diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,5 +1,4 @@
-
-# Welcome to the [CTSM mini-tutorial](https://ncar.github.io/CTSM-Tutorial-2022/README.html) 
+# Welcome to the [NEON-NCAR Main Tutorial Repository](https://ncar.github.io/CTSM-Tutorial-2022/README.html) 
 
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@@ -8,8 +7,7 @@
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-
-This tutorial was first offered during Spring 2022.
+Numerous versions of this tutorial can be found in this repository's branches.
 
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diff --git a/notebooks/Day2a_GenericSinglePoint.ipynb b/notebooks/Day2a_GenericSinglePoint.ipynb
deleted file mode 100644
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--- a/notebooks/Day2a_GenericSinglePoint.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial 2a: *Running a generic single-point case*\n",
-    "\n",
-    "This tutorial is an introduction to running single-point simulations of the Community Terrestrial Systems Model (CTSM) at locations that do not have preconfigured options.  It will guide you through setting up the required driver data (i.e. surface and atmosphere data) as well as setting up and submitting a single-point CTSM case.\n",
-    "\n",
-    "In the previous tutorial, `Day1a_GlobalCase`, we set up and ran a global CTSM case. Many of the steps required to run a single-point case are similar, with some changes and additional required steps which we will cover here.\n",
-    "<br><br>\n",
-    "\n",
-    "### Questions about this tutorial? \n",
-    "- Please post them on the [CTSM forum in the CESM Bulletin Board](https://bb.cgd.ucar.edu/cesm/forums/ctsm-clm-mosart-rtm.134/). Note that this resource will require you to register and log in so that you can be notified of responses to your inquiries. \n",
-    "- You can also file issues on the [NCAR CTSM-Tutorial GitHub repository](https://github.com/NCAR/CTSM-Tutorial-2022).\n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## In this tutorial\n",
-    "\n",
-    "The tutorial has several components. Below you will find steps to: \n",
-    "1. Generate subset surface and atmosphere data files at a single latitude and longitude point.\n",
-    "2. Set up and submit a single-point case.\n",
-    "\n",
-    "Specifically, we will simulate the Harvard Forest site for one year using data extracted from global datasets that are available for CTSM. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note:</b> This tutorial assumes you have completed the previous tutorials! Content in the Day 0a Git Started tutorial is required, and Day 0b NEON and Day 1 global tutorials are strongly recommended. If you haven't completed the Day 0 and Day 1 tutorials, go back and do these first.\n",
-    "</div>\n",
-    "\n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
-    "\n",
-    "<b>TIP:</b>  Before we get started, make sure you're in a bash kernel \n",
-    "<ul>\n",
-    "    <li> Switch kernel (upper right of your current notebook)</li>\n",
-    "    <li> Select either one of the Bash Kernels from the pop-up window</li>    \n",
-    "    <li> Click select</li>    \n",
-    "</ul>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "<div>\n",
-    "<img src=\"https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/kernel.png\" width=\"670\" />\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h1> 1. Subset global surface and atmosphere files </h1>\n",
-    "\n",
-    "CTSM uses a surface data file to read in important grid cell-level information like vegetation, crop, and glacier grid cell fractions, the fractional cover of each plant functional type (PFT), and soil characteristics.\n",
-    "\n",
-    "A global surface data file is located and read by default for global CTSM cases, depending on the chosen _component set_ and _resolution_. To run CTSM at a single point, we will need to supply a surface data file at a specified latitude and longitude.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "Remember from Day 1 that a <b>component set</b>, or colloquially a \"compset\", specifies a configuration for your case, including the component models, time period of simulation, and model physics options. The <b>resolution</b> defines the model resolution or grid size.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Similarly, when running a land-only simulation, that is when using a \"data atmosphere model\" (e.g., in _DATM mode_), with climate data (e.g. temperature, precipitation, solar radiation, etc.) driven by an input file, CTSM needs DATM files. We can also provide subset global DATM for single-point runs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>Tip:</b> It is not strictly required to provide CTSM/CIME with subset DATM data in order to run a single-point case, as CTSM can just use the global files. However, your simulations will run much faster if you use subset climate data.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h2> 1.1 Use <em>subset_data</em> to subset surface and DATM files</h2>\n",
-    "\n",
-    "We have created a python script, *subset_data*, which will subset default global surface and DATM files at a user-specified latitude and longitude.\n",
-    "\n",
-    "This script is located in the CTSM source code, in the `tools/site_and_regional` folder.\n",
-    "\n",
-    "Navigate here now:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd ~/CTSM/tools/site_and_regional\n",
-    "ls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to use the `subset_data` python script you must have some required python packages installed. On NCAR machines (like Cheyenne), you can load the NCAR python library, _ncar pylib_, by running `module load python` and then `ncar_pylib`. **This is not necessary in the cloud because your python environment is already configured in CESM-Lab.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>Tip:</b> On NCAR or other machines, you can also use your own python environment if you want. Required third-party python packages are <i>scipy</i>, <i>xarray</i>, and <i>numpy</i>.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use the built-in print help to see what options are available for the subset data script:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "./subset_data --help"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### There are a lot of options, but for now we will just use a few of the most commonly used:\n",
-    "\n",
-    "**Type of subsetting:**<br>\n",
-    "`point` : this tells the script to subset data at a single point (region is the other option)<br>\n",
-    "\n",
-    "**Location-related information:**<br>\n",
-    "`--lat` : this tells the script which latitude to subset at (*must be between -90 and 90*)<br>\n",
-    "`--lon` : this tells the script which longitude to subset at (*can be between 0 and 360 or -180 and 180*)<br>\n",
-    "`--site` : optional, specifies a site name or tag<br>\n",
-    "\n",
-    "**Type of files to create:**<br>\n",
-    "`--create-surface` : tells the script to subset surface data<br> \n",
-    "`--create-datm` : tells the script to subset DATM data<br>\n",
-    "\n",
-    "**Time information:** <br>\n",
-    "`--datm-syr` and `--datm-eyr`: starting and ending years for the DATM data to subset (*must be between 1901 and 2014*)<br>\n",
-    "\n",
-    "**Data management information:**<br>\n",
-    "`--create-user-mods` : tells the script to create a *user_mods* directory (see below). Note that if you don't use this option, you will have to modify scripts in your simulation to point to the modifed files.<br> \n",
-    "`--outdir` : specifies the directory to place subset data and user mods directory in<br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Call the script with these options by running the line of code below \n",
-    "A few notes:\n",
-    "- We will use a latitude of 42.53562 and longitude of 287.82438 (i.e. Harvard Forest). Note that your latitude and longitude points do not have to be this precise for your own sites!\n",
-    "\n",
-    "- We will run the simulation from 2001 to 2002. Note that because we are only simulating years around present day, we are not using land use change. If you want to run a transient simulation, you will also need to create land use data (the `--create-landuse` option).\n",
-    "\n",
-    "- This is also pretty time consuming, so we'll use `qcmd_serial` here to put this in the queue on a single processor.  \n",
-    "\n",
-    "- As before, please be patient while this runs, and don't worry if you see `WARNING: No dominant pft type is chosen.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qcmd_serial -- ./subset_data point --lat 42.53562 --lon 287.82438 --site my_point --create-surface --create-datm --datm-syr 2001 --datm-eyr 2002 --create-user-mods --outdir /scratch/$USER/my_subset_data\n",
-    "\n",
-    "echo \"------------------------\"\n",
-    "echo \"Successfully subset data\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Depending on the speed of your computing system, it may take a bit of time to subset all the climate data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h2> 1.2 Check on the subset files </h2>\n",
-    "\n",
-    "Once the subsetting has successfully finished, let's navigate to the specified output directory to check on the data that we just created:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd /scratch/$USER/my_subset_data\n",
-    "ls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You should see a surface data file (e.g. *surfdata_0.9x1.25 ... .nc*) and two folders: **datmdata** and **user_mods**. \n",
-    "\n",
-    "* **datmdata** houses the subset DATM files\n",
-    "* **user_mods** is a directory created that houses several files we will use to set up our single-point case\n",
-    "\n",
-    "Let's navigate into the **user_mods** directory to look at the contents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd user_mods\n",
-    "ls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You should see three files: *shell_commands*, *user_nl_clm*, and *user_nl_datm_streams*.  \n",
-    "\n",
-    "The *shell_commands* file contains *xmlchange* commands required to set up a single point case at the specified latitude and longitude.\n",
-    "\n",
-    "Take a look at this file if you want:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat shell_commands"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that many of the xml commands are changing aspects of the model configuration that are communicated to **[CIME](https://github.com/ESMCI/cime)** (Common Infrastructure for Modeling the Earth), which is the infrastructure that generates model executables and associated input files. Below are explanations of the commands included in this script. \n",
-    "\n",
-    "`./xmlchange CLM_USRDAT_DIR` - this tells CIME the location of an argument *CLM_USRDAT_DIR* which we can use to specify the main directory of subset data files  \n",
-    "\n",
-    "`./xmlchange PTS_LON` and `./xmlchange PTS_LAT` - this tells CIME that we are running at a specified latitude and longitude  \n",
-    "\n",
-    "`./xmlchange MPILIB` - this specifies a specific MPI (*Message Passing Interface*) library to use required for single-point runs on NCAR machines.  \n",
-    " \n",
-    "   \n",
-    "If you remember from the Day 1 tutorial, *user_nl_clm* is a Fortran namelist file used to set up different namelist options for CLM. Here, we are using it to specify the location of our subset surface data. Note the use of the variable `$CLM_USRDAT_DIR` set up in the *shell_commands* file.\n",
-    "  \n",
-    "    \n",
-    "Similarly, *user_nl_datm_streams* specifies the location and a few other options for our subset DATM data.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will use this **user_mods** directory when we create our single-point case (see below)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note:</b> If for whatever reason you end up moving the subset data directory (i.e. here <b>/scratch/$USER/my_subset_data</b>), you will need to modify the xmlchange command that specifies the <i>CLM_USRDAT_DIR</i> to be the full path to the directory's new location. \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h1> 2. Create a single-point CTSM case </h1>\n",
-    "\n",
-    "Now that we have our subset data ready to go, we can set up our single-point case with CIME.\n",
-    "\n",
-    "The steps required here are very similar to the global case that we set up in the Day 1 tutorial, with a few differences. Mainly, we are going to specify a `--user-mods-dir` in our `./create_newcase` command as the full path to the **user_mods** folder we just created with *subset_data* to point to all the data required to run the single point (rather than global) simulation. We will also choose a different component set and resolution.\n",
-    "\n",
-    "<h2> 2.1. Create the case </h2>\n",
-    "\n",
-    "As in our Day 1 Tutorial, we will navigate into the CTSM **scripts** directory to run the `create_newcase` script. Today we are going to be running a CLM-BGC simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd ~/CTSM/cime/scripts\n",
-    "./create_newcase --case ~/clm_tutorial_cases/I2000_CTSM_singlept --res CLM_USRDAT --compset I2000Clm51BgcCrop --run-unsupported --user-mods-dirs /scratch/$USER/my_subset_data/user_mods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This command should look fairly familiar to you, with some updated values and arguments.\n",
-    "\n",
-    "`--res` - defines the model resolution, or grid:\n",
-    "* we are now using `CLM_USRDAT`, which should be used when we have user-specified domain (i.e. a subset surface data)\n",
-    "\n",
-    "`--compset` - defines the component set for the case:\n",
-    "* `I2000Clm51BgcCrop` is an alias that describes using year 2000 initialization time, data-driven atmosphere (GSWP3v1 data), CLM 5.1 BGC with prognostic crop, along with some other component settings.\n",
-    "\n",
-    "`--user-mods-dirs` - this is where we tell CIME where our **user mods** directory is.\n",
-    "* it should be the path to the directory that was created during our *subset_data* scripting\n",
-    "* the namelist files (i.e. *user_nl_clm* and *user_nl_datm_streams*) will be copied into the case directory, and the commands within *shell_commands* will be executed. Remember that the information included in these files ensures the model uses the data subset for our site."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>Tip:</b> Depending on your machine (e.g. this is required on Cheyenne), you may also have to provide a project id (<code>--project {PROJECT_ID}</code>) which specifies accounting or directory permissions when on a batch system. By default the script uses the shell environment variable <code>$PROJECT</code>, which can be set in your bash profile\n",
-    "\n",
-    "<i>Remember that you can see script parameters and definitions using</i> <code>./create_newcase --help</code>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h2> 2.2. Set up the case and build the executable </h2>\n",
-    "\n",
-    "As with our global case, we will change into our case directory, set up our case (`./case.setup`) and then build the executable with qcmd (`qcmd -- ./case.build`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd ~/clm_tutorial_cases/I2000_CTSM_singlept\n",
-    "./case.setup\n",
-    "./xmlchange MPILIB=impi\n",
-    "qcmd -- ./case.build"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note:</b> You'll notice we changed the <code>MPILIB</code> parameter from what was set in the <b>user_mods</b>. This is a special change we have to make to run on this cloud system. On NCAR machines (e.g. Cheyenne) you should keep <code>MPILIB=mpi-serial</code>.\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "-----\n",
-    "\n",
-    "You can read on, but before executing any code blocks in the notebook **wait for the model to build.**\n",
-    "This can take a while, especially while you're wating for your `qcmd` job to start and as code for the land model compiles.\n",
-    "\n",
-    "You'll see text stating `MODEL BUILD HAS FINISHED SUCCESSFULLY` when it's finished.\n",
-    "\n",
-    "<h2> 2.3. Customize the case </h2>\n",
-    "\n",
-    "As in our global case, we will invoke a few XML commands to update some runtime values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "./xmlchange STOP_OPTION=nyears\n",
-    "./xmlchange STOP_N=1\n",
-    "./xmlchange RUN_STARTDATE='2001-01-01'\n",
-    "./xmlchange DATM_YR_ALIGN=2001\n",
-    "./xmlchange DATM_YR_START=2001\n",
-    "./xmlchange DATM_YR_END=2002\n",
-    "./xmlchange PIO_REARRANGER_LND=2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3> Spinup </h3>\n",
-    "\n",
-    "When running a model like CLM, the initial conditions (i.e. state variables like carbon and nitrogen pools and soil moisture) have an impact on the results of the simulation. Often, we don't know the precise values of these initial conditions. To get around this issue, we can initialize the model with arbitrary values and then run the model with some cycle of atmospheric forcing for many years (e.g. 200) until the model attains an equilibrium state. Then, we can simulate the model response to some perturbation (e.g. changing climate, CO<sub>2</sub>, etc.). This process -- establishing an equilibrium state -- is called _spinup_.\n",
-    "\n",
-    "We have already run such a spinup simulation at Harvard Forest. We can see how the soil C pools evolved for this simulation over time.\n",
-    "\n",
-    "*Note that you'll see **AD mode** for accelerated decomposition mode and **Post-AD mode** on this figure. See the tip below or visit the [Model Equilibrium and its Acceleration](https://escomp.github.io/ctsm-docs/versions/release-clm5.0/html/tech_note/Decomposition/CLM50_Tech_Note_Decomposition.html#model-equilibration-and-its-acceleration) section of the CLM Tech Note for more information*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div>\n",
-    "<img src=\"https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/ad_mode.png\" width=\"525\" height=\"375\" alt=\"Evolution of different C pools during the accelerated decomposition spinup.\"/>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "The steady-state, or equilibrium, size of carbon (C) and nitrogen (N) pools are proportional to their turnover time. This spinup simulation was conducted using \"accelerated decomposition\", or \"AD\" mode. This accelerates the turnover time of \"slow\" ecosystem C and N pools (soil, wood, and coarse woody debris) so they come into equilibrium more quickly. \n",
-    "\n",
-    "We ran the model in AD mode for 100 years cycling through atmospheric forcing for 1981 to 2000.\n",
-    "AD mode was invoked with the commands: <code>./xmlchange CLM_FORCE_COLDSTART=on</code> and <code>./xmlchange CLM_ACCELERATED_SPINUP=on</code>.\n",
-    "\n",
-    "We then ran a \"post-AD\" simulation (using the end of our \"AD\" simulation as the starting point) for another 100 years with <code>./xmlchange CLM_ACCELERATED_SPINUP=off</code>. In returning the turnover times of slow C and N pools to their intended rates, we have to adjust the pool sizes from their \"AD\" steady-state. For example if the turnover of \"passive\" soil C was 10x faster in AD mode, the passive soil C pool needs to be 10x larger starting the post-AD simulation (the model automatically handles this conversion for you). Running the simulation for 100 years in post-AD mode allows the state variables to equilibrate with non-accelerated decomposition.  In post-AD mode the history files are monthly, whereas AD output is set to annual averages by default.  This difference in history file output frequency is reflected in the variability in the post-AD output.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "After spin up is complete, we have to tell CIME to use the spinup simulation's end point as the starting point, or initial conditions, for our simulation. \n",
-    "\n",
-    "We do this via the <i>user_nl_clm</i> file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "echo \"finidat='/scratch/data/day2/finidat_file/I2000_CTSM51_spinup.clm2.r.0281-01-01-00000.nc'\" >> user_nl_clm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>Tip:</b> You can also edit the file using any text editing software (e.g. vi, emacs, etc.).\n",
-    "</div>\n",
-    "\n",
-    "Let's check the file to make sure our command worked:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat user_nl_clm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h2> 2.4. Submit the case </h2>\n",
-    "\n",
-    "Finally, let's submit the case as we did in Day 1:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "./case.submit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You should see a confirmation that it successfully submitted.\n",
-    "\n",
-    "### Congratulations! You've created and submtted a single-point CLM case!\n",
-    "\n",
-    "You can check the status of your case as in previous tutorials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qstat -u $USER"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "Once your jobs are complete (or show the 'C' state under the 'Use' column, which means complete), we can check the CaseStatus file to ensure there were no errors and it submitted, ran, and completed successfully.  To do this, we'll 'tail' the end of the CaseStatus file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tail ~/clm_tutorial_cases/I2000_CTSM_singlept/CaseStatus"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember from Day1a that you can check on files in your runs in the **scratch/{USER}/{CASE_NAME}/run** directory (e.g. `/scratch/$USER/I2000_CTSM_singlept/run`). \n",
-    "\n",
-    "Archived history files will be in the **scratch/{USER}/archive/{CASE_NAME}/lnd/hist** directory (e.g. `/scratch/$USER/archive/I2000_CTSM_singlept/lnd/hist`).\n",
-    "____"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, check out the next Generic Single Point Tutorial [2b_GenericSinglePoint_Visualization](Day2b_GenericSinglePoint_Visualization.ipynb) to walk through how to visualize and analyze some of the output produced. **Note that you don't need to wait for this job to finish before moving on to the Day2b tutorial**"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Bash",
-   "language": "bash",
-   "name": "bash"
-  },
-  "language_info": {
-   "codemirror_mode": "shell",
-   "file_extension": ".sh",
-   "mimetype": "text/x-sh",
-   "name": "bash"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/Day2b_GenericSinglePoint_Visualization.ipynb b/notebooks/Day2b_GenericSinglePoint_Visualization.ipynb
deleted file mode 100644
index f81aa18..0000000
--- a/notebooks/Day2b_GenericSinglePoint_Visualization.ipynb
+++ /dev/null
@@ -1,9319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial 2b: *Single-Point Visualizations*\n",
-    "\n",
-    "This tutorial is an introduction to analyzing results from a single-point simulation.  It uses results from the case you ran in the 2a Tutorial, but you don't have to wait for those runs to complete before doing this tutorial too. We've prestaged model results from this simulation in a shared directory. This way, you can get started on analyzing simulations results before your simulations finish running. We also ran this case for longer (10 years instead of 1), so that we have more data to work with.\n",
-    "\n",
-    "## In this tutorial\n",
-    "\n",
-    "The tutorial has several objectives: \n",
-    "1. Increase familiarity with Jupyter Notebooks\n",
-    "2. Increase knowledge of python packages and their utilities\n",
-    "3. Visualize output from a single-point CTSM simulation.\n",
-    "\n",
-    "***\n",
-    "\n",
-    "*As in other visualization notebook, this should be run in a Python3 kernel*\n",
-    "\n",
-    "*We'll start by loading some packages*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import xarray as xr\n",
-    "import functools\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "\n",
-    "<b>NOTE:</b>  This example largely uses features of xarray and \n",
-    "    matplotlib packages. We won't go into the basics \n",
-    "    of python or features in these packages, but there are lots of \n",
-    "    resources to help get you started. Some of these are listed below. \n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "- [NCAR python tutorial](https://ncar.github.io/python-tutorial/tutorials/yourfirst.html), which introduces python, the conda package manager, and more on github.\n",
-    "- [NCAR ESDS tutorial series](https://ncar.github.io/esds/blog/tag/python-tutorial-series/), features several recorded tutorials on a wide variety of topics.\n",
-    "- [GeoCAT examples](https://geocat-examples.readthedocs.io/en/latest/), with some nice plotting examples\n",
-    "\n",
-    "***\n",
-    "\n",
-    "## 1. Reading and formatting data\n",
-    "\n",
-    "### 1.1 Find the correct directories and file names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/scratch/data/day2/I2000_CTSM_singlept/lnd/hist/I2000_CTSM_singlept.clm2.h0.2010-12.nc\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = '/scratch/data/day2/'       # path to archived simulations\n",
-    "case = 'I2000_CTSM_singlept'       # case name\n",
-    "years = list(range(2001, 2011, 1)) # look at 10 years of data (python won't count the last year in the list, here 2011)\n",
-    "months = list(range(1, 13, 1))     # we want all months (again, python will go up to, but not including month 13)\n",
-    "\n",
-    "# build a list of file names based on the year and month\n",
-    "file_names = [f\"{case}.clm2.h0.{str(year)}-{str(month).rjust(2, '0')}.nc\"\n",
-    "              for year in years for month in months]\n",
-    "\n",
-    "# create their full path\n",
-    "full_paths = [os.path.join(path, case, 'lnd/hist', fname) for fname in file_names]\n",
-    "\n",
-    "# print the last file in our list\n",
-    "print(full_paths[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "These are the raw history files that CTSM writes out.  \n",
-    "By default, they include grid cell averaged monthly means for different state and flux variables.\n",
-    "<br><br>\n",
-    "Typically we post-process these data to generate single variable time series for an experiment. \n",
-    "This means that the full time series of model output for each variable, like rain, air temperature, or latent heat flux, are each in their own file.\n",
-    "A post-processing tutorial will be available at a later date, but for now we'll just read in the monthly history files described above.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 Open files & load variables\n",
-    "\n",
-    "This is done with the xarray function `open_mfdataset`\n",
-    "\n",
-    "To make this go faster, we're going to preprocess the data so we're just reading the variables we want to look at.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    <b>TIP:</b> If you want to look at other variables, the <b>fields</b> variable in the cell below is where you can modify what we're reading off of the CTSM and CTSM-FATES history files.\n",
-    "</div>\n",
-    "\n",
-    "As in our Day 1 tutorial, we will begin by looking at 'all sky albedo', which is the ratio of reflected to incoming solar radiation (**FSR/FSDS**).\n",
-    "Other intereresting variables might include latent heat flux (**EFLX_LH_TOT**) or gross primary productivity (**GPP**). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-- your data have been read in -- \n"
-     ]
-    }
-   ],
-   "source": [
-    "# define the history variables to read in\n",
-    "fields = ['FSR', 'FSDS', 'GPP', 'EFLX_LH_TOT', 'FCEV', 'FCTR', 'FGEV', 'ELAI', \n",
-    "          'H2OSOI', 'HR', 'TBOT', 'TSOI']\n",
-    "\n",
-    "\n",
-    "def preprocess(ds, fields):\n",
-    "    '''Selects the variables we want to read in \n",
-    "       Drops lndgrid because we are on a single point'''\n",
-    "    \n",
-    "    return ds[fields].sel(lndgrid=0)\n",
-    "\n",
-    "\n",
-    "def fix_time(ds):\n",
-    "    '''Does a quick fix to adjust time vector for monthly data'''\n",
-    "    nmonths = len(ds.time)\n",
-    "    yr0 = ds['time.year'][0].values\n",
-    "    ds['time'] = xr.cftime_range(str(yr0), periods=nmonths, freq='MS')\n",
-    "\n",
-    "    return ds\n",
-    "\n",
-    "\n",
-    "# open the dataset -- this may take a bit of time \n",
-    "ds = fix_time(xr.open_mfdataset(full_paths, decode_times=True,\n",
-    "                                preprocess=functools.partial(preprocess, fields=fields)))\n",
-    "\n",
-    "print('-- your data have been read in -- ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Printing information about the dataset is helpful for understanding your data.**\n",
-    "- *What dimensions do your data have?*\n",
-    "- *What are the coordinate variables?*\n",
-    "- *What variables are we looking at?*\n",
-    "- *Is there other helpful information, or are there attributes in the dataset we should be aware of?*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
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-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 120, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi       (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "Data variables:\n",
-       "    FSR          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FSDS         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    GPP          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCEV         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCTR         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FGEV         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    ELAI         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    H2OSOI       (time, levsoi) float32 dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;\n",
-       "    HR           (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TBOT         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TSOI         (time, levgrnd) float32 dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6559dc03-0c54-4df3-b13a-5301f9a6fa87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6559dc03-0c54-4df3-b13a-5301f9a6fa87' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levgrnd</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9aa23be-6653-4175-a8ac-f4b5591dda36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9aa23be-6653-4175-a8ac-f4b5591dda36' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-b67ef872-96f0-44d5-b411-42b5602d5c8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b67ef872-96f0-44d5-b411-42b5602d5c8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd3c6da6-eb29-4d69-936e-b77643cc7553' class='xr-var-data-in' type='checkbox'><label for='data-cd3c6da6-eb29-4d69-936e-b77643cc7553' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-3d5e1aae-ba31-4eb5-b679-a5cc366ea27e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d5e1aae-ba31-4eb5-b679-a5cc366ea27e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65da7e9f-74a1-459a-bf9c-6bd2422c7756' class='xr-var-data-in' type='checkbox'><label for='data-65da7e9f-74a1-459a-bf9c-6bd2422c7756' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2010-12-...</div><input id='attrs-ee7a39f5-31ff-4190-842b-84fe8c57623c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee7a39f5-31ff-4190-842b-84fe8c57623c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2f768fe-dff1-4c0e-89bc-37b65898a71d' class='xr-var-data-in' type='checkbox'><label for='data-f2f768fe-dff1-4c0e-89bc-37b65898a71d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd0c3e06-13ae-41dc-adf6-56d52bc948c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd0c3e06-13ae-41dc-adf6-56d52bc948c3' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FSR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bd86fe20-4a3f-4f62-b832-fd01e8bb6286' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd86fe20-4a3f-4f62-b832-fd01e8bb6286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f58736b-9e01-4607-95a0-deebd760cd44' class='xr-var-data-in' type='checkbox'><label for='data-4f58736b-9e01-4607-95a0-deebd760cd44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reflected solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSDS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7df1f88a-bb59-44d8-88f2-96f2565c8bd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7df1f88a-bb59-44d8-88f2-96f2565c8bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bf5095e-60ed-4855-a3ed-10a43d5863a8' class='xr-var-data-in' type='checkbox'><label for='data-2bf5095e-60ed-4855-a3ed-10a43d5863a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-278f422e-fd71-4ada-abae-0c92ebab461f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-278f422e-fd71-4ada-abae-0c92ebab461f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24256001-eac8-4d22-8964-c2327ead155b' class='xr-var-data-in' type='checkbox'><label for='data-24256001-eac8-4d22-8964-c2327ead155b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFLX_LH_TOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c12533f1-977c-4a81-8e50-7315f87542d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c12533f1-977c-4a81-8e50-7315f87542d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d5d9945-6f34-43df-8441-07a0cae94835' class='xr-var-data-in' type='checkbox'><label for='data-3d5d9945-6f34-43df-8441-07a0cae94835' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total latent heat flux [+ to atm]</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
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-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-38e6a180-635a-40f8-a3ab-c24ef5726e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e6a180-635a-40f8-a3ab-c24ef5726e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7cf9a47-4fde-494b-a6be-9655302b76f2' class='xr-var-data-in' type='checkbox'><label for='data-c7cf9a47-4fde-494b-a6be-9655302b76f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-faa57cdb-b8da-4c82-be1c-bcbe8d33ec83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faa57cdb-b8da-4c82-be1c-bcbe8d33ec83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-265098e6-db71-4411-83f8-18a166a017c1' class='xr-var-data-in' type='checkbox'><label for='data-265098e6-db71-4411-83f8-18a166a017c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
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-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-dcaac74f-6aa3-4258-9e33-307aee1af01d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcaac74f-6aa3-4258-9e33-307aee1af01d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5de12b9f-7e8f-49e9-b479-a1244b5f989d' class='xr-var-data-in' type='checkbox'><label for='data-5de12b9f-7e8f-49e9-b479-a1244b5f989d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-12a23c58-1eca-4e3f-9b84-215cf538e29a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12a23c58-1eca-4e3f-9b84-215cf538e29a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e15b59a0-90fd-49c7-8316-82c3334e8a5a' class='xr-var-data-in' type='checkbox'><label for='data-e15b59a0-90fd-49c7-8316-82c3334e8a5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time, levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;</div><input id='attrs-42568293-6e89-4106-b03b-d58408504262' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42568293-6e89-4106-b03b-d58408504262' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be8028e3-a8f8-4f25-95c4-82f7fddf8042' class='xr-var-data-in' type='checkbox'><label for='data-be8028e3-a8f8-4f25-95c4-82f7fddf8042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.38 kiB </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120, 20) </td>\n",
-       "                        <td> (1, 20) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bab34103-464b-457e-9766-9c70b110eeea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bab34103-464b-457e-9766-9c70b110eeea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f0a24a1-9cca-4b23-a7b3-aa0eae474e3a' class='xr-var-data-in' type='checkbox'><label for='data-1f0a24a1-9cca-4b23-a7b3-aa0eae474e3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TBOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-dc114408-000c-4c49-bb9d-388629cd36f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc114408-000c-4c49-bb9d-388629cd36f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e9dac7-620f-48e5-b399-e5a3690d928e' class='xr-var-data-in' type='checkbox'><label for='data-a0e9dac7-620f-48e5-b399-e5a3690d928e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric air temperature (downscaled to columns in glacier regions)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSOI</span></div><div class='xr-var-dims'>(time, levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;</div><input id='attrs-47d4d326-21b1-49f6-b96f-35afe68cdd8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47d4d326-21b1-49f6-b96f-35afe68cdd8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-055f8186-e348-4d1a-a419-9eed33412720' class='xr-var-data-in' type='checkbox'><label for='data-055f8186-e348-4d1a-a419-9eed33412720' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil temperature (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 11.72 kiB </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120, 25) </td>\n",
-       "                        <td> (1, 25) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b1b48fe-34e3-4fb5-9b7a-cefca49c3130' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b1b48fe-34e3-4fb5-9b7a-cefca49c3130' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>CLM History file information</dd><dt><span>comment :</span></dt><dd>NOTE: None of the variables are weighted by land fraction!</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>history :</span></dt><dd>created on 05/19/22 14:47:50</dd><dt><span>source :</span></dt><dd>Community Terrestrial Systems Model</dd><dt><span>hostname :</span></dt><dd>cheyenne</dd><dt><span>username :</span></dt><dd>afoster</dd><dt><span>version :</span></dt><dd>unknown</dd><dt><span>revision_id :</span></dt><dd>$Id: histFileMod.F90 42903 2012-12-21 15:32:10Z muszala $</dd><dt><span>case_title :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>case_id :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>Surface_dataset :</span></dt><dd>surfdata_0.9x1.25_hist_16pfts_Irrig_CMIP6_simyr2000_my_point_c220519.nc</dd><dt><span>Initial_conditions_dataset :</span></dt><dd>I2000_CTSM51_spinup.clm2.r.0281-01-01-00000.nc</dd><dt><span>PFT_physiological_constants_dataset :</span></dt><dd>ctsm51_params.c211112.nc</dd><dt><span>ltype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ltype_crop :</span></dt><dd>2</dd><dt><span>ltype_UNUSED :</span></dt><dd>3</dd><dt><span>ltype_landice :</span></dt><dd>4</dd><dt><span>ltype_deep_lake :</span></dt><dd>5</dd><dt><span>ltype_wetland :</span></dt><dd>6</dd><dt><span>ltype_urban_tbd :</span></dt><dd>7</dd><dt><span>ltype_urban_hd :</span></dt><dd>8</dd><dt><span>ltype_urban_md :</span></dt><dd>9</dd><dt><span>ctype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ctype_crop :</span></dt><dd>2</dd><dt><span>ctype_crop_noncompete :</span></dt><dd>2*100+m, m=cft_lb,cft_ub</dd><dt><span>ctype_landice :</span></dt><dd>4*100+m, m=1,glcnec</dd><dt><span>ctype_deep_lake :</span></dt><dd>5</dd><dt><span>ctype_wetland :</span></dt><dd>6</dd><dt><span>ctype_urban_roof :</span></dt><dd>71</dd><dt><span>ctype_urban_sunwall :</span></dt><dd>72</dd><dt><span>ctype_urban_shadewall :</span></dt><dd>73</dd><dt><span>ctype_urban_impervious_road :</span></dt><dd>74</dd><dt><span>ctype_urban_pervious_road :</span></dt><dd>75</dd><dt><span>cft_c3_crop :</span></dt><dd>1</dd><dt><span>cft_c3_irrigated :</span></dt><dd>2</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd></dl></div></li></ul></div></div>"
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-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
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-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
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-     },
-     "execution_count": 4,
-     "metadata": {},
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-   "source": [
-    "# This cell will print information about the dataset\n",
-    "ds"
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-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also print information about the variables in your dataset. The example below prints information about one of the data variables we read in. You can modify this cell to look at some of the other variables in the dataset.\n",
-    "\n",
-    "*What are the units, long name, and dimensions of your data?*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
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-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;FSDS&#x27; (time: 120)&gt;\n",
-       "dask.array&lt;concatenate, shape=(120,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * time     (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "Attributes:\n",
-       "    long_name:      atmospheric incident solar radiation\n",
-       "    units:          W/m^2\n",
-       "    cell_methods:   time: mean\n",
-       "    landunit_mask:  unknown</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'FSDS'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c07eae78-8051-421e-a736-1f09aeffc583' class='xr-array-in' type='checkbox' checked><label for='section-c07eae78-8051-421e-a736-1f09aeffc583' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-0295ca44-42cd-4be3-b45a-0e22d2f85f1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0295ca44-42cd-4be3-b45a-0e22d2f85f1b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2010-12-...</div><input id='attrs-fe2f5dc5-dcf8-432b-83de-7552df93b596' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe2f5dc5-dcf8-432b-83de-7552df93b596' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3181adeb-fd5c-4d8f-be1d-eb8feb0263af' class='xr-var-data-in' type='checkbox'><label for='data-3181adeb-fd5c-4d8f-be1d-eb8feb0263af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8aa80555-10c8-4b63-9e0d-ed1ec5c18937' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8aa80555-10c8-4b63-9e0d-ed1ec5c18937' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'FSDS' (time: 120)>\n",
-       "dask.array<concatenate, shape=(120,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time     (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "Attributes:\n",
-       "    long_name:      atmospheric incident solar radiation\n",
-       "    units:          W/m^2\n",
-       "    cell_methods:   time: mean\n",
-       "    landunit_mask:  unknown"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "ds.FSDS"
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-  {
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-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    <b>Tip:</b> In <i>xarray</i> you can access variables and coordinates using dot notation (<code>ds.ASA</code>) or using brackets with the variable or coordinates in quotes (<code> ds['ASA']</code>).\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.3 Adding derived variables to the dataset\n",
-    "\n",
-    "As in Day 1, we will calculate the all sky albedo (ASA). Remember from above that this is the ratio of reflected to incoming solar radiation (**FSR/FSDS**).\n",
-    "We will add this as a new variable in the dataset (which requires using quotes; e.g., `ds['ASA']`) and add appropriate metadata.\n",
-    "\n",
-    "*When doing calculations, it is important to avoid dividing by zero. Use the `.where` function for this purpose*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds['ASA'] = ds.FSR/ds.FSDS.where(ds.FSDS > 0.0)\n",
-    "ds['ASA'].attrs['units'] = 'unitless'\n",
-    "ds['ASA'].attrs['long_name'] = 'All sky albedo'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 2. Filtering and indexing data\n",
-    "\n",
-    "*xarray* allows for easy subsetting and filtering using the `.sel` and `.isel` functions. `.sel` filters to exact (or nearest neighbor) values, whereas `.isel` filters to an index.\n",
-    "\n",
-    "Let's filter to a specific date. Note that because our output was monthly and at one latitude and longitude point, this should only give us one point of data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
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-       "    ...           ...\n",
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-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
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-       "    ...                                   ...\n",
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-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c93ec8c8-c9bc-4dbf-92be-1aa621b80f5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c93ec8c8-c9bc-4dbf-92be-1aa621b80f5f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levgrnd</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b7057b21-dfb2-4dc8-ac7b-4e12402b428d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7057b21-dfb2-4dc8-ac7b-4e12402b428d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-0bb5c1f5-db5e-40d2-b688-025c747d084b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bb5c1f5-db5e-40d2-b688-025c747d084b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f78a035-911a-47f3-878f-84b5a2248912' class='xr-var-data-in' type='checkbox'><label for='data-7f78a035-911a-47f3-878f-84b5a2248912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-66fceebf-b5b0-4813-a03e-161f30d801c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66fceebf-b5b0-4813-a03e-161f30d801c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c7377d4-282f-4ba9-be2b-6d17ec358c6d' class='xr-var-data-in' type='checkbox'><label for='data-5c7377d4-282f-4ba9-be2b-6d17ec358c6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00</div><input id='attrs-1b693d95-f336-46fc-996a-7c6a1824bf59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b693d95-f336-46fc-996a-7c6a1824bf59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03009f44-233f-4f72-8618-b870c39bef0b' class='xr-var-data-in' type='checkbox'><label for='data-03009f44-233f-4f72-8618-b870c39bef0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa538d71-d9e3-4c58-9cb0-138a9c78e801' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa538d71-d9e3-4c58-9cb0-138a9c78e801' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FSR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f3bd56d0-6018-421b-9373-508624ebee6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3bd56d0-6018-421b-9373-508624ebee6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a7b233d-9c92-4d97-9268-a6269785f55d' class='xr-var-data-in' type='checkbox'><label for='data-4a7b233d-9c92-4d97-9268-a6269785f55d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reflected solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSDS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-db134ecb-bef1-4b79-b6de-3547967cf56b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db134ecb-bef1-4b79-b6de-3547967cf56b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78827c7d-5bca-42ae-9ac7-c3e3c8f00010' class='xr-var-data-in' type='checkbox'><label for='data-78827c7d-5bca-42ae-9ac7-c3e3c8f00010' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-571b7b7b-d53b-4219-a5f2-813f9fb7bcec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571b7b7b-d53b-4219-a5f2-813f9fb7bcec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ade74fec-d229-44e7-9ca7-09c8187af506' class='xr-var-data-in' type='checkbox'><label for='data-ade74fec-d229-44e7-9ca7-09c8187af506' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFLX_LH_TOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-647ed9f9-811f-4453-bb69-e3c80182511b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-647ed9f9-811f-4453-bb69-e3c80182511b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b01affe4-6318-40ae-9ead-27fecaa2f946' class='xr-var-data-in' type='checkbox'><label for='data-b01affe4-6318-40ae-9ead-27fecaa2f946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total latent heat flux [+ to atm]</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e7ed5266-6428-4d65-8eb0-ffdc9c66421f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7ed5266-6428-4d65-8eb0-ffdc9c66421f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c36556cc-8656-4736-a435-f0445dcb88da' class='xr-var-data-in' type='checkbox'><label for='data-c36556cc-8656-4736-a435-f0445dcb88da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-123e59d2-a116-4ba0-bca2-4a2ec410cfc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-123e59d2-a116-4ba0-bca2-4a2ec410cfc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b1198c5-0553-4f8e-ad9c-4fdd84c2a332' class='xr-var-data-in' type='checkbox'><label for='data-5b1198c5-0553-4f8e-ad9c-4fdd84c2a332' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b4ee15c9-d341-4110-a2f1-7c1c22d9c8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4ee15c9-d341-4110-a2f1-7c1c22d9c8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec88c2d3-719f-41e2-b91a-aec936bc3edf' class='xr-var-data-in' type='checkbox'><label for='data-ec88c2d3-719f-41e2-b91a-aec936bc3edf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-6bef4865-79be-45e8-aa7f-dced276a1d4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bef4865-79be-45e8-aa7f-dced276a1d4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ff1a4cb-f2fe-467d-9f5a-84b8dce59a17' class='xr-var-data-in' type='checkbox'><label for='data-3ff1a4cb-f2fe-467d-9f5a-84b8dce59a17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time, levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;</div><input id='attrs-472ece78-230c-4744-86bb-115fd8e8d097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-472ece78-230c-4744-86bb-115fd8e8d097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc676f7e-2f37-4305-ac85-ec6b064376b9' class='xr-var-data-in' type='checkbox'><label for='data-bc676f7e-2f37-4305-ac85-ec6b064376b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 20) </td>\n",
-       "                        <td> (1, 20) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n",
-       "  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d8ad7c12-5a62-4de3-adc1-bb413f0d0ad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8ad7c12-5a62-4de3-adc1-bb413f0d0ad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-178a4f19-d4c8-4346-ba3f-c3aa494fa31e' class='xr-var-data-in' type='checkbox'><label for='data-178a4f19-d4c8-4346-ba3f-c3aa494fa31e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 481 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 1, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
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-       "  * time         (time) object 2001-01-01 00:00:00\n",
-       "Data variables: (12/13)\n",
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-       "    FSDS         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
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-       "    EFLX_LH_TOT  (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCEV         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCTR         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    H2OSOI       (time, levsoi) float32 dask.array<chunksize=(1, 20), meta=np.ndarray>\n",
-       "    HR           (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TBOT         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TSOI         (time, levgrnd) float32 dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
-       "    ASA          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# filter to specific date\n",
-    "ds.sel(time=\"2001-01-01\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also filter to a date range using the `.slice` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
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-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 6, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi       (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2001-06-01 00:00:00\n",
-       "Data variables: (12/13)\n",
-       "    FSR          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FSDS         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    GPP          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCEV         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCTR         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    H2OSOI       (time, levsoi) float32 dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;\n",
-       "    HR           (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TBOT         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TSOI         (time, levgrnd) float32 dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;\n",
-       "    ASA          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5f161966-6880-42da-ba87-584d331a7459' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f161966-6880-42da-ba87-584d331a7459' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levgrnd</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-255c65e0-0636-4cde-ae84-e9d21c55688e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-255c65e0-0636-4cde-ae84-e9d21c55688e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-c20bcb97-6936-4f39-bc52-fe521235f0b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c20bcb97-6936-4f39-bc52-fe521235f0b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecbda4bd-1215-48cc-89ff-dccc67aa5f3d' class='xr-var-data-in' type='checkbox'><label for='data-ecbda4bd-1215-48cc-89ff-dccc67aa5f3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-b4fc9193-32aa-46c9-84ab-48f13e53d11f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4fc9193-32aa-46c9-84ab-48f13e53d11f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a34a8410-d085-4861-a307-a4cec63f4f46' class='xr-var-data-in' type='checkbox'><label for='data-a34a8410-d085-4861-a307-a4cec63f4f46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2001-06-...</div><input id='attrs-7085ff99-ea8f-476d-a5a7-42acd2a7cac5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7085ff99-ea8f-476d-a5a7-42acd2a7cac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5a9e0c2-dc0e-412d-84d9-ce48afbfa811' class='xr-var-data-in' type='checkbox'><label for='data-c5a9e0c2-dc0e-412d-84d9-ce48afbfa811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06efd63f-0678-48ff-9667-3506ca3c2ad2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06efd63f-0678-48ff-9667-3506ca3c2ad2' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FSR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f57f4ef3-64a4-42cc-a3a0-ebba064c2676' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f57f4ef3-64a4-42cc-a3a0-ebba064c2676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c2a529c-88b4-4b65-aaf7-37920c4266f4' class='xr-var-data-in' type='checkbox'><label for='data-4c2a529c-88b4-4b65-aaf7-37920c4266f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reflected solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"91\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"41\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
-       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"41\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"41\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSDS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-9b2c951b-c627-42f3-9535-cf633786ccfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b2c951b-c627-42f3-9535-cf633786ccfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e73b1a2-0db4-4792-93f9-de1a09fe6f9f' class='xr-var-data-in' type='checkbox'><label for='data-7e73b1a2-0db4-4792-93f9-de1a09fe6f9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"91\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"41\" />\n",
-       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"41\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
-       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"41\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"41\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.20382942136741 0.0,41.20382942136741\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-44a9ee3c-9092-4107-a5a9-cc6c8533fc59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44a9ee3c-9092-4107-a5a9-cc6c8533fc59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-323401e8-d5ad-41e0-a21b-abf84bbdf6d7' class='xr-var-data-in' type='checkbox'><label for='data-323401e8-d5ad-41e0-a21b-abf84bbdf6d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"91\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"41\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
-       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"41\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,41.20382942136741 0.0,41.20382942136741\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFLX_LH_TOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-10698fc9-5509-4c44-a018-33d9225e7df0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10698fc9-5509-4c44-a018-33d9225e7df0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d7c6766-17d4-47a3-ae2c-5bef808ee033' class='xr-var-data-in' type='checkbox'><label for='data-8d7c6766-17d4-47a3-ae2c-5bef808ee033' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total latent heat flux [+ to atm]</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"91\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"41\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
-       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"41\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-de8d45f2-51ae-4f18-ad2c-fd849987c151' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de8d45f2-51ae-4f18-ad2c-fd849987c151' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-511e4e32-b338-4a42-8983-4eebf133d548' class='xr-var-data-in' type='checkbox'><label for='data-511e4e32-b338-4a42-8983-4eebf133d548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-52e92b3c-5c3f-4797-a093-8d607035a618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52e92b3c-5c3f-4797-a093-8d607035a618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0646bd09-fab0-4614-aa3c-3041673f4773' class='xr-var-data-in' type='checkbox'><label for='data-0646bd09-fab0-4614-aa3c-3041673f4773' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"41\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"41\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eb6c4332-62cb-4de0-8686-4c55e4e39293' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb6c4332-62cb-4de0-8686-4c55e4e39293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af5f93bc-0db4-4157-adac-2a797f9b8e3a' class='xr-var-data-in' type='checkbox'><label for='data-af5f93bc-0db4-4157-adac-2a797f9b8e3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"41\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-dff95005-7371-4d0a-a9a7-b7336892884e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff95005-7371-4d0a-a9a7-b7336892884e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-854533ee-76c6-4222-86ae-bd76a1e32e52' class='xr-var-data-in' type='checkbox'><label for='data-854533ee-76c6-4222-86ae-bd76a1e32e52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"61.203829\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
-       "  <text x=\"140.000000\" y=\"20.601915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.601915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time, levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;</div><input id='attrs-208b7ef0-d655-4b86-acb5-454aa9012d83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-208b7ef0-d655-4b86-acb5-454aa9012d83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f854e0-0a28-46bd-9873-22bf33d7a8e4' class='xr-var-data-in' type='checkbox'><label for='data-d5f854e0-0a28-46bd-9873-22bf33d7a8e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6, 20) </td>\n",
-       "                        <td> (1, 20) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <text x=\"60.000000\" y=\"63.544815\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bc6c3377-9bcb-4254-9241-13bd875d826a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc6c3377-9bcb-4254-9241-13bd875d826a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77d85e1a-a6c6-445b-a263-1a4253b12567' class='xr-var-data-in' type='checkbox'><label for='data-77d85e1a-a6c6-445b-a263-1a4253b12567' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TBOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2b463235-ae7d-425f-ac46-9a17df948e84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b463235-ae7d-425f-ac46-9a17df948e84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d34522fd-359d-4f87-af12-d7e8fdde3643' class='xr-var-data-in' type='checkbox'><label for='data-d34522fd-359d-4f87-af12-d7e8fdde3643' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric air temperature (downscaled to columns in glacier regions)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
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-       "                        <td> 4 B </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSOI</span></div><div class='xr-var-dims'>(time, levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;</div><input id='attrs-54ce7489-0ac2-4d1e-aa0c-2e9a77a364c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54ce7489-0ac2-4d1e-aa0c-2e9a77a364c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a0b9cfa-f771-465b-ab0e-3ba1d1cbc1b2' class='xr-var-data-in' type='checkbox'><label for='data-7a0b9cfa-f771-465b-ab0e-3ba1d1cbc1b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil temperature (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Bytes </th>\n",
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-       "                        <td> 100 B </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 25) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 486 Tasks </td>\n",
-       "                        <td> 6 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ASA</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-0cc03d8c-6fce-451f-a4f7-cf31f3ed17d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cc03d8c-6fce-451f-a4f7-cf31f3ed17d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0772942b-5eaa-4a0b-8f30-b34d6b764c9b' class='xr-var-data-in' type='checkbox'><label for='data-0772942b-5eaa-4a0b-8f30-b34d6b764c9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>unitless</dd><dt><span>long_name :</span></dt><dd>All sky albedo</dd></dl></div><div class='xr-var-data'><table>\n",
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-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 6, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi       (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2001-06-01 00:00:00\n",
-       "Data variables: (12/13)\n",
-       "    FSR          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FSDS         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    GPP          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCEV         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCTR         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    H2OSOI       (time, levsoi) float32 dask.array<chunksize=(1, 20), meta=np.ndarray>\n",
-       "    HR           (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TBOT         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TSOI         (time, levgrnd) float32 dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
-       "    ASA          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# filter by a date range\n",
-    "ds.sel(time=slice('2001-01-01', '2001-06-01'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also filter to a whole year:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
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-       " *\n",
-       " */\n",
-       "\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
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-       "\n",
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-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
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-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
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-       "\n",
-       ".xr-section-details {\n",
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-       "  margin-bottom: 5px;\n",
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-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-array-wrap {\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
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-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
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-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
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-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 12, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi       (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2001-12-01 00:00:00\n",
-       "Data variables: (12/13)\n",
-       "    FSR          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FSDS         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    GPP          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCEV         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCTR         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    H2OSOI       (time, levsoi) float32 dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;\n",
-       "    HR           (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TBOT         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TSOI         (time, levgrnd) float32 dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;\n",
-       "    ASA          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-514050d9-fb1e-46d7-a5ea-171fdcc6a1c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-514050d9-fb1e-46d7-a5ea-171fdcc6a1c7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levgrnd</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-01c29c22-57ab-49dd-b054-09585e8b449a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-01c29c22-57ab-49dd-b054-09585e8b449a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-4f34e0c9-a05b-4a4c-8566-401b243d0a7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f34e0c9-a05b-4a4c-8566-401b243d0a7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aca980cb-b84c-4042-aed3-9a76d6310d61' class='xr-var-data-in' type='checkbox'><label for='data-aca980cb-b84c-4042-aed3-9a76d6310d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-06533d4a-23de-4c75-ad4b-9a7a68c35345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06533d4a-23de-4c75-ad4b-9a7a68c35345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c44337e-44ce-4b1b-b272-7487ce908bd8' class='xr-var-data-in' type='checkbox'><label for='data-1c44337e-44ce-4b1b-b272-7487ce908bd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2001-12-...</div><input id='attrs-88b2e6e4-97a6-410c-b2c8-23fd6866db77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88b2e6e4-97a6-410c-b2c8-23fd6866db77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e746a83-623b-4a33-bebf-2207d36bdc34' class='xr-var-data-in' type='checkbox'><label for='data-4e746a83-623b-4a33-bebf-2207d36bdc34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67dfe332-a191-46f7-9c07-7f470c3af648' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67dfe332-a191-46f7-9c07-7f470c3af648' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FSR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-67491c11-1368-46a0-a0c1-6763c590d2d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67491c11-1368-46a0-a0c1-6763c590d2d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c1c228-c572-4b6c-b628-ae10aa85e7a6' class='xr-var-data-in' type='checkbox'><label for='data-25c1c228-c572-4b6c-b628-ae10aa85e7a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reflected solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"37\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
-       "  <text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSDS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-fd9e841f-f6a0-402f-80ab-20dccab40aff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd9e841f-f6a0-402f-80ab-20dccab40aff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a998481-64ac-4a4f-bd2a-485b1b846ca0' class='xr-var-data-in' type='checkbox'><label for='data-5a998481-64ac-4a4f-bd2a-485b1b846ca0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"37\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"37\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"37\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"37\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eb034023-bf61-42f9-8dd6-f78b7360f8a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb034023-bf61-42f9-8dd6-f78b7360f8a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f76e3be-4409-40e0-ab5d-201595d4c9c1' class='xr-var-data-in' type='checkbox'><label for='data-9f76e3be-4409-40e0-ab5d-201595d4c9c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFLX_LH_TOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-4a6ec2b3-ff37-4cbf-b4fe-d64e865c38bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a6ec2b3-ff37-4cbf-b4fe-d64e865c38bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-556dbb98-19f3-494e-97b2-5a82f090589f' class='xr-var-data-in' type='checkbox'><label for='data-556dbb98-19f3-494e-97b2-5a82f090589f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total latent heat flux [+ to atm]</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
-       "  <text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-294b13dd-4f16-4386-a918-78fc6c2e1cc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-294b13dd-4f16-4386-a918-78fc6c2e1cc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be7840c1-5e43-4c40-ae68-08f544f179aa' class='xr-var-data-in' type='checkbox'><label for='data-be7840c1-5e43-4c40-ae68-08f544f179aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-78f8ba9f-af23-4171-9131-76b51829bd20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78f8ba9f-af23-4171-9131-76b51829bd20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4108d41-81ea-4ddd-8e2f-9c73ec86b094' class='xr-var-data-in' type='checkbox'><label for='data-b4108d41-81ea-4ddd-8e2f-9c73ec86b094' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-23463af6-c1a3-4477-85d4-6a233815918f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23463af6-c1a3-4477-85d4-6a233815918f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58cb47c1-ba66-44ad-b14d-128872e33ae7' class='xr-var-data-in' type='checkbox'><label for='data-58cb47c1-ba66-44ad-b14d-128872e33ae7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d3699ba6-7558-487d-bf80-8e3c122cebc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3699ba6-7558-487d-bf80-8e3c122cebc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f36cb81d-dfba-4ba5-82e4-00a656782a2b' class='xr-var-data-in' type='checkbox'><label for='data-f36cb81d-dfba-4ba5-82e4-00a656782a2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time, levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20), meta=np.ndarray&gt;</div><input id='attrs-0595549d-7113-42d4-bbad-d1db25ac6c5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0595549d-7113-42d4-bbad-d1db25ac6c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecae9ffa-ca9c-469c-9f18-24924b94544b' class='xr-var-data-in' type='checkbox'><label for='data-ecae9ffa-ca9c-469c-9f18-24924b94544b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
-       "            <table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 0.94 kiB </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12, 20) </td>\n",
-       "                        <td> (1, 20) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "  <text x=\"140.000000\" y=\"36.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,36.000000)\">12</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bcb8d93b-4efa-4edf-823b-5b3cfb4cf0e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb8d93b-4efa-4edf-823b-5b3cfb4cf0e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2654c28d-d407-483f-be77-b8d53ff03b27' class='xr-var-data-in' type='checkbox'><label for='data-2654c28d-d407-483f-be77-b8d53ff03b27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TBOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-48aa082f-f968-4abf-a878-2582d6b9d391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48aa082f-f968-4abf-a878-2582d6b9d391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40107f39-2ab8-4462-b874-2a8dc45c5a0e' class='xr-var-data-in' type='checkbox'><label for='data-40107f39-2ab8-4462-b874-2a8dc45c5a0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric air temperature (downscaled to columns in glacier regions)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 48 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSOI</span></div><div class='xr-var-dims'>(time, levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;</div><input id='attrs-0f4040cb-1697-4855-8c59-14181b02f321' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f4040cb-1697-4855-8c59-14181b02f321' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1b4e819-bc86-4323-b828-1ec08871aac8' class='xr-var-data-in' type='checkbox'><label for='data-c1b4e819-bc86-4323-b828-1ec08871aac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil temperature (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.17 kiB </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (12, 25) </td>\n",
-       "                        <td> (1, 25) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 492 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
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-       "                        <td> 1332 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef05472b-3ebd-4616-b123-57b14a12330c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef05472b-3ebd-4616-b123-57b14a12330c' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>CLM History file information</dd><dt><span>comment :</span></dt><dd>NOTE: None of the variables are weighted by land fraction!</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>history :</span></dt><dd>created on 05/19/22 14:47:50</dd><dt><span>source :</span></dt><dd>Community Terrestrial Systems Model</dd><dt><span>hostname :</span></dt><dd>cheyenne</dd><dt><span>username :</span></dt><dd>afoster</dd><dt><span>version :</span></dt><dd>unknown</dd><dt><span>revision_id :</span></dt><dd>$Id: histFileMod.F90 42903 2012-12-21 15:32:10Z muszala $</dd><dt><span>case_title :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>case_id :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>Surface_dataset :</span></dt><dd>surfdata_0.9x1.25_hist_16pfts_Irrig_CMIP6_simyr2000_my_point_c220519.nc</dd><dt><span>Initial_conditions_dataset :</span></dt><dd>I2000_CTSM51_spinup.clm2.r.0281-01-01-00000.nc</dd><dt><span>PFT_physiological_constants_dataset :</span></dt><dd>ctsm51_params.c211112.nc</dd><dt><span>ltype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ltype_crop :</span></dt><dd>2</dd><dt><span>ltype_UNUSED :</span></dt><dd>3</dd><dt><span>ltype_landice :</span></dt><dd>4</dd><dt><span>ltype_deep_lake :</span></dt><dd>5</dd><dt><span>ltype_wetland :</span></dt><dd>6</dd><dt><span>ltype_urban_tbd :</span></dt><dd>7</dd><dt><span>ltype_urban_hd :</span></dt><dd>8</dd><dt><span>ltype_urban_md :</span></dt><dd>9</dd><dt><span>ctype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ctype_crop :</span></dt><dd>2</dd><dt><span>ctype_crop_noncompete :</span></dt><dd>2*100+m, m=cft_lb,cft_ub</dd><dt><span>ctype_landice :</span></dt><dd>4*100+m, m=1,glcnec</dd><dt><span>ctype_deep_lake :</span></dt><dd>5</dd><dt><span>ctype_wetland :</span></dt><dd>6</dd><dt><span>ctype_urban_roof :</span></dt><dd>71</dd><dt><span>ctype_urban_sunwall :</span></dt><dd>72</dd><dt><span>ctype_urban_shadewall :</span></dt><dd>73</dd><dt><span>ctype_urban_impervious_road :</span></dt><dd>74</dd><dt><span>ctype_urban_pervious_road :</span></dt><dd>75</dd><dt><span>cft_c3_crop :</span></dt><dd>1</dd><dt><span>cft_c3_irrigated :</span></dt><dd>2</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd></dl></div></li></ul></div></div>"
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-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 12, levsoi: 20, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi       (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2001-12-01 00:00:00\n",
-       "Data variables: (12/13)\n",
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-       "    FSDS         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
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-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
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-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.sel(time='2001')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we wanted to select the first time instance, we would use the `.isel` function. Note that the `.values` function returns an array with just the values for `FSDS` in it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(77.89855194)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# grab the first value of the incident solar radiation\n",
-    "ds.isel(time=0).FSDS.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also filter by other coordinates, like `levsoi`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
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-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "    levsoi       float32 0.01\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "Data variables: (12/13)\n",
-       "    FSR          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FSDS         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    GPP          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCEV         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    FCTR         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    H2OSOI       (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    HR           (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TBOT         (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    TSOI         (time, levgrnd) float32 dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;\n",
-       "    ASA          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2bfe95c1-c267-45c6-a393-d48d0ee41395' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bfe95c1-c267-45c6-a393-d48d0ee41395' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>levgrnd</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fdd5672b-45bf-458f-894b-18bc452b0c8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdd5672b-45bf-458f-894b-18bc452b0c8d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-455d31eb-8e22-4890-982c-d7f672796f51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-455d31eb-8e22-4890-982c-d7f672796f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-485dedfd-c55e-441f-a979-6a6ba7ba8180' class='xr-var-data-in' type='checkbox'><label for='data-485dedfd-c55e-441f-a979-6a6ba7ba8180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>levsoi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01</div><input id='attrs-d86c61a5-7239-447d-a65c-5d52a11f7bed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d86c61a5-7239-447d-a65c-5d52a11f7bed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa5d09e-410d-4a95-ac45-e512383da245' class='xr-var-data-in' type='checkbox'><label for='data-eaa5d09e-410d-4a95-ac45-e512383da245' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0.01, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2010-12-...</div><input id='attrs-53a02745-c711-4e5e-8dd2-a5b66be9cf45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53a02745-c711-4e5e-8dd2-a5b66be9cf45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ffadbed-3566-4716-b270-b1a13f1ba090' class='xr-var-data-in' type='checkbox'><label for='data-3ffadbed-3566-4716-b270-b1a13f1ba090' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92d7dbe0-852e-40b7-92bb-afebfa994ac3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92d7dbe0-852e-40b7-92bb-afebfa994ac3' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FSR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-23ca9ad0-eecb-4f85-862e-a8b1bcfb9f51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23ca9ad0-eecb-4f85-862e-a8b1bcfb9f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96b411d3-6182-47ce-b9fe-67529d2a4591' class='xr-var-data-in' type='checkbox'><label for='data-96b411d3-6182-47ce-b9fe-67529d2a4591' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reflected solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSDS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a3508191-1db3-4138-bdea-2e3cb30ddbde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3508191-1db3-4138-bdea-2e3cb30ddbde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3886f1e5-ff11-4a89-bf70-18f6f95c16da' class='xr-var-data-in' type='checkbox'><label for='data-3886f1e5-ff11-4a89-bf70-18f6f95c16da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric incident solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d0509671-f402-4f72-9d60-2bfdebb5c3dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0509671-f402-4f72-9d60-2bfdebb5c3dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c28fe438-c132-43c7-a83f-60489e788407' class='xr-var-data-in' type='checkbox'><label for='data-c28fe438-c132-43c7-a83f-60489e788407' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFLX_LH_TOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c8f04914-fe32-4aaa-b0e0-daabab75ccc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8f04914-fe32-4aaa-b0e0-daabab75ccc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a5f22d7-7b33-4755-8acf-23d8fc75a8e5' class='xr-var-data-in' type='checkbox'><label for='data-3a5f22d7-7b33-4755-8acf-23d8fc75a8e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total latent heat flux [+ to atm]</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
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-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-1ac8c31c-93b9-4ac9-b0d6-768c6e314315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ac8c31c-93b9-4ac9-b0d6-768c6e314315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058afcdb-ebd0-4cf1-8aca-40831df91735' class='xr-var-data-in' type='checkbox'><label for='data-058afcdb-ebd0-4cf1-8aca-40831df91735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
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-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f9fa9d1c-7782-48b7-a1c3-b294f4d01920' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9fa9d1c-7782-48b7-a1c3-b294f4d01920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6051bfa-7f44-4c61-8f21-e0ee890afa52' class='xr-var-data-in' type='checkbox'><label for='data-e6051bfa-7f44-4c61-8f21-e0ee890afa52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-0a2f480f-8c55-471a-b6bd-4568403b9dd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a2f480f-8c55-471a-b6bd-4568403b9dd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7fb8608-7a92-4b54-9b07-2c9a55de5471' class='xr-var-data-in' type='checkbox'><label for='data-a7fb8608-7a92-4b54-9b07-2c9a55de5471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-221e1c5c-272d-4506-8ff7-1aa6c586692e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-221e1c5c-272d-4506-8ff7-1aa6c586692e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a856ea9d-5c9c-48e7-889d-bbeb988f62b4' class='xr-var-data-in' type='checkbox'><label for='data-a856ea9d-5c9c-48e7-889d-bbeb988f62b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
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-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-26b2ee0c-9775-460f-84c2-fc82e9b3373c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26b2ee0c-9775-460f-84c2-fc82e9b3373c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b40be8a2-12a3-47e2-8b44-ef41e086a671' class='xr-var-data-in' type='checkbox'><label for='data-b40be8a2-12a3-47e2-8b44-ef41e086a671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 600 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-36b8e643-81e8-4f3e-ae96-1c98eb8750bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36b8e643-81e8-4f3e-ae96-1c98eb8750bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb4059a-72a6-4379-b6c3-42ffe59eb4dd' class='xr-var-data-in' type='checkbox'><label for='data-8bb4059a-72a6-4379-b6c3-42ffe59eb4dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "            </table>\n",
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-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TBOT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8ef1d849-4f07-4f93-9cbe-641b68021b7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ef1d849-4f07-4f93-9cbe-641b68021b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8587df9a-d9d2-4f5d-9b2f-90bd85858e3f' class='xr-var-data-in' type='checkbox'><label for='data-8587df9a-d9d2-4f5d-9b2f-90bd85858e3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric air temperature (downscaled to columns in glacier regions)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSOI</span></div><div class='xr-var-dims'>(time, levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25), meta=np.ndarray&gt;</div><input id='attrs-27567ab2-adcc-4b95-b3a5-d95669c7cff8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27567ab2-adcc-4b95-b3a5-d95669c7cff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd02a53b-5c27-4784-ae73-39c4cf6568f1' class='xr-var-data-in' type='checkbox'><label for='data-fd02a53b-5c27-4784-ae73-39c4cf6568f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil temperature (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 11.72 kiB </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120, 25) </td>\n",
-       "                        <td> (1, 25) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 480 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ASA</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-62318278-2229-4537-ae5e-3fcf2fd79975' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62318278-2229-4537-ae5e-3fcf2fd79975' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047384fc-b6a3-4e54-9f8b-6b40b13cc4fb' class='xr-var-data-in' type='checkbox'><label for='data-047384fc-b6a3-4e54-9f8b-6b40b13cc4fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>unitless</dd><dt><span>long_name :</span></dt><dd>All sky albedo</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 1320 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-61698988-340e-45b1-bde0-2fd2aaf32dc8' class='xr-section-summary-in' type='checkbox'  ><label for='section-61698988-340e-45b1-bde0-2fd2aaf32dc8' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>CLM History file information</dd><dt><span>comment :</span></dt><dd>NOTE: None of the variables are weighted by land fraction!</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>history :</span></dt><dd>created on 05/19/22 14:47:50</dd><dt><span>source :</span></dt><dd>Community Terrestrial Systems Model</dd><dt><span>hostname :</span></dt><dd>cheyenne</dd><dt><span>username :</span></dt><dd>afoster</dd><dt><span>version :</span></dt><dd>unknown</dd><dt><span>revision_id :</span></dt><dd>$Id: histFileMod.F90 42903 2012-12-21 15:32:10Z muszala $</dd><dt><span>case_title :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>case_id :</span></dt><dd>I2000_CTSM_singlept</dd><dt><span>Surface_dataset :</span></dt><dd>surfdata_0.9x1.25_hist_16pfts_Irrig_CMIP6_simyr2000_my_point_c220519.nc</dd><dt><span>Initial_conditions_dataset :</span></dt><dd>I2000_CTSM51_spinup.clm2.r.0281-01-01-00000.nc</dd><dt><span>PFT_physiological_constants_dataset :</span></dt><dd>ctsm51_params.c211112.nc</dd><dt><span>ltype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ltype_crop :</span></dt><dd>2</dd><dt><span>ltype_UNUSED :</span></dt><dd>3</dd><dt><span>ltype_landice :</span></dt><dd>4</dd><dt><span>ltype_deep_lake :</span></dt><dd>5</dd><dt><span>ltype_wetland :</span></dt><dd>6</dd><dt><span>ltype_urban_tbd :</span></dt><dd>7</dd><dt><span>ltype_urban_hd :</span></dt><dd>8</dd><dt><span>ltype_urban_md :</span></dt><dd>9</dd><dt><span>ctype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ctype_crop :</span></dt><dd>2</dd><dt><span>ctype_crop_noncompete :</span></dt><dd>2*100+m, m=cft_lb,cft_ub</dd><dt><span>ctype_landice :</span></dt><dd>4*100+m, m=1,glcnec</dd><dt><span>ctype_deep_lake :</span></dt><dd>5</dd><dt><span>ctype_wetland :</span></dt><dd>6</dd><dt><span>ctype_urban_roof :</span></dt><dd>71</dd><dt><span>ctype_urban_sunwall :</span></dt><dd>72</dd><dt><span>ctype_urban_shadewall :</span></dt><dd>73</dd><dt><span>ctype_urban_impervious_road :</span></dt><dd>74</dd><dt><span>ctype_urban_pervious_road :</span></dt><dd>75</dd><dt><span>cft_c3_crop :</span></dt><dd>1</dd><dt><span>cft_c3_irrigated :</span></dt><dd>2</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 120, levgrnd: 25)\n",
-       "Coordinates:\n",
-       "  * levgrnd      (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "    levsoi       float32 0.01\n",
-       "  * time         (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "Data variables: (12/13)\n",
-       "    FSR          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FSDS         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    GPP          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    EFLX_LH_TOT  (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCEV         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    FCTR         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    ...           ...\n",
-       "    ELAI         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    H2OSOI       (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    HR           (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TBOT         (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    TSOI         (time, levgrnd) float32 dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
-       "    ASA          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Attributes: (12/37)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/19/22 14:47:50\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             cheyenne\n",
-       "    ...                                   ...\n",
-       "    ctype_urban_shadewall:                73\n",
-       "    ctype_urban_impervious_road:          74\n",
-       "    ctype_urban_pervious_road:            75\n",
-       "    cft_c3_crop:                          1\n",
-       "    cft_c3_irrigated:                     2\n",
-       "    time_period_freq:                     month_1"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.isel(levsoi=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*For more information about filtering and indexing data see the [xarray documentation](https://xarray.pydata.org/en/v0.11.0/indexing.html).*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 3. Plotting\n",
-    "### 3.1 Easy plots using xarray and matplotlib\n",
-    "\n",
-    "As shown previously, *xarray* provides built-in plotting functions. For a quick inspection of albedo, we can use `.plot()` to see it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.ASA.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    The package <i> xarray </i> automatically sets the x and y axis names based on the metadata/attributes that are set for each variable. Notice that above we set the <code>long_name</code> and <code>units</code> for our new variable <code>ASA</code>, which is how <i>xarray</i> knew what to put in the y-axis label.\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "We can also just plot one year of data (2001) from the simulation, selecting the year using the `.sel` function. Note we also changed the color and marker for this plot.\n",
-    "\n",
-    "*More plotting examples are on the [xarray web site](https://docs.xarray.dev/en/latest/user-guide/plotting.html)*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.ASA.sel(time='2001').plot(color=\"purple\", marker=\"o\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot multiple graphs by using the keyword `ax`. Here, `axes` is an array we create consisting of a left and right axis created with `plt.subplots`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1OpKJA6o6TVVPcO+/oKovuPdVVUeqakdV7aGqtsKpqRYboOed5s2dIrlFC2fVvZ9/9jpRdLAC2US17NXZAPRq3svjJMHTvWl33hv+HnPXzuXcj86luKTY60jGGLNfcnOt/7GXWrZ0Bus1bQrHHQfTp3udKPJZgWyiWs6aHDo27kiD2g28jhJUx3c6nmcGP8OEhRP4v//+n9dxjDGm2jZsgHXrrAXZa61aOUVySopTJM+saP4aYwWyiW6lS0zHopH9RzKy30ie+N8TvJb9mtdxjDGmWhYscG6tBdl7bdo4RXLDhnDssZCT43WiyGUFsolaW3ZtYdHGRVE/g0VFnhr8FMcccAxXTryS75Z953UcE0YikiUit4rIPaWb15mMqQ6b4i2ytGvnFMn16sExx8Ds2V4nikxWIJuoNXuN81sdqy3IADUSavDe8Pfo0LgDp753Kks2LfE6kgkDEXkBOBO4FmexjtOBdp6GMqaa8vIgKckpzExk6NDBKZKTk+G007xOE5msQDZRK9ZmsChP4+TGTDh7AkUlRQx7Z5jNbBEfDlPVC4BNqvpP4FD2XrDDmKiRmwudO0NiotdJjK+OHeGmm2DRIlizxus0kccKZBO1sldnk1onlZb1W3odJeQ6p3Tm/dPfJ3ddLud8dI7NbBH7Ct3bHSLSEtgDdPAwjzHVZlO8Ra7MTOfWBuztywpkE7Vy1jgr6ImI11HC4pgDjuHpwU8zceFEbv/ydq/jmNCaKCKNgMeAX4GlOMtBGxNVdu6EJUus/3Gk6t3buf31V09jRKQaXgcwpjr2FO9h7tq5XH/w9V5HCauR/Ucyb908HvvxMQ5MO5CLel/kdSQTAqp6v3v3QxGZCCSp6mYvMxlTHfn5UFJiLciRqkEDp/uLtSDvy1qQTVTKW5/H7uLdMd//2J+nBz/N0R2O5sqJV/LD8h+8jmNCQETqiMjdIvKSqu4CmorICV7nMqaqbAaLyJeZaQWyP1Ygm6iUXeCsoBfLU7yVp2ZiTd47/T3aNmzLKe+ewtI/lnodyQTfa8AunMF5ACuBB7yLY0z15OWBiNNKaSJTZiasWOEs5mL+UmmBLCJpInKHiLwoIq+WbuEIZ0x5cgpySK6RTOeU+DzrNkluwoSzJ7C7eDfDxtnMFjGoo6o+ijM4D1UtxJnuzZiokpcH7ds704mZyJSR4dxaP+S9BdKC/CnQEPgvMMlnM8YzOQU59GjWg8SE+J03qGtqV947/T3mr5vPeR+fR4mWeB3JBM9uEUkGFEBEOuK0KBsTVXJzrXtFpCstkK2bxd4CKZDrqOr/qep7qvph6RbyZMaUQ1XJLsiOy+4VZR3X8Tj+dfy/GL9gPHd8eYfXcUzw/AOYArQRkbeAL4FbvY1kTNWUlDjLTNsAvcjWsCF06mQtyGUFMovFRBEZqqqTQ57GmAAs37ycP3b+EZcD9Py5pv81zFs3j0d+eIQD0w7kgl4XeB3J7AcRSQAaA6cCh+B0rbheVdd7GsyYKlq+HAoLrQU5GmRkwC+/eJ0isgTSgnw9TpG8U0S2utuWUAczpjzxsoJeoESEZ4c8y1Htj+LyCZfz44ofvY5k9oOqlgDXqOoGVZ2kqhOtODbRKC/PubUW5MiXmQlLl8KGDV4niRyVFsiqWl9VE1Q1yb1fX1UbhCOcMf5kF2QjCD2a9vA6SsSomViT909/nzYN2nDKu6ew7I9lXkcy+2eqiNwiIm1EpEnp5nUoY6rCpniLHqUr6lk3i78ENM2biAwTkcfdzebiNJ7KKcihS2oX6taq63WUiJJSJ4UJZ09gZ9FOhr0zjG27t3kdyVTfJcBI4FtgprvN8DSRMVWUlwepqc5mIlsfd0iPFch/CWSat4dxulnMd7fr3eeM8UROQY51ryhHt7RuvDv8Xeauncv5H59vM1tEKVXt4Gc7wOtcxlSFzWARPZo0gQ4dbCYLX4G0IA8FjlXVV1X1VWCw+5wxYbepcBPLNi+jd7PeXkeJWIM7DebJ457kk7xPuOuru7yOY6pJRA4SkTNE5ILSzetMxlRFXp4VyNHEVtTbW6Ar6TXyud8wBDmMCUjpAL0+LWyKt4pcd/B1XJ5xOQ99/xBjZ4/1Oo6pIhH5B/Csux0FPAoM8zSUMVWwYYOzMpsN0IsemZmweDFs2uR1ksgQSIH8EJAtIq+LyH9w+sI9GNpYxvhXWiD3atbL2yARTkQYPXQ0R7Y7ksvGX8ZPK3/yOpKpmuHA0UCBql4M9AJqexvJmMCVzmBhLcjRo3TBkOxsb3NEikBmsRiHMxfnR+52qKq+E+pgxviTsyaHFvVa0KxeM6+jRLxaibX48IwPadWgFSe/czLLNy/3OpIJXKE73VuRiDQA1gJB7YMsIkki8ouIzBKReSLyTz/7DBSRzSKS4273BDODiV02xVv0KZ3JwrpZOMotkEWkq3ubAbQAVgIrgJbuc8aEXfbqbBugVwWlM1sUFhVy0jsnsX33dq8jmcDMEJFGwEs4V+1+BYI9jf8uYJCq9gJ6A4NF5BA/+32nqr3d7b4gZzAxKjcXkpKgbVuvk5hApaRAu3Y2k0WpilbSuwm4AnjCz2sKDApJImPKsbNoJ7nrczmx84leR4kqB6YdyDunvcMJ407g/I/P54MzPiBBAh1+YLygqle7d18QkSlAA1WdHeTvUKB0LsCa7qbB/A4Tv/LyoEsXSEz0OompiowMa0EuVe7/JVX1CvfuEFU9ynfDZrEwHpi/bj5FJUXWglwNQ9KH8Pixj/Nx3sfc87VdJY8mqro02MVxKRFJFJEcnC4cU1X1Zz+7Hep2w/hMRLpX8FlXiMgMEZmxbt26UMQ1UcSmeItOmZmQnw+bN3udxHuBNCP5W7fW1rI1YZe92hk5YAVy9dxwyA1c2udSRn03irfnvO11HBMBVLVYVXsDrYH+InJQmV1+Bdq53TCeBT6p4LNeVNW+qto3LS0tVJFNFNi5E5YssQI5GpX2Q7aBehX3QW4uIplAsoj0EZEMdxsI1AlXQGNK5RTkUK9WPTo26eh1lKgkIozJGsMR7Y7gkk8v4eeV/hoLjZdEpIMX36uqfwDTcOa5931+i6puc+9PBmqKiK2LZiq0cCGo2gC9aFQ6k4X1Q664Bfl44HGcloUnfLYbgTtCH82YveWsyaFXs17Wf3Y/lM5s0bJ+S05+92RWbF7hdSSztw8AROTLUH+RiKS5AwERkWTgGCCvzD7NRUTc+/1x/p+xIdTZTHSzKd6iV9Om0Lq19UOGCgbpqep/gP+IyGmq+mEYMxmzjxItYVbBLC7oZYuJ7a/UOqlMOHsCh75yKCe9cxLfXfwddWvV9TqWcSS4i4R0FpGbyr6oqk/6PhaRQPomr1PVo/083wLnHJ+IU/i+p6oTRWSE+10v4MzHfJWIFAGFwFnu4D5jypWXByLQubPXSUx12Ip6jopmsSiVKSJfupfgEJHGwM2qamvYmrBZvGkxW3dvpU9zW0EvGLo37c6408Zx4rgTufCTC3nv9PesZT4ynAWcjHNurh/A/olUPGhagPH+XnAH/u3zC+UWxqX3RwOjA8hhzJ9yc6F9e0hO9jqJqY6MDBg/HrZuhfqBnIViVCAF8hBV/bNLhapuEpGhQNALZPdy38vAQTjTDV2iqv8L9veY6FO6gp4N0AuerM5ZPHbsY9wy9RbunXYv9x1lU9x6TVUXAI+IyGxV/SyAt1ypqssq2kFErq7odWOCLS/P+h9Hs8xMpw95Tg4MGOB1Gu8EUiAnikhtVd0Ff/ZVC9WSp08DU1R1uIjUwgYDGldOQQ6Jkkj3puXOMmWq4aZDb2Leunnc/+39DE0fyiGt/a0TYcJNVT8TkSygO5Dk8/x9Zfb7PoDPqnQfY4KlpAQWLIBBtlJC1PJdUS+eC+RArqmOBb4UkUtF5BJgKvCfYAdxl1M9AngFQFV3l3brMCa7IJtuad1IqpFU+c4mYCLC04OfplX9VoyYOIKikiKvIxlARF4AzgSuxekmcTrQzs9+DUTkIRF5U0TOKfPamLCENcbH8uVQWGgD9KJZ8+bQsqXNZFFpgayqjwKjgG44rRn3u88F2wHAOuA1EckWkZdFxEYOGcBpQbb+x6FRv3Z9nh78NLPWzGL0L9bdNEIcpqoXAJtU9Z/AoUAbP/u9hlNAfwicJSIfikjpFT67HGDCLjfXubUuFtHNVtQLrAUZVf1MVW9R1ZtV9fMQZakBZADPq2ofYDtwm+8OtlJTfFq7fS2rtq6y/schdGq3UxnSaQh3f303v2/53es4xpkxAmCHiLQE9gD+5kjuqKq3qeonqjoMZ2GPr0QkJVxBjfFlU7zFhsxM57/l9u1eJ/FOpQWyiGwVkS3utlNEikVkSwiyrARW+ix1+gFOwfwnW6kpPs0qmAXYAL1QEhFGDx1NUUkRN3x+g9dxDEx0By0/hlP0LgXe8bNfbZG/ph9R1VHAi8C3gBXJJuxycyE11dlM9MrMdPqTz5rldRLvBNLFor6qNnC3JOA0QjDtj6oWACtEpIv71NHA/GB/j4k+2QW2xHQ4HND4AO4acBcfzP+AKYumeB0nrqnq/ar6hzsHfTugq6re7WfXCcCgMu/9D3AzsDv0SY3ZW16etR7HgtIV9eK5m0WVJz5V1U8oc0IOomuBt9zJ73sDD4boe0wUySnIoW3DtjRJbuJ1lJh3y2G30CWlCyMnj6RwT2HlbzAhISJ1RORuEXnJnUGoqYicUHY/Vb1VVf/r5/kpqpoelrDG+LAp3mJDy5bQrJkVyBUSkVN9tuEi8jDOHMVBp6o5bheKnqp6sqpuCsX3mOiSU5BjrcdhUrtGbcZkjWHxpsU89P1DXseJZ68Bu3AG54HTBe2B8nYWkTiezt9Eig0bYN06a0GOBSK2ol4gLcgn+mzHA1uBk0IZyphSO/bsYMGGBTaDRRgN6jCIc3ucyyM/PMKC9Qu8jhOvOrqzBe0BUNVCnNkq9iEirYCJYcxmjF82QC+2ZGTA/PmwY4fXSbwRSB/ki322y1V1lKquDUc4Y+asmUOJllgLcpg9cdwTJNdI5urJV6MakgtGpmK73UWZFEBEOuK0KO9FRLoDU4D/C288Y/ZlU7zFltKBerNne53EG+WupCciz1JBVwpVvS4kiYzxYUtMe6NZvWY8dPRDXD35asbNHcc5Pc6p/E0mmP6BU/i2EZG3gMOBi/zs9zVwkqr+FMZsxviVlwdJSdC2rddJTDD4rqh3SBzOql5RC/IMYCbOMqcZQL679QaKQ57MGJwZLBolNaJdw30WETMhdkXmFfRr2Y+bPr+JP3b+4XWcuKKqU4FTcYricUBfVZ3mZ9fpODMLGeO53Fzo0gUSE71OYoKhdWtIS4vfFfXKLZBV9T/udEHpwFGq+qyqPosz/VrvMOUzca50gJ6I3+6XJoQSExJ54YQXWLdjHXd9dZfXceKCiGSUbjjTu60GVgFt3efKGgY0EJFQrG5qTJXYFG+xRSS+V9QLZJBeS8B3hHQ99zljQqq4pJjZa2bTu1lvr6PErYwWGYzsN5Ix08cw/ffpXseJB09UsD1edmdVLVbVK4Bt4QxpTFmFhbBkifU/jjWZmTBvHuzc6XWS8Cu3D7KPh4FsEfnafXwkcK+/HUVkfACft1FVLwoonYlr+RvzKSwqtP7HHrv/qPv5YP4HXDXpKn6+7GcSE+z6aaio6lHVfN99wc5iTFXk54OqtSDHmsxMKCqCOXOgXz+v04RXpQWyqr4mIp8BB+MM2rvNXfXOn27AZRV8nADPVTmliUvZq50V9Pq0sCnevNQwqSH/Ov5fnPXhWTw/43mu6X+N15GMHyLSGGiDz3ldVeO096AJt9Ip3qwFObb4rqhnBbJ//YEB7n3FWd7UnztV9ZuKPkhE/hngd5o4l1OQQ63EWnRNtSYJr53R/QxeyX6FO7+6k9O6nUaL+i28jmR8iMj9OAP6fuOv2YeU0K16asxecnOdPqvptn5jTGnXDpo0ic9+yIGspPcwcD0w392uExG/S2yp6nt+3p8gIg0q2scYf3LW5NA9rTu1Emt5HSXuiQjPDX2OXUW7uOmLm7yOY/Z1Bs7iIgNV9Sh3s+LYhE1eHrRvD8nJXicxwRTPK+oFMkhvKHCsqr6qqq8Cg4Gsit4gIm+LSAMRqYtTVC8Qkb/vf1wTL1SV7NXZtoJeBElPSee2v93GO3PfYepvU72OE5N8Z7Hwt1Xw1rlAozDFNGYfubnWvSJWZWTA3Lmwa5+limJbIAUy7H3ibRjA/geq6hbgZGAy0BY4v0rJTFxbvW0163asswF6Eea2v91GpyadGDl5JDuL4nBYc+iVzljxHPAz8CLwknv/mQre9xDOYOrPRWR86RbytMbgrLa2YIEN0ItVmZmwZ49TJMeTQArkB3FOvK+LyH9wFg95sJL31BSRmjgF8qequocKVuUzpixbQS8yJdVIYszQMeRvzOfRH2zq3WAr7R4BLAMyVLWvqmYCfYBFFbz1P8AjOLMO+U4NZ0zILVvmTANmLcixyXdFvXhS4SA9EUkASoBDgH44s1D8XwWzWJT6N7AUmAV8KyLtgC37ndbEjdIZLHo17+VxElPWsR2P5czuZ/Lgdw9yTo9z6NSkk9eRYlFXVZ1T+kBV54pI7wr2X6+qFbUw70NEkoBvgdo4/y/4QFX/UWYfAZ7G6Wq3A7jIZsYwZZXOYGEtyLGpQwdo1Cj+VtSrsAVZVUuAa1R1taqOV9VPKyqOReRQERFVfUZVW6nqUFVVYDlQrfk9TXzKWZNDx8YdaVC7QeU7m7B78vgnqZVYi5GTR+L8ipsgyxWRl0VkoIgcKSIvAbkV7D9TRB5yz8GB9FkG2AUMUtVeOKujDhaRQ8rsMwRnNdV04Arg+er9OCaW5br/Mq0FOTbF64p6gXSxmCoit4hIGxFpUrqVs++FOCfqd0TkIhFpDqCOoqClNjGvdIlpE5la1m/JqEGj+OK3L3h//vtex4lFFwPzcGYQugFnsPPFFezfB+dK34NUsPKeL/e8XLoCX013K/vXzknAG+6+PwGNRMTm+DN7ycuD1FRISfE6iQmVzEyYPRt27/Y6SfgEMg/yJe7tSJ/nFDig7I6qOgJARLritDy8LiINga+BKcAPqlq8X4lNzNu6ayuLNi7iwl4Xeh3FVODqflfz+qzXuWHKDQzuNNha+4NIVXeKyAvAZFVdUN5+InIo8FN1V+ATkUSccSWdgOdU9ecyu7QCVvg8Xuk+t9rPZ12B08pM27ZtqxPHRKm8POteEesyM53ieP586N3b6zThUWkLsqp28LPtUxyXeU+eqv5LVQfjTFT/PXA6zkhsYyo0a80sAJviLcIlJiTyQtYLFGwr4J6v7/E6TkwRkWFADk7DAiLSu5xZKfxetQuUqharam+gNdBfRA4qG8Xf28r5rBfdQYV909LSqhLDRDmb4i32+a6oFy8CWSgkSURuEpGPRORDEbnBHdwREFUtVNXJqnqtqvbdv7gmHtgMFtGjX6t+jOg7gmd/eZZfV8fZCI7Q+gfOCqZ/AKhqDtC+7E6qOkJVM4B7gcY4V+3+JyIPisgRbgtxpVT1D2Aazjz3vlbiLF9dqjWwKvAfw8S69eudzVqQY1vHjtCggRXIZb0BdAeeBUYDBwJv+ttRRHqIyE8iskJEXhSRxj6v/RKMwCb25RTkkFonlZb1W3odxQTgwaMfJLVOKldNuoriEutBFSRFqro50J2rc9VORNJEpJF7Pxk4Bsgrs9t44AJxHAJsVtV9uleY+FU6g4W1IMe2hASnFTmeZrIIpEDuoqqXqurX7nYF0LmcfZ/HacnoASwEvheRju5rNfc7rYkL2QXOCnrODFMm0jVKasSTxz3JL7//wku/vuR1nFgxV0TOARJFJF1EngV+DOSNVbhq1wL4WkRmA9OBqao6UURGiMgId5/JwGKcOZhfAq6u9k9kYpJN8RY/MjJg1iwoipMpFwIpkLN9p/4RkYOBH8rZt56qTlHVP1T1ceAaYIr7fpsLylRqT/Ee5q6da90rosw5Pc5hUIdB3P7l7azZtsbrOLHgWpwrd7uAcTjzyN9Qdqf9uWqnqrNVtY+q9lTVg1T1Pvf5F1T1Bfe+qupIVe2oqj1UdUbwfkQTC3JzISkJ2rXzOokJtcxMZ0GY+fO9ThIegRTIBwM/ishSEVkK/A84UkTmuC0PvsSdtQIAVf0aOA2nS4b9+phK5a3PY3fxbiuQo4yI8NzQ59i+ezu3TL3F6zhRT1V3qOqdqtrPHfh2p6r6W9vbrtoZT+XlQZcuziV4E9vibUW9QKZ5KztooyKPAN2An0qfUNXZInI0cHcVs5k4lF3grKBnBXL06ZralVsPv5VR343ikt6XcFQHWxuoqkRkAhVcbVPVYWWeqqeqU9z7j4vITJyrdudX9DnGBEtuLvTv73UKEw7p6VCvntMP+eKKZmWPEYFM87asoq3Mvm+7k8mX/Yzlqnp5MIOb2JRTkENyjWS6pHTxOoqphjsH3EmHRh24evLV7C6Ooxnlg+dxnEU+lgCFOP1+XwK2AXP97G9X7YxnCgth6VIboBcvEhKgT5/4aUEOyUUREekZis81sS+nIIcezXqQmBDQ7FQmwiTXTGb00NHkrc/j8R8rXMjN+KGq36jqN0AfVT1TVSe42znA3/y8pfSqne9nzAaOBj4KfWITz/LzQdUG6MWTzEzIyYmPgXpBL5BF5BhgTLA/18Q+VXWWmG7W2+soZj8MTR/Kad1O4/5v72fxpsVex4lWaSLy54JMItIB2Gf1DbtqZ7yUm+vcWgty/MjMdK4cLCh3fc/YEchCIdf4joyuZN9zgUeBU/Y3mIk/yzcvZ9POTfRpYSvoRbunBj9FjYQaXPvZtahaV9hquBGYJiLTRGQa8DV+ZrEoZVftjBfy8kDE6Ztq4kM8ragXSAtyc2C6iLwnIoOl4slpXwGGq+q64MQz8cRW0IsdrRu05p8D/8nk/Ml8nPex13GijjvwLh243t26qOrn/va1q3bGK7m50KEDJCd7ncSES5cuULeuFcgAqOpdOCfqV4CLgHx3GdOOfna/D3jFXZXJmCrJKchBEHo07eF1FBME1x18HT2b9eT6Kdezbfc2r+NEBREZ5N6eCmQBHd0ty32u7P521c54Ji/P+h/Hm8RE6N07PlbUC6gPsjrXSAvcrQhoDHwgIo+W2e9BnEL6k+DGNPEguyCbLqldqFurrtdRTBDUSKjBC1kvsHLLSu6ddq/XcaLFke7tiX62E/zsb1ftjCeKi51+qFYgx5+MDMjOdv4NxLJA+iBf586t+SjOCno9VPUqIBNnOqG9qOpYd19jqiSnIMe6V8SYQ9scyuUZl/PUT08xe03ZdYVMWar6D/f2Yj/bJX7eYlftjCeWL3dWVbMBevEnMxO2b4eFC71OElqBtCCnAKeq6vGq+r6q7gFQ1RL8t2igql8GMaOJA5sKN7Fs8zKbwSIGPXzMwyTXTGbMdOsmGygRuV5EGojjZRH5VUSOK7ufXbUzXimdwcJakONPvKyoV2GBLCIJwGllFwQppaq55bwvUUSGua3PN5VuQchrYpQN0ItdTZKbcOwBxzIpf5LNaBG4S1R1C3Ac0BS4GHjY34521c54IS/PubUW5PjTtaszMDPW+yFXWCC7rcSzRKRtFT93As6AvhSgvs9mjF9WIMe2rPQsVm5ZyZy1c7yOEi1KZwsaCrymqrN8ntuHXbUz4ZaXB6mpkJLidRITbjVqQK9esd+CXCOAfVoA80TkF2B76ZOqOqyC97RWVZuX0wQsZ00OLeq1oFm9Zl5HMSEwNH0oAJMWTqJnMzs1BGCmiHwBdABuF5H6QEl5O4tIIs6sF+3xOa+r6pMhzmniVG6utR7Hs8xMeOMNKClxlqCORYH8WP/E6Wt8H/CEz1aRz/z1lzOmPDZAL7a1qN+CjBYZTMqf5HWUaHEpcBvQT1V3ALVwulmUx67ambCyKd7iW0YGbN0KixZ5nSR0Km1BVtVvqvG5PwEfu32Y9+BcGlRVbVCNzzIxblfRLuavm88J6X7HfJoYkZWexajvRrFhxwZS6th12Yq43dt+BRCRe1X1XmBDBW+xq3YmbNavdzZrQY5fvgP1Onf2NkuoBDLN2yEiMl1EtonIbhEpFpEtlbztCeBQoI6qNlDV+lYcm/LMWzePopIia0GOcVnpWZRoCZ//5ndBOFO+irqzlbKrdiZsSgfoWQty/DrwQKhdO7b7IQfSxWI0cDaQDyQDl7nPVSQfmKs2ZN0EwAboxYd+rfqRVifNullUXbmD83yUXrUrFJEtIrI1gIYMY6rFpngzNWs6A/VieSaLQAbpoaqLRCRRVYuB10Tkx0reshqYJiKfAbt8PqfCASPuQJMZwO+qatfb40T26mzq1apHxyb+Vi83sSJBEhiaPpQJCydQXFJMYkKi15GiRWYA+5RetZtjDRMm1PLyICkJ2rXzOonxUkYGjBsHqiCB/BkfZQJpQd4hIrWAHBF5VERuBCpbC3gJ8CXOwJKqDBi5HvA7t7KJXTlrcujVrBcJEqNDYc2fstKz2Fi4kZ9W/uR1lIgmIp1F5EsRmauqJSLSU0TuquAtdtXOhE1uLnTpEruzF5jAZGbC5s3w229eJwmNQFqQzwcSgWuAG4E2+FliGkBEbgemqOo/qxpERFrjTFM0CrBFReJEiZYwq2AWF/S6wOsoJgyO63gcNRJqMCl/Eoe3PdzrOJHsJeDvwL8BVHW2iLwNPFDO/tW6amdMdeTlwcEHe53CeK10oN6vv0KnTt5mCYVK//5T1WWqWqiqW1T1n6p6k6qWN7HHEuB6EckWkddF5EwRaRxglqeAW6l4rs8rRGSGiMxYt25dgB9rItniTYvZunur9T+OEw2TGvK3tn+zfsiVq6Oqv5R5rqiC/at71c6YKikshKVLrf+xge7doVat2B2oV2kLsoicANwPtHP3L3fKNlV9B3jHfV8fYDDwkdu3+L84rctlT/ql37FWVWeKyMDysqjqi8CLAH379rVLiTGgdIBen+Z9vA1iwiYrPYu/T/07KzavoE3DNl7HiVTrRaQjoAAiMhynldival61awO8ATTHaZh4UVWfLrPPQOBTnAIc4CNVva+q32Vix8KFTp9Tm+LN1KoFPXrEboEcSA+ip4ALgZSqTNmmqtmq+pCqHoWz0Mg8nBkw/DkcGCYiS3EK7EEiMjaQH8BEt5yCHBIlke5Nu3sdxYRJVnoWAJPzJ3ucJKKNxOle0VVEfgduAK4qu5OI3FvZB1WwTxFws6p2Aw4BRorIgX72+05Ve7ubFcdxzqZ4M74yM50uFrE4+iGQPsgrCHDwh4icB4iqvlnmpTOB7ap6hb/3qertwO3uZwwEblHV8wLIZqJcTkEO3dK6kVQjyesoJky6pnalQ6MOTMyfyJV9r/Q6TkRS1cXAMSJSF0hQ1a3l7HpZJdO5CXAWcK+f71iN2yqtqltFJBdoBczfn+wmtuXmOjMWpKd7ncREgowMePFFp9tNhw5epwmuQArkW4HJIvINlQ/+uBk4ws/z7wDTgLerkdHEsOyCbI7ucLTXMUwYiQhZ6Vm8kv0KhXsKSa6Z7HWkiCMitXEGQ7cHaog7h5KfFtyXqLyv8UsBfF97oA/ws5+XDxWRWcAqnMaLeZV9noldeXlOIZRsv7aGvVfUi8cCeRSwDUjCGQBSkUR/LR1u60TNQAKp6jScYtrEuLXb17Jq6yoboBeHsjpnMXr6aKYtncaQ9CFex4lEnwKbgZn4NEyUVZ2+x2WJSD3gQ+AGVS3bGv0r0E5Vt4nIUOATwG/boYhcAVwB0LZt2/2NZSJUbq51rzB/6dHDWTRk5kwYPtzrNMEVSIHcRFUDXcK0pojUVdXtvk+KSH0qL65NnJlVMAuwFfTi0cD2A6lTsw6T8idZgexfa1UdHOovcRsuPgTeUtWPyr7uWzCr6mQRGSMiqaq63s++Nog6xhUXO4P0jj3W6yQmUtSuDQcdFJsr6gUySO+/IhJogfwK8IF7uQ7489LdO+5rxvwpuyAbsAI5HiXVSOLoDkczKX8StraFXz+KSI9QfoE4/TZeAXLLmy9ZRJq7+yEi/XH+n7EhlLlM5Fq2DHbutBZks7eMDKcFOdZO5YEUyCOBKSJSKCJbRGRreYNCVPVxnEuD34jIBhHZAHwDTFTVx4IX28SCnIIc2jZsS5PkJl5HMR7ISs9i6R9LyV1vi2f68TdgpogsEJHZIjJHRGZX5QPcFVArcjjOQlCDRCTH3YaKyAgRGeHuMxyY6/ZBfgY4y1bri1+lM1jYFG/GV2YmbNgAy5d7nSS4Ku1ioapVmmxeVV8AXnD7tUkFo69NnMspyLHW4zg2NH0oAJMWTuLANH+zi8W1KvU7EZFpwEWqutR93B9ncF6v8t6jqt/jzHJRLlUdDYyuShYTu2yKN+OP74p67dp5myWYyi2QRaSrquaJSIa/11W13B4nInKTz/3Su5uBmaqaU72oJpbs2LODBRsWcPqBp3sdxXikTcM29GzWk0n5k/j74X/3Ok5EUdVlItILGOA+9Z2qzqrgLQ/hXOl7BmeqtiHAxSGOaeJMbi6kpkJKitdJTCTp0QMSE51uFqec4nWa4KmoBfkmnBHJT/h5TYFBFby3r7tNcB9nAdOBESLyvqo+Wo2sJobMWTOHEi2hTwtbQS+eZaVn8egPj/LHzj9olNTI6zgRQ0SuBy4HSgfOjRWRF1X1WX/7q+rnbreIqcB6oI+qFoQnrYkXeXnWvcLsKznZWXY61lbUK7cPsqpeISIJwF2qelSZraLiGCAFyFDVm1X1ZpxiOQ1njuSLghXeRK/SJaati0V8y0rPoliL+eK3L7yOEmkuBQ5W1XtU9R6cle4uL29nEbkbeBbnHHsvME1EssIR1MQPm+LNlCczM/YG6lU4SE9VS4DHq/G5bYHdPo/34MylWUgFc3qa+JFTkEOjpEa0axhDHZZMlR3S+hCaJDdhUv4kr6NEGgGKfR4XU3F/4VSgv6r+T1X/DRyPszy1MUGxfr0zEMtakI0/GRmwbh38/rvXSYInkHmQvxCR04CPqjB6+W3gJxH51H18IjDOXTbVljE1ZBdk07t5b98+6iYOJSYkMrjTYD7L/4wSLSFBAplYJy68BvwsIh+7j0+mgqkyVfV6EUkWkS6qukBVlwE2W60Jmlx3shlrQTb++K6o17q1t1mCJZD/G90EvA/srmyat1Kqej9O/+U/cAbnjVDV+1R1u6qeu7+hTXQrLilm9prZ9G7W2+soJgJkpWexbsc6pv8+3esoEcOdl/hiYCOwCbhYVZ8qb38RORHIAaa4j3uLyPjQJzXxwqZ4MxXp1QsSEmKrH3JQp3kTkV9VNcN93wxgRkX7mPiUvzGfwqJC639sABjcaTAJksCk/Ekc3Ppgr+NEDHemoEDXp7oX6A9Mc9+bIyIdQpPMxKPcXGcwlq0ibvypU8f54ymWVtQLpIsFInIqzsT1ijPd0Cfl7NqtksnsBWhYpYQm5mSvthX0zF+aJDfh0NaHMil/EvcddZ/XcaJVkapuLtNlKYaGyxiv5eVBly5OK6Ex/mRmwhcxNN660gJZRMYAnYBx7lMjRORYVR3pZ/dAeicVV76LiWU5BTnUSqxFtzS7VmccWelZ3PHVHazeupoW9Vt4HScazRWRc4BEEUkHrgN+9DiTiSF5eXCwXeAxFcjMhDfegFWroGVLr9Psv0D+FjwSOF5VX1PV14ChwEB/O6rqsgC2lUHMb6JQzpocuqd1p1ZiZSvhmniR1dmZkWxy/mSPk0Sta4HuOLMEjQO2YLNYmCApLISlS22AnqmY74p6sSCQAnkBzrRtpdoAFXWjMKZcO/bsYMaqGda9wuylR9MetGnQJu6neysdBF3eVt77VHWHqt6pqv1Uta97f2c4s5vYtXChM7+tDdAzFenVC0RiZ6BeIH2QU4BcEfnFfdwP+F/pCGlVHRaqcCb2PPTdQ2ws3MiFvS70OoqJICJCVnoWY+eMZVfRLmrXqO11JE+UDooWkfuAAuBNnLEb5wL7DJgWkQlU0NfYzs8mGGyKNxOIevWcfyPxVCDfE/IUJi7kb8jn0R8f5dwe53Jk+yO9jmMiTFbnLF6Y+QLfLf+OYw44xus4XjteVX17fD4vIj8Dj5bZr3Qhp1OB5sBY9/HZwNKQJjRxIy/PaRns3NnrJCbSZWbC1197nSI4Apnm7ZtwBDGxTVW59rNrSaqRxOPHVWdxRhPrBnUYRFKNJCYunGgFMhSLyLnAOzgtxGfjZ4Bz6flZRO5X1SN8XpogIt+GJamJebm50KEDJCV5ncREuowMGDsW1qyBZs28TrN/bMIWExYf533M5799zn0D76N5veZexzERqE7NOhzV/qi474fsOgc4A1jjbqe7z5UnTUQOKH3gzoGcFtKEJm7k5Vn/YxMY3xX1op0VyCbktu/ezg1TbqBH0x6M7O9vdkBjHFnpWSzauIiFGxZ6HcVTqrpUVU9S1VRVTVPVk1V1aQVvuRGYJiLTRGQa8DU2i4UJguJiWLDA+h+bwPTp49zGXYEsIo1FpGeowpjYNOq7UazYsoLnhj5HjYSA1qYxcap0urdJC+O7FVlEOovIlyIy133cU0TuKm9/VZ0CpAPXu1sXVf08PGlNLFu2DHbtsgLZBKZ+faeveixM9VZpgey2SDQQkSbALOA1EXky9NFMLFiwfgGP//g4F/S6gAHtBngdx0S49o3ac2DagdbNAl4Cbgf2AKjqbOCsSt6TiTMXci/gTBG5IKQJTVzIy3NurYuFCVRmZvy0IDdU1S04o6RfU9VMIO5H0JjKlQ7MS66ZzKPHlB18b4x/WelZfLvsW7bu2up1FC/VUdVfyjxXVN7OIvImzowWf8OZirMf0Dd08Uy8sCneTFVlZsKKFbBunddJ9k8gBXINEWmBM2BkYojzmBjyYe6HTF08lQeOeoBm9aJ8OKsJm6z0LPaU7GHq4qleR/HSehHpiDvHsYgMB1ZXsH9f4HBVvVpVr3W368IR1MS2vDxIS4OUFK+TmGiRkeHcRns3i0AK5PuAz4FFqjrdHSmdH9pYJtpt272NGz+/kd7Ne3NVv6u8jmOiyGFtDqNh7Ybx3g95JPBvoKuI/I4z4K6iX6S5OPMgGxNUubnWemyqprRAjvZuFoHMg/w+8L7P48XAaaEMZaLf/d/cz8otK3l3+Ls2MM9USc3Emhzf6XgmL5pMiZaQIPE32Y57nj1GROoCCapaWX+TVGC+u+LpLp/PsZX0zH7Jy4PT7P/4pgoaNoROnaK/QA5kkN6j7iC9mu6o6vUicl44wpnolLsulyd/epKLe1/MYW0O8zqOiUJZ6VkUbCsge3W211E8ISLXi0gDYAfwLxH5VUSOq+At9wInAw8CT/hsxlTbunWwYYO1IJuqy8yMjy4Wx7mD9E4AVgKdgb+HNJWJWqrKNZ9dQ71a9Xj4mIe9jmOi1JBOQxAknmezuMQ97x4HNAUuBsr9hVLVb/xtFX2BiLQRka9FJFdE5onI9X72ERF5RkQWichsEcnY3x/MRI/SGSysQDZVlZEBS5c6f2BFq0AK5Jru7VBgnKpuDGEeE+Xem/ceXy35ilGDRtG0blOv45golVY3jf6t+sdzgSzu7VCc2YNm+Tz3104i37u3W0Vki8+2VUS2VPIdRcDNqtoNOAQYKSIHltlnCM78yunAFcDz1f+RTLQpncHCpngzVVW6ol40tyIHUiBPEJE8nFHSX4pIGrAztLFMNNq6ays3fXETfZr34crMK72OY6JcVnoW03+fztrta72O4oWZIvIFToH8uYjUB0rK7qSqf3Nv66tqA5+tvqo2qOgLVHW1qv7q3t8K5AKtyux2EvCGOn4CGrmzGpk4kJcHycnQtq3XSUy0iYWBepUWyKp6G3Ao0FdV9wDbcU6axuzlvm/uY9XWVYzJGkNiQqLXcUyUy+qchaJ8lv+Z11G8cClwG9BPVXcAtXC6WYSEiLQH+gA/l3mpFbDC5/FK9i2iSz/jChGZISIz1kX7BKgGcArkLl0gIf7GyZr91LgxdOgQ4y3IIlITOB94V0Q+wDlxR3GvEhMK89bO46mfn+KyPpdxSOtDvI5jYkCf5n1oUa9FXHazUNUSoDVwl4g8DhzmrqYXdCJSD/gQuMHt97zXy/7i+fscVX1RVfuqat+0tLRgxzQesCnezP6I9hX1Avm78HmcJUzHuFsG1g/N+CgdmFe/Vn0eOuYhr+OYGCEiDE0fyue/fc6e4j1exwkrEXkYuB6Y727XiUjQf7ncBpAPgbdU9SM/u6wE2vg8bg2sCnYOE3l27IBly6z/sam+zExYvBg2bfI6SfUEUiD3U9ULVfUrd7sYZxlTYwAYN3cc05ZO46GjHyK1TqrXcUwMyUrPYsuuLfyw4gevo4TbUOBYVX1VVV8FBgNZwfwCERHgFSBXVZ8sZ7fxwAXubBaHAJtVtaIV/UyMWLgQVK0F2VRfaT/k7CidrTOQArnYXfIUAHclveLQRTLRZMuuLdz8xc30bdmXyzIu8zqOiTHHHHAMNRNqxuuqeo187jcMwecfjtN9bpCI5LjbUBEZISIj3H0mA4uBRcBLwNUhyGEikE3xZvZX6UwW0drNIpAlzm4BvhaRxTj90doRwsEiJrrcO+1e1mxbw/izxtvAPBN09WvX58j2RzIpfxKPHfeY13HC6SEgW0S+xjnvHgHcHswvUNXv8d/H2HcfxVn22sSZ3FwQgc6dvU5iolVKCrRrF6MFsogkAr1w5sDsgnMyzVPVXRW9z8SHOWvm8MzPz3B5xuX0a2W9bkxoZKVncePnN7Jk0xI6NO7gdZywUNVxIjKNv7qz/Z+qFngYycSZvDxnFoKkJK+TmGgWzSvqVdjFQlWLgWGquktVZ6vqLCuODTgD80ZOHkmjpEY8ePSDXscxMeyEzicAxONsFocCA4Ej3fvGhE1urg3QM/svIwPy82HzZq+TVF0gfZB/FJHRIjJARDJKt5AnMxHtrTlv8d3y73j4mIdJqZPidRwTwzo16UTnlM5MXDjR6yhhIyJjgBHAHGAucKWIPOdtKhMvioudQXrW/9jsr9J+yNE4UC+QPsiHubf3+TynwKBgBhGRNsAbQHOcFaNeVNWng/kdJjg279zMLV/cQv9W/bmkzyVexzFxICs9izHTx7B993bq1qrrdZxwOBI4yO0DjIj8B6dYNibkli2DXbusBdnsv9KZLH79FQYO9DRKlQWykt5RfragFseuIuBmVe0GHAKMFJEDQ/A9Zj/d8/U9rN2+ljFDx5AgtsSSCb2s9Cx2Fe/iyyVfeh0lXBYAvgv8tgFCslCIMWXl5jq31oJs9lfTptC6dXQO1Ku0BVlEbvLz9GZgpqrmBCuIO7fmavf+VhHJxVnSdH6wvsPsv1kFsxg9fTQj+o4gs2Wm13FMnBjQbgD1a9Vn0sJJDOsyzOs44ZAC5IrIL+7jfsBPIjIeQFXj4iAYb9gUbyaYonVFvUC6WPR1twnu4yxgOjBCRN5X1UeDHUpE2gN9gJ/LPH8FcAVA27Zt932jCakSLWHk5JE0SW7CA4Me8DqOiSO1EmtxbMdjmbxoMqqKs8ZFTLvH6wAmfuXmQlqaM02XMfsrMxPGj4effoIDDoDUVEiIgovPgRTIKUCGqm4DEJF/AB/gzMs5EwhqgSwi9XCWPr1BVbf4vqaqLwIvAvTt21eD+b2mcm/OepMfVvzAK8NeoUlyE6/jmDiTlZ7FR7kfMXvNbHo17+V1nFBbp6p7XT0TkYGqOs2jPCaO5OVZ67EJnsMOc1ZlPNSdi6dmTWjVyul6UbqVfdy8OdQIpEINoUC+vi2w2+fxHqCdqhaKSFCnfBORmjjF8Vuq+lEwP9vsnz92/sHfp/6dQ1sfykW9L/I6jolDQ9OHAs50b3FQIL8nIm8AjwFJOA0RfbHp3kyIqTotyMOHe53ExIpBgyAnBxYvht9/h5Ur/9qmT4ePP3YGhfpKSIAWLfYtnH0L6latoHbt0OUOpEB+G6fv26fu4xOBcSJSlyD2DxbnmukrQK6qPhmszzXBcfdXd7OhcAOfD/3cBuYZTzSv15zMFplMyp/EHQPu8DpOqB0MPAL8CNQH3sJZGtqYkFq/HjZutBZkEzwi0KuXs/mj6vyb8y2cV678q5jOzYUvvoBt2/Z9b1raX0XzgAHw978HL3elBbKq3i8ik4G/4aykN0JVZ7gvnxu8KBwOnA/MEZEc97k7VHVyEL/DVEP26mzGzBjD1X2vpk+LPl7HMXEsKz2LB757gA07NsT6/Nt7gEIgGacFeYmqlngbycSD0hksbIo3Ey4iTn/3lJTyi2iALVv2bYEuLaSXL/9rcGmwBNTDQ1Vn4vQ3DhlV/R6nADcRpERLuHry1aQkp3D/oPu9jmPiXFbnLO779j6mLJrCuT2D+fd5xJkOfIoze0UK8G8RGa6qduHbhJTNYGEiVYMGzhauP97sWrmp0Os5r/PTyp947NjHaJTUyOs4Js71bdmXpnWbxsOy05eq6j2qukdVC1T1JJyC2ZiQysuD5GSwiaJMvLMC2ZRrY+FG/u+//8fhbQ7n/F7nex3HGBIkgSGdhjBl0RSKSoq8jhNKM0XkPBG5B0BE2uIsHmJMSOXmQpcu0TENlzGhZL8Cplx3fXUXGws38tzQ52xgnokYWelZbNq5iZ9W/uR1lFAagzNjxdnu463Ac97FMfHCpngzxmFVj/Fr5qqZvDDjBa7tf208TKlloshxHY+jRkINJi2M6W4WB6vqSGAngKpuAmp5G8nEuh07YNkyG6BnDFiBbPwoHZjXtG5T/jnwn17HMWYvDZMa8re2f4v1fsh7RCQRUAARSQNsFgsTUgsXOlNuWQuyMVYgGz9ezX6VX37/hcePe5yGSQ29jmPMPrLSs5izdg7LNy/3OkqoPAN8DDQVkVHA98CD3kYysc6meDPmL1Ygm72s37Ge2/57GwPaDuDcHjE9jZaJYlnpWQBMzo/NadJV9S3gVuAhYDVwsqq+720qE+u+/toZnJee7nUSY7xnBbL508SFE8l8MZPNuzbz3NDncBY3NCbydE3tSodGHWK6m4Wq5qnqc6o6WlVzvc5jYtvbb8NLL8GIEZCU5HUaY7xnBbJh1dZVnP7+6Zw47kTq1arHtAun0aNZD69jGVMuESErPYsvF39J4Z5Cr+MYE9WmT4dLL4Ujj4R//cvrNMZEBiuQ41hxSTFjpo+h23PdmLhwIqMGjSL7ymwOb3u419GMqVRW5ywKiwqZtnSa11GMiVqrVsHJJ0Pz5vD++1DL5koxBrACOW7NKpjF4a8ezsjJI+nfqj9zrprDHQPuoFainR1NdBjYfiB1atZh4sKJXkeJWiLyqoisFZG55bw+UEQ2i0iOu90T7owmdHbuhFNOgc2b4dNPIS3N60TGRA4rkOPM9t3buXXqrWS+mMniTYsZe8pYvjjvCzo16eR1NGOqJKlGEkd3OJpJ+ZNQVa/jRKvXgcGV7POdqvZ2t/vCkMmEgSpcfjn88guMHQs9e3qdyJjIYgVyHPks/zMOev4gHvvxMS7qfRF51+Rxbs9zbTCeiVpZ6Vks27yM+evmex0lKqnqt8BGr3OY8Hv8cacwvv9+p4uFMWZvViDHgYJtBZz1wVkMfXsoSTWS+Oaib3h52Ms0SW7idTRj9ktWZ2e6t1iezSICHCois0TkMxHpXt5OInKFiMwQkRnr1q0LZz5TRZMnw//9H5xxBtx5p9dpjIlMViDHsBIt4d8z/k3X0V35OO9j7ht4HzlX5nBEuyO8jmZMULRu0JpezXpZgRw6vwLtVLUX8CzwSXk7quqLqtpXVfumWWfWiJWbC2efDb17w2uvgV1ANMY/K5Bj1Ny1cxnw2gBGTBpBRosM5lw1h7uPvJvaNWp7Hc2YoMpKz+KH5T+wqXCT11FijqpuUdVt7v3JQE0RSfU4lqmmjRth2DBnnuNPP4U6dbxOZEzksgI5xhTuKeSOL++gz7/7sGD9Av5z8n/48oIv6ZzS2etoxoREVucsirWYL377wusoMUdEmos7SEFE+uP8P2ODt6lMdRQVwZlnwrJl8PHH0KaN14mMiWw1vA5ggmfqb1MZMWkEizct5qLeF/HYsY+RWscae0xsO7jVwaQkpzApfxJnHnSm13GiioiMAwYCqSKyEvgHUBNAVV8AhgNXiUgRUAicpTZlSFS6+Wb473/h1VfhsMO8TmNM5LMCOQas3b6WGz+/kbfnvE3nlM58dcFXHNXhKK9jGRMWiQmJDO40mM8WfUZxSTGJCYleR4oaqnp2Ja+PBkaHKY4JkZdfhmeegRtvhIsv9jqNMdHBulhEsRIt4eVfX6br6K58MP8D/nHkP5g1YpYVxybuZKVnsX7Heqavmu51FGMiyvffw9VXw3HHwaOPep3GmOhhLchRav66+YyYOILvln/HEe2O4N8n/JuuqV29jmWMJ47vdDwJksCkhZM4pPUhXscxJiIsWwanngodOsA770AN+z++MQGzX5cos7NoJ6O+HcUjPzxCvVr1eGXYK1zc+2Jb7MPEtSbJTRjQdgCP/PAI89bN47ye55GVnmWztpi4tX07nHQS7N4N48dD48ZeJzImuliBHCVKtIRJCydx8xc3k78xn/N7ns/jxz1O07pNvY5mTER485Q3+ddP/+LtOW/zcd7HNEpqxOkHns75Pc/n8LaHkyDWo8zEh5ISuPBCmDMHJk6ELl28TmRM9LECOcJtLNzIq9mv8vyM51m8aTEdG3dk6vlTOeaAY7yOZkxEadOwDU8e/ySPHvsoXy7+krFzxvLWnLd46deXaNewHef2OJfzep5Ht7RuXkc1JqTuvx8+/NBZTnrIEK/TGBOdJFpn7Onbt6/OmDHD6xghM2PVDMZMH8O4uePYWbSTAW0HMLLfSE7pdgq1Emt5Hc+YqLBt9zY+zfuUN2e/ydTFUynREjJaZHBej/M4u8fZNK/XPCw5RGSmqvYNy5dFgFg/P0eyDz+E4cOdFmRbKc+YypV3frYCOYLsLNrJe/Pe47npz/HL779Qt2Zdzu95Plf3u5oezXp4Hc+YqFawrYB35r7D2Nljmbl6JgmSwLEHHMt5Pc/j5K4nU69WvZB9txXIJhxmzXLmOO7ZE77+2lkxzxhTMSuQI9iSTUt4YcYLvJL9ChsKN9A1tStX972aC3pdQMOkhl7HMybm5K7L5a05bzF29liWbV5GnZp1OKXrKZzf83yOPuBoaiQEt/eZFcgm1NauhX79oLgYpk+HFi28TmRMdCjv/Gx9kD1SoiV8vuhzxswYw6SFk0iQBE7qehIj+43kqPZH2awUxoRQt7RuPDDoAe476j5+WP4DY2eP5b357/HWnLdoVrcZZx90Nuf1PI+MFhn2u2gi3u7dcNppTpH8/fdWHBsTDFYgh9nGwo28lv0az894nt82/Uazus24c8CdXNn3Slo3aO11PGPiSoIkMKDdAAa0G8DTQ55mcv5kxs4ey3PTn+Opn5+iW2o3zut5Huf0OIf2jdp7HdeYfajCyJFOYTxuHGRmep3ImNhgXSzC5NfVv/LcL8/x9ty32Vm0k7+1/Rsj+43k1G6n2qA7YyLMxsKNfDD/A8bOHst3y78DYEDbAZzX8zxOP/B0GidXbVJZ62JhQuXZZ+G66+COO2DUKK/TGBN9rIuFB3YW7eT9ee/z3PTn+Pn3n6lTsw4X9LyAq/tdTa/mvbyOZ4wpR5PkJlyReQVXZF7Bkk1LeHvO27w5+02unHglqXVSObXbqV5HjCnbtjmrvNmgsqr573/hxhudBUHuv9/rNMbEFiuQQ2DpH0v/HHS3fsd6uqR04enBT3NBrwtolNTI63jGmCro0LgDdx5xJ3cMuINfV/9K96bdvY4Ucx55BB54ABo2hObNoVmzim+bNoVacX7hbdEiOOMM6NoV3nwTEmwdHGOCygrk/bCxcCP5G/JZuGGhs210bmcVzEJEGNZlGCP7jeToDkfbQB9jopyIkNnSOniGwnHHOa3HBQWwZo1zm5Pj3G7Z4v89TZpUXkg3a+YU0zVi7P90mzfDsGFOUTx+PNSv73UiY2JPjJ02gm/77u0s2riI/I0+hbC7bSjc8Od+CZJAh0Yd6JzSmWFHDOPSjEtp27Cth8mNMSY6DBjgbP4UFjpFc2nh7O92+nTndtu2fd8vAikpfxXMjRr9tTVsWPH9+vUjr2W2uBjOPRfy82HqVDjgAK8TGRObrEAG9hTvYckfS/4sfPM35P/ZGrxyy8q99m1ZvyWdUzpzWrfT6JzSmfSUdDqndOaAxgfYYDtjjAmy5GRo397ZKrN9e+XF9KpVTgvsH3/Ajh0Vf54INGhQfgFd9rl69fxvdeoEr9C+806YNAnGjIGBA4PzmcaYfcVNgayqrNyy8q8i2KdFePGmxRRr8Z/7Nk5qTOeUzhzV/ig6p3T+c+vUpFNIV9syxhhTfXXrOi2qgbaq7tnzV7Fc9ra855Yv/+u5zZuhpCSw76pTp/wCuuxWt67/52fOdPprjxgBV11V9eNjjAlc/BTIKF1Gd6GwqBCA5BrJdE7pTO/mvTn9wNP3KoRT6qR4nNYYY0yo1awJqanOVh0lJU63jtICevt253FFm+8+W7Y4Ldq+r+/cWfF3DhwIzzxTvbzGmMDFTYGcIAm8dtJrpNVNo3NKZ1rWb0mCRFjnMmOMMVEjIcHpgtGgAbRpE5zPLCryX2hv3w67dsGxxzqFvTEmtOKmQAY486AzvY5gjDHGlKtGDac/c8OGXicxJr5FTBOqiAwWkQUiskhEbvM6jzHGGGOMiU8RUSCLSCLwHDAEOBA4W0QO9DaVMcYYY4yJRxFRIAP9gUWqulhVdwPvACd5nMkYY2KaiLwqImtFZG45r4uIPONe2ZstIhnhzmiMMV6IlAK5FbDC5/FK97m9iMgVIjJDRGasW7cubOGMMSZGvQ4MruD1IUC6u10BPB+GTMYY47lIKZD9rcOs+zyh+qKq9lXVvmlpaWGIZYwxsUtVvwU2VrDLScAb6vgJaCQiLcKTzhhjvBMpBfJKwHeSnNbAKo+yGGOMcQR0dQ/sCp8xJrZESoE8HUgXkQ4iUgs4CxjvcSZjjIl3AV3dA7vCZ4yJLRExD7KqFonINcDnQCLwqqrO8ziWMcbEO7u6Z4yJS6LqtzEg4onIOmBZNd6aCqwPcpxYY8cocHasAhfPx6qdqkZks6qItAcmqupBfl7LAq4BhgIHA8+oav8APrM65+d4/vdRFXacAmPHKXDxfqz8np8jogW5Oqr7PxsRmaGqfYOdJ5bYMQqcHavA2bGKPCIyDhgIpIrISuAfQE0AVX0BmIxTHC8CdgAXB/K51Tk/27+PwNhxCowdp8DZsfIvagtkY4wx+0dVz67kdQVGhimOMcZEjEgZpGeMMcYYY0xEiMcC+UWvA0QBO0aBs2MVODtWpiL27yMwdpwCY8cpcHas/IjaQXrGGGOMMcaEQjy2IBtjjDHGGFMuK5CNMcYYY4zxEfEFsoi0EZGvRSRXROaJyPXu801EZKqI5Lu3jX3ec7uILBKRBSJyvM/zo0RkhYhsq+Q7M0VkjvsZz4iIuM8fISK/ikiRiAwP1c9cVZF0jHxeHy4iKiIRM3VMJB0nEfmXiOS420IR+SNEP3a1eXS8/O4nIrVF5F33s3925+41Hoqw36eIPDdDZB0nn9ft/Ez0np/t3BwmqhrRG9ACyHDv1wcWAgcCjwK3uc/fBjzi3j8QmAXUBjoAvwGJ7muHuJ+3rZLv/AU4FGeZ1c+AIe7z7YGewBvAcK+PTSQeI58M3wI/AX29Pj6Repx89rkWZ/VIz49RBBwvv/sBVwMvuPfPAt71+vjE+xZJv09E6Lk50o6TTwY7P1dynHz2ibjzs0fHKu7OzZ4HqMY/jE+BY4EFQAuffywL3Pu3A7f77P85cGiZzyj3H4L7WXk+j88G/l1mn9cj7SQcSccIeAo4AZgWSSfgSDtOPs//CBzr9fHw+nhVtJ/vZ+HM374ed5CxbZGxRcLvU6SfmyPhONn5OfB/T+7zEX9+tnNzaLaI72Lhy2267wP8DDRT1dUA7m1Td7dWwAqft610nwtUK/c91X2/p7w+RiLSB2ijqhOrkz9cvD5OPjna4fxF/1UVPjfswnS8KvLnZ6tqEbAZSAnSZ5v9FCm/T5HO6+Nk5+e9xMT52c7NoRM1K+mJSD3gQ+AGVd1SpkvVXrv6eU6r8lX7+X7PeH2MRCQB+BdwURU+K+y8Pk5lHp8FfKCqxVX43LAK4/GqMEYIP9vshwj7fYpYXh8nOz9X6/0RfX62c3NoRUULsojUxPlH8JaqfuQ+vUZEWrivtwDWus+vBNr4vL01sKqCz0706Yx/n/v+1oG+P1JEyDGqDxwETBORpTh9lsZH2ECQSDhOvs4CxlX35wm1MB+vivz52SJSA2gIbKzqz2OCKwJ/nyJShBwnOz/H0PnZzs1h4HUfj8o2nL9O3gCeKvP8Y+zdGf1R93539u6Mvhi3M7rPeyvrjD4d5+RR2nF/aJnXXyeC+rlF4jFy95lGBPVxi7TjBHQBlhKh/bW8OF7l7QeMZO+BIO95fXzifYu03yf39Yg6N0fqcXL3sfNzlJ6f7dwcpuPsdYAA/mP8Dae5fjaQ425Dcfq4fAnku7dNfN5zJ84ozQXsPXr3UZy/dkrc23vL+c6+wFz3M0aX/oIA/dz3bQc2APO8Pj6RdozK7DONyDoBR9RxAu4FHvb6uETY8fK7H5AEvA8swhl5foDXxyfet0j6fSJCz82RdpzK7DMNOz9H5fnZo2MVd+dmW2raGGOMMcYYH1HRB9kYY4wxxphwsQLZGGOMMcYYH1YgG2OMMcYY48MKZGOMMcYYY3xYgWyMMcYYY4wPK5BNzBORRiJytXu/pYh84HUmY4wxdn42kcumeTMxz12rfqKqHuR1FmOMMX+x87OJVDW8DmBMGDwMdBSRHJwJ1Lup6kEichFwMpCIswTrE0At4HxgF86qShtFpCPwHJAG7AAuV9W8cP8QxhgTg+z8bCKSdbEw8eA24DdV7Q38vcxrBwHnAP2BUcAOVe0D/A+4wN3nReBaVc0EbgHGhCO0McbEATs/m4hkLcgm3n2tqluBrSKyGZjgPj8H6Cki9YDDgPdFpPQ9tcMf0xhj4o6dn41nrEA28W6Xz/0Sn8clOL8fCcAfbuuGMcaY8LHzs/GMdbEw8WArUL86b1TVLcASETkdQBy9ghnOGGPimJ2fTUSyAtnEPFXdAPwgInOBx6rxEecCl4rILGAecFIw8xljTLyy87OJVDbNmzHGGGOMMT6sBdkYY4wxxhgfViAbY4wxxhjjwwpkY4wxxhhjfFiBbIwxxhhjjA8rkI0xxhhjjPFhBbIxxhhjjDE+rEA2xhhjjDHGx/8DdY/qbags/DIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(ncols=2, figsize=(10,4))\n",
-    "ds.GPP.sel(time='2001').plot(ax=axes[0], color='green')\n",
-    "ds.ELAI.sel(time='2001').plot(ax=axes[1], color='blue')\n",
-    "plt.tight_layout() ; "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we wanted to compare years? We can start by creating columns for our years. The `time.dt` accessor allows us to access DateTime information. Next we will create a `groupby` object across our years. We will use this grouping to plot each year.\n",
-    "\n",
-    "*For more information about working with time in xarray, see the [xarray documentation](https://xarray.pydata.org/en/v0.11.0/time-series.html).*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# create month and year columns\n",
-    "ds[\"year\"] = ds.time.dt.year\n",
-    "ds[\"month_name\"] = ds.time.dt.strftime(\"%b\")\n",
-    "\n",
-    "# group by year\n",
-    "groups = ds.groupby(\"year\")\n",
-    "\n",
-    "# plot each year\n",
-    "colors = plt.cm.rainbow(np.linspace(0, 1, 10))\n",
-    "for count, group in enumerate(groups):\n",
-    "    year = group[0]\n",
-    "    df = group[1]\n",
-    "    plt.plot(df.month_name, df.ASA, label=year, color=colors[count])\n",
-    "plt.legend(loc='upper right')\n",
-    "plt.xlabel(\"Month\")\n",
-    "plt.ylabel(\"Albedo [unitless]\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot a histogram across all years from the summer (June, July, and August). This is done by using `.dt` and the `.isin` accessors to subset the data (`time.dt.month.isin([6, 7, 8])`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.ASA.isel(time=(ds.time.dt.month.isin([6, 7, 8]))).plot.hist() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 Plotting in multiple dimensions\n",
-    "When the data is two-dimensional, by default *xarray* calls `xarray.plot.pcolormesh()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# pcolormesh plot\n",
-    "ds.sel(time='2001').H2OSOI.plot(x='time', yincrease=False, robust=True) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The depth to bedrock for this gridcell is ~ 4 meters, which means no soil moisture or soil temperature are calculated for deeper soil horizons.\n",
-    "\n",
-    "You can see this by running the following code block, or entering it into the terminal (see `zbedrock` all the way at the end of the `ncdump` output):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!ncdump -v zbedrock /scratch/$USER/my_subset_data/surfdata_*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also make a contour plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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tf7yk7QWSNop5jEIIYXA6mXTP9iGSfgtsnh/a0/Y/mpp91PZNbbZZFgOWUCcgLWf7olQ+bpGqiZwA9iaNBj4ZwPblkl5e0XYL4AM5TfwRIu07hBAGodNJ96YD/yLFjGdIekZxFvBcPaelOm3qBKQ7JW1ATv+T9HbaTNRk+5amAPZ4RdNtamw/hBBCb9WedE/S94B3AVez5LX+cwttngJ8LT//VeCTpIII1wK75UlV26oTkHYlzSn0LEm3kubN2KFF+1vyuUlLWpo0L9K1ZQ3bHeKFEELoi04m3Xsz8Mw20wgdCvwGWB44izTG9PWkig4HUrOyQ52AdJPtV+UkhWm2F7RpvwuwD6mi7HxSeuCuxQaSLrW9WauV1GkTQgihK1/voO31pEzoVgFpddv7QrpWZPt7+fF9JVVN/DdGnYB0g6TTSIOi/rddY9t30voICuDZbdIEBaxUo28hhBBqkiQnlcWtG20KDz0IXCapebxosVJDcW695sSI2vPu1QlIzyQV19sV+FnOWT+6+QJVh2UmnlVju1XXnUIIIXTnLEnHAyfZvrnxYL68sgXwAdIpt0MLy5xM+5TtkyStYPt+218urPfpwP/V7VydWnYPAccAx0hahXQ67hxS1kXRJc3LtlhnXDsKIYTB2wb4EHCUpPWAe0jVvqeTLq/8t+3Ligs0Kja0YvurFY9fRyrcWkutGWMlbUnKstgWuJg0+V7zhtt2OoQQwsSx/TCwP7B/nlZoVeAh2/dULSNpQ+C/SPXsilNVrF/SdmXg/RQG3Oa2tQqx1qnUcANp8NQxwB62H2jT/hnAZ0s69Io6HQohhNB/uTZdnXTsQ0gp3f9Nmq5iR6pn7D4VuIAuZ3Coc4S0qe1W8x81O5aU5ncwNa8D5Qy+h23Xbb8sKQd+GdJrOM721zroYwghhHpm2j4zJzvcBHxd0nmkINVsWdvtZoqoVCcgPUXSidSvZbfQ9gGtVihpGqka+A7AC0iZG8tI+hcpws613TztedEjwCts358PO/8g6be2L6jxekIIIdT3cP7O/pukTwC3Ak+uaHuEpJ2AU1gyI+/uivZLqJOO91PgC8BjecVXkIJJlV9L+rikp0qa3bg1tTmLVEfpC8BTbK9t+8nAy0iHe9+V9N6qDeS0xfvz3Rn51pc53kMIYTKRNF3SGR0ssjtpdtlPAc8H3kfKxivzKGlKi/NJcybNo4OEt37Usmt0dI/CYwaKF8Be1ZhboyhH0eNJJdFntOqUpOmkF/t04Ce2LyxpszOwM8AyM1dutboQQpgSbD8u6UFJK7WYcqLY/uL86/2k60etfAZ4eh6P2rGe17Kz3TzrYJkVmwJc8zruLgtYTW0eB56bszpOlLSx7aua2swllT1ihVXWjiOoEEJIHgaulPR7YFGiWjEbTtKvaT22tKzM0NWkgbRd6baWXeXptJrmkV6ogHWAf+ffVwZuZuxUupVs3yPpbFJ+/VVtmocQQkh1537Tps0Pu1jv46SqDmdRXdWhUp2BsdcDndSya6txFJWnwj3Z9qn5/rbAq9otL2k14LEcjGbmZb7XZrEQQhhJkrYhFSWYDhxs+7slbbYiTf8zA7jT9pZV67N9WP7uXMf2XyvaVJYXauFX+daVyoAkqTR1r3GqzfZe3W604AW2d2ncsf1bSd9qtUD2VOCwfB1pGnCM7VN60J8QQhgq+XvuJ8CrSQWrL5Z0cnFi03zpYn9gG9s3S6rKgmu0fwPpCGhpYD1JzwW+WXEarrbxFkhodYS0YrcrlfRWUl0kA3+wfWJF0zslfRn4RW77XuCuduvPmX7P67Z/IQS4bUuxxjlxaXUEvBC4Lp+tQtLRpOkcijNtvwc4oVGfzvYdbdb59bzes3P7y3IpoXGRtB3wLWBdUnxpTLo6q87ylQHJ9je67ND+pMy3o/JDH5X0Ktu7ljTfnjS4qhGwzs2PhRAGIIJSfyx4YCbnXbBR3earSiqmRs/NCVkNawK3FO7PZ/F04g3PAGbk6+krAvvYHjMdecFC2/c2JZe1/CBImkUKLq0u2+wNvBW4soPZaBepVcuuQ1sCGzc6I+kwUhmJMXKa92596EMIIYyKO23PafF8WUpy85f9UqQxQq8EZgLnS7rAdlWl7askvQeYnmvVfQr4U+nGpTmk8kErpru6B/iQ7XklzW8BruomGDVeRK/9lZQ516jovTZQOvdR1L0LIYS25pO+RxvWAm4raXNnrjX6gKRzgU2pnvrhk8CXSJlwRwKnk061lfk58HHb5wFI2oIUoDYpafs54FRJ57Bkll2tnIN+BKQnAdfmqXEhlQY6X9LJuWPFi2Yd170LIYQp5mJgw3yN51ZSpZz3NLU5CdhP0lKkRIXNScVQq7ze9pdIQQkASe8gfSc3W9AIRgC2/yCp6rTdd0gDaJfN/ehIx1l2hU5VRbzSeTEqtK17F0IIU5nthbmG3OmktO+f275a0i75+QNtX5tn9r6CVGX74OZCAU2+wNjgU/YYwEWSDiLlBZg0FdHZkjbL27+00Ha27dd0/iqTnmfZ2T5H0uqkIyOAi1pkfPxa0sdJSQ0dF+ILIYxfJDYMvzxW89Smxw5suv8DUh25Snms5+uANSX9uPDULKpLwj03/2yu7v0SUoAqXmI5Q9JrbP+uVT+q9CPL7p2knXI26WLcvpL2sH1cSfM6de9CCD1225bVpbvCpHYbqdjpG0kVcxoWAJ8uW8D21h2sf1fgc5IeIRXk7k3at6TP2f6+pH0pSQdsUQriS6QBr3fk9awGnAEsEZByOfM9bf9PnY6GEEIYH9uXA5dLOrJdvdAGSSuRjo5enh86hzSI9t5Cm6VsL7Td9fhVaH3K7tr8s3bp8Gxa0ym6uyiZ5sL2E5J2BSIghRDCYD1NUq1pyUlZdlcB78z330fKsntroc0FkuYDpwGn2b6xm061OmX36/zzMABJK6a7i+YhqnKapNNZPDD2XcBvK9r+XtJnSUGpWHE2riGFEEL/dDIt+Qa231a4/w1JlxUb2J4jaV1gW2BvSWsCfyB9959j+xFqaDtBn6SNJf2ZFCGvkTRP0nOq2tveAziIlKO+KWnU8ecqmn+IdM7xXLqYzCmEbty2peIaSpjqZto+E5Dtm2x/nSWTE4oeymOPAJD0UuCh5kZ5PQfafjMp4eHXpMLX50lqV1kcqDcOaS7wGdtn5c5sRZpF9iVljSV9z/bngRNKHmt+AeOunRRCJ4qBKLLLFot9MeV0Mi35LsDh+VoSpOmCqmaMBSBfn/rffCMfMbVVJyAt3whGeUNn56koqrwaaA4+25Y81ijC2uxeUh2kdsUBQwghdGd3Fk9L/i3SabsxQSZXGn+v7U1zLTts31fS7imkU4BPkMaifpJ0jekvwG62b63TqToB6XpJXwGOyPffS5qkr7lDHwM+DqwvqVgqaEXgjxXr/jDwYqAR8LYCLgCeIembto+oWC6EjsVpuhAWuSvnA7ScltxpuvPn59/HBKKCQ0kT/i1P+j7/JbAdqSr5gflnW3UC0oeAb5BOwYmU8lf2Ao4kXcD6L2DPwuMLWiQpPAE82/Y/AfKA2gNIZS/OZXEQDF1qfAnH6ZhycaoqTFGH5tNoF5O+a8+zXVoEG/hzLv12LEsmn51QaLO67X0BJH3cdmPC1H0lfbhup+rMGPtv0mFd4/Bt+bJImXPS76Wz6SOe1ghG2R3AM2zfLalWjnyoZxBfvPHlPtri/Zs6bL9c0tKkijpbAb+RtILt2SXNZ5OG7xSTHkwhT4AlE+Sap71omzzX0DYgSTqSdFHrcVIW3EqS9splKsbrPEmnsLh+0tuAc/M1qnt6sP4prfkU1aCCEsQRWRi8+OzVl7PmXpZvKwOnAOeVtbVdeUqv4KQc0O63/eXCdp5OdcXxMepEro3yEdGbSbWU1iENjOqFXUn58M8lzQB7OLCr7Qc6LFcRahrUdZS4XrNYI828zj6JlPTxi31Yyzmk7/S5wFa2P277qLKGkg7LU6Q37q8i6efFNra/WjZG1fZ1tt9et1N1riHNkDQjd34/249JajezYN3iqrsDx9o+vmZ/m7cznTRu6Vbb23WzjtAbo/wF0M8jx26PUgfZp/FudxiOTEb58zdBngS8lFQO6FOSngDOt/2Vkrab2L6nccf2vyU9r2ylOXC9n7Fz3FWVmltCnSOkg4AbSdkT5+bRuJXZFrm46kXAO0ilJi6UVBUhZwGnSzpP0q45kHViNxaXOBoZxf+YJ+IPaSoeJQ1bX8r6Uxa8hl0v+jgKr3OyyQHmelLG9O3ABiyuVddsmqRVGnckzab6YOZUUjC6ksXFDspmli1VJ6nhx8CiMuWSbiblrFepVVw1r/sbpDIUm5BKDJ0jab7tV7Xrl6S1gNeTJoRqOXfTVNSrP/KJuNDdyX/cvfrvvM7rbN6nk/VaRbfv+XiOrvpxlBVJGtUk/Z00u/d5pLTsHW0/WtH8R8CfJB1HSmZ4J+l7t8yytrv+Pq6d/dDgpGreDKhZXLXJHcA/ctuq0cLN9iZNl/tEzfZDoc5/xhPVj6o27dr2sv911zXoo8tu3rdOnq9qO1FHD518Puqur84RYLvTiYN436fIEduGtl9n+79sn9ciGGH7cFLC2T+BfwFvbTFG9AhJO0l6qqTZjVvdTnUckGo4TdLpkj4o6YOkwVKlxVUlfUzS2cCZwKrATrbL5mlvXm474A7bLQ8FJe0s6RJJlyx8pHVN2EF8CNv9sQ2TXp0+6vRLq86y/Qrq3QSFYXvfeqn51HK3AbR5uXZBpexz0Oqz0cv3ZzK/n0W2O/pH3vY1tvezva/ta1o0fZQ0H975dFGftE5SQ0ds75FLAm1BGkg71/aJFc3XBXa3fVmHm3kp8EZJryOVTp8l6Re239vUl7mkLBJWWGXtymP3fp42aN5Guza92HYnRxnN26vzx11cphevq5enF2F8719zX7t5feMJwq3aDPKzMZ7le/E5bqyjV/9oxKm7gfkM8HTbd3azsOzWb5Sk5YD/BNaxvZOkDYFn2j6lov2YQqpVxVXzc1uQDh8PydebVrA9pjRRi/5tBXy2XZbdMuus7TU/u3vd1dZS9iHvx39YnQSNMLXEZ6N7rfbdDbt9dp7tOd2uu5Pvm/Fua5jkig7vtv1gN8vXOUI6hHTY9eJ8fz5pIGtpQKKz4qpfA+YAz8zbmQH8gnQENPSmYrZaGC7x2ejeVNx3klomHNjea5ybeBy4TNJZwKI5kOqmfdcJSBvYfpek7fOKH5I05p3ssrjqW0gDYi/N674tTwRYm+2zgbM7WSaEEKao2t+vkhaQsupK2Z5V8vCv8q0rdQLSo5JmkjsmaQMKka+gm+Kqj9p2Y6Btm2ktQgghjEMealO37YoAkr5JyoI+gpQXsAMVga0xw3i36gSkr5PmSV9b0i9Jp9PG1DbqsrjqMZIOAlaWtBOpsvhPO1g+hBBCTZI+Z/v7kval5Oin4tTaa21vXrh/gKQLge+XrH870vxK65Lii9JqS4+mxqgzMPZ3kuYBL8or363bDIqSdf9Q0qtJlR+eCXzV9u97se4QQghjNCrb1E7FBh6XtANwNCmIbU+6VlRmb9LEfFe6XcZciTrVvs+0/UrSeKLmx8YtB6C+B6EZCyZ+ZH27i6i9HqXeTqep2FXte/W66m6zanu9SC3vJnOy2/1SXLZV215/bsdzMb+b/nazvTpp9HX6UtafViZ7ooPtX+efhwHka/YuK4xa8B5gn3wzKSfgPRVtbwGu6iYYQYu0b0nLkqa4PYs0X0bjnZoF/Nb2syuWa5v23eJiWUeHd51YYZW1/dxX7Nbr1Q61if6Sm+h/AJoNcn+022adwNfP/dfLfdHP8Xvd9KNhPP354wl7TOq0b0kbk64JzSZ97/4LeL/tq8e53heQTtmdw5JZdrWy91odIX2UVI17DVLad+Pdvg/4SYvl2qZ9Ny6Whf4a5BfERH8Z1TERfexkm6P6fg3Lez8s/RgRc4HP2D4LFo3n/CnwkuaGeXzoToyt4P2hkvV+hzQt+rLA0p12qjIg2d4H2EfSJxtT07bSZdp3CCGEwVu+EYwgDZ9pkeV8EqkI6xlUXztqmG37Nd12qk5Sw7758G4jUtRrPN48TW03ad8hhBAG73pJXyGdtgN4L2kqijLLVVXaKXGGpNfY/l03nWpbXDVXU9g337Ympfq9sbmd7Xtt32h7e1I1h8dI14lWkLRO0zqX6aazIYQQeuJDwGrACcCJpOLWY4bzZKfkuqF17EoqsP2QpPskLZBUOX9eszrjkN4ObAr82faOeRK9g6saS/oEaezSP1k8NYSBYhXv84HNJB1hu1fToYcQQqjB9r+BT8GimbeXt10VOHYDvijpEdKBRmXy2XjzA+oEpIdsPyFpoaRZpLmL1m/RfndS8dW7WrRZWtIHgJfkyuBLsH1CjX6FEELogqQjgV1I14TmAStJ2sv2D5rb1gkykp5i+x/jbVMnIF2S50n/Kanj95OmKK9yC6liQyu7kMpPrAy8oek5kw4jQwgh9MdGtu/LA15PJWVBzyPNZTRGnsJ8Q5bMIzi30ORUYLM222zbpk5Sw8fzrwdKOg2YZfuKFotcD5wt6TdU5KHb/gPwB0mX2P5Zuz6EEELoqRmSZgBvBvaz/VijpmgzSR8hnbZbC7iMVLXnfOAVhWabtrlWJNKQoZZqTdAnaU0W1yZC0subomPRzfm2NO3z0I+Q9Cng5fn+OcCBth+r069OTLvnAWaeeOGYxx96y+YlrSlt262qbVQZz7bHu606yzeW6dXraree4nKd9K+TbYxne83LdbpfOl3/ePSrb816sS9mnnhhrc9Gt5+JMoPaP0PgIOBG4HLgXEnrUh0wdgNeAFxge2tJzwKWKNJqe3ovOlVngr7vAe8CrmFxDrptj8m0a1puedsPtGlzMGkOpEaF2PcBj9v+SI2+d2SWZntz9aTaUQhhkjvDxw1VpQZJ25BK90wHDrb93Yp2LwAuAN5l+7i6/c1TCk23vbDkuYttv0DSZcDmth+RdJnt59Zdf111jpDeTEpSKJtyYgxJLwZ+BqwArCNpU+CjhVN/RS+wvWnh/v9KurzOdkIIYSrIWXA/IVXBmQ9cLOlk29eUtPsecHqn28i158YEo2x+ziP4FfB7Sf8Gbut0G3W0HYdEuiY0o4N17g28FrgLwPblLD4l1+zxPL8SAJLWp/1I4BBCmEpeCFxn+3rbj5Kqbr+ppN0ngeNJmdA9Y/sttu+x/XXgK6QDjjf3chsNdY6QHiRNSXsmNaektX1L06SyVUFmD+AsSdeTLnqtS/XgrBBCGAmdzC5wA6wqqTgdxFzbcwv31yRlLzfMB5a42JWv87+FlGjwgq46XYPtc9q1yRl5awAPATfafqLNIovUCUgn51tdt0h6CWBJS5MGX11b1tD2mZI2JM2FJOAvHZwavBFYQAp2CwddLTeEEHrkzjbfX2Wl2Zuj3d7A520/3nQwUL5CaVfgl7bvyfdXAba3vX+tHo9d30qkKg3bk5LZ/kVKEV9d0gXA/sXaeVXqpH13OiXtLqSLb2uSIvnvcker1v8I0CqNvJWtezVZYAghDKn5wNqF+2sx9hrOHODoHIxWBV4naaHtX1Wscyfbi2ZtsP3vPGt3VwEJOA44HHhZI8g1SHo+8D5J67cb5lMZkCQdY/udkq6kfKrbTUoWIweIHdr3P4QQQg0XAxtKWg+4FXg3TRPk2V6v8bukQ4FTWgQjgGmS1JhILydEdDxdRGH7r27x3DzSoNu2Wh0hNWaz267OiqrmaC90qvKaU5cM/C4P5jqo6ZxrCCFMCrYX5hqhp5PSvn9u+2pJu+TnD+xitacDx0g6kPRdugtwWrFBi4lUG/0aU8tO0vHAz0mTuNa+dtTQaj6k2/PPm2quq5M52gGQ9FLgMtsPSHovqazEPjW3+VLbt0l6MikV8S/Ng3Ul7QzsDLAsy3XavRBCGAq2TyWV3ik+VhqIbH+wxio/T5qE9WOka1S/o6lodqOGnaRvAv8gTVUh0hmwqvp2B5AS034s6VjgUNt/qdEfoPUpu46iYxfXmiB1ftM8VulzpHTCw4Et2y1o+7b88w5JJ5JSI89tajOXNDMiszQ7ppMMIQQgH70ckG/tvNZ2MavvAEkXkqYial7vGaQ5kVYiJTj8XtItpFqov2hXhafVEVI30bEx3e3nGTuh3ytKmi+0bUlvIh0Z/SxXAW8pz2w4zfaC/PtrgG+2Wy6EEKayLnMDHs9FWI/Oy2xPi/Gikp5EmvDvfcCfgV8CWwAfALZq1b86ad+1o2P2S+B/gNeTzkt+gJQCWGaBpC/kzr88X1irMwh3deDEnFGyFHCk7dNaLxJCCFNeR7kB2XtImdP7kALSH2lKqmiQdALwLNIBzBsal36A/2kaa1WqTkDqKDoCT8pHOrvlQVTnSKoaTPUu0gv7sO1/5JllS8ufF9m+njRpYAghhJps357/8f+Z7VfVXOZGyitDlNnP9v9WrKftWNE6Aal2dMwa5whvl/R6Ur78WhUd/AdQnJbiZtI1pBBCCH2QB88+KGkl2+3mrkPSM0jXmla3vbGkTYA32v52oc1by34vbLPWHHctA1KOpLvarhsdAb6dL2j9J7AvMAv4dNN6qxImKqfGDSGE0DMPA1dK+j2waFaGiuE5PyWVeTsot7kizzj77UKb5olWi2pPutoyIOVI+vw6Kyq4MEfde4GtK9Y7rnnXQwghjMtv8q2oKhN5OdsXNZUkWqIyuO2e1CCtc8ruz5JOBo5lyUhaFfH+JOkGUmLDCbb/3WrlkrYANrR9iKRVgRVt31Cv+yGEELqwsu19ig9I2q2i7Z15VoZGVYe3A7eXNZS0DPA24GkU4ovtWlnQdaafmE2aSuIVpMOyN9AiQ8P2hsCXgecA8ySdkge9lnX+a6QU8S/kh5YGflGn4yGEELpWNrzmgxVtdyWdrnuWpFuB3UkZ1GVOIiVALCQdwDRutdQprtrxoZjti4CLJP0/UtLCYZQHmrcAzwMuzcvdJilO54UQQh9I2p6UlLZePvPVsCJ5DrsStv2qpvGf61W0Xcv2Nt32r21AkrQWKTnhpaRDtj8Au9meX9F+FinQvBvYAGhUUSjzaB4Y2zgUXL7jVxBCCKGuP5FOt60K/Kjw+AKqZ104HtjMdvFI5zigLL/gT5L+w/aV3XSuzjWkQ4AjgXfk++/Nj1VVd72cNNXtN22f32bdx0g6CFg5lz7/ECmjI4QQQo/lOqE3AS+WtC7p+v0ZkmYCM0mBCQBJzyJdelmpKZV7FoUqPE22AD6Y8wgeYXHmdOnsEM3qBKTVbB9SuH+opN1btF+/UdK8Hds/lPRq4D7SJH1ftf37OsuGEELoTj4A2JmUI7ABaazogcArC82eScoXWJkl07oXADtVrHrb8fSrTkC6MyclHJXvb0/JuUZJe9veHTi5cQquyPYbS5b5NHBsBKEQQhioXUmXUi4EsP23PHPCIrZPAk6S9OIaZ7say9yUi2W/LD90nu3L63aqTkD6ELAf8N/5/h/zY82OyD9/WHfjpEO/0yXdTSpNdJztf3awfAghhM49YvvRxtgiSUtRPQ7pz3nK8+ewZMHsMXEgp47vxOKBsL+QNNf2vnU6VSfL7mZgzNFNSbt5+WdV3bqyZb4BfCOXongXqe7d/Lo1lkIIIXTlHElfBGbmyyYfB35d0fYI4C/Aa0mzKuwAXFvR9sPA5o0ECEnfA84nJca11XYckqS1JJ0o6Q5J/5R0fM68q2p/g6Trm29tNnMHaYqLu4Ant2kbQghhfPYkzcJwJWmivlNJ40fLPN32V4AH8rx3rwf+o6KtWLL49uP5sVr6kWVXrOi6bF5udllDSR8jHRmtRkoj3Mn2NTX6FEIIoUt5gr6fUi+ruVEw+x5JG5MOHp5W0fYQ4MI8aSrAm0kTr9bS8yw7280JD3tL+gPw1ZLm6wK7276sRj9CCCH0gKTtgG+RvoOXonVh67mSViEdQZ0MrAB8pWy9tveSdDYp/VvAjrb/XLdfPcuya5C0WeHuNNIRU2n1Bdt7StpC0o65lt1qwApRyy6EEPpqb+CtwJU1humcmWuSngusD9CiUgO2LyVX3+lUp1l2Jo30LcuyayiO/l0I3Ai8s6xhrmU3h5TvfghptthfkKpChBBC6I9bgKtqjhk9Htis6bGqSg3j0rMsu0L70iknKkQtuxBCGLzPAafm2bwfaTxoe9GEqV1WahiXOrXsDiPVrrsn318F+FFZDnp+/jOt1ld8wUQtuxBCmAjfAe4nBZalK9p0U6lhXOqcstukEYwAbP9b0vNatJ8DvIB08QvSCzmXdIjYrOtadpJWBg4GNiadSvxQ3dHEIYQwxc22/ZpWDbqp1DBedQLSNEmrNCbakzS7zXKrkirDLsjtv04qD/SR5objrGW3D3Ca7bdLWhpYruZyIYQw1Z0h6TW2f1ej7V2SzgRWt71xLmTwRtvfbrdgp+oEpB+RSoofRzoSeSfpcK/KOsCjhfuPUp2zTg5AHdWyy1NcvJw8oZTtR5u2GUIIodquwOckPUIaZ9Qq7funwB6kSfqwfYWkI4HBByTbh0u6hDRjrIC3thm8egRpcr4TSQHsLcDhxQaSFlBeN6nVTilanzTK+JBcyG8e6TrXEjMTStqZVNGWZeMAKoQQALDdSfLYcrYvatS9yxb2uEtAvSMkcgCqVUHB9nck/ZbF1V7HDIzqcGeUWYqUhvhJ2xdK2odUCmOJwVq25wJzAWZpdq0pMUIIYbKT9PKyx22fW/LwnZI2IB9ESHo7aZK/nqsVkLqwHHBfY7CrpPV6PNh1PjDf9oX5/nGkgBRCCKG9PQq/L0uaimIe6UxYs11J/9g/S9KtwA2kEnI91/OANIjBrrb/IekWSc+0/VfSpFJRAy+EEGqwXUzjRtLawPcr2l4PvCoPy5nWSFjrh34cIQ1qsOsngV/mDLvrgR37sI0QQpgK5pOG0IzRPLY0X0u6F5jX6zqk/QhIAxnsmnfEnHbtQgghLEnSvixOLJsGPBeomtl1Tr415kt6PXAxsIukY22XHll1ox8BqevBriGEEAbiksLvC4GjbP+xou2TSGNL74dFl2WOIw29mUfFqb5u9DwgjXOwawghhD7LE+3V1Ty29DFgXdsP5XFMPdOzgCRJjcqxVYNdi21CCCHUI2kbUnWa6cDBtr/b9PwOwOfz3fuBj9kecwpO0pW0HgO6SclzRwIXSDop338DcFS+HNPTZLJeHiGdJel44KRcIRyAnHSwBfAB4Czg0B5uM4QQJjVJ04GfkGbpng9cLOnkpgIFNwBb5lqj25LStDcvWd12nW7f9rckncriSfd2sd045bdDp+trpZcBaRvS9aKj8uRN9wAzSRfMfgf8d8wMG0IIHXshcF1Ov0bS0cCbKByd2P5Tof0FwFplK7J9U+N3SauTCmEDXGT7jhZ9mEl/x5YCPQxIth8G9gf2lzSDVGT1oWKl8BBCmAqm3fMAM0+8sH3DZNVcnq1hbq4y07AmS86WMJ/yo5+GDwO/bbVBSe8EfgCcTTrq2VfSHraPK2k7sIlU+1KpwfZj9Km0RAghTDJ32m41hEUlj5Vei5e0NSkgbdFmm18CXtA4KpK0GnAGKXuu2cAmUu1X6aAQQgi9MR9Yu3B/LeC25kZ5WoiDgW1t39VmndOaTtHdRbq8UmZgE6lGQAohhOF2MbBhvjZ/K/Bu4D3FBpLWAU4A3mf7/2qs8zRJpwNH5fvvAk6taDuwsaURkEIIYYjZXijpE8DppLTvn9u+WtIu+fkDga+SBrDun0v7LGx1GtD2HpLeyuLMubm2T6xoO7CxpRGQQghhyNk+laYjmByIGr9/BBgzK3cVSZ8mzeR9Qgdt+17goOqcYQghhMlrFnC6pPMk7ZpTwHvRdlwiIIUQwhRj+xu2n0Oa62gN4BxJZ4y37XhFQAohhKnrDuAfpCy7J/ewbVciIIUQwhQj6WOSzgbOJBUx2Kmijl1HbccrkhpCCGHqWRfYvWY5t07ajksEpBBCmGJs79mPtuMVp+xCCCEMhZEMSJKeKemywu0+SbtPdL9CCCF0byRP2dn+K2kO+MZcIbcCpaOMQwghjIaRPEJq8krg78V5PkIIIYyekTxCavJuFhcIXIKknYGdAZZluUH2KYQQQodG+ggpT4/+RuDYsudtz7U9x/acGSwz2M6FEELoyEgHJGBb4FLb/5zojoQQQhifUQ9I21Nxui6EEMJoGdmAJGk54NWkSalCCCGMuJFNarD9IGlCqhBCCJPAyB4hhRBCmFwiIIUQQhgKEZBCCCEMhQhIIYQQhkIEpBBCCEMhAlIIIYShEAEphBDCUIiAFEIIYShEQAohhDAUIiCFEEIYChGQQgghDIUISCGEEIZCBKQQQghDIQJSCCGEoRABKYQQwlCIgBRCCGEoREAKIYQwFCIghRBCGAojG5AkfVrS1ZKuknSUpGUnuk8hhBC6N5IBSdKawKeAObY3BqYD757YXoUQQn9I2kbSXyVdJ2nPkucl6cf5+SskbTYR/RyvkQxI2VLATElLAcsBt01wf0IIoeckTQd+AmwLbARsL2mjpmbbAhvm287AAQPtZI+MZECyfSvwQ+Bm4HbgXtu/a24naWdJl0i65DEeGXQ3QwihF14IXGf7etuPAkcDb2pq8ybgcCcXACtLeuqgOzpeS010B7ohaRXSG7AecA9wrKT32v5FsZ3tucDcvMy/zvBxNw2oi6sCdw5oW5NV7MPxi33YvXXHs/AC/n36GT5u1ZrNl5V0SeH+3Pzd1bAmcEvh/nxg86Z1lLVZk/QP+8gYyYAEvAq4wfa/ACSdALwE+EXVArZXG1DfkHSJ7TmD2t5kFPtw/GIfThzb2/RwdSrbRBdtht5InrIjnap7kaTlJAl4JXDtBPcphBD6YT6wduH+Woy9Zl6nzdAbyYBk+0LgOOBS4ErS65jbcqEQQhhNFwMbSlpP0tKkjOKTm9qcDLw/Z9u9iHRdfaRO18HonrLD9teAr010PypEcBy/2IfjF/twErC9UNIngNNJQ1x+bvtqSbvk5w8ETgVeB1wHPAjsOFH9HQ/ZI3eaMYQQwiQ0kqfsQgghTD4RkEIIIQyFCEg1SFpb0lmSrs3183bLj8+W9HtJf8s/Vyks84VcxuOvkl6bH1tO0m8k/SWv57sT9ZoGrVf7MD9+dn7ssnx78kS8pkHr8T7cXtKVuczMaZLqjpkJoX9sx63NDXgqsFn+fUXg/0glPL4P7Jkf3xP4Xv59I+ByYBnS4N2/ky5GLgdsndssDZwHbDvRr2+U9mF+7mxSHcMJf12juA9JyUx3AKvmdt8Hvj7Rry9ucYsjpBps32770vz7AtKYpzVJ1SIOy80OA96cf38TcLTtR2zfQMp8eaHtB22fldfzKCltfa2BvZAJ1Kt9ONBOD5ke7kPl2/J5HN8sRnDMSph8IiB1SNLTgOcBFwKrO+f655+NU0dVZTyK61kZeANwZn97PHx6tA8PyafrvpK/VKeU8exD248BHyON4buNdCT1s8H0PIRqEZA6IGkF4Hhgd9v3tWpa8tii/Ppcofwo4Me2r+9tL4dbj/bhDrb/A3hZvr2vt70cbuPdh5JmkALS84A1gCuAL/S8oyF0KAJSTfmP+Hjgl7ZPyA//s1FRN/+8Iz/erozHXOBvtvfua6eHTK/2oVO198ZpqyOZQqfyerQPnwtg+++2DRxDqgUZwoSKgFRDPiX0M+Ba23sVnjoZ+ED+/QPASYXH3y1pGUnrkeYouSiv69vASsDuA+j60OjVPpS0VCMjLH85bwdcNYjXMNF6+Dm8FdhIUqPg8KuJWpBhCESlhhokbUHKiLsSeCI//EXS+ftjgHVIBV/fYfvuvMyXgA8BC0mnVn4raS3SOf2/wKIJmvazffCgXstE6eE+XB44F5hByhg7A/iM7ccH+HImRK/2YX58F2A34DHgJuCDtu8a3KsJYawISCGEEIZCnLILIYQwFCIghRBCGAoRkEIIIQyFCEghhBCGQgSkEEIIQyECUph0JK0s6eP59zUkHTfRfQohtBdp32HSyXXeTrG98UT3JYRQ31IT3YEQ+uC7wAaSLgP+Bjzb9saSPkiqhD0d2Bj4EWkakPeRBiq/zvbdkjYAfgKsBjwI7GT7L4N+ESFMNXHKLkxGewJ/t/1cYI+m5zYG3kOqf/cd4EHbzwPOB96f28wFPmn7+cBngf0H0ekQpro4QgpTzVm5KOsCSfcCv86PXwlskitpvwQ4tjCrxTKD72YIU08EpDDVPFL4/YnC/SdIfw/TgHvy0VUIYYDilF2YjBaQpvjuWJ5f6AZJ74BUYVvSpr3sXAihXASkMOnkqtV/lHQV8IMuVrED8GFJlwNXk6YCDyH0WaR9hxBCGApxhBRCCGEoREAKIYQwFCIghRBCGAoRkEIIIQyFCEghhBCGQgSkEEIIQyECUgghhKHw/wHbAg7UbFGR+QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.H2OSOI.plot.contourf(x='time', yincrease=False) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>CHALLENGE:</b> Instead of a contour plot, can you plot the volumetric soil water (<code>H2OSOI</code>) for year 2001 as a linegraph, where each line is a different <code>levsoi</code> value? (<i> Hint: check out the <code>hue</code> parameter for plotting in xarray</i>).\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot your graph here\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### 3.3 Visualizing Relationships\n",
-    "\n",
-    "Plotting can be useful for looking at relationships between different variables. What do you expect the relationship between latent heat flux and GPP to be? Below we are breaking up the data by season (`time.dt.season` is a built-in accessor)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds[\"season\"] = ds.time.dt.season\n",
-    "grouped = ds.groupby('season')\n",
-    "for key, group in grouped:\n",
-    "    plt.scatter(group.EFLX_LH_TOT, group.GPP, label=key)\n",
-    "plt.legend(loc=\"lower right\")\n",
-    "plt.xlabel(\"Latent Heat Flux [W m$^{-2}$]\")\n",
-    "plt.ylabel(\"GPP [gC m$^{-2}$ s$^{-1}$]\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Questions:** \n",
-    "- How is albedo related to GPP?\n",
-    "- How does this relationship play out over different seasons?\n",
-    "\n",
-    "**What other relationships can you elucidate from these simulations?**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot some relationships here\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 4. Aggregating and averaging\n",
-    "### 4.1 Basic aggregation methods\n",
-    "\n",
-    "Often we will want to aggregate our simulations from monthly to yearly or larger sums or averages. There are various ways to do this using *xarray*. A very common step would be to apply some function over the whole dataset, such as `sum()`, `mean()`, `min()`, or `max()`. Let's explore some of these methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(3.610761e-05, dtype=float32)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# compute the average GPP across the whole time series\n",
-    "# NOTE these values are not weighted by the number of days in each month.\n",
-    "ds.GPP.mean().values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also calculate the average GPP for a specific year, weighting by the number of days in each month and coverting to different units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1103.6826305738941"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# calculate GPP for year 2005\n",
-    "# weight by the number of days in the month (non-leap years)\n",
-    "days_in_month = [31,28,31,30,31,30,31,31,30,31,30,31]\n",
-    "gpp_pm = ds.sel(time='2005').GPP*days_in_month/365.\n",
-    "      \n",
-    "# convert from GPP in gC/m^2/s to gC/m^2/year (x 60 seconds per minute x 60 minutes per hour x 24 hours per day x 365 days per year)\n",
-    "gpp_pm.sum().values * 86400. * 365.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Can you find the maximum soil volumetric water content for the soil at 0.26 m?**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate answer here\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 Split-Apply-Combine\n",
-    "You saw in our plotting above that we used *xarray*'s `.groupby` function to group our dataset by a specific variable (in that instance, years). This `groupy` operation allows us to aggregate our data conditionally on some coordinate label or group. This then allows us to perform **split / apply / combine** methods on *xarray* DataArrays and Datasets:\n",
-    "\n",
-    "* **Splitting** the dataset into groups based on some criteria\n",
-    "* **Applying** a function to each of those groups (e.g. aggregating, performing a transform, or filtering)\n",
-    "* **Combining** the results back into one data structure\n",
-    "\n",
-    "Let's use this methodology to remove seasonality from our dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# calculate temperature in Celsius\n",
-    "ds['tair'] = ds.TBOT - 273.15\n",
-    "ds['tair'].attrs['units'] = 'degrees Celsius'\n",
-    "ds['tair'].attrs['long_name'] = 'air temperature'\n",
-    "\n",
-    "# plot temperature across whole time series\n",
-    "ds.tair.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we will split the data by month:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DataArrayGroupBy, grouped over 'month'\n",
-       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.tair.groupby(ds.time.dt.month)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can apply a calculation to each group - either an aggregation function, which will reduce the size of the group, or a transformation, which will preserve the group's full size. Here we will take an average. Notice that the dimensions are now 1 x 12, because we have averaged across all years for each month."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tair&#x27; (month: 12)&gt;\n",
-       "dask.array&lt;stack, shape=(12,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c757221e-4553-427d-9ef4-40db255e9e5d' class='xr-array-in' type='checkbox' checked><label for='section-c757221e-4553-427d-9ef4-40db255e9e5d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-d061af66-5fa0-4bbc-895e-9e1826094c7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d061af66-5fa0-4bbc-895e-9e1826094c7d' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-ce900887-8a92-4cca-98c3-1998532433c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce900887-8a92-4cca-98c3-1998532433c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6464c6b-3d18-435d-9cde-0a8cc747d6a5' class='xr-var-data-in' type='checkbox'><label for='data-e6464c6b-3d18-435d-9cde-0a8cc747d6a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ded0dc2-3b0b-41b0-892e-0533ecf5fead' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ded0dc2-3b0b-41b0-892e-0533ecf5fead' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tair' (month: 12)>\n",
-       "dask.array<stack, shape=(12,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# split-apply-combine\n",
-    "t_clim = ds.tair.groupby(ds.time.dt.month).mean()\n",
-    "t_clim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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z6JwN1OpCcW6jv2nKuo9X7uDT1TuZNDKdjDbRtuMoLxAYINx1dgb5eyuYsaTAdhy/pQWgrCoqq+K+D9fQp30LJgzpZDuO8iLDMhI5qWNLnpqfS3l1ne04fkkLQFljjGHy+2uoqKnn8Uv76Br/6kcOLRS352ANL3y92XYcv6S/ccqaD1ZsZ97a3fx5VAZpiVG24ygv1C8ljrN7tuGFhZspLqu2HcfvaAEoK3YdqOKvH+aQmRrHdad1tB1HebE7zsqgqq6BZ77ItR3F72gBKI8zxnDPzFXU1Dfw2KV9CAzQNf7V0XVOiGLcwPZMX1xA/t5y23H8ihaA8rh3sgpZsKGYu0d3pUO8nt5RHduk4ekEBwbw2FxdKK45aQEoj9q+v5IHP1nL4E4t+c3JHWzHUT4iMSaM64d05OOVO1hVuN92HL+hBaA8xhjDXe+uwhjDo5f0IUAP/ahfYMLpnYiLCGbKZ+sxRpeIaA5aAMpjpi8u4Ju8Pdw7phvtW0bYjqN8THRYMLecmc53m/byda4uFNcctACUR2wrqeCRWesYkh7PlYNSbMdRPuqqwSkkx4Uz5TNdKK45aAEot2toMNzxzkoCRfjHxb0R0UM/6sSEBgXy57MyWLuzlI9X7bAdx+dpASi3e+X7rSzeUsJ953YnqUW47TjKx53XO4nubWN4dM4GquvqbcfxaVoAyq227CnnH7PXMywjgUszk23HUX4gIKBxiYjCfZVMX6QLxf0aWgDKbepdh35CAgOYood+VDMakh7PqWmtePqLXEqram3H8VlaAMptXvpmC9n5+3jg/B60jgmzHUf5ERHh7tHd2FdRywsLdaG4E6UFoNwir6iMR+duYFT31lzQt53tOMoP9UqO5bw+Sfz36y0UlVbZjuOT3F4AIvKSiBSJyJom97UUkXkikuu6jnN3DuU5dfUN3P72SiJDAnn4wl566Ee5zR2julBb38C0+bpQ3InwxB7Ay8Dow+67G5hvjEkH5rtuKz/xn4WbWVl4gIcu6ElCdKjtOMqPpbaK5KqTUnhr6TY2FR+0HcfnuL0AjDELgZLD7j4feMX19SvABe7OoTxj/a5Spn2+kTG92nJu7yTbcZQD3DI8nbCgAKbOXm87is+x9RlAa2PMTgDXdaKlHKoZ1boO/cSGB/PQBT1tx1EOER8Vyk1DOzMnZzezVu+0HceneP2HwCIyQUSyRCSruLjYdhz1M55dkEfOjlL+dkEvWkaG2I6jHOSGMzrTJzmWe99fzW79QPi42SqA3SLSFsB1XXS0BxpjnjfGZBpjMhMSEjwWUP0ya7Yf4Jkv8rigbxKje7axHUc5THBgAE+M60tVbT13vLNS1wk6TrYK4CNgvOvr8cCHlnKoZlBd1/hL1zIyhPvH9rAdRzlU54QoJo/pzte5e3j1+6224/gETwwDfRP4HsgQkUIR+R0wBRgpIrnASNdt5aP+OT+X9bvKmHJxL1pE6KEfZc/VJ6UwLCOBv3+2ntzdZbbjeD1PjAK6whjT1hgTbIxJNsa8aIzZa4wZboxJd10fPkpI+YiV2/bz3JebuHRAMmd2bW07jnI4EeEfl/QmMjSISW+toKauwXYkr+b1HwIr71VVW8/t76ykdUwY953X3XYcpQBIjA7j7xf1ImdH45BkdXQ/WwAiEtB0Bq9STT05byN5RQf5x8W9iQkLth1HqR+c1aMN4zLb8++vNrF0qx5gOJqfLQBjTAOwUkT0FE7qR7LzS3j+681cMSiF07vo6Czlfe47rzvJcRHc+tYKynTF0CM6nkNAbYEcEZkvIh8durg7mPJelTX13PHOKtq1CGfymG624yh1RFGhQTw5ri879lfywMdrbcfxSkHH8ZgH3J5C+ZSpc9azZU85b/z+JKJCj+dHSCk7BqTG8cdhaTz9RR4juiUyumdb25G8yjF/e40xX3kiiPINizfv5X/fbmX8yamc0jnedhyljmni8HS+2ljMPTNX0z8ljkQ9N8UPjnoISES+cV2XiUhpk0uZiJR6LqLyFg0Nhgc/WUtyXDh3nd3VdhyljktwYABPjutLZW09d7y7CmN0lvAhRy0AY8xprutoY0xMk0u0MSbGcxGVt5ids4ucHaXcNrILESF66Ef5js4JUUw+pxsLNxbz6vf5tuN4jeOeByAiiSKScujizlDK+9Q3GJ6Yt5G0xCjO1zN8KR909eBUhmYk8MisdeQV6SxhOI4CEJGxriUbtgBfAVuBz9ycS3mZD1dsJ6/oILeN7EJggJ7hS/keEWHqxb2JCAnUWcIux7MH8BAwGNhojOkIDAe+dWsq5VVq6xuY9nkuPZJiGN1DV/pUvisxJoy/X9SbNdtLeWq+zhI+ngKoNcbsBQJEJMAYswDo695Yypu8nbWNgpIK7hiVQYC++1c+bnTPNlw6IJnnvtxElsNnCR9PAewXkShgITBdRJ4CdFqdQ1TV1vP0/DwGpMYxNENn/Cr/8NexPWgXF86tbzt7lvDxFMBKoAK4FZgNbAL05JsOMX1xAbtKq7h9VBdE9N2/8g9RoUE8eVlftu+r5EEHzxI+ngIYZoxpMMbUGWNeMcb8Exjo7mDKvvLqOv61II9T01rppC/ldzI7tOSmoZ15J7uQ2WuceS7hn5sIdpOIrAa6isiqJpctwCrPRVS2vPzdVvaW13D7qAzbUZRyiz8N70LPdjHcM3M1RQ48l/DP7QG8AZxH4+kaz2tyGWCMudoD2ZRFBypr+c9XmxjeNZH+KXG24yjlFiFBAUwb15eKmnrufM95s4R/bibwAWPMVtcZvfKbXJz9sblD/PfrzZRW1XHbqC62oyjlVmmJ0Uwe040vNxTz+iJnzRLWM4Kpn9h7sJqXvtnCmN5t6ZEUazuOUm53zeBUzuiSwMOz1rGp+KDtOB6jBaB+4rkvN1FZW8+tI/Tdv3IGEeHRS3oTHhzIpBkrqK13xixhLQD1I7sOVPHaonwu7JdMWmKU7ThKeUzjLOFerN5+gKc+z7UdxyO0ANSPPLMglwZjmDQi3XYUpTxudM+2XDIgmX99mUd2vv9/3KkFoH6wraSCGUu2MW5ge9q3jLAdRykr/nped5JahHPrWys5WF1nO45baQGoH0z7PJfAAOGWM/Xdv3Ku6LBgnhzXl8J9FTz4cY7tOG6lBaAAyCs6yPvLC7lmcCqt9ZR5yuEGdmjJjWd05u2sQubk7LIdx220ABQAT36+kfDgQG4a2tl2FKW8wqQRTWYJl/nnLGEtAEXOjgN8umon153WkVZRobbjKOUVDs0SLq+u404/PZewFoDiibkbiQkL4vohnWxHUcqrpCVGc8/ZXRtnCS8usB2n2WkBONyygn3MX1/EDWd0JjY82HYcpbzOb07uwJD0eB7+dK3fzRK2WgAislVEVovIChHJspnFqR6fu4H4qBCuPaWD7ShKeaWAAOGxS/sQFhzIrW/51yxhb9gDGGaM6WuMybQdxGm+27SHb/P2ctPQNCJDg2zHUcprtY4J45ELe7Gq8ABPz/efWcLeUADKAmMMj8/dSJuYMK46KcV2HKW83jm92nJR/3Y8syCP7Px9tuM0C9sFYIC5IpItIhOO9AARmSAiWSKSVVxc7OF4/uvLDcVk5+/jluFphAUH2o6jlE94YGwPklqEc9vbK/xilrDtAjjVGNMfOBv4o4icfvgDjDHPG2MyjTGZCQl6UvLm0NBgeGzuBlJaRnBZZnvbcZTyGdFhwTxxWV8KSiqYOtv3T41utQCMMTtc10XA+8Agm3mcYnbOLnJ2lDJpRDrBgbbfAyjlWwZ1bMlvBqfy+qJ8Nuwqsx3nV7H22y8ikSISfehrYBSwxlYep6hvMDwxbyNpiVGc37ed7ThK+aRJI7oQHRbMQ5+s9ekJYjbf/rUGvhGRlcAS4FNjzGyLeRzhwxXbySs6yG0juxAYILbjKOWT4iJDmDQinW/y9vDF+iLbcU6YtQIwxmw2xvRxXXoYYx62lcUpausbmPZ5Lj2SYhjdo43tOEr5tKsHp9IpIZKHP11HTZ1vzg3QA8AO8nbWNgpKKrhjVAYB+u5fqV8lODCA+8Z0Z/Oecl79fqvtOCdEC8AhqmrreXp+Hv1TWjA0Q0dTKdUchnVN5IwuCTw1P5eS8hrbcX4xLQCHmL64gF2lVdxxVgYi+u5fqebylzHdqKip58l5G21H+cW0ABygvLqOfy3I49S0VpzSOd52HKX8SnrraK4+KYXpi31vWKgWgAO8/N1W9pbXcPuoDNtRlPJLh4aF/u1T3xoWqgXg5w5U1vKfrzYxvGsi/VPibMdRyi8dGhb6da5vDQvVAvBz//16M6VVddw2qovtKEr5NV8cFqoF4Mf2HqzmpW+2MKZ3W3okxdqOo5Rfazos9LVF+bbjHBctAD/27682UVlbz60j9N2/Up4wNCOB07sk8NTnG31iWKgWgJ/aXVrFq9/nc2G/ZNISo2zHUcoRRIT7xnSj3EeGhWoB+Kmnv8ilwRgmjUi3HUUpR/GlYaFaAH5oW0kFM5ZsY9zA9rRvGWE7jlKOM2lEF6JCg7x+WKgWgB96an4ugQHCzcP03b9SNjQOC+3i9cNCtQD8TF7RQWYuK+Sawam0iQ2zHUcpx7rmZO8fFqoF4Gee/Hwj4cGB3DS0s+0oSjmaLwwL1QLwI2t3lPLpqp1cd1pHWkWF2o6jlON5+7BQLQA/8sS8DcSEBXH9kE62oyil8P5hoVoAfmJZwT4+X1fEDWd0JjY82HYcpZSLNw8L1QLwE4/P3UB8VAjXntLBdhSl1GG8dVioFoAf+G7THr7N28tNQ9OIDA2yHUcpdZimw0IXbPCeYaFaAD7OGMPjczfSJiaMq05KsR1HKXUUh4aF/u0T7xkWqgXg497NLiQ7fx+3DE8jLDjQdhyl1FF447BQLQAftmb7Af7ywRpO7tSKcZntbcdRSh2Dtw0L1QLwUfvKa7jhtWxaRYbwzJX9CArU/0qlvJ2I8BfXsNBpn9sfFqqvGj6ovsEwccZyisuqee7qATrpSykf0qV1NFe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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t_clim.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we combine steps we can use these methods to remove the seasonality from our original dataset. Note here that the dataset is now 1 x 120 in dimension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tair&#x27; (time: 120)&gt;\n",
-       "dask.array&lt;getitem, shape=(120,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * time     (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d530f69a-6d04-4050-809c-e2607dc58580' class='xr-array-in' type='checkbox' checked><label for='section-d530f69a-6d04-4050-809c-e2607dc58580' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 480 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (120,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 1284 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >120</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-447255da-09f2-4576-9284-df07b0bece2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-447255da-09f2-4576-9284-df07b0bece2e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-01 00:00:00 ... 2010-12-...</div><input id='attrs-e0b069e4-db19-4e0b-ad82-b7fe38962aa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0b069e4-db19-4e0b-ad82-b7fe38962aa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9f5180a-bc5d-416d-a371-7278e1521679' class='xr-var-data-in' type='checkbox'><label for='data-e9f5180a-bc5d-416d-a371-7278e1521679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2001, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2002, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2003, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2004, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2005, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2006, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2007, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2008, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2009, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-cb4c0f23-53eb-434f-b4bd-71dad6baaec2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb4c0f23-53eb-434f-b4bd-71dad6baaec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82f8bdca-a637-483a-af11-39973d164a75' class='xr-var-data-in' type='checkbox'><label for='data-82f8bdca-a637-483a-af11-39973d164a75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
-       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
-       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
-       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
-       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
-       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
-       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
-       "       12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-735264aa-480f-447f-a24e-e455fda3cf90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-735264aa-480f-447f-a24e-e455fda3cf90' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tair' (time: 120)>\n",
-       "dask.array<getitem, shape=(120,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time     (time) object 2001-01-01 00:00:00 ... 2010-12-01 00:00:00\n",
-       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gb = ds.tair.groupby(ds.time.dt.month)\n",
-    "t_anom = gb - gb.mean(dim = 'time')\n",
-    "t_anom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t_anom.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also calculate rolling means using the `rolling` function. Here we will calculate a 6-month rolling average of GPP."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mv_avg = ds.GPP.rolling(time=6, center=True).mean()\n",
-    "mv_avg.plot(size = 6)\n",
-    "ds.GPP.plot()\n",
-    "plt.legend(['6-month moving average', 'monthly data']) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>CHALLENGE:</b> Calculate the average seasonal GPP values.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate value here\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/Day2c_CodeModification.ipynb b/notebooks/Day2c_CodeModification.ipynb
deleted file mode 100644
index c57584b..0000000
--- a/notebooks/Day2c_CodeModification.ipynb
+++ /dev/null
@@ -1,905 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c02b043a-7a7e-43bf-9d87-2e1320077fb2",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Tutorial 2c - *Code Modifications (Workflow + Simulation)*\n",
-    "\n",
-    "This tutorial is an introduction to familiarizing yourself with **Git** and **Github** workflow and terminology and making **simple code modifications**. Specifically, we will work through an example that modifies CTSM code related to grass phenology and test this modification for the Konza Prairie NEON Flux Tower site. Below, you will find steps to:\n",
-    "1. Check your cloned CTSM repository for any changes to date\n",
-    "2. Create a Git branch for code modifications\n",
-    "3. Make code modifications\n",
-    "4. Run CTSM with new code modifications\n",
-    "5. Save changes to your GitHub branch\n",
-    "6. [optional] Share your changes with others\n",
-    "\n",
-    "Once you have completed this tutorial, you can compare the changes from the new code to the original in tutorial [Day2d_CodeModification_Visualization.ipynb](Day2d_CodeModification_Visualization.ipynb).\n",
-    "________________________\n",
-    "<div>\n",
-    "    <img src=\"https://git-scm.com/images/logos/downloads/Git-Icon-1788C.png\" \n",
-    "         style=\"float:left; padding-right:10px; width:60px; height:60px;\" />\n",
-    "</div>\n",
-    "<strong>Git</strong> is an open-source version control software to track your changes in the source code.\n",
-    "\n",
-    "<br><br>\n",
-    "\n",
-    "\n",
-    "<div>\n",
-    "    <img src=\"https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/github.png\" \n",
-    "         style=\"float:left; padding-right:10px; width:60px; height:60px;\" />\n",
-    "</div>\n",
-    "<strong>GitHub</strong> provides a centralized online service to host the source code and version control using Git.\n",
-    "<br><br>\n",
-    "\n",
-    "\n",
-    "\n",
-    "If you want to know more about Git and GitHub, here is a tutorial you can use: \n",
-    "https://swcarpentry.github.io/git-novice/\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "\n",
-    "<b> TIP:</b> Before attempting any code modifications on your own, familiarize yourself with the suggested CTSM workflow with Git that we'll go over here \n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "In this tutorial, we assume you have already cloned CTSM repository during the [Day0a_GitStarted.ipynb](Day0a_GitStarted.ipynb) tutorial. If not, please follow the [Day0a_GitStarted.ipynb](Day0a_GitStarted.ipynb) to do this. \n",
-    "\n",
-    "It is also recommended that you go through the [Day0b_NEON_Simulation_Tutorial.ipynb](Day0b_NEON_Simulation_Tutorial.ipynb) tutorial and run simulations for KONZ so that you can compare the results of the code we'll modify here with the original code. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "022d4658-b2aa-45f1-8e43-e4f467a45194",
-   "metadata": {},
-   "source": [
-    "## 1. Check the cloned CTSM repository\n",
-    "\n",
-    "First navigate to your cloned CTSM repository:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "18d5acee-519a-47f3-97fc-25dd309a3d88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd ~/CTSM/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25ea4ad3-cbfd-47c2-9454-0f0a788dfeb3",
-   "metadata": {},
-   "source": [
-    "Next, check the status of your clone on the cloud. \n",
-    "The command below shows if you have already made any changes to your code in the cloud:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6173319d-6920-4c9f-adc1-7758728a13da",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "On branch master\n",
-      "Your branch is up to date with 'origin/master'.\n",
-      "\n",
-      "nothing to commit, working tree clean\n"
-     ]
-    }
-   ],
-   "source": [
-    "git status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14b38c55-bf8f-4cab-89c1-667927e68b47",
-   "metadata": {},
-   "source": [
-    "You will likely see this message: \"Your branch is up to date with 'origin/master'.\" Note that if you have already made changes to the model code, it will show up as the output of `git status`. \n",
-    "\n",
-    "To check what changes you have made to the code at any point, you can use the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c96ce683-7e63-4c9f-b0b1-a31e679983c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git diff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8663353-b6a5-4a41-99cf-12844dea404d",
-   "metadata": {},
-   "source": [
-    "If you have been following this tutorial consecutively, you should have a clean copy of the repository and will not see any differences. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67df694b-aef1-46fa-a058-0c585d2c1b14",
-   "metadata": {},
-   "source": [
-    "## 2. Create a branch for your code modifications\n",
-    "\n",
-    "Now we will create a git branch for our code modifications. Creating a branch in GitHub allows you to make modifications and develop new features to the code while not changing the original code directly. \n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>NOTE: </b> \n",
-    "    A <code>branch</code> of a repository is a copy of the original, or <code>main</code> branch. Branches allow you to preserve the original code (the 'main' branch) while making any modifications in a copy (the new branch) and therefore can help to contain errors so that they do not get propogated into the 'main' code base. Using branches also helps to work on multiple features or bugs simultaneously while keeping a base branch that you know works.\n",
-    "</div>\n",
-    "\n",
-    "The below line of code creates a branch for your development called `phenology_change`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "788823ad-a1a1-4d7e-a20c-ae8f8f041c6b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Switched to a new branch 'phenology_change'\n"
-     ]
-    }
-   ],
-   "source": [
-    "git checkout -b phenology_change"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1dd26c2b-b6e6-4baf-89bd-568030404aee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "On branch phenology_change\n",
-      "nothing to commit, working tree clean\n"
-     ]
-    }
-   ],
-   "source": [
-    "git status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2eb73ea7-db22-4379-a14a-96b967393eba",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>NOTE: </b> \n",
-    " GitHub branches give us the flexibility to work on the same code base at the same time while keep tracking of what and where things have changed. When playing a video game, we save our progress at checkpoints so we can go back to these points and start from specific part of the game. Similarly, Git and Github give us the flexibility to save snapshots of the code so we can revert back to these snapshots if we want to rework part of our changes. The image below shows how different branches might be developed at the same time and merged back with each other.\n",
-    "</div>\n",
-    "\n",
-    "![github](https://nvie.com/img/main-branches@2x.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6513804-29de-46a1-9176-fc3d90022590",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## 3. Make your code modifications\n",
-    "\n",
-    "Now that you have a`branch` of the code, you can start changing the code. \n",
-    "\n",
-    "In this tutorial, we are going to change one aspect of grass phenology. In particular, we will change the threshold for the amount of rain required for leaf onset and compare the results for Konza Prairie Biological Station (KONZ).\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "\n",
-    "<b> WARNING: </b> To compare the results from modified code with original code, make sure you have already run the original CTSM code for Konza (KONZ) or have previously completed the [Day0b_NEON_Simulation_Tutorial.ipynb](Day0b_NEON_Simulation_Tutorial.ipynb) for the KONZ site.\n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "To find more information about NEON's KONZ site, please visit NEON's website: https://www.neonscience.org/field-sites/konz\n",
-    "    \n",
-    "    \n",
-    "**Questions:** \n",
-    "- Where is Konza Prairie Biological Station located? \n",
-    "- Is rain necessary for leaves to green up here?\n",
-    "    \n",
-    "The CTSM model code is located under `src` directory.\n",
-    "    \n",
-    "### 3.1 Navigate to the `src` directory and look at the content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6d746176-a453-4db4-926b-e5f3f500c21e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd ~/CTSM/src"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d706ac7e-2db0-4f32-b218-7a6e2182cd77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "biogeochem\tdyn_subgrid  README.unit_testing  unit_test_stubs\n",
-      "biogeophys\tfates\t     self_tests\t\t  utils\n",
-      "CMakeLists.txt\tinit_interp  soilbiogeochem\n",
-      "cpl\t\tmain\t     unit_test_shr\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a5fbdc4-5c4c-45ca-b693-999b0bb489c2",
-   "metadata": {},
-   "source": [
-    "As you can see there are multiple directories and multiple files listed in this directory.\n",
-    "\n",
-    "<div class=\"alert alert-success\">\n",
-    "  <strong>Fun Fact:</strong> There is roughly ~252,919 lines of Fortran code in CTSM repository. \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7967208c-fc90-4f80-b0e1-073a1ff1e30d",
-   "metadata": {},
-   "source": [
-    "In this example, we will make code modifications to the `CNPhenologyMod.F90`, which is in the `biogeochem` subdirectory.\n",
-    "    \n",
-    "First, let's navigate to this directory:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "6069b8d2-dab3-45e8-bcbb-7132a9a6d4d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/negins/CTSM/src/biogeochem\n"
-     ]
-    }
-   ],
-   "source": [
-    "cd ~/CTSM/src/biogeochem\n",
-    "pwd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24d9a9ad-8949-4319-aa39-50c3ce432371",
-   "metadata": {},
-   "source": [
-    "Next, list all the files in this directory: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "abc3c9fd-f08e-43d3-b77e-c1e80279e105",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ch4Mod.F90\t\t\tCNRootDynMod.F90\n",
-      "ch4varcon.F90\t\t\tCNSharedParamsMod.F90\n",
-      "CMakeLists.txt\t\t\tCNSpeciesMod.F90\n",
-      "CNAllocationMod.F90\t\tCNVegCarbonFluxType.F90\n",
-      "CNAnnualUpdateMod.F90\t\tCNVegCarbonStateType.F90\n",
-      "CNBalanceCheckMod.F90\t\tCNVegComputeSeedMod.F90\n",
-      "CNC14DecayMod.F90\t\tCNVegetationFacade.F90\n",
-      "CNCIsoAtmTimeSeriesReadMod.F90\tCNVegNitrogenFluxType.F90\n",
-      "CNCIsoFluxMod.F90\t\tCNVegNitrogenStateType.F90\n",
-      "CNCStateUpdate1Mod.F90\t\tCNVegStateType.F90\n",
-      "CNCStateUpdate2Mod.F90\t\tCNVegStructUpdateMod.F90\n",
-      "CNCStateUpdate3Mod.F90\t\tCropReprPoolsMod.F90\n",
-      "CNDriverMod.F90\t\t\tCropType.F90\n",
-      "CNDVDriverMod.F90\t\tDryDepVelocity.F90\n",
-      "CNDVEstablishmentMod.F90\tDUSTMod.F90\n",
-      "CNDVLightMod.F90\t\tdynCNDVMod.F90\n",
-      "CNDVType.F90\t\t\tdynConsBiogeochemMod.F90\n",
-      "CNFireBaseMod.F90\t\tEDBGCDynMod.F90\n",
-      "CNFireEmissionsMod.F90\t\tFATESFireBase.F90\n",
-      "CNFireFactoryMod.F90\t\tFATESFireDataMod.F90\n",
-      "CNFireLi2014Mod.F90\t\tFATESFireFactoryMod.F90\n",
-      "CNFireLi2016Mod.F90\t\tFATESFireNoDataMod.F90\n",
-      "CNFireLi2021Mod.F90\t\tFireEmisFactorsMod.F90\n",
-      "CNFireNoFireMod.F90\t\tMEGANFactorsMod.F90\n",
-      "CNFUNMod.F90\t\t\tNutrientCompetitionCLM45defaultMod.F90\n",
-      "CNGapMortalityMod.F90\t\tNutrientCompetitionFactoryMod.F90\n",
-      "CNGRespMod.F90\t\t\tNutrientCompetitionFlexibleCNMod.F90\n",
-      "CNMRespMod.F90\t\t\tNutrientCompetitionMethodMod.F90\n",
-      "CNNDynamicsMod.F90\t\tSatellitePhenologyMod.F90\n",
-      "CNNStateUpdate1Mod.F90\t\tSpeciesBaseType.F90\n",
-      "CNNStateUpdate2Mod.F90\t\tSpeciesIsotopeType.F90\n",
-      "CNNStateUpdate3Mod.F90\t\tSpeciesNonIsotopeType.F90\n",
-      "CNPhenologyMod.F90\t\ttest\n",
-      "CNPrecisionControlMod.F90\tVOCEmissionMod.F90\n",
-      "CNProductsMod.F90\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f279eb73-7b15-49bd-b2f8-8ad4ea7bfa7b",
-   "metadata": {},
-   "source": [
-    "Below we will modify the `rain_threshold` parameter in the `CNPhenologyMod.F90` file. You can open up the file you'd like to modify by double clicking on it in the sidebar.\n",
-    "\n",
-    "### 3.2 Locate the CNPhenologyMod.F90 file\n",
-    "\n",
-    "#### **To Do**: Navigate to `CTSM/src/biogeochem/CNPhenologyMod.F90` and double-click on the `CNPhenologyMod.F90` file to open it:\n",
-    "\n",
-    "\n",
-    "***Do you only see the tutorials listed in the sidebar?** You can navigate to the files listed above in the sidebar interface by following the file path. Start by clicking on the folder icon (above 'Name'), then click on `CTSM` -> `src` -> `biogeochem`. From here, find and double-click on the `CNPhenologyMod.F90` file.* \n",
-    "\n",
-    "![image1.png](https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/file_listing_1.png)\n",
-    "\n",
-    "\n",
-    "It will open up the file under another tab:\n",
-    "\n",
-    "![image2.png](https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/file_listing_2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4381349e-9592-4f5a-8823-906e25d314ad",
-   "metadata": {},
-   "source": [
-    "This will open up a Fortran code, which you can read and edit. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb4e5157-5542-456c-9dcc-65d61b0e73b0",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "<b>TIP:</b>  You can also access the file with any text editor. To do this, open the file with <code>vim</code>, <code>emacs</code> or another text editor of your choice from a terminal window.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3149a617-99ec-414b-ab95-fec2c4fec062",
-   "metadata": {},
-   "source": [
-    "### 3.3 Modify the `rain_threshold`\n",
-    "In the below exercise, we will change the rain threshold for stress deciduous vegetation, which includes C3 grasses. The rain threshold is the amount of rain required to initiate leaf onset. Reaching the rain threshold is one of several requirements for stress deciduous vegetation leaf onset. If you are interested, you can find more information about the [stress deciduous phenology representation](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Vegetation_Phenology_Turnover/CLM50_Tech_Note_Vegetation_Phenology_Turnover.html#stress-deciduous-phenology) in the CLM Technical Note.\n",
-    "\n",
-    "**Question:**\n",
-    "* Can you find `rain_threshold` in the code? What is the current value set to? Tip: Try using a search function (e.g., cmd+f on a Mac or ctrl+f on a PC).\n",
-    "\n",
-    "**Answer:**\n",
-    "\n",
-    "The current value of `rain_threshold` is 20mm as specified in the line 1349 in the code:\n",
-    "\n",
-    "```\n",
-    "rain_threshold = 20._r8 \n",
-    "```\n",
-    "\n",
-    "_______\n",
-    "\n",
-    "#### **To Do**: Change `rain_threshold` for leaf onset to 1mm in this file. \n",
-    "Your modified code should look the same as this:\n",
-    "```\n",
-    "rain_threshold = 1._r8 \n",
-    "\n",
-    "```\n",
-    "\n",
-    "Now that you've changed the value of the rain threshold, **save and close** this file. *Note that JupyterLab automatically saves your changes at a regular interval. However, to ensure your changes are saved, go to the \"File\" menu (upper left) and click on \"Save File\".* \n",
-    "\n",
-    "\n",
-    "**Questions to consider:**\n",
-    "* Will changing the rain threshold from 20 mm to 1 mm cause leaf onset to be earlier or later than the original simulation? \n",
-    "* How might changes in leaf onset impact simulated carbon, water, and energy fluxes?\n",
-    "\n",
-    "---\n",
-    "\n",
-    "Let's quickly check that our code modifications are reflected using git."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "daabd410-cbd9-4d96-9c8f-df0bdf078327",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "diff --git a/src/biogeochem/CNPhenologyMod.F90 b/src/biogeochem/CNPhenologyMod.F90\n",
-      "index f87a59eba..82ea12458 100644\n",
-      "--- a/src/biogeochem/CNPhenologyMod.F90\n",
-      "+++ b/src/biogeochem/CNPhenologyMod.F90\n",
-      "@@ -1346,7 +1346,7 @@ contains\n",
-      "       avg_dayspyr = get_average_days_per_year()\n",
-      " \n",
-      "       ! specify rain threshold for leaf onset\n",
-      "-      rain_threshold = 20._r8\n",
-      "+      rain_threshold = 1._r8\n",
-      " \n",
-      "       do fp = 1,num_soilp\n",
-      "          p = filter_soilp(fp)\n"
-     ]
-    }
-   ],
-   "source": [
-    "git diff ~/CTSM/src/biogeochem/CNPhenologyMod.F90"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0de9c60-0549-4f89-846e-f2484b8ad2a6",
-   "metadata": {},
-   "source": [
-    "You should see that changes you made to `rain_threshold` reflected in the output above.\n",
-    "\n",
-    "*Specifically, git will list the name of the file, the lines of code before and after your changes, and your changed code. The changes you made will be denoted with '-' and '+' symbols, illustrating what was deleted ('-') and what was added ('+').* "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afe91764-af42-4acf-b361-5ace0df9926c",
-   "metadata": {},
-   "source": [
-    "## 4. Run a CTSM simulation using your modifications:\n",
-    "In this step, you will test your modifications by running the modified code.\n",
-    "\n",
-    "You can do so by either:\n",
-    "1. Using `./run_neon.py` script. (easiest method)\n",
-    "\n",
-    "2. Following the steps for running an unsuported single point case similar to [Day2a_GenericSinglePoint.ipynb](Day2a_GenericSinglePoint.ipynb).\n",
-    "\n",
-    "*We recommend using the `run_neon.py` script for any NEON flux tower simulation, as this simplifies the steps of running a NEON tower simulation and points to the NEON flux tower meteorological data that are already created. The generic single point tutorial does not use flux tower meteorological data, but instead extracts data from the global datasets that are used to run CTSM.*\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "  <strong> Reminder: </strong> <code>./run_neon.py </code> is a user-friendly script that simplifies all the steps of running NEON tower simulations into one command.\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "Now, let's run a CTSM simulation for our NEON site, KONZ, with these modifications. Note that we have changed `output-root` to `~/scratch/CLM-NEON-phenologychange`. Creating a new output-root allows us to test the changes at several sites. We also have to specify `--overwrite` so that the script will run the KONZ site another time. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1261a908-af43-4e37-b9a9-45e9dfc5b6cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Submitting command to Slurm:\n",
-      "    run_neon --neon-sites KONZ --output-root /home/negins/scratch/CLM-NEON-phenologychange --overwrite\n",
-      "\n",
-      "Waiting for job 1056 to start ... \n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "qcmd -- run_neon --neon-sites KONZ --output-root ~/scratch/CLM-NEON-phenologychange --overwrite"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e165439-ad4a-4436-a374-2cdec7feb4ce",
-   "metadata": {},
-   "source": [
-    "**Note:** Your simulation has been submitted, but may take some time to download required data and run the simulation. You can check the status of your simulation using the same commands you used in the [Day0b_NEON_Simulation_Tutorial.ipynb](Day0b_NEON_Simulation_Tutorial.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "7925a3c6-b7ec-4d77-87af-ea1f7656b35b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "ctsmworkshop2022.cesm.cloud:\n",
-      "                                                                               Req'd  Req'd   Elap \n",
-      "Job id               Username Queue    Name                 SessID NDS   TSK   Memory Time Use S Time \n",
-      "-------------------- -------- -------- -------------------- ------ ----- ----- ------ ----- - -----\n",
-      "1056                 negins   build    qcmd                 --         1     8     -- 60:00 R 60:00\n"
-     ]
-    }
-   ],
-   "source": [
-    "qstat -u ${USER}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "43ac0320-178f-4add-b473-2b1aaeefe863",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-05-25 19:09:13: model execution starting 719\n",
-      " ---------------------------------------------------\n",
-      "2022-05-25 19:30:51: model execution success 719\n",
-      " ---------------------------------------------------\n",
-      "2022-05-25 19:30:51: case.run success 719\n",
-      " ---------------------------------------------------\n",
-      "2022-05-25 19:30:52: st_archive starting 720\n",
-      " ---------------------------------------------------\n",
-      "2022-05-25 19:30:54: st_archive success 720\n",
-      " ---------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "tail  ~/scratch/CLM-NEON-phenologychange/KONZ.transient/CaseStatus"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f13ba0b-cfea-4c9d-99a5-97b62928b52f",
-   "metadata": {},
-   "source": [
-    "Next, we're going to have a quick look at changes in LAI and GPP from our phenology changes to make sure the changes worked as intended. After ensuring that the code modifications worked and do not have any bugs, we suggest saving your code (see section 5) and running an AD and post-AD spinup to generate a new initial conditions file.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "\n",
-    "<b>WARNING!</b> \n",
-    "    \n",
-    "We strongly recommend running a full spinup after making code modifications (see example in [Day2a_GenericSinglePoint.ipynb](Day2a_GenericSinglePoint.ipynb)) before evaluating new code development. \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c145283-1fdc-476b-9cb7-5e3ff7b7297a",
-   "metadata": {},
-   "source": [
-    "## 5. Save your changes to your github branch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "963e57d6-de98-4474-843e-e1003dc952ac",
-   "metadata": {},
-   "source": [
-    "When you are happy with your changes, make sure you have committed these changes and submitted them to your GitHub repository. \n",
-    "\n",
-    "Below, we walk you through the easiest way to do so.\n",
-    "*****"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e80f76a-d61d-45cf-bb60-7458dfb75180",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**First**, check the status of all files. The following command will will show all the files that have been modified."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "0488b42b-bae6-4593-8dfe-f2681bd32fdc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "On branch phenology_change\n",
-      "Changes not staged for commit:\n",
-      "  (use \"git add <file>...\" to update what will be committed)\n",
-      "  (use \"git restore <file>...\" to discard changes in working directory)\n",
-      "\tmodified:   CNPhenologyMod.F90\n",
-      "\n",
-      "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "git status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37d1c495-ec10-49ab-a0b1-5cd7f003589a",
-   "metadata": {},
-   "source": [
-    "*****\n",
-    "**Next**, add any file (or all files) to be saved.\n",
-    "\n",
-    "*Note that specifying a single file will add only that file. Using '.' will add all files.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "9728c2ff-87d6-41f9-91b5-d0b304f53251",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git add CNPhenologyMod.F90"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a967e93-3afd-4ec8-90b4-27e6e99b3895",
-   "metadata": {},
-   "source": [
-    "****\n",
-    "**Then**, commit your changes (effectively saving them on version control) using a meaningful commit message:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "fbb421e8-e848-444b-9857-b0780c3816ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[phenology_change ff59e3121] Changing rain threshhold for leaf onset to 1mm\n",
-      " 1 file changed, 1 insertion(+), 1 deletion(-)\n"
-     ]
-    }
-   ],
-   "source": [
-    "git commit -m \"Changing rain threshhold for leaf onset to 1mm\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fda9a325-2bcf-445f-b7a1-4976436cb3d3",
-   "metadata": {},
-   "source": [
-    "****\n",
-    "**Last**, you can compare the original (unmodified) branch to your modified branch to see the submitted changes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "9706d0ad-981b-483b-910d-76592cab8388",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "M\tsrc/biogeochem/CNPhenologyMod.F90\n"
-     ]
-    }
-   ],
-   "source": [
-    "git diff --name-status origin/master phenology_change"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c091debe-ba7e-4ec0-a9c9-93bf3fc93fd5",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "  <strong>Congratulations!</strong> Now you have successfully changed the rain threshhold for leaf onset, run a simulation with the updated code, and saved your code to your local branch.\n",
-    "</div>\n",
-    "\n",
-    "The next tutorial, [Day2d_CodeModification_Visualization.ipynb](Day2d_CodeModification_Visualization.ipynb), guides you through visualizing the output of original simulation compared to the modified code for KONZ site. It also helps you to compare both simulations with evaluation data from the NEON flux tower. \n",
-    "\n",
-    "**Go To [Day2d_CodeModification_Visualization.ipynb](Day2d_CodeModification_Visualization.ipynb)**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8c10b43-9869-4425-be27-2dbfbc38803b",
-   "metadata": {},
-   "source": [
-    "## 6. [Optional] Sharing your changes with others & pushing changes back to your GitHub Repository\n",
-    "*Note: If you plan to contribute your code developments to CTSM, you will need to use these optional steps to share your code with CTSM model developers. If you already have a GitHub account and have a CTSM fork, start at step 6.3.*\n",
-    "\n",
-    "So far, we made a code change and saved it to a `local` branch. In reality, we usually want/need to push our changes back to GitHub so our collaborators can see, comment, or use our code modifications. \n",
-    "Imagine saving your progress in a video game or in a Word document on a local computer. If you use a different computer, you can not load your progress. However, if you save your video game progress or your Word document on the cloud, you can easily access it from any computer. Nowadays, video games save your progress via a profile/account and Word documents can be saved and shared through Google or Dropbox accounts. \n",
-    "\n",
-    "Similarly, **you need to create an account on GitHub to be able to share your changes** so:\n",
-    "- you can access your code and changes from anywhere.\n",
-    "- you can share with collaborators. \n",
-    "- you can contribute back to CTSM repository. \n",
-    "\n",
-    "### 6.1. Create a GitHub account\n",
-    "\n",
-    "Visit the [GitHub website](github.com) and create an account if you don't already one. You can skip step this if you already have a GitHub account. \n",
-    "\n",
-    "\n",
-    "### 6.2. Create a fork from CTSM repository\n",
-    "You don't have access to write directly to the main CTSM repository (that right is reserved for the CTSM main software engineers), so you need to create your own copy of the repository to save your changes. For this, you will fork the CTSM repository.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "  <strong> NOTE: </strong> \n",
-    "    A fork is a copy of a repository. \"Fork\"ing a repository is similar to creating a branch in that it allows you to freely experiment with changes without affecting the original project. However, we recommend using your CTSM fork as an unmodified copy of CTSM and making changes using branches.\n",
-    "</div>\n",
-    "\n",
-    "#### To Do: Create a fork\n",
-    "You can create your own fork of the CTSM repository by using the fork button in the upper right corner of the CTSM reository page.\n",
-    "\n",
-    "- Login to your GitHub account.\n",
-    "- Navigate to the original [CTSM repository](https://github.com/ESCOMP/CTSM) (escomp/CTSM).\n",
-    "- Use the `fork` button to create a fork of CTSM repository in your account\n",
-    "\n",
-    "![image3.png](https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/fork_image.png)\n",
-    "\n",
-    "\n",
-    "Your forked repository will be under your account name:\n",
-    "\n",
-    "https://github.com/YOUR-USER-NAME/CTSM\n",
-    "\n",
-    "For example, for the username (negin513) the forked repo is:\n",
-    "\n",
-    "https://github.com/negin513/CTSM\n",
-    "\n",
-    "You can make any modifications you'd like to your forked repository. Note that you only have to fork a respository once -- it will always be connected to your GitHub account unless you delete it.\n",
-    "\n",
-    "### 6.3. Pushing your changes to the outside world:\n",
-    "To start, connect your forked repository to the computing system you are using. You can do so by using the following:\n",
-    "\n",
-    "<div class=\"alert alert-block alert-danger\">\n",
-    "\n",
-    "<b>WARNING!</b> \n",
-    "    \n",
-    "Please replace \"YOUR_USER_NAME\" in the code below with your own GitHub username (created in step 6.1).\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8199e4b0-f584-4f98-bf3e-e06f2bcad4e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git remote add YOUR_USER_NAME https://github.com:YOUR_USER_NAME/CTSM.git"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2795462b-5098-436d-ba81-737cdd9633f9",
-   "metadata": {},
-   "source": [
-    "Finally, push your changes to the remote repository. Note that 'pushing' the changes makes the changes visible to anyone who looks at your GitHub repository, including your collaborators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de521a1a-969e-4288-a13a-c5901c64a36c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git push -u YOUR_USER_NAME phenology_change"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e2b518c-b5f2-4c2b-8608-c76a7eace5c4",
-   "metadata": {},
-   "source": [
-    "To see your changes now you can go to your fork and look for your 'phenology_change' branch on [github.com](github.com). You will be able to see your recent changes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56b1a922-2416-479d-84bd-c1a866863c11",
-   "metadata": {},
-   "source": [
-    "### 6.4 Submitting a Pull Request to CTSM\n",
-    "\n",
-    "In the future, you might want your code changes to be included on the CTSM main branch. The benefits of this are that everyone who forks CTSM can see and use your code modifications. Additionally, this will ensure that you do not need to continually update and resolve conflicts when new code developments are added to code you are using. We recommend talking with CTSM scientists and software engineers early in your code development process so that we are aware of your proposed code changes and can let you know about any potential conflicting code developments that are also in progress. \n",
-    "\n",
-    "To contribute your changes to the main CTSM respository, you will need to submit a GitHub Pull Request. \n",
-    "\n",
-    "Creating a Pull Request (PR) is easy and is a great way to contribute scientific changes to the community code.\n",
-    "To create a PR:\n",
-    "\n",
-    "- Navigate to CTSM PR page on GitHub (https://github.com/ESCOMP/CTSM/pulls)\n",
-    "- Next, click the `New pull request` button on the top right corner of the page. \n",
-    "- Then, click on `compare across forks` link. \n",
-    "- Choose your base and head repository and branches. \n",
-    "    - In the `head repository` choose your own fork. \n",
-    "- Next, click on the `Create pull request` green button.\n",
-    "- In the \"Open a pull request\" page, confirm the forks and branches being used for the pull request. On the left you should see \"base fork: ESCOMP/CTSM\" and \"base: master\". On the right you should see \"head fork: YOUR_USER_NAME/CTSM\" and \"compare: MYBRANCH\" (where YOUR_USER_NAME will be your git username, and MYBRANCH will be the branch you'd like brought to the main CTSM code base).\n",
-    "- Enter a short but descriptive title for this pull request\n",
-    "- In the comment box, give a more detailed description of this pull request\n",
-    "- Click the green \"Create pull request\" button\n",
-    "\n",
-    "Our scientists and software engineers will review the code and start a conversation with you about the modifications you made. Most times they will ask for clarification and modifications. If the code meets CTSM scientific and software engineering standards, they will eventually merge it with the CTSM main branch.  You can see some [active pull tequests on GitHub](https://github.com/ESCOMP/CTSM/pulls).\n",
-    "\n",
-    "**Resources:**\n",
-    "You can find more resources the CTSM wiki on: \n",
-    "- [Code development and suggested workflows with CTSM and git](https://github.com/ESCOMP/CTSM/wiki/Tutorials);\n",
-    "- [Coding guidelines](https://github.com/ESCOMP/CTSM/wiki/CTSM-coding-guidelines); and \n",
-    "- [Common problems to be aware of](https://github.com/ESCOMP/CTSM/wiki/List-of-common-problems)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "118d05b0-611d-4615-90f5-01d35d79ded0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Bash",
-   "language": "bash",
-   "name": "bash"
-  },
-  "language_info": {
-   "codemirror_mode": "shell",
-   "file_extension": ".sh",
-   "mimetype": "text/x-sh",
-   "name": "bash"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/Day2d_CodeModification_Visualization.ipynb b/notebooks/Day2d_CodeModification_Visualization.ipynb
deleted file mode 100644
index 422f248..0000000
--- a/notebooks/Day2d_CodeModification_Visualization.ipynb
+++ /dev/null
@@ -1,11182 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d17e3c92-d037-435a-b59d-8d8140640ccc",
-   "metadata": {},
-   "source": [
-    "# Tutorial 2d - *Code Modifications (Visualization)*\n",
-    "\n",
-    "This tutorial is an introduction to analyzing results from your code modifications for the NEON Konza Prairie simulation. It uses results from the case you ran in the Day0b and Day2c tutorials, but you don't have to wait for those runs to complete before doing this tutorial. We've pre-staged model results from this simulation in a shared directory so that you can analyze results of your simulations regardless of whether they've completed.\n",
-    "\n",
-    "You can also check [NEON visualization](https://ncar.github.io/ncar-neon-books/notebooks/NEON_Visualization_Tutorial.html) tutorial for more advanced visualization features for NEON tower site simulations. \n",
-    "\n",
-    "## In this tutorial\n",
-    "\n",
-    "The tutorial has several objectives: \n",
-    "1. Increase familiarity with `xarray` and `pandas`.\n",
-    "2. Increase knowledge of python packages and their utilities\n",
-    "3. Compare results from original code with the modified code for a NEON tower.\n",
-    "\n",
-    "\n",
-    "***\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>NOTE:</b> In Day 2c, executable code blocks used a Bash shell or had to be executed on the command-line.  In this tutorial, we will be using Python code, and you should directly execute the contents of code blocks by running individual cells in this Jupyter notebook, similar to the Day0b <i>Run NEON</i>, Day1b <i>GlobalVisualization</i>, and Day2b <i>GenericGinglePoint_Visualization</i> tutorials.\n",
-    "</div>\n",
-    "\n",
-    "***\n",
-    "\n",
-    "There are countless ways of analyzing and processing model data. This tutorial uses Matplotlib, a comprehensive data visualization and plotting library for Python. For more information on Matplotlib, please see the [User's Guide](https://matplotlib.org/stable/users/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d83ae0ac-3a83-4a92-b7f1-80b78c914866",
-   "metadata": {},
-   "source": [
-    "## 1. Load our python packages\n",
-    "\n",
-    "Here we are importing python package and libraries we will use to analyze these simulations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "87240e2f-46c1-4179-8932-67ada9ca9b5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import Libraries\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "import time\n",
-    "import datetime\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import xarray as xr\n",
-    "\n",
-    "from glob import glob\n",
-    "from os.path import join, expanduser\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from scipy import stats\n",
-    "\n",
-    "from neon_utils import download_eval_files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82839ff5-b508-40bc-a6c3-d6eea3fefe49",
-   "metadata": {},
-   "source": [
-    "Before diving in, we need to specify the NEON site that you simulated in the cell below.   \n",
-    "\n",
-    "*The tutorial is currently set to use the KONZ site, which is the site we recommended in the Day0b_NEON_Simulation_Tutorial and Day2c_CodeModification tutorials. If you ran simulations for a different tower site, please change the 4-character site name in quotes below to the same as your simulation.*\n",
-    "\n",
-    "For simplicity, we focus on analyzing and evaluating a single year of data. <p>\n",
-    "The code below uses data for **2018**, but data are available through this year. You can select a different year by changing the year in the quotes below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "83adce12-a7fd-48f2-8950-f4bfab607c54",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Change the 4-character NEON site below to point to your NEON site:\n",
-    "neon_site = \"KONZ\"\n",
-    "\n",
-    "# Select a year for analysis\n",
-    "year = \"2018\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e908987-f782-4507-9d6f-609516052a3b",
-   "metadata": {},
-   "source": [
-    "## 2. Load and explore CTSM data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96a59fd1-da01-4872-b380-6bd56d39f6bf",
-   "metadata": {},
-   "source": [
-    "When a simulation completes, the data are transferred to an archive directory. In this directory, there are files that include data for every day of the simulation, as well as files that average model variables monthly.\n",
-    "\n",
-    "Run the cell below to see a subset of the files listed:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c14e9446-38bc-4f18-acf7-4463a7838058",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-01.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-02.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-03.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-04.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-05.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-06.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-07.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-08.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-09.nc\n",
-      "/home/negins/scratch/CLM-NEON-phenologychange/archive/KONZ.transient/lnd/hist/KONZ.transient.clm2.h0.2018-10.nc\n",
-      "ls: write error: Broken pipe\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls ~/scratch/CLM-NEON-phenologychange/archive/{neon_site}.transient/lnd/hist/*2018*.nc |head -n 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd1bda1f-1f4c-424f-a000-65b4be27649d",
-   "metadata": {},
-   "source": [
-    "**Note:** you won't see these files if your simulation from 2c has not finished.\n",
-    "\n",
-    "The NEON tower simulations generate two types of files:\n",
-    "* `*h0*`: Variables that are averaged monthly. One file is available for every month of the simulation.\n",
-    "* `*h1*`: Variables that are recorded every 30 minutes. Values are aggregated into one file for each day of the simulation. Each file includes 48 data points.\n",
-    "\n",
-    "**Note:** Only a subset of CLM variables are included on the `*h1*` files, with many more variables included on the monthly-averaged `*h0*` files. A full list of variables that are simulated by CLM is available [on this website](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_nofates.html).\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>TIP:</b> For future simulations you can add or remove history file output by modifying the list of variables in <i>hist_fincl2</i> found in your <i>user_nl_clm</i> file (e.g. ~/scratch/CLM-NEON-phenologychange/KONZ.transient/user_nl_clm)\n",
-    "</div>\n",
-    "\n",
-    "****"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ecc4b4e-8459-4d36-880b-8c5732cf445b",
-   "metadata": {},
-   "source": [
-    "### 2.1 Load data from unmodified CTSM simulations\n",
-    "\n",
-    "Here, we want to read and analyze the data from the original (unmodified) CTSM code. \n",
-    "The below code lists the 30-minute (.h1.) CTSM files for 2018:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "62cabd93-c938-4e7c-9b22-0456382b429b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All simulation files: [ 365 files]\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-01-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-02-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-03-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-04-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-05-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-06-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-07-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-08-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-09-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-10-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-11-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-12-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-13-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-14-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-15-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-16-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-17-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-18-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-19-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient/lnd/hist/KONZ.transient.clm2.h1.2018-01-20-00000.nc\n"
-     ]
-    }
-   ],
-   "source": [
-    "# pre-staged simulation:\n",
-    "sim_path = \"/scratch/data/day2/KONZ.transient/lnd/hist/\"\n",
-    "sim_files = sorted(glob(join(sim_path,neon_site+\".transient.clm2.h1.\"+year+\"*.nc\")))\n",
-    "\n",
-    "print(\"All simulation files: [\", len(sim_files), \"files]\")\n",
-    "## for brevity we'll just print the first 20 files\n",
-    "print(*sim_files[0:20],sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0f2e94e-1206-41ee-a1e9-1a0df0999fc0",
-   "metadata": {},
-   "source": [
-    "Next, let's read and load CTSM history files into memory. \n",
-    "\n",
-    "For this purpose, we will use `open_mfdataset` function, which opens up multiple netcdf files at the same time into one xarray Dataset. You can find more information about this function [here](https://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html). \n",
-    "\n",
-    "**Note:** there are many files, so this will take about a minute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5480b8d0-6a38-4b01-b2e8-0f2714a292ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading original simulation files took: 55.31088423728943 s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "ds_ctsm_orig = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',parallel=True)\n",
-    "end = time.time()\n",
-    "print(\"Reading original simulation files took:\", end-start, \"s.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9c3c3bba-9da3-4e56-9c2b-d0d35ad39002",
-   "metadata": {},
-   "source": [
-    "The next step, exploring the data, is not required, but will allow you to explore the python dataset we just created and become familiar with the data structure.\n",
-    "\n",
-    "Run the below cell to find more information about the data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b606aa81-d91f-4575-b96e-5c4bd127f43f",
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:       (time: 17520, levgrnd: 25, levsoi: 20, levlak: 10,\n",
-       "                   levdcmp: 25, hist_interval: 2, lndgrid: 1)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:29:59.12...\n",
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-       "Dimensions without coordinates: hist_interval, lndgrid\n",
-       "Data variables: (12/42)\n",
-       "    mcdate        (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    mcsec         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
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-       "    nstep         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    time_bounds   (time, hist_interval) datetime64[ns] dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
-       "    ...            ...\n",
-       "    HR            (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
-       "    NET_NMIN_vr   (time, levdcmp, lndgrid) float32 dask.array&lt;chunksize=(96, 25, 1), meta=np.ndarray&gt;\n",
-       "    SNOW_DEPTH    (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
-       "    SOILC_vr      (time, levsoi, lndgrid) float32 dask.array&lt;chunksize=(96, 20, 1), meta=np.ndarray&gt;\n",
-       "    TBOT          (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
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-       "Attributes: (12/99)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/18/22 20:38:53\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             aws-hpc6a\n",
-       "    ...                                   ...\n",
-       "    cft_irrigated_switchgrass:            60\n",
-       "    cft_tropical_corn:                    61\n",
-       "    cft_irrigated_tropical_corn:          62\n",
-       "    cft_tropical_soybean:                 63\n",
-       "    cft_irrigated_tropical_soybean:       64\n",
-       "    time_period_freq:                     minute_30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8657ef5a-bc9f-4a06-8e91-37bd93830864' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8657ef5a-bc9f-4a06-8e91-37bd93830864' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17520</li><li><span class='xr-has-index'>levgrnd</span>: 25</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levlak</span>: 10</li><li><span class='xr-has-index'>levdcmp</span>: 25</li><li><span>hist_interval</span>: 2</li><li><span>lndgrid</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a630ce13-57ac-4144-bc71-2456d44c17ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a630ce13-57ac-4144-bc71-2456d44c17ab' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-12-31T23:29:...</div><input id='attrs-21e910e6-64fe-4cbc-8514-ad236a56ee33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21e910e6-64fe-4cbc-8514-ad236a56ee33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ad76a9e-30a3-465a-8c83-543725fcd7d2' class='xr-var-data-in' type='checkbox'><label for='data-9ad76a9e-30a3-465a-8c83-543725fcd7d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:30:00.000053644&#x27;,\n",
-       "       &#x27;2018-01-01T01:00:00.000107288&#x27;, ..., &#x27;2018-12-31T22:30:00.000000000&#x27;,\n",
-       "       &#x27;2018-12-31T23:00:00.878906248&#x27;, &#x27;2018-12-31T23:29:59.121093752&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-9c36b67b-7d45-4f8e-a6ea-11fadc8bae5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c36b67b-7d45-4f8e-a6ea-11fadc8bae5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af41dc3b-53f0-490a-aed6-d95e16adb0d8' class='xr-var-data-in' type='checkbox'><label for='data-af41dc3b-53f0-490a-aed6-d95e16adb0d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-60c42051-807c-4af3-9674-6332aae352d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60c42051-807c-4af3-9674-6332aae352d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ee77598-fea2-4003-9790-d1919e8959fd' class='xr-var-data-in' type='checkbox'><label for='data-2ee77598-fea2-4003-9790-d1919e8959fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levlak</span></div><div class='xr-var-dims'>(levlak)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05 0.6 2.1 ... 25.6 34.33 44.78</div><input id='attrs-2e1a62b7-9061-4c0e-8cd3-203a886754e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1a62b7-9061-4c0e-8cd3-203a886754e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a031f44-9950-4f49-86c8-89441581e983' class='xr-var-data-in' type='checkbox'><label for='data-3a031f44-9950-4f49-86c8-89441581e983' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate lake levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 0.05 ,  0.6  ,  2.1  ,  4.6  ,  8.1  , 12.6  , 18.6  , 25.6  , 34.325,\n",
-       "       44.775], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levdcmp</span></div><div class='xr-var-dims'>(levdcmp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-1be57a14-df6e-4bcc-b721-106343b7d83f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1be57a14-df6e-4bcc-b721-106343b7d83f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bbbfe6e-fced-40ef-92ff-64ffb6fe3ca2' class='xr-var-data-in' type='checkbox'><label for='data-9bbbfe6e-fced-40ef-92ff-64ffb6fe3ca2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate levels for soil decomposition variables</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3af4c969-d5f0-44b4-9931-7307c0a91800' class='xr-section-summary-in' type='checkbox'  ><label for='section-3af4c969-d5f0-44b4-9931-7307c0a91800' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mcdate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-3e4509bd-ef5a-4163-912e-579efa793d85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e4509bd-ef5a-4163-912e-579efa793d85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0323a1dc-91a6-4c0c-a892-9b8a2dfd4dbf' class='xr-var-data-in' type='checkbox'><label for='data-0323a1dc-91a6-4c0c-a892-9b8a2dfd4dbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >17520</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcsec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-e5b46564-6b00-45d8-8b0b-3347477f0d32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5b46564-6b00-45d8-8b0b-3347477f0d32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98608074-99e3-4eff-84f1-99b3700180bb' class='xr-var-data-in' type='checkbox'><label for='data-98608074-99e3-4eff-84f1-99b3700180bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current date</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
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-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
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-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
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-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
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-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
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-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mdcur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-40f7f0c8-5b91-4152-8667-e281155245f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40f7f0c8-5b91-4152-8667-e281155245f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fa3741-f45f-452d-838d-ffb3629de883' class='xr-var-data-in' type='checkbox'><label for='data-b9fa3741-f45f-452d-838d-ffb3629de883' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current day (from base day)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mscur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-56017754-c149-40c8-a839-aeb5cb29b256' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56017754-c149-40c8-a839-aeb5cb29b256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b77370-a97c-43b6-a1c1-cbb453a80403' class='xr-var-data-in' type='checkbox'><label for='data-c7b77370-a97c-43b6-a1c1-cbb453a80403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current day</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nstep</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-e7dd614f-6c64-4129-8819-fd7a5548a8d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7dd614f-6c64-4129-8819-fd7a5548a8d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c540abf8-c820-423f-aa4a-f1dee26cfeaa' class='xr-var-data-in' type='checkbox'><label for='data-c540abf8-c820-423f-aa4a-f1dee26cfeaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time step</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, hist_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-ab65fe29-7fbf-4adb-b0ef-b930d523169b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab65fe29-7fbf-4adb-b0ef-b930d523169b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e2e1a64-e476-4108-9712-5983d6c9137b' class='xr-var-data-in' type='checkbox'><label for='data-7e2e1a64-e476-4108-9712-5983d6c9137b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>history time interval endpoints</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 273.75 kiB </td>\n",
-       "                        <td> 1.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 2) </td>\n",
-       "                        <td> (96, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> datetime64[ns] </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-084ef777-0236-4014-bfd5-a13604f4aae0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-084ef777-0236-4014-bfd5-a13604f4aae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a63cb41-4afa-4d86-816b-a63d2e2fe82d' class='xr-var-data-in' type='checkbox'><label for='data-9a63cb41-4afa-4d86-816b-a63d2e2fe82d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-e80f0c99-337d-42a0-abe0-d6ff3f880bed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e80f0c99-337d-42a0-abe0-d6ff3f880bed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48e7d24-0ca8-4494-8b66-8cc0ba3e098a' class='xr-var-data-in' type='checkbox'><label for='data-b48e7d24-0ca8-4494-8b66-8cc0ba3e098a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-e6896cfa-aafc-4574-8086-ddc47f644d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6896cfa-aafc-4574-8086-ddc47f644d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6638f92a-91ea-40b8-8251-df32c693849d' class='xr-var-data-in' type='checkbox'><label for='data-6638f92a-91ea-40b8-8251-df32c693849d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-516cc56d-f6e5-4c13-acb3-3e64cf38a200' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-516cc56d-f6e5-4c13-acb3-3e64cf38a200' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b32a2c89-b24b-4a52-adfc-e985186dbdd5' class='xr-var-data-in' type='checkbox'><label for='data-b32a2c89-b24b-4a52-adfc-e985186dbdd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-3c0f2646-3749-4298-bf99-2f56599a9f16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c0f2646-3749-4298-bf99-2f56599a9f16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1974398e-bf4d-45f0-bb2a-ebd1f2b8f67d' class='xr-var-data-in' type='checkbox'><label for='data-1974398e-bf4d-45f0-bb2a-ebd1f2b8f67d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>grid cell areas</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landfrac</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-b8c6a8a9-1cac-4d45-9c4c-e434d607ed14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8c6a8a9-1cac-4d45-9c4c-e434d607ed14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c9d6ae0-4cf6-4c11-add8-bfff6bdd4d9d' class='xr-var-data-in' type='checkbox'><label for='data-5c9d6ae0-4cf6-4c11-add8-bfff6bdd4d9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land fraction</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landmask</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-1d323ab0-9ead-4321-b3d4-ccb94ced1615' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d323ab0-9ead-4321-b3d4-ccb94ced1615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56397710-17d2-4922-bf92-204e89713d2c' class='xr-var-data-in' type='checkbox'><label for='data-56397710-17d2-4922-bf92-204e89713d2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land/ocean mask (0.=ocean and 1.=land)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pftmask</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-a5ac58c7-7b6a-4011-963a-bbb57d011a05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5ac58c7-7b6a-4011-963a-bbb57d011a05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b01fcf41-c172-4e84-8426-5bfd5e6f0dbc' class='xr-var-data-in' type='checkbox'><label for='data-b01fcf41-c172-4e84-8426-5bfd5e6f0dbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pft real/fake mask (0.=fake and 1.=real)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbedrock</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-7bf36055-a0d5-4c57-88a2-2cfcce23864b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bf36055-a0d5-4c57-88a2-2cfcce23864b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df1ff46f-fc36-4a7e-8939-f87a2eccd5de' class='xr-var-data-in' type='checkbox'><label for='data-df1ff46f-fc36-4a7e-8939-f87a2eccd5de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>index of shallowest bedrock layer</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZSOI</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-0eeb4d75-3fd0-4632-89fa-680d6f1af9fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eeb4d75-3fd0-4632-89fa-680d6f1af9fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e6edebe-6500-43ef-88c3-0ad7034a30df' class='xr-var-data-in' type='checkbox'><label for='data-2e6edebe-6500-43ef-88c3-0ad7034a30df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>nonurb</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
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-       "                    <tr>\n",
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-       "                        <td> 100 B </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZSOI</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-c0e94742-46de-47e5-814d-06628c067204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0e94742-46de-47e5-814d-06628c067204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81524a49-4cd7-44b6-83f4-2c8297606d6d' class='xr-var-data-in' type='checkbox'><label for='data-81524a49-4cd7-44b6-83f4-2c8297606d6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>nonurb</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
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-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WATSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-6035ed2a-2f7c-43f5-a5a2-6cac2ad85fe5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6035ed2a-2f7c-43f5-a5a2-6cac2ad85fe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4825c3-e43e-4774-a4db-b1ad0a53848c' class='xr-var-data-in' type='checkbox'><label for='data-8e4825c3-e43e-4774-a4db-b1ad0a53848c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated soil water content (porosity)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SUCSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-354c02f8-6b5d-4954-a1bb-dc594605a5ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-354c02f8-6b5d-4954-a1bb-dc594605a5ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f668f2-e5c6-4c25-ad9d-f0249877a951' class='xr-var-data-in' type='checkbox'><label for='data-d3f668f2-e5c6-4c25-ad9d-f0249877a951' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated soil matric potential</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
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-       "                    <tr>\n",
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-       "                        <td> 100 B </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BSW</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-dbcffaf0-dd76-4145-98ce-58d142e46e73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbcffaf0-dd76-4145-98ce-58d142e46e73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e64a59a-caba-418d-9246-44c77bca3420' class='xr-var-data-in' type='checkbox'><label for='data-4e64a59a-caba-418d-9246-44c77bca3420' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of soil water retention curve</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HKSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-94794f82-7ea6-4828-b119-8a826c4e513b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94794f82-7ea6-4828-b119-8a826c4e513b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-153de185-42a6-490d-96b3-328e2771d7bc' class='xr-var-data-in' type='checkbox'><label for='data-153de185-42a6-490d-96b3-328e2771d7bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated hydraulic conductivity</dd><dt><span>units :</span></dt><dd>mm s-1</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZLAKE</span></div><div class='xr-var-dims'>(levlak, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 1), meta=np.ndarray&gt;</div><input id='attrs-47579f26-5da7-4b7e-a22c-9519c4544877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47579f26-5da7-4b7e-a22c-9519c4544877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ab6edb-c1a7-46ab-999e-67a21a8c3330' class='xr-var-data-in' type='checkbox'><label for='data-36ab6edb-c1a7-46ab-999e-67a21a8c3330' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lake layer node depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>lake</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 40 B </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZLAKE</span></div><div class='xr-var-dims'>(levlak, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 1), meta=np.ndarray&gt;</div><input id='attrs-cf355263-4e53-4802-87f4-ec3bfc4400e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf355263-4e53-4802-87f4-ec3bfc4400e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6aa78804-40e1-4322-b88c-8c6987492aa8' class='xr-var-data-in' type='checkbox'><label for='data-6aa78804-40e1-4322-b88c-8c6987492aa8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lake layer thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>lake</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 40 B </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PCT_SAND</span></div><div class='xr-var-dims'>(levsoi, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 1), meta=np.ndarray&gt;</div><input id='attrs-21305608-accd-4935-8ebc-89a5800e3d36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21305608-accd-4935-8ebc-89a5800e3d36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebf07596-8665-4948-bb0e-d78332a9b839' class='xr-var-data-in' type='checkbox'><label for='data-ebf07596-8665-4948-bb0e-d78332a9b839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>percent sand</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PCT_CLAY</span></div><div class='xr-var-dims'>(levsoi, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 1), meta=np.ndarray&gt;</div><input id='attrs-b753700b-ec5a-42d2-9aa8-7a836a6e4edf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b753700b-ec5a-42d2-9aa8-7a836a6e4edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47c0e1da-b988-4183-829b-6e264cf1132c' class='xr-var-data-in' type='checkbox'><label for='data-47c0e1da-b988-4183-829b-6e264cf1132c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>percent clay</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-0917dcef-3bbe-4c6d-9a77-34d1ddd7fb32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0917dcef-3bbe-4c6d-9a77-34d1ddd7fb32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b179327-dc57-4ebf-bf7c-b9a7f70ff294' class='xr-var-data-in' type='checkbox'><label for='data-3b179327-dc57-4ebf-bf7c-b9a7f70ff294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>autotrophic respiration (MR + GR)</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-5875b9f8-9cf6-4107-8313-871a7b1033d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5875b9f8-9cf6-4107-8313-871a7b1033d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da0e73fc-0a36-4fcc-af75-ee7b59e6211d' class='xr-var-data-in' type='checkbox'><label for='data-da0e73fc-0a36-4fcc-af75-ee7b59e6211d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-d72df118-dfda-41d1-ae7b-63d6dd250b65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d72df118-dfda-41d1-ae7b-63d6dd250b65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f85387ea-e0cd-4256-96ef-d3baefccd84f' class='xr-var-data-in' type='checkbox'><label for='data-f85387ea-e0cd-4256-96ef-d3baefccd84f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-3477c614-ad11-4a1c-ba84-c3d1acc75b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3477c614-ad11-4a1c-ba84-c3d1acc75b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90deb368-94a5-48bb-a145-cd252cfbd4cb' class='xr-var-data-in' type='checkbox'><label for='data-90deb368-94a5-48bb-a145-cd252cfbd4cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-008a8898-d3c2-489a-b97d-8d8bee019351' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-008a8898-d3c2-489a-b97d-8d8bee019351' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f49cd9-e7f0-4571-85af-057a6d12616d' class='xr-var-data-in' type='checkbox'><label for='data-c4f49cd9-e7f0-4571-85af-057a6d12616d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FIRA</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-f400536c-05a7-469b-8224-2b30a8484fc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f400536c-05a7-469b-8224-2b30a8484fc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b8b5d9-0aa9-4afc-81d6-d35634c4f0b7' class='xr-var-data-in' type='checkbox'><label for='data-27b8b5d9-0aa9-4afc-81d6-d35634c4f0b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>net infrared (longwave) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSA</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-120e70f1-938b-45b9-a152-8cb777c223e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-120e70f1-938b-45b9-a152-8cb777c223e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5199a2b-1be0-4023-b27a-6b6ddd87c2c0' class='xr-var-data-in' type='checkbox'><label for='data-e5199a2b-1be0-4023-b27a-6b6ddd87c2c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absorbed solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSH</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-07cffcf6-7592-4d10-be7e-27f5bc417dfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07cffcf6-7592-4d10-be7e-27f5bc417dfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68db2b9-0127-4efb-8feb-4703795d9640' class='xr-var-data-in' type='checkbox'><label for='data-b68db2b9-0127-4efb-8feb-4703795d9640' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensible heat not including correction for land use change and rain/snow conversion</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-5690a8b3-922a-497e-b226-1061b305b1ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5690a8b3-922a-497e-b226-1061b305b1ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6107869f-502a-4083-ade1-90d18b3f5072' class='xr-var-data-in' type='checkbox'><label for='data-6107869f-502a-4083-ade1-90d18b3f5072' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>friction velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-e6b5d40c-c086-49aa-9954-19f09a931de2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6b5d40c-c086-49aa-9954-19f09a931de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c5853d-ad60-473e-96af-c66b83a807a7' class='xr-var-data-in' type='checkbox'><label for='data-81c5853d-ad60-473e-96af-c66b83a807a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5466 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
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-       "                    <th> Type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-ae23eea5-ec80-4f84-9714-3e213d50908f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae23eea5-ec80-4f84-9714-3e213d50908f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7c3d883-fb0a-4917-abd2-b7b897d46263' class='xr-var-data-in' type='checkbox'><label for='data-c7c3d883-fb0a-4917-abd2-b7b897d46263' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                <thead>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "                    <tr>\n",
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-       "                    <tr>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
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-       "        </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3909593f-024b-42ea-bd31-2d2510280cda' class='xr-section-summary-in' type='checkbox'  ><label for='section-3909593f-024b-42ea-bd31-2d2510280cda' class='xr-section-summary' >Attributes: <span>(99)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>CLM History file information</dd><dt><span>comment :</span></dt><dd>NOTE: None of the variables are weighted by land fraction!</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>history :</span></dt><dd>created on 05/18/22 20:38:53</dd><dt><span>source :</span></dt><dd>Community Terrestrial Systems Model</dd><dt><span>hostname :</span></dt><dd>aws-hpc6a</dd><dt><span>username :</span></dt><dd>wwieder</dd><dt><span>version :</span></dt><dd>unknown</dd><dt><span>revision_id :</span></dt><dd>$Id: histFileMod.F90 42903 2012-12-21 15:32:10Z muszala $</dd><dt><span>case_title :</span></dt><dd>KONZ.transient</dd><dt><span>case_id :</span></dt><dd>KONZ.transient</dd><dt><span>Surface_dataset :</span></dt><dd>surfdata_hist_78pfts_CMIP6_simyr2000_KONZ_c211102.nc</dd><dt><span>Initial_conditions_dataset :</span></dt><dd>KONZ.2021-09-02.clm2.r.0268-01-01-00000.nc</dd><dt><span>PFT_physiological_constants_dataset :</span></dt><dd>ctsm51_params.c211112.nc</dd><dt><span>ltype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ltype_crop :</span></dt><dd>2</dd><dt><span>ltype_UNUSED :</span></dt><dd>3</dd><dt><span>ltype_landice :</span></dt><dd>4</dd><dt><span>ltype_deep_lake :</span></dt><dd>5</dd><dt><span>ltype_wetland :</span></dt><dd>6</dd><dt><span>ltype_urban_tbd :</span></dt><dd>7</dd><dt><span>ltype_urban_hd :</span></dt><dd>8</dd><dt><span>ltype_urban_md :</span></dt><dd>9</dd><dt><span>ctype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ctype_crop :</span></dt><dd>2</dd><dt><span>ctype_crop_noncompete :</span></dt><dd>2*100+m, m=cft_lb,cft_ub</dd><dt><span>ctype_landice :</span></dt><dd>4*100+m, m=1,glcnec</dd><dt><span>ctype_deep_lake :</span></dt><dd>5</dd><dt><span>ctype_wetland :</span></dt><dd>6</dd><dt><span>ctype_urban_roof :</span></dt><dd>71</dd><dt><span>ctype_urban_sunwall :</span></dt><dd>72</dd><dt><span>ctype_urban_shadewall :</span></dt><dd>73</dd><dt><span>ctype_urban_impervious_road :</span></dt><dd>74</dd><dt><span>ctype_urban_pervious_road :</span></dt><dd>75</dd><dt><span>cft_c3_crop :</span></dt><dd>1</dd><dt><span>cft_c3_irrigated :</span></dt><dd>2</dd><dt><span>cft_temperate_corn :</span></dt><dd>3</dd><dt><span>cft_irrigated_temperate_corn :</span></dt><dd>4</dd><dt><span>cft_spring_wheat :</span></dt><dd>5</dd><dt><span>cft_irrigated_spring_wheat :</span></dt><dd>6</dd><dt><span>cft_winter_wheat :</span></dt><dd>7</dd><dt><span>cft_irrigated_winter_wheat :</span></dt><dd>8</dd><dt><span>cft_temperate_soybean :</span></dt><dd>9</dd><dt><span>cft_irrigated_temperate_soybean :</span></dt><dd>10</dd><dt><span>cft_barley :</span></dt><dd>11</dd><dt><span>cft_irrigated_barley :</span></dt><dd>12</dd><dt><span>cft_winter_barley :</span></dt><dd>13</dd><dt><span>cft_irrigated_winter_barley :</span></dt><dd>14</dd><dt><span>cft_rye :</span></dt><dd>15</dd><dt><span>cft_irrigated_rye :</span></dt><dd>16</dd><dt><span>cft_winter_rye :</span></dt><dd>17</dd><dt><span>cft_irrigated_winter_rye :</span></dt><dd>18</dd><dt><span>cft_cassava :</span></dt><dd>19</dd><dt><span>cft_irrigated_cassava :</span></dt><dd>20</dd><dt><span>cft_citrus :</span></dt><dd>21</dd><dt><span>cft_irrigated_citrus :</span></dt><dd>22</dd><dt><span>cft_cocoa :</span></dt><dd>23</dd><dt><span>cft_irrigated_cocoa :</span></dt><dd>24</dd><dt><span>cft_coffee :</span></dt><dd>25</dd><dt><span>cft_irrigated_coffee :</span></dt><dd>26</dd><dt><span>cft_cotton :</span></dt><dd>27</dd><dt><span>cft_irrigated_cotton :</span></dt><dd>28</dd><dt><span>cft_datepalm :</span></dt><dd>29</dd><dt><span>cft_irrigated_datepalm :</span></dt><dd>30</dd><dt><span>cft_foddergrass :</span></dt><dd>31</dd><dt><span>cft_irrigated_foddergrass :</span></dt><dd>32</dd><dt><span>cft_grapes :</span></dt><dd>33</dd><dt><span>cft_irrigated_grapes :</span></dt><dd>34</dd><dt><span>cft_groundnuts :</span></dt><dd>35</dd><dt><span>cft_irrigated_groundnuts :</span></dt><dd>36</dd><dt><span>cft_millet :</span></dt><dd>37</dd><dt><span>cft_irrigated_millet :</span></dt><dd>38</dd><dt><span>cft_oilpalm :</span></dt><dd>39</dd><dt><span>cft_irrigated_oilpalm :</span></dt><dd>40</dd><dt><span>cft_potatoes :</span></dt><dd>41</dd><dt><span>cft_irrigated_potatoes :</span></dt><dd>42</dd><dt><span>cft_pulses :</span></dt><dd>43</dd><dt><span>cft_irrigated_pulses :</span></dt><dd>44</dd><dt><span>cft_rapeseed :</span></dt><dd>45</dd><dt><span>cft_irrigated_rapeseed :</span></dt><dd>46</dd><dt><span>cft_rice :</span></dt><dd>47</dd><dt><span>cft_irrigated_rice :</span></dt><dd>48</dd><dt><span>cft_sorghum :</span></dt><dd>49</dd><dt><span>cft_irrigated_sorghum :</span></dt><dd>50</dd><dt><span>cft_sugarbeet :</span></dt><dd>51</dd><dt><span>cft_irrigated_sugarbeet :</span></dt><dd>52</dd><dt><span>cft_sugarcane :</span></dt><dd>53</dd><dt><span>cft_irrigated_sugarcane :</span></dt><dd>54</dd><dt><span>cft_sunflower :</span></dt><dd>55</dd><dt><span>cft_irrigated_sunflower :</span></dt><dd>56</dd><dt><span>cft_miscanthus :</span></dt><dd>57</dd><dt><span>cft_irrigated_miscanthus :</span></dt><dd>58</dd><dt><span>cft_switchgrass :</span></dt><dd>59</dd><dt><span>cft_irrigated_switchgrass :</span></dt><dd>60</dd><dt><span>cft_tropical_corn :</span></dt><dd>61</dd><dt><span>cft_irrigated_tropical_corn :</span></dt><dd>62</dd><dt><span>cft_tropical_soybean :</span></dt><dd>63</dd><dt><span>cft_irrigated_tropical_soybean :</span></dt><dd>64</dd><dt><span>time_period_freq :</span></dt><dd>minute_30</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:       (time: 17520, levgrnd: 25, levsoi: 20, levlak: 10,\n",
-       "                   levdcmp: 25, hist_interval: 2, lndgrid: 1)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:29:59.12...\n",
-       "  * levgrnd       (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi        (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * levlak        (levlak) float32 0.05 0.6 2.1 4.6 ... 18.6 25.6 34.33 44.78\n",
-       "  * levdcmp       (levdcmp) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "Dimensions without coordinates: hist_interval, lndgrid\n",
-       "Data variables: (12/42)\n",
-       "    mcdate        (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mcsec         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mdcur         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mscur         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    nstep         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    time_bounds   (time, hist_interval) datetime64[ns] dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
-       "    ...            ...\n",
-       "    HR            (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    NET_NMIN_vr   (time, levdcmp, lndgrid) float32 dask.array<chunksize=(96, 25, 1), meta=np.ndarray>\n",
-       "    SNOW_DEPTH    (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    SOILC_vr      (time, levsoi, lndgrid) float32 dask.array<chunksize=(96, 20, 1), meta=np.ndarray>\n",
-       "    TBOT          (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    TSOI          (time, levgrnd, lndgrid) float32 dask.array<chunksize=(96, 25, 1), meta=np.ndarray>\n",
-       "Attributes: (12/99)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/18/22 20:38:53\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             aws-hpc6a\n",
-       "    ...                                   ...\n",
-       "    cft_irrigated_switchgrass:            60\n",
-       "    cft_tropical_corn:                    61\n",
-       "    cft_irrigated_tropical_corn:          62\n",
-       "    cft_tropical_soybean:                 63\n",
-       "    cft_irrigated_tropical_soybean:       64\n",
-       "    time_period_freq:                     minute_30"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_ctsm_orig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57dbf598-6220-4dd8-9774-2a02b332bff7",
-   "metadata": {},
-   "source": [
-    "In the output, you can click on Dimensions, Coordinates, Data Variables, and Attributes to expand and see the details and metadata associated with this dataset.\n",
-    "\n",
-    "If you click on Data Variables, you will see a list of all the available variables. You can click on the ‘note’ icon at the right end of the line for each variable to see a description of the variable (the long_name) and its units, as well as other information. Here are a few questions to consider:\n",
-    "\n",
-    "Questions to consider\n",
-    "\n",
-    "1. What variables are available in the dataset?\n",
-    "\n",
-    "2. What is the long_name and unit of the variable FSH?\n",
-    "\n",
-    "3. Can you find the dimensions of this variable?\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "\n",
-    "<b>💡 Tip: </b>  Xarray has built-in plotting functions. For quick inspection of a variable, we can use .plot() to see it. Xarray plotting functionality is a thin wrapper around the popular `matplotlib` library.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "baba8e48-23e3-4ba3-bdba-145d618cf79c",
-   "metadata": {},
-   "source": [
-    "Let's quickly inspect GPP from original simulation.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "\n",
-    "<b>Defined:</b>  Gross Primary Production (GPP) is the total amount of CO<sub>2</sub> that is fixed by plants through photosynthesis.\n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "The code below will make a basic plot of the Gross Primary Production (GPP) variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "030f3fb8-b3b2-4ce9-8c44-99b7c1f4ca94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_ctsm_orig.GPP.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dbd79d2-4e15-4db5-b049-71fc8f23bebd",
-   "metadata": {},
-   "source": [
-    "One thing that jumps out from this plot is a dip in GPP in August-September.\n",
-    "\n",
-    "This points to some of the challenges in representing stress deciduous phenology used for some plant functional types (PFTs).  The [CLM5 technote has more information about phenology](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Vegetation_Phenology_Turnover/CLM50_Tech_Note_Vegetation_Phenology_Turnover.html). Thisis an area that additional research and development can help!\n",
-    "\n",
-    "In this specific case, the dip in GPP is related to biases in the input data from NEON, specifically, the zero precipitation that's reported for much of the summer.  Other nearby precipitation sensors suggest that this is not realistic and is potentially a problem with the NEON precipitation sensor. We're working with NEON scientists to address this issue.\n",
-    "\n",
-    "Caveats aside, let's go back to the results we have.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "You can select to plot only specific time period using `.sel` option. \n",
-    "\n",
-    "For example,let's check GPP for June of 2018:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8cbb8a20-e154-4030-b039-27b9c78e7623",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_ctsm_orig.GPP.sel(time=slice('2018-06-01', '2018-06-30')).plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ce096b5-04a6-470a-8459-73d2b7338d3d",
-   "metadata": {},
-   "source": [
-    "By now you might have noticed the units of GPP in the CTSM history output files. If not, you can check:  \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5069c098-2eab-4dcc-b592-71ee277474c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'gC/m^2/s'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_ctsm_orig.GPP.units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38c7393f-a66f-4182-8fa8-678e75257050",
-   "metadata": {},
-   "source": [
-    "Let's change the unit from g C m<sup>-2</sup> s<sup>-1</sup> to g C m<sup>-2</sup> day<sup>-1</sup>:\n",
-    "\n",
-    "**NOTE:** We're still looking at half hourly data so converting to a daily flux is somewhat misleading, but let's go with this (for now). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "db8526c5-e2ce-4cfb-87fc-613db4d90304",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Don't accidentally run this cell more than once!\n",
-    "ds_ctsm_orig['GPP'] = ds_ctsm_orig['GPP']*60*60*24\n",
-    "ds_ctsm_orig['GPP'].attrs['units'] = 'gC/m^2/day'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb306250-9f4c-47be-b7ad-17a9c16cd593",
-   "metadata": {},
-   "source": [
-    "Let's remake the plot from above, using the new unit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c448c229-ca3a-40b3-8e7d-7a032be42935",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_ctsm_orig.GPP.sel(time=slice('2018-06-01', '2018-06-30')).plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "643954f8-c256-4fd6-9f10-650a55c1b5a6",
-   "metadata": {},
-   "source": [
-    "### 2.2 Load data from modified CTSM simulations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3370161-c774-4c1b-a98d-11aada84aa3d",
-   "metadata": {},
-   "source": [
-    "We'll follow a similar procedure as above to load data from the modified CTSM simulations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ba3911a9-e2d5-47fc-aa3b-c600afb321b9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All simulation files from modified simulation: [ 365 files]\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-12-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-13-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-14-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-15-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-16-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-17-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-18-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-19-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-20-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-21-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-22-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-23-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-24-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-25-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-26-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-27-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-28-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-29-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-30-00000.nc\n",
-      "/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/KONZ.transient.clm2.h1.2018-12-31-00000.nc\n"
-     ]
-    }
-   ],
-   "source": [
-    "# pre-staged simulation results (with rain_threshold = 1):\n",
-    "sim_path = \"/scratch/data/day2/KONZ.transient_phenologychange/lnd/hist/\"\n",
-    "\n",
-    "sim_files_mod = sorted(glob(join(sim_path,neon_site+\".transient.clm2.h1.\"+year+\"*.nc\")))\n",
-    "\n",
-    "print(\"All simulation files from modified simulation: [\", len(sim_files_mod), \"files]\")\n",
-    "# Here, just printing the last 20 files\n",
-    "print(*sim_files_mod[-20:None],sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "723f1287-80ed-4937-bb71-af670ebd2b6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading modified simulation files took: 50.38145565986633 s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "ds_ctsm_mod = xr.open_mfdataset(sim_files_mod, decode_times=True, combine='by_coords',parallel=True)\n",
-    "end = time.time()\n",
-    "print(\"Reading modified simulation files took:\", end-start, \"s.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dccc486-2e7a-4765-a15a-ff7d6736eee3",
-   "metadata": {},
-   "source": [
-    "Similar to above, we can get a quick preliminary look at GPP from the modified simulation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a95c54d2-4213-4eb2-9e35-d3ea19bb9497",
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:       (time: 17520, levgrnd: 25, levsoi: 20, levlak: 10,\n",
-       "                   levdcmp: 25, hist_interval: 2, lndgrid: 1)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:29:59.12...\n",
-       "  * levgrnd       (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi        (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * levlak        (levlak) float32 0.05 0.6 2.1 4.6 ... 18.6 25.6 34.33 44.78\n",
-       "  * levdcmp       (levdcmp) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "Dimensions without coordinates: hist_interval, lndgrid\n",
-       "Data variables: (12/42)\n",
-       "    mcdate        (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    mcsec         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    mdcur         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    mscur         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    nstep         (time) float64 dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;\n",
-       "    time_bounds   (time, hist_interval) datetime64[ns] dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
-       "    ...            ...\n",
-       "    HR            (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
-       "    NET_NMIN_vr   (time, levdcmp, lndgrid) float32 dask.array&lt;chunksize=(96, 25, 1), meta=np.ndarray&gt;\n",
-       "    SNOW_DEPTH    (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
-       "    SOILC_vr      (time, levsoi, lndgrid) float32 dask.array&lt;chunksize=(96, 20, 1), meta=np.ndarray&gt;\n",
-       "    TBOT          (time, lndgrid) float32 dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;\n",
-       "    TSOI          (time, levgrnd, lndgrid) float32 dask.array&lt;chunksize=(96, 25, 1), meta=np.ndarray&gt;\n",
-       "Attributes: (12/99)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/24/22 03:01:22\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             aws-hpc6a\n",
-       "    ...                                   ...\n",
-       "    cft_irrigated_switchgrass:            60\n",
-       "    cft_tropical_corn:                    61\n",
-       "    cft_irrigated_tropical_corn:          62\n",
-       "    cft_tropical_soybean:                 63\n",
-       "    cft_irrigated_tropical_soybean:       64\n",
-       "    time_period_freq:                     minute_30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6487930e-c825-4cf2-ad18-94dee7b4c083' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6487930e-c825-4cf2-ad18-94dee7b4c083' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17520</li><li><span class='xr-has-index'>levgrnd</span>: 25</li><li><span class='xr-has-index'>levsoi</span>: 20</li><li><span class='xr-has-index'>levlak</span>: 10</li><li><span class='xr-has-index'>levdcmp</span>: 25</li><li><span>hist_interval</span>: 2</li><li><span>lndgrid</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-adbf163d-3b1e-4810-b8f7-8e977c3abbf6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-adbf163d-3b1e-4810-b8f7-8e977c3abbf6' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-12-31T23:29:...</div><input id='attrs-b957f0ec-5dd0-48ca-a042-de93edcb094c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b957f0ec-5dd0-48ca-a042-de93edcb094c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9ed37d5-01e4-464a-90e7-9ed69452fde9' class='xr-var-data-in' type='checkbox'><label for='data-b9ed37d5-01e4-464a-90e7-9ed69452fde9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:30:00.000053644&#x27;,\n",
-       "       &#x27;2018-01-01T01:00:00.000107288&#x27;, ..., &#x27;2018-12-31T22:30:00.000000000&#x27;,\n",
-       "       &#x27;2018-12-31T23:00:00.878906248&#x27;, &#x27;2018-12-31T23:29:59.121093752&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levgrnd</span></div><div class='xr-var-dims'>(levgrnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-c2839da1-c484-41d0-ac1c-cd190a2828be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2839da1-c484-41d0-ac1c-cd190a2828be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d64bfe-9259-4377-9c45-1bf57fc2c251' class='xr-var-data-in' type='checkbox'><label for='data-84d64bfe-9259-4377-9c45-1bf57fc2c251' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate ground levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levsoi</span></div><div class='xr-var-dims'>(levsoi)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 5.95 6.94 8.03</div><input id='attrs-914432cf-2e01-43c5-a438-eb1bec962b03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-914432cf-2e01-43c5-a438-eb1bec962b03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41147a13-2083-403e-acde-56dfe223943c' class='xr-var-data-in' type='checkbox'><label for='data-41147a13-2083-403e-acde-56dfe223943c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate soil levels (equivalent to top nlevsoi levels of levgrnd)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.01, 0.04, 0.09, 0.16, 0.26, 0.4 , 0.58, 0.8 , 1.06, 1.36, 1.7 , 2.08,\n",
-       "       2.5 , 2.99, 3.58, 4.27, 5.06, 5.95, 6.94, 8.03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levlak</span></div><div class='xr-var-dims'>(levlak)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05 0.6 2.1 ... 25.6 34.33 44.78</div><input id='attrs-900d3be3-afd1-4e1c-a389-0adde1796539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-900d3be3-afd1-4e1c-a389-0adde1796539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e2bf2d-c541-421f-b02c-192580b0cda8' class='xr-var-data-in' type='checkbox'><label for='data-e0e2bf2d-c541-421f-b02c-192580b0cda8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate lake levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 0.05 ,  0.6  ,  2.1  ,  4.6  ,  8.1  , 12.6  , 18.6  , 25.6  , 34.325,\n",
-       "       44.775], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>levdcmp</span></div><div class='xr-var-dims'>(levdcmp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01 0.04 0.09 ... 19.48 28.87 42.0</div><input id='attrs-29cd99da-ba99-4c88-9ef4-d6d3e46c186b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29cd99da-ba99-4c88-9ef4-d6d3e46c186b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-327799ac-9b12-4e54-ab67-60b3b86ff22e' class='xr-var-data-in' type='checkbox'><label for='data-327799ac-9b12-4e54-ab67-60b3b86ff22e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate levels for soil decomposition variables</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-02, 4.000000e-02, 9.000000e-02, 1.600000e-01, 2.600000e-01,\n",
-       "       4.000000e-01, 5.800000e-01, 8.000000e-01, 1.060000e+00, 1.360000e+00,\n",
-       "       1.700000e+00, 2.080000e+00, 2.500000e+00, 2.990000e+00, 3.580000e+00,\n",
-       "       4.270000e+00, 5.060000e+00, 5.950000e+00, 6.940000e+00, 8.030000e+00,\n",
-       "       9.795000e+00, 1.332777e+01, 1.948313e+01, 2.887072e+01, 4.199844e+01],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-968a460f-22d2-4ffb-b62d-3363e4742907' class='xr-section-summary-in' type='checkbox'  ><label for='section-968a460f-22d2-4ffb-b62d-3363e4742907' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mcdate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-50ca4a72-be48-466d-a99e-51bf15ebea53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50ca4a72-be48-466d-a99e-51bf15ebea53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a6f979-b78c-449c-a0df-d82d6a4a98a4' class='xr-var-data-in' type='checkbox'><label for='data-44a6f979-b78c-449c-a0df-d82d6a4a98a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current date (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
-       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
-       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
-       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
-       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
-       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >17520</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcsec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-be865b76-2c7f-4e32-be2a-53045acc4da8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be865b76-2c7f-4e32-be2a-53045acc4da8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a880c8-4407-4fa4-94ae-b4f2d911f214' class='xr-var-data-in' type='checkbox'><label for='data-45a880c8-4407-4fa4-94ae-b4f2d911f214' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current date</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
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-       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
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-       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
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-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
-       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
-       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
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-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mdcur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-6e152197-3b04-4311-b078-89087c76571a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e152197-3b04-4311-b078-89087c76571a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1735af74-1591-43f6-9483-5ec3333b6951' class='xr-var-data-in' type='checkbox'><label for='data-1735af74-1591-43f6-9483-5ec3333b6951' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current day (from base day)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mscur</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-40f96758-96fa-4392-9b8a-65f1eb03722a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40f96758-96fa-4392-9b8a-65f1eb03722a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8b2ee30-da7b-46ea-87ef-30704e2722ee' class='xr-var-data-in' type='checkbox'><label for='data-b8b2ee30-da7b-46ea-87ef-30704e2722ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>current seconds of current day</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nstep</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-dcc58779-445a-4eeb-b23c-1f4bfc1dc85e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc58779-445a-4eeb-b23c-1f4bfc1dc85e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e90209-e176-4a30-8b49-90bad064df3b' class='xr-var-data-in' type='checkbox'><label for='data-50e90209-e176-4a30-8b49-90bad064df3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time step</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, hist_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-10a497f9-a04b-406e-af68-94f4a993c1aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10a497f9-a04b-406e-af68-94f4a993c1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-446e6fdf-44f2-46a1-8f36-21c24749ab4c' class='xr-var-data-in' type='checkbox'><label for='data-446e6fdf-44f2-46a1-8f36-21c24749ab4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>history time interval endpoints</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 273.75 kiB </td>\n",
-       "                        <td> 1.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 2) </td>\n",
-       "                        <td> (96, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> datetime64[ns] </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-ee9aedcc-eb59-4c79-8107-bf1d4f55c89b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee9aedcc-eb59-4c79-8107-bf1d4f55c89b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-391e1754-a825-4dd8-bc51-01e8c9e64dd3' class='xr-var-data-in' type='checkbox'><label for='data-391e1754-a825-4dd8-bc51-01e8c9e64dd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_written</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96,), meta=np.ndarray&gt;</div><input id='attrs-159573b1-9a92-49cc-a3dd-9b97da9593bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-159573b1-9a92-49cc-a3dd-9b97da9593bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-096ecf66-7a35-4328-a39c-68d0f1ef4ce5' class='xr-var-data-in' type='checkbox'><label for='data-096ecf66-7a35-4328-a39c-68d0f1ef4ce5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520,) </td>\n",
-       "                        <td> (96,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5098 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-eba5e3ba-8aba-4ccb-84ce-5b06be2336e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba5e3ba-8aba-4ccb-84ce-5b06be2336e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-947667bc-f6b8-4ed1-a213-c68306815f55' class='xr-var-data-in' type='checkbox'><label for='data-947667bc-f6b8-4ed1-a213-c68306815f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-004c7162-c484-46f7-a1d5-cc0282b94442' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-004c7162-c484-46f7-a1d5-cc0282b94442' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee30514-58f2-43f4-bdf3-16b940945d4f' class='xr-var-data-in' type='checkbox'><label for='data-1ee30514-58f2-43f4-bdf3-16b940945d4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coordinate latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-43544077-a5e9-4aa3-9869-61f5c623a9c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43544077-a5e9-4aa3-9869-61f5c623a9c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bf62798-fe82-4fba-b416-e5f4ee902642' class='xr-var-data-in' type='checkbox'><label for='data-4bf62798-fe82-4fba-b416-e5f4ee902642' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>grid cell areas</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landfrac</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-d263c286-a90e-48bd-a5da-bf0e65f40f94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d263c286-a90e-48bd-a5da-bf0e65f40f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5229ba68-8c4d-45c5-bfaf-c46ee42ade2f' class='xr-var-data-in' type='checkbox'><label for='data-5229ba68-8c4d-45c5-bfaf-c46ee42ade2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land fraction</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landmask</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-2178db4e-73c9-43e3-aedf-beba5dd6c3fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2178db4e-73c9-43e3-aedf-beba5dd6c3fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e975e689-c48e-46ed-bee9-229cd6d5b176' class='xr-var-data-in' type='checkbox'><label for='data-e975e689-c48e-46ed-bee9-229cd6d5b176' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land/ocean mask (0.=ocean and 1.=land)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pftmask</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-6ffac2d8-91e3-48f1-bcbc-9842c7e595b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ffac2d8-91e3-48f1-bcbc-9842c7e595b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b4b389-ea40-4ee5-85c5-24411e27842a' class='xr-var-data-in' type='checkbox'><label for='data-c9b4b389-ea40-4ee5-85c5-24411e27842a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pft real/fake mask (0.=fake and 1.=real)</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbedrock</span></div><div class='xr-var-dims'>(lndgrid, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 96), meta=np.ndarray&gt;</div><input id='attrs-443fbc3e-924c-49b0-a4cb-8838386d1318' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-443fbc3e-924c-49b0-a4cb-8838386d1318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22d7a1b6-aa74-4a10-865d-460d02d53f1c' class='xr-var-data-in' type='checkbox'><label for='data-22d7a1b6-aa74-4a10-865d-460d02d53f1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>index of shallowest bedrock layer</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 768 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 17520) </td>\n",
-       "                        <td> (1, 96) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4372 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
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-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZSOI</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-f56e8417-eef1-40ae-adf4-e6302a712967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f56e8417-eef1-40ae-adf4-e6302a712967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd34099a-1337-429c-b5d4-6931e836880b' class='xr-var-data-in' type='checkbox'><label for='data-fd34099a-1337-429c-b5d4-6931e836880b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>nonurb</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZSOI</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-8511c566-5570-4e30-86b3-e554579ca4e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8511c566-5570-4e30-86b3-e554579ca4e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46987d67-4a8c-4593-b5a8-b6babfcc95c6' class='xr-var-data-in' type='checkbox'><label for='data-46987d67-4a8c-4593-b5a8-b6babfcc95c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>soil thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>nonurb</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WATSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-b2ed5da2-4180-4167-8be4-825bb43a7f82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2ed5da2-4180-4167-8be4-825bb43a7f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea2dbb47-80cc-46ab-aa0b-364b8469e1c8' class='xr-var-data-in' type='checkbox'><label for='data-ea2dbb47-80cc-46ab-aa0b-364b8469e1c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated soil water content (porosity)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SUCSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-83363201-fba3-49a1-912c-a43cda1225c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83363201-fba3-49a1-912c-a43cda1225c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b77182f-9535-4e75-a521-decf98a735d6' class='xr-var-data-in' type='checkbox'><label for='data-2b77182f-9535-4e75-a521-decf98a735d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated soil matric potential</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"16.566940\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"53.133879\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,53.133879,60.000000)\">25</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BSW</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-79c9c5cc-4b37-42dc-9136-613df420148c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79c9c5cc-4b37-42dc-9136-613df420148c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7619b5-7bda-4b19-861a-2bef6ff87637' class='xr-var-data-in' type='checkbox'><label for='data-2e7619b5-7bda-4b19-861a-2bef6ff87637' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of soil water retention curve</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HKSAT</span></div><div class='xr-var-dims'>(levgrnd, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 1), meta=np.ndarray&gt;</div><input id='attrs-a82b83ef-b13a-48fe-81a4-6ee8793810bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a82b83ef-b13a-48fe-81a4-6ee8793810bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5970cb2-62ad-454f-8b7f-b0f0042ed85c' class='xr-var-data-in' type='checkbox'><label for='data-f5970cb2-62ad-454f-8b7f-b0f0042ed85c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>saturated hydraulic conductivity</dd><dt><span>units :</span></dt><dd>mm s-1</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100 B </td>\n",
-       "                        <td> 100 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                        <td> (25, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZLAKE</span></div><div class='xr-var-dims'>(levlak, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 1), meta=np.ndarray&gt;</div><input id='attrs-8711cf71-410f-4f68-8ede-b1557e4ad082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8711cf71-410f-4f68-8ede-b1557e4ad082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7bfd1f6-3609-4913-8ae6-2a06aa221ca8' class='xr-var-data-in' type='checkbox'><label for='data-e7bfd1f6-3609-4913-8ae6-2a06aa221ca8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lake layer node depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>lake</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 40 B </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZLAKE</span></div><div class='xr-var-dims'>(levlak, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 1), meta=np.ndarray&gt;</div><input id='attrs-29dddb23-644d-4644-b279-deb2a42cd73b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29dddb23-644d-4644-b279-deb2a42cd73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24867800-c16c-413e-b318-73d2ea7b58d0' class='xr-var-data-in' type='checkbox'><label for='data-24867800-c16c-413e-b318-73d2ea7b58d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lake layer thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>landunit_mask :</span></dt><dd>lake</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 40 B </td>\n",
-       "                        <td> 40 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                        <td> (10, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PCT_SAND</span></div><div class='xr-var-dims'>(levsoi, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 1), meta=np.ndarray&gt;</div><input id='attrs-29e9f668-63b5-4c34-9870-6897d0f0321f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29e9f668-63b5-4c34-9870-6897d0f0321f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-393c7f2c-bff6-4dfc-9afa-675d1d49a0fc' class='xr-var-data-in' type='checkbox'><label for='data-393c7f2c-bff6-4dfc-9afa-675d1d49a0fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>percent sand</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PCT_CLAY</span></div><div class='xr-var-dims'>(levsoi, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 1), meta=np.ndarray&gt;</div><input id='attrs-47fbbfd2-a3ce-4d90-b8c4-071a88a73a28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47fbbfd2-a3ce-4d90-b8c4-071a88a73a28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cad34c2-89d3-4db7-a6c2-01c1675c1cb3' class='xr-var-data-in' type='checkbox'><label for='data-9cad34c2-89d3-4db7-a6c2-01c1675c1cb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>percent clay</dd><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                        <td> (20, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 2 Tasks </td>\n",
-       "                        <td> 1 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-8ed57f5a-e413-4697-99ac-808a1ff7f289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ed57f5a-e413-4697-99ac-808a1ff7f289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-477585b6-fab4-4560-ba28-3c7c5e70d40f' class='xr-var-data-in' type='checkbox'><label for='data-477585b6-fab4-4560-ba28-3c7c5e70d40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>autotrophic respiration (MR + GR)</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELAI</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-508c1612-b8ad-409e-95a0-fef53410d1d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-508c1612-b8ad-409e-95a0-fef53410d1d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88752f81-17a7-4533-ac08-e10d5f917d69' class='xr-var-data-in' type='checkbox'><label for='data-88752f81-17a7-4533-ac08-e10d5f917d69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>exposed one-sided leaf area index</dd><dt><span>units :</span></dt><dd>m^2/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCEV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-eaabcf02-7ca0-4e06-8ce0-369ea18cfcca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaabcf02-7ca0-4e06-8ce0-369ea18cfcca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6532d49-9854-4a12-b2ab-854f3c94ab36' class='xr-var-data-in' type='checkbox'><label for='data-a6532d49-9854-4a12-b2ab-854f3c94ab36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FCTR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-5e9867dd-880f-4158-af8f-8dfbfda37f94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e9867dd-880f-4158-af8f-8dfbfda37f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d8db24-787e-47cd-a385-502a81af29e7' class='xr-var-data-in' type='checkbox'><label for='data-c4d8db24-787e-47cd-a385-502a81af29e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>canopy transpiration</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" />\n",
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-       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
-       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
-       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
-       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
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-       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
-       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
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-       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FGEV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-7578d0d9-b230-42a3-ae11-2801615df2d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7578d0d9-b230-42a3-ae11-2801615df2d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd061c50-f288-48c5-b0b9-e4c89ebd89f1' class='xr-var-data-in' type='checkbox'><label for='data-cd061c50-f288-48c5-b0b9-e4c89ebd89f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ground evaporation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FIRA</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-252e149a-30a7-42da-b106-2fcd6466e68a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-252e149a-30a7-42da-b106-2fcd6466e68a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d264fa9-78dd-448c-b616-6cac7f1b7383' class='xr-var-data-in' type='checkbox'><label for='data-0d264fa9-78dd-448c-b616-6cac7f1b7383' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>net infrared (longwave) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSA</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-7d4e3ef0-13a3-4002-9d5b-1e121cba81f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d4e3ef0-13a3-4002-9d5b-1e121cba81f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75864536-c3d0-4362-aaf7-5740d9562336' class='xr-var-data-in' type='checkbox'><label for='data-75864536-c3d0-4362-aaf7-5740d9562336' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absorbed solar radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSH</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-d7e026b5-331f-4af0-b90e-615a102a7896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7e026b5-331f-4af0-b90e-615a102a7896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6164e3cb-2ec5-49a6-b215-1795c7f6fbcb' class='xr-var-data-in' type='checkbox'><label for='data-6164e3cb-2ec5-49a6-b215-1795c7f6fbcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensible heat not including correction for land use change and rain/snow conversion</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FV</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-691e447d-8c65-4e7a-9bb9-2fb08aac9865' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-691e447d-8c65-4e7a-9bb9-2fb08aac9865' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31af040e-63c2-4ab7-8a72-ca3c8603c27d' class='xr-var-data-in' type='checkbox'><label for='data-31af040e-63c2-4ab7-8a72-ca3c8603c27d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>friction velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
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-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPP</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-a2d24cc4-1ae1-40e6-b00b-f125ebfa6b5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2d24cc4-1ae1-40e6-b00b-f125ebfa6b5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f638b13-583a-461b-a589-4d39bc751290' class='xr-var-data-in' type='checkbox'><label for='data-8f638b13-583a-461b-a589-4d39bc751290' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>gross primary production</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 68.44 kiB </td>\n",
-       "                        <td> 384 B </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (96, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>H2OSOI</span></div><div class='xr-var-dims'>(time, levsoi, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 20, 1), meta=np.ndarray&gt;</div><input id='attrs-b0f569ee-ed78-4c27-b07a-32effbdec38f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0f569ee-ed78-4c27-b07a-32effbdec38f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7a57193-3047-4fbd-b294-85dd132b3036' class='xr-var-data-in' type='checkbox'><label for='data-b7a57193-3047-4fbd-b294-85dd132b3036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>volumetric soil water (natural vegetated and crop landunits only)</dd><dt><span>units :</span></dt><dd>mm3/mm3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>veg</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
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-       "                        <td> 1.34 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 20, 1) </td>\n",
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-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5466 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HR</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-4d378e14-26b0-46c7-a312-8962f301836d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d378e14-26b0-46c7-a312-8962f301836d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c23a35-d346-43a2-bffc-4172b57efc32' class='xr-var-data-in' type='checkbox'><label for='data-19c23a35-d346-43a2-bffc-4172b57efc32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total heterotrophic respiration</dd><dt><span>units :</span></dt><dd>gC/m^2/s</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 5100 Tasks </td>\n",
-       "                        <td> 364 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOW_DEPTH</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-b0fc034c-a5e3-4edd-8f31-508b1afce079' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0fc034c-a5e3-4edd-8f31-508b1afce079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd96eb0d-1047-4603-93f9-11e7b4a6ec3e' class='xr-var-data-in' type='checkbox'><label for='data-fd96eb0d-1047-4603-93f9-11e7b4a6ec3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>snow height of snow covered area</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TBOT</span></div><div class='xr-var-dims'>(time, lndgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 1), meta=np.ndarray&gt;</div><input id='attrs-1f0dfdd7-553a-44a0-b029-13fb3a44c55a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f0dfdd7-553a-44a0-b029-13fb3a44c55a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40ff2e94-5584-4d81-bb4e-26536cbe7c08' class='xr-var-data-in' type='checkbox'><label for='data-40ff2e94-5584-4d81-bb4e-26536cbe7c08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric air temperature (downscaled to columns in glacier regions)</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>landunit_mask :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><table>\n",
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$</dd><dt><span>case_title :</span></dt><dd>KONZ.transient</dd><dt><span>case_id :</span></dt><dd>KONZ.transient</dd><dt><span>Surface_dataset :</span></dt><dd>surfdata_hist_78pfts_CMIP6_simyr2000_KONZ_c211102.nc</dd><dt><span>Initial_conditions_dataset :</span></dt><dd>KONZ.2021-09-02.clm2.r.0268-01-01-00000.nc</dd><dt><span>PFT_physiological_constants_dataset :</span></dt><dd>ctsm51_params.c211112.nc</dd><dt><span>ltype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ltype_crop :</span></dt><dd>2</dd><dt><span>ltype_UNUSED :</span></dt><dd>3</dd><dt><span>ltype_landice :</span></dt><dd>4</dd><dt><span>ltype_deep_lake :</span></dt><dd>5</dd><dt><span>ltype_wetland :</span></dt><dd>6</dd><dt><span>ltype_urban_tbd :</span></dt><dd>7</dd><dt><span>ltype_urban_hd :</span></dt><dd>8</dd><dt><span>ltype_urban_md :</span></dt><dd>9</dd><dt><span>ctype_vegetated_or_bare_soil :</span></dt><dd>1</dd><dt><span>ctype_crop :</span></dt><dd>2</dd><dt><span>ctype_crop_noncompete :</span></dt><dd>2*100+m, m=cft_lb,cft_ub</dd><dt><span>ctype_landice :</span></dt><dd>4*100+m, m=1,glcnec</dd><dt><span>ctype_deep_lake :</span></dt><dd>5</dd><dt><span>ctype_wetland :</span></dt><dd>6</dd><dt><span>ctype_urban_roof :</span></dt><dd>71</dd><dt><span>ctype_urban_sunwall :</span></dt><dd>72</dd><dt><span>ctype_urban_shadewall :</span></dt><dd>73</dd><dt><span>ctype_urban_impervious_road :</span></dt><dd>74</dd><dt><span>ctype_urban_pervious_road :</span></dt><dd>75</dd><dt><span>cft_c3_crop :</span></dt><dd>1</dd><dt><span>cft_c3_irrigated :</span></dt><dd>2</dd><dt><span>cft_temperate_corn :</span></dt><dd>3</dd><dt><span>cft_irrigated_temperate_corn :</span></dt><dd>4</dd><dt><span>cft_spring_wheat :</span></dt><dd>5</dd><dt><span>cft_irrigated_spring_wheat :</span></dt><dd>6</dd><dt><span>cft_winter_wheat :</span></dt><dd>7</dd><dt><span>cft_irrigated_winter_wheat :</span></dt><dd>8</dd><dt><span>cft_temperate_soybean :</span></dt><dd>9</dd><dt><span>cft_irrigated_temperate_soybean :</span></dt><dd>10</dd><dt><span>cft_barley :</span></dt><dd>11</dd><dt><span>cft_irrigated_barley :</span></dt><dd>12</dd><dt><span>cft_winter_barley :</span></dt><dd>13</dd><dt><span>cft_irrigated_winter_barley :</span></dt><dd>14</dd><dt><span>cft_rye :</span></dt><dd>15</dd><dt><span>cft_irrigated_rye :</span></dt><dd>16</dd><dt><span>cft_winter_rye :</span></dt><dd>17</dd><dt><span>cft_irrigated_winter_rye :</span></dt><dd>18</dd><dt><span>cft_cassava :</span></dt><dd>19</dd><dt><span>cft_irrigated_cassava :</span></dt><dd>20</dd><dt><span>cft_citrus :</span></dt><dd>21</dd><dt><span>cft_irrigated_citrus :</span></dt><dd>22</dd><dt><span>cft_cocoa :</span></dt><dd>23</dd><dt><span>cft_irrigated_cocoa :</span></dt><dd>24</dd><dt><span>cft_coffee :</span></dt><dd>25</dd><dt><span>cft_irrigated_coffee :</span></dt><dd>26</dd><dt><span>cft_cotton :</span></dt><dd>27</dd><dt><span>cft_irrigated_cotton :</span></dt><dd>28</dd><dt><span>cft_datepalm :</span></dt><dd>29</dd><dt><span>cft_irrigated_datepalm :</span></dt><dd>30</dd><dt><span>cft_foddergrass :</span></dt><dd>31</dd><dt><span>cft_irrigated_foddergrass :</span></dt><dd>32</dd><dt><span>cft_grapes :</span></dt><dd>33</dd><dt><span>cft_irrigated_grapes :</span></dt><dd>34</dd><dt><span>cft_groundnuts :</span></dt><dd>35</dd><dt><span>cft_irrigated_groundnuts :</span></dt><dd>36</dd><dt><span>cft_millet :</span></dt><dd>37</dd><dt><span>cft_irrigated_millet :</span></dt><dd>38</dd><dt><span>cft_oilpalm :</span></dt><dd>39</dd><dt><span>cft_irrigated_oilpalm :</span></dt><dd>40</dd><dt><span>cft_potatoes :</span></dt><dd>41</dd><dt><span>cft_irrigated_potatoes :</span></dt><dd>42</dd><dt><span>cft_pulses :</span></dt><dd>43</dd><dt><span>cft_irrigated_pulses :</span></dt><dd>44</dd><dt><span>cft_rapeseed :</span></dt><dd>45</dd><dt><span>cft_irrigated_rapeseed :</span></dt><dd>46</dd><dt><span>cft_rice :</span></dt><dd>47</dd><dt><span>cft_irrigated_rice :</span></dt><dd>48</dd><dt><span>cft_sorghum :</span></dt><dd>49</dd><dt><span>cft_irrigated_sorghum :</span></dt><dd>50</dd><dt><span>cft_sugarbeet :</span></dt><dd>51</dd><dt><span>cft_irrigated_sugarbeet :</span></dt><dd>52</dd><dt><span>cft_sugarcane :</span></dt><dd>53</dd><dt><span>cft_irrigated_sugarcane :</span></dt><dd>54</dd><dt><span>cft_sunflower :</span></dt><dd>55</dd><dt><span>cft_irrigated_sunflower :</span></dt><dd>56</dd><dt><span>cft_miscanthus :</span></dt><dd>57</dd><dt><span>cft_irrigated_miscanthus :</span></dt><dd>58</dd><dt><span>cft_switchgrass :</span></dt><dd>59</dd><dt><span>cft_irrigated_switchgrass :</span></dt><dd>60</dd><dt><span>cft_tropical_corn :</span></dt><dd>61</dd><dt><span>cft_irrigated_tropical_corn :</span></dt><dd>62</dd><dt><span>cft_tropical_soybean :</span></dt><dd>63</dd><dt><span>cft_irrigated_tropical_soybean :</span></dt><dd>64</dd><dt><span>time_period_freq :</span></dt><dd>minute_30</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:       (time: 17520, levgrnd: 25, levsoi: 20, levlak: 10,\n",
-       "                   levdcmp: 25, hist_interval: 2, lndgrid: 1)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:29:59.12...\n",
-       "  * levgrnd       (levgrnd) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "  * levsoi        (levsoi) float32 0.01 0.04 0.09 0.16 ... 5.06 5.95 6.94 8.03\n",
-       "  * levlak        (levlak) float32 0.05 0.6 2.1 4.6 ... 18.6 25.6 34.33 44.78\n",
-       "  * levdcmp       (levdcmp) float32 0.01 0.04 0.09 0.16 ... 19.48 28.87 42.0\n",
-       "Dimensions without coordinates: hist_interval, lndgrid\n",
-       "Data variables: (12/42)\n",
-       "    mcdate        (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mcsec         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mdcur         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    mscur         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    nstep         (time) float64 dask.array<chunksize=(96,), meta=np.ndarray>\n",
-       "    time_bounds   (time, hist_interval) datetime64[ns] dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
-       "    ...            ...\n",
-       "    HR            (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    NET_NMIN_vr   (time, levdcmp, lndgrid) float32 dask.array<chunksize=(96, 25, 1), meta=np.ndarray>\n",
-       "    SNOW_DEPTH    (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    SOILC_vr      (time, levsoi, lndgrid) float32 dask.array<chunksize=(96, 20, 1), meta=np.ndarray>\n",
-       "    TBOT          (time, lndgrid) float32 dask.array<chunksize=(96, 1), meta=np.ndarray>\n",
-       "    TSOI          (time, levgrnd, lndgrid) float32 dask.array<chunksize=(96, 25, 1), meta=np.ndarray>\n",
-       "Attributes: (12/99)\n",
-       "    title:                                CLM History file information\n",
-       "    comment:                              NOTE: None of the variables are wei...\n",
-       "    Conventions:                          CF-1.0\n",
-       "    history:                              created on 05/24/22 03:01:22\n",
-       "    source:                               Community Terrestrial Systems Model\n",
-       "    hostname:                             aws-hpc6a\n",
-       "    ...                                   ...\n",
-       "    cft_irrigated_switchgrass:            60\n",
-       "    cft_tropical_corn:                    61\n",
-       "    cft_irrigated_tropical_corn:          62\n",
-       "    cft_tropical_soybean:                 63\n",
-       "    cft_irrigated_tropical_soybean:       64\n",
-       "    time_period_freq:                     minute_30"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_ctsm_mod"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "83682f3e-a5c2-4603-8228-e733a378c56b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_ctsm_mod.GPP.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b24e70c9-8ed3-4558-bb08-96492e80da43",
-   "metadata": {},
-   "source": [
-    "We should change the GPP units to match the change we made in the unmodified simulations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "23bf8d8a-30b9-4dcc-913a-556981d0c45c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Don't accidentally run this cell more than once!\n",
-    "ds_ctsm_mod['GPP'] = ds_ctsm_mod['GPP']*60*60*24\n",
-    "ds_ctsm_mod['GPP'].attrs['units'] = 'gC/m^2/day'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05b1007e-527d-4de2-9b3b-06df47ea7c5f",
-   "metadata": {},
-   "source": [
-    "### 2.3 Compare modified and unmodified simulations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c3df526-0f89-4bb2-952a-57d931560512",
-   "metadata": {},
-   "source": [
-    "Since our code modifications change phenology, let's start by looking at leaf area index. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "69c9433e-5c06-43d6-aff6-cfdcd3ceb12e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows=2, figsize=(13,7),sharex=True)\n",
-    "ds_ctsm_orig.ELAI.plot(ax=axes[0], color='orange',marker=\"o\")\n",
-    "plt.suptitle('Default',weight='bold')\n",
-    "ds_ctsm_mod.ELAI.plot(ax=axes[1], color='blue',marker=\"o\")\n",
-    "plt.title('Phenology Mods',weight='bold')\n",
-    "\n",
-    "plt.tight_layout(); "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7249cd60-66d7-48e8-9f13-adfcab0a82bc",
-   "metadata": {},
-   "source": [
-    "**Question**: Can you see any diffences between the two simulations? How does changing the rain threshold change patterns (onset, peak, etc.) of LAI?\n",
-    "\n",
-    "Next let's inspect GPP from both simulations for April 2018. Note that we can overlay the two simulations onto a single plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "bd408dc7-5440-487d-ae61-c20dad93ebe2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Pu+22G/bee28MGzYMF1xwAdrb23OOcc011+DUU0/FQQcdhH79+m3ef/LJJ2P16tUYMWIEpk+fjp133jnnu8OHD0dZWRn23HNPCjQmiJjwmhiCCgAqZaNnT1IICCItqMaHYowJUlk+JWlLt+zXhVMu9CxfLrZXrRJuSGlSGhlXRc+FPTBjDMBMAKs55z+27N+Gc7488/pSAPtxzk93O9bIkSP5/Pnzu+z75JNPsOuuu0bebqJw0DklksiQId4rXIxlV4J0KSlxDl4OciyCIPQwTREMK33fa2uBadOCK+Kq8aGuTviTFxM6Y2VMImdgVOMwAMya5e+60Pn/kkKO84yxdzjnI53ei9NScACA8QAOt6UfvZEx9iFj7AMAhwG4NMY2EARBhEJVT8BKkKBg1XcGDfJ/LIIgvDFN4KyzugbDNjWJDGBTpgQ7pioFZTHGBHkJxHV1+WmHH1TjcBCLrR/rUFITScSZfWge55xxzodzzkdkHnM55+M553tk9h8vrQYEQRBJRNYTkKZve12BoAKAStnQqH9IEEQApk5Vr84GzTYjx4cePcT2gAHFGxPk5h6UVEWpoQEoL8/d39bm/3pQCfpRzRn5oHgqGhMEQQTEMICttgLOPRd47DFhcgbClayXwoSckLbcUjyfemokTSYIwoYqXSaQzTYTBMMARmWiI4tVIQBENXYVSe0XwwB69crdv2mT/+tBZTW68EKRkhQIN2fkg1QrBXHFQxD5h84lkXTa24GyMjGYDx0KnHFG+JL1hiHchQwjOwFRPAFBFIYwwcFyocAhyWDR4OYeVF+f3Iw7a9Y47/d7PciFni22ENuDB4vte+4BfvhDYPvtw88ZcZNapaCyshJNTU0kTHYDOOdoampCZWVloZtCEEra27Or+j17Rjf5t7SI1SRpeielgCDiobbW/f0wft5SKVi/Pvgx0k5DA6CaxpOcinPrrZ33u6UrVWEYwjJQXi7+s1QAqqvTUdE4L8XL4mDgwIFYtmwZVq5cWeimEBFQWVmJgQMHFroZRDfCNMXq1JIlYrIPWzCmrU1YCgBhbo5q8m9uFqZlKVSQUkAQ8TBtGjBhgtrNZcyY4Mf++mvxfMEFwHXXJbtAVZxUVACqWqsyFWfS+mXKFOCXv8zdv26dmEf8tnf9+tzCllTROGbKy8sxdOjQQjeDIIgEEkeVUek+ZJrAW2+JbcbCpzRsaemqFLj55RIEERzDAC65BPjmG+f3584NdlzTBD79NLudpqrGUS2e2MdcFUms33DEEc5KgYwr8Nsf69blKgVpsRSk1n2IIAhCRRxVRtvbgQULgIkTxWtJUxNwzjnBzOIdHWLiqa4mSwFB5AOV/zgQXGCtr8+9b5NcoEoiBfnGRhFoHcbFx63yu5UkpuJ0K7jm95owTeD3vxd9OWRIti+rqsTvJH3Rh5QCgiC6HVFXGeVcuA+98op4thMkUwWQXTmqqqKYAoLIB1ttpX4vqMCqys+vW8iqUES5eKIztpaXh0vFaZpC0C4p6Spwh8VNKfBzTUglS47rViVLZiVKurWAlAKCILodqoE86KQvBfVvv1V/JojCISdkshQQRH445xzn/RUVwQRW08zNQy9hLJmBtZIolRmdsVXVTzo4WTXGjw9edM6KVArsdRb8KjFuSpZMSZr0uAJSCgiC6HY4ZcEIUzBGugvJWgJOBFE4rJYCiikgiHgxTeD++3P39+0LPPRQMF/6+nohpDoRpvZBPlAVG3MrQqZCp/J7UIsqIArP2QVqzoHp08MrXlIpKLFJxH6VGNXCUGMjcPXV4vXeeydbUSSlgCCIbodhdK0MHLZgjHQZOvZY5+qXQVYZTRMYPVq8vuIKEbwMkKWAIOJArjRbExZK4febb4Ln0feyECYxsFaiWoAIsjAhc/TbA2ztBOkP03QvPDd1qv9jWpFKgd011K8S41bRWLb/iy+Sm5oVIKWAIIhuylFHieeddw5fMEZaCkaNAh5+ODd/tddEaEcKKF9+KbZXrQJmzhSvSSkgiOhxcu2Qwm+YIFsvC2ESA2slqmJjbkXI3DAM4OSTgYED1ccI0h9egrmbwqBDa6v6PT9KjJO1hLFcS1KSg9BJKSAIotthmsD3videL1wYflVGriCVl4uJ7447ur7f1ORPoHASUORqFSkFBBE9OsJdEGHNzW0mjMtiPojazRLIpuN06pegx47b2hJVoLG0lkg3pLo6tWtZUi1IpBQQBNGtkKvwsphQe3t4c620FMjiZU6Cgx+Bwm1CoJgCgogeXeHOr7DmtEgAhHdZzAeGAVx1VXZ74MBwbTZN4C9/AT75RIyFEyaI6u9AuP7wqizsVanaC6kUyGBgSRAlxjDEcS67TFioo7SY5ANSCgiC6FbEVaMAyMYThE156jYh7LJLtOn2CILQC4QFgglr0iopqa0N77KYLw4/PPv6n/8MpxBMmpStZtzYKFwi998f6Ncv3v6YNi34d00T+MUvxOvKSqBHD/E6qBJjmsCGDcCtt4pxfMyY6Cwm+YCUAoIguhVR1ygAsu5D0lIQNuWpm4AStogQQRC5GIZYuZYZZRjLzbITVFhbt67rdpjUm/nG6k+/fn3w46gWY956K3xu/tWr1e9NnhxekZEVrr/5Rlhqe/YMpsTI40mkYjRhArDNNmJfv37JtiAplQLG2J81Ho/ksa0EQRCeRF2jAMh1H3ISHPwIFNL3VJq9nVIAJjkYjSDShmkKAU36eHPeNQVlmJXhQw/tus+e2jLJWJWC4cODWylViy7r1omxTOVbr4Pb2D13bvDFEydFpr09eC0BlWI0dy7w6qti+9Zbk6sQAECZy3u7AjjP5X0G4O5om0MQBBGOhgbg/PO7rk6FNdfa3Yfs1NYKE7afwd4wgDVrgIsvVgcXJzUYjSDShpPA5lSd3A9yZdh+XOlCkwaefz772mqlBPyNZ4MHOxc923JLMc5t2pR1zfGL05guCdpeQD2+ci7GZL/KnZuVWsYrpLmicT3n/BWXx8sAfu3yfYIgiLxjGMD112e3y8vDm2ut7kN2EzEQfKCX3xs40Pn9pAajEUTa8FKwg7jsOSkagPApTwuzZuXuiyoLU3U18P3vZ48ZFMMAbrpJ/X5Qq6rb+BpkTHezUsssT0lXGJVKAed8DgAwxoZ5fYYgCCJJHH109vUuu4Q311rdh6IMZJbHaWjIXUVLcjAaQaQNHQXb732sUjTSlEFs1Srn/UGyMM2Ykd2uqxO+9H/9q9jeY49wMVLWMd2JIFZVJ0VGWoODKDFuKV6lpSC1SoGFexljbzHGpjDGtoy7QQRBEGEwza4ZNdasCX9Mq/tQlIHMLS3imOPHCzciyaBByQ5GI4i0oZt9yM99rFI0nGKEkooq3WcQK+Upp4jnhgbxmDkzGyQctpKvDILu39/5/SDtlYpM795iu7Y2uziz117+22oY2UxGjHWNU5HKQprdhwAAnPMDARgABgGYzxh7nDF2VOwtIwiC8Il07fnqq+y+pUtFMZ2gk5FpAiecIF6ffXa0k2hzc3YF6YgjsvvnzyeFIGpMUwRRlpRQytdixF5YSiW4+7mPVYoG5+m5vo47LndfUCulXF2vro4+NbR0yTr33GhTfBoGcOGFwgrc0pJVPoIqMYcdJp7nzu2awaikBKio6B6WAnDOPwPwCwBXADgEwB2MsU8ZYz9UfYcxNogx9hJj7BPG2H8YY1Mz+/syxv7OGPss89wnij9CEASh8vFdvx445xz/A7y9ENqKFcDatWJwtxJ0UmppyU5wViElTT7JaUCex8ZGSvlazBgG0KsXMHWqWMUOK1xKRcN+nM7O9Fxfw4d33Q5TZMyqFERpUTVN4OSTxetHHgG++93se6Wlwk0pzCJKZ6dw+YpCiZFCv70QGiCsBam3FDDGhjPGbgPwCYDDARzHOd818/o2l6+2A/hJ5rP7A7iIMbYbgCsBvMA53wnAC5ltgiCI0LhNOJs2+R/gVRlLamqylSp79w6eyvDxx4VVY8gQ4KWXsu+RUhAtcRS0I9JJe7tYFZYCfdiKu4aRu0gApOf6sqYkvfbacEXGrEpBVKmhpUK/YoXY/uor4IUXsu93dAgFL4wC1tmpTpnqV4mRQr9dKTBNsTg1bVqyLZU6loK7ALwLYE/O+UWc83cBgHP+JYT1wBHO+XLLZ9dBKBXbATgBwMzMx2YCODFw6wmCICx4TTh+B3jV51evFpNnz57AeecFL3Ijhf/GRuA2yxILKQXRojqPTikUie5NW1s2mNQwRDxP//7hhGFV3FLSry/TBH73u+z222+HO55VKVBlI/JrUVVZf+2/G0YB6+xUF5zzq8RIpcAacCzHe5l6OsmWSp2YgoM5549xznOMHpzzx3R+hDE2BMBeAN4EsBXnfHnm+8sBDPDVYoJQQD7DxI47ur/vd4D3Wu3q0SOYj6jTRGddsfvud+kajhLVeWSM+rjYkJYCSVVVeJcOGahqJ8nXlxRUv/02uy9MITDTBI45RryePFk8z5gRvpKv7kJOmJounZ1CiI9CiXFyH0qTpVLHfWgnxthTjLGPGWML5UP3BxhjvQD8AcCPOedrfXxvEmNsPmNs/sqVK3W/RhQp5DNMmCbw4ovq9ysq/A/wDQ25rgHW4/To0VWY18VrAqNrOFoaGpxXAjlP5sRMxIN0E7EWIYzCz/vAA533J/n6UlXzDdJep9grWcslbCVf3YWcMDVdOjuFEG9VYvr3D6bEOLkPRRlfETc67kMPA5gOESNwGIBHAehaCMohFAKTc/7HzO6vGWPbZN7fBsAKp+9yzmdwzkdyzkf2V+WgIogMadLEiXior1f7hQLAQw8Fm5Tsx7RuB1UKdCcwuoajwTCi8xkm0ou1CKGkqkr4pYepbjx0qPq9pF5fUQqqbvOvXH0PqnjppJINW9NFVi82jGy8wh13BJsvnJSCqOIr8oGOUlDFOX8BAOOcN3LOr4EIMnaFMcYAPAjgE875rZa3/gxgQub1BADP+GsyQeSi8t1M6oBMRI/bua6rCzbA19fnCgttbWK/aQLLlomKoH5dfXRzpgN0DUeFDAy3k8SJmYgHa70RSRRFpVpbo0lxmk9U7Sop8W+ddFMwZP8GrWgsA8K32EJsDx4s3JPCBohbkUoBkK1TEPR6kN+zxhREFV+RD3SUgo2MsRIAnzHGLmaMnQS9OIADAIwHcDhj7L3MYwyAGwAcxRj7DMBRmW2CCMyRR6rfU+WUJ7ofbpNv0MHXLUB10qSskOHX1UdOdNKlpa4uO+nZSapQkTacroHy8mROzEQ8qCwFQDgXoo0bgT590iP4AeqFiY4O/26LbivhYS0FgBgvJ08WrpuNjcA990QTIC6xKgVSmA9iATZN4LrrxOvddsv2oRzvpcIRhSITFzpKwY8BVAP4EYB9IAT9CW5fAADO+TzOOeOcD+ecj8g85nLOmzjnR3DOd8o8rw71D4iixjS7picjihe31fdx44Id061iaVh3tU2burq0OCm3SRYqugOqjCNE98RuKTBN4Oqrxeu99w4ev7NxY9YHXQYdJ1nwA3IXJqz4HcvcVsIrKoTAHdRSINm4MStUA9EEiEs6OnItBX6VAnvg9pIlXZUrwwAOOQTYf/9oFJm40Mk+9DbnfD3nfBnnfCLn/Iec8zfy0TiC8MJr4FpNKmfRICe5LbcU24MGAaeeKl4H9RdWTXYdHc6f13X1MU3goouy242NwF/+0vUzSRcq0sbUqbn7gtSuINKL1VIghbimJrEvaAVb0xT37iefiGvp8IxzdZIFP0lUsTZOY68cuxiLRoBvbY1PKXByH/KrFOjENVZUiDEnySiVAsbYs4yxP6se+WwkQajwGrjI9aK4MAzgssvE64ULgf32E6+DDsRyspP+wlJQD+ufXl+fO6FZfVhvuikdQkVaMM2s8GeHYjaKB2kpKCuLJjmFVCzkvWxV7mVO+qRjFbSt+J07DQO49FLxetGi7NhlmqJ/brklXJplJ6UgbIC4pLMzO8YHVQp0ArdTrRQAuBnALQAWAWgBcH/msR7AR/E3jSC8cRu4ysrI9aIYaW0Vqz5lZdl0omEGYsMQ/sKTJ2cF9bCBY16CaNInjrThZCWQ0MJB8SAFyPLyaLLvqCqeA2prYtKoqXHeP2aM/2M1NwuhWgrYURbtclIK5G+GxWopKCsT1g2/SoFOhqFUKwWc81c4568A2Itzfhrn/NnM4wwAiqy8BJFf3PzIVYGbRPfG6nsqn8MOxC0tXVPMSQuCDErz6+rjFQCf9IkjTUyZorYSALRwUExYLQVRpIl0UyDSohRYi5dZmTvX/7Gam7vOx1GmCrcqBaaZvW+HDQtfy8WqFDAWLNW0zkJRqpUCC/0ZY9vLDcbYUABUOIBIBFI4k+nJrDQ1UfGnYsQ6eUhLQZBMEhLOhVJgH/ANAzjiCBGg6MfVxzSBtQ5lHK1pEpM+caQF0wTuvVf9fm0tuWgVE9ZA4yjSRLopEGlRClTuN0Hc6uxKQZS1EFpbxSKMtD58843Yv2xZ+HneqhQA4nf8piSVsohcPHJaKOouSsGlAF5mjL3MGHsZwEsAXIyxBJFfDAM46qiuQpWEij8VH3LyAKJxH9q0KVvx0k5ZWVbQ0MWp9gHQVbFN+sSRFrwK2k2blr+2EIXHGmgshThZwbZfP/+B/U6KhZyH0qIURFlfwa4URFm0S1qA4yhUalUK5KLNnXf6j4EwDGD0aPFwWigqL0/+2O4WaLwNAHDO/wZgJwhFYCqA73DOn89P8wjCG9ME/vrXaFc8iPRitRRE4T7kVKFSUl7uXylQXY9WM34UwXOE+71forMkRnQr7ClJDQN49VXx+tZb/VuNpGIhj1dXB4wdK16nQSkwTed2BkmFbJrA008Dn32WFaajLNolx/UorQ8SqRREEQPR0tK1cJmVtFsKHmKMvcEYuwHAdwH8h3P+Puc8hCGeIKJF3sRu7iEUSFhcWGMKorAUSKXAKXYliKVAdT3KFUsg+RNHWnC79zs7i8+90DSFwFZSEi4TTFqJo3iZYQA77QScfLJYHd5/f7Hf77iQb6ZMEQXA7PTu7d9i4pSFadIk8TpM7JUVqRREaX2QSKUgCivExo3OC0hAypUCzvn3ABwK4GUAJwF4gzH2R8bYJMYYiVlEInC6ia1Q8afiI+qYgieeEM8XXpgrSAVRClSrZ5dfnt1O+sSRFtwSEQDF5V4ohcDGRuFSFSYTTFqxBhpLoqho3NGRdcORz0m2FMhYGyfXuh49/AvtbsK0YQBHHw2MGBE8zbJpAu++Czz/PLB+fXZcl4Sd56VSEIUVwp6UwkqqlQIA4Jxv5Jz/jXM+lXM+EsBPAJQBuIsx9lZeWkgQLrjdrFT8KR1EvXoZZUyBaQJXXZXdtgtSQZQCJ5eDGTOAk07KfibpE0dasPe1E8XgXqgSAotJKQK6piSVSAHOb2CplY6OrKKRBqXALdZm1Sr/x/MSpoNk85FIK4Q8d01Nou0yu6CsJB1mnpdKQRRWCC+loLMz2deGL69Kzvkizvk9nPPjQWlJiQSgullraqj4UxqQA35Uq5emCbz0EvD220LBeOUVsT+okF1fnyssWAWpIEoBIK7LbbYBJkzIXqfWSZpiCqLjtdfc+7MY3AvdhMBiUIoAMTacfrp4ffrp2TFGLiCEsRS0t6fLUtDYqH6vttb/8byE6SDZfCSqWhDSAnjHHcHnebkg9cwzwMcfi9oMYWMgNm50jykAkj2+uwUaD2KMPcEY+ydj7CrGWLnlvac557SWRRQclXvAPvvkvy2Ef6LMJGGPL2lszA7mxxwTzArhtQJWVhZ8gF+3rmvhoD/9Kfv6z38uLreOuPBKSVos7oVuQmAxKEVybFixQmx//XV28YExIcQVk/uQKuMQIGIj/OIVUBzGUqAag7/6SjwHPW/WBSlAjOMzZ4qFGim8B/E26GIpWGQCTw8BHi8Bnh6CPXqLQT3JlmDXQGOIeIJLAGwD4BXGmNQh62JuF0FoYRiifLqkrg7Yaitg660L1yZCH5Ww4ibEqHBSMOQqflArhNcKWFBLgWmKPNt33SWUlSlTuroptbQUn793HHilJC0W90I3IbAYlCKvxYcolII0uQ+5tW3fff0fT7rpyT6wC9M9egS3FKjG4O22E89Bz5vqmpg7V8Q/HHNMMG+DzUrBIhN4axLQ3AiAA82N+N4W43HnhCnYfffkju1uSkF/zvm9nPP3OOeXALgHwKuMsR0AuAyzBJFfjjpKPM+cKSa41atFcGgxZtdIGyphxU2IUaHjBuHXCtHQkA1allhXwIKkJJUrVJLGRrGa7eamRATD7Zro27c4FALAXQgshj5ws/iFyUsvSZv7UJ3Lsm5Qy6dhAEOHAuPG5QrTlZXBLQUqK8TVV4vXQZUCt2uitDTY+evsFFaAykoA79cDHV21jhLGMeWo6ThosJnYRR83paCcMbbZM4pzPguiTsH/QVgOCKLgmCZw0EHi9cUXA+eckx3UijG7RtpQDbxBBmRdNwg/PtSGIa4pQLgZ2FfAghYvs69QFbu/d1y4XRM/+EH+2lFoVEJgEP/xNKK6Dvr2DZ+XHujqPiRXy5OckrShITcYVm6H8Xe3KkdWwlgKDAOYPj27LcfgCRPEdlClwM0KXFqavSb8IP9jVRWAZufBu4QB9068ILGLPm5KwQMA9rPu4Jz/A8CpAD6Ks1EEoYNccV2+XGyvW5frq5fUG48QqISSIMKKV/pJiV8f6r33Fs+NjbkrYEGUAj+Cft++/o5NdMXpmmBMPA8blv/2FIqGBucMTOvWFceiiWq1GYgmpqm9PV3uQ4YB3H57druuTgTsAuGUAqsblZXKSvFe0D458UTxfMst2TG4vFz0dVClwC0OoqQkWFsfe0w8X3klsHqDevCuqdqAcaPNRC76uNUpuI1z/orD/n9zzo+Kt1kE4Y1XjQJJEm88Imu2dyKosGJ19enZM1cQChJYKq+xuIuXOVEsQltcGIZwC5HU1Qk3QyBcCsq0YRjifrCzaVPyF02iSFksfd5lGsvBg8X26tXOn/c7Z6Qt0BgAjj9ePE+fLgTtM84Q22EtBXalwDRFtWhAuBcFOX9OY7BpitX8664Ldl3Ia0LSo0fWChzEfcg0gUsvzW6XMfUAwxhw3dj6RAb5e6YkZYxtl4+GEIRfdAfuJN54hBBGVBOQX2FFWo2++Sa7j3PgzDOz20HrVsiVKKfc02Vl4nf8mJobGnJT1lVXA7165X42DUJb0jnuOPF8551C+Bk/Xpy3MIGlacM0gTVrnN9L8qJJlCmLDQM4/3xx7zU2iu2oquOmUSmQVnWZaUcK81EqBfL8yWtv6dJg58+uFMjjSrfLoNeFdS747nez20GUgvr67JgybrSJmqoNrp8f3G9JIoP8XZUCxtgeAJ7KU1sIwhc6A3expBxMI17CiB9hRZVJ4u9/F69vvz143Qo50Dvlng4ykRqGMC8DXeMUNijmkCQLbWnASamrqioepcAe2G4nyS5qUaYsBkQ1XGsaYK9UmrqkzX0IyFUKpFU1SqUgqvMnjyHv4aiOa1Ui/vWv7HYQpcA6Tl83tn6zm6KKZgxOZJC/W52CwwA8AWB8/ppDEPo4Dejl5VnhjSoaJxsvpc7Pap1KcP7iC/EcZqJrbhbXVInDaBk0qPCQQ8TzCy9klZWoVi2JrtgFCiBcMaW04eVmmWQXNa86IX4wTeDRR4GVK7PuJtKFJOyc0R0sBYyJtkcZaBzV+bNbCqI4rl1Zbm3NWhtKSvwHGlvH6cH9PBpSWo1eo5O5WulmKfgzgFM55//LV2MIwg9yQO/dW2wPHgw8/DBw0UVi8KCKxslmzBgoV1P8rtapBOdBg8Rz0IlOFr/auNHZb1WurvlVCqRAarU+RLVqSXRFWgSsrgerV4vzWgxpi70EpSS7qEWlKEsBUAqXVncTwxBprffaK9icId0H024pAMR4FqWlIIrzZ5rZ+IdzzhHbURzXzdoQxFJgTV/dtM7F/MZKgaETgKHJFE7clILHAfyKMeYZd+AEY+whxtgKxthHln3XMMa+YIy9l3mMCXJsgpAYhkhNtuWWWT9RuQroVrSIKCymKQI+nc5RkNU6lUD929+K10ELjE2aJFZSAWe/1aCWAielQCq5MraALF3RYHUfkudUTvjFkLZYR1BKoouaaQp3HztBFGUvd5OKiuBVZuW1lKaUpEA8SoE9+1DYhQ55v379tdhesUJsjxkTfgEl6joFhgFMnarxQd4BLJopipslELfsQxdApB6dFfDYjwA41mH/bZzzEZnH3IDHJggAYtB46CERyCRX/SorxcpN0gflYkbl0lBVFWy1TgrUffqI7YEDxfb48cIaEWSi0/FbDSoAyEI+9jgFwwAmThT/gyxd0WB1PYjaRz0N6KTqTZqLmhQGm5q67q+tDaYoe7mb9OgRnVJAloLsthyXBwwQ21tv7e/8uVUdDuv25VWnIMj5++53xXO/3k3uH+xoFsXNEoirFYBz/lsAfwtyYM75qwAUCb8IIjxy4pABmnLV7+OPxXax+AynEdUk3dISfNXWGsD73/9mJ4igE52O32qUlgJJeXlwAYXoimlmUy2OHSvGCCeSuFIeFfZ0nHaXvSS6qKkWDXr1CqYoe7mbVFQEr7grhce0uQ/J/xuVUsC5c50CwwBmzxavn3jC3/lzG4MNAzjyyOBuX25WjDDFy8aNNgF4RBkDyuJmhcbTNYhz/mjEv3kxY+yDjHtRn4iPTRQRqlWEv2XUWFIKkovbymSYVVt5PViF7SC1BAA9v9U4lIIwrgxEFrlosHKl2P7qK3UMS9JWyqNGWqBqaoALL8zuLy0V7pdJs0hFGWAMeLuxhLnn5L1f7JYCu8XEivS196t4eY3BVVV6tYqcMAzgnnuy22VlWWtD0OJlLS2ZzEPQ8F2uTuagoxUvwBgbzhg7njH2Q/kI+HvTAewAYASA5QBucfnNSYyx+Yyx+SvlqE4QFlQThMxVT0pBcnFbmQyzatvcLCYKa6agoBOdjj9sXEpBWxvFxITFadGA83SslMfB+vXivpDF2wAh+MycmbyYiqgzcUlribxf7e4mPXqEtxSkXSkwTWD5cuCRR4IF4MsxUFXRGPDfx15jcNjUwqeemn09cGC4OgWAaItn5iEAKK0G9kzmoKNTvOwhAA8BOBnAcZnHD4L8GOf8a855B+e8E8D9AEa5fHYG53wk53xk//79g/wc0c1RTRC1teKZlILkYhjZ82QnzKptc3PuJFJWFkwpkIKEXOWyCxKmCVx2mXh98MH+JlEvpQCgmJiwqJRLzoszbfH69cLVMg0xFao4iDDKm2EIYXfcuFx3kygCjdPmPmRVCqIIwLf3gxU5hvqdk1WVqOW5q64OpxRYz5H1dVClYONGYMkq1QSWWY2oHgyMmpHK7EOS/TPC+QTO+cTM45wgP8YY28ayeRJEIDNBBEK1ijBypHj9ne8UR8rBtDJtWq6puaws3MTvpBSUlwcXsA0D2Gcf4btqFSTkJLpqldj+8kv9SdQ0geuvF6932UWd5pRciMKhUi7r6oDvfQ8YNqy4grnXr1ffB0mLqZDCoNP+MLS1Ze8vK2EsBXb3ISkUJ1kpME1g8mTx+phjRNacsMpiHJYCQJzziy4Sx5UZBiVhLQXWc2S9N8JYCp779xhn56GSzGoPDxCskEd0lIJ/McZ283tgxthsAP8C8B3G2DLG2LkAbmSMfcgY+wDAYQAu9XtcgpDIiUO6A9TVCf/Yl18W25wXR8rBtGIYwKhRYrCXhXOspeb9YprAk08CS5d2VQbDZtRobe3qdwsEz2IjlYlvvxXbS5bkXp/yt0gpCIeb60EYITAfmKa4hktKolnYMM1sdW8nkhhTMW5c9MdUKQUVFUIo9BtcaprZRahf/EJsS+UgqZY+e2anL7/MzfIkUQXmO+GmFAS1FEicFnuA8EqB9RxZlYAgxcsA0Zbj9p7rHGbcmRlwWpYBb01KX0pSCzMhFIP/ZgKEpVDvCud8HOd8G855Oed8IOf8Qc75eM75Hpzz4Zzz4znny8P/BaKYOf10Ifxfc41Y9Zs7N3eyT6J5nBAMGADsvrsYgLffHthuu2DHcStOFNR9SLJpU3ZSkwQNhNRRJkgpiAY314MkB3PLa7mxMZqFDXk8lRKU1JgKJ6E6bJyNPWWmRN7ffq4J2a+yavrq1WJ7bibRelItBV4Vrq04BQ2rsFtMrISxFADuSkFHR/jgaCC8pcA0gTvuAAbVapjd0pqSNMNDAMZD1ByQ8QTHxdkogtBFFpaSVY2jzlpBxIt1sA+zeusmbIdxHwKEoGC3FAQNhNS5PuVvhVFkCIFhABdfLFb+rK4HSbYUqK5lrcJImseTDBqU3JgKp3s2rNXEyVJgmsCNN4rXO++sf3zVebrtNvE6qUqBn7nQz3/It6XANIFbMqlqdtgh2HWhshT4VQqshS7VMQU20pqSFMASzvmfOeeLOOeN8hF7ywjCA9MUfsGAqFwbVflzIn/YlYKgq7duwnYUlgK7UhC0UqfO9UmWgmhpbc219CRZKVBdy01NwQQfNyHwzTeTqRAAzvesk7ud32NalQIpzMmMdUuX6h9f1a/LM/4PSVUK/MyFdXX6n9UJNA5yz5km8Ic/AAsXZpVCed7WrBGf8XPenNpsf+1XKbAqiFfNacCGVo9qgUCqU5J+yhh7nDE2LoKUpAQRCSrT7ZgxuRldkmoeL3ZME3jrLeCFF4B+/YD33wf+8pdgq4FuwnYcMQXSNWWrrcT2VlvprbjqKBNvvimed9yRAuWjYOPGXKUgye5DUdfwcDvedtsl9xpTWffCuIPalYIwFa5V/brttuI5qUqB0xhUXp47xvmdN90sBU89JZ7r6/1db3Kel3ED0pUuisBowF0p8BNTYFcQW1rFgMM5gNKe2SDjzT+Q4pSkAKoAtAI4GiFTkhJEVLiVP7/uuuy+Yko5mCbkYC+F9aam7KQSxIfaTdiOwn3ILlQC4pqSk91jj+ldY1KZqKoS205pTh94QLymQPloaG3NXShIsqXATRDzE/hpPZ683uwk+Rpzu2eDuoPalYIw7qaqMeeII8Tr885LpsIlx6CaGrFdVwc8/DDw0EMixgsAtt7a/7ypUgrkWC/xc72p5nlVYLTf68J6jVlf+y1eJhXEcaNN3H/eJPTrLUxPIgkKB7Y/Fyjvm/1CqeKGTABKpSBjGai1pCGdGDYlKUFEhdtgPnaseD1jRnGlHEwTXsFufld95EQng9yswvY33wAvvhg8k4uT+5AkSPEywwAOOQTYd9/c67O+PncFmwLlw6FyHwqSbSYfyIqqTvgJ/LRi/f9Ox07iNeZ2TwVxB+3sFA+rUhDG3dQwRLY7SUmJyJ72+99n9yVV4TIM8ejfPzsGGQbw+OPi/Sef9D9vqgKNw1hj/Ar5fq+LqNyHpIJ43dh69Oxh+7MdzUDjHKDTElCxqSmxGYjcLAV1AH7PGPsnY+waxth+jKmKxBNEfnEbzOWqIBUvSy46g73fCcEwRMD5JZdkJzrTBBYtEtdC0FVRN6VAChh+LRFOq9cABcrHgZNSkPS4DZWyEiQjitX32u3YSbvGVPdUUHdQaZW0rmQHjQ0CRN9aq0N3dgpXSHuKzCQqXIBop/2/h7kvVJaCMGOaW4HSoOfNivUas94XfpUCuSilrGbc1iSUAysJzUCkVAo45zdwzg8HMAbA+wDOAfBuJr7gLMbYVvlqJEHYcRvMSSlIPjorOkFWA+1ZKqZOzRWC/E7SKvchIJilABDXppNSQIHy0aOyFMj3kogqwJMxfwqtn/STSbvGnOKABg4M7g4q71GrpUAKc/37i20/bjN++jZpChcg2m53KwujFKgCjcOMaap5fto0cZ6ku5NuTJeqzXb8xhQA4rdXbvB5EyUwA5FnTAHnfB3n/E+c8ws453sB+C2A/gAejb11BKFADub9+ontbbbJDgph058R8eM02FsJsurT2SmEPHlc0wzveypdTKJ0HwLUSoEsrGWFAuXDYQ80Nk2RrQwQ2cuS5toBqM835/4UWt3rPInXmNM99cYbwd1BpZJhT0kqLYqAcP3RPb4fQT9pChfgbikIkpjhuefE80kndXXTDGONkfO8dHmzuoUaRtZVyzSDXRdWpWDcaBN4egjweAku33kITh3lf2C4918N2Nhm+7Ol1UBFrfMXEpiByC2m4BjG2CkOb+0F4APO+THxNYsgvDEM4M47xesXXsgOCmVl4kFKQXIxDOCuu7LbtbVZITlocLg028sJyE140p2k5YpZvpQCwwCuukq8Zqw4A+WjruZrddWyp6Bctiy5Pt8qohBG+1piHpN6jTndU2ESBqiUAiBY8TLdMSSJChfgbCmQfePXUmCa2f9od9OUgr0cK/1eb4YB9OwJXHppbgxWWIufvJ7unDAFs6aMB5obAXD0qWjEjHMngfv0+Z/7sYHnP58AyJrGrBQYOgHYZ5pQDqwkNAORm6Xg1wBecdj/AoDfxNMcgvCHaqCvrCSlIOkclymBeOedwKpVwI9+JM5b0OBwacqXSoGb8KQ7SUth8YornAXUqJUCADj+ePH8xz8WX6C8UzXf8eOBKVOCH+/114F588T5iyqVYT5wEl4Bf6vOqlXaO+4QSld9fXKvMad7KkyaTzelIIjbjM4YklSFyzTFPfHaa13HtaDuQ/X1uYK59b4yDJFi+dRT/V9vnKsrGodVCjo6hIVgylH3ooR1LZfds0cz8OYFvo53+PYmjt5xJoDMsXgHsCgTeDJqBlCSGfSr68T20IRdGHBXCqo55yvtOznnXwHoGV+TCEIfUgrSi12Ir64W5yxoRhiZOeOii8REZ10NtdKzp96kZJpdq8g6BSnHoRQkPQg2Tpz8tDkH7r3X/2q+VDCkwNDYGF0qw3ygSiM6Zoz+MeQqbW3Ge2HbbbNCak1NtiJ8EsmnUhBEuDQM4IcOFZvkeHbttclUuOR9IccX67gWdOzRCSYOmhq6rU2cdyelQLY3aFG0Y48VGYPsCsFmOjcAb+mvSFx8UD0qyxQBxUMNYLsxwBbDgBMXJ1IhANyVgkrGWE4ZCsZYOUTtAoIoOKQUpBcnpQDIzd6hg2kCV16Z3W5sBNaudU7h2NamnyPbK5OIVAr8+uCSUuCMSrjw60sPpDvIFgA2bHDeP3euv+MYBnDTTeL1a69lhdQ0KgVh3If+8AfxfO65uVa/oPfcZZeJ5wEDsu5+990n9iW1eJmbtSxoTIFOMHHQyvL2ecJKELcvIKsYrV3rkjEIGSegz2doH3ebLRTHkgHFpdVAu+LGTghuSsEfAdzPGNtsFci8vjfzHkEUHFUaNFIKko8UeuxKga4gZ6W+Pvd8t7VlKkra2LQpXI5s++oX4E9YMU2RIvLOO51dkoL69XYHVNYdwP9qfpqDbAG1UBnEqmEfJ00T+OorUbQqiQW2AGcBMqigbZpd73m71S/oirNs4+zZwsK5eDFw5plCQQijwMSFV/KFoMpRQ4N3ReSysmB9oqMU+D1v1gWDJas8VgS4/kX35RrFsWRAcVl1bmrShOGmFPwCwNcAGhlj7zDG3gWwGMDKzHsEUXCcLAWmKQb9xx9P7oRH5A720l0iiFKgEpTC5GTXXf0C9Cc7uUIllRUnl6RithS44Xc13y3HedhUhnHjNmYFsWpY03HKazBMBfF8EKWlwGnRwGr1C7ri7JTmFPCf5z5feCVfCLogYRjAWWeJ16oECeXlwSwFTzwhni+4IHc+D6oUWMf/q+Y0oJO7lOBi+hUDn//PGMeFKGyb8fkrrQbaU6oUcM7bOedXAhgE4GwAEwAM5pxfyTkPcGoJInrsSoGc8OT+pE54xY5pAqdkcpudeabYfvddsT10qH9lTiUoqSrA6ubI9koP6lcp0KnuGSYtYNpZvVr9nh9fesA9x/lTT4ntWbOSpxBMmSKCq52IonBXmAqz+STKmAIvq19YS0FalAKv5AthFiSGDxfPK1Y4x1IEsRR4WXiCKgXW8X/26wb+/uHhjsI8B4D+h2of96jd5sKxxO+XGZ8/aSlw1BySgWedAgDfA7BT5vE9xtgPGWNHMMYGxNs0gvDGPiinZcIrZqTitmKF2P76a2DiROCee8S2zDozcaK+YqAS4CdNCpcje/Jk8Vq1+uVXKdBxSSpmS4GbshbEl16V4zypfWyawPTpzjJDaWk0hbvSUjU7SqXAy+oX1FKQNqXAK/lCmAUJe0poO0EsBboWHr9KgXXBYNxoEwd+51+OwjwDgPX/0z7udn1cYgoWmcCCe4Q70jNDxHYC0VEKzgXwAAAj87gfwGUAXmOMKdYzCCI/2AfltEx4xYyT4ubk/9/W1jX7jxuGAZxzjnhtFeDvuUc89+ol3vObInDvvcXzggXq1S9AXynQcUlKqsCaD9ysAUHuYcMAttxSZKSynr+kVjR2u97DCJnWmIK0VM12uqf8KoYSL6tfsVgKVMikB9JV51e/8m+tlWO6KoFCEEuB13wu+93vebNWsr5ubL1IP6rCR9XhpU2Km6i8L/DWJKBtTfaYb01KpGKgoxR0AtiVc34y5/xkALsBaAWwH4Ar4mwcQXjR1iaEQLkamJYJr5jxI9ypguKc2Gsv8bx0aVcB0DCACRPEKpnfFIEyO0tNjfP7fpWChobcVJN2y0WxBhqbJjBzpvr9oPdwW1uu0JZUpcDreg/qCmkVXsNUmM0nL76Yu++GG4L9f8MAfvIT8drJ6lcslgKVe97q1VkLrsSv621zs1AIShRSZZCUpF7z+ezZ4vk3v/GvxBiGWDRyyz4EAOu53sDDOfDzJxvQ3mmLuC6tFiYHe4CxTFWaMHSUgiGc868t2ysA7Mw5Xw2gCL1eiSTR3t51QE7LhFfMxKWguWWpCGK6tvqz7ruv84RTUiKEDN1jGwbwu99lt50sFyUlQqgoNqXALYVomHvYSSlIqzUmqCukFMZKS7OrpEGtZ/li1qzcfa2twV1BDz1UPL/6au7iQGmpuI+jshQEzbQTN25CdljXW1WBMUmQlKRuWY3CKjGAOEdu2Yc2tFbjkgcatI752GMiPmHOGycBADhn2SJlmxTamA8rRL7QUQr+yRh7jjE2gTE2AcAzAF7NpCddE2vrCMID+4QvJzw5OCV1witmnBQ3FapVJyeiVArkhLNmjdheulQ94fgVAH7wA/H8yCNqy0VFRfEFGrtZkMLcw2myFMgiY24EcaNqaxP3kryfDEPk699ii+QW2Fq1yvm9xsZgWeXcipc9/rhY6f3tb/0dO22WArdFs7Cuty0t7uN6EEuBYQAnCRk7x8ITRfxgR0cm+xDLLb21fmNPnP/ADDzysuF5TNMELrxQxCeMGfFXcA4sbRqEeRsaRJGyaoXiodpfQHSm3IsAPAxgBIC9ADwK4CLO+QbO+WExto0gPHGa8A0DOPlkMbgnccIrdqTi1qeP2B44UP1ZP9WNm5uF0GNfWQL8KwV+Jhy/k51cnXYSTiQVFelbxQ6LmwWpvj6Y2wjnYuJPi1IwbZr7dQEET0nqVOAxSKHAuLGvADsRZFVYJcCHWXG2ZnWyklSlQLrMSKxCdljX2zgsBaYJ/PWv4vWgQUJ5kfN5FPGD7e1idf+bgdcCEOPFklWDAAC/ffoXmP26oXXM+nrgxL1M3H/eJGxZvRaMCbekvdsycQN7Ngg3Iiul1WJ/wvBUCrjgD5zzSznnP+acP8V5gvMpEUWFk1IAiEm/2ISqNGEYwC9/KV5/9JGYnJxQ7bdjmkKg6uwUKU2dCoJ1dOhngvMz4fi1FLitWErKy4vv+nWzIAVNLawS2pLqPmQYwL33ZrftGVGCulG1tzsXeNy0yZ/inQ90K1H7XRVW3XdhVpzTVqcAAE44QTzfemvXRbOwrrfNzbnxUlb8Lp5Yqw4DYuy1jgFRxA/Kc7Sp39EAgItmz8GVT14PzoHrT/s5Ft0+BONGm57HXLLEOWC5uiITNzDUEG5E1XUALG5FQ5O3YqlUChhjz3l92e0zjLGHGGMrGGMfWfb1ZYz9nTH2Wea5j/8mE0QWlVJQUZG8VUCiK9aMKGEmJDl5yKBgJwFSXiO6K1V+Jpw4lIJitBRIC5JKsAjiT6/q66RaCgDguOPE8113CT9laUnr0ye4G5XTOCmzxCStD/ys9DY26n9WdS2EWXFOm/sQoHazlPefVB79uN6aJvD888D776vdr/xaClTK2ptzTODpIVjUUILGaUJol/hVmuU5KmXiJrh43Bu4/7xJYEwo5EP6N+L+8yZhVoP7asTgwS4ByzJuYKgBnLgYOKNTPCdQIQDcLQUHMsb+7PJ4FiITkYpHABxr23clgBc45zsBeCGzTRCBaWvLXQEDxKSftMmO6Ip1FVdOSFtvLfYNGKA/Iems9PlVCvwoKX6VAinsO7k5SYpRKQDE+T74YPX7fv3pVSu5SbUUAFmXnqoq0R8LFojtn/0suCukk6VAKl9JcyHys9KrKk7ohEqAD7PinEal4MknxfOFF+YK8IYB7LyzKCyp63orF2VkPQGVVc+vpcDpXr9zwhTcPnY80NwIxjgG92vErCnjceeEKYHiBzcH4EMIC7tVzspZ7e/ZoxkH9nRfjWhocElHmsC4ATfclIITANzi8rgZom6BI5zzVwHYQ65PACCTzs0EcGKQRhOExMlXFiheoSpN2AU2w8jmIb/vPv3BXWelz69SYF+1dptworYUmCawbJlYJQ4SUJl2mptzc8pL/PrTq/q6pESctyQuHNhXcmWax/Xrgx9T5T4E5BaHKjR+EhH4Ebytlkmv39NdcVZdXxs2AE8/Lc5bku5h0wSuuiq77STA+507dd2v/FoK7Pf6uNEmphx1L0pYVx/QEsZx0VH3YvFLpm+lWV4/ZRlLAVpXOn/QI0uQYQDvdDRgQ2s64gbcUCoFnPNXNB7/8vl7W3HOl2eOvxyAsioyY2wSY2w+Y2z+ypWKE0UUNaYpBt7PP88deGVMAUW/JJf29q41JoBgbh06K31+lQIgu2o9apT7qpnfyc5NKZCrblKACepLn2aam4Fdd80tghTEn94tuLSjA7j++mQJbUBWwJIKqcyM09AQvK1u7kNJUwpkXREddGOOAPW1EMZtxumYpimqtDc3Z6uzJ+Ue9qoQDPiPZ9J1v/K7eGJX1q4bW5+jEEgY44Fy/sv2lEiloHIr5w9qrPb328fA+Q/MQCcyF1KC4wbc8JHwL79wzmdwzkdyzkf279+/0M0hEoYUnuQEah94w5RrJ/KDk+tXEKVAZ6UviFIg26Gq0CkJailwch+KIs1e2mluBnbaCbjlluy+oKmFVULbpEnZBYMkCW1A1p2nujq6ttotBaYJ/PSn4vWBBybnvwPeRewkfpVEN2XcMIQieuKJ/jLW2VO9AuJetS9GJeUe1hHg/VoKdN2v/GaAk8ra5sKkHkXG0OwjwCTDZktBxn0IO18cOEtQS4vIZNRWuhWw/TmJjhtwI99KwdeMsW0AIPO8Is+/T3QTvISnJAcSEgIn168g501npS+oUrBxo9qVxXpsXaXANIEzzxSvTz45VxiLIs1eWjFNsRL+ySfCjUyulN9wQ/DUwk6CYNIVL6v7UFRttVoKpKIh6wAsX54spUgn+9BWW/lXEr3c9oIUOHSywCT5HtYR4P3WSNF1vwpa0E1mx1rT3Nf9g0w/wESONVIxfvNfmQln0EnAqBnoRA9wDrSU6K/2S2W+jK8FyntrtyVpuCoFjLG9GGOnMMZ2jej3/gxAGgZlITSC8I3XwBu0bD2RP5z8nIMqc4YBbLMNMHFirgBpmiJIEwBGj/Yn/GzcGJ2lQApj0hvyq69yhbEo0uylEdk3MpvMhg2iuBYQzpfeSRBMstBmmtlr95RT1Nl1ggRcy3st6UqRzn+z9pMuUSsFpglMny7GKqtbV5LvYbcKwRK/lgK5KCPT56qserJ/dV16TRM4//zsdhnz8HPjegEmU6YA48dn761xo00Ma71IbLx4FABgQ9X+ePXTg/H36sXaq/0tLQBjnSjtXAeU12h9J4m4pST9FYAnAZwM4C+MsfNVn1V8fzaAfwH4DmNsGWPsXAA3ADiKMfYZgKMy2wThG6+BVw58ZClILlG5DwHZ4NyHH+46QUths6lJbH/5pb9V0SiVAh1hLGyu8LTi1DdSePBbZdaKkyCYVKHNSWm01ymQ+G2r1SqXZKUI0PtvQVaco1QK3NIgO92rSbmHDQMYO1a8tlcIlgSpkXLGGeJ+/dWv1FY9Odbr1sWor8+uvo8bbaKmaoP7F6q9A0xMU9QBkWPLuNGi4Fi/XpkJouVL4K1JKO9chfLSNl/90NICnH3IQ2Ljo2uBp4eIwmUpw81ScBqAEZzzcQD2BeBRY7ArnPNxnPNtOOflnPOBnPMHOedNnPMjOOc7ZZ7t2YkIQgs34ck0s4LWvvsmxyxOdMXJfShIqkg3v+uwq6JRxhToCGN+sh51J7wE0rDFy6zXWVIVL5ViFEUBM6sCnlSlSNLQoFaGJEFixVSF7CR+3FvcxhX7vZq0e3jnncVza6uzAB8kc58MXlbVGDFN4LbbxGun4pJOWMeE68bWu14Tm9rLtfz+7fEeTgXH0NGMik2f+1YKBrabuOusH2V3NDcCb01KnWLgphRs5Jw3AwDnvMnjswSRV6TwJPNUy4EXCLcyTOQPJ/ehIBYetwk67KqoTkyBrjChK4wZBnDSScAOOwT3pU8bOgJpmOJl1utMjh1SUUiK0Ka6JjnPKjFB2mqawAsvAO+8IywuY8YkUymSGIbIoe9GEKWgvT03KNiKH0tB0q0tKkwTuOkm8XqnnZznRb8xBQDw6KPi+ec/z7XqyUWbNWvE9tKlenOydUzwDDKGhxaZwX5+VMdlfCPKy9p8WY52aa1HdQ9b0Y+O5kBZkQqJm6C/g61QmXX7z/lqIEGoMAxgiy2Ej6AUnpLuL0tkcXIfKikRk7MfpcBtgg67KurlPmSawHvvAf/3f94uLn5WqCsrk5cqMk7GjNH7nF+hyy0N5fDh4nfDKF4yYDGKfPSqa7KuTrh8DBrkv61OhaVmzhQpP7fZRuzr1y8ZSpGVe+4RwmlNTdbNxXoOg7oPuVUR96MUuI0r9msgKdmtdCq/A/4tBaYJXHppdtt+3KBzckNDdkFmySr3AbuibJOW8G0/b6rj8pIqbUuB7NeBfT0qGqcE3eJlNyO3mBlBFJx168TEIUnrCk4xoio857catdsEHcZVxDTF6tZddzkLfHIykG31mvzlCnWfPmJ74EC1MFZVlbxKs3Eii9Z54cfFxTRFhidABBY6VVgNk7LYGhwdRT56t2u1V69gAdcqgWzuXGDePLF9883JUggknAMXXyx80Bcv7jpWBHUfikopcDtXTsJuEhamdIVzvzEFVt9/p+MGnZMNI5sdqH5OAza1u5SAB7SEb/t5u2qOc8Gx9l57aisFsl+Vikt3qWjMOX8FwFoA/QGssBcuy1sLCULBpk1iELcqBUn3lyWyOLkPmaYYYG+7TX/ltaFBXehKCuLbbiv219bqrYrq5IcPsgJmGMAvfylef/ihuh3FZinQUdr9uLjI87cik/R6xYrc8xdWKVCd/6lTgx1PXqs9e4ptq6tQUKXATSBLctrmzk5xbqQ7oWl2FTxffdX/Mb2UAj8xBfJcyfZZz1VSF6Z02+XXUuB13DBz8kEHieerrwYqyj3SFpV7pCxF9rzJ+WL26wamPDIDKMukEJUFx2p2QnmpnvuQ/J9XzWnAxjab4tKdKhqHzT5EEHHz8MPi+Re/yAqQSQ0iJHKxuw9JQU5mp9BdeTUM4LLLxGunjBqGIfypAeDaa/VWRXUE/qCTv1cWFCBrKSiWitwqAcErzaEKnfMXVilQneempuDWAsMADj8cGDGiq6tQz56irX4DQN0EsiQrBfK89OiRq6ADYuwPEnQelaUAEOdm8GBg3Liu5yqpC1O67fIbU+B13DBzsrw269bUA9yjUR3rtIJ6DSOrbADAnz8wgB3OAcpqNhccY6XlKC/TsxTI/zn7dQMzXxEZ9zs5w7Jvul9F41DZhwgiTlR+jIAQHrbKVCsfMCB5/rKEwO4+FCYeZNQo8Tx/vrPftbQmSX9aL3QE/qCTv45SUFkphKBiqcitEhwOPVTcw3596eOo3GrH7TwHdRUxTeD550WcilzoME3g1lvF+zvs4E8YdhPIpFKQRIuUFAYrKpzHhU2bggWdqzIPAcHqFCxcCMye3dWq6STslpcXfmFKVzj3e19YryXJ2YeaWNDQC3icwWAM62cwrH2wBuNGm9h2W/05WV6bFe0a1Yo79eIKgK4WobIyAB0bgdLsnygpK9e2FFj79eMvdwcA1F26Eq9suTh1CgFA2YeIlOLmx2gYwNNPi32PPEIKQVKxuw+FMbtLYb+3opDkn/4knq+4Qs8tSUfgD7oCJidcL0sBUDxxBYYB3HlndltaBvbeO5jbjM75C2spcDvPQVxFnGJUJk4EzjkH+OYbsW/ZMn9xC26FpaQLRRItBfIe6dFDXcBNtV+FKoZJEqROgZNV87XXcj/vlWI1H+hm3QpSvOyii8RrxoBLjjfx4HnjUcE2bP4MY0BN5Xo8Onk83v69qT0nt7aKWgK62YV0g3qt/6+0FEBnK1BiUQpKy7VjCmS/nn2oiWtOvhoA8Olte8MYnc6Uh5R9iEglXgKk9PUslpXWNGJfuQtjdl+7VjzXOBSSNE3ggguy2zpuSQ0NuTm37QK/nAz8pouU/9tNUJC/ncRV3Lg4/njxfMcdWctATY1Q9jv0ipVuRpXNyLo/rFJgGCJGxYkgriJOK+JOLkN+g1ZPO01YnX79664WF3kNJk0pME3hPgUIX3JVClGZjlr3mE89Je591aJAeXn4OgVTp4riWHaCWDbiwDDEOGV3eZKYpkis0NEhPqerfEpL7X/+A9wxfipKmLPfY1kJx4Al+kE3ra2ZGgXQ9KPUDOqV99S40Sbm/3IIsPBhYOPXm92PPl0glILf/EZvEckYbWL6xEno0/NbAEBPtiSVNQoAyj5EpBQvAVKuhoRxDyDixb5yF3Tl3atYXdCA4N/+NrutEvgNQwizO++s7+Li5dtsmtlg5H32KXwqw3xhL4BkmsDtt4vXugWPJKpsRtb9YZUCAJg2LXdfUFcRP9YFP599KFNk9eqruwo4jPnP9BU3cgX+iy/E9urV6gq4uoqiPKYcA1SLAmVl4esUNDWp44AKHWgsaWlxLjIm++lbIddiyRJ9q5Ts26oqAJuaXD9b2un+vpXWVp0aBZk2bKrGvA16N96mTUIheHjSORjYJ2Ny4u3Am+dgnmli7l/LUF4qLgZpsXPth/frUVmWWwQtbTUKAI/sQ26PfDaSIOw0NGStARKrACmFLrIUJBe7+5BbRg8VOoVxgrolHXOMeJ4zx13g9yNMAGJCsl+7Evl/ZPG9L75IRo7zfCBdpaqqghc8kuic8yiUAiDX4hPUVcSPdUH3s6YJ/PjH2W27QJw0pcBJgVdRVxf8mKpUnGHrFET9nThoacldfAHCxXTJ7/X5NtqBauNGdarPTbwnOjsZOAfaO0rx0MsTcMwkQ2uM2LQJmDZ+KnqU21YNOzdh1w1TsXGTCDSWtLV5ZBVTuS2lrEYB4J596ATG2EWW7TcZYwszj1Py0zyCcMYwgLPPFq+dMs6Q+1CyMU3grbeAF1/sunppGMB++4kAU52Vd52JLKhbkly5jqqiscTNUlDMxfesSkHYfshHTAEg2mNfGQ7qKuLkslZe7r74odM+txzySVMKdFfT/VhjdBcF/NYpcEqDrHInY6zwgcaS5mZnS0GYmC55r9Ys1Lnw9cNTW1uB5/49xtF5qKSzFSUlHIwBZaUdmHjITJwwwtS69zZtAvrVOFss+vZsQltHOUpLOsFY1kzV5GbgULktpaxGAeB+dn4GwBo70AMiC9GhACbH2CaC0GLYMPH89de5AiS5DyUXuQosz4199dJPSjydiSyoW5JUCtwqGgP+fJEBd6UgqTnO84EUXisrw/eDzjmPQimI+nxZFdDaWpF686GHgK23Fvv69/eXTc2rfUlTClTKXG1tV8tAW5tQbHRWhXUURNME7rtP3PNaPuQGcNVV4rV1UWraNOdV+AsvTEbCC87Ff3RSCsLEdMl7l7XoRH8r/MEcaG0FfrDXXMcw47LSroNuzx7NuG5svda9JwP3VbR1iAG6rERzYN+zAS1tuUXQ0lajAHBXCio450st2/M4502c8yUAesbcLoLwRGYlcQouJfeh5OK1CuynoqbORKabdcOOrlIQpaUgqTnO84HVUhC2HwwDOOus7HZpKTBhQu7CQdhFg76Kekl+z5fdXQrI9odhAC+9JF5Pm+ZPuPTqxx49khXMrnILnTYtV9HTrWPipSDKvpfJCnSPe/DB4vkf/8guSsmxpq4u60Z2yinAPfe4Hytf2ON2rISJ6bolE2Xa2aljBWDaAbitrUBdP/00U4Nrl3jee6YpYlVWrXM266zeULtZKbC6EKmsQACAoQZ+9dwMtGe+t7kIWjdLSdrHusE5v9iy2T+e5hCEHqYJ3HCDeP2d7+QO4OQ+lFx0Mkf5MePrTGSGITKaHHusfkCwXEGNWilwiyko1uJ7pimy5ADAGWeILEFh+sE0gZkzs9sdHWLbOk74LdLk9BtSkLRSUeH/fHkpykFT1DrlkJf9aJpCAH7iCf3q4XFjGMDo0dntkpKsMhfUpUwK6jJ+yb4oEPS48jv269QwxBjz2GNCMXjqqeT076OPiuef/zy3TbKfpPC73Xb6MV1r14rA3RLmbQVg4FoBuKYJrHjbBNdNRwpg2erBnveePK9TH5uGjW221RlWjk96TgNHRinIBBtXVDgnFbC29fanDSxpGog//vtMmBsWp1IhANyVgjedqhgzxi4A8FZ8TSIId+wrO05ZEshSkFx0MkfpruD6CU726y5SiJgC+X+22UZs9+vX/Yvvyft5xQqx/dVXQoCfMCHbD37dZrx86YHw7kP19c7fr6nxf768FGUpeOoG4UoMA5gyRby2urkAos9l+3VXx+PGNIHXX89ud3ZmlbkwrloyfeykSbmLAkGPq1IKgNwqzEnoX1XBT7tiIAvlvfKKv5iu68bW6wfZewTgyv771fH1yvSmKOm6stK8qRpLahs82yzP6+zXDZwz4+HN+zkHsP/DOHA08NvTrwUAfHjDHrjo+yYeekjdF7Kt7e1A76q1WL6qd8HPdRjclIJLAUxkjL3EGLsl83gZwNkAfpyHthGEIzorOxRTkFy8VsP9ruAaBrD99sJM75UlyI/wXgj3IUC0/5VMfrfbbuveCgGgvp/nzs26zdx+e/QVjcMqBarfWL3a/7G8FOWgSgEA7L+/eP7ww+z9kdSA9vp6dV2GsC5lGzYAvXrpf9/ruG5KQRL7V0dRBrJjk86Y1qXCu2bqUACeAbiy/1yPud9DQKVYNVixtj++2WkGDtQYJKzndfbrZ2x+vVmheWsSejARdDCodhnuOHOSayGy7Lnm6F21Fmtbehf8XIfBLSXpCs75aADXAlicefyGc/5dzvnX+WkeQeSiO+EDZClIInI1XBYlsq/u+/X1Nk3gs8+8zfRBLQU6SkFUKUkl8jeT5O8dF273c1Bh2EvQk0WaOjv9FWny8xt+8FKUw1S4drJ4JTWg3a1dYXze6+pEDNqDD+ae66DHdVMKkti/um2SblY6SoH1Wm9apwiwscEBYFtFdUFbm1TpSFFdJ9xzjvonAOCn5s1oqtFbNbCe1x7ltij7NyaI+gIWSjrd6w1sDtovb0VFWRvWtvTusj9teEaFcM5f5JzfmXm8mI9GEYQbOpNxaakQOkkpSCayWu3Uqbmr+34sBdJ0KwsZuZnp/WQJMk3g8svF6wMPdBcapaVAVbTIjpelAAgnBKYNt/s5qFLg5UsftEiT/TfsAZtVVcHiP6SivOWWYnvQoK6K8uzZ4vmaa/z7p8vYGGt/JDWgXRW43bdvto9kJWM/dUykgPbtt8D48VmXKiB7XPnbAwfqHffnPxev99sv93wksX912ySVAp0x2Emh8oIBwJeK6oK2Nl01pwGtbbYVlJKKbFafciGA11St015Iss4BZx7wWNc3uaIinou7k2zr2QcLV6Trxl6FRbcPwcXHpdN/SD9hLEEkBN2VnaiKExU7pikEkZKSaAPmWludV8z9WAr8mOl1V/SlICHdQJYtcxcapYCvqrxqh5SCrjhlnJHBumF86c89V7y21zGJyrXD6n8tCePuZRjARReJ+6yxMXsceT1K/PqnOykFaQ1oNwwxBslg3iB1TDgH7r0315f+d78Tr19/XU/RkGktncaHJPavm6JsxY/7kDWmq7bGh9+cR0yBtf+4vUqBdfUlo1zcNeFiDPt8iGdWI9MEJlsS6l/9w9/otdfF3amhATj7UBO3nnkZADHeDOnfiFvHTtLOspQkSCkg8k5YIVM3xWQUKQeLHTkBNjaKsTjKgDmVG03UdQokukqiX6HRj7kd0FMKisl9CMi1ssht2Q9BfOn33ls8NzZ2FSCjdO0Yk/GCGDVKPB93nP9jWFm/XljQrAGbYZUYpyxacgyVgpdumt64URWIssZp+HHXU51TztW+9F7H1jkfsn9lnyehfw0jq1w6FfyU+B3PDAPYdVdgXaue+xAAz5gC2X/Xn1aPynLbCeFtwp1nkQm8LUw+jAGVHY3AW+6CuD2uYru+X3g2tYO51xswDODOc+tRXdF1sC5j7m5HSYWUAiKvOAmZdnOuDoYBbLut+K5qxShsykEivoC5jg6xsu6U2SfqOgUS3YBgv0Kj30lUJ6agtFT0QzFYCpyy+MjiVI8/Lib8a6/1v4Agr1u7i0+Urh3y/Ei3nzCFwEwTeOAB4eJi/a9hlRgnSwEgxsyTTxbCoW6a3jgxTSiz19grUevea27nNKgvve75MAzgiCOEcpqE/gWAvfYSzwsXqtvkdzwDgMN3MFFd7pCfF6U5WYI4Z54xBYBo26BaRWc3LxECt83/Hx16/v+S5d9so/ikEI2/+nYAFvX3rjfQs8SlnSmjIEoBY2wxY+xDxth7jLH5hWgDURh0zblemCawdKnIBa0SFsh9KDxxBcxJQSWspcDJr1tlpte9HvwIjaYJXH+9eL3rrnrXsI6lABCrjMVgKVBdS9IqFTSto7UYmpUoXTvkb/TJVPUJqhTIxZING8S29b+GUWJME7jxRvF6551z+y5JFY3r653jchjrem78WArczqm9/3QtBX7OR48eybJWy4KfTlmYJEGSdFx2eP3mnP5dD7YlsP25gKXWAGMcWDRTy9VnaZMq0HiwWuDW8P+X3PX3i3I/VFoN7HENAOD8+x/A11Xe2lxHD5d2poxCWgoO45yP4JyPLGAbiDzjx5yrQk6g0ofbSVgwTWDVKlG6PimFY9JIXAFzcqJUWQp0A3cNQ6wiS9zM9LqWAl2h0e5bvHSpntCqoxSYphAQb7+9+1+/qmuptDSclUqlFNiLNG27bXDXDtm+sJYCN4tcmKw7XgHVlZXJUQrc5gZ7JWpdgdUwgFNPzd3v5kvvdWw/56OiIn1KQRBLwbZbKk5e2+qM379tMPdY0ZfX7s+fbMCGVltnl2bceVQCt4f/v3XOeenjwwEAX387AJ2dDMu+yVQiHnwyAKCqokXrWlu1nUNAdKm721FSIfchIq/4Meeq8HJp8ZORhnBnzJhck34UAXNelgJAf+I/8kjx/Ic/uJvpdYUJKTTKiVOlaARxrTJNYMEC0VaVsK+j9HYnVEJWhyIRiO440dwsBIASh1nOMLIVSl96KbhrR1TuQ24WOT8F+qzoXJ9JshSoMg9J5U3ix30IAH760+xrN196XaVAno+ePcW22/moqEhO/wJCKSgtdS/IGEQpiHpFX167s1838OirZwIQymF7RykwdIJw59mzQQjeVjwEccMALrkku92jTJyc0+96AqXjO3HdfxaLY5eKlYTqHs1a/bCqlwHzNVnzgImUqaO83Y6SSKGUAg7gecbYO4yxSZ6fJroNO+6ofk939dnLpSWJhWPSiGmKaqL2FXsnIcsvcvXMrhSYJnDTTeL1jjvqCcKqFWE7foQJwxDF0AYNUisafl2rrJUvAbWwX2zXrxSyJFLIqqtz/rzuONHS4p4uMYpaJvLaW7RIPO+/fzDLjpdFzjCAffYBjjpK3z9d5/pMklKgi9+6INa4nOXL1f3nN+vO8ceLMcrtfCTJUmCawJ13CmV76FDvbGq6ffzoo8Cz745xtuxuOybQir68RseNNjHhYJE2lDGgrLQj63o01ABGzUAn6wHOgQ3QE8T32Sf7urJc+Gdu3CQiwufKTKkZZaO6olmrHzZuBP67fBexMXY9cOLiVCoEQOGUggM453sD+B6AixhjB9s/wBibxBibzxibv3Llyvy3kIgc0wRedKl0obv67DWBJrFwTBpxEk4BsdoUduXayX1ICs1r1ohtXXccXaXArzCxcaN74TK/rlW6wn4xXr8nnSSeb7ghK2SF8f03TeChh4Rrl1vMERCNUvCHP4hnmTzhnHP83R86/9WvgKlzffboIYRElVUmn6gqQdv3+40V+8tfsq/33Vd9Xvzk5weEMuW24g4kRymQY+u6dWLbzfrox1JgmsCFFwI/2Guuc5D4l3MDrejLa/S6sfWo7mHLtmB1PRpqYGPvw/DW56PwVNtiT0HcNEXaX0AoHI9NGQ8A+MOPT8a40WZ2jC0Tk4mu+1CvVSZ+fsJ1wknquV1TmYpUUhClgHP+ZeZ5BYA/ARjl8JkZnPORnPOR/fv3z3cTiRhQBZL5paEhV1izTqBJLByTRtyE0OZmUXgsCKYJHHSQeH3ZZdmJKegKuR9LQZRKgV+hVVfYL8brVworNTXZfdKCIAUvXbcZt6BdK2GVAmvOc7tQvWmTv/tDZS2xF/XzI2DKdKlu+2XfJsFaoHvd+y1CeMcd2W23hQa/14OOUpCUQGO/9VwAvT6WKT4H93NxEcqs6POSSnAOrGnzXtGXc7zrcSXlPdGzxwbPfrbWnxk32sT9503CgN5iwXnbPstx/3mTsgXH3v0ZAODmM36KH6wvA95ySY+4yMROqydhy+q1Ipy6eYlnatQkk3elgDHWkzFWI18DOBrAR/luB5F/vFY6dd0jDAO44grx2slHNImFY9KIlxDa1OQ/lawcmJcvF9urVmUn6aAr5H4sBX78ZFtb3ZUCKcj16ye2t9nGXWjVFXqK8fqVSoE9ANIwgAMOEA9dtxldAUgKgUGENnkdq/LqA+7vOeGVftavUrDZFcJl/4cfiudevQof0K5bXMuPxa++PlfhUQnDfpUCndTCSbEUuI6ti0zg9/2AxxnwOMPOH/bDuNGmr/TNS1Z5uAgNNcC2PRYfLB2OmxYs9lzRNwxRUb5pnSLQxOJ6xMqqUd3D283HOi5cN7YePXt0HSR69mjGdWPrhQLw+X3i2AwoQQfwv+lqxeD9epTCX2rUJFMIS8FWAOYxxt4H8BaAv3DO/1aAdhB5xkvI9OMeMXq0eH711VxhIYmFY9KIaqXRyvTp/gQJN4Et6Ap5nJYCr5VAwwAefFC8fvZZ92tMV9gvtuvXNIGDMw6kP/1p+LSZusql34B2KyrXOju694ZpAj/6UXbbybrhV8D06ocpU7LHj7owYRAMQ7iiAN4BwbrKvd/ihkD07kOtrdFYyMOgGkMvPs4E3pgItGU12LKOJjw86RwMbPe+EORxr5rjkiVo84F7oaZyvfa5++HeJmqqHGoflFR0OW5JebWWpcB6zgf3a3T8TC+2BPh8huN7yv0BAqmTTN6VAs75Qs75npnH7pzzbrz+RVhpaHBfWVFln3BCplazuhtYMQwh1A4blpzCMWlDtdJox08AbKPzWIzGxmAr5KaZddM47DB3gaa8XLh56E7QXu5D1uMC+llLJG7Cvrx+d9+9e1+/dsvRypXOaTP91Gvw44YCBFMK/GRK0/2c3VJgX9GOMqbANEVtGDuFDmiXlaE//ti9uJbuOVPNKarihkC0RQjl+34slHGgGluvG1svKgTb6FG+CXuVeF8I0roz+3UD5z8wA5yL8XU9t7kILTLRtuhpDO2/EJO3HYIfnWB6Kp87NTtUMwaA0poux61Y/jgG9F6BiX2HuLrsyHM+brQJa92ELlQPBrgiwEa1P0AgdZKhlKREXnETyL79Vn+VSuVuYKVYij/FhUqAt+PHwlNaqt7v1x3H6iMKAF984b7S6XfS11UK/Kw4n5HJWverX3kL+8Vw/caRNlNXuQyjFITNlKb7Oet+v0qB0yKM7Ae3+K5CBrSrqi9b0bX4mSaw1mGhuaJCXdwQiN5SABTehUiOrXIMlAsSvZj6ZPdi3hOAYYi4MAB4ev4PwRjwHq5HL2NxF8G9/fVJKGfrwRgwpH8jrj9xEv7xgFoxME2girvUPsgcF29NAmtfB8aALcoaXX355bgwbfxUlDCni58JCwRTTFKK/fM2NKDZy0qSIkgpIPJGfb37gNverhecZ5rApZeK14ceqhYCq6q8/XQJZ0wztz6BCj8BsKosJ3K/1R3nuefchWa/gclxTPrW4+pM/FIh0aloXAzXbxxpM1UCUNC89E44KR5O6Fo/dawbfpUCa+EuuzuOm+BfyIB2eZ7dlHFd9yHVfFNT456SNMqYAjl+FFopAMR/7t8fOO88y4JEhQ/zvIL99xfPn/zlYQDAXuznwNNDssL5+/UoY7n++1efWO84Vm+O11HFE8g2v18vfPetuPjyGwZw509M9KtRBftwocjsoMiS77DfNIFjJhk474EZaGsvA+fAklV1mNeezhoFACkFRB7RWXn2Cs6zV5Fdtky9OlxZ2f2FqrjwkylKJ/ZAoso9b92vu7rmx1/YNEW6SwDYZRdvi5ROkTF7e3WECfkZHaWgGCwFumkz/faDYYgaE2ee6Z2XPohSYC9gFZaGhtxrImxKUtPMpuMcNEgcS/aDqt8ZK2xAu46lQNd9SDU+qFKfRr1oYJrAb34jXg8fXvjig6YJfPUV8MADljEtgliH5mbhkjNo5U8sOy2r9grf+sG1SxzPkWe8jmyz4rh8g/N+0wSO7FevXuyqzkxCo+4BdpwMnhGPO3kpsONksd+lrYx1Zj4PzJnj0v6EQ0oBkRf8rDy74Wd1uKqq+wtVceHHhUA39gDQc+3QnZx1fcf91j/QLTJmb6+OwCY/Q5YCgc71UFkZLGVm3MXLDAM49lj3z6gEUCekZQMQVXzDpCS1X/NLlnS9hlWWjsMPL2z8SpTuQ34TFwSJKVC10+7a6LZ4pYtpCtdKxsSjXz9/geyTJmUXeuSYxjepV+F09YUNG0RsQgm3TbZy1V7hW7+kabDjuZBzT22N4uaR7kOK4y5tGuzYL/X1wMBal4nN6u4z6h607CMk+8dW/dtRIZBtlelNy0o7u7hHUUpSgnBBd+XZXtLejp/VYWkpKHTmBxVTpmQHeMZEpWC/KT7jwo8LgR8FQq6wSgVRlY8d8BaAdH3H/boZxemWJD/j5XZgmsD994vrt66u8KuMcWEYwIQJ2euhtFRsW6+HoFV3m5vdM1KFUQpMU6y2yqJlKnTuIymwWRVAJ2XQj1LgdQ3b+13yr38V9lrTVQp0BHe/iQuCWApU93HUVclNE5g4saslvalJv0ieU3tOGOH+RQZg5iVTXI9vmiJ1qGs9gT0b0M67nogNrdX49dMNjudicxFSrzSnezageVPuca98ssGxn5cscTmmA6+9IS7Cu+7YqLQWDx6sTm9KKUmJbomc/EpKwuWx1hEcS0qAadPcP+Nndfiuu4RCUOj8204ceaRI52mFc7EvCYqBrs804N8H2TDEqtyVVzq7dvjN5uOVutNv/QO/+/0EE+q4D0kh8dtvs79byFSRcWKawMyZWcW9o0NsW/+rVAr8KvdeloKgQaCmKYQxHXdIHdc6XQHSTyEsnWt47tzcPi109qGNG8U8YLWa2NF1H5Ljg1QMvVL7+lEKTBNYsUIcz2l+iboq+YUXOrdr0ya98+X0u9PGT3W13jMGGKNmKMceqyuvaz2BoQbKRs/ARi4ySHzxzTb4+dMzcOR5huO5kAHyV81pQGubTeuyBvAONXD+/TOw4tv+AIAvv9ka5z8wA7NfNxz/7+DB4pidXPGnLUK8aQLT7hQTS2X5RqW1uKFBs8BaiiClgFAib/rGxvB5rHUER7eJQKKz+uNlOi80pgm88IL6fadUgflGTqheikGQolodHWKCU63i+hXWOjvd3/frRuB3fxBLgZtSEPUqY5Lx+q+mCdx5p3/lvr3d/RoDglsKpk7VvzbtCo4TOgKk7IeODj3Lkc41HLXgGhb5Hzs73c+1n3ojsvjdd7/rne1L93pQueJY2xtlVfIpU7IpuJ3QUU6dflcdcJultKRDOfbIe3fcaI16AkMNVB7yAADgtnefwx3POCsEEs5FmlPztXEAgE7OctOcAnjtCwNn3P04AOC0O+dg9uuG8v82NAB/eteA0jHKIsTX1wNr1gmloEeZMF859YNhAM2glKREkTB1anTCic6Kmc6qh87qcNKFKq8MS5wnQ4ExDJE6ExCBiowJ9y6pKAwcGKyollexMb+TsxTQ3FZz/LgRNDTkts3t836UGPkZN/ehpAlrceL2X8NYTGbOFM+/+pVawAyqFPipVKwz7ngJkEH6Qeeaj1JwDYv8jzLVtNsClN/K5OvWAb17e39ON6ZAZ36Jsir5ffd5f8bLutzQ0DWj050T9M3R40abjvep3HfdWI16AgBQKgbVUrgHSsmsUeNGmzhp32cAAEubBuKqOQ05GX0aGoAOJqL9qyvESfFyEVuySpHtwiLEL1kCtLYL96HKio1d9tvpNboBbZ22dFmUkpTobpimevILIpzoBqPqHNswgL33Bo44wnn1J+lClY5QkRQFZv16Yc5vbBQreKtWiYwugKgLUF/vX4HxUgp0hWxd5U8qkttsI7b79XNXZgwDuPnm7HaUbgc6loIkCWtx4/Zfgyr3pglcckl2WyVghg001sVr3PESIIP0g7zmpW++0zUcpeAa1s3Uz3/0W4Rw7Vp1kUsrTz0lnq+80v0/6Mwvsv+33VZsOwWO6zBlirclFPCuLG8YwM9+Jl4zBlx4xAytxB+MCaHf6T6V+5TuMzIgWCKVAu6uFFiDd/v0XAMAqOu31DF41zCAn14pLuLqHs3KsdqqdD737zEO1w4Dts2uXA4eDGxsy7oP2f9zF4YaeH7ZFdnjVOdaNNIEKQWEI24Tjp/KwxJdgVxX8Fm3Tl24LKxQFVUcRRiSosCsXy/6WU4g0gccCO5SNnu2eL7kEuf+1RXW/Ch/hgH84hfi9apV3srM0UeL50cf9XY7iDolaZTCWtJRWRDHjAmu3OtUBwaCKwVeyRDseI07MuhXYg+2DtoPhgHsuCPwwx86X8NScO0vXLI9iwWqkEGwVjfTM88UY0ZNjd7YoPsfTRO4/XbxWmdslqmFn3rK/fOmCUyenN12G9d05xfDAP79b/H6178O1q/2uDM3vCzQo0eL53nzgLJSRcEYBwb3W+I49sh717OegCSjFJR5WAr8Bu9+/3hhKTj0wA3Ksdrq6jTxkJkOChEHFj64WeloaABYaVf3Ibcx+PNvM8Uajn4dOHFxahUCgJQCQoHbhBMkzaeOQK4r+Jgm8PHHwDPPOA/0YYSqKOMoVMfXIYjiFTWmKXJar12b7ef6+txMMH5cs0wTuOKK7LZT/+paCvwofzJLhtvvWtmwQTy7VcyWRJ2SVAprAwaI7a23DiaspQGVBXHu3ODKvcrH2j6myZXhn//cn/LvlQzBSnm597hjVbSB3GDrMIsca9e6u84YBjBrlnj91FPBrrGpU9WK1fr1wNlnRxMD4deNSn5eFkZ0u+f9WCr8uBfK6/vii/0vMOkU8rTiZYGWcQkD2/1NZs0Y7HhdeFr/7avx0lLA3JUC38G7pWKyL4O6uIG896eNn5qjbGymcxPwjuh0wwB+/duM+1D5Rndr8SITE78zXrx+9YepTUUqIaWAcMRtwtmwwb+Q7DSQlpd3Fd7dggIlOgO9Xajaait9oSrueIS43IKitm7IfpYTiexnXYFLRX19rlJp718/KUl1J2e/51X+b53iVEEsBV4pSQ0jWwBn1qzuqRAA7ivEQZR7t3oodgHzwguz236Uf8PouqosKS/PTZag46LhdW0GXeQwTZEf/5FH3McEeQ+5FoxywUsYbW/3HvdkxhkrFRVd/2OcqYX9Wh1lUTJA7V5omsBFF2W3/S4w+Yldsf6mis1KwZcXqj8E2wXLytFrtPOFlq0noGio3X2oLGMp8FAKAGD1Bk3rAwB8+RcAwJSRF3StpGxh8GBhJfAMrrbUbThpX3HcB84/D4tvHwJjtEPnLjKBtyahpmKV2N64PFu0LaWQUkA40tDgPqEFEW6tg35trSi3bqWpyXvQ9ONH/vvfi9emqS9UqYRenQwPYY5vx0/RIyfz/cSJwh81qKKg6ufSUufP67pm6Uy+flKSyirFgLvvv18XDDmB6lgK5HV21VXe/eynorFUSIIKa2lAZREbPDiYcq+qh2Kv1BtW+b/1VvG85Zbi2HV1YkXeHqSqkzzB69qU/bD11mJ7wADvftDJkCORCoffQnmmqZcxDtBbNLCfN/t2nKmF/VpjDj9cPD/9tNq9MExMjFudBjdUxzZN4NJLRYAx63BJZVRiG5hchAApaOcoEhJ79p0v/woAuOqws5XC++brVhVHYb+3F5nAOz/ONtVaSdlCQwNww2ku1YztLDKBf//E87h4v14UabMii7alFFIKCEcMo+tKmh0/Pu8yr7c0+wIiJmDOHP+Dpt/iZYA/dyeV0BtFNWY/QrmfoFIn831bm/BHDeoGpernjo5w/u46k6+fbD5HHSWeZ8929/3362okg6lPOcW9z0wTuOCC7LZXPwdRCqQrU3fDNIV7ix3rCrFhCMELECveXsq96rrlvOt3wyQjME1gp53Ea8aAxx4T155Kkfc6pptiJDEM4KWXxOvbb/fuB7+V3+X7upgmMH581mLrhdd4phrDrO2NM7WwX2uMvCfdUjYHWWAyTeCss/zXzpA4XWvWegKTj7zPfS7r3JS7rRBwx4zJVDJmThHfrGv2nUUm8G/hv+kmZMvr1rOasURTKDcMYJBbNWNJea3luDYt2UnYV9UiSGmNAoCUAsKFAw5Qv+dXaLUPcps2Bctu5Gegl5OdnxUw1SQXRZpQP9YVnRSuEl0zs5+VUFU/19W5B0V64fS/7JOvnwBQ3RV93UlfTqCrMtbg5cvdhXy/q4E6KUmt7QO6r1IgUw/aqanpej356Qe361bnc17j2pFHCoVx2TKx/c03wipnmurvusUH6ShGkhdfFM9nnOFtkfKj9Mj+9aMUTJ3qr5icmxuebqa7IKmFKyv1Pi+tMdLy4ZVxTF6Lbv9LtcCk2g+Ie0In25AKp2vQOkaVsAAHdxBwZRyM0vcfvGuwraaQLc+3azE0j7ap9rMeHoF6rBwYOc3fcVW1CFJaowAgpYBQIIUjFXEIrRK3idnPQB9EKXDLLBI2HsCPdUU3hatfdN2XVBPwmDEi+FjS0SG2dRSmKVOcs2nYlQrpjlNf7y386EzOQHbSlwqHatL3K+T7XXEm96Esqj6yr7j76Qe3bEZWgvjpH3mkc9HBtjYhJDc0OJ/XdevclUodxchvoLwfpcevUuAmxKv4+GN1Ln23gFq7tcSaWrh/f+/UwvK+lS5eXp8fPhz4/vfdrY7SSgK4WxJVC0xu1hWd8VkquPYVf9X1K++zcU4+8VaYQltxEHDlOLlklUootmnhmkK2dEnyLIbm0jblfjcltroO2P/hrCKje9w9GzYHOm8mxTUKAFIKPElCesp8Y5pCUHObJHSFVq/+8jsxG4bIIw14D/RB3IfcCBtXoBO0GuS3dPzerehew9ZJp6REXBNz5jib+a0uNKrfVKXXkwG18nNWZdRN+DFNYOxY8fqMM7z/l6xvccwx6knfr5Dvd8XZj1Lw7LPi+Uc/8jf2TJkiVjwZE89ehY0KhY7bDODPUuCWzciK35Vh03SvQt7UJL7rlOnHLa5AVzHyq6zqWOQk8jrTyZBjFYj9MmOG8343BcPeXsPIZi9budI7tbC0dr/4ondqYUAsJLktIvmxJNqtU177ATHOusGY+B+cC7c1netX3k/XjfXwqd9hkraAK69b55z/6JLzH4C2kC19/7WKoQH+hPI21YXGctOI6h53qAGMmoHmTb1EP6S8RgEAgHOe+Mc+++zDC8GsWZxXV3MubkHxqK4W+7srkyd3/b+qB2N6x6urUx+jtrZrH9fVefftrFmcDxggPr/VVu6fX7VKfG7aNN1/L/5X2P+sardOv1p/S+c6mzWL89JSf8euq/M+Znl57vcqKtyP69Zet+sA8P6cvc1B781DDuH84IP9t1PVZ37aMWsW5/36ic9su617W4P+vyOOcG7/bru5fy/fzJrFeUmJc1snT+762fvv73oe3PrA7727zz6cjxnj3V6v61dew6rxQ/X7utebn+M6XTuM5far6rNu11llpb+xRnWfW/Hz+VmzOK+q0r/f+vcXn9l6a73x9KijON9/f/X7fsYHP+dBft6r/+zf3Wsvzn/wA/f/JOf0jlng3FQ83swceOEszp/cgnMTvHMWOP99rdin6IdFt9c5H+9Ptg5ZOIvzJ6q7fuaJasdjd7q104mFs3jbY+Xie3+qczwmXziLc5PptbXLcUvdj5vhr786i3957xDl+0kDwHzOneVtx51JexRKKfArIBSKyZNzBUMdAduOH8G1Z0+9Y7odQ7Zv0iQh4Ou0z88Etn69+MzvfqfXVs6FouLW5iOO0D+WFR2hIsh15tVeP5Nz2GO6tddN2bK2R1f4CXpvHnss56NGqd8PIoxblSjVfef3uEH+n9f9W1kZfkFj1qzctpWWqgUdFW73g/U/+lW6VNePqt8OOojzww7zbq/XtV9S4v6/VL+vWoSx96ef48b1WZ0FI7cFCpUCo/p8bW3w9gZVqo8/nvM991S/71fpmzw59zuqdnjNEU732IEHcn744e7/SY7n7Y+VKIRtS+MXzuL8iSpP4V32r1rRcOiQhbN422Nl3kK2qWjn46XK//jfu77LX/31UepO+FOdup0uwv6iu4fxF64+SX3cDC9ccwpffPeunp9LCqQUBMTvAJBvdAZpP5N1r17ex7M+dAQM1SQh+3DWLM579xb7Bg8OttqsmnAffbTrZ7zaq2slCaIY+OlXP9dZkOOWqsdW7T5QPVR4WYy8Phdm5dTKiSdyvsce7p+ZNSt7L+gq1wcdxPmhh6rf93vtBvl/Oqu4paXBFQMnQcv+6NVL7/i6FrkoBFw3q9vRR3O+337ubVVZX5yufb/CqEoBDyPk+rk//VxnOhbJWbPU/VVWlttet3HB6b/FvWhw2mmc77yz+n2/x/Xzebd7wu36dbNsSKVr3OhZeivwKuHZYTX9n7NcjqlYfV80fSR/4RfHqhu8cJa6jSpLAef8ozuO4G83jFYfV2UlcDkm55z/9879+Lxrj3b9DOecv3rt9/lnd+zt+bmk4KYUUEyBCyqf1yRUm919d70S6NOne/siT5kifBXXu6QvdkIn8NYrm8+kSdnsG14VKv34e/stTnTkkfol5V94wX9siZuvqCrWwOs6O/JIf22QqM6Jm9+/xC0QWx7DiR13dN7PWNcKsboBoEGzx1RVeceYGAZwwgnADjvo+SEDIq7D7f6JM1ZB5ovXiZ3p6BDZc0pK/McanHeedzDq+vXi+F7Hdru2rf/RT7+p4nA4V5/D6mr3/+QVSyCRfuIyVkFml3Lz9dbNumM9royTcjuuaqxxynrj5zrzSj96xBGiPf/4h3Ock1MRM7fkC27+8V77g6ScNU3gueeABQvUsRV+A9SjqJVQWxv8+pX9PW38VHU8gTUo2Ec2nwPLL1Qf08Gn3zSBxi9rUM7Wq2NX3PL724OXLcddvLQnStGsPq4ypsH5mJJNnT3Ro9Q7mKmipAWbOl3y06YIUgoCEDRw1TTFYMlY10dVlb6QKQWAjz/W/1237A66yoUTXsGwboJBba06eE7VXj8TmJ/AvClT9CZ+KzqCj8Q03dPM2bMpSdyus913999mCWPO15uOkmcV4J1wOoabYNW3b9cJT1f4CVrltbJSLxvV+vX+Arh79nQPhPWrxPhJoXrWWfr54iWci/u+vNw7sFSOWX7GvenT1ffHlClqYbisrOt/1O03t//glgLSK7BU556wnxfDAA45BNh/f3el0q0OjOp/ewWiuo01TteIn+vMjSOOEMqARHUv2IVh3RSykrgWDeQClWy3ahFJjk+yDV4B6n7aoVo4kckUnPBSCmR/u1bytQrwull3FplAu8sqiC3QVvbvmg01qKlap16kc8vvr1A0/vGAiUN3+QdG1L2Hly8dgn88YOYeN2CWoDbeE1Xl3kpBj7JmtPEqz8+lASYsCXn+UcaOBTANQCmABzjnN7h9fuTIkXz+/Pl5aZuVkhLgjrOmYMqR0yMpXkXEh/38FOCyLgoYi75vozx3bvdpEo/rduxivYat/RFVHzj1cZT9G3ebk94PcYy/cd0XcfRB3PdwFG1WjmFnZA80zzQxsn1il+w/qt9xlYnO6PqlIUOA0duZeOSCs1Be6l4rwfG45bXAqatydv/oBBPXnzgJPXtkNSOdfuGcYQG7ELsY9yg/M880MarDu71W1m/shfcr78WBuoV7CgRj7B3O+Uin9zSLlEfamFIAdwM4CsAyAG8zxv7MOfex9p0fHr5oCs7anxSCNELnLD7i7tu4jk/HTR9x9kGazlva+oHaG+9xIzt2eVd/0DlzgP1PaQ/3O+W5PqYHbGfi4QvOQkWZ/+JpnAP/3TQWuzi8d9nh9V0UAkCvvYxx7NBxP+aZBzgK8FIh8Nvemqr12K/jbMwzkXjFQEUh3IdGAfgf53wh53wTgCcAnFCAdngyfv8ZNDETBEEQBNH9kBV8M1x2eD3KSoObNzjPPSYA/O6M+kAKASCE/F5rnAuQqCsqe1Ne2o4ha5z9A4esCd5et+OmgUIoBdsBWGrZXpbZ1wXG2CTG2HzG2PyVK1fmrXFWSphPR12CIAiCIIg0YPP9DyNkAxmlwKFw13Zbhjvutls4f78ZHpklAh5325jamwYKoRQ4rb3nqKac8xmc85Gc85H9+/fPQ7McUJX9JgiCIAiCSCsObj5hhewvv3WOEGc9wx7X+fu9RjeEittQHffLNfG0Nw0UQilYBmCQZXsggC8L0A5vdphUtMF+BEEQBEF0Pzo5c3Tz6TW6Ae0dwcTCto4yLN5Skc1nzwa0d8Zw3KEGPvnmiEBymttxF2/ZgE3tMbQ3BeQ90BjA2wB2YowNBfAFgNMBnFGAdngz6h4wAJ2fTXc0bySFNGRvcTt+Etubz2wlURw/bVl36LjpPK7bsZPY3u503DDH7k79QP0b7rjt6IXy0fc6uvlgqIEyAC3/vACVGvn5JZ5Zd+I6LoDdLv4HXr5pCg7eRj8pjNdxDzQMzDOB4S0XoKYy2vYmnUKlJB0D4HaIlKQPcc5d1apCpSQlCIIgCIIgiO5ColKSAgDnfC4A53BygiAIgiAIgiDyClU0JgiCIAiCIIgih5QCgiAIgiAIgihySCkgCIIgCIIgiCKnIIHGfmGMrQTQWOBm9AOwqsBt6M5Q/8YL9W+8UP/GC/VvvFD/xgv1b7ykrX8L3d46zrljAbBUKAVJgDE2XxWtTYSH+jdeqH/jhfo3Xqh/44X6N16of+Mlbf2b5PaS+xBBEARBEARBFDmkFBAEQRAEQRBEkUNKgT4zCt2Abg71b7xQ/8YL9W+8UP/GC/VvvFD/xkva+jex7aWYAoIgCIIgCIIocshSQBAEQRAEQRBFDikFBEEQBEEQBFHkkFJA5BXGGCt0GwiCSB6MsfJCt4EgwkDzG5F2SCnIwBgrzTzTTR0xTHApY2wgpyCWWGCM7cQYqyx0O7orjLHhjLFehW5HdyQzPlwD4Mdyu6AN6obQ/BYfNL/FT9rmtzTfb0WvFDDGzmaM/RvA1EK3pTvCGDsLwEsA9gKwNo03SZJhjJ3AGPscwG8APMAY61voNnUnGGMGY+wDAL8G8CRjrKLQbepOMMbOhBgfzgJwJgCQYBUdNL/FC81v8ZK2+a073G9FrRQwxnYBMAXAcwAOZoxtzznnjLGi7peoYIwdAOARAD/lnJ/FOV8rJ3waPMOTGSDPA3AG53wcgBUA6hljOxe2Zd0Dxtj3AFwAYDLn/CQAOwA4LvMeXb8hYIyVMsbOBXA+gJ9xzrcH8AVjbPcCN63bQPNbvND8Fi9pm9+6y/2WqsZGAWOsRr7mnH8KsUJ1G4CPAVyc2d9ZmNalH1v/vgbgbQC7Zt67kjF2HGOsF60GBsPav3IXAHm9PgHgZABjaEU7GNLsm+FlzvnBnPPXGGNbAFiY+Qyj6zcYsn855x0AnuGcH8I5f4sxtiuAdRDXMxEQq4sbzW/RY+tfmt8ixsFFM9HzW3eUJ4tKKWCMXQng34yx3zHGzs7s/i/nfDWAPwHYgTF2cOazRdU3UWDr33Mzu6cAmMkYew/AlgAuAXBTRqsmfGDp3xsZY2dkrtsPAUxgjPUBMBLAfABbA9iugE1NJYyx3wD4FWOsf2ZXa2b/VgDmAlgDMSnR9RsAS/8OAADO+arMfsY5/wTAEAAjMvto/PUJY+xnAF7OjA9nAUJQofktGmz9OzGzm+a3iLD0702MsdMBfIMEz2/dVZ5MTUPDwhg7HMAYAEcB+BuA6xljwy0a/ScQvoEXAEK7s60aEi449O9vGWN7c87fgRgoz+OcXwnAALANgLqCNTaF2Pr3rwBuyZhR7wWwCcAsAAcAuBrA/gBopUoTxlgPxtjPAUwAMBzCP3jzCg/n/GsAYzjnZ0Cs/uwMYPsCNTd1OPTvCNtH5DxkAjgQSN/qWiFhjNUyxh6BEJrOA/AWgIsYY1bBiea3gCj690LGWJ1lfjuX5rdgKPr3UgB9AdyPBM5v3VmeLBqlAEA5gH9zzhdxzl8CMA3A9Zb3NwB4CsB6xti1jLEbIVauCD2c+vc6AOCc3805n595vRLAaogbntDH3r93AriFc97IOb8MYlIyOOf/AbAM1L9+aIPwA90NwBsADmOMDbV+gHP+beZ5NYRva598NzLFuPZvxpUIEJaZb5mgmOamsGwA8DznfCzn/D0AL0CssA60fYbmt2Co+nc7YPP89k7mNc1v/rH37z8AfARge875woTOb91WniymgbcaQC3LpLXinN8AYBvG2KmZbQ5gI4A9AEwGsJJz/nmhGptCnPp3gOxfQAQOMcZugVgtfLswzUwt9v69DsB2jLHTMttfMcYGMcbuhpis/lu4pqaLzKr0As75BgBPQghToxhjPYBs0GDm+r0ZYqWbrl9NNPpXrqB9CmAiF5ClQBPO+UYAz1p2tUNco8uBLjEwNL8FwKV/l1k/R/NbMBT9uyeAry2fSdr81m3lyW6nFGQC1nLgnP8JInvIDyy7bwRwmWX7egD/ATCYc35TbI1MMUH7N7MyOBtCwz6Ec/6/mJuaSgL0748t23cBKAXw/YwARthw6d/WzPNiAPMAHAJgF8v3hgOYg+z1uyD2xqaQIP1rsRS8DuA6xliZVMSIrrj07zrLZi2AFZzzJZn3pEvD70DzmytB+jfzve0hAmFpfnMhaP9muBt5nt8YY4daYsw2063lSc55t3lAuFQsAjDEsq8EQI/M69MBvCrfBzAY4kKryWxXFvo/JPkRon8rAFQC6Fvo/5DkR4j+7ZXZri70f0jyw6V/SzKvSzPPvTOfPQPAeAA/yOzvX+j/kORHwP49E8BJhW57Gh4++vdAAGbm9TEAjs68rir0f0jyI0T/Hpp5XVvo/5DkRwTXb888tvXYzFx7P4Delv1MYz5OtTyZakuBw2pSX4iI9SOlaZpz3sk5b81o8k8CeBnALxhjFwGYAXEhrst8dmPeGp8CIuzfTZzzjVz4YxMZIuzf9ZnPNuet8SnAR/92Zvq3JrNvLYDPIAb4q5FJiceFvzCRIaL+vQaZLE9EV4L2L4CDAVQwxqYD+CWEGwM45y35aXk6iKh/fwWgI/PZpvy0PB1E2L8tmc/Gah2QsUyMsXEQVuE7OefnZ8arzW543V2eTK1SYPGTtPqkvgFgOkQGgJ0sn70CIqL9QAC3AHgYwCgAL3DOL8xnu9MC9W+8UP/GS4D+fR3AdzMTwy4Qblk3cc535JzPzWvjUwD1b7wE7N8DMrt2B7AfgE855wdyzl/NW8NTQoT9ewDn/J95a3hKSFv/WgT+TgBfAngUwP8y753KGBsI4RoGxlg9gDfRTefjskI3IAiMsYsBHMEYewXAHM75l0wUszgWIu3dNgBOZ4y9BaARwFoAO3HOv8kc4jXG2Bs868tKWKD+jRfq33gJ2L+7yv5ljC0GsEfcK1Nphfo3XsL2L0Ts1kWc8zX5b33yof6Nl7T1r6W9/4RQBuYB+A6Ae5ioj/AhhKvj1wAuBPAtgJ277XxcCJ+lMA8AJ0FE9h8GoaHdBWDvzHu/zjyPg7jQPoHFzw8iSIUV+j8k+UH9S/2b5kfI/i0rdPuT/qD+TXT/lhe6/Ul/UP9S/3q0926IOjTbArgBwF6Zz9UCaAKwp+W73XI+Zpk/lxoYYzdApLd7iDFWB+AUCC3zPMbYfAj/sxoI7a4dwGTO+UarOYtQQ/0bL9S/8UL9Gy/Uv/FC/Rsv1L/xkrb+dWjvWADfybS3klviAhhj90MEQL/cna+H1MQUMLY5aGUhhCkHnPNGAH8BsCVj7LsQBSRe55yP4JyPhyiHvWvms93yBEYF9W+8UP/GC/VvvFD/xgv1b7xQ/8ZL2vrXpb3PAqhhjB1vUwh+CRHr8Ekh2ptPEmspYIxtwTNVRG1BK30BPALgQc75M4yxLSACV2ogAtc6LcfooukRWah/44X6N16of+OF+jdeqH/jhfo3XtLWvz7bewaALTjnNzDGDgLwG4hCdT/jnC/PR3sLSeIsBYyx/RhjzwC4nzF2DmOsB+ecs2wE+zcA/gRgcubkfgugJ0RO2E7GWCljrARIb0qoOKH+jRfq33ih/o0X6t94of6NF+rfeElb/wZsby8AVZn3F0MEPY8vBoUASJhSwETV0LsBPJV5HA5gR6BL1csqAP8HUcJ9BmNsWwB7QfingXPeYdVGiSzUv/FC/Rsv1L/xQv0bL9S/8UL9Gy9p69+Q7d2U+dxSzvnH+WhvUkiUUgBgHwD/45w/BuDvEFVwlzAm/L8YY9dCaHVbAfgJRIqoxwGsgYgUJ9yh/o0X6t94of6NF+rfeKH+jRfq33hJW/+mrb2JoKAxBYyxQwBs5Jy/mdkeAGApgJsh8tkuA/Bx5nEXgIcA/Ipz/j/LMao5VXJ1hPo3Xqh/44X6N16of+OF+jdeqH/jJW39m7b2JhZegDyoEEEnfwSwGuLE9LG8twuA3wE4K7N9CIC5yOS6zewrKUS70/Kg/qX+TfOD+pf6N80P6l/q3zQ/0ta/aWtv0h+Fch/aBOBFAGdClJQ+Vb7BOf8U4kQuy+x6J/MZafIp4eTz5wX1b7xQ/8YL9W+8UP/GC/VvvFD/xkva+jdt7U00eVMKGGNnMcYOYYxtyTlvBfAAgH8AWABgJGNsZ8vHnwdwdcb363QAwwCsAgA6gc5Q/8YL9W+8UP/GC/VvvFD/xgv1b7ykrX/T1t40EWtMQeYkbA0RvNEJ4HOI9FRTOeerMp/ZCcLfq5Vzfm1mXxWAGQAGQJSS/hEvsghwHah/44X6N16of+OF+jdeqH/jhfo3XtLWv2lrb2qJyy8JQGnmeWcAszKvywDcCeAPts+eBOAeADsBqLZ8dut8+lKl6UH9S/2b5gf1L/Vvmh/Uv9S/aX6krX/T1t40P8oQMYyxMogKcKWMsbkAegPoAADOeTtj7EcAvmSMHcI5fyWz/0+MsV0B/BVAL8bYYZzzTwB8FXX70g71b7xQ/8YL9W+8UP/GC/VvvFD/xkva+jdt7e0ORBpTwERKqHcA9AHwPwDXAmgDcBhjbBQAcKG2/QbANZbvnQqgHsBLAIZnTiBhg/o3Xqh/44X6N16of+OF+jdeqH/jJW39m7b2dhcijSlgjB0EYAgXxSLAGLsHwIcAWgBcwjnfh4kS1wMA3AHgCs75osz3wDn/Z2SN6YZQ/8YL9W+8UP/GC/VvvFD/xgv1b7ykrX/T1t7uQtTZh94BMIcxVprZfg3AYM75IxDmn0u4iPYeCKCDc74IECePTqAW1L/xQv0bL9S/8UL9Gy/Uv/FC/RsvaevftLW3WxCpUsA5b+act3LOOzK7jgKwMvN6IoBdGWPPAZgN4N0of7sYoP6NF+rfeKH+jRfq33ih/o0X6t94SVv/pq293YXIA40BIKPZcQBbAfhzZvc6AFdB5IhdxDn/Io7fLgaof+OF+jdeqH/jhfo3Xqh/44X6N17S1r9pa2/aiat4WSeAcogCEcMz2twvAXRyzufRCQwN9W+8UP/GC/VvvFD/xgv1b7xQ/8ZL2vo3be1NNbEVL2OM7Q/g9czjYc75g7H8UJFC/Rsv1L/xQv0bL9S/8UL9Gy/Uv/GStv5NW3vTTJxKwUAA4wHcykUZaiJCqH/jhfo3Xqh/44X6N16of+OF+jde0ta/aWtvmolNKSAIgiAIgiAIIh3EFVNAEARBEARBEERKIKWAIAiCIAiCIIocUgoIgiAIgiAIosghpYAgCIIgCIIgihxSCgiCIAgljLEtGWNTMq+3ZYw9Veg2EQRBENFD2YcIgiAIJYyxIQCe45wPK3RbCIIgiPgoK3QDCIIgiERzA4AdGGPvAfgMwK6c82GMsbMBnAigFMAwALcAqIDIJ94KYAznfDVjbAcAdwPoD6AZwPmc80/z/ScIgiAId8h9iCAIgnDjSgCfc85HALjc9t4wAGcAGAWgAUAz53wvAP8CcFbmMzMAXMI53wfATwHck49GEwRBEP4gSwFBEAQRlJc45+sArGOMfQvg2cz+DwEMZ4z1AjAawO8ZY/I7PfLfTIIgCMILUgoIgiCIoLRaXndatjsh5pcSAGsyVgaCIAgiwZD7EEEQBOHGOgA1Qb7IOV8LYBFj7FQAYII9o2wcQRAEEQ2kFBAEQRBKOOdNAF5jjH0E4KYAhzAAnMsYex/AfwCcEGX7CIIgiGiglKQEQRAEQRAEUeSQpYAgCIIgCIIgihxSCgiCIAiCIAiiyCGlgCAIgiAIgiCKHFIKCIIgCIIgCKLIIaWAIAiCIAiCIIocUgoIgiAIgiAIosghpYAgCIIgCIIgihxSCgiCIAiCIAiiyPl/XudNAXSGUmsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 936x252 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# You can plot the two simulations together\n",
-    "fig = plt.subplots(nrows=1, figsize=(13,3.5))\n",
-    "ds_ctsm_mod.GPP.sel(time='2018-04').plot(color='blue',marker=\"o\", label='Phenology Mods')\n",
-    "ds_ctsm_orig.GPP.sel(time='2018-04').plot(color='orange',marker=\"o\", label='Default') \n",
-    "# and add a legend (this is facilitated with the 'label' information) \n",
-    "plt.legend() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54105941-f427-4c6f-9b86-900c1f4f850c",
-   "metadata": {},
-   "source": [
-    "**Questions**: \n",
-    "1. How did our code modificatons change LAI and GPP relative to the default parameterizaiton for `rain_threshold`?\n",
-    "2. Can you explain why this occured in 2018?\n",
-    "3. Is this modification justified, based on observations from the site? Let's find out in the next section!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bcececfb-0def-46c9-8a82-3fa4cdeb20bd",
-   "metadata": {},
-   "source": [
-    "______________________________________________________________\n",
-    "\n",
-    "## 3. Explore NEON Tower Observation Data\n",
-    "\n",
-    "### 3.1 Download NEON data\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "\n",
-    "<b>💡 NOTE: </b>  NEON provides observational flux data at 30 minute time intervals that we can use for model evaluation. Here, NEON data with the least restrictive quality control flag are pulled from the API for model evaluation. The data have been preprocessed by NEON, including unit conversions and gap-filling using a redundant data stream regression and/or filled using a Marginal Distribution Sampling (MDS) gap-filling technique when redundant data streams are unavailable. The data are formatted and supplied as monthly netCDF files.\n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "The next step uses a pre-established function (`download_eval_files`) to download the NEON observational data files for the site and year specified above. The preprocessed NEON data are available for download from NEON’s GCS (Google Cloud Storage) bucket, with the full listing of available data [here](https://storage.googleapis.com/neon-ncar/listing.csv).\n",
-    "\n",
-    "If you would like to download all available NEON evaluation data from this site, change the word year to \"all\" (quotes included) below: `download_eval_files(neon_site, eval_dir, \"all\")`\n",
-    "\n",
-    "Run the cells below to download available NEON data from the site and year we selected above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "158a7173-ac6b-4c7b-8f1b-c2c249d8cdb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we need to create a directory to store the data \n",
-    "# This cell uses bash magic, sounds exciting doesn't it?  (Nothing to worry about, just run the cell!) \n",
-    "!mkdir ~/scratch/evaluation_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ef24dd53-b4b0-4a1b-b056-0dd92e22fd0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading evaluation files for KONZ for year 2018.\n",
-      "Download finished successfully for listing.csv .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-01.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-02.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-03.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-04.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-05.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-06.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-07.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-08.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-09.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-10.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-11.nc .\n",
-      "Download finished successfully for /scratch/neginsevaluation_files/KONZ/KONZ_eval_2018-12.nc .\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Then we'll dowload the data from NEON (this is in python)\n",
-    "eval_dir = \"/scratch/\"+os.environ['USER']+\"evaluation_files/\"\n",
-    "download_eval_files(neon_site, eval_dir, year)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "552454c2-1366-49bd-a125-102edf085377",
-   "metadata": {},
-   "source": [
-    "### 3.2 Load NEON data\n",
-    "\n",
-    "Now, let's read these downloaded evaluation files from NEON:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "77e04522-aed3-4625-8611-23f6d5ae81ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading all observation files took: 0.18925261497497559 s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "eval_path = os.path.join(eval_dir,neon_site)\n",
-    "eval_files = sorted(glob(join(eval_path,neon_site+\"_eval_\"+year+\"*.nc\")))\n",
-    "\n",
-    "start = time.time()\n",
-    "ds_eval = xr.open_mfdataset(eval_files, decode_times=True, combine='by_coords')\n",
-    "end = time.time()\n",
-    "print(\"Reading all observation files took:\", end-start, \"s.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb8a91ed-7ac7-45c5-81b8-afdc49705d80",
-   "metadata": {},
-   "source": [
-    "### 3.3 Inspect NEON data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d61fd5d-d9e7-43aa-9ec5-78b093df227b",
-   "metadata": {},
-   "source": [
-    "Let's inspect the evaluation files from NEON quickly: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "22d46289-e372-428b-8bbe-3f9101c03544",
-   "metadata": {},
-   "outputs": [
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-       "Coordinates:\n",
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-       "    FSH              (time, lat, lon) float64 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT      (time, lat, lon) float64 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    GPP              (time, lat, lon) float64 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    ...               ...\n",
-       "    NEE_fqc          (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    FSH_fqc          (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    EFLX_LH_TOT_fqc  (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    GPP_fqc          (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    Ustar_fqc        (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "    Rnet_fqc         (time, lat, lon) int32 dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    created_on:     Mon Nov  1 23:06:47 2021\n",
-       "    created_by:     David Durden\n",
-       "    created_from:   /home/ddurden/eddy/tmp/CLM/KONZ/KONZ_2018-01-01_2021-10-0...\n",
-       "    NEON site:      KONZ\n",
-       "    TimeDiffUtcLt:  -6\n",
-       "    created_with:   flow.api.clm.R\n",
-       "    supported_by:   This data development was funded by the National Science ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-662b00bb-3942-46e8-a81b-329270ad6293' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-662b00bb-3942-46e8-a81b-329270ad6293' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>time</span>: 17520</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d525606c-9c1c-4c31-89d7-b3a532df9888' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d525606c-9c1c-4c31-89d7-b3a532df9888' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>39.1</div><input id='attrs-70563bc7-3786-422a-b80d-79ef8473b745' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70563bc7-3786-422a-b80d-79ef8473b745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fac7b3c4-d9ec-468e-8e0c-8c1b11f5b257' class='xr-var-data-in' type='checkbox'><label for='data-fac7b3c4-d9ec-468e-8e0c-8c1b11f5b257' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>lat</dd></dl></div><div class='xr-var-data'><pre>array([39.100781])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>263.4</div><input id='attrs-11a82b8b-04d6-4c48-8c71-17fd546d2f8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11a82b8b-04d6-4c48-8c71-17fd546d2f8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8047eed7-c28a-4261-b2e7-7e1711e79bb8' class='xr-var-data-in' type='checkbox'><label for='data-8047eed7-c28a-4261-b2e7-7e1711e79bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>lon</dd></dl></div><div class='xr-var-data'><pre>array([263.436917])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-12-31T23:30:00</div><input id='attrs-4b5fa80e-5074-4165-99d7-8994cde53680' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b5fa80e-5074-4165-99d7-8994cde53680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a311bb8a-cf06-49d7-8563-8f281c506ad9' class='xr-var-data-in' type='checkbox'><label for='data-a311bb8a-cf06-49d7-8563-8f281c506ad9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:30:00.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T01:00:00.000000000&#x27;, ..., &#x27;2018-12-31T22:30:00.000000000&#x27;,\n",
-       "       &#x27;2018-12-31T23:00:00.000000000&#x27;, &#x27;2018-12-31T23:30:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e30411eb-aa50-4bd5-b186-4ecdf38ce605' class='xr-section-summary-in' type='checkbox'  ><label for='section-e30411eb-aa50-4bd5-b186-4ecdf38ce605' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>LATIXY</span></div><div class='xr-var-dims'>(time, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1488, 1), meta=np.ndarray&gt;</div><input id='attrs-5b3181ce-c0ff-46b6-b377-0dc967da29ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b3181ce-c0ff-46b6-b377-0dc967da29ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b622116-2e46-4e21-8eda-8fcaeaf8d7f1' class='xr-var-data-in' type='checkbox'><label for='data-8b622116-2e46-4e21-8eda-8fcaeaf8d7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees N</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 11.62 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (1488, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 48 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LONGXY</span></div><div class='xr-var-dims'>(time, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1488, 1), meta=np.ndarray&gt;</div><input id='attrs-ec66cbbe-8008-451c-a55d-7a53421aaaf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec66cbbe-8008-451c-a55d-7a53421aaaf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae71ecc4-5329-4e48-be6a-e8cdb7e732e8' class='xr-var-data-in' type='checkbox'><label for='data-ae71ecc4-5329-4e48-be6a-e8cdb7e732e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees E</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 11.62 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1) </td>\n",
-       "                        <td> (1488, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 48 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEE</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-36b7657e-1a38-46ca-b70d-f2d020d9be1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36b7657e-1a38-46ca-b70d-f2d020d9be1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a2eee7e-3f69-4979-b973-29dbea578221' class='xr-var-data-in' type='checkbox'><label for='data-2a2eee7e-3f69-4979-b973-29dbea578221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>umolm-2s-1</dd><dt><span>long_name :</span></dt><dd>net ecosystem exchange</dd><dt><span>mode :</span></dt><dd>time-dependent</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 11.62 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1, 1) </td>\n",
-       "                        <td> (1488, 1, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 36 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                    <tr>\n",
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-       "                    <th> Type </th>\n",
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-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:          (lat: 1, time: 17520, lon: 1)\n",
-       "Coordinates:\n",
-       "  * lat              (lat) float64 39.1\n",
-       "  * lon              (lon) float64 263.4\n",
-       "  * time             (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:30:00\n",
-       "Data variables: (12/15)\n",
-       "    LATIXY           (time, lat) float64 dask.array<chunksize=(1488, 1), meta=np.ndarray>\n",
-       "    LONGXY           (time, lon) float64 dask.array<chunksize=(1488, 1), meta=np.ndarray>\n",
-       "    NEE              (time, lat, lon) float64 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    FSH              (time, lat, lon) float64 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    EFLX_LH_TOT      (time, lat, lon) float64 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    GPP              (time, lat, lon) float64 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    ...               ...\n",
-       "    NEE_fqc          (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    FSH_fqc          (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    EFLX_LH_TOT_fqc  (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    GPP_fqc          (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    Ustar_fqc        (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "    Rnet_fqc         (time, lat, lon) int32 dask.array<chunksize=(1488, 1, 1), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    created_on:     Mon Nov  1 23:06:47 2021\n",
-       "    created_by:     David Durden\n",
-       "    created_from:   /home/ddurden/eddy/tmp/CLM/KONZ/KONZ_2018-01-01_2021-10-0...\n",
-       "    NEON site:      KONZ\n",
-       "    TimeDiffUtcLt:  -6\n",
-       "    created_with:   flow.api.clm.R\n",
-       "    supported_by:   This data development was funded by the National Science ..."
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "ds_eval"
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-  },
-  {
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-   "id": "b96a13ac-eea3-4adf-9849-9f770a006b6a",
-   "metadata": {},
-   "source": [
-    "Let's check GPP from NEON files. What are the units of GPP from NEON files?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "2019c082-8a86-48a1-9fb4-d161c2decd97",
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-   "outputs": [
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-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
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-       "\n",
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-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
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-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
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-       "\n",
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-       "  display: none;\n",
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-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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-       "\n",
-       ".xr-var-name span,\n",
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-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
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-       "\n",
-       ".xr-attrs dt,\n",
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-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
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-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;GPP&#x27; (time: 17520, lat: 1, lon: 1)&gt;\n",
-       "dask.array&lt;concatenate, shape=(17520, 1, 1), dtype=float64, chunksize=(1488, 1, 1), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 39.1\n",
-       "  * lon      (lon) float64 263.4\n",
-       "  * time     (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:30:00\n",
-       "Attributes:\n",
-       "    units:      umolm-2s-1\n",
-       "    long_name:  gross primary productivity\n",
-       "    mode:       time-dependent</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'GPP'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17520</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4e853a98-f66d-4baf-8f01-80467112bb51' class='xr-array-in' type='checkbox' checked><label for='section-4e853a98-f66d-4baf-8f01-80467112bb51' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1488, 1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 136.88 kiB </td>\n",
-       "                        <td> 11.62 kiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (17520, 1, 1) </td>\n",
-       "                        <td> (1488, 1, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 36 Tasks </td>\n",
-       "                        <td> 12 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
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-       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">17520</text>\n",
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-       "       &#x27;2018-01-01T01:00:00.000000000&#x27;, ..., &#x27;2018-12-31T22:30:00.000000000&#x27;,\n",
-       "       &#x27;2018-12-31T23:00:00.000000000&#x27;, &#x27;2018-12-31T23:30:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c2ad77a-734f-422d-8510-18d40752cbb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c2ad77a-734f-422d-8510-18d40752cbb0' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>umolm-2s-1</dd><dt><span>long_name :</span></dt><dd>gross primary productivity</dd><dt><span>mode :</span></dt><dd>time-dependent</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'GPP' (time: 17520, lat: 1, lon: 1)>\n",
-       "dask.array<concatenate, shape=(17520, 1, 1), dtype=float64, chunksize=(1488, 1, 1), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 39.1\n",
-       "  * lon      (lon) float64 263.4\n",
-       "  * time     (time) datetime64[ns] 2018-01-01 ... 2018-12-31T23:30:00\n",
-       "Attributes:\n",
-       "    units:      umolm-2s-1\n",
-       "    long_name:  gross primary productivity\n",
-       "    mode:       time-dependent"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_eval.GPP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2b0cbd5-3ff7-4e1c-b8ef-78ad2c091f00",
-   "metadata": {},
-   "source": [
-    "**Question:** Do you remember the units of GPP from CTSM simulations?\n",
-    "\n",
-    "We should convert the units from NEON data to match the CTSM units (keep in mind that we also converted the units). \n",
-    "\n",
-    "We can convert umol m<sup>-2</sup> s<sup>-1</sup> to g C m<sup>-2</sup> s<sup>-1</sup> by using molecular weight of carbon. We also want to convert time units from seconds to days. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "d4678f24-7c62-4167-b170-161c1aa17124",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-- convert GPP units from  umolm-2s-1 to gc/m2/s\n",
-    "ds_eval['GPP'] = ds_eval['GPP']*(12.01/1000000)\n",
-    "\n",
-    "#-- convert from gc/m2/s to gc/m2/day\n",
-    "ds_eval['GPP'] = ds_eval['GPP']*60*60*24\n",
-    "ds_eval['GPP'].attrs['units'] = 'gC/m^2/day'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "6c40a628-68b7-421a-977b-879a8ab89ebb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_eval.GPP.plot() ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fb19fbf-d79d-48c7-9ac1-88a7015a33b8",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Flux towers actually measure net ecosystem exchange, or NEE, and a statistical model is then used to estimate GPP. In evaluating CLM simulations of GPP against NEON observations, we are actually comparing a process model (CLM) against an observationally-constrained statistical model (NEON).\n",
-    "\n",
-    "___________________________"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02b1e064-335f-4c7e-9377-a776edb2a6fd",
-   "metadata": {},
-   "source": [
-    "## 4. Compare CLM and NEON data¶\n",
-    "### 4.1 Format all data\n",
-    "So far, we have loaded observational data from NEON and model data from CLM for two simulations (original and modified). In this section we will compare observed and simulated GPP fluxes. You can also explore other available variables using the below code.\n",
-    "\n",
-    "<div class=\"alert alert-danger\">\n",
-    "  <strong>A note about model timestamps:</strong> \n",
-    "    \n",
-    "The CTSM history includes an initial 0th timestep for each model simulation. This offset in the time dimension can cause challenges when analyzing and evaluating model data if not treated properly. You may notice in the last line of the below cell, we shift the value by -1 to address this issue. In tutorials from Day1b and Day2b, we also handled it using the fix_time function when loading the netCDF files. \n",
-    "</div>\n",
-    "\n",
-    "Run the following cells of code to extract the variables needed for this notebook and create a single dataframe that includes all the extracted variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "0142ad35-d3bb-4f49-ab99-2f8080b85cf9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Convert NEON data to a Pandas Dataframe for easier handling:\n",
-    "#-- fields to extract\n",
-    "eval_vars = ['GPP','NEE','EFLX_LH_TOT']\n",
-    "\n",
-    "df_all = pd.DataFrame({'time':ds_eval.time})\n",
-    "\n",
-    "for var in eval_vars:\n",
-    "    field = np.ravel ( ds_eval[var])     \n",
-    "    df_all[var]=field"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54b9e689-cc91-4b80-8dde-d2be2046897c",
-   "metadata": {},
-   "source": [
-    "We can inspect the dataframe created:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "8e82f339-5666-4bdb-ae88-e9d097088b36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>GPP</th>\n",
-       "      <th>NEE</th>\n",
-       "      <th>EFLX_LH_TOT</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>-0.293934</td>\n",
-       "      <td>0.532182</td>\n",
-       "      <td>-1.200724</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2018-01-01 00:30:00</td>\n",
-       "      <td>-0.229756</td>\n",
-       "      <td>0.459569</td>\n",
-       "      <td>-4.083161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>-0.367755</td>\n",
-       "      <td>0.583214</td>\n",
-       "      <td>-3.010144</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2018-01-01 01:30:00</td>\n",
-       "      <td>-0.340121</td>\n",
-       "      <td>0.548177</td>\n",
-       "      <td>-2.603810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>-0.394974</td>\n",
-       "      <td>0.593901</td>\n",
-       "      <td>-2.281186</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 time       GPP       NEE  EFLX_LH_TOT\n",
-       "0 2018-01-01 00:00:00 -0.293934  0.532182    -1.200724\n",
-       "1 2018-01-01 00:30:00 -0.229756  0.459569    -4.083161\n",
-       "2 2018-01-01 01:00:00 -0.367755  0.583214    -3.010144\n",
-       "3 2018-01-01 01:30:00 -0.340121  0.548177    -2.603810\n",
-       "4 2018-01-01 02:00:00 -0.394974  0.593901    -2.281186"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_all.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "21ba691c-4e47-4efb-aece-c52dd0201de1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Convert CTSM data to a Pandas Dataframe for easier handling:\n",
-    "ctsm_vars = ['GPP','AR','HR','ELAI','FCEV','FCTR','FGEV']\n",
-    "df_ctsm = pd.DataFrame({'time':ds_ctsm_orig.time})\n",
-    "\n",
-    "for var in ctsm_vars:\n",
-    "    sim_var_name = \"sim_\"+var+\"_orig\"\n",
-    "    df_all[sim_var_name]=np.ravel(ds_ctsm_orig[var]) \n",
-    "    df_all[sim_var_name]=df_all[sim_var_name].shift(-1).values\n",
-    "\n",
-    "    sim_var_name = \"sim_\"+var+\"_mod\"\n",
-    "    df_all[sim_var_name] = np.ravel(ds_ctsm_mod[var])\n",
-    "    df_all[sim_var_name]=df_all[sim_var_name].shift(-1).values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4b06080-6e02-445b-a9ed-ecb30d91e518",
-   "metadata": {},
-   "source": [
-    "### 4.2 Plotting GPP Time Series (Daily Average)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "930ea010-1b4a-46ee-86fd-811220e451d3",
-   "metadata": {},
-   "source": [
-    "This creates a time series plot comparing daily average latent heat flux from observations (NEON) and simulations (CLM). To start, we need to calculate the daily averages. Run the below cells of code to create the averages and plot. *Now our conversion of GPP to g C m<sup>-2</sup> day<sup>-1</sup> makes sense!*\n",
-    "\n",
-    "First, we need to extract year, month, day and hour from time column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "784bc3d1-0e13-4153-8ad4-19a87d461ae6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-- extract year, month, day, hour information from time\n",
-    "df_all['year'] = df_all['time'].dt.year\n",
-    "df_all['month'] = df_all['time'].dt.month\n",
-    "df_all['day'] = df_all['time'].dt.day\n",
-    "df_all['hour'] = df_all['time'].dt.hour"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "488fcc92-2dab-4be3-99f6-3decf008b3e5",
-   "metadata": {},
-   "source": [
-    "Next, calculate daily average:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "87a71b65-a93e-4a17-8dc0-2f01151383d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_daily = df_all.groupby(['year','month','day']).mean().reset_index()\n",
-    "df_daily['time']=pd.to_datetime(df_daily[[\"year\", \"month\", \"day\"]])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a35f7899-dd68-421e-ae6a-1de982d534b5",
-   "metadata": {},
-   "source": [
-    "Using the daily averages, we will create a plot using Python’s [matplotlib package](https://matplotlib.org/).\n",
-    "\n",
-    "Run the below cell to create the plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "7806686b-d793-40ba-a03c-26908c45f24d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(num=None, figsize=(13, 5),  facecolor='w', edgecolor='k')\n",
-    "        \n",
-    "ax = plt.gca()\n",
-    "df_daily.plot ( x= 'time', y = 'GPP' , marker = 'o' ,ax =ax , color = 'g',label=\"NEON\")\n",
-    "df_daily.plot ( x= 'time', y = \"sim_GPP_orig\" , marker = 'o' ,ax =ax , color = 'orange',label=\"CLM Original\")\n",
-    "df_daily.plot ( x= 'time', y = \"sim_GPP_mod\" , marker = 'o' ,ax =ax , color = 'b',label=\"CLM Modified\")\n",
-    "\n",
-    "plt.xlabel('Time')\n",
-    "# here keeping track of units is helpful agian!\n",
-    "plt.ylabel(\"Gross Primary Production (\"+ds_eval.GPP.attrs['units']+')')\n",
-    "plt.title(year+\" \"+neon_site)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "faf29b8c-1501-4296-9725-91c035c23429",
-   "metadata": {},
-   "source": [
-    "**Are the simulations and observations in the plot similar?**\n",
-    "\n",
-    "Remember that there are some problems with the precipitation data available for the model simulations that affect phenology during the summer and fall.\n",
-    "Also keep in mind that NEON GPP is calculated from observed NEE using a statistical model.\n",
-    "\n",
-    "**Questions:**\n",
-    "1. When is NEON GPP highest at this site? When is it lowest? <br>\n",
-    "1. Do patterns of CLM GPP match NEON data? Why or why not? \n",
-    "1. How do the CLM simulations compare to NEON data **in the spring** for the default and modified cases?\n",
-    "1. As we saw above, the modified code changed phenology so that LAI and GPP were higher earlier in the simulation. Does this change improve simulated GPP?\n",
-    "\n",
-    "---\n",
-    "\n",
-    "In addition to looking at means, it is important to also look at variability, as this gives us an indication of when and where simulations are outside the range of observed values.\n",
-    "\n",
-    "Let’s explore variability by adding the daily standard deviation as a shaded area to the plot:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "4f08bd6c-9db4-400a-a60e-e6542e5e961d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_daily_std = df_all.groupby(['year','month','day']).std().reset_index()\n",
-    "df_daily_std['time'] = pd.to_datetime(df_daily_std[[\"year\", \"month\", \"day\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "0c930188-6d01-48d5-b303-1f248c79974d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(num=None, figsize=(13, 7),  facecolor='w', edgecolor='k')\n",
-    "\n",
-    "plt.plot ( df_daily.time, df_daily['GPP'], marker = 'o' , color = 'g',label=\"NEON\")\n",
-    "plt.plot ( df_daily.time, df_daily['sim_GPP_orig'], marker = 'o' , color = 'orange',label=\"CLM Original\")\n",
-    "plt.plot ( df_daily.time, df_daily['sim_GPP_mod'], marker = 'o' , color = 'b',label=\"CLM Modified\")\n",
-    "\n",
-    "plt.fill_between(df_daily.time, df_daily['GPP']-df_daily_std['GPP'], df_daily['GPP']+df_daily_std['GPP'] ,alpha=0.1, color = 'g')\n",
-    "plt.fill_between(df_daily.time, df_daily['sim_GPP_orig']-df_daily_std['sim_GPP_orig'], df_daily['sim_GPP_orig']+df_daily_std['sim_GPP_orig'] ,alpha=0.1, color = 'orange')\n",
-    "plt.fill_between(df_daily.time, df_daily['sim_GPP_mod']-df_daily_std['sim_GPP_mod'], df_daily['sim_GPP_mod']+df_daily_std['sim_GPP_mod'] ,alpha=0.1, color = 'b')\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time', fontweight='bold',fontsize=17)\n",
-    "plt.ylabel(\"Gross Primary Production (\"+ds_eval.GPP.attrs['units']+')',fontweight='bold',fontsize=14)\n",
-    "plt.title(year+\" \"+neon_site, fontweight='bold',fontsize=17)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53037c81-5938-4c58-adda-3abbf89e78c5",
-   "metadata": {},
-   "source": [
-    "The standard deviation allows us to see when CLM underpredicts or overpredicts the NEON tower observations.\n",
-    "\n",
-    "#### **Questions to consider:**\n",
-    "\n",
-    "1. Do fluxes simulated by CLM with different `rain_threshold` fall within the range of NEON tower observation variability? <br>\n",
-    "1. What times of year does CLM shows the best and worst performance in predicting GPP? <br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bfbeaba-688c-475c-acc8-307369d00007",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### 4.3 [Optional] Extract and save your data in `.csv` format:\n",
-    "If you are unfamiliar with reading and using the netcdf file format that model and evaluation data are provided, you can save data different formats. The next cell of code will save the data we processed (e.g., loaded, averaged) in .csv, or comma-seperated file format.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "b802afb6-885e-46e2-a197-fd2207ee1315",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mkdir: cannot create directory ‘/home/negins/preprocessed_data/’: File exists\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create a directory to write out data\n",
-    "!mkdir ~/preprocessed_data/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "2976cc9b-a64c-4a6c-b261-00e9da0097fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "csv_dir = \"~/preprocessed_data/\"\n",
-    "\n",
-    "#create the directory if it does not exist:\n",
-    "if not os.path.isdir(csv_dir):\n",
-    "    os.makedirs(csv_dir)\n",
-    "    \n",
-    "csv_out = os.path.join(csv_dir, \"preprocessed_\"+neon_site+\"_\"+year+\".csv\")\n",
-    "df_all.to_csv(csv_out,index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "2e3c3238-7c12-4b44-9b60-b91c7b654fe5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "preprocessed_KONZ_2018.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check the .csv file was written out\n",
-    "!ls ~/preprocessed_data/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b639cc7-0a1c-4e22-94bd-5919521ff405",
-   "metadata": {},
-   "source": [
-    "## 5. Compare CLM and NEON latent heat flux\n",
-    "In this section we will compare observed and simulated **latent heat fluxes**. You can also explore other available variables with this code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28e25ed4-cc18-496e-94ad-1ce3a5b5bc99",
-   "metadata": {},
-   "source": [
-    "### 5.1 What is latent heat flux?\n",
-    "\n",
-    "Below we explore how well CLM simulates latent heat flux, which is directly observed at NEON towers. Latent heat flux is the energy for water evaporation from the ecosystem. Latent heat flux is a combination of plant transpiration, evaporation from leaf surfaces (e.g., from dew, after precipitation events, etc.), and evaporation from the soil:\n",
-    "\n",
-    "$$ Latent Heat Flux = Transpiration + Canopy Evaporation + Ground Evaporation $$\n",
-    "\n",
-    "Although NEON towers cannot distinguish how much each of these processes contributes to latent heat flux, CLM simulations can help us to disentangle the role of each. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a7a9973-294b-442b-8bbf-2ddb6b67f2e2",
-   "metadata": {},
-   "source": [
-    "First we will calculate latent heat flux simulated by CLM by summing the component fluxes in the above equation. The CLM variables are:\n",
-    "\n",
-    ">$FCEV$: Canopy evaporation (W m<sup>-2</sup>) <br>\n",
-    ">$FCTR$: Canopy transpiration (W m<sup>-2</sup>) <br>\n",
-    ">$FGEV$: Ground evaporation (W m<sup>-2</sup>) <br>\n",
-    "\n",
-    "*Run the below cell to calculate simulated latent heat flux*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "dec49b85-17ae-46c0-aab8-843ca574ef17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sims = ['orig','mod']\n",
-    "\n",
-    "for sim in sims:\n",
-    "    clm_var = 'sim_EFLX_LH_TOT_'+sim\n",
-    "\n",
-    "    #EFLX_LH_TOT = FCEV + FCTR +FGEV\n",
-    "    df_all [clm_var] = df_all['sim_FCEV_'+sim] \\\n",
-    "                     + df_all['sim_FCTR_'+sim]\\\n",
-    "                     + df_all['sim_FGEV_'+sim]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b01382af-25cc-4981-8a92-77555b9097ba",
-   "metadata": {},
-   "source": [
-    "Let's calculate daily averages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "c96b94da-612c-4991-90d0-1677a786e2cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_daily = df_all.groupby(['year','month','day']).mean().reset_index()\n",
-    "df_daily['time']=pd.to_datetime(df_daily[[\"year\", \"month\", \"day\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "3144ad7d-d678-4492-ad2b-1e1f083fb11d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(num=None, figsize=(13, 5),  facecolor='w', edgecolor='k')\n",
-    "        \n",
-    "ax = plt.gca()\n",
-    "df_daily.plot ( x= 'time', y = 'EFLX_LH_TOT' , marker = 'o' ,ax =ax , color = 'g',label=\"NEON\")\n",
-    "df_daily.plot ( x= 'time', y = \"sim_EFLX_LH_TOT_orig\" , marker = 'o' ,ax =ax , color = 'orange',label=\"CLM Original\")\n",
-    "df_daily.plot ( x= 'time', y = \"sim_EFLX_LH_TOT_mod\" , marker = 'o' ,ax =ax , color = 'b',label=\"CLM Modified\")\n",
-    "\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel(\"Latent Heat Flux [W m$^{-2}$]\")\n",
-    "plt.title(year+\" \"+neon_site)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9c43f669-8d0f-4270-8af4-33c8c59de51b",
-   "metadata": {},
-   "source": [
-    "#### **Questions to consider:**\n",
-    "1. How well does CLM simulate observed latent heat flux? What times of year does CLM overestimate or underestimate observed latent heat flux? \n",
-    "1. How different are the original and modified simulations? Is one noticably more accurate than the other? \n",
-    "1. Which component flux (transpiration, canopy evaporation, ground evaporation) seems most likely to contribute to the mismatch between simulations and observations? "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d33499b6-dfe5-47e3-ac8d-9b12c15a35b2",
-   "metadata": {},
-   "source": [
-    "### 5.2: Monthly Averages & Component Fluxes of Latent Heat Flux\n",
-    "\n",
-    "Next we will disentangle whether transpiration, canopy evaporation, or ground evaporation is the dominant contribution to latent heat flux during each month using CLM data. As mentioned in section 5.1, NEON observations cannot distinguish how much each of these processes contributes to latent heat fluxes. \n",
-    "\n",
-    "*Run the cell below to calculate monthly averages:*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "923a8874-bc7e-4fee-95dc-7b51b5aff722",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_monthly = df_all.groupby(['year','month']).mean().reset_index()\n",
-    "df_monthly[\"day\"]=15\n",
-    "df_monthly['time']=pd.to_datetime(df_monthly[[\"year\", \"month\",\"day\"]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02ec804a-3929-4dbb-8957-c6ecaec56050",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**Next, create a stacked bar chart showing components of simulated latent heat flux over different months.**\n",
-    "\n",
-    "*Run the cell below to create the plot*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "43d4308e-cc6f-4f88-9e11-2147b2d837f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def line_format(label):\n",
-    "    \"\"\"\n",
-    "    Helper function to convert time label to the format of pandas line plot\n",
-    "    \"\"\"\n",
-    "    month = label.month_name()[:3]\n",
-    "    if month == 'Jan':\n",
-    "        month += f'\\n{label.year}'\n",
-    "    return month\n",
-    "\n",
-    "\n",
-    "plt.figure(num=None, figsize=(13, 5),  facecolor='w', edgecolor='k')\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "df_monthly.plot ( x= 'time', y = 'EFLX_LH_TOT' , marker = 'o' ,ax =ax , color = 'black',label=\"NEON Latent Heat Flux\",use_index=False)\n",
-    "df_monthly[['time','sim_FCEV_orig','sim_FCTR_orig','sim_FGEV_orig']].plot.bar ( x= 'time',stacked='True',ax=ax,rot=0,align='edge',width= -0.25,edgecolor = \"black\")\n",
-    "df_monthly[['time','sim_FCEV_mod','sim_FCTR_mod','sim_FGEV_mod']].plot.bar ( x= 'time',stacked='True',ax=ax,rot=0,align='edge',width= 0.25,edgecolor = \"black\", alpha =0.5)\n",
-    "# Modified case has lighter shading.\n",
-    "ax.set_xticklabels(map(line_format, df_monthly.time))\n",
-    "\n",
-    "#set labels for the legend\n",
-    "ax.legend([\"NEON Latent Heat Flux\", \"Canopy Evaporation\", \"Canopy Transpiration\", \"Ground Evaporation\"]);\n",
-    "\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel(\"Latent Heat Flux [W m$^{-2}$]\")\n",
-    "plt.suptitle(year+\" \"+neon_site)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3089573-6ac7-4e9d-932a-0b6f9260138e",
-   "metadata": {},
-   "source": [
-    "The monthly averages of NEON latent heat flux observation are plotted as a line on top of the barplot of CLM data for reference.  The modified case has lighter shading.\n",
-    "\n",
-    "Both simulations have high biases in latent heat flux in the summer.  This pattern is similar to the high GPP that we saw in earlier plots.\n",
-    "\n",
-    "#### **Questions to consider:**\n",
-    "1. Which components of latent heat flux changed by changing the rain threshhold for leaf onset? When are these changes most noticable?\n",
-    "1. Which months does original CLM simulation overestimate and underestimate observed latent heat fluxes for this site? \n",
-    "1. What times of year is *canopy transpiration* the largest contributor to the total CLM latent heat flux? How does it change between the two simulations?\n",
-    "1. What times of year are *canopy evaporation* and *ground evaporation* important contributors to the total CLM latent heat flux?  How do these change between the two simulations?\n",
-    "1. What is the dominant component flux when CLM overestimates observed latent heat fluxes? When CLM underestimates latent heat fluxes?\n",
-    "****"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de264565-5e93-4087-8480-a42c5d85f4bf",
-   "metadata": {},
-   "source": [
-    "### 5.3 Annual Correlations\n",
-    "Scatter plots can help to describe the relationship between latent heat flux and the component transpiration and evaporation fluxes. We can look at these relationships using data from CLM simulations.\n",
-    "\n",
-    "First, plot annual average relationships for the original simulation.\n",
-    "\n",
-    "*Run the cells below to first define a generic function that plot scatter diagrams and add a regression line, and then to generate the plots.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "ad747f2e-884a-425d-8208-fafceaa31d27",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Defining generic function for scatter plots\n",
-    "def detailed_scatter (x, y, color):\n",
-    "    plt.scatter (x,y, marker=\"o\",color = color)\n",
-    "    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n",
-    "    line = slope*x+intercept\n",
-    "    plt.plot(x,line,'black', label='y={:.2f}x+{:.2f}'.format(slope,intercept)+\" (R2=\"+\"{:.2f}\".format(r_value)+\")\")\n",
-    "    plt.legend(fontsize=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "165df377-846a-46be-b47c-09d3117f3252",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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i9SiAQQghhBBCCCGEEK9HAQxCCCGEEEIIIYR4PQpgEEIIIYQQQgghxOtRAIMQQgLAuHHjUL16dTRr1kzY9swzz6BVq1Zo1aoV1Go1WrVqBYDP+hweHi7smzBhgodaTQghhBBCSBmfrkJCHFNSUoLz58/j7t27nm4KIX6vQoUKiIuLQ0hIiEfbMWbMGLz00ksYNWqUsO3zzz8X/j5lyhRERkYKr+vXr4+MjAxZrq3X63Ht2jXk5+dDp9PJck5CiLigoCBUqVIFMTExUCjocypCCCH+hQIYAej8+fOoVKkS1Go1OI7zdHMI8VuMMeTl5eH8+fOoW7euR9vSsWNHZGdni+5jjOGLL77Anj17XHLt8+fPg+M4qNVqhISEUL9DiIswxlBSUoLLly/j/PnziI+P93STCCGEEFlRaD4A3b17F9HR0TSIIMTFOI5DdHS018922r9/P2rUqIGEhARhW1ZWFlq3bo1OnTph//79Tp3/zp07qF27NkJDQ6nfIcSFOI5DaGgoateujTt37ni6OYQQQojsKIDhZbRaQK0GFAr+q1brmuvQIIIQ9/CFf2vr16/HsGHDhNexsbHIzc3FX3/9hXfffRfDhw9HQUGB6HtTU1ORmJiIxMREXL16VfIaNJWdEPehf2+EEHfRHtVCvUQNxTwF1EvU0B510eCFkPvofzgvotUCSUlATg7AGP81Kcl1QQxCCCktLcVXX32FZ555RtgWFhaG6OhoAECbNm1Qv359nDp1SvT9SUlJSE9PR3p6OqpVq+aWNhNCCCHE87RHtUjaloScmzlgYMi5mYOkbUkUxCAuRQEML5KcDBQWmm4rLOS3E+Lt7t69i4SEBJw8edLTTXGb48ePo1GjRrh3756nm+KwH374AY0aNUJcXJyw7erVq0KyzbNnzyIzMxP16tXzVBMJkUT9DjH37rvvYtasWWCMebophPi95N3JKCwxHbwUlhQieTcNXojrUADDg8yXi+TkiB+Xm+vOVvmn5557Dk2bNkVwcDCee+45q8fPmDEDTZs2ReXKlVGrVi2MHz8e169fF/YfPnwYPXr0QGxsLDiOw88//+yytn/wwQdo2LAhqlSpgujoaDz11FM4cuSIyTFvv/026tevj0qVKiEhIQEfffSRxXNeuXIFAwcORKVKlVCtWjXMmDEDer1e2G/t/sUsXboU7du3xwMPPAAA2LdvHziOQ0REBCIiIhAbG4tRo0YhLy9PeM9vv/2GXr16oUaNGoiMjESbNm2wZcsWO79DZU6fPo0uXbqgYsWKiIuLwzvvvGPxeGv3uXHjRjRr1gxVq1ZF1apV8eijj+LHH38U9jdt2hStW7fGsmXLHG6zuwwbNgzt27fHyZMnERcXhxUrVgAANmzYYLJ8BAB++ukntGjRAi1btsTgwYOxfPlyREVFeaLZPs3efken02HatGmoVq0aKlWqhEGDBuHatWsm+//zn/+gbt26iIiIwGOPPVauL5DLO++8gwcffBCRkZGoUaMGnn76aeSa/Wf07bffomnTpggPD0ezZs3w/fffWzznkSNH0LlzZ1StWhWxsbGYPXu2ySAzLy8Po0ePRs2aNREZGYnhw4fjxo0bFs/pi/2OtfucMGGC0H7DH47j8O677wLwrX7H3Vq3bo0pU6Zg/vz5nm4KIQEh56b44CX3Jg1eiAsxH9amTRtPN8Fha9cyplQyxi8W4f9wnOlrwx+VSt5rnzhxQt4T+oClS5eyb7/9lg0YMIA9++yzVo9//fXX2Z9//smKi4vZlStXWPfu3Vnfvn2F/SdOnGCpqans4MGDDADbv3+/U+1TqVRs7969ovtOnz7Nrl69yhhj7N69e+ztt99mtWvXZnq9njHG2NatW5lSqWS//vorY4yxAwcOMKVSyb7//nvJ63Xp0oUNGDCA5efnszNnzrCEhAS2aNEim+/fXGlpKYuLi2M//fSTsG3v3r0sKChIeJ2dnc2aNGnCRo8eLWz75ptvWFpaGrty5QrT6XRs8+bNrEKFCuzgwYPS3ywLbWjUqBF76aWX2J07d9ihQ4dYtWrV2IYNGyTfY+0+c3Nz2b///ssYY0yn07HPP/+cKZVKduPGDeGY77//nqlUKqbT6SSvY+nfnC/3Y+ak7iUQ+xzG7O935s+fzxISEtiZM2dYfn4+GzhwIOvevbuwf/Hixaxhw4bszJkz7N69e+z1119nNWvWZAUFBQ61z1K/s2DBAvbzzz+zoqIiVlBQwMaMGcOaN28u7D9z5gwLDw9na9asYffu3WNr165lSqWSZWVliZ4vPz+fxcbGsrfeeouVlJSwEydOsDp16rD//ve/wjE9e/ZkAwcOZAUFBSwvL49169aN9erVS7L9vtrv2Huf33//PQsODmYXLlww2eZMv+Nvbt++zQAIf7Kzsx06TyD0x4TIZe2RtYybyzHMRbk/qvdUnm4e8QNS/RgFMDxEpRIPVpgHMZRKPtghJ194qPnoo49YixYtTLadPn2aBQUFOfxgwhhjo0ePtmkgYW779u2scuXKovvEAhjJycmsefPmrLCwkDHG2N9//80qV64sGVSwNJAwdu/ePfbee+8xAOzmzZuMMcbeeecd1r59e5Pj2rVrZzIwMHb27FkGgJ0+fVrY9tlnnzG1Wi15XUv3zxhjv/32G6tYsSIrKSkRtpkPJBhjbOrUqaxp06bSN8gYS0xMZO+++67FY8Ts2bOHhYeHs1u3bgnb3njjDfb444/bfA5L91laWsq+/PJLBoAdOXJE2H737l0WFhbGDh06JHleCmB4f5/DmOf7nfj4ePbZZ5+ZXBuAEBR46KGH2JIlS4T9xcXFLCQkhKWlpTHGXNfvMMbYsWPHGAB2/fp1xhhjs2fPZo8++qjJMY8++iibO3eu6Pu/+eYbVrVqVSHwyhhjc+fOZXXr1mWM8QNQjuNYRkaGsH/fvn0WB6O+2O84cp+DBg1iAwYMMNnmbL/jT44fP24SvCguLnb4XIHQHxMiF9V7KtHgBTeXY2uPyDx4IQFJqh8L9sCkDwLpZSGMASoVvz8+HkhJATQa17Zl8uTJyMjIcO1F7mvVqhWWLFli9TiNRoNp06bhjz/+wEMPPQQAWLFiBbp06QKVSgUAaNGiRbkpzca2b9+ORx99VJZ27969Gy1atLD5+Hnz5uHAgQN48cUXsWzZMgwePBiTJ09G165dHbr+zz//jN69ewuVIKZNm4bKlSsDAIYOHYqVK1fil19+Qfv27fHLL7/g1KlT6N69u+i5Dh8+jMjISNSvX1/Y9uCDDyI7OxsFBQXCeY1Zu/8///wTDRs2RHCwdJdy9uxZbN++HU2aNJE85tKlSzh+/LjJtWz9OR8+fBgNGzZERESEyX19+OGHku81J3afubm5aNGiBW7dugW9Xo9nnnkGzZs3F/aHhYUhISEBf/75Jx588EGbrxXIvLHPATzb79y8eRO5ublo06aNsK1+/fqoXLkyjhw5ArVaDb1eX25dP2MMGRkZGDVqlOz9jrHdu3cjLi4OVatWBcD3I8ZtBfh/b4cPHxZ9v1jb9Xo9srKyUFBQAIVCAcZ/qGKy33Atw/ffmC/2O4Z7tPU+L126hK+//hrbt2832U79Di8tLQ1jxowBAPTs2RPffPONZxtESACRWibCwKBp7uLBCwloFMDwkPh48ZwXKhWQnW3/+bRaPtmnOwMfrlS5cmUMHToUK1aswEMPPQSdToe0tDS8//77wjGuWvttbtOmTfj0009Nch9YExQUhHXr1qF169bo0KEDatasiTlz5jjchkcffRT5+fnIz89HWlqaScLF6tWrY/DgwXjiiSeEB+ElS5agWbNmoue6desWIiMjTbZVqVIFAEQDGLbc/40bN0QDHzqdDlWqVEFJSQkKCwvx2GOPST7Y37lzB4MGDULfvn3RuXNnYbutP2ep+5Iq/2lO6j7j4+ORn5+PO3fuYOPGjaKJ8ypXrmw1Rwjxfp7sdwy/p5Z+h/v06YMPP/wQvXr1Qp06dTBnzhzodDphv9z9jsGBAweQnJyMDRs2CNuk/r0dP35c9ByPPPIIFAoFFi5ciKlTp+LUqVNYuXKlcO9xcXF4/PHHMXfuXKxevRolJSVYsGCByffGnC/2OxEREXbd54oVK1CnTh3RIFSg9zvPPPMMvvjiCwDA8uXL8fzzz3u4RYR4H+1RLZJ3JyP3Zi7iI+OR0jlFtuBCfGS8aA4MVWT5gDMhcqIAhoekpPAlUo2rjiiV/HZ7GcqvGs5lKL8K2BbEsPXTSXd7/vnn0aVLF7z77rvYvXs3SktL0bdvX7e2YePGjXj++efx9ddf2/0pV82aNfH000/j/fffx549e6BQlOXMXbduHSZOnCi8LigoQO/evYVPEh999NFyn7gB/IPxyy+/jKioKDRp0gSNGzfGf/7zH6xfvx4ZGRlo3LgxTpw4gb59+yI8PBzPPvtsuXNUqlQJN2/eNNmWn58v7HPk/qtWrSr68B0UFIT8/HwwxvD9999j5MiRuHjxImrWrGly3K1bt9CrVy9Ur14d//vf/ySvY4nUfYkNcMzZcp8VK1bEmDFj0KRJE6jVajz11FPCvoKCAkpyaQdv7XMAz/U7hn97ln6HZ86ciTt37qBbt264c+cOxo4di8aNGyMmJkY4Xu5+Z//+/ejXrx9SU1PRq1cvk/ba8+8tKioK33zzDaZPn4533nkHKpUK48aNw/z584VZHWvXrsVrr72Gxo0bo0KFCpgyZQp++OEHk/sz5qv9jq33qdfr8emnn+KFF14Ax3HlzhOo/U5paSlCQkKE13/99RdatWrluQYR4qUMJU4NVUIMJU4ByBLESOmcYnJ+AFCGKJHS2YHBDCH2cMsCFhfx9fV9a9fyuTA4jv/qaK4LqXwaUsk/fWldbOvWrdnq1atZv3792PTp0032NWnShFWsWFHyj3FiNwN7cmCsXLmSVa1alf38888Wj4NEEs99+/axSpUqsXHjxrHGjRuz27dvS57DnrXoJSUlLDw8nG3bto0xxlivXr3KfW9ee+011r9/f9H3G3JgnDlzRti2YsWKcjkwbL1/xhj79ddfWcWKFVlpaamwTWwt+uzZs9mDDz5osg4+Ly+PPfTQQ+yZZ54xWctuYOvP2bAW3fj7PGvWLKs5MOy5T8YYS0hIYB988IHw+t69eywsLIylp6dLvodyYPhOn8OY5/qd+Ph4tmLFCuH1mTNnTHJgmLt69SqrUKEC27lzp7BNzn7n22+/ZZGRkWzTpk3l9s2ePZs99thjJtsee+wxyRwYYqZOncoefvhhyf3bt29nFSpUMEmaa8yX+x1b7vObb75hoaGh7MqVK+Xe42y/46vOnz9vku/C0QS2UgKhPyaBQypHhZwJNtceWctU76kYN5djqvdUlPuCyIqSePoxqeolHCd+vC891Hz88cesefPmLCQkhJ06dcrh89y7d48VFRWxESNGsDFjxrCioiJ27949yeOXLl3KoqKiJLPS6/V6VlRUxIqKihgAtnv3blZUVCQ8SF+6dInFxsaylStXMp1Ox7p27cpGjhwpeT1LA4nly5ezc+fOMb1ez65evcqef/55VrVqVeGhdsGCBaxhw4bC9+fEiROsXr167M0335S8XpcuXdigQYPYzZs32dmzZ1nDhg3ZwoULbb5/c6Wlpax27domgRyxgcTNmzdZ1apV2fr16xljjF28eJE1a9aMjRo1ymQQ4ghDNYBXXnmFFRYWsr/++otVr15duJYYa/eZlpbGMjMzmU6nYwUFBWzevHksLCyMHT9+XDhm165dLD4+nqqQMP8JYHiq35k/fz5r2LAhO3v2LLt58yYbPHgwe+qpp4T9Fy9eFIIZubm5rFevXuyJJ54QBuZy9jtffvklq1SpkhAoNXf69GkWHh7O1q1bx4qLi9m6dessViFhjLFDhw6xoqIidvfuXfbFF1+wypUrsx9++EHY/88//7C8vDym0+nYwYMHWYMGDSwGRHy137H1Pvv27cuGDh0qeg5n+x1f9N133wmBi4YNG5oEpOQSCP0xCRxSFUK4uRIDBEK8DAUw/Jg/z8AoKChgERERdn2aJaZTp04mn9oAYJ06dRL2d+/enT3//PPCawAsODi43KduBllZWeXOB4CtWrWK6XQ61rlzZ5OyfZcvX2axsbEmFQaMWRpIjBs3jsXGxjKlUslq1KjB+vTpY5J5vqSkhM2YMYOpVCpWsWJFVqdOHfbaa6+ZZGKvWLEiW2s0xefy5ctswIABLCIigkVHR7Np06aZPAhbu38xCxcuNLlnsYEEY4z95z//YQ0aNGAlJSVs7ty5DABTKpUm10lJSbF4LSmZmZnsySefZOHh4Sw2NrZcJRZ7f87JycksPj6eKZVKFhMTw5588kmTARdjjA0bNkyy4osBBTB8p89hzHP9TmlpKZsyZQqLjo5mERERbMCAAUIJZcYY++OPP1iDBg1YeHg4q169OnvxxReF6hdy9ztqtZoFBQWV+7eRk5MjHLNz507WpEkTVqFCBdakSRP23XffmZzDvN8ZP348q1KlClMqlaxt27bljk9NTWU1a9Zk4eHhrEGDBiYVV6T4Yr9jy32eP3+eBQUFsX379ole09l+x9fMmDFD+Df0f//3f9IHnl3L2GYVY1qO/3rWvk+DA6E/JoHDHTMwCHElqX6MY8wsLbgPSUxMRHp6uqeb4XHmOTAAPp9Gaqp4Doy///4bjRs3dl8DncAYQ7169ZCSkoLhw4d7ujnEgqKiIrRo0QLbt2/HAw884OnmuMWJEycwYMAAHDlyBGFhYZLHWfo350/9mNS9+FKfA1C/40uo33Gs3/EVhn+L2fezm+/atQtdunQRPzhLCxxMAnRGD0NBSqBtKlDXtvX+gdAfk8BhngMD4HNUpPZJpSohxCdI9WOUxNMPGIIU/lSFxECr1aK4uBiDBw+2+715ecCFC0BxMRAaCtSuDURHu6CRBAAQHh6OzMxMTzfDrZo0aYKTJ096uhlEZs70O8S9qN/xXwUFBSYVXv7991/ExsZKv+HQJNPgBcC/PpxscwCDEH9gXHkkKjwK4cHhuF50XfYqJIR4CgUw/IRG4x8BC2PVqlVDcHAwVqxYgdDQULvem5fHV2O5X1UUxcVlZWspiEEIkeJMv0MIkceff/6JNm3aCK9LS0sRFBQk/YYsLVCcJ76vMFfm1hHivcxnXeQV5UEZosSagWsocEH8hsL6IcSTtFpArQYUCv6rVuvpFrnP1atXcfHiRfTs2dPu9164UBa8MNDr+e2EECLFmX6HEOK8jz76SAheDB8+HIwxy8ELgJ9lIUUZL2PrCPFuybuTTZaMAEBhSSGSd5f/N6I9qoV6iRqKeQqol6ihPRpAgwzi0yiA4cUMuS1ycvi0nDk5/OtACmI4qrjYvu2EEEII8azu3bvjxRdfBACsWbMGWlsfeCzNsmiZIkPLCPENuTfF/y3k3MwxCVAYZmrk3MwBA0POzRwkbUuiIAbxCRTA8GLJyaaJOQH+dbKFDxoIL1hicZTUdkIIIYR4RnFxMTiOw3fffQeAT0A6YsQI208gNcsiJJryX5CAEh8pPePIOEBhz0wNQrwNBTC8WK7EBwpS2+3hw8VnCPEp9G+Npzdf00UIcRlf+veWlZVlUk3lzp07aNSokQ1v1AJb1MA6BVByG1CY5awJUgKJS+VtLCFeLqVzCpQhStF9xgEKqZkaUtsJ8SYUwPBi8RJBVKnttqpQoQLy8vL8emBVWmrfdkJcgTGGvLw8VKhQwdNN8aiKFSviwoULKC4u9ut+hxBPY4yhuLgYFy5cQMWKFT3dHKu2bNmCevXqAQAeeughMMagVIoPvkwYSqYW5gBgQEkev9Y2NBoAByhVdpVPJcRfaJprkNonVXK/IUAhNVPD0gwOQrwFTaj3YikpfM4L42UkISHA7dt8Uk9Hy6XGxcXh/PnzuHr1qrwN9iI3bgA6XfntQUHA33+7vz0kcFWoUAFxcXGeboZHxcXF4dq1a8jJyUEpRREJcang4GBERkYiJibG002xaOLEifj4448BAAsXLsTMmTNtf/Ph5PIlU1kJEBwBDL4mYysJ8S2GEqpSDAGKlM4pJtVKAEAZokRKZ8oZQ7wfBTC8mCEwkZzMLxuJigJu3eJLhAJlST2Nj7VFSEgI6tatK29jvcyff5YP/iiVQGoq0LWr59pFSCBSKBSoXr06qlev7ummEEI8TK/XIzo6Gvn5+QCAn3/+GR06dLDvJFJJO6lkKglg5iVUzRkHKAwlVZN3JyP3Zi7iI+OR0jmFSq0Sn0ABDC+n0ZQFJ9TqsuCFgSGpp72zMPydefDH0dkqhBBCCJFHXl6eycyQq1evOjZTRBl/f/mIyHZCApRYYk4DVaSqXIBC01xDAQvikygHhg9xZVJPf6TRANnZgF7Pf6XgBSGEECIT4ySaW9T8awsOHDggBCsqV64MnU7n+DKXlil8kk5jQUoqmUoCmlQCTg4csidnU7CC+A2XBTDGjRuH6tWro1mzZsK2uXPnonbt2mjVqhVatWqFHTt2CPsWLlyIBg0a4IEHHhDKaBFTrkrq6W5aLT+bRKHgv9pa5p0QQgghXsA8iWZhDv9aIoixePFiYZnIhAkTcPPmTSgUTjyC1tXwSTqVKvhi0k56RiZy0h7VQr1EDQbxJNmUmJP4G5ctIRkzZgxeeukljBo1ymT7q6++iqlTp5psO3HiBDZs2IDjx4/j33//RZcuXXDq1CkEBQW5qnk+SSypp1LJb/cVWq3pPTiax4MQQgghHiKWRFNXyG83CyK0a9cOv//+OwBg06ZNGDhwoPXzZ2n5cxXm8stCWqaUD07U1fhMwMIcPSMTudiT94IQf+GyGRgdO3ZEVFSUTcdu3boVQ4cORVhYGOrWrYsGDRrg4MGDrmqaz9Jo+CSUKhXAcfzX1FTPDfwdmUmRnGwagAH416NHyz8jg2Z6EEIIITIxXjIiln8CMEmiWVRUBI7jhODFmTNnbA9e2DG7wxfRMzJxhGGmhWKeAuolaqHiiFTwIjo8Gql9UmnpCPE7bs+BsWzZMrRo0QLjxo3DjRs3AAAXLlxAnTp1hGPi4uJw4cIF0fenpqYiMTERiYmJPlkG1NlBtbfkdTDMpMjJ4UuvG2ZSWLsfqXwdOp1953FV+wghhBBixjyoIOV+Es1//vkHSmVZjoq7d++iXr16tl3L0uwOPxfoz8hEmmGmRc7NHDAw5NzMEV5LKbhX4NB1zIMkhHgbtwYwXnjhBZw5cwYZGRmIjY3FlClTAACMlf/PkOM40XMkJSUhPT0d6enpqFatmkvbKzd/GlRLzaRItvJ8YUu+DlvOY42j7SOEEEKIGbGggrn7STTXrl2Lxo0bAwC6dOkCxhjCwsJsv1aAlkgN9GdkYpnYTIvCkkIEcdJLiUr0JUjebfuDr1SQhIIYxNu4NYBRo0YNBAUFQaFQYPz48cIUuLi4OJw7d0447vz586hVq5Y7m+YW/jSodrQiSkoKn7fD0fPbiiq2EEIIITKxGDwoS6KpeWMHRo4cCQD44IMPsGvXLvuvJVUK1c9LpAb6MzKxTKrCiI7pHHqfGKkgiT1BEELcwa0BjIsXLwp/37x5s5B9uW/fvtiwYQPu3buHrKwsZGZmom3btu5smltIDZ5zcnwvR4OjFVHM83hI5aBytrKKv1RsIYQQQjxOMqigAobroetzBly9EVi3bh0AID09HS+99JLt5zfOr1F6G+BCTPcHQInUQH9GJpZJVRJRRaoQHR5t9/uMGZaNSC1HsScIQog7uCyAMWzYMLRv3x4nT55EXFwcVqxYgenTp6N58+Zo0aIF9u7di/feew8A0LRpUzz99NNo0qQJunfvjg8//NAvsytbGjzLuZzEHckrxWZS2FoRxTiPR1qa4+dxVfsIIYQQYqRlCh9EMHY/qHDx4kUEB5cVtcvPz0ebNm1sP7d5fo3iPP4TjpBo+GKJVFvQMzKxV0rnFIQGhZpsCw0KRUrnFCztsbTcPgAIUYRYrUBivGxECpVhJd7GZWVU169fX27bs88+K3l8cnIykn1xLYUdxMqgGjMsJ3EmMae7ypQazpWczM8siY/n78/ea8h1HnedlxBCCAk4huCBWWnTH87UQNeu/HIGlUqFrKwsyfwMksTya+iLgQoRwJBrMjTe+9AzMnGEeT4Uw2tDlZFJOychrygPAF+BZGmPpSYVSAxVS3Jv5iI+Mh4pnVMsVjEBqAwr8U4cE8sO5CMSExORnp7u6WbYRavlB9U50oFOOPMTUavFz61S8bMeCCHexV392Lhx47B9+3ZUr14dx44dAwDMnTsXn376qZDsbcGCBejZsycAYOHChVixYgWCgoLw/vvv46mnnrJ6DV/skwkhjnnjjTeQcn9a47Rp07B48WLHTrROAfHKJhwwXO9w+xzhT32YP90LgeQSD1WkCtmTs62+3zDTwjhYoQxRWgxeqCJVSOmcQmVYicdI9WNuL6Ma6AzLJ6Rm/zk7K9BS8kp3LC3xJoF2v4RYMmbMGHz77bfltr/66qvIyMhARkaGELw4ceIENmzYgOPHj+Pbb7/FxIkTodNZThRGCAkAWVqwzSo0jOWE4MXOnTsdD14AAZu0kxBbaY9q7cpPIVYK1d4qJobACAUviDeiAIaHSI0FdDrnBt5SeTaiovynhKst/KlkbSChoJPrdOzYEVFRUTYdu3XrVgwdOhRhYWGoW7cuGjRoIGTEJ4QEqCwtbv04HoqBuci8xG86/1EFdH8gz7nzWsivQYi/EQsuWDs+aVuS5H7z/BRSpVClAiA6poMyxPTfHy0bId6OAhgeolKJb4+Odm7gLZW8EvCfEq628KeStYGCgk6esWzZMrRo0QLjxo3DjRs3AAAXLlxAnTp1hGPi4uJw4cIFTzWREOJqxlVAtqj512bbD38+CpXHFglvKfkfUDvyLvDb6LLjHVFXwyfpVKrgr0k7CQGkgwuWghiWclSIBRqkZlpIUUWqkNonFapIFThwwmuaeUG8GQUwPMRVgQbzMqUqFf/6+nXx46WWnPg6S0tpiHeioJP7vfDCCzhz5gwyMjIQGxuLKVOmACifKAyAZGK+1NRUJCYmIjExEVevXnVpewkhLmBeBaQwh399cKKw/ZPdDK1e5/NRDHoIYFog2DDznOn446SCGFLBEWN1NUD/bD7nRf9sCl4QvyQVXEjeLf2gY6mEqVigwZ6Spxw4IcdF9uRs6OfoadkI8QkUwPAQVwYajMuUZmfzr6WWllgq7erLAu1+/QEFndyvRo0aCAoKgkKhwPjx44VlInFxcTh37pxw3Pnz51GrVi3RcyQlJSE9PR3p6elCMlBCiA8RqwKiKwTOpAK6QvT+LzBhJb95VRLw5WSRc+gK+fOYkwqOODNjgxAfJRVcsBR0kCphqopUiQYa7Cl5ysAoWEF8EgUwPMidgQapGR8pfrrELdDu1x9Q0Mn9Ll68KPx98+bNaNasGQCgb9++2LBhA+7du4esrCxkZmaibdu2nmomIcSVCsUHTyUlOnAa4JsM/vWxt4Axnew8j1RwRCzYQYifkwouWAo6pHROsStHhdjxHMRnUKoiJdazE+LlKIDhZVw18Jaa8aHx08BroN2vP6Cgk2sNGzYM7du3x8mTJxEXF4cVK1Zg+vTpaN68OVq0aIG9e/fivffeAwA0bdoUTz/9NJo0aYLu3bvjww8/RJCzJZIIId5JpNpHzlUgdHTZ69srgKZxNp7n4ERgfTCwjrs/80KERNCEEH9mbzACADTNNXblqBA7fkLiBErUSfwKx8QWO/sIf61xrdXy6/5zc/lPn1NSaOBNAkMg/u77Uz/mT/dCSMAwLPO4P1Ni259A33f4XS1VHDIWGD0mBimBuqOBrDTTmRVBSj7x5tVfgNMfW7+mUsXnuvAy/tSH+dO9+BNDSdPcm7mIj4wXclD463UJcYZUPxbsgbYQKzQa1w7aAnGQSHyDq3/3CSGEmDEkzDycjEnLc/D+d/zLN998E7NG1OOXexTm8jMsWqbwx1frIL7915HWr0clUkkA0zTXuDVwYB64WDNwDQUuiM+jAEaAMZSqNFR7MJSqBGjgSAghhAQiph6OGg+/CkMhoX379qFTp/sJLwwBjiwtH7T4daRp0ML0TBauwll4HyFEboayrYbKJ4ayrQAoiEF8GuXACDCBVKpSqwXUakCh4L9qKek5IYQQV7KlZKi7WWnT9evXoVAohDLIly9fLgteGJ9DqtSq8bktoRKphDhNe1QL9RI1FPMUUC9RQ3tUuo9xpGwrIb6AAhgBJlBKVWq1wLhx/AwTxviv48ZREIMQQoiLeGPJUCtt+v333xEdHQ0AqFChAnQ6HapXr17+PFLVRE4vNz23lOAIee6HED9ka1DCMKMi52YOGJgwo0LqeEfKthLiCyiA4WOcnVXgbaUqXTVLYtIkoLjYdFtxMb+dEEIIkZ03lgy10KZ33nkH7dq1AwCMGzcORUVFUCgkHgslq4bYkAeeCwYeWm57mwkJINqjWozdMtYkKDF2y1jRoMSknZPsmlHhSNlWQnwBBTB8iCF/hfGsgqQk+wb93lSqUo77kZKXZ992QgghxClSg3xPlgyVuPZjM3MwdepUAMDnn3+OFStWWD6PSKlVy8erwOe8UAHtVtOyEUIkTNo5CSX6EpNtJfoSTNpp+omb9qgWeUXiD7FSMyocKdtKiC+gAIYPkSN/hUYDpKYCKhXAcfzX1FTPJPCUup/RoylvBSGEEB8jNci3d/AvJ7Nr3y0GOA3w80n+dWZmJp5++mnr52mZwlcPMcFJXPN+iVTKeUGIVVJBCfPtlvJWSM2o0DTXILVPKlSRKnDgoIpUIbVPKiXwJD6PAhg+RK78FRoNkJ0N6PX8V09VH5Fqt05XNiNj7FggJsb+gEbFivZtJ4QQQpwiNsj3dMlQozZlXgLCx5btKioqQoMGDWw7T10N0DbVdGZFgwned7+EeCF7Em+av8/AUt4KSzMqNM01yJ6cDf0cPbInZ1PwgvgFCmD4EFvzV/hK9Q1b8m6UlPDLPuxdYlKhgn3bCSGEEKeIDfLbpnp2BsL9Nm04FIOGU/hNHds2AmMMFez9D7GuxnRmRduPvO9+CfEy1hJvKjjpoVjy7mQh+MEk8s1Eh0dTUIIEHApg+BBb8le4Mq+E3MTuxxpbl8xcv27fdkIIIcRp5oN8LxjMj33zBwx79xoA4N1338WPv/8tX7lXL7xfQryJtVKmeqaXfK8h2JFzU7zCjzJEiaU9lsrXWEJ8BAUwfIh5/oroaCA8HBg5smymhRx5MtzF/H6Cgmx7ny1LZryt2gohhBDiTjqdDiEhIVi9ejUAvmTqq6++6p3lXgnxU9ZKmaoiVZLvDeKCygU/DCzls3B0yQohvoICGD7GkL9izRqgqKj88oociTLs9ubJcBfjfBxpabbNyIiPt75MJiUFCAkx3RYS4plqK4QQQojT7Jg1cfnyZQQHB6O0tBQAcP36dbRt25bf6Y3lXgnxU1IJNqPCo6BeopacXREaFAod04nu48BJ5rOwtmTFVhQEId6MAhg+SmqmhdQsBl+YeSA2wyQ01PQYpRLo2ZNP7mm8TGbs2PJBDMYsvyaEEEJ8gtisiV9HAuu4csGMffv2oWbNmgCAmjVrQq/Xo2rVqmXn8sZyr4T4KbFSpqFBoSi4VyAZvFBwCjzb+lnJ2RnGQRHzQMOknZMsLlmxhVxBEEJchQIYPspSBQ9reTK8mfGMjGvXgJUry5d8/eILPrmnsZISYJJRyexJk4D7HzwJSktNj/ElvpKYlRBCiAuIzZowJPUrzAF+Gwt8GYN5gzg88cQTAIDJkyfj4sWL4Dizcqf2lnuVK18GIQFIrJRppdBKKNGXSL5Hz/RIO5yGngk9ywU/lCFKoeqIWKBBqiyrpSom5qzl7SDE0yiA4aOkZlQYBvnmg35PlUp1lljJ1zzxvtlkuy3H+ApfSsxKCCHEBazMjmD6EjR/NQ9zv+Jfb5sWhvdeSRQ/2J5yr5QvgxCnmZcylQoyGCssKcSOzB3lgh/GeS/EAg1SpJayiLGWt4MQT6MAho+yVJFEbNBPyvjaTAZfSsxKCCFEZllawEKpxdt3AcUI4Nh5/nXu+0DvVvekc1rYU+6V8mUQIgvjpR62yr2ZWy74YZz3wtaAgvGsDVtIBTvsCYIQ4koUwPBR5vkifH2mhT2io+3bbs7XZjJILRfy1sSshBBCZGKYASGRzO/YOaDSs2Wvi9OAOob/C+XIaUH5MghxmvlSD1tZCxhI7Y8Oj5actWELsbwd9gZBCHElCmD4sECdabF0afnknqGh/HYDW4MZvjCTgUrCEkJIgBLNfcFbuQ9oPpP/e782ANMCIcFGB1jKaWHrshB782UQQsqxZ6mHgS0BA6lAw9IeSyVnbdhCLG+HvUEQQlyJAhjE52g05ZN7rlxpGsARC3JIMcxk8NZEmZaWCxFCCPFjEjMdBi0Bnv2U//unzwFbXhM5SCynBWDfshB78mUQQkTZmzsiOjzapoCBKwMNlpaueBKVdyUAEGz9EEK8j0ZjecaJYV9yMh+giI8Hbt8WT+IZH1+WKNOQa8KwvMT4XJ4idi+GXCeEEEJ8QJaWDxAU5vKzF1qmiOecMKeMvz9TgleqA0JGle0+vBBoITUZoq5G/Lr2LAsxtNGRthNCAPBLPcRKpgZxQdAxnfBVFalCSucUu4IFmuYarwkuuJphKY5hNouhvCuAgPkeEB7NwAhw3jrrQA7mS2yWLpWeyeDtiTIDdbkQIYT4PHuWbJiXLK3VU5gBcT7PNHhx69YttGikEr+mUiV93ZAoifdIRELqaoD+2cBwPf+VgheE2EVqqUfagDSsHbgWcZXjwIGTeDcxoPKuxMBlAYxx48ahevXqaNasmbBt2rRpaNSoEVq0aIEBAwYgPz8fAJCdnY3w8HC0atUKrVq1woQJE1zVLGJEqwXGjjVNajl2rHcHMewJuJgfC0gnPpVKiJmT45/BHUIIIU4yDzZIlRa1tGRDOAfHn+fXEaYBh6w0oO5o7Pi7Ouq8wr+1SUJt6PV6REREWF7iIXVdDrQsxMPoGTmwiC31GN1yNCbtnIQRX40QknsaZhRM/Gai1y+TkGMph73noPKuxMBlAYwxY8bg22+/NdnWtWtXHDt2DEeOHEHDhg2xcOFCYV/9+vWRkZGBjIwMLF++3FXNIvdptcDIkUBJien2khJg0iTPtMkawzIP44DLyJHAxIm2HWtYEiI2k8FSQkxfqlhCCCHEDeyZVSG5ZCPH6Bzgz2NOV4ipC7ToNf8KAGDOnDk4fuo8OO7+p7WWSqJKXbf4uu1lVIlL0DNy4DHOKZHSOQVph9OQV1R+XXNhSSE+Tv+4XFDDeHDv6TwQ5lVVxNroinNQeVfflJ6ejnv37sl6TpcFMDp27IioKNNpit26dUNwMJ92o127djh//ryrLk8sMAzumUQlJ7E8Ed5AbJkHY8Dy5eUDC/YsCdFq+fwY1njTkhJCCCEeZE8iTMmKHZxkhRGA//+tzsvAO1sLAAB79uzB3Llzyx8otcTDUgURWhbiUfSMHNgm7ZxkV1US42UScgQPnCXHUg5HzkHlXX3L5s2bwXEcHnroIXzzzTeynttjOTBWrlyJHj16CK+zsrLQunVrdOrUCfv375d8X2pqKhITE5GYmIirV6+6o6l+R2xw7wn25t+QWubBWPnAgtSx5tsNwRxbgzZS5yWEEBJA7EmEKbbMgwuB6IyL+/LvAIoRwPnr/OtLly7hiSeesK+NVEHEZ9Ezsv+a+M1E0ZkX1hiWSXhDHgg5lnI4cg4q7+obzp49C47jMHDgQABArVq10KtXL1mv4ZEqJCkpKQgODobm/vz92NhY5ObmIjo6GocOHUL//v1x/PhxVK5cudx7k5KSkHR/LUBiYqJb2+0vvGEQ7kjVj/h4/jgx5vcUFSUelDD7wEMymBMUBOh04m0ghBAS4Myqg5hsNydWyaP0NlAsPohJPws8NIv/e5ACuHfyfwiqUcP+NlIFEZ9Ez8j+S3tUi+Xpji0BMiyT8IY8EFJVVexZyuHoOQKp6oqvuXfvHhITE3Hs2DFh27Fjx9C0aVPZr+X2GRhpaWnYvn07tFqtsIYzLCwM0dHRAIA2bdqgfv36OHXqlLubFjCsDcLv/yhcypGqHykpfPJNMY4GFqSCOTqddMUSQgghPsbWhJu2snd2g/mSjeLrooe9/21Z8GLUY0Dp6bUIajDS8XbSUhGfQs/I/i15dzKYhZlXUoyXSXhDHgg5lnLQchD/8vrrr6NChQpC8CItLQ2MMZcELwA3BzC+/fZbvPXWW/j666+hNBodXr16Fbr7H3efPXsWmZmZqFevnjub5lVcXdo0JaX84NwgJIQvN+pqti7xMKbRABMmlA9iiAUWros/G5bbLhX4MFQoEatYQgghxIfYk3DTVpaSZ9pCZKZG5wXApDX839e9FIS0tLUUcAgg9Izs/xyZJREdHi0sk9Ae1eJ2cfmkbe4e+MuxlIOWg/iHb7/9FhzHYdGiRQCAESNGQK/XY9SoUVbe6RyXLSEZNmwY9u3bh2vXriEuLg7z5s3DwoULce/ePXTt2hUAn6Ro+fLl+OmnnzB79mwEBwcjKCgIy5cvL5fcKFA4srTCXobzJCfz5zcsl1Cp+ECAOwbpUstBrM2k+OgjoEMHvu25ufzxYm229fwpKabfb6AsIKLRUMCCEEJ8nqWEm84ECOpqHH9/yxQ+iKIrxL0SoMKYsl0nl9VCw56LLZ87S0tLQ3wYPSMHJqllE8aCuCDomR7xkfFI6ZwiDOgNyTvN819Eh0djaY+lbh/4y7GUg5aD+K7z58+jTp06wuvKlSsjJycHVapUccv1OcakalEAr7zyitUTVK5cGfPnz5e1UbZKTExEenq6R67tKmq1+MBbpeLLfvoL80ANwAcO5JrlYM/5tVrrARFCXMWefsyZPnncuHHYvn07qlevLkzxmzZtGrZt24bQ0FDUr18fq1atQpUqVZCdnY3GjRvjgQceAFD2IC3nvRDiNusUEE+YyfFLK2zlSNDA0nuytDj73XTUf+Ff4fDCwkKEh4dbPk9oFFBSADCjOuhBSiqFKgN39cfuQP2x95EKQhgoQ5SSsxDUS9SiwQ9VpArZk7PlbiohokpKStCxY0f89ttvwrZDhw7hwQcfdMn1pPoxiwEMlUqFN9980+KJFy1ahL///tv5FjrAHztnhUK8vCnHAXo7nrN8gasDBxSYIL7Ann7MmT75p59+QkREBEaNGiUEML7//ns8+eSTCA4OxowZMwAAb731FrKzs9G7d2+TRExy3wshbrMxBigRSZgZEg0MuWbbOQzLUIxncogFDYwDDSFRgO4WoC8Wfc+XX36JIUOGAADat2+PAwcOiF/74ETg9HJYqloCgF/G0j/btvshotzVH7sD9cfeSXtUi+Tdyci9mYuocH4mzfWi6+VmXJhTzFOI5s/gwEE/x88GCMQrzZ8/H7NmzRJef/zxx5gwYYJLrynVj1lcQvLqq69i9OjRFk9848YN51pGTDi6tMJd5AwKuHqJBi0BIf7GmT65Y8eOyDabxtWtWzfh7+3atcOXX37pdBsJ8ToSyZ8lt4uxtgwlSwscmmRaWUQsaKIrBA5NQtLzE/DpLn4t+39fH4apC9aJXzdLa1vwApAu7Upcgp6RiSOMl00YghnXiyQStxmRo/IHIY7Yt2+fSRnv/v37Y9OmTVAo3F4LRGDxypMnT7Z6AluOIbYTS7DpyeoXxglFY2KAceP4AAtjZfk5nEky6uqEpYT4E1f2yStXrkSPHj2E11lZWWjdujU6deqE/fv3S74vNTUViYmJSExMxNWrVx26NiEuJVHxQ3K7GKngQGFu2ewMibKoxvR6IGJEnhC8ODAXmNpyq3RC0cPJsCl4AYiXcCUuQ8/IxBbao1qol6ihmKeAeoka2qNaYXvStiTk3MwBA0POzRyM/GokuHmcyXEGVLWDuNvly5fBcZwQvAgKCsK1a9ewefNmjwYvABuqkHz33XdYsWJFuU/uVq5c6ao2BTSNxnuqXxjySBgCFnl5QHGx6THWSp/ac345AiKE+DtX9MkpKSkIDg6G5n5HExsbi9zcXPz111949913MXz4cBQUFIi+NykpCenp6UhPT0e1atUcbgMhLiM1sLdnwG/pHGKzM0RcLQCCRgJ37vGv8z4B2iegbCaHGFtnVVgq4Upchp6RiSViQYqkbUnCzAvzXBiGJSLGxxlQ1Q7iLjqdDl27dkXNmjWFbQcOHEBpaalQ0tnTLAYwXn/9daSkpODo0aPo3LkzPvjgA2HfsmXLXN64QKXR8Ak79Xr+q6eWQSQnmybBlJKTY98sCsOsixEjyp/fmYAIIf7OFX1yWloatm/fDq1WC+5+jeKwsDDhP6k2bdqgfv36OHXqlPM3QIgntEzhB/jG7B3wWzqHDUGGn08C1V/g/x5TCdCvBaIijA6QOoctQRZ7S7gSWdAzMhFjPONi9ObR5YIUhSWFQg4MSwzHGdM01yB7cjb0c/TInpxNwQsbSM2AIeLee+89BAcH44cffgAAvPPOO2CMoX379h5umSmLAYzt27djz549WLJkCQ4dOoSdO3fi1VdfBQBYyP1J/ESujR/8cJztsyiMZ104e11CAo3cffK3336Lt956C19//TWURmvXrl69Cp1OBwA4e/YsMjMzUa9ePXlughB3q6vhB/hKFQDOsQG/pXNYCTIs2Ao8dj/X40vdK+Dqcv7/TRNS5xALnBgEKYH2a/nEnRS8cDt6RibmzGdc6JhO9Ljcm7k25a4wBDloEO4YSzNgiKnffvsNHMfhtddeAwB06dIFpaWlwmtvYzGAUVpaiuBgPs9nlSpVsG3bNhQUFGDIkCEoNl9LQDzClTkkbE0cav7/tKVZFLbM6vCWhKWEeBtn+uRhw4ahffv2OHnyJOLi4rBixQq89NJLuHXrFrp27YpWrVoJ2aR/+ukntGjRAi1btsTgwYOxfPlyREVFufz+CHGZuhp+oD9c7/iAX+ocFoIMbZKB5C/4v2/ZsgUffPSZfbNB6mqAuqNRPuMox2+nwIXH0DMyMSe2LESMoeKIeU4LseM8NQj3h6CJ2M9DbGZLIMvLy0NwcLDJDIuLFy9i165dCAoK8mDLLLMYwKhfvz5+/PFH4XVQUBBWrFiBBx54wGNloUgZV+eQEEsoaiupWRTWZld4MmEpId7OmT55/fr1uHjxIkpKSnD+/Hk8++yzOH36NM6dO4eMjAxkZGRg+fLlAIBBgwbh+PHjOHz4MP7880/06dPHpfdFiE8zmZ0BABwK7wGcBvgzm9+SvX8J+vXr59hskH93oHwiT3Z/O/EUekYm5qwtCwHKEm8a57QA+HKoYsd5YhDuLzMXpH4etvyc/J1er8eAAQMQExMjzLjds2cPGGMmuS+8lcUAxsaNG9G2bdty2+fPn49z5865rFHENmKzGeTMIWGcUNReUrMoLM2u8GTCUjlQRRXiatQnE+KlhNkZDCdiFqHiuLJd9/5ZDdWjk0SOtXE2iKUKKMRjqD8m5qSWhQRxQaKJNw05LdgchjUD14gm6LQ0CHfVLAl/mbkg9fMI9NKzn3zyCYKCgrBlyxYAwJtvvgnGmEmpVG9nMYARHh6O8PBwk21z584FANSuXdtljSK2cXSWgz0MCUUtBTHM1/JamkUhVSZ27VrPJiw1cDQIQRVViDtQn0yId/vf//6Hpk/NAAD06NEDjDGEPjCaL5P6ZQywjuP/bIyRLp1qTo4qKkR21B8Tc1KlTtMGpEE/Ry/MqBALOGiaa5DSOQXxkfHIvZmL5N3J0B7VSg62o8KjXDZLwl9mLlDpWVMZGRngOE5YLtyuXTsUFxdj1qxZHm6Z/ewu4vr111+7oh3EAbbMcpBrVoCl5SSMlQUxrM2i8KYyseacCUJMmkQVVYhnUJ9MiHd45plnMHr0aADAx2OBHUkn+CBFlhb4fRxQnFd2cEke8NtY24IYclRRIW5B/XFgMywLiQ4vKzUZHswHuawtyxDbP/KrkWgQ1UB0EA7AZbMk/GXmApWe5d28eRNVq1ZF69athW25ubn49ddfERIS4sGWOc7uAAZlVvYeUrMZDLMf5JwVYG05CWP8PltmUXhLmVhzji7J0WqBvDzxfe6sqEJLWAIT9cmEeFZpaSk4jsMXX/DZOv9MASZ0AVCYA/w6EvhjAqAXSerISoDDNgw25KiiQtyC+uPAZVjOMeKrEbhedF3YnleUh6RtSZi0c5LFgIPYsg0Ghj1ZezC65ehyg3DjaxiTY5aEP81cCOTSs4wxjBw5ElWqVEF+fj4AYMeOHWCMoU6dOp5tnJPsDmAcOnTIFe0gDrA2m8HeAbm1AbAh8FCu/Nt9vlr+1HDfUqVdpe7L8L4RI6TPLUdFFVsCE7SEJXBRn0yIkSwtsEUNrFPwX21dpuGgCxcumHyCdfNToLXa+AgGlN6WPoGteSzkqKJCXI7648BkPHsC4AMPxgpLCpFXJP5JlyHgIBV4YGDYkbmj3CDclbMkaOaCOF+qzLJmzRooFAqsXbsWADBjxgwwxtCjRw8Pt0wewbYclJWVhQ8++ADZ2dkoLS0VttNUOc/TaKRnMNiTI8MwADYEPAwDYMM1jMXHiw/2fbH8qfl9ixG7L1veBzhfUcXWn4ulYJW3zHAh8qE+mRARWVrgYBKgu98ZFubwrwGXDPi///57PPXUUwCABjUVOPW2XjLAL4nyWPg86o8Dj/aoFsm7k5F7MxfxkfG4XXzbpvKpYqLC+RLl8ZHxQgDEnFhwI6VzCpK2JZlcV85ZEprmmoAPWBgzBKkM32/DEiAAXvV9OnHiBJo2bSq8btq0KdLT01GhQgUPtkp+NgUw+vfvj2effRZ9+vSBQmH3pA3iIfYEGuwZAKeklB+8+2r5U7H7NiZ1X9beBwDR0c4HD2z9ubgjoSvxHtQnEyLicHJZ8MJAV8hvlzmAMXPmTLz11lsAgNf7KrDgGb39J+FCKI+FH6D+OLCIDWTlkNI5BSO/Gllu9gYgPqvCMGg2DqQYyrMS+VmqzOIN3/M7d+6gUaNGOH/+vLDt9OnTqF+/vgdb5To2BTAqVKiAV155xdVtITKzJ9BgzwDYeIlKbi4fEElJ8c1P+i0N8FUq6fuyFhjgOGDpUufaZuk65tv9aVYMsY76ZELMZGn5GRdiZCw3yhhDvXr1kJ2dDQD4fk51dG14xfobQ6IBDmWJPEOigcSltBTED1B/HFjEBrLOMOSy0DTX4JfcX7A8fblJEMPSrAqaJeE+3lqZhTGGiRMnYvny5cK2r776CgMGDPBgq1zPpgDGpEmTMG/ePHTr1g1hYWHC9gcffNBlDSPOsyfQYO8A2NLSFV8idd+GhKT2vs+AMce/P1pt2c9MoQB0OvHrG/P0rBjjNvtyQMtXUJ9MiBHD0hEpMi3TKCgoQGRkpPD6woULqLUvzvobg5QUrPBj1B8HFrkHrFHhUVAvUQuzKCYkTsCOzB00q8LLSC3x8WRlli+//BJDhgwRXr/00kv44IMPPNYed7IpgHH06FGsWbMGe/bsEabHcRyHPXv2uLRxxHm2Bho8PQD2FEfvW+x9xqSqtYgxHvxHRQEFBUBJCb9PLHgh1j5PzoqxJ38KkQf1yYQYEVs6YhCkBGr1BDbG8KVLASA0GmhjX0Dhzz//RJs2bYTXJSUlCA4OBkKiys4rRqnil4lQ8MJvUX8cWKQGstHh0YgIjRACDz0TeiLtcJrV2Ro3im4ICT5zbuYg7XAaJcz0Qq7OOWKP06dPIyEhQXitUqlw/PhxVKxY0e1t8RSbAhibN2/G2bNnERoa6ur2EA/xp2Uh9nD0vg37J00qX0LVnsCP+eBfqhxrUBBfdtZS+zw1K4YSiLof9ckkoGVp+aBFYS4/u0Jq6QgA1B0NnPmML1lqUJwH/D7u/n7rndRHH32EF198EQAwdOhQrF+/vmynpaSdodEUvAgA1B8HFqmB7NIeS8sFHTrEd8CknZMkK5AAgB6m+XO8Ka8CKeMNOUfu3r2L1q1b459//hG2nThxAo0bN3ZbG7yFTdmGWrZsKdSPJb5PqiynoUyqXs9/DZTBp6P3rdEA164Ba9dKl7KVYlyC1VoyUIBvm7f+XCiBqPtRn0w8xs1lSkWvfzDpftCC3f8qEUVQqoB/d5gGLwz0xUD6JKuX6969uxC8+N///mcavACA4uvSby7O49vq7u8RcSvqjwOLvSVG8+/m230NT+dVIOI0zTXlytm6y7Rp0xAeHi4EL9auXQvGWEAGLwAbZ2BcvnwZjRo1wkMPPWSyvo9KRPkemu4vP3tnPthagtWYNyfjpASi7kd9MvEIN5cpFXVokshyEQY+iGGUvT9Iyc9++HWk9LlK8vh7Eml7cXGxyb8tyU+5rM0AcVEVFOI9qD8OPLYkzzRUK9ExkbXAVngyrwLxLt988w169+4tvB4zZgxWrlwJzu6a3f7FpgDGvHnzXN0O4iaWpvsb9gfSEhJPsKUEqzFvz0USqPlTPIn6ZOIRbixTKipLW1bFoxzGz7gwLCsxLN1In2Q5R4VI27OyslCvXj3h9Z07d6BUKsXf3zKFX46iL5a+hoxVUIj3of6YiHG0Womn8ioYaI9qqTSrF8jNzYXKKKFedHQ0zp49i8qVK3uwVd7DpgBGp06dXN0O4iZS0/oNMzFoZobrWVtaERoKVKoEXL/uG4GkQM2f4knUJxOPkBqIu2uAfjhZep9SBfTPLr/d2odUZm3fsmWLUH6uTZs2SE9Pt/x+Q5BEbymRJ32a6s+oPyZiHF0G4skEnoZZI4bAS87NHCRt4wcDFMRwj+LiYnTo0MHk/56//voLrVq18lyjvJBNOTCI/5Ca1h8UZHlmhpykcnAECktLK1QqYOVKPreGcc4Lb/+eBWr+FEICitRAXGy7XLkyjM9jaalGS4lPLC3lqABM2j5x4kQheLFgwQLrwQuDEgvXMCxlIYQEFKllIEFcEKLDo0X3RYRGIHl3MhTzFFAvUUN71L0Pe2KzRgxJRYnrzZs3D2FhYcL/PZ9++ikYYxS8EGHTDAziP8Sm+4eElJXtNCd3IkbKwSG95EIq+Sd9zwghXqFlimkODEB8gC5Xrgzz80gJiZY+r6UcFffbrtfrER0dLSRi3L9/Px599FHb2yl1DS4IaJtK+S8ICRDGyy+iwqMQGhSKYl3Z8jJliBKpfVIBAOO2jjPZBwC3i2/jdvFtAJ6Z/SA1a4SSirrW7t270aVLF+H14MGD8fnnnwtlmUl5Fr8zW7ZswZUrV9zVFuIGGg0/UDZUzYiO5r9KkTsRo7UcHIHA/GdgrXIJfc+IAfXJxKPqavgBuVIFgOO/ig3QLeXKsIfYecwFKYHEpdL7W6bwx5gLjQbapiKvcncEBQUJwYurV6/aF7yQukaQEmiXRsELP0b9MTFmWH6RczMHDAx5RXlgjCE6PLpctRJNcw0qhVayek53z36QmjVCSUVd4+LFi+A4TghehIaGIi8vDxs3bqTghRUWvztr165F69atkZCQgDFjxiA1NRXHjx93V9uIixhP94+IAIolco+5IhEjldzk2bPkgr5nxID6ZOJxdTV8ronhev6r2ABdrlwZ1o6XCqAYEwu6tF8LDL6GAxfrIiYmBgBQqVIl6HQ64XU5lpbE2BrYIX6F+mNiTGz5RYm+BBGhEaJlN68XWVnedp87Zz+kdE6BMsQ0GOvppKL+qLS0FE888QRq1aolbPv9999x7949REVFebBlvsNiAOPLL7/EhQsXsGvXLnTr1g1HjhzBqFGjUK1aNfTs2dNdbSQuZGkQbGlWgKOkZnRQyU1p9D0jBtQnE59gT64MS0IsPchx0gEUcyJBl8WLF6NDhw4AgKSkJBQUFEh/4mVYylKYA4CVLYkxD2JYC+wQv0L9MTFm7/ILW2c1uHP2g6a5Bql9UqGKVJWbNULk8d///hchISHYt28fAGDJkiVgjKFt27aebZiPsSkHhlqtxt27d1FUVISioiLh78T3xcfzORXMqVSuya9AJTftR98zYo76ZOLVbM2VYY2lCiK2BEOytPwyFLPSqu3atcPvv/8OgB+EDho0yPJ5PF0+lng16o8JwAcacm6Wf6CWCkCkdE4xqfghxhOzHwxLXIi8fvnlF5Plid27d8f27dsRFBTkwVb5LoszMBYsWIA+ffqgXbt2WLhwIYqLi/HSSy/hyJEj2Lt3r8UTjxs3DtWrV0ezZs2EbdevX0fXrl2RkJCArl274saNG8K+hQsXokGDBnjggQfw3XffOXlbxFYpKfxg2JgrB8f25n8g9D0jZZzpkwlxG6klFYB9lUksVRCxFgwRmTVR9PN4cBwnBC/OnDljPXgBeL58LPFK9IxMjNm7/EJstsMLiS/Q7Ac/c+3aNXAcZxK8uHz5Mnbu3EnBCydwjDEmtbNRo0aIiIhA79698cgjj+Dhhx9GZGSkTSf+6aefEBERgVGjRuHYsWMAgOnTpyMqKgozZ87EokWLcOPGDbz11ls4ceIEhg0bhoMHD+Lff/9Fly5dcOrUKas/2MTERNvLnBFJWi2fEDI3l5+RkZLi3YNjX2svIZbY04850ye7A/XJRJJYRZEgpeVcEVvU4tU9QqOBwdcsX8/svSf/BRpNK9t99+5dhIWFWW6vYfYGpwCYrvwxShW/XIT4DXf1x/SM7J+Mq5DER8YjpXOKZADCnmOJ79Hr9ejXrx+2b98ubPvxxx/RsWNHD7bK90j1YxZnYPzzzz/4/vvvkZiYiH379mHAgAFo27Ytxo8fj1WrVlm8YMeOHcslItm6dStGjx4NABg9ejS2bNkibB86dCjCwsJQt25dNGjQAAcPHrTn/ogT7Eko6WmGkqI5OQBjZSVFte4tlU2IRzjTJxPiUY5UJpGq7tHGQtURA6PZEet+KQtedG4KsA1KhP37pfR7zWdviAUvHFkSQ/wKPSMTc5rmGmRPzhZN2mnMvGKJoWSq9ig9zPqDjz76CEFBQULwYsGCBWCMUfBCRlZrtERFRaF379548803sXDhQgwZMgR79+7Fc889Z/fFLl++jNjYWABAbGysUH7qwoULqFOnjnBcXFwcLly4YPf5if+jkqIk0MnZJxPiNo4sw3Cmusf9HBkjPgI0H/Gb3h8F/PB/sB44kSrfygXZ3w7i1+gZmThCrGKJu0umEvkdOnQIHMfhxRdfBAB06NABxcXFeP311z3cMv9jMYDx9ddfY+bMmXjsscdQvXp1TJ06FdeuXcM777yDS5cuydYIsVUsHCeevSs1NRWJiYlITEzE1atXZWsDcZ5WC6jVgELBf3XFrAhvKynqjnsmxMBdfTIhsnO0MomD1T10zf4DTgNof+Ff//Ef4OWnjA6wFDiR2sf0VGWECOgZOXBpj2qhXqKGYp4C6iVqk5kTlvYZ2FuxhHi3/Px8VKpUCYmJicK2c+fO4eeff0ZISIgHW+a/LFYhWb16NR555BEsXrwYbdq0QWhoqFMXq1GjBi5evIjY2FhcvHgR1atXB8BHk8+dOyccd/78eZPauMaSkpKQlJQEACa/KMSzDEs7DLMjDEs7AHmXpEhVTfFESVF33TMhBnL3yYS4jVyVSWxw6dIlxCaMEl7fSAWqVDQ7yFLgRBkvnnvD3jKwxK/RM3JgMiz/MMygMCz/MJDaZ7ycxN6KJcQ7McYwfPhwbNiwQdj23XffoVu3bh5sVWCwOAPjq6++wtSpU9G+fXtZHpT79u2LtLQ0AEBaWhr69esnbN+wYQPu3buHrKwsZGZmUj1cH+OupR3urppiCS1nIe4md59MiNs4sxzEDrt37xam4cfHx0N/Zg2qVBbJo2EpcCKVe4NyXhAj9IwcmCwt/7B1aYi9FUuI91m9ejUUCoUQvEhOTgZjjIIXbmIxgNG7d2+rJ5A6ZtiwYWjfvj1OnjyJuLg4rFixAjNnzsSuXbuQkJCAXbt2YebMmQCApk2b4umnn0aTJk3QvXt3fPjhh1Raxse4a2mHN5UU9bblLMT/OdMnU9k+4nEOLgex1axZs9ClSxcAwNSpU5GTkwOu3gj7AyduCrYQ30bPyIHJ0vIPW5eGiJVQpZKpvuHYsWPgOA5jx44FALRq1Qp3797F/PnzPdyywGKxjGqVKlUsZkxljOH48eM4e/asSxpnDZWI8h5qtfjSDpWKr2zijwLxnon87OnHnOmTqWwf8WnGZU2V8fxsiPsBBcYYGjdujJMnTwIAduzYgR49eniytcRHuas/dgfqj11DvUQtuvxDFakCAMl92ZOzXd004kK3b99GQkKCSX6bs2fPom7duh5slf+T6scs5sDYunWr1RPTNGbvpNXySxlyc/n8ECkprp2lkJJimg8C8NzSDncJxHsmnuVMn9yxY0dkm0XWtm7din379gHgy/Y9/vjjeOuttyTL9rVv397ZWyDEfoaypob8GYU5/GsAt2L6onLlysKh586dQ1xcnCdaSQIMPSMHppTOKSZ5LgxuF9/G002fRtrhNJN9tDTEtzHGkJSUhM8++0zYtmXLFmGJF/EMiwGMTp06uasdxEnGAYuoKKCgACgp4fe5IrmkWIAkNdW9QRNPM9ybu+7Z3UEp4n3k7pMtle1r166dcByV7SMuY2FmhUCsrKmuEEe2TkPLV0cIm0pKShAcbPGxhhDZ0DNyYDIs85i0cxLyivKE7XlFeUg7nIbRLUdjR+YO5N7MRXxkPFI6p9i0NER7VIvk3ckm7wNQbpvxucTeQ8tQ5PP5559j6NChwutJkyZhyZIlnmsQEdD/9H5AqwXGjQOKi/nXeXnljzEkl5RjwCtVfSM11T+XTlgKHGg07gkiUMUT4k72lu1LTU0FACrbR+xjYWaFSRBDpKxp6h7g+RUXAQCDBg3Cl19+aVswhBBCnKRprkHy7mSTAAbAJ+zckbnD6nIR88BDz4SeJjM3cm7mYOyWseA4DsW6YmGbcUUTS9VQKIjhnMzMTDRs2FB4Xb9+fRw9ehTh4eEeaQ8FqsqzmMST+IZJk8qCF5bIlVzSluobWi2fI0Kh4L9qy5fB9gmGwEFODsBYWeDA3fdDFU+IKxjK9gFwqmxfeno60tPTUa1aNdc3mvgPiZkVOGzWsZmVL+37DvD8Cv7vq1atKgteHEy6X/6UlQVDsnz0Px9CiNfQHtVCvUQNxTwF1EvU0B7V2pywU+xcSduSkHMzBwwMOTdzsDx9ebklKSX6EiF4YWBc0cTWiifEdkVFRUhISDAJXvzzzz84ffq0R4MX5r8vSduSoD0a2P+32RTAWLp0qU3biGeIzbgQEy9TeWlr1Te8ZdAvB28JHFDFE2JMrj6ZyvYRjxKZWSG6/X5Z05JSgNMA2/7kNx/7dhHGjBnDv7A1GEKIzOgZ2b9JDSCjwqNEj4+PtPywLRZ4YJCsp1COIUDiaACFiHv11VehVCpx+vRpAMD69evBGMMDDzzg0XZRoEqcTQEMwwOusdWrV8vdFuJCtiSXtHXWhFQgxLDdWwb9cvCWwIG17znxnL179+LRRx/FoUOH3HZNR/pkKttHvI5SogMz315Xg9zaCxA6umzT7WOfoelTM8o22BoMIURm9Izs+8RmWBhIDSABPkGnMVsSdjobYDAESKQCJdYCKMTU119/DY7jhNwWzz33HPR6vUnuC0+iQJU4izkw1q9fj3Xr1iErKwt9+/YVtt+6dQvR0dEubxyxTXS0+CwMw5J1WxI+iuVYGDkSGDGCLwtq/H5r1Te8ZdAvh/h48VKp7g4cUMUT73LkyBEMHjwYmZmZwrZdu3ahTZs2Lr2uM33y+vXrRbfv3r1bdHtycjKSfTHqSHxHyxTTHBgAEKTktxvZvn07+vSZzL+lZUtkZGSUP5cy/v7yEZHtlBuDuAA9I/sHsVwSI78aiRFfjYAqUiVaFhUArhddx5qBa+zOTRAfGS96Tg6cyUyMEEWISQ4MwDRAIlYNhSqe2C47O9ukBGqNGjWQmZmJSpUqebBV5Un9vgR6oMpiAOORRx5BbGwsrl27hilTpgjbK1WqhBYtWri8ccQ2S5cCY8eWVR0BgJAQYNUq2xM8is2aMOTxM08Yaa36hrcM+uXgLYEDd1c8IeXl5ORg5MiR2L9/v8n2KlWqYPPmzXj88cdd3gbqk4nbuGPQbzif8XVq9eRf/zoSUMZj8tZGWLr6OwDAm2++iVmzZom3tfR2+e1BSv58tiQKJcRO1B/7DktJEC0t6ci5mVMusGAQHxkPTXON3ckUpQIPYtVLDO0Ta7dx+ym5o+2Ki4vx8MMPmwTCjxw5gubNm3uuURZQoEocx8TSzfuIxMREpKene7oZXsHZEpsKRVnAQophJoa160ycCHz8cfn3v/AC8NFHtrfJW1D50sCVl5eHCRMm8EkCzXzxxRcYMmSI09fwp37Mn+4lIEgFKcyrgwB8MKBtquVBv7NBD6PrMgbUnAhcKeB37du3T7xspVhbASAkGkhcer89YjMzVED/bNvbRgKCP/Vh/nQvzjCfYQHwA8DUPqnQNNdAMU9hNQeFeRDD+P1S16Typ95n1qxZmD9/vvB65cqVGDt2rAdbZJtA/n2R6sdsCmD89ttvePnll/H333+juLgYOp0OFStWREFBgUsaayvqnOWjVovPmjCnVJafjZCaajqglzqXSuX+MqsUfCD2KiwsxIwZM7Bs2bJy+z744AO8+OKLkiVFHeFIP0Z9MnGapSCFI4N+R4MexraogcIc3LgDRCWVbb68Kg7Vx5wru45xkKT0NlAssobS0NZ1CkB0cMIBw/W2tYsEDOqP/Y96iVp0Cr4qUoXsydmS+8WOt2UAqT2qxbit40yWfoQGhWJlv5UBM+j0Nrt27UK3bt2E10OHDsW6detkfZYjriHVj9mUxPOll17C+vXrkZCQgKKiInz22Wd4+eWXZW8k8ZyUFD4YYUlQkG3JOb0lB4aj1VD8pQQssV1paSlSUlLAcRwqVqxoErx44403cO/ePTDG8NJLL3nFf3jUJxOnWara4UhCTDmqgBTm4uCZsuBFhRBAtwaoHnqB3yBWKlUseGHcVlsThRLiIOqPvZu1JIgpnVPKJeM0Zwh26OfokT05WwhEiCX/nLRzUrnyp8W6YkzaOUmGuyH2+Pfff8FxnBC8qFixIm7cuIH169d7xbMccZxNAQwAaNCgAXQ6HYKCgjB27Fjs3bvXle0ibqbR8DMpVCr+tfm/a6US0OnE32semPCWihmOVEPxpxKwxDLGGFasWAGO4xASEoI33nhD2Pfcc8+hoKAAjDH85z//QWhoqAdbKo76ZOIUS0EKRwb9MlQBeXdXFTw8m//7uE5A0Wo+kCxcVyxIIonxMzpq9eRnghgTSRRKiDOoP/Ze1qp1aJprkNonFapI/gGYg+kDsFS+AanyqnlF4kFVqe1EfqWlpXjsscdQu3ZtYdsff/yB27dvo0qVKp5rGJGNTQEMpVKJ4uJitGrVCtOnT8d7772HO3fuuLptxM00Gn6JB2PAmjV8MIPj+K/GwQ1z5oEJsdkctia+lHP2gyMzQfypBCwRt337digUCigUCjz33HPC9j59+uDSpUtgjOHTTz/1ukzUxqhPJk6zFKRomWL/oN/JmQ4dO3bElNU3AACfvwysMCwhMb6uvSVRC3OArDSg7mh+SQk4/qs9y1oIsYL6Y+8mNsOCA4eeCT2F15rmGmRPzgabw7Bm4BqoIlXgwEEVqZLMdWGpvCrxnEWLFiEkJAQ///wzAGDZsmVgjCExMdHDLSNysimAsWbNGuj1eixbtgwVK1bEuXPnsGnTJle3jXiQIZih1/NfNRrbAxPGszmMAyDWck9otcC4caazH8aNczyI4chMEG9Z/kLk9euvvyI2NhYcx6FPnz4wpP556KGHkJmZCcYYvv76a9SoUcPDLbUN9cnEaZaCFHU1/CDfnkG/I0EPAHfv3gXHcUJ1n8w9b+PpJyWu68iyD10h8O8OPh/GcD3/lYIXREbUH3s3TXMNRrccbTKzgoEh7XAatEfLP2Aaghnmy0XMSS1NkRId7r7SumJLW/zd/v37wXEcXn/9dQBA7969odPp8OKLL3q4ZcQVLJZRNVCpVCgqKsLFixcxZ84cV7eJeCl7Snkal1u11aRJQLHpskEUF/PbHUm86UgJVH8qARvoTp48iaFDh5qUygKA+Ph4fPnll3jooYc80zAZUJ9MnCZWwtS4akhdjX0DfWvnE5GZmYmGDRsKr4uKilDh4ibgsMQbWqbw5VWtVAwox96ZG4TYgfpj72NeteF28e1ylUYKSwqRvDvZ4cSa8ZHxosk/o8OjUXCvACX6EmFbiCIES3ssdeg69jKvumJY2gLAL5OIXrlypdyHT1euXEG1atU81CLiDjbNwNi2bRtatWqF7t27AwAyMjLQt29flzaMeCexmRlyyZNYHii13RpHZoI4s/yFeN7FixfRvXt3cByHRo0aCcGL0NBQ7NixA4wx5OTk+HTwAqA+mcikrsZjMxO++OILIXjRsWNHMMb44IV5ks6DSXzyTkN77Q1eAJSwk7gU9cfeRSw3hVT+CXtnURgTW5qiDFFiaY+lWNV/lckylFX9V7kteCC1tCV5t3+thdbpdOjRo4dJ8OLnn38GY4yCFwHApgDG3LlzcfDgQSHxSatWrZDt7nqYhDjA3oCLo8tfiOcUFBRgzJgx4DgOtWrVwnfffSfsW716NfR6Pe7du4cePXp4sJXyoj6ZeIUsLZ8oc50C+DIG+G2sdPDByLPPPotnnnkGAPDuu+/ixx9/5HfYUslEKZGMSQol7CQuRv2xdxEbwEuRSvBpC+Pkn+b5MmxdhuIK1qqu+IP3338fwcHB+PbbbwEAb731Fhhj6NChg4dbRtzFpiUkwcHBiIyMdHVbiA20WtuWcBDHObL8hbjXvXv3MHv2bCxevLjcvrfeegtTpkxBUFCQB1rmHtQnE48zlDQ1BBzEypkagg/3Z3bo9XqEh4ej+P5awd9//x1t27YtO96WSiYtU0yva04RCgRVAkqu27SMhRBnUX/sXWwdqEtVF7GHIVjhDubLYlI6p4heW2ppizPBGm9x8OBBPPzww8Lrxx9/HLt27UJwsE3DWeJHbJqB0axZM6xbtw46nQ6ZmZl4+eWX8cgjj7i6bcSMv5f4jJbIbyS1XW5yVkAh8tPr9XjvvffAcRwqVKhgErx47bXXUFRUBMYYpk+f7tfBC4D6ZOIBxrMttqiBQ5NsK2l6P/hw5coVBAUFCcGL69evlwUvDOeWWh5ivATEPMFoSDQQGg0h6efDK4Eh1yhhJ3Eb6o+9i9RAPTo82qbqIt5IqmSrWHJOqaUtzgZrPOnGjRsIDw83CV78+++/2Lt3r18GLwIxCau9bApgfPDBBzh+/DjCwsIwbNgwVK5cGUuWLHFx04g5fy/xuXQpEBpqui00lN/uav4eHPJl69evB8dxCAoKwmuvvSZsHz58OK5fvw7GGN555x1UqFDBg610L+qTiVsZZlsYLw8Rm3EhRhmPH3/8UVinXLNmTej1elStWrXs3L+Pu39uEWJLQIxzdwy5BgymgAXxHOqPvYul3BSeWtbhLHvyWlha2uJrGGMYMmQIoqKicPfuXQDADz/8AMYYYmNjPdw617AnWBXIOGaoJ+iDEhMTkZ6e7ulmuI1CwQ+uzXEcn+PBH3hqiYxaLV59RKXic2fYi5b6OGf37t3o168f7ty5Y7L9ySefxOrVq1GnTh0PtUx+/tSP+dO9ECNb1NIBBkuClHjztx6Ys4QvKTl58mS89957psd8GSMdDFGqaAkIcSt/6sP86V7sZetyC285rzWKeYpyVVQAgAMH/Rw/GQCYWbFiBZ577jnh9ezZszFv3jwPtsg91EvUokuAVJEqZE/Odn+DPEyqH7M476ZPnz7gOE5y/9dff+18y4jNAqHEp6fyT+RKLJmU2m6JYTaHYbaMYTYHQEEMSzIyMjBo0CCcPXvWZHuTJk3w+eefo1mzZh5qmfegPpl4hK1lSM3yT7R8neHIP3zwYtu2bejdu3f591iaydE/2/62EuIm1B97L1fkpvBkeVJ/zmth7siRI2jZsqXwuk2bNjhw4ABCzado+6lASMIqB4sBjKlTpwLgp/CMHz8en332mVsaRcSlpJgOjAEq8SkXOYNDlpb6UADDVFZWFkaMGIEDBw6YbI+OjsbmzZvx2GOPeahl3on6ZOIRynjxGRgh0UBIBB/gMEqYeefOHURERAiH5eTkIN68M83SmlYXIcTHUH8cWCwt43B1ACOlc4pJ8ATw/bwW5goKClC/fn1cu3ZN2JadnQ2Vys7KUz4ukIJVzrAYwOjUqZPw94iICJPXxP0Mg19amiA/OYNDcs7m8EfXrl3D888/j6+++qrcvk2bNmHgwIEeaJVvoD7ZyxgG4WYDeL8jVvkjSAkkLi13v8eOHUPz5s2F18XFxQgJCTE9n3kFEzEhbsreTIiDqD8OLJ78ZNwQIPHE8hVXY4xh3LhxWL16tbBNcsZeAAiEYJUcbEriCcDiNDniPhoNn5NBr+e/UvBCHhoNkJrK57zgOP5raqpj31+pWRv+tNTHXoWFhXjxxRfBcRyqVatmErz46KOPoNfrwRij4IUdqE/2MLHElgeT+O3+xrzyh1LFvzYLXqxatUoIXvTt2xeMsfLBC4AP+lgKXnAhfHCEEB9B/bH/k/oE3F2fjGuaa3w2CamUdevWQaFQCMGLKVOmgDEWsMELwL+SsLqSxRkY169fF/6u0+lw48YNGOf8jIqKcl3LCHEzufJv0FIfXmlpKRYsWIA5c+aU2zd79my88cYb4oMbIon6ZC8iNgjXFfLbfWUWhj0zSOpqLN7XoEGDhMBkamoqxo8fL31dSzk1KHEn8RHUHwcW+mRcPv/88w8aN24svH7ggQfw119/ITw83IOt8h6uyOHibywGMNq0aQOO44QO+cEHHxT2cRxXLtkeISSwl/owxvDpp5/i+eefL7fv+eefx9tvv22yNp7Yh/pkLyI1CLc14aWnmS/jMMwgAewKHpSWlpoEIg8fPowWLVpYfpNUTg2lihJ3Ep9B/XFg8edlHO5SWFiIpk2bItuovN+pU6eQkJDguUYRn2QxgJGVleWudhDiVzxVTcVTtm7div79+5fb3r9/f6SmpqJatWrub5Qfoj7Zi0gOwn1krZgMM0jOnz9vUtK4oKAAlSpVsv5GqZwaLemTTOI7qD8OPPTJuONefvllLFu2THj9xRdfYMiQIR5sEfFlNufAIIQQYwcOHECNGjXAcZxJ8KJdu3Y4ffo0GGPYvHkzBS+If2qZwg+6jfnSINzJGSQ7duwQgheNGzeGXq+3LXgB2JxTgxBCiG/bvHkzOI4TghcTJkyAXq+n4AVxCgUwiMtotYBaDSgU/FetTLntXHVeYt3ff/+Nli1bguM4dOjQAVeuXAEAqNVqpKengzGGX3/9FfXr1/dwSwlxMV8fhEvNFLFhBsnUqVPRq1cvAMCsWbNw4sQJ+5MY1tXwy0WG6/mvvvJ9I4T4PO1RLWIWx4Cbx4GbxyFmcQy0R+lhUk5nz54Fx3FCcvbatWvj1q1b+PjjjynpLXGaxSUkrnDy5Ek888wzwuuzZ8/izTffRH5+Pj799FPh09oFCxagZ8+e7m4esZFWaznHg1ZrmsgyJ4d/DTi3tMJV5yXSLly4gDFjxuCHH34w2V6hQgVs2bIFTz31lIdaRoiHWUls6dUcWMbBGEN8fDzOnz8PANi9ezeefPJJV7eUkIBBz8iupz2qxejNo6FjOmFbXlEexm4ZCwC0RMRJ9+7dQ2JiIo4dOyZsO3bsGJo2berBVhF/Y9MMjJEjR9q0zRYPPPAAMjIykJGRgUOHDkGpVGLAgAEAgFdffVXYRx2zZ9gyu8EQRMjJARgrCyIYH5ucbFqFA+BfJyc71z5XnZeYys/Px8iRI8FxHOLi4kyCF2vXroVer0dRUREFLzxEzj6ZBCg7Z5Dk5+dDoVAIwYuLFy9S8IIQ0DOyr5m0c5JJ8MKgRF+C5N30MOmM119/HRUqVBCCF2lpaWCMUfDCS2mPaqFeooZingLqJWqfmoVkUwDj+PHjJq91Oh0OHTrk9MV3796N+vXrQ6VSOX0u4jyxwMSIEUBEhP3BiVyJZdRS223lqvMSPmo+bdo0cByHqlWrYu3atcK+t99+GzqdDowxaDQamv7nYa7qk0mAsXEZR3p6OqpWrQoAUCgUKC0tRc2aNd3XTkK8GD0jewdbB2N5RXmS58i96RsPk9428Pz222/BcRwWLVoEABgxYgT0ej1GjRrl0XYRadqjWiRtS0LOzRwwMOTczEHStiSP/y7ZymIAY+HChahUqRKOHDmCypUro3LlyqhUqRKqV6+Ofv36OX3xDRs2YNiwYcLrZcuWoUWLFhg3bhxu3Ljh9PmJfcQCEwBw5w4wdmxZEMOWIEK8xDJqqe22ctV5A5Ver8c777wDjuNQoUIFvP3228K+qVOnoqioCIwxTJkyBQoFpczxNFf3yYSYe//99/HQQw8B4B9KdTodgoKCPNwqQjyPnpG9h1yDsfhI73+Y9KaB5/nz58FxHHr06AEAiIyMRH5+PtasWUMfdHm55N3JKCwxHfQVlhT6zCwkjhkKWFvw+uuvY+HChbJeuLi4GLVq1cLx48dRo0YNXL58GTExMeA4DrNmzcLFixexcuXKcu9LTU1FamoqAODq1avIyREpY0ccolDwMy+kqFRAdja/tETs227YD5TPVQEASiWQmipvDgy5zhtIGGNYt24dRowYUW7fyJEj8cEHHyAyMtIDLQtMiYmJSE9Pt+s9ruiT5eDIvRDv1fmRptjz6wkAgPbVGAx/eUnZLI0sLV9ytTCXT/xZqyfw746y1y1TfDc/CAlY3tIf0zOyfdRL1Mi5Wf5eVZEqZE/ONtkWszhGchbG2oFrvT4Hhj336iolJSXo2LEjfvvtN2HboUOH8OCDD7rl+sR5inkKMJQf9HHgoJ+j90CLxEn1yTYFMAA+kV9OTg5KS0uFbR07dnS4QVu3bsWHH36I77//vty+7Oxs9O7d2yQBjBh6WJaXVGDCgOMAvd72IIK1RJ+OctV5/d3333+Pfv364e7duybbu3btilWrVqF27doeallgc7Qfk6tPljNpXED0yeYDdz8cqN+7dw8VKlQQXv/zX+CBWuCTfLblB0flEoCaMxzrZ98b4t883R8b0DOyfewZjGmPajFu6zgU64pNtr+Q+AI+6vWRS9spB08PPOfPn49Zs2YJrz/++GNMmDDB5dcl8vKGQJgtpPoxm6qQzJw5Exs2bECTJk2EqaMcxznVOa9fv95katzFixcRGxsLgK8Z3KxZM4fPTRyTkgKMHCk9C8OwTMMQLLAWRNBoXBNYcNV5/dGff/6JQYMGIdswNea+Fi1aYMOGDWjcuLFnGkacImefbEgaB/Brt2vXro0BAwZg1apVePXVVzF16lQ5m+7bsrSmA/fCHP414DcD9bNnz5qUQS5cBYSH3n+hK+SDN4a/W2I41k++L4RIoWdkz4uPjBcdjIktCTHMsEjenYzcm7mIj4xHSucUr595YWDPvcpp3759eOKJJ4TX/fv3x6ZNm2h5sY9K6ZyCpG1JJstIlCFKpHSWrkTmTWwKYGzevBknT55EWFiYLBctLCzErl278Mknnwjbpk+fjoyMDHAcB7VabbKPuIdGA/zyC/Dxx+X3hYTwQQrjY30tiBAoMzfOnj0LjUZjMrUPAKpXr46vvvoKHTp08FDLiFzk7pMNKGmcFYeTyw/cfXmgbjab5MsL/TDkpfcBAO0aAL/OE3lPoR1J7gpzgC1qv5ylQogBPSN7ni2DMe1Rrc8GLYy5e+B5+fJlk6TNQUFBuHz5MqKjo11yPeIevh7IsymAUa9ePZSUlMjWOSuVSuTlma4/W7NmjSznJs756COgQwdg0iTA8COKjgaWLvXtwb75shdD6VfAt+/L4OrVqxg/fjy2bt1abt/mzZvRv39/9zeKuIzcfbKBWNK4//3vf0hMTMQ777wjVKIIWFKDd3sG9d7CbDZJ0vs5+HQvH7xYvHgxpiV8yAcgzCnvf8ontk+MH85SIcQYPSN7nrXBmCHxpWHQb0h8afxeX+GugadOp0P37t3xww8/CNsOHDiA9u3by3od4jma5hqf+/03sCkHxqBBg3D48GF07tzZpIN+//33Xdo4awJpfV8gk2PmhC2JR33NnTt3MGXKFNFPYlJTU/Hcc89RFmgf4Eg/5oo+mZLG2WCLWmJQr+LLkPqS+/ei1wOVnwPu3OM3/7KwBh6Zean8chnAvhwY5nzxe0QCjrf0x3KgZ+QyvrLe31u89957eO2114TXVftVRX7rfJ/7lJ74PqdyYPTt2xd9+/aVvVGEWCPXzAlbSr/6gpKSEsyfPx9vvvlmuX1vvvkmXn/9dQQH2/TPmvgwV/TJO3fuxIMPPogaNWoAgPAVAMaPH4/evXuLvi8pKQlJ9/9RJiYmytomr9MyRXxQ39I31oyaKMzF1QKg+gtlm64tB6IrXeFfGGZLWEpYKlqFRCKA5YuzVAixAT0je7/cm+L9j9T2QPX777+jXbt2wutm7ZrhTK8zuKHjy/b68swV4l9sGumMHj0aRUVFyM3NxQMPPODqNhE/Z8+MiuRk02onAP86Odm+AEZ8vPgMjHjX5jySBWMMy5cvx8SJE8vtmzhxIhYvXoyKFSt6oGXEU1zRJ1PSOBvYMqj3ET9nV8djyZcBAFERfPCC41C2RATg70vq3qT2Sc5S8YHOlhAH0DOy97Ml8aW/5MhwRF5eHmrWrGlSRefixYtot6Edim4WmRxbWFKI5N3JAfO9Id7JptSx27ZtQ6tWrdC9e3cAQEZGBkWbiUMMMypycvhqJ4YZFVqt+PFyzZxISeFLvRpTKk0Tk3qbzZs3g+M4KBQKk+DFwIEDcfXqVTDG8OGHH1LwIgDJ3ScbksYNHDhQ2DZ9+nQ0b94cLVq0wN69e/Hee+853W6/UFfDL4UYrue/envwIkvLBxXWKfivWVosWLBACF5M7ALkfXI/eCHHbJKWKfx5jPnqLBVCbEDPyN4vpXMKlCGm/ZJx4ktDjoycmzlgYMJMA+1RiYdTP6HX6zFgwADExMQIwYs9e/aAMYaaNWvSzBXitWwKYMydOxcHDx5ElSpVAACtWrVCVlaWK9tF/JSlGRVipGZI2DtzQqMBUlP5nBccx39NTfW+BJ4///yzkHfAeDD5yCOP4OzZs2CMYdOmTfjuuxio1YBCwef3kAoAEf8kd59sSBoXGRkpbFuzZg2OHj2KI0eO4OuvvxZmYxAfYshjUZgDgAGFOWjTeSSS73e4Wz55FR++qALA8Tkq2qY6H5Cpq+HPo5T5vIR4KXpG9n6a5hqk9kmFKlIFDhyiw6MRHhyOkV+NhHqJGpN2TjKp6gGUzTTwV5988gmCgoKwZcsWAPwyZMaYSalUqdKsri7ZSog1Ni0hCQ4ONnmwBUDJAYlD7J1RkZJimgMDcHzmhLeWfj1+/DiefvppnDhxwmR7/fr18eWXX6JVq1Ym2/29ogqxjvpkP2FWxlT25ShGZV8L7wEVxwEAn7c7KysLarUawLvyXc/A0tITQvwM9ce+wVBxQawiiRR/nGmQkZGB1q1bC6/btWuHn376CSEhIeWOdXfJVkJsZdMMjGbNmmHdunXQ6XTIzMzEyy+/jEceecTVbSN+yN4ZFe6cOaHVwm2zGs6fP4/OnTuD4zg0a9ZMCF5ERETghx9+AGMMp0+fLhe8AOyfxUL8D/XJfkBkdgQOJvHb5XI/ceaJ84bgBe9eGu4HLwghzqL+2L20R7VQL1FDMU8B9RK13cs8kncnl5ttIcWfZhrcvHkTVatWNQle5Obm4tdffxUNXgDlZ66oIlVI7ZNK+S+Ix9kUwPjggw9w/PhxhIWFYfjw4YiMjMTSpUtd3TbiJ4wDA7dvA6GhpvutzajQaPhSp3o9/9WW4IW9wQh7c3M4cs38/HxoNBpwHIc6depgz549wr5169aBMYZbt26hc+fOFq/jLxVViOOoT/YDRrMjBLpCfrtclPH4336g6Qz+ZfcWANMCocEQ8mEQQpxD/bHzbA1KyJGrwtZZFaFBobhdfBuKeQrELI5BzOIYh4MmnsQYw8iRI1GlShXk5+cD4KuOMcZQp04dq+/XNNcge3I29HP0yJ6cTcEL4hU4xhizdtDGjRsxZMgQq9vcjWpcez/z5Q4AEBICVK4MXL9uvQqJXNdUKi3P3FCrxauUqFR80MTRay5bdhdHj/6faALE9957D6+88goUCpviiLK1lXgXR/ox6pN9XJYW+HWExE6OTxAqg6F92uPz7b8BAD4cA0zsanZAkJLyUxBihPpj9zNf0gHwyxTEPulXL1GLLvlQRaqQPTnbputJnSM6PBoRoRHIvZmLqPAoFNwrQIm+RPQcUu3zNmvWrMGoUaOE1zNmzMCiRYs82CJC7CPVj9k0clq4cKFN2wgxJ7bcoaQEiIiQnlHh7FIOR5ZYODurwfSaOgCLUVjIYdy4cJPgxYwZM3D37l0wxjB58mS7gxeAb1ZUIfKiPtmHGZaOSJGh3GhpaSk4jhOCF3++E1s+eAHIP+ODkABE/bFzxJZ0SCXQlKMqhlRFkqU9lgozDSJCIySDF5ba5y1OnDgBjuOE4EXTpk1RVFREwQviNywm8dy5cyd27NiBCxcu4JVXXhG2FxQUIDjYpvyfJMDZGxiQI0GlI8GI+HjxWQ22VjvJyWEA/gdgTLl9Y8aMwdKlS1G5cmXbTmaF4fuQnMzfkytmsRDvRH2yHxBbOmLgaLlRo2Sg/96thdrPXhB23bx5k+971ilgSOBpopDWnhHiCOqP5WFPUCI+Ml509oQ9uSoMsyaSdycj92Yu4iPjkdI5xWQ2hS0BEW9M8Hnnzh00atQI58+fF7adPn0a9evX92CrCJGfxY9/a9WqhcTERFSoUAFt2rQR/vTt2xffffedu9pIfJi9STvlSFDpSOlVR2c1fPvttwgLCwP/T2mM0Z7uAP6FSsWwatUq2YIXBo7kBSG+j/pkP2ApYCC1nCNLy+esWKfgvx6cWPb6yxjgt7FAYQ52HWVC8KJeDQX0a4HKe1rw75ea2SHDjA9CAhH1x/Kwp1Sn1OwJe6tiWMvrYEtAxJsSfDLG8MILLyAiIkIIXmzatAmMMQpeEL9kMUTcsmVLtGzZEsOHD5fMUEuIJfaWQZUjQaUjpVftmdWQnp6OQYMGIdesURzXCoytB9DIpmsSYi/qk/2AMv5+5RHz7Srp4MXBpLJZG4U5wOmPy/YX5wEAXt8ALNrGb5rZB1g4VF92/MEkoO5oICvNdPaHozM+CCHUH8vEnlKdtsyecFWbxI7xBps2bcLgwYOF1y+99BI++OADD7aIENezaY5bdnY2Xn/9dZw4cQJ3794Vtp89e9ZlDSP+wd7lDs4u5XDkmsbvkzrmzJkzGDZsGP744w+T7TVr1sRXX32F9u3bQ6ulZR3EPahP9mEtU0wDEoDlQIKlJSfgqyY1eA04e4V//d0MoFsLs4N0hcC/O/gZHveXmkAZz1+TEngS4hTqj51jb1BC01zj8uSZhvOP3jwaOqYrtz86PNrjCTxPnz6NhIQE4bVKpcLx48dRsWJFD7aKEPewKYPg2LFj8cILLyA4OBh79+7FqFGjMHLkSFe3jXiAswk0xdiz3EGuBJVyLLG4cuUK+vbtC47j0KBBAyF4oVAosG3bNjDGcPHiRbRv3162axJiC+qTfVhdDR9IUKoAcPxXS5VALCw5KSgEFCPKghcXlokEL4zPU1cD9M/mq5z0z6bgBSEyoP7YeYYlHWsGrgEAjPxqpMfLlWqaa5A2IE0y4aen3L17F40bNzYJXpw4cQLZ2dkUvCABw6YARlFRETp37gzGGFQqFebOnYs9e/a4um3EzQwJNHNy+E/1DAk05Qhi2Eqj4cudqlQAx/FfLZU/ldvt27cxfvx4cByHGjVqYNu2bcK+zz77DHq9HjqdDr1793ZPg+5zRWCJ+C7qk32cPYEEiRwVf2UDkePLXpf8D6hV1cI1KdcFIS5B/bE8DOVUc27mgIEh52YOkrYleTyIkdonFapIFThwUEWqPFo+ddq0aQgPD8c///wDAFi7di0YY2jcuLFH2uNPtEe1UC9RQzFP4fHgGbHOpgBGhQoVoNfrkZCQgGXLlmHz5s24cuWKq9tGZGZtECxHAk05ODuTwd7BfklJCWbNmgWO41CpUiV89tlnwr758+ejpKQEjDE8++yzWLeOc3sgwRsCS0SaJ4JL1CcHkJYp/BITIx//ADx4v19+ph3AtEBwEABwQGg0wJmtx6dcF4S4DPXH8rCnnKqBI4NO8/dM/GaixXNYS/jpDt988w04jsPbb78NgK9up9froQnwqb5yBR28MXhGLOMYYyJ11Uz98ccfaNy4MfLz8zFr1iwUFBRg+vTpePjhh93RRkmJiYlIT0/3aBt8hXl5UoBfmmE8u0Gh4AfI5jiODyb4AlvuE+AzNn/44Yd4+eWXy53j5ZdfxqJFi6A0W8ti67nlplaL5wVRqfgAD/EcOX4nHOnHqE8OMEZlUnssZvj2ML85bQIw6rH7xyhV/GwOs+Mp1wUhtqP+2HMU8xRgIqWeOXDQzyn/EGoYdJon/7Q0Q0LsPeasncOdcnNzoVKphNfR0dE4e/as7JXtfJEjP38p6iVq0fK8qkgVsidnO9tU4gSpfsymAIaYqVOnCpFAT/G1ztmTbBkE+8NA2do9fPnllxgyZEi5/UOGDMHHH3+M6Ohoh8/tKv4QWPIUVydWleN3Qq5+jPpk/1ZcXHy/ZDPvxGKgce37L4KUlvNoWELBDkIE1B97jqVBZErnlHJJPpN3J9s96JS6hj3ncIfi4mJ06NDB5OeXkZGBli1beqxN3kbOoIO9wTPiPlL9mE1LSMR88cUXTjWIuJct5UnlSqDpSeL3+SNycqqC4ziT4EXHjh2RnZ0Nxhi++OILi8EL6XPbV+LVEVIVWOypzBKI3LH0xlO/E2J8sk/O0gJb1MA6Bf81S+SHY8sxfi47O9skeHHn+Ao0TrAxCaglhhKthTkAWFnJ1QD8HhMiJ5/sjz0spXOKaMLMngk9Raf3SwUicm9K/wdsaZ8jx7nCvHnzEBYWJgzaPv30UzDGKHhhRupn5MjPLj5S/IFaajvxPIcDGA5O3CAeYssg2NMJNOVQdj9HATQGwAF4HEA+AKBhw4Y4fPgwGGP48ccfTabm2X5u27bLxR8CS57gjpwu3hRc8rk+2ZbBs7cOsB0JqjgYiNma+hrq1q0LAGhTPxTs7Foom4yTp5qIWIlWXSG/nRDiMJ/rj72AVMLMHZk7RHNjBHFBouexNOi0dUDqiYHrnj17wHEc5s6dCwAYPHgwdDodnnvuObe3xRfIGXSQCp6ldKYHbW9lMYBx/fp10T95eXnUOfsYWwfBvlwK9Ny5c1AqnwAftGgB4J/7eyrj//5vDxhjOHnyJFq0kKozaJm9gQS5kjv6Q2DJE9wxO8LdwSW/6pNtGTw7O8B2xewNR4IqDgZiXhrVDf2ffw8AsOBpIP3NYnkDOFIlWi2UbiWE8PyqP/YSYgkzpT5R1zGd3YNOsYGqOcM53FWV4uLFi+A4Dp07dwYAhIWFIS8vDxs3boRC4fDnzH5PzqCDt1WbIdYFW9rZpk0bcBwn2hGHhoa6rFFEfobBrivzAXjCjRs3MHHiRGzYsEFk7+dQqZ5Gz5588GDhQufu257voXlyR8PyBePz2HttX/9ZuVt8vHh+CjlnR7j735Vf9cm2DJ6dGWAbggaGAIghaAA4l+PBUlBF6rx2vocxhpiYGFy/fh0AsH828OgDNl7LHsr4+0EVke2EEIv8qj/2YvGR8XblxgD4/AjG2wwDUcNX4/f0TOiJHZk7yp3DOEGkYdmK8TmcVVpaiq5du2Lfvn3Ctt9//x1t27aV5fz+TuxnafyzduR8FLDwHQ4n8fQGvpagiMijqKgIr7/+OpYuXVpu3/vvv48XX3xRiFpT5ZDA5amfvb38qR+z6162qCUGz0bVNGw5xpnzO2KdAhBJ9gVw/JIOJ9+Tl5eHmJgY4fXV5UBMJTuuZQ/zIA/gXEJQQnxcwPbHXsyeahNyVaZwdVWK//73v5g+fbrweunSpXjllVecPi8h/kb2JJ6EuJNOp8PChQvBcRyUSqVJ8OL//u//cO/ePTDG8PLLL5tMuXNHHgQx3pTcMVDR0hsv1zKFHywbC1Ly2+05RoqrlkdIzU6wNGvBxvf8+uuvQvCiUqVK0G2KFwleWLmWPepq+GCFUoaEoIQQ4gL2TO9P3p0smi8jebd9D31yJog09ssvv4DjOCF40b17d5SWllLwghA7WVxCQognMcawevVqjBs3rty+cePG4b333rNaC9uTlUNcvXyBWEdLb7yYYZBsqYSnLcdIcdXyiFo9gdMfi2+X0jKl/EwHcHz7tqiBlin475f/Cg+1SUlJ+OSTT6RnSNgSwLFVXQ0FLAghXs3W6f1yBR6klq04mtzz2rVrqFatmsm2y5cvo3r16g6dj5BARzMwiNfZsWMHgoODoVAoTIIXvXr1wsWLF8EYw4oVK6wGLwDbq0TIlXDTwJsrh8h9r4Q4rK7GejUNW44R48zsDUv+3WHfdsBspgPAJxq+v6SkMAftnxolBC++/PJLPnhR7n00Q4IQQiyxpzKFpSSdciWI1Ov16NOnj0nw4scffwRjjIIXXspdyVuJcxyqQmL4QwKTKwbABw8eRO3atcFxHHr16gWdTgeAX/t06tQpMMawfft21KxZ067z2hJIMORKyMkBGCtLuOnMfXnr8gVX3CtxH+qT7eCqwb+jS1MMgRilCobgRVExwGmA3zL5fBZnzpzBoEGDxN/nbMlUQoisqD/2PrYGHgy5MnJu5oCBCUk6DYNVOapSfPTRRwgKCsL27dsBAAsWLABjDB07dnTqHmmA7TrWfi+I97CYxFOhUCAuLg7BwfxKE+NDOY7D2bNnXd9CC/wlQZG7abWOV02QMzHiqVOnMGzYMPz5558m2+Pi4rBp0ybZMjFbu99ASrgZSPfqK+zpx6hP9gLOJge9n9Dz1EXggallm++uBsJG+2xObUL8AvXHvk97VGu1MoUrk3QeOnQIiYmJwusOHTpg7969CAkJceq8gHxJSok4Vydv9Wa2/LvxBKl+zGIOjJdffhn79u1Dhw4dMGzYMDz66KPgOM5ljSSu52x5T0tJMW15/6VLl/Dss89ixw7T6dbBwcHYunUreva0sI7cQdbyIARSws1Auld/RH2yk7K0juXTMCaWz8KepSnKeKzblQPNR/zLzk2BH/4PRstLCCG+gPpj72RLvgxXJOnMz89HnTp1cPv2bWHb+fPnUbt2bYfPac5SklJvGGz6Olclb/V25oExV5QNlpvFJSRLly5FRkYGhgwZgjVr1qB169aYPn06srKynLqoWq1G8+bN0apVKyFKef36dXTt2hUJCQno2rUrbty44dQ1fI278hI4W5XDkQHwrVu38Oyzz4LjOMTGxpoEL1atWgW9Xo+SkhK7gxdyfc9szZPhDwLpXv2Rq/pkj8nS8jMa1in4r1kunKZpSIhZmAOA8V8PJtl/TSeXpoz6Xx0hePH+qPvBC7kTcxJCXI6ekV3DHUsk7MmVYQ1jDMOGDUPVqlWF4MV3330HxpiswQsgcAfY7iLn74Uvkat6jxhX/Xu2msST4zg88cQTWLx4MSZMmIBVq1bhhx9+cPrCe/fuRUZGhjAtZNGiRejcuTMyMzPRuXNnLFq0yOlr+Ap35iVw9hN4WwfAxcXFSE5OBsdxqFy5MlauXCnsW7RoEUpLS8EYw5gxYxz6xELO75k3J9yUWyDdq79yVZ/sdnIFFGx1ONmsCgj414cd+A/aPC8FYDUQo9PpoFAosGbzzwCAP/5bEy8/RYk5CfFl9IwsL3flIJArSefq1auhUCiwYcMGAEBycjIYY+jWrZtsbTUWqANsd5Hr98LXuCow5sp/zxYDGHfu3MG6devQr18/9OzZE7dv38aff/6J8ePHO31hc1u3bsXo0aMBAKNHj8aWLVtkv4a3cnZWhD2c/QTe0gBYr9dj6dKl4DgOYWFhWLBggXDM5MmTUVhYCMYY4uJmoH79IKdmTsj5PfPWhJu2smcmiq/fa6BzZ5/scnIGFGzhaPJNa2wIxFy6dAnBwcHCGvkbN24gcepFSsxJiA+jZ2T5ufKTYGPOJuk8duwYOI7D2LFjAQCtWrXC3bt3MX/+fFnbac7aAJsSfDpHjuStvshVgTFX/nu2mMSzYsWKSEhIwLBhw9CgQYNyn5QPHDjQoYvWrVsXVatWBcdxeP7555GUlIQqVaogPz9fOKZq1apWp8j5S4IihYKfRWCO4wC9Xt5ryZGE0zwpZp8+n2PZsqHljmvXbijOn/8QFy5ECckzAXmSgLrze+bN5EyqSjzDnn7MVX2yXOzqk9dZmHk13AXJLJ1Nvungeffs2YPOnTsDAOLj45GdnQ0ue53zuTgIIbLzhv44kJ+RuXnS/y+wOZ5Pcnz79m0kJCTg0qVLwrazZ8+ibt26bmuDVLJFSvDpXbw1KaYYV/3uKOYpwFD+3y0HDvo5tg3WHEriOWTIEHAch3/++Qf//POP6cU5zuHO+ZdffkGtWrVw5coVdO3aFY0aNbL5vampqUhNTQUAXL161aHre5v4ePHKEK7IS2AY1DpahcRwjlq19qJ///7IySnAsmVl+5544gmkpaXhp5/qiCYLDQ+3LwmoVAURd37PvJmzSVWJb3FVn6xWq1GpUiUEBQUhODgY6enpuH79Op555hlkZ2dDrVbjiy++QNWqVeW4jfsUAMT+A7O6stExzibflGJhZsecOXPw5ptvAgCmTp2K//73v2UzNgztMMzYACiIQYgPoWdk+QVxQdAxneh2T2KMISkpCZ999pmwbevWrejbt6/b2yKVpJQSfMpDjsCDryXFNLRJ7oBLfGS8aFUXOZY8WZyBYcmmTZvK16t3wNy5cxEREYFPP/0U+/btQ2xsLC5evIjHH38cJ0+etPheX4wui/GVT9GPHDmCwYMHIzMz02R748aN8cUXX6BZs2bCNqlynVLEZk5Y+r4A5fcBQHQ0sHSpd33fHGFrqVuaieL75OrHnOmT1Wo10tPTERMTI2ybPn06oqKiMHPmTCxatAg3btzAW2+9ZfE8Xj0DA5CuQiJszwG4IIDp+BkUtsyMEJmBwRjQZEYw/rlQCgDYsWMHevToIXk8AOdnghBCnOYN/bGxQHtGdmQGhqs/6f78888xdGjZLONJkyZhyZIlsp1fLs5+2u1LMwZcRa6ZCIFcjtWYHN9PqX7M4Y+6Xn31VYfed+fOHdy6dUv4+/fff49mzZqhb9++SEtLAwCkpaWhX79+jjbN53hzXoKcnBx07NgRHMehZcuWQvAiKioKP/74IxhjOHHihEnwArC/LKfYzAlrswtSU/mAhbG8PNclQHUXexKUUlURYuBonyzFL9dcmyffNAQvhBwW4IMXgO1JRVum8DM57rt9F1CMgBC8OHfuXFnwAnBdLg5CiNegZ2THqCLFy0lLbXdlksDMzExwHCcEL+rXr4/CwkKvDF4AzuUxcFfyVG8nV84GqhbDc2VOEYcDGA5O3MDly5fx6KOPomXLlmjbti169eqF7t27Y+bMmdi1axcSEhKwa9cuzJw509Gm+SSNBsjO5j81z872bPAiLy9PmBqpVquxf/9+Yd/GjRvBGENeXh46duwoeQ6pAXR0tO1VMKxVTNFogIiI8vtdlQDV1QzJOEeMsD1BKVUVIQaO9skAP925W7duaNOmjTD9+PLly4iNjQUAxMbG4sqVK6LvTU1NRWJiIhITE+2bshwabd92VxFLJmpgS1JRo7KqR3KBSs+W7SopKUFcXJzp8UqJzlFqOyHE59Azsv20R7W4XXy73HZLVSBckSSwqKgICQkJaNiwobDt5MmTOH36NMLDwx0+r6s5U0HDXclTvZ1cgQeqFlNG01yD7MnZ0M/RI3tytmyzeizmwLDEkdKXAFCvXj0cPny43Pbo6Gjs3r3b0eYQG1hallBYWIgZM2ZgmXFCi/uWLVuGiRMn2vUzT0kRX/6xdCn/d1uWR9iS58LZsrCeZviZ5OTws28sPfOI3ZMcOU2If3C0TwacW3OdlJSEpCR+bWdiYqLtF63SCrgi0udXaWX7OeRgbeaDtf33l598uj0HSSv4TQMHDsSmTZvEj3dVLg5CiNegZ2T7iE01BwAFpzAZSBuSVRqWOogtmQAc/6T71VdfNZlhsX79epPlI97MmTwGNGOAJ1fOhpTOKaJLJ/y9HKs7WQxgNG/eXLQTZozh8uXLLmsUkZ95PomcHGD8+FJs3foWNm58o9zxs2bNwqxZsxASEuLQ9awNrC0NsC0N6s1nF/hyMk/zn4m1D2yk7kmjoYCFga25Q3yVq/rkWrVqAQCqV6+OAQMG4ODBg6hRowYuXrworLmuXr26w+cXdWWvfdtdRRkvnpPCeL+U+8tP+i0uxNd/8ptWPB+KcTMsJO8z5NSgKiSE+DR6RpaP2AwAANAzPneDYUnDL7m/IO1wmuixxuIj4+3K6bBt2zaThJzPPfccUlNTnfpgwBOkEnxa48pki75ErsCDq5JikjIWAxjbt293VzuIi5Xlk2AAVgJ4DkVFwMaNZcckJSXhnXfeQYTYugwHODKwFhvUG4IYKlX5AanUTA9vXEJhPri+fbv8UhEpISHeeU/eRCxId39igN8EMVzRJ9+5cwd6vR6VKlUS1lzPnj1bWHM9c+ZMF625lkoq5ubssy1TgN/HAfri8vuszIwoOfR/CB1a9o/46CKgWZ1iPjhhKSBRV0MBC0J8HD0jy0ds8GyusKQQqYdSRauUGFOGKNEzoadNVSCys7NNSqDWqFEDmZmZqFSpkiO34TBPJ9CkGQM8OQMPjgaTiG0s5sAoKSnB+fPnoVKpTP7k5uaitLTUXW0MCIb8BwoF/1XuJJQ5OdsBcOB/5M8Z7emLy5cvgzGGTz75RDJ44er2GYgl7jQEL8Ryg3hzAlRjYok58/Jsf7+PfQjgEZaSvvoLV/TJgbzmGlla4NAk8eBFSDSf20Ii0JCbm4vQIWXTa2+tAJrVuf/CkYScWVq+Qsk6Bf/VWvJQQohH0TOyfGwtk2oteMGBw+iWo7Ejc4fFnA7FxcVo3bq1SfDiyJEjuHTpkkeCF55OoOnKZIu+Rixng/aoFuolaijmKaBeova65Kbe3j5XsBjAmDx5sug/5PDwcEyePNlVbQo49lSdsMe5c+ewePHi+1Pg+hjtaQsgEwCDSrXV6tRwV7VPjCM5LVyZAFWuwI3Y4NoexcWeG4i7K3jlLF/Ph2ILV/TJhjXXhw8fxvHjx5F8/xfNsOY6MzMTu3fvRlRUlDNNL09RUWJHqHsG84bqI8USkcSQCMngxfbPpkKl4rPit4gHmBaIqGB0gL0JOU0qoTDbK6AQQjyGnpHlYy0wYWAt0MHAsCNzh8WcDrNmzUJYWBgyMjIAACtXrgRjDM2bN7erzXLxlgSarkq26Ou8IcBkibe3z1UsBjCys7PRokWLctsTExORnZ3tqjYFDEeqTlhz7do1LF++HJ06dUJ8fDxmzJgBAIiOjkdY2B/gl5D8DqCBzUst3PnJtjeVBZUzcGPrINrSTAtPDMTdGbxyljf97riKX/XJD38ivp3TA7+Ndc1g3jgw8tto6eojgOQsislju6PP+HcAAPMGAYcXmh3gSEJOsUootlRAIYR4jF/1xx4mVSbV3OPqx8tV2jBnmPpfzhmAzWWYP38+AGDo0KHQ6/UYO3as3e2VEyXQ9G7eEmAyMJ9tMWnnJK9qn7tYDGDcvXtXcl9RUZHsjfEHtn5abTwwlGLrgPXWrVtYu3YtevbsidjYWLzwwgu4evUq/vOf/yAzMxOMMVy7loMVKxIdWmrhzk+2vaksqJyBG0tlZY1/JhMmAEESHzB4YiDuS8syvOl3x1X8qk+uqwEUFcpvZ6UAKzHdJsdg3nyWg7VP/MxmUTDGUKNGDSxd/R0AYN8bwGzzXJ1KlcVlJ5Kklpw4shSFEOIWftUfe1hK5xRwsL5W9vT108JSBynxkfHomdCz7HwFAOYCWMO/rFixIm7cuIH169d7RZJOKrnp3bwpwCQ22yKvSHwWac7NHL+ehWExgPHQQw/h008/Lbd9xYoVaNOmjcsa5avs+bTaliUFlgas9+7dw5YtW/D000+jRo0aGDlyJI4fP44pU6YgIyMDx48fxxtvvIEGDRoI73F0qYU7P9n2ppwWcgZupAbXS5eW/UxSUoC0NEAnMq7y1EDcl5ZleNPvjqv4VZ98cCKglx4AlFOY69zSErFZDpI4oFZP4dWNGzegUChw5coVAMDlj4BOjUXe0z/bseScUktO7F2KQghxG7/qjz1M01wjWRLVWO7NXGGpw9qBa8vNxjAk8Ew7nAamY3zO+nfL9v/xxx+4ffs2qlSpIu8NOCGlc4rofQRaAk1v5U0BJqlqPVL8eSkJx5h08cbLly9jwIABCA0NFTrj9PR0FBcXY/PmzahZs6bbGiomMTER6enpHm2DMbVafEaFIQGlMYXCctlMpbL84Ku0tBR79+7F+vXr8dVXX+HmzZuoVq0ann76aQwbNgzt27eHQsHHpOQsJ2le3UGqff7Gnp+nLaz9TKSuFxTEBzY88b2W+3tAyrOnH/OrPnl9sPVZEMZCogF9kWkQIkjJz3gArJclXacAbHhANj/3wasJePjhhwEAoaGhKFpfE4q7IhE8pYoPYDjCMDtE7N6oWgkhbhOw/bEXUC9RW61GoopUIXtytvBarHpH8u5k5GzPAXYbvbEnoOpq+l5v4ukqJJ7iC/dtmPVgXqHFE0lOFfMUNgX6jJn/m/E1Uv2YxQCGwd69e3Hs2DEAQNOmTfHkk0/K30IHeFvnLBWU4Dj+E3ZjUgNDwLRc6Nq1DNOm/YZLl9ZDofgCev1lVK5cGQMGDMDw4cPx5JNPIjjYtBquKwIOcgZE5OTKdrk7cGPP74+7BGrwyp0c6cf8ok9eZ2HqLhdiuowkSAkEhYsn3AyNBnQSgQ3jwf8W9f3lI+bXCpIMpLy7qyqmrL4BABg7dixWrlzpumBDltZ6EIYQ4lIB2x97AbGBorEQRQgqh1XG9aLrkoPdn3/+GY899ljZhoYAhgJQ8BVK9HM89DBFyvGmwIA1jgZa5A7QSAX5osOjJZeS+PrvvVMBDG/lbZ2zPZ9WWxsYrl+/HsOHDzd6RxiAPggNHYbly3ti7FiRteMOtMMW3hy8cPXg2p337q2zHVzxPfDW3ylP8LZ+zBmyBTDary0/mP91JOyaQWE+I8JS4EHk3B3fBPaf5P++YcMGPPPMM6bnomADIX4nYPtjDxAb3AEQtkWF85WvrhddR1R4FG4V30KxrqzktfFg98qVK6hRo4bpBaYBMCp25eufRPsbqcG4vT8nb53F4YoAjaVzJu9OluX76W2k+jGLOTCIfexJIii2Xv+VV3bj+ecjwHGcWfAiDcAVABtRXDwQ8+ZJBy8AefMWeHMVCnckmHS2RKs9JUi9NQml3GVqvfl3iniJuho++DBcX5ZXwt58EOYJMOtq+GCFUgWAM024aXTuu8UApykLXpza/bZp8EKqfYQQQmwiVfoRgFDKc2mPpYgIjQAA5N/NNwleAHylhf/b9X/o0aOHSfBidtpsKFOUJsELyinhfeRIjunNJURdUb1E01wjJLHlwEEVqRICIoGWS4UCGDKyN4mgRgNs2ZKBunXrIyeHw6JFXXDnzh0AQLNmzQAcB/+p4CgAlYX3WQtEyJl005urUHh7gklbBurGAY7kZGD0aPuSUNoTILGn3XKf05g3/04RL9YyhZ8xYSuxgIdU4OH+uTMvAeFGFfWKVgEJV2fLU8KVEEIIAFgs/ag9qkXM4hiM+GqEMDDViS3z+x3InZKLb7/9FgCwePFiMMYwb9Q8jG45GkEcX9ItiAvC6JajveJTeVJGjuSY3lbi1JirqpcYktjq5+iRPTlb+L22FNzwRxTAkJktn1ZnZWWhQ4cO4DgOrVu3xtmzZwEAlSrFYP/+/WCM4ejRo1Cpmohew1ogQs5P8r05SODO6iiOsDZQFwtwpKXxPydbZjvYOpPBnoCEO2ZHePPvFPFi5jMoOIl6wwAf6GhpR4dXV4Mvro5Dwyn8y8ceAJgWqBAKeUq4EkIIEYITlko/Jm1LktwPALgAvizqTv7l448/jpKSEkybNk24RtrhNCHooWM6/H97dx4XVb3+AfxzZgBlAFFxFwEVF0RWcSVNI8rUzFxyGU0jU7F+2d69ccvlSstNU7puWbkkuFRXs3Irt0pxQwX3JWJRUxNQXECBmfP743QOM8M5Z85szADP+/XiBTNz5sz3DPCdOc883+dZnbXaJT6VJ5XskTHgCi1O006mIWhhEFSzVQhaGCT8nTmje4lUcKM2ogBGNSkoKMCIESPAMAzatWuH9PR0g1v/B4DF3bs30LfvQ8JJprWBCHu2k3TlIIGrLrngmTtRtzUTQcn9LQ1IVEd2hCv/TZEaRK6DidqTq2uhsNXq888/j9H/twgAMF8L/PqeyQamy1EIIYRYhE/3lwtOqBm1dJvIUgBzARh0rv3vrv9iz549RsXsXflTeVckdQLuaPbIGHB2i1O5JSx1bUlHdaMAhgOVlJQgPv5FMAyDpk2bYuPGjcJtjRsvA6AHt0RkOIDKDhT8SSZgfSDCXnULXDlIYM9AjSOYO1G3NRNByf2VZIEYZmdIdcaxZ3aEK/9NEReRk8YFH9aquO+HpwNf+wAHxv/dSUSumCfzd7cSltv28BTJIIZer0e9evW47iIADn7UAq8NEtnQ0vobhBBCjIgFFkyJLhVhAXwN4CMAFdxVzRKbIfVEKl565KUqm7vCp/I1hbNrSNiaMeDsIIFUsGzipokAUKeWdFQ3CmDYWUVFBebMmQOGYeDl5YWdO5cY3DoLnp5lSE1lcfPmVADSVfj5k0xbAhH2qGXg6kECexeYtCdzJ+q2ZiIoub9ckEMsO4OR+JO0Z3aEq/9NERdweEploKIkD/h9KVBxV8EdGVQJbhguATEIjPy1qg3UajXKyrjCcEVFReg5al7VOhuWLkchhBBShbkAgpe7FwJ9A42vPAZgNoAz3MWnpz4NlmVxfcl1yRNBZ38qX5PUhmwVTzdP4Wc/T79qDRJI/U3rWF2VorS1fUlHdaMAhh2wLIvly5eDYRi4u7tj5syZBrdOA3AX3JvqmSgtdUdSkrITQls+9bZnLQNXDhK4MnMn6rZmIii5v1yQQyw7g2WrBjEckR1Bf1MEKi/p23Tyn9JV9XdXEanMjJL8yjaqJXn45SyL5s9dBgA0b+ILvV6PRo0ayXcqIYSQOs6W5QbmAgj13epXfqJ+DVydi++529St1Vh1dBU2LtsosweOsz+Vr0lqcraK2JKk0opSi+5v69IZub/pmhYIqmkogGGDzZs3g2EYqFQqTJ06Vbj+6aefxl9//QWGYQEshVEvJ3CBCbGTT1ONG1s/NmtrGTi6A0VdI3eibmsmgpL7ywU5pJaLsCxlR5BqoJLOQLOIJrCyq4gmUGKbAC4LQ1eCORuB/nO5q2cMBK593hCMYdSOWqQSQkgVti43EAssGCoqLcKTgU9CPU8NLKu8fuH2hai4XIGJ0RMVPU5d68Zgi+rOVrFnvQ1bskfstXTG3N90TQgE1VQUwLBQeno6mjdvDoZhMGzYMOH63r17Izs7GyzLYuPGjWjatKnsp9+GJ5+OYE0tg+roQEGM2ZqJIHf/tLTKQJb674YNhgEJtUQTB7WasiNINVC0JMQcxnh5h1irVX4JSEk+Iv4JzPwfd/X3rwMLJ4AKdBJCiAK2njDK1sBgAeYjBr6+vrhz8w4A4IcffgDLspjx+AyLx1qXujHYojqzVZQEDSwJcNiSPWKvpTN8sEwt0RWNli05DgUwFDh79izCw8PBMAxiY2Px119/AQDatWuHo0ePgmVZpKeno127dkb3M5fiz598StUdKCqybrxpadbVMlDa1YIyNOzHUc+nYTAKAHS6yr89PiChk2jiIHU9IS4neJpxhoTEEpB7zYaB0bI48ff7mrwU4Mnov+9DBToJIcQsa08YDU9cRe0CMBvQl+oBAH2H9oXfR3548uiTYGYzaPKfJtQC1UGqM1vFXNDA0qwIW7JH7Ll0RhumxeqnV9OypWpGAQwJV65cQXx8PBiGQZcuXXDy5EkAgEajwU8//QSWZZGdnY3o6GjJfShdImDvtpJJSZUdTQwxTNVaBoYn0OayNihDw74c+XwqCUZJZf84KiuIELtx9wN6pwI9llS9zWQJyKl7EfD29hZuLlsNBDT5+wIV6CSEEEWsPWGUyrxQXVJxdS5+q7zu8wOf42DMQaO6BoWlhUjYnEBBDAdxRLaKWCaFuaCBpVkRtmSP2HvpDC1bqn4UwDBw69YtTJgwAQzDwN/fHzt37hRuS01NhV6vx7179xAfH694n0qWCNi7raTUMhGW5U5g+RNk0xNoKQEB3LYTJ7pWhoY1j+VKGSTW1ikxJHU8SlqsUjtTUjMxwKgCRbUpVq5cibCwMADAkCFDwP6RCvcGCgp0mrZxlWjDSgghdYW5E0ap9P8qJ65lABYC+i/1wlUHDx4Ey7KYe3AuyvXlVR67TFeGGdtmIGhhEJjZDNzmuIGZzdhcR8GR7FnvwRmsHb9YJsWEjRPAShTZ5oMGlmZF2BI0cMTSGVq2VL3qfADjwYMHePPNN8EwDBo1aoTU1FThtk8++QQ6nQ4sy0Kr1RoXerOR4YlnUhIXHJDL1LDkxFsuc8PwU36xE2hT7u5AYSEwfrz00gJnZGhY81iulkGiJMggR+54lGT1UDtTUiMpXPIxYsQIJCQkAAA+++wz/PDDD8oKdBp0KxHauB6eQkEMQkidJnfCKJf+b/Sp9lYA7wO4xV38/PPPwbIsevbsCUA+hb+wtFBYhqJjuTekecV5GL9xvMstM7FXkUhnkQpCKAkaiWVSSAUvDIMGUtkPKkYl+Xh80GDN8DUAgAkbJygKtlDGRM3HsKzcZ++uLSYmBhkZGRbfT6/XY8GCBXjjjTeq3PbWW29hzpw5qFevnj2GKIo/8TQMHmg00ieP9tjeVGAgd6Js7rfv5gZUVMhvExjIZZcEBYkvQ+FvtydrHkvqPvz9DOtDVAep8ajVXMZOQID8mOSeg+Rky/5miPNYO4+5IouOZa01AWEG6L1GOvsiJw0Vx96B+8jKN8GZmZmIiIiosh2ykrgCnpoAbhkJv8/vgv4OXpjQBHJBD0JIrVVn52MbBS0MEq1xEegbiOS4ZCR8mICytWXC9W493LDy85UYHz5e0X6U0LhrzJ6E8sVE84vzEeAbgOS4ZIectMo9H7mv5Nr98ezN3O9B7rlWzVZJBiwM8X8b/D74oInYciO5xxO7n5K/BVIzSM1jdS4DIz09HWq12ih48eyzz+LWrVtgWRYfffSRQ4MXgOVLByzdXkmHk/x8ZTU2zAUvDJcc2JpRYAlrHkvuNmdkY0i10tXplGWIyD0HlF1BaidWNnhxedtko+DF7ZWeiGhwqsp2VTIsDowHvmnC3SbVlYS6lRBCiCipzIm8nDyMDx9fGbzwAVTvqFAxqAL/2v2vKp+UJ8clQ2XlqYlpvQTTJRDTt0yvtqwIexaJrA6mz5W5IJJcbQoldSQYMFWWWch19JB7PHt1FCE1S50LYOj13Jq7xx57DFeuXAHLsli9ejV8fX2rbQxSJ555eeLLQyw9WeeXh+TnS7fK5D/dFzuBVkqtNj4ptlcxUiXLZax5LHPjkAoK2aNuhtg+lASa5AJV5p4DW1u0EuJyNNL/LNtWvIY2L94HAHRqCehTAR+PUi7TwlBWEqATSU8rL+QCG+6NJR6bupUQQoiYKietFQCWAPi08irfV32B1wG9B/c+3DCAwJ9Ay9VKUIIPEIgtgViWsazaTnTtXSTS3gwDFk3+0wQJmxOMnisG5jMk84rzROtjiNWXMCX2PPDZMfzyIFOWBoVcNVhE7KPOBTAeeughsCyLHTt2oFWrVk4Zg9IaFea2F7vetC6CWN0Kw3aa5k6gpWg0wOrVxifF9igKqbROhTWPlZzM1fSQYxoUskfdDLl98EEGc9kyUsdDRThJnaHykOwY8uabb2LQXK699b+GAefmGbSSNs2ckMuk0JUADLjuJIaoWwkhhEgyOmndCWAugL/+vnEYgFlAsW9xlfuVlJdgxrYZRsEGWwIY/ImxJXUYHHGi64gikfZiGtwpLC1Ema7MaBsWrKIghlgmi2F9CQBV9iP2PJhttQvLg0KuEiwijlHnAhiuwFzmg+Gn7mlpwN27VbeROlGVKsypVosvJeBPoA1ql5rl5ye+HEFq2QIANGnCXccw3M9SAQCly2WsWSKh1QINGsgfm2lQyB6dQpTsQ255i1QAi5aJkDpFXwYcnQEcni50CGE3BSKgVRPMmzcPALDzn8C/R5ncTxNg3FWEMfOyV1bEdSfRKOhWQgghBNowLV5q/BLXFnXf31eGA5gJIFL+voWlhaJ1D0z5efoJJ8UqkXnc8MTYkqCEI050rSkSOX3LdKG7itscN0zfMt3u4wKk29qaYsFKBiFM8YEoPqsjaVcSkuOSwc5ksWb4GrPPg7kxyQV/XDlYRBzHzdkDqIv4E8ykJOmikvn50sU4/fyAlBTxE1WpE2G9nvuSwzDyRT2VFLrUaqt2T3nuOaDcoCtWYSHwd3OAKvuyZLmM6WMpUVQkfRvDVA0K2aOuh5J9BASI/y2IjcmQNc8BITVWWSHw+1IAQHEJ0FBb+U909dAitMh5y3h5iFoDtBrELQ3hr5dITxVoArhgBQUsCCHErMuXL6NNmzaVV9QH8Mrf3+0o5YkUoxNfuYKcAb4Bop/mM2CMMjEceaKrDdMqLiI5fct0LM1YKlzWsTrh8pLBS6x6fKnnR2lwx7DgqOG+pDJZCksLUVhaCKByeRCg7HmQG5NpsU9T/PXVUZyVuA7KwHACpTUqpLIpvL2lT1qlPq1XqaSzHvhAibnghTV1FJKSjIMXvLIy8SwGe9XRkCK3n2nTqh6fNeMxrXchlW3T2GCpvVhWDsOIj4mQuu5oDtDwhcrLFd8GoEWPF8UzJ/7cKl7zQuzlj5aKEEKIIuXl5ejdu7dR8OLYsWMI/CBQcfDCQ+2haDs/T78qJ6R8C039TH2VgpBSn8pPi5nmkq0zlx9dbtH15ihuayvBNLBj+FzzWRnm8PVFTAuEihVNlRoTH0Qx9zuS+1sgtVO1BzAuXbqEAQMGICQkBKGhoUhJSQEAzJo1C61bt0ZkZCQiIyOxdevW6h5atVBSo8LdnVs2IpedIUWus4VU7QapQAnPlroKlnYFcXRdB6lAQWIisMQkyC21fIdhgEGDKrcxDFZMn1613sW9e+bHJbYcZM2aqmMipK777w4g5l/cz+P6AGwaoH5wibuirZZrdTpOz31vq5WpecECvVNpqQghxGXUlPfIc+fOhYeHBw4ePAgAWLp0KViWRVRUlKIijgCXDWFae0FKYWkhmNkMmvyniaKuIVJLOJYMXuKSJ7pShSulrjdHrjOHud+Pn6efbGBnUIdBiupjAJWZGOY6v9AyEGKpal9C4ubmhvnz5yM6Ohp37txBt27dEB8fDwB49dVXjdqb1kZyNSr0eu7k+t49bpmFFLlP//lP6ydOrBocKSkBxo+vvI1fEiIXZFCybESO1NII/jap8fMZKny3FK3WOHPF8HpLyO3fkNTyHYALTKxezf28enXlNnl5wLJl8pkshkyXs9ByEELkPfo+sOs093PqdEAb+/cNch1CNAF/t0wVuZ6WihBCXIirv0feu3cvBgwYIFweNmwY/ve//0Glqvw8VCylf1CHQfgq6yvcK6/8RMeaYp2FpYV47rvnjB4HkF4u4SoBCnPUjFo0WCHWUlQO/zxIFcPML84XnpOJmyaKPqa3h3eV581wv6bLcBgw0LhrjH63huOXCqSYtlAFaBkIUa7aMzBatmyJ6OhoAICPjw9CQkJw5cqV6h6G08jVqFizRj4TAuCyM8xlI2i18vUu+MBGXh4wYYL0Cbe1y0YMSXX+8PCQPg6x9p/26AZiDp9NwTBcoEfud1FSwmVMmG6jNHgB2G9ZDCG2qAmf+D0oBxhtZfDi7McGwQtzyz4ikqmrCCGkRnDV98jXr18HwzBC8MLNzQ0FBQXYtGmTUfCCZ5rSD0D0BFeOWKFOACjXlxu1PpVbLlFTTOk2RfH1UksylHTyaOzJrV3WhmmhZ8VPFEzbo5ru1zTwxIJFfbf6ohkUlrREpWUgxBJOrYGRm5uL48ePo2fPngCARYsWITw8HAkJCbh586Yzh+YwcjUVkpLMnwAzyrK2FJ8cSz2evZZtaLXAypVc4VGenx+wYoVlgRF7dAMBpAMhhks/lBJb/qMUtTslroL/xO/s2bM4ePAgFi9ejDNnzgDgPvHLzMxEZmYmBvHrpqrZH38B9SdVXi755QV0DrZg2UdbLXUVIYTUOK7wHlmn0yE+Ph4tWrQQrjtw4ADKy8vh5+enqL4BYF0tB6kTbABGJ+lyyyVqiiWDlyAxJlHIuFAzaiTGJFYp4CkXrFHaXYQnVwvDcN8zts0wu9+i0iLRJTtS9TKoxSmxldMCGHfv3sWIESOwcOFCNGjQAImJicjOzkZmZiZatmyJ119/XfR+y5cvR0xMDGJiYnDjxo1qHrVyprUR+EwBuRoPSjpbSBW/NGWuVasce7fj1GqBggIuYMCyXAeVpCTjuhFiz5Uhe3QDAaQDIWLZFOYoDSbx/Pyo3SlxPa76iR8AbDwCtH+V+7lXMMBuCoRnv+VV61yY01bLZVxoAriaGFlJXGtVQghxQa7wHnnBggVwc3PDzp07AQDz588Hy7Lo1asXAGWZD3yAw9paDlIMl1ZIdbAwzSSwlNLgjL0sGbwEFe9VgJ3JouK9CsQGxFZ5fLFgAh+sUdJdpKi0cu2yklolJeUlQmcROQG+AaIZFFTbgjiKUwIY5eXlGDFiBLRaLYYPHw4AaN68OdRqNVQqFV544QUcPnxY9L5TpkxBRkYGMjIy0LRp0+octmJyyx34Yo2GGQmentx3pVkThiftYkUkg4K4pSGWnmAb7j8pyfblGWJBHLHnZulS80tDlHYDkQocGR6bGGuyKSxZLgJw3WMMl8UQ4mqs+cTPUUHlaV8CIxZyP/9nLHBgNmQKcpqRk8a1Ui3JA8By3w9PoSAGIcTlOPs98qFDh8AwDF577TUAwKOPPoqKigrhMs9c5oOSJQ3W0rE6IaCgNJPAkgBE2sk0JGxOMArOjN84XnERUVuJBYcSNidIBhPyivPAKHjTb/hcmRY6tcWgDuIZmlLFVGl5CLFVtQcwWJbF888/j5CQEKPJ8OrVq8LPmzZtQteuXe3+2OZObu1F6lP+GTMqL5eWVv5cWMidtA8apCxrgj9pNxcMUNL9QoySGhPmnkupIM6MGeYzHcSWhohlr5vWA1FSJ8PRdSfqy7QOszRbhJDqZO0nfvYOKuv1gM/zwGe7ucv7ZwJvDvn7RrlinXKykqq2UtWVcNcTQoiLcOZ7ZACYM2eOkGHBP+7PP/8MtbpqMUmpT/z56y1d0mApPiihNJPAkiUlM7bNEO2QUlhaKJllYs9MDbHnzlzHFrklN4B45oM17VHFbL0oXSNLaW2L6s54ITVbtXch2b9/P9asWYOwsDBERkYCAN5//32sW7cOmZmZYBgGQUFB+Oyzz+z6uKZdJfiTW8D+n4ZLnagWFlZ20pBaxqDTcR1JdDouS+P2baC8vHI7w9oJ5tqf2ooPuph27ADMP5dSx6h0vKZZJl9+WXWb8vLKoJBWK18ngx9XcrJ0dxF7ePCAy7QQa79KRTuJq5L6xI/3wgsvYMiQIVJ3t5uCO0DTaQaXlwF+PvwlBmhlZR0OqcwNazM6CCHEAZz1HpnXuDFX5HH37t1G3UbEBPgGiGZXNPZsjKCFQQ7JvDDEByVyX8nF/vz9WJqxVHZ7JUsseHLLJkrKSzBx00RM2DgBjT0b407ZHSG4wGd7ALApy8CSsUphwMDLwwt3y7g3pJ5unrLbJ8clY8LGCVZ1h7F1vHzGCR+0sdfzSGovhmUtTYR3HTExMcjIyFC0bVCQeIFGvtOGPUk9Fv94+fnmlx9oNFxAAxAPICQlWVZw0hAfILGGRsMteRFr82r4XKpUli+xENtXWpp4S1jTMS1fLt9RJTW1MoihZJ/W4tvOmgZJ+DHS0hFiypJ5zBFYlsXEiRPRuHFjLFy4ULj+6tWraNmyJQBuLfShQ4ewfv162X1ZdCxrjVNW958HHprD/dzYmwteVMmIVWusK8D5XZBEK9VAro4GIYTA+fOxPTn6WExPOgHAXeUOhmHMZgvYEzuTVRQwCfQNRHJcstAOlG9dyl9veKLMzLZtSUWgb6DQfcUa9goAadw1Rr8fjbtGdgmHtcftqOO1db+k5pOax5zahaQ62asIpBJy3SX4QIQ5pssoWBa4fJlr7zlhgvXBC42GO7m2tsBnSYl48ALgxsQvKxHpqqUYn2XCZ82YCzTwz5Xc82q4lMRcm1mlTDMq+XHzdU4CA6loJ3F9/Cd+u3fvNmqZ+tZbbyEsLAzh4eHYs2cPFixY4LAxfLC5Mngx/VGg8DOJGj6myz5y0rjgxFoV912qpgW1UiWEELsSq2/QoF4DuwUvGDBIHZ4KdiZrVLTTEH+9uQwAjbsGwY2DMWHjBOFEmS8sKlbfws/TT3JfSuQV59m0BELJshhz1Iza4u4s1iwjsUdRTnPLkQgxVe1LSJwlIED8pN8Raf1aLbe0QexEn8+iULKMgV+awW/Hn8hbktng7g40aAAUFVU+Nn8ivWyZbVkSphim8jlWmt2gVnPHuHWrcZaJVssFQyxZcrJmjfTzarqUROrvwdxY+SU+U6YAsbFVs2P4/Wu1FLAgNcNDDz0EsUS86mqbyhj8n3z3KvBUjJk78Ms++MKcfG0LvjAnUDVDg7+clcTdXxPABS+olSohhFhNG6Y1+jRfNdt+n4uyYIV9S3Ux4a+XWs4CACpGBQYMduXskn08vr4FAKQ8kYLnvnsO5fpy2fvIsWUJBH+fGdtmKOoCYso088KQXFAgOS65SlaNKT9PP3h7eCO/OB8BvgFVslesIfX7o3arREqdycCQa1/qCCkp0o9n+gm9SG0kANz1ltRqCAwEEhONP/lfuZJrYWra/WLrVtuCF6YZFgxj3f70emDJEm5spmO0JDumcePK51WK4f6Sk7ngjlKBgUBFBXeMFRXcmLVa8XETQpRJv1D5c85CBcELoLKQp6WFOdtqLW+/SgghRDGpE05rOl0YZgNIZQbw1yfHJcND7VHldjWjhppR4165sqr2JeUlGL9xPJJ2JWFy9GRh/3ymh4pRftpkaeFQU9owLbw9vBVt6+fpBz9PP6NOH1LPmVxQwDCrBkCV35nGXYOUJ1IUFeW0BLVbJZaqMwGM6k7rN/d4hie/q1dXDXYwjGU1GviaEVLBAFO2Lp3R6wEvr8pjszYYIpcBY0l2zP373HetlhuPGH5pS1AQd3nlSuN2tn5+XACoOgNdhNRlfToCK6YA91cBQUobmPDLPqgwJyGEOIVUxwi5E1FLPk03PXk1d4KrDdNixVMrjJZ++Hn6oWH9hlZlUeQV52F11mokxyWDncmi4r0KsDNZNKrfyKL9mGY7WNppQ8kSCgYMCt4qQMFbBUZBBWuDAnzXEHYmizXD11RLC1Rqt0osVWeKeLoavhtJfj6XPXD3LtfBwpAlWQ1+flzWh9KAjFyhUaXUai4bwdr9SRUqHTSIyxDJy7PsOeC3M+04I/fYYs+X4e/GdGkIIfZWk+cxU7YU8VTEww8YWcD9TIU5CSF2VmfnYwuIFe80LA6ZdjINSbuSqiwxmL5lutlOIQDg5e6Fz578rMrJq9R+5ahmq6zqqsEzLSJpzf74IqEAZJ83nuFxqhiV5PIZqTEasuY5I8SVSM1jFMBwAiUn2NawpNOFvcYgFzTQaLgAxD2RzD2G4WpWAPZ7Lgz/kg2DECqVeDaLIzrQEGKpmjqPiXFoAMO0A4lpDQyxbQghxAJ1dj62gDUdI8SCHnISYxKxZPASW4YJAGjynyZW1ZAwxM6sfHMpdex+nn4orSiVPD6Nuwaebp6iY2HAYM3wNULwx5LnyVxXEUJqujrfhcSVJCXZdsIeGGi89IFXUsIVDxWTllbZIYRfQqGkDoccwzoYUktmPvtMvCPJ363GbX4ueKbPh+ESHamOI47oQEMIsSNGDYDhsipMAxNttdx1mkDpbQghhNiVNR0jknYlKT4pB4ClGUsVL7OQknYyDbcf3Lbqvqb74cl1B5kYMVGy7kRJeYlkIIUFi+e+e07IllD6PNEyC1KX1ZkuJK7ElhNnPmtAqk1pYWFlu1DDJSq3bwPlfy8D5LubLF/OLY9ISrJuOYmnp/Flsc4baWmAmxtQZtJVq7DQvlkohYVcYEZsuUd1dqAhhNgRq+eKbkppq6WABSGEVKPGno1FT8YNa1yknUyzuoMGjwWLvOI8q7t5JO1KsqmLCM/08T3dPKsEGQpLC7E6azWWP7kcEzZOsHiZSbm+XFjqoQQDRjLbhZC6gDIwnMCWE2c++MFnMIiZMYMLDuTlccsqCgsrgxe8khJg2jRgwgTra2EoCT4kJVUNXhje35rMD0OMQRY6H5hJMwnWV3cHGkKInWhkJsucNK4OxloV9z3Huk/pCCGEKCOV1eCh9hDqPKSdTEPC5gSbl27wzHXzkCqMqTQYoPTx+eUdUsfFbydVrNSwwKgYvk6FEtRelNR1FMBwINNlG/yJtdgJtVJKgh+FhcqCC3fv2t5KNS1N+jgB89kmOp11z4VGwy0bMR1/SQkXNDFU3R1oCCH2wFR2HDHF178oyQPAct8PT6EgBiGEOJBUVoOPh4+QoZC0KwllOolPrqyUV5wnupSEDyrkFecZZWyknUxTfJLPt0iVa/OaX5yvaHlHfnG+ZPePlCdSZIMYfJFNqSUqhvui9qKkrqMAhoPwRS35LAjD7ADTE2o/P64lqTnu7lzQgWG4IIWz6XTA+PHcl+lxTp/OBTPMBUj4YAL/XCjB36eoSPx2saCJYU0MufayhBBXwUovD8lKMi7eCXCXs6Q/pSOEEGIbqayGotIis9vYig9MGBILKvCZEMlxybJBCZ6e1QstQ/lghqnGno0VHZfGXSPbEjTliRS4qcRX7w/qMEj0vokxidRelBATVAPDQcSKU/LZAXytCP4kmg92yGEYZYELhuGCIXfvWj92W5WUAMuWmQ9e8Ms4DJ8Lc+1YDTuHSNXuoNoWhNQCGvFiaACAEok3klLXE0IIsVmAb4BoFw7DbAepbWxluJSErxchVWsirzgP+/P3w8vDC3fL5N8QqxgV0k6mQRumxf78/aKtXm/dvyVZ+8PQvfJ7wr7EggzaMK1kbZCtF7cK21CAghB5lIHhIFJLJ8SuV9KJg2Wla0kYCgkBHjwwv52cevVsuz9gPnihVosv45BbXmNat4JqWxBSSzHuQMVd6foWUrUx5GpmEEIIsYnU8gjDJQ3JccnwUHs45PH5JSL8khE5SzOWmg1eAICO1WHKD1Mwfct0LD+6XHIbAGaXdwCQrdcBGGerGHJU5gohtREFMBxEKgtApapaK8Ke7TzPn69asNNSDx5UXc7B15ywF71evGMJH8zhi3vy38XqVlBtC0JqKYYBygohWd8iIhlQm7yRVGuka2YQQgiRJVUM05DpEgc/Tz94unliwsYJwn20YVqseGqF2aKV1rKkHasl+1yWsUwIVIgpLC1ESXmJ5DITnrlAhFRtDirMSYhyFMBwEKlMAp2uak0Mey550EnPvRZh2cogBh8YSElRVnBTSS0L02M2rBkCVBb3XL2aG4tU3QqqbUFILaQ3STczrW/RVgv0WP73MhOG+95jObVUJYQQK8gVwzSlDdMi95VcrBm+BqUVpSgsLaxyH22YFgVvFSDQV2YpoItR2vpUx+pkM0zMBSKUZLEQQuRRAMNBTLMDxNqF8jUxbOlK4kgsW1lzgq9TsXy5fOtTlco4+CFGbJmHXM0QQgjhOo4YaKsFhuUC4/TcdwpeEEKIVeSKYYpJO5mGiZsmmr2Pkq4aNVGZrgxe7l5VioQqCUTIFfkkhChDAQwHMswO0OvFt8nPVxbscBbT5S1aLZcVIRZwUasrj9MwiOHnx33JLfOwpGYIIaQOMpO2SwghxDpSyx7EruezNaSWWxjex/Bk3ZUp6VZiqqS8BGuGr7EqEMFnsehn6pH7Si4FLwixEHUhcQC+lkN+PrdUIjmZ+y7WMUOlqgxa6HSV312F2PIWPvhgeIx371btkGKYwaHkcaijCCFEkszaZEIIIdZT0l2EJ5atIXcfvqtG0MIgh3QnsYdpMdOwOms1SspL4Ma4YWbUTEQ3iUaDeg1QWl4Knb7q649apYa/mz+2Pb7N6PqzZ89W17AJqfG8vLzg7+8PlcqynAoKYNgZX8uBXw7B17qYOJHLXDBdJsEHK0y/uwqpjh6GrU8BLhAjRmkGRXKy8fMGUEcRQogBubaqhBBCrJYcl4wpP0wxCkxILYeQK1IpdZ+0k2kuG7wI9A3EksFLAADLMpZhVNAoPBT4ELz9vKFSqdDGsw0KSwuhZytTqVWMCoG+gfDTOKZQKSF1gV6vx5UrV1BQUIBmzZpZdF9aQmJnUrUctm41XiZiYaDJKby9lRfFlMqUUJpBQR1FCCEC6jBCCCHVxpK6DFJFKtWMWvQ+/JITV2QYcNl6cStYsHi67dPwbuQNMICe1aP4QTECfQOFwp0eag8KXhBiByqVCs2bN0dxcbHF96UMDDuTq+VgmLWgpFOHM3l4AMuWKd/eHhkUplkdhJA6qsdyrutIST6gCeCCF1SkkxBCHIZf6mGOVLaGVMDD3JITZzIcM59Z4u3mbfTxbpmuDH4aPwpYEOIA7u7uqKiosPh+NSAPoGaxNRPBmfjioYGBwIoVlgUTKIOCEGI31GGEEEJckqVdNOyxdIQBg3rqejbvx1Cgb6DRmPnMEgYMDGt6yrVMJYTYhrHyE30KYNiZWEtU00yEtKpttZ0qMZEruFlRwX3n26ZayrDrirX7IIQQQgghrkuqi0bayTQELQyCarYKQQuDkHYyDSrG9lMNFiwe6B7YvB+eWK0OsZavKkaF1j6t7fa4pO66f/8+OnTogPPnzzt7KNXm9OnT6Ny5Mx48sN//Lo8CGHamJBMhSbytthFHLDEx3adGA6SmAkuW2P+xCCGEEEJI3cDXusgrzgMLFnnFeUjYnGBU/NKR6qnrSbZD9fP0g5+nn2zGCJ9ZolZx6ch1qdbF5MmTERoaCjc3N0yePNns9oWFhZg4cSJatGgBX19fjBs3Djdv3hRu1+l0ePvtt9GmTRv4+PggLCwM3377rUPGPn/+fERHR8PX1xfNmzfHM888g3yT9fx//fUXJk6cCD8/PzRo0ACRkZH4888/RfeXn58Pb29voy8PDw80aNBA2CY0NNTodk9PTzAMg2PHjkmOMyUlBb1790anTp0AAHv37gXDMMI+WrZsiWeffRaFBi0dDx48iMGDB6N58+bw9fVFt27d8N1331n9XP3+++949NFHhc4f8+fPV3Q/vV6PPn36gGEYXL58Wbj+q6++Qp8+fdCoUSM0adIETzzxBE6ePCncHhoaiqioKCxatMjqMUuhAIYDmMtEUNKZg2XtOya1Gpg2jZZ4EEJqgBwXS1MjhJA6SCyjQopYrYsyXZmjh2j0WGuGrzFa2pI6PBXsTBYFbxWg4K2CKhkjprRhWvg38EdMqxiENw+vE8ELAAgPD8cnn3yCoUOHKtr+2Wefxd27d3Hx4kXk5OSgsLAQEyZMEG5fvHgx1qxZg507d+L27dv497//jXHjxuHcuXNWjS8oKAh79+4Vva2srAz//e9/cf36dfz+++/w8vLCkCFDhNvv37+PuLg4eHh44Pz587h16xbS0tLg7e0tur+AgADcvXvX6Kt79+4YP368sM3p06eNbn/ttdfQpUsXREdHi+5Tp9Nh0aJFeOGFF4yuV6vVwj4OHjyIo0eP4vXXXxduLyoqwujRo3Hq1CncvHkT7777LsaOHYsjR44ofeqMxvDkk08iJCQEN27cwPfff4+PPvoIGzZsMHvfBQsWQGO6vADAnTt3MHv2bFy+fBlXrlxBdHQ0HnvsMZSWlgrbJCQk4L///S/0evsGMimA4QTOqIeh03FtXJOTaYkHIcTFZSlIUyOEEOIwYhkVU36YIhnEkGuvWh0CfAMkl7bUNkuXLkVERITRddnZ2XBzc0NenuU1R15++WU8/vjjRlkGUu7du4dt27bhvffeg4+PDxo3box33nkHW7ZsER77999/R//+/dGpUycwDINhw4bBz88Pp06dAgD861//Qnh4uHCie+7cOfj6+uLnn3+2eOz//Oc/ERsbi/r168PHxwdvvPEGTp48KWSErF69Grdu3cKSJUvQpEkTqFQqhIaGKjpWADh16hTS09Mxbdo00dsrKiqwYsUKTJ06VXIfGRkZuHnzJnr37i25TWBgIAYNGoSMjAzhukGDBuHZZ59F06ZNoVKpMGzYMHTt2hX79u1TNHZDv/76K/Ly8vDBBx9Ao9EgOjoaU6dOxTIzHRsuXLiAJUuWYN68eVVue/HFFxEfHw8vLy/Uq1cP77zzDq5du2YUqOrXrx+uXbuGzMxMi8cshwIYTiBWJ6M6lJQoW75CCCFOVeLcN8KEEFJX8VkX4zeOr5JRUVJegqRd4m8kG3s2dsh4/Dz9kBiTWKU+hSGxmha1mVarRXZ2ttEn8V9++SUeffRRBAYGAuCyKho2bCj5Zc1JMACwLCt88fhP17OysgAAL7zwAk6dOoUzZ85Ap9Ph22+/RUVFBfr16wcAmD17Npo0aYIXX3wRJSUlGDlyJF555RXEx8dbNSZDu3btgr+/Pxo1agQA2LNnD7p06YKpU6fCz88PnTt3xieffKJ4f8uWLUPv3r0RHh4uevt3332H4uJiPPvss5L7OHbsGDp27Ag3N+nmn3/88Qd+/PFHYYmJmGvXruH06dNGY1H6e87KykLHjh2NMk+io6OF35kYvV6PhIQEfPzxx2jYsKHkdrxdu3ZBo9EgODhYuK5evXro0KGD7PIaa1AbVQdKS+MCBvn5XNZFcrJxq9AZMwB+qRPD2H/ZiBgly1cIIcSpNDWgbRMhhNQyfNaFXNtTR2daBPoGIjkuuUr2RGxALJJ2JSG/OF8IlhSVFiHAN0B0e3t55ZVX7P7psZTIyEgsXLjQ7HYNGjTAmDFj8OWXX6J79+7Q6XRYvXo1Pv30U2GbEydOOGSM3t7e6N+/P2bNmoVVq1ahvLwc77//PgDg9u3bAIB27dqhb9++6Nq1K1QqFerVq4c1a9agWbNmALilE2vXrkVUVBRiY2PRokULzJw50+axpaenIykpCevXrxeuKygowK5du7Bw4UIsW7YMJ06cwMCBA9G8eXNozaSil5SUIDU1FSkpKZLbfPbZZxg9erTsCf7NmzdFMz50Oh0aNmyI8vJylJSUoG/fvli8eLHoPu7du4cRI0Zg6NChiIuLE65X+nu+c+cOfH19ja5r2LCh8DsTk5KSghYtWmD48OHIzc2V3f+FCxcwefJkzJ8/Hz4+Pka3NWjQAEVFRYrGqRRlYDhIWhowZQqQl8cFJvLyuMuGHUgMlghZHbzg61kkJnLfzakJ7VwJIXVcq0HOHgEhhNQ5YnUsTPHtRk0VlhaKXm+JxJhEyaUfhstDlNa0qM2mTp2KdevWoaSkBFu3bkVFRYXiGha2Sk1NRb169RASEoIePXrgqaeeAgA0adIEADB9+nQcP34cOTk5KCsrw88//4xp06bhp59+EvbRokULPPPMM8jMzERSUhJUqspT0rVr1xplEeTn52PIkCHCZcMaF7zffvsNQ4YMwfLlyzF48GDheh8fH7Ru3RozZsyAh4cHYmJiMH78eGzevNnsca5fvx4qlQqjR48WvT07Oxu7du2SXF7Ca9SokWigQK1W49atW7h79y62b9+Oc+fO4erVq1W2u3PnDp544gk0a9YMX331ldlxi/Hx8UFxcbHRdbdu3ZJcSvP7779j/vz5igpwnjlzBgMGDMAbb7wh+lzcvn0bjRvbN0OLMjAcJCmJW7JhyHAJx8SJXF0KWwQGcrUsDKlU0sEQ03auhBDikv7c6uwREEJInWMuu0JuqYaKUSnuOMKAAYuqb1a3XnS9uV9JRoQzdO/eHe3bt8c333yDTZs2YdKkSXB3dxduDw0Nla2HsW3bNvTt29eqx27durVR8cctW7agfv366NWrFwDg6NGjePHFF4XlLH369EHfvn2xbds2PPbYYwCAX375BStXrkRCQgJefPFFHDlyBF5eXgCAcePGYdy4ccL+g4KCsGrVKvTv3190PDt27MDo0aOxYsUKDB8+3Oi2yMhIo7oSPEZBu8dly5Zh4sSJqF+/vujtn332GSIiItCzZ0/Z/URFReHChQvQ6XRQq9WiY3n88ceRmJiIyZMnIyMjQxhfUVERBg4ciHbt2iE1NbXKMhSlv+eIiAhcuHAB9+7dE57n48ePV6mlwtu3bx9u3LiBrl27AqhcJhQeHo65c+di+vTpALjlMQMHDsS7776L//u//6uyn7KyMly8eBFRUVGyz5GlXC4DY/v27ejUqROCg4Px4YcfOns4VpNaqsFnYlgSvPDyqlozQyoYIZVhoVZT1xFCiGWcNh9TDQxCCDFSHfOxVHYFAMn2owC39ERp8CLQN1A0eAE4vxBoTTNlyhTMnz8fW7durdL+1LRThumXYfCirKwM9+/fh06ng06nw/3791FWJt1B5vz58ygqKoJer8eRI0fwyiuv4B//+IewjCI2NhZpaWm4cuUKAODQoUPYu3ev0KXj+vXrGDt2LFJSUvD555/D398fiYmJVj0H//vf/zBq1CikpqZWCV4AwKRJk1BYWIjFixdDp9MhKysLaWlpotsaOn78OI4cOSJZnLOsrAyrVq0ym30BcMGmhg0b4sCBA7Lbvf7668jJyRGCQ9euXcPDDz+MkJAQpKWlidbQUPp77tevHwIDA/HOO++gtLQUmZmZ+OyzzySP75lnnkF2djYyMzORmZmJrVu54OJPP/0k1PvYv38/4uLiMHfuXNHgBcAVD23evLndAxhgXUhFRQXbrl07Njs7m33w4AEbHh7Onj59WnL7bt26WbT/1FSWDQxkWYbhvqem2jZeOYGBLMvlQhh/qdXi10t9MQw3TqVjT01lWY3GeB8ajWOPlRBiPUvnsepi6XzMshYeSxqkvzYF2jZ4QgixQp2dj/+WeiKV1SRrWMyC8KVJ1rCpJ+TfRAYuCDS6j9iX4X6ktg9cEKh4nIELAllmFsMGLgg0Oz5LnDlzxm77crTbt2+z3t7ebP/+/W3az8MPP8wCMPp6+OGHhdsHDhzITp06Vbi8fPlytkWLFqynpycbHBzMLly40Gh/xcXF7NSpU9lWrVqx3t7ebPv27dnk5GSWZVlWp9OxcXFx7MSJE4Xtr1+/zrZs2ZL94osvRMcXGBjI7tmzR/S2oKAgVq1Ws15eXkZfeXl5wjZ79uxhIyMjWY1GwwYHB7OLFi0y2oeXlxebanKiNHXqVNnndd26day3tzd7584dyW0MffDBB0bHvGfPHlatVlfZ7t///jcbHBzMlpeXs7NmzWIBsBqNxujY+OfSUhcvXmQfeeQR1tPTk23ZsiX78ccfG91u+ns2lJOTwwJgL126JFzXv39/lmGYKs/9r7/+KmwzduzYKo9jSu5/TmoeY1i2OkpHKnPgwAHMmjULO3bsAAB88MEHALgWOWJiYmJE04LE8DUpDJd1aDSOy0qQejzTZSVyGAaYNg1YssTyxxYrHkoIcT2WzGPVydL5GLDwWNbKpG/2TgXa0qRFCKledXY+NpB2Mk0olqm0QKZqtkoyqwLgOomkPJEi7EesWKjGXSOZ4WE6Pmvvq8TZs2cREhJi836qA8uyaNeuHZKTk42WXBDXU1paivDwcLOdRmqTM2fO4Omnn8aJEydQr149ye3k/uek5jGXWkJy5coVtGnTRrjs7+8vpB/ZylxNCnvTarngSGBgZaFN/rIYtbqyECe//Zo1lgcv+MfOzQX0eu47BS8IIZZy5HwMAGCqrgPlqCh4QQghBhw+HxswLJaptECm1NITFaNC6vBUFLxVYLQfbZgWy59cjkDfQDBgZJenmBIrNCrX3rU2S0tLQ1lZGUaOHOnsoRAzPD09cfHixToTvACALl264Pz587LBC2u5VBFPsWQQ0yIry5cvx/LlywEAN27cULxvqZoUjmwratgy1VB1ZoIQQog1lMzHgPVzMlipQkDK1lETQkhd4fD52EbJcckWZ0Vow7RWZUxI1cmoa/UzmjZtCjc3N3z55Zfw8PBw9nAIqVYulYHh7++PS5cuCZcvX76MVq1aGW0zZcoUZGRkICMjA02bNlW8b6niltXdVlQqM4OCF4QQV6JkPgasn5OhkUhHk7qeEELqKIfPxzayJaPCUlLZHnIFSGujGzdu4OrVqxg0iNqOk7rHpQIY3bt3x8WLF4W+wevXr7dbT+PkZOWdPByNlngQQlydI+djAEBEMqA2mZTVGu56QgghAofPx3ZgzdITayTHJUPjbvzaIdfelRBS+7jUEhI3NzcsWrQIjz/+OHQ6HRISEhAaGmqXffNBAipuSQgh5jlyPgZQWeciK4lrm6oJ4IIXVP+CEEKMOHw+rkH4wIilhUYJIbWHS3UhsZSrVosmhBClatM8VpuOhRBS99SmOaw2HUt1Onv2LDp37ixaY4QQYl8sy+LcuXM1uwsJIYQQQgghhDhD/fr1UVhYKFo4lRBiPyzLorCwEPXr17f4vi61hIQQQgghhBBCnMHf3x+XL1+u1i4uhNRV9evXh7+/v8X3owAGIYQQQgghpM5zd3dH27ZtnT0MQogMWkJCCCGEEEIIIYQQl0cBDEIIIYQQQgghhLg8CmAQQgghhBBCCCHE5VEAgxBCCCGEEEIIIS6PAhiEEEIIIYQQQghxeQxbgxsdN2nSBEFBQc4ehlVu3LiBpk2bOnsYdkfHVbPQcTlfbm4uCgoKnD0Mu7B2Tnbl35crjw2g8dmKxmeb2jY+mo+Vc/XfvbXouGqW2nhctfGYAOuOS2pOrtEBjJosJiYGGRkZzh6G3dFx1Sx0XMQVuPLvy5XHBtD4bEXjsw2Nr+6qrc8tHVfNUhuPqzYeE2Df46IlJIQQQgghhBBCCHF5FMAghBBCCCGEEEKIy6MAhpNMmTLF2UNwCDqumoWOi7gCV/59ufLYABqfrWh8tqHx1V219bml46pZauNx1cZjAux7XFQDgxBCCCGEEEIIIS6PMjAIIYQQQgghhBDi8iiAUc22b9+OTp06ITg4GB9++KGzh2OThIQENGvWDF27dhWuKyoqQnx8PDp06ID4+HjcvHnTiSO03KVLlzBgwACEhIQgNDQUKSkpAGr+cd2/fx89evRAREQEQkNDMXPmTAA1/7h4Op0OUVFRGDJkCIDac1y1navNhzXh/9/V/9Zv3bqFkSNHonPnzggJCcGBAwdcZowLFixAaGgounbtirFjx+L+/ftOH5ulr6MffPABgoOD0alTJ+zYsaPax/bmm2+ic+fOCA8Px9NPP41bt245ZWxS4+PNmzcPDMMYtd+r7vHVZq42d1uL3sfWHLX5fayrv65bIygoCGFhYYiMjERMTAwA+x4XBTCqkU6nw4svvoht27bhzJkzWLduHc6cOePsYVlt0qRJ2L59u9F1H374IeLi4nDx4kXExcXVuBc2Nzc3zJ8/H2fPnsXBgwexePFinDlzpsYfV7169bB7925kZWUhMzMT27dvx8GDB2v8cfFSUlIQEhIiXK4tx1WbueJ8WBP+/139b33GjBkYOHAgzp07h6ysLISEhLjEGK9cuYJPP/0UGRkZOHXqFHQ6HdavX+/0sVnyOnrmzBmsX78ep0+fxvbt2zF9+nTodLpqHVt8fDxOnTqFEydOoGPHjvjggw+cMjap8QHcCdzPP/+MgIAA4TpnjK+2csW521r0PrbmqM3vY139dd1ae/bsQWZmptA61a7HxZJqk56ezj722GPC5ffff599//33nTgi2+Xk5LChoaHC5Y4dO7J//vkny7Is++eff7IdO3Z01tDsYujQoexPP/1Uq47r3r17bFRUFHvw4MFacVyXLl1iH3nkEXbXrl3s4MGDWZatfX+HtVFNmA9d7f/f1f/Wi4uL2aCgIFav1xtd7wpjvHz5Muvv788WFhay5eXl7ODBg9kdO3a4xNiUvo6a/o889thjbHp6erWOzdDGjRvZcePGOW1sUuMbMWIEm5mZyQYGBrI3btxw6vhqo5owd1uC3sfWPLXpfayrv65by3D+5dnzuCgDoxpduXIFbdq0ES77+/vjypUrThyR/V2/fh0tW7YEALRs2RJ//fWXk0dkvdzcXBw/fhw9e/asFcel0+kQGRmJZs2aIT4+vtYc1yuvvIL//Oc/UKkqp7PacFy1navPh674/+/qf+t//PEHmjZtiueeew5RUVGYPHky7t275xJjbN26Nd544w0EBASgZcuW8PX1xWOPPeYSYzMlNSZX+59ZsWIFnnjiCQCuM7bvv/8erVu3RkREhNH1rjK+2qC2P5euOCdYyxVfx2xRG9/HuvrrurUYhsFjjz2Gbt26Yfny5QDse1wUwKhGrEjDF4ZhnDASYs7du3cxYsQILFy4EA0aNHD2cOxCrVYjMzMTly9fxuHDh3Hq1ClnD8lmP/74I5o1a4Zu3bo5eyjEQq48H7ri/39N+FuvqKjAsWPHkJiYiOPHj8PLy8tlUl9v3ryJzZs3IycnB3/++Sfu3buH1NRUZw/LIq70P5OcnAw3NzdotVoArjG2kpISJCcnY86cOVVuc4Xx1Rb0XNYMrvg6Zqva9j62JryuW2v//v04duwYtm3bhsWLF+PXX3+16/4pgFGN/P39cenSJeHy5cuX0apVKyeOyP6aN2+Oq1evAgCuXr2KZs2aOXlElisvL8eIESOg1WoxfPhwALXjuHgNGzZE//79sX379hp/XPv378f333+PoKAgjBkzBrt378b48eNr/HHVBa46H7rq/39N+Fv39/eHv78/evbsCQAYOXIkjh075hJj3LlzJ9q2bYumTZvC3d0dw4cPR3p6ukuMzZTUmFzlf2b16tX48ccfkZaWJpy4usLYsrOzkZOTg4iICAQFBeHy5cuIjo7GtWvXXGJ8tUVtfy5dcU6wlKu+jtlLbXkfWxNe163FzwnNmjXD008/jcOHD9v1uCiAUY26d++OixcvIicnB2VlZVi/fj2GDh3q7GHZ1dChQ7F69WoA3Jucp556yskjsgzLsnj++ecREhKC1157Tbi+ph/XjRs3hGrxpaWl2LlzJzp37lzjj+uDDz7A5cuXkZubi/Xr1+ORRx5BampqjT+uusAV50NX/v+vCX/rLVq0QJs2bXD+/HkAwK5du9ClSxeXGGNAQAAOHjyIkpISsCyLXbt2ISQkxCXGZkpqTEOHDsX69evx4MED5OTk4OLFi+jRo0e1jm379u346KOP8P3330Oj0RiN2dljCwsLw19//YXc3Fzk5ubC398fx44dQ4sWLVxifLWFK87d9uSKc4IlXPl1zBa18X1sTXhdt8a9e/dw584d4eeffvoJXbt2te9xWV09g1hly5YtbIcOHdh27dqxc+fOdfZwbDJmzBi2RYsWrJubG9u6dWv2iy++YAsKCthHHnmEDQ4OZh955BG2sLDQ2cO0yG+//cYCYMPCwtiIiAg2IiKC3bJlS40/rqysLDYyMpINCwtjQ0ND2dmzZ7Msy9b44zK0Z88eoQBSbTqu2szV5sOa8v/vyn/rx48fZ7t168aGhYWxTz31FFtUVOQyY3zvvffYTp06saGhoez48ePZ+/fvO31slr6Ozp07l23Xrh3bsWNHduvWrdU+tvbt27P+/v7C/8fUqVOdMjap8RkyLSJX3eOrzVxt7rYWvY+tOWr7+1hXfl23VHZ2NhseHs6Gh4ezXbp0EeYIex4Xw7Iii9kIIYQQQgghhBBCXAgtISGEEEIIIYQQQojLowAGIYQQQgghhBBCXB4FMAghhBBCCCGEEOLyKIBBCCGEEEIIIYQQl0cBDEIIIYQQQgghhLg8CmDUQteuXcOYMWPQvn17dOnSBYMGDcKFCxeQm5uLrl27Vtl+0qRJ0Gg0Qs9eAJgxYwYYhkFBQUGV7e/evYupU6eiffv2CA0NRb9+/XDo0CGHHpMl9u7dC19fX0RGRgpfO3fudPawjHz33Xc4c+aMcPm9996zyxhXrVqFpk2bYvLkyQCAqKgoZGZmAgAqKirg5eWF1NRUYftu3brh2LFjNj/upUuXMGDAAISEhCA0NBQpKSnCbW+++SZatGiBefPm2fw4hNQUdXke3rFjhzD3ent7o1OnToiMjMSzzz5breOwZl6luZmQ2q065ubExES0b98eUVFR6NatGz7//HOHHpMYb29v0evVarXR++MPP/ywmkcmLzMzE1u3bhUuf//993YZY25uLjw9PREZGQkAePXVV7Fw4ULh9scff1yYnwHg9ddfxyeffGLz4wKAVqtFp06d0LVrVyQkJKC8vBwAsGHDBgQHB2PIkCF2eZy6hAIYtQzLsnj66afRv39/ZGdn48yZM3j//fdx/fp12fsFBwdj8+bNAAC9Xo89e/agdevWottOnjwZjRs3xsWLF3H69GmsWrVKdBJ3pr59+yIzM1P4evTRR6t9DDqdTvI20zfJc+bMsdsYR48ejS+++AIA0KdPH6SnpwMAsrKy0KlTJ+HyvXv38McffyAiIsLmx3Rzc8P8+fNx9uxZHDx4EIsXLxaO7+OPP8a0adNsfgxCaoq6Pg8//vjjwtwbExODtLQ0ZGZm4quvvhK2kZsf7UVqXqW5meZmUjdV19zcqFEjXLx4EcePH8f27dtRVFRUZbvqmAPFeHp6Gr0//sc//lHtY6ioqJC8zTSAMXToULuNsX379kLg2HAO1uv1KCgowOnTp4Vt09PTERsba5fH1Wq1OHfuHE6ePInS0lLhdcDwNYFYhgIYtcyePXvg7u5u9KYkMjISffv2lb3f2LFjsWHDBgBcBkNsbCzc3NyqbJednY1Dhw5h7ty5UKm4P5927dph8ODBAIBhw4ahW7duCA0NxfLly4X7eXt7IykpCREREejVq5fwYpGXl4e4uDiEh4cjLi4O+fn5ALiI97Rp09C3b1907NgRP/74I4DKwAQvNjYWJ06cUPTcvP3221iyZIlwedasWZg/fz7u3r2LuLg4REdHIywsTHiRys3NRefOnTFx4kSEh4dj5MiRKCkpAQDs2rULUVFRCAsLQ0JCAh48eAAACAoKwpw5c/DQQw/hm2++weeff47u3bsjIiICI0aMQElJCdLT0/H999/jzTffRGRkJLKzszFp0iR8++23Zvc9c+ZMYZznzp0ze8yxsbHCBJ2eno5p06YJz9/hw4cRHR0NtVptdJ9Vq1Zh2LBhePLJJ9G2bVssWrQIn3zyCaKiotCrVy/RF+KWLVsiOjoaAODj44OQkBBcuXJF0e+FkNqG5mFxSuZH/nFffvll9OnTB+3atRPmxqtXr6Jfv36IjIxE165d8dtvvwnH9frrryM6OhpxcXG4ceOGsB/+vjQ309xMSHXMzYcPHzaam5s2bYq3335buO+AAQMwbtw4hIWF4f79+3juuecQFhaGqKgo7NmzBwD3v/7SSy8J+x0yZAj27t0LQHoez8nJQe/evdG9e3e8++67Fj0v27ZtwzPPPCNc3rt3L5588kkAQGJiImJiYhAaGoqZM2cK2wQFBeHtt99Gjx490KNHD/z+++8A5F9PXnvtNQwYMABvv/02Dh8+jD59+iAqKgp9+vTB+fPnUVZWhvfeew8bNmxAZGQkNmzYYPRcyO1b7DVDjuEcfPr0aXTt2hU+Pj64efMmHjx4gLNnzyIqKsroPvx5weTJk9G1a1dotVrs3LkTsbGx6NChAw4fPiz6WIMGDQLDMGAYBj169MDly5cV/V6INApg1DKnTp1Ct27dLL5fhw4dcOPGDdy8eRPr1q3DmDFjRLc7ffo0IiMjq7yx4q1YsQJHjx5FRkYGPv30UxQWFgLgPlHq1asXsrKy0K9fPyGd7qWXXsKzzz6LEydOQKvV4uWXXxb2lZubi19++QVbtmzBtGnTcP/+fUyePBmrVq0CAFy4cAEPHjxAeHh4lXH89ttvRily2dnZGDNmjPACBABff/01Ro0ahfr162PTpk04duwY9uzZg9dffx0sywIAzp8/jylTpuDEiRNo0KABlixZgvv372PSpEnYsGEDTp48iYqKCixdulTYb/369bFv3z6MGTMGw4cPx5EjR5CVlYWQkBB8+eWX6NOnD4YOHYqPP/4YmZmZaN++vXBfc/tu0qQJjh07hsTEREVpv4YR5vT0dPTr1w/16tXDnTt3ZKPLp06dwtq1a3H48GEkJSVBo9Hg+PHj6N27t9GnqGJyc3Nx/Phx9OzZ0+z4CKmNaB6WZm5+5F29ehX79u3Djz/+KHz6tnbtWiG7IysrS0gFvnfvHqKjo3Hs2DE8/PDDmD17tlWPTXMzIbVbdczNERERQvBCzOHDh5GcnIwzZ85g8eLFAICTJ09i3bp1mDhxIu7fvy87Fql5fMaMGUhMTMSRI0fQokULyfuXlpYavT/esGED4uPjcfDgQdy7dw8At7Rh9OjRAIDk5GRkZGTgxIkT+OWXX4yC1Q0aNMDhw4fx0ksv4ZVXXgEg/3py4cIF7Ny5E/Pnz0fnzp3x66+/4vjx45gzZw7eeecdeHh4YM6cORg9ejQyMzOFMfDk9i32miGnVatWcHNzQ35+PtLT09G7d2/07NkTBw4cQEZGBsLDw+Hh4VHlfr///jtmzJiBEydO4Ny5c1i7di327duHefPm4f3335d9zPLycqxZswYDBw40Oz4ijwIYRDB8+HCsX78ehw4dMhuNlvLpp58KUeFLly7h4sWLAAAPDw9hjVe3bt2Qm5sLADhw4ADGjRsHAJgwYQL27dsn7OuZZ56BSqVChw4d0K5dO5w7dw6jRo3Cjz/+iPLycqxYsQKTJk0SHYfpEhJ+LeJff/2FP//8E1lZWWjUqBECAgLAsizeeecdhIeH49FHH8WVK1eEiHabNm2EN5Ljx4/Hvn37cP78ebRt2xYdO3YEAEycOBG//vqr8NiGE+6pU6fQt29fhIWFIS0tzSg9TYy5fQ8fPrzKcygnKCgIZWVluHbtGs6dO4dOnTqhe/fuOHToENLT09GnTx/R+w0YMAA+Pj5o2rQpfH19hUh8WFiY7OPevXsXI0aMwMKFC9GgQQOz4yOEGKtN87AYpfPjsGHDoFKp0KVLF2E+7t69O1auXIlZs2bh5MmT8PHxAQCoVCphv/w8bctji6G5mZC6zZq5OTk5GZGRkWjVqpVwXY8ePdC2bVsAwL59+zBhwgQAQOfOnREYGIgLFy7I7lNqHt+/fz/Gjh0LAMI+xZguIRk9ejTc3NwwcOBA/PDDD6ioqMCWLVvw1FNPAeA+7IuOjkZUVBROnz5ttMSOf7yxY8fiwIEDAORfT0aNGiUE3ouLizFq1Ch07doVr776qtk52Ny+xV4zzOGzMPgARu/evYXLUnNw27ZtERYWBpVKhdDQUMTFxYFhGLNzMABMnz4d/fr1s/q1nVSiAEYtExoaiqNHj1p13zFjxuDdd99FfHy8ZPQ4NDQUWVlZ0Ov1VW7bu3cvdu7ciQMHDiArKwtRUVFCJNnd3R0MwwDgCghJrX/jtzH9mb+s0WgQHx+PzZs34+uvvxYmMqVGjhyJb7/9Fhs2bBAi6Glpabhx4waOHj2KzMxMNG/eXBi32Bj47AwpXl5ews+TJk3CokWLcPLkScycOdNsZN3cvuvVqwdA/jk01bt3b3z77bdo2bIlGIZBr169sH//fhw+fBi9evWSfRyAOzngL6tUKlRUVODSpUtC9H7ZsmUAuMjyiBEjoNVqhTfzhNRFNA9LUzo/Gs5B/LzYr18//Prrr2jdujUmTJggmXFgOmZLH1sMzc2E1HyOnpu7dOliNDcnJSUhMzMTt2/fFrYxnIek5hU3Nzej+d1wfpKbx6XmPiVGjx6Nr7/+Grt370b37t3h4+ODnJwczJs3D7t27cKJEycwePBgo7HIvVaIXW947O+++y4GDBiAU6dO4YcffjA7B5vbt9hrhjl8JtzJkyfRtWtX9OrVCwcOHJDNgjM3BwNcHajIyEijoqCzZ8/GjRs37FYYtK6jAEYt88gjj+DBgwdGFY+PHDmCX375xex9AwICkJycjOnTp0tu0759e8TExGDmzJnCBHHx4kVs3rwZxcXFaNSoETQaDc6dO4eDBw+afcw+ffpg/fr1ALhAwkMPPSTc9s0330Cv1yM7Oxt//PEHOnXqBIArkPTyyy+je/fuaNy4sdnHMDRmzBisX78e3377LUaOHAmAiwI3a9YM7u7u2LNnD/Ly8oTt8/PzhajyunXr8NBDD6Fz587Izc0V1vutWbMGDz/8sOjj3blzBy1btkR5eTnS0tKE6318fIwqWvMs2bdSsbGxWLBgAXr37g0AQqpxixYt0LBhQ6v22aZNGyF6P23aNLAsi+effx4hISF47bXXbBovITUdzcPKSM2PUvLy8tCsWTO88MILeP7554UuHXq9XljzvHbtWqPxW/rYNDcTUns5em4ODg5GTEwM/vWvfwlFOu/fvy95Qt2vXz9h/rlw4QLy8/PRqVMnBAUFITMzE3q9HpcuXZKsrWAoNjbWaB63VP/+/XHs2DF8/vnnQrba7du34eXlBV9fX1y/fh3btm0zug+/LHvDhg3CPCb3emKouLhYKITKL0kEpOdgS/atVGxsLH788Uc0btwYarUajRs3xq1bt3DgwAHheKyxY8cOZGZmCgU6v/jiC+zYsQPr1q2TXV5ElKNnsZZhGAabNm3Czz//LLTXmzVrlpC+dv78efj7+wtf33zzjdH9+bZ8cr744gtcu3YNwcHBCAsLwwsvvIBWrVph4MCBqKioQHh4ON59913JT5AMffrpp1i5ciXCw8OxZs0aoxZvnTp1wsMPP4wnnngCy5YtQ/369QFwKXMNGjTAc889J7lf0xoY/Jvb0NBQ3LlzB61bt0bLli0BcNWBMzIyhGr5nTt3FvYTEhKC1atXIzw8HEVFRUhMTET9+vWxcuVKjBo1Skgjk6rk/u9//xs9e/ZEfHy80X7HjBmDjz/+GFFRUcjOzhaut2TfSsXGxuKPP/4QJuOWLVtCp9NJpsdZY//+/VizZg12794tPOeGVaQJqUtoHlZGan6UsnfvXkRGRiIqKgr/+9//MGPGDADcp3qnT59Gt27dsHv3brz33ntWPzbNzYTUXtU1NxcWFiI4OBjdunXDo48+io8++kh02+nTp0On0yEsLAyjR4/GqlWrUK9ePcTGxgpLFd544w2hEK+clJQULF68GN27d0dxcbHkdqY1MPh6EWq1GkOGDMG2bduEJSoRERGIiopCaGgoEhISqmQlPHjwAD179kRKSgoWLFgAQP71xNBbb72Ff/7zn4iNjTXqyDJgwACcOXNGqM9hSOm+lQoLC0NBQYHR62RYWBh8fX3RpEkTm/ZtaNq0abh+/Tp69+6NyMhIzJkzx277rqsYVmmeDSHVaNKkSRgyZIiQJWHozz//RP/+/XHu3DmHRjJzc3MxZMgQnDp1ymGPYW+rVq1CRkYGFi1a5OyhGJk1axa8vb3xxhtvOHsohBCFXGEeVsLb2xt379516hjMobmZEFKbBAUFISMjw64n+o7kqu/p9+7di3nz5gldvogylIFBapSvvvoKPXv2RHJystPfNLsiT09PbNu2zWjdnbO9+eabSE1NNVr7SAipuWgethzNzYQQ4jxqtRrFxcVC9ypXsGHDBkyfPh2NGjVy9lBqHMrAIIQQQgghhBBCiMujj04IIYQQQgghhBDi8iiAQQghhBBCCCGEEJdHAQxCCCGEEEIIIYS4PApgEEIIIYQQQgghxOVRAIMQQgghhBBCCCEujwIYhBBCCCGEEEIIcXn/D+w9uxwpQ+6KAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Generating plots\n",
-    "plt.figure(num=None, figsize=(15, 5),  facecolor='w', edgecolor='k')\n",
-    "\n",
-    "plt.subplot(1, 3, 1)\n",
-    "detailed_scatter (df_daily.sim_FCEV_orig, df_daily.sim_EFLX_LH_TOT_orig, 'b')\n",
-    "plt.ylabel('CLM Latent Heat [W m-2]')\n",
-    "plt.xlabel('CLM Canopy Evaporation [W m-2]')\n",
-    "\n",
-    "plt.subplot(1, 3, 2)\n",
-    "detailed_scatter (df_daily.sim_FCTR_orig, df_daily.sim_EFLX_LH_TOT_orig, 'orange')\n",
-    "\n",
-    "plt.ylabel('CLM Latent Heat [W m-2]')\n",
-    "plt.xlabel('CLM Canopy Transpiration [W m-2]')\n",
-    "\n",
-    "plt.subplot(1, 3, 3)\n",
-    "detailed_scatter (df_daily.sim_FGEV_orig, df_daily.sim_EFLX_LH_TOT_orig,'green')\n",
-    "\n",
-    "plt.ylabel('CLM Latent Heat [W m-2]')\n",
-    "plt.xlabel('CLM Ground Evaporation [W m-2]')\n",
-    "\n",
-    "plt.suptitle(year+\" \"+neon_site+\" Scatter Plots\", fontweight='bold')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f33483c1-4219-4709-8667-df5618813bd9",
-   "metadata": {},
-   "source": [
-    "#### **Questions to consider**: \n",
-    "1. Which component flux has the strongest relationship with total latent heat flux\n",
-    "1. The plots show data for the full year. How do you think the relationships might change by season? \n",
-    "\n",
-    "#### **Challenge:** Can you make a similar plot for the modified simulation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "02a9031a-99a1-41b9-a226-f5be9c8a173f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/Day3_CreateFork.ipynb b/notebooks/Day3_CreateFork.ipynb
deleted file mode 100644
index ab4f8d7..0000000
--- a/notebooks/Day3_CreateFork.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e8c10b43-9869-4425-be27-2dbfbc38803b",
-   "metadata": {},
-   "source": [
-    "## Day 3 On your own:\n",
-    "### *Sharing your changes with others & pushing changes back to your GitHub Repository*\n",
-    "*Note: If you plan to contribute your code developments to CTSM or the CTSM-Tutorial, you will need to use these optional steps to share your code with CTSM model developers. If you already have a GitHub account and have a CTSM fork, start at step 2.*\n",
-    "\n",
-    "So far, we made a code change and saved it to a `local` branch. In reality, we usually want/need to push our changes back to GitHub so our collaborators can see, comment, or use our code modifications. \n",
-    "Imagine saving your progress in a video game or in a Word document on a local computer. If you use a different computer, you can not load your progress. However, if you save your video game progress or your Word document on the cloud, you can easily access it from any computer. Nowadays, video games save your progress via a profile/account and Word documents can be saved and shared through Google or Dropbox accounts. \n",
-    "\n",
-    "Similarly, **you need to create an account on GitHub to be able to share your changes** so:\n",
-    "- you can access your code and changes from anywhere.\n",
-    "- you can share with collaborators. \n",
-    "- you can contribute back to CTSM tutorial repository. \n",
-    "\n",
-    "### 1. Create a GitHub account\n",
-    "\n",
-    "Visit the [GitHub website](github.com) and create an account if you don't already one. You can skip step this if you already have a GitHub account. \n",
-    "\n",
-    "\n",
-    "### 2. Create a fork from CTSM-Tutorial-2022 repository\n",
-    "You don't have access to write directly to the main CTSM tutorial repository (that right is reserved for the tutorial developers), so you need to create your own copy of the repository to save your changes. For this, you will fork the CTSM tutorial repository.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "  <strong> NOTE: </strong> \n",
-    "    A fork is a copy of a repository. \"Fork\"ing a repository is similar to creating a branch in that it allows you to freely experiment with changes without affecting the original project. However, we recommend using your CTSM fork as an unmodified copy of CTSM tutorial repository and making changes using branches.\n",
-    "</div>\n",
-    "\n",
-    "#### To Do: Create a fork\n",
-    "You can create your own fork of the CTSM tutorial repository by using the fork button in the upper right corner of the CTSM tutorial reository page.\n",
-    "\n",
-    "- Login to your GitHub account.\n",
-    "- Navigate to the original [CTSM tutorial repository](https://github.com/NCAR/CTSM-Tutorial-2022).\n",
-    "- Use the `fork` button to create a fork of CTSM repository in your account\n",
-    "**NOTE:** The image below is from the CTSM repository, but the GitHub interface looks the same for the tutorial repository.\n",
-    "\n",
-    "![image3.png](https://github.com/NCAR/CTSM-Tutorial-2022/raw/main/images/fork_image.png)\n",
-    "\n",
-    "\n",
-    "Your forked repository will be under your account name:\n",
-    "\n",
-    "https://github.com/YOUR-USER-NAME/CTSM-Tutorial-2022\n",
-    "\n",
-    "For example, for the username (wwieder) the forked repo is:\n",
-    "\n",
-    "https://github.com/wwieder/CTSM-Tutorial-2022\n",
-    "\n",
-    "You can make any modifications you'd like to your forked repository. Note that you only have to fork a respository once -- it will always be connected to your GitHub account unless you delete it.\n",
-    "\n",
-    "### 3. Pushing your changes to the outside world:\n",
-    "To start, connect your forked repository to the computing system you are using. You can do so by using the following:\n",
-    "\n",
-    "<div class=\"alert alert-block alert-danger\">\n",
-    "\n",
-    "<b>WARNING!</b> \n",
-    "    \n",
-    "Please replace \"YOUR_USER_NAME\" in the code below with your own GitHub username (created in step 1).\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8199e4b0-f584-4f98-bf3e-e06f2bcad4e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git remote add YOUR_USER_NAME https://github.com:YOUR_USER_NAME/CTSM-Tutorial-2022.git"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2795462b-5098-436d-ba81-737cdd9633f9",
-   "metadata": {},
-   "source": [
-    "Finally, push your changes to the remote repository. Note that 'pushing' the changes makes the changes visible to anyone who looks at your GitHub repository, including your collaborators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de521a1a-969e-4288-a13a-c5901c64a36c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git add -a\n",
-    "git commit -m 'all my notebook changes'\n",
-    "git push YOUR_USER_NAME "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e2b518c-b5f2-4c2b-8608-c76a7eace5c4",
-   "metadata": {},
-   "source": [
-    "To see your changes now you can go to your fork and look for your 'main' branch on [github.com](github.com). You should be able to see your recent changes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56b1a922-2416-479d-84bd-c1a866863c11",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "118d05b0-611d-4615-90f5-01d35d79ded0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Bash",
-   "language": "bash",
-   "name": "bash"
-  },
-  "language_info": {
-   "codemirror_mode": "shell",
-   "file_extension": ".sh",
-   "mimetype": "text/x-sh",
-   "name": "bash"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/Day0a_GitStarted.ipynb b/notebooks/GitStarted.ipynb
similarity index 100%
rename from notebooks/Day0a_GitStarted.ipynb
rename to notebooks/GitStarted.ipynb
diff --git a/notebooks/Day1a_GlobalCase.ipynb b/notebooks/Global/1_GlobalCase.ipynb
similarity index 100%
rename from notebooks/Day1a_GlobalCase.ipynb
rename to notebooks/Global/1_GlobalCase.ipynb
diff --git a/notebooks/Day1b_GlobalVisualization.ipynb b/notebooks/Global/2_GlobalVisualization.ipynb
similarity index 100%
rename from notebooks/Day1b_GlobalVisualization.ipynb
rename to notebooks/Global/2_GlobalVisualization.ipynb
diff --git a/notebooks/Day3_GlobalVisualization.hydro_vers.ipynb b/notebooks/Global/3_GlobalVisualization.hydro_vers.ipynb
similarity index 100%
rename from notebooks/Day3_GlobalVisualization.hydro_vers.ipynb
rename to notebooks/Global/3_GlobalVisualization.hydro_vers.ipynb
diff --git a/notebooks/Day0b_NEON_Simulation_Tutorial.ipynb b/notebooks/SinglePoint/1_NEON_Simulation_Tutorial.ipynb
similarity index 90%
rename from notebooks/Day0b_NEON_Simulation_Tutorial.ipynb
rename to notebooks/SinglePoint/1_NEON_Simulation_Tutorial.ipynb
index 5e03bad..18e3b0f 100644
--- a/notebooks/Day0b_NEON_Simulation_Tutorial.ipynb
+++ b/notebooks/SinglePoint/1_NEON_Simulation_Tutorial.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial 0b: *CTSM Simulations at NEON Tower Sites*\n",
+    "## Tutorial 1: *CTSM Simulations at NEON Tower Sites*\n",
     "\n",
     "This tutorial is an introduction to running the Community Terrestrial Systems Model (CTSM) at [NEON tower sites](https://www.neonscience.org/).  It will guide you through running a simulation and provides example visualization of the simulation results. If you want to dive deeper, after you complete a NEON tower simulation in this tutorial, you can use the [NEON_Visualization_Tutorial](https://github.com/NCAR/NEON-visualization) to explore observation and model data further.\n",
     "<br><br>\n",
     "\n",
-    "![NEON Tower](../images/STER_tower.png)\n",
+    "![NEON Tower](../../images/STER_tower.png)\n",
     "\n",
     "\n",
     "**A few notes about the model:** There are several configuration options of CTSM, and throughout this tutorial we will use the Community Land Model (CLM) configuration which is the climate and biogeochemistry mode of CTSM. Throughout the rest of this tutorial, we refer to the model as CLM and will use version 5.1 with active biogeochemistry (CLM5.1-BGC).\n",
     "\n",
-    "Additional information about CTSM and CLM is available [on the website](https://www.cesm.ucar.edu/models/cesm2/land/), including [technical documentation](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/index.html), a [user's guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/index.html), and a [quickstart guide](https://escomp.github.io/CESM/release-cesm2/quickstart.html#create-a-case) for running various model configurations beyond what is covered in this tutorial.\n",
+    "Additional information about CTSM and CLM is available [on the website](https://www.cesm.ucar.edu/models/cesm2/land/), including [technical documentation](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/index.html), a [user's guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/index.html), and a [quickstart guide](https://escomp.github.io/CESM/release-cesm2/quickstart.html#create-a-case) for running various model configurations beyond what is covered in this tutorial. Details on [history field (or output variables)](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_nofates.html) and variable naming conventions are also available.\n",
     "\n",
     "Specifically, this example runs a single point case with data from a flux tower that's part of the National Ecological Observatory Network (NEON).  You can find out more about the [NCAR-NEON collaboration at this website](https://github.com/NCAR/NEON-visualization).  This effort aims to links NCAR’s modeling capabilities with NEON’s measurement network through an NSF supported cyberinfrastructure project.\n",
     "\n",
@@ -45,9 +45,17 @@
     "***\n",
     "<h4>1.1 Select a NEON tower site to simulate.</h4>\n",
     "\n",
-    "NEON towers currently available for simulation include:  \n",
+    "NEON towers currently available for simulation includes:  \n",
     "\n",
-    ">ABBY, BART, BLAN, CPER, DCFS, DSNY, GRSM, HARV, JORN, KONZ, NOGP, OAES, ORNL, OSBS, SCBI, SERC, SRER, STEI, TALL, TREE, UKFS, UNDE, WOOD.\n",
+    "```\n",
+    "ABBY,BARR,BART,BLAN,BONA,CLBJ,CPER,DCFS,DEJU,DELA,DSNY,\n",
+    "GRSM,GUAN,HARV,HEAL,JERC,JORN,KONA,KONZ,LAJA,LENO,MLBS,\n",
+    "MOAB,NIWO,NOGP,OAES,ONAQ,ORNL,OSBS,PUUM,RMNP,SCBI,SERC,\n",
+    "SJER,SOAP,SRER,STEI,STER,TALL,TEAK,TOOL,TREE,UKFS,UNDE,\n",
+    "WOOD,WREF,YELL\n",
+    "```\n",
+    "\n",
+    "**NOTE:** NIWO and PUUM have data gaps that prevent us from running for a full calendar year\n",
     "\n",
     "The [NEON website](https://www.neonscience.org/field-sites/explore-field-sites) describes tower sites in more detail.\n",
     "\n",
@@ -67,7 +75,7 @@
    "outputs": [],
    "source": [
     "#Change the 4-character NEON site below.\n",
-    "neon_site= \"KONZ\"\n",
+    "neon_site= \"SJER\"\n",
     "\n",
     "# Set an environment variable to the site we want to use, for easier use in scripts\n",
     "import os\n",
@@ -98,10 +106,10 @@
     "table td, table th, table tr {text-align:left !important;}\n",
     "</style>\n",
     "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>NOTE:</b> You might see lines that say 'ERROR' or 'file not found'; this is ok if the simulation continues running to completion, and will be addressed in future changes to our data download process.\n",
+    "<b>NOTE:</b> You might see lines that say 'ERROR', 'WARNING' or 'file not found'; this is ok if the simulation continues running to completion, and will be addressed in future changes to our data download process.\n",
     "</div>\n",
     "\n",
-    "Run the command below - note that it will take several minutes without printing additional updates, so be patient!  We are using a 'helper' script here, 'qcmd', which will allocate a small compute node for each individual to build and run the case.  \n",
+    "Run the command below - **note that it will take several minutes without printing additional updates, so be patient!**  We are using a 'helper' script here, 'qcmd', which will allocate a small compute node for each individual to build and run the case.  \n",
     "\n",
     "<style> \n",
     "table td, table th, table tr {text-align:left !important;}\n",
@@ -119,7 +127,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qcmd -- run_neon --neon-sites $site  --output-root ~/scratch/CLM-NEON"
+    "qcmd -- run_neon --neon-sites $site  --output-root ~/scratch/NEON_cases"
    ]
   },
   {
@@ -156,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!tail  ~/scratch/CLM-NEON/${site}.transient/CaseStatus"
+    "!tail  ~/scratch/NEON_cases/${site}.transient/CaseStatus"
    ]
   },
   {
@@ -225,7 +233,7 @@
     ">\n",
     ">If you run a simulation for a NEON site that you have previously simulated, you will see an error like the following:\n",
     ">```\n",
-    ">Case already exists in /home/<user>/scratch/CLM-NEON/ABBY.transient, not overwritting.\n",
+    ">Case already exists in /home/<user>/scratch/NEON_cases/ABBY.transient, not overwritting.\n",
     ">```\n",
     ">The overwrite option (e.g., adding `--overwrite` to the end of the `run_neon` command) can be used to avoid this error. \n",
     "\n",
@@ -277,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!ls ~/scratch/CLM-NEON/archive/$site.transient/lnd/hist/*2018*.nc |head -20"
+    "!ls ~/scratch/NEON_cases/archive/$site.transient/lnd/hist/*2018*.nc |head -20"
    ]
   },
   {
@@ -324,7 +332,7 @@
     "import os\n",
     "from neon_utils import plot_soil_profile_timeseries\n",
     "username = os.environ['USER']  # Get our username, for use in paths\n",
-    "sim_path = \"/scratch/\"+username+\"/CLM-NEON/archive/\"+neon_site+\".transient/lnd/hist/\"\n",
+    "sim_path = \"/scratch/\"+username+\"/NEON_cases/archive/\"+neon_site+\".transient/lnd/hist/\"\n",
     "\n",
     "case_name = neon_site+\".transient.clm2\"\n",
     "year = 2018\n",
@@ -362,7 +370,7 @@
     "</div>\n",
     "\n",
     "\n",
-    "**Additional examples of visualizations and evaluation of your NEON simulation against NEON observations are available in the  [NEON Visualization Tutorial](https://ncar.github.io/ncar-neon-books/notebooks/NEON_Visualization_Tutorial.html).**\n"
+    "**Additional examples of visualizations and evaluation of your NEON simulation against NEON observations are available in the  [NEON Visualization Tutorial](./2_NEON_Simulation_Visualization.ipynb).**\n"
    ]
   },
   {
@@ -389,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Day0c_NEON_Simulation_Visualization.ipynb b/notebooks/SinglePoint/2_NEON_Simulation_Visualization.ipynb
similarity index 93%
rename from notebooks/Day0c_NEON_Simulation_Visualization.ipynb
rename to notebooks/SinglePoint/2_NEON_Simulation_Visualization.ipynb
index d34d919..8d87596 100644
--- a/notebooks/Day0c_NEON_Simulation_Visualization.ipynb
+++ b/notebooks/SinglePoint/2_NEON_Simulation_Visualization.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial 0c: *CTSM Evaluations at NEON Tower Sites*\n",
+    "# Tutorial 2: *CTSM Evaluations at NEON Tower Sites*\n",
     "\n",
-    "This tutorial is an introduction to evaluating CTSM at a NEON tower site.  It provides example visualizations and evaluation of simulated latent heat flux using NEON tower observations.  This tutorial uses results from the case you ran in the Day0b tutorial, but you don't have to wait for those runs to complete before doing this tutorial. We've pre-staged model results from this simulation in a shared directory so that you can analyze results of your simulations regardless of whether they've completed.\n",
+    "This tutorial is an introduction to evaluating CTSM at a NEON tower site.  It provides example visualizations and evaluation of simulated latent heat flux using NEON tower observations.  This tutorial uses results from the case you ran in `1_NEON_Simulation_Tutorial`, but you don't have to wait for those runs to complete before doing this tutorial. We've pre-staged model results from this simulation in a shared directory so that you can analyze results of your simulations regardless of whether they've completed.\n",
     "\n",
     "------\n",
     "\n",
@@ -55,14 +55,23 @@
    "source": [
     "Before diving in, set the NEON site that you simulated in the cell below. As a refresher, NEON tower sites include:\n",
     "\n",
-    ">ABBY, BART, BLAN, CPER, DCFS, DSNY, GRSM, HARV, JORN, KONZ, NOGP, OAES, ORNL, OSBS, SCBI, SERC, SRER, STEI, TALL, TREE, UKFS, UNDE, WOOD.\n",
+    "```\n",
+    "ABBY,BARR,BART,BLAN,BONA,CLBJ,CPER,DCFS,DEJU,DELA,DSNY,\n",
+    "GRSM,GUAN,HARV,HEAL,JERC,JORN,KONA,KONZ,LAJA,LENO,MLBS,\n",
+    "MOAB,NIWO,NOGP,OAES,ONAQ,ORNL,OSBS,PUUM,RMNP,SCBI,SERC,\n",
+    "SJER,SOAP,SRER,STEI,STER,TALL,TEAK,TOOL,TREE,UKFS,UNDE,\n",
+    "WOOD,WREF,YELL\n",
+    "```\n",
+    "\n",
+    "**NOTE:** NIWO and PUUM have data gaps that prevent us from running for a full calendar year and cannotbe plotted\n",
+    "\n",
     "\n",
     "*The tutorial is currently set to use the KONZ site. If you ran a simulation for a different tower site, please change the 4-character site name in quotes below to the same as your simulation.*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,46 +95,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "KONZ.transient.clm2.h0.2018-01.nc\n",
-      "KONZ.transient.clm2.h0.2018-02.nc\n",
-      "KONZ.transient.clm2.h0.2018-03.nc\n",
-      "KONZ.transient.clm2.h0.2018-04.nc\n",
-      "KONZ.transient.clm2.h0.2018-05.nc\n",
-      "KONZ.transient.clm2.h0.2018-06.nc\n",
-      "KONZ.transient.clm2.h0.2018-07.nc\n",
-      "KONZ.transient.clm2.h0.2018-08.nc\n",
-      "KONZ.transient.clm2.h0.2018-09.nc\n",
-      "KONZ.transient.clm2.h0.2018-10.nc\n",
-      "KONZ.transient.clm2.h0.2018-11.nc\n",
-      "KONZ.transient.clm2.h0.2018-12.nc\n",
-      "KONZ.transient.clm2.h0.2019-01.nc\n",
-      "KONZ.transient.clm2.h0.2019-02.nc\n",
-      "KONZ.transient.clm2.h0.2019-03.nc\n",
-      "KONZ.transient.clm2.h0.2019-04.nc\n",
-      "KONZ.transient.clm2.h0.2019-05.nc\n",
-      "KONZ.transient.clm2.h0.2019-06.nc\n",
-      "KONZ.transient.clm2.h0.2019-07.nc\n",
-      "KONZ.transient.clm2.h0.2019-08.nc\n",
-      "ls: write error: Broken pipe\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls /scratch/data/flux/CLM-NEON/archive/{neon_site}.transient/lnd/hist |head -20"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls /scratch/data/NEONv2/hist/{neon_site}.transient/lnd/hist |head -20"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each line of the list above includes the file path (`//scratch/data/flux/CLM-NEON/{simulation_name}/lnd/hist/`) and file name. The file names are automatically generated and comprise:\n",
+    "Each line of the list above includes the file path (`/scratch/data/NEONv2/hist/{simulation_name}/lnd/hist/`) and file name. The file names are automatically generated and comprise:\n",
     "* the simulation name, which includes:\n",
     "    * the NEON site\n",
     "    * the type of simulation \n",
@@ -190,20 +171,20 @@
     "import os\n",
     "import time\n",
     "import datetime\n",
+    "from glob import glob\n",
+    "from os.path import join, expanduser\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import xarray as xr\n",
     "\n",
-    "from glob import glob\n",
-    "from os.path import join, expanduser\n",
-    "\n",
-    "#import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
     "from scipy import stats\n",
+    "from neon_utils import download_eval_files\n",
     "\n",
-    "from neon_utils import download_eval_files"
+    "# suppress Future Warnings that are annoying...\n",
+    "import warnings\n",
+    "warnings.simplefilter(\"ignore\", category=FutureWarning)"
    ]
   },
   {
@@ -264,10 +245,10 @@
    "outputs": [],
    "source": [
     "#--The following line points to your directory:\n",
-    "#sim_path = \"/home/\"+username+\"/scratch/CLM-NEON/archive/\"+neon_site+\".transient/lnd/hist/\"\n",
+    "#sim_path = \"/home/\"+username+\"/scratch/NEON_cases/archive/\"+neon_site+\".transient/lnd/hist/\"\n",
     "\n",
-    "#-- But for now let's look at the pre-staged results:\n",
-    "sim_path = \"/scratch/data/flux/CLM-NEON/archive/\"+neon_site+\".transient/lnd/hist/\"\n",
+    "#-- But for now let's look at the pre-staged results:  TODO check for latest directory\n",
+    "sim_path = \"/scratch/data/NEONv2/hist/\"+neon_site+\".transient/lnd/hist/\"\n",
     "\n",
     "\n",
     "sim_files = sorted(glob(join(sim_path,neon_site+\".transient.clm2.h1.\"+year+\"*.nc\")))\n",
@@ -302,7 +283,7 @@
     "\n",
     "start = time.time()\n",
     "\n",
-    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',parallel=True,preprocess=preprocess)\n",
+    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords', preprocess=preprocess)\n",
     "\n",
     "end = time.time()\n",
     "print(\"Reading all simulation files took:\", end-start, \"seconds.\")"
@@ -418,7 +399,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "eval_dir = \"/home/negins/scratch/evaluation_files/\"\n",
     "eval_dir = os.path.join(\"/home\", username, \"scratch/evaluation_files\")\n",
     "\n",
     "download_eval_files(neon_site, eval_dir, year)"
@@ -428,14 +408,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Hint: If the download does not work, you can directly point to the pre-staged results:"
+    "Hint: If the download does not work, you can directly point to the pre-staged results by uncommenting the cell below\n",
+    "\n",
+    "**NOTE, this is also faster for more years / sites**"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "eval_dir = \"/scratch/data/flux/evaluation_files\""
+    "#eval_dir = '/scratch/data/NEONv2/eval'"
    ]
   },
   {
@@ -602,13 +586,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "csv_dir = os.path.join(\"/home\", username, \"preprocessed_data\")\n",
-    "\n",
-    "#create the directory if it does not exist:\n",
-    "if not os.path.isdir(csv_dir):\n",
-    "    os.mkdir(csv_dir)\n",
-    "\n",
-    "csv_out = os.path.join(csv_dir, \"preprocessed_\"+neon_site+\"_\"+year+\".csv\")\n",
+    "# Make the figures directory if it's not already there\n",
+    "out_dir = os.path.realpath(os.path.expanduser('~/scratch/NEON_cases/postprocessed_data'))\n",
+    "if not os.path.isdir(out_dir):\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "    \n",
+    "csv_out = os.path.join(out_dir, \"preprocessed_\"+neon_site+\"_\"+year+\".csv\")\n",
     "df_all.to_csv(csv_out,index=False)"
    ]
   },
@@ -656,7 +639,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_daily = df_all.groupby(['year','month','day']).mean().reset_index()\n",
+    "df_daily = df_all.groupby(['year','month','day']).mean('numeric_only').reset_index()\n",
     "df_daily['time']=pd.to_datetime(df_daily[[\"year\", \"month\", \"day\"]])"
    ]
   },
@@ -759,7 +742,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_monthly = df_all.groupby(['year','month']).mean().reset_index()\n",
+    "df_monthly = df_all.groupby(['year','month']).mean('numeric_only').reset_index()\n",
     "df_monthly[\"day\"]=15\n",
     "df_monthly['time']=pd.to_datetime(df_monthly[[\"year\", \"month\",\"day\"]])"
    ]
@@ -1075,7 +1058,7 @@
    "outputs": [],
    "source": [
     "# Calculate annual average diel cycle and bias\n",
-    "diel_df_mean = df_all.groupby('local_hour').mean().reset_index()\n",
+    "diel_df_mean = df_all.groupby('local_hour').mean('numeric_only').reset_index()\n",
     "diel_df_std = df_all.groupby('local_hour').std().reset_index()\n",
     "\n",
     "diel_df_mean['EFLX_LH_TOT_bias'] = diel_df_mean['sim_EFLX_LH_TOT']- diel_df_mean['EFLX_LH_TOT']\n",
@@ -1196,6 +1179,13 @@
     "You can also use this script to explore additional CLM and NEON data by changing the variables in section 3 of this script.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1220,7 +1210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/SinglePoint/ProjectExamples/Basic_CTSM-NEON_Plots.ipynb b/notebooks/SinglePoint/ProjectExamples/Basic_CTSM-NEON_Plots.ipynb
new file mode 100644
index 0000000..b1f992c
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/Basic_CTSM-NEON_Plots.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51f96e25-587b-4108-a7d9-34666f7a50e6",
+   "metadata": {},
+   "source": [
+    "# Basic plotting \n",
+    "## Examples of typical plots used to look at CTSM variables\n",
+    "\n",
+    "This tutorial uses [xarray](https://docs.xarray.dev/en/stable/user-guide/terminology.html) and [matplotlib](https://matplotlib.org/stable/index.html) to create several types of plots. You will find examples for how to:\n",
+    "\n",
+    "1. Load datasets with xarray\n",
+    "2. Manipulate data & make plots for:\n",
+    "> 2.1 Raw dataset; <br>\n",
+    "> 2.2 Diel averages for given months of the year; <br>\n",
+    "> 2.3 Daily and annual fluxes; and <br>\n",
+    "> 2.4 Annual climatologies\n",
+    "3. Export data to other file types (*e.g., .csv files*).\n",
+    "\n",
+    "------\n",
+    "\n",
+    "**This tutorial uses a Jupyter Notebook.** \n",
+    "For more information on Jupyter notebooks please see the information in the Getting Started tutorials or visit the [Jupyter Notebook Quick Start Guide](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html). \n",
+    "\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7edf783f-9442-4a1b-ab7c-e9455be3ac5b",
+   "metadata": {},
+   "source": [
+    "# 1. Load Datasets\n",
+    "\n",
+    "## 1.1 Load Python Libraries\n",
+    "It is necessary to start by loading in the libraries that will be used in the script. Note that this list is a good starting point for most of your plotting needs. Not all the libraries loaded here will be used in this tutorial.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87882ecf-c37c-4b01-bfb4-24e56d671777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from glob import glob\n",
+    "from os.path import join\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "from neon_utils import fix_time_h1\n",
+    "\n",
+    "# suppress Future Warnings that are annoying...\n",
+    "import warnings\n",
+    "warnings.simplefilter(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2ea9556-75d1-4aa8-921b-8cd347edf263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('numpy '+np.__version__) ##-- was working with 1.23.5\n",
+    "print('pandas '+pd.__version__) ##-- was working with 1.5.3\n",
+    "print('xarray '+xr.__version__) ##-- was working with 2023.1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db2ec48-9370-422a-a4e4-893bc39906e2",
+   "metadata": {},
+   "source": [
+    "## 1.2 Point to history files \n",
+    "\n",
+    "### 1.2.1 Where are my simulation results?\n",
+    "When you run simulations, history files are all saved in the archive directory within your scratch directory. For the NEON simulations, this directory is `/scratch/NEON_cases/archive/`.\n",
+    "\n",
+    "We can print a list of the simulations in this directory. Because this is a python script and we want to use a [bash](https://opensource.com/resources/what-bash) command, we have to use bash magic, `%%bash` or `!` which turns the python cell block below into a bash cell.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acf88bc1-a2ab-4ae5-bfcb-232cc5d6ad82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "ls ~/scratch/NEON_cases/archive/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2819f0e-486e-4e39-b019-1cc83842d30e",
+   "metadata": {},
+   "source": [
+    "#### Alternatively, we have all these cases you can run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9cdd273-7082-4c9f-bcb8-1e76abb96c76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "ls /scratch/data/NEONv2/hist/\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf652c-e127-4cb8-89cb-a4423b29f2b4",
+   "metadata": {},
+   "source": [
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.2 Point to the data folder with history files \n",
+    "**The below code sets the following:**\n",
+    "- the NEON site; \n",
+    "- the path to the archive directory;\n",
+    "- and the subdirectory with data.\n",
+    "\n",
+    "**To Do:**\n",
+    "In the code below, change the name of the NEON site you simulated and want to create plots for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e3bee8-fa6b-4c37-ae55-f62151640598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neon_site = 'KONZ'  #NEON site we're going to look at\n",
+    "archive = '~/scratch/NEON_cases/archive' #Path to archive directory\n",
+    "\n",
+    "# Alternatively, prestaged data from all NEON sites are here\n",
+    "# archive = '/scratch/data/NEONv2/hist/'\n",
+    "\n",
+    "# this unpacks the and expands the shortcut we used above\n",
+    "archive = os.path.realpath(os.path.expanduser(archive)) \n",
+    "\n",
+    "# Create a path to the data folder\n",
+    "data_folder = archive+'/'+neon_site+'.transient/lnd/hist'\n",
+    "data_folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d25741-5010-496f-b61a-f15e1c135989",
+   "metadata": {},
+   "source": [
+    "**Is this the path for the simulated data, `data_folder`, correct?** \n",
+    "If not, you can set the `data_folder` variable manually. \n",
+    "<br>*HINT:* You can check in the terminal window or using bash magic.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.3 Create functions to use when opening the data files\n",
+    "1. `preprocess` reads in a subset of variables using an xarray function. This saves time and memory resources.\n",
+    "2. `fix_time` adjusts a time offset that is a feature of the CTSM history files.\n",
+    "<br>**A note about model timestamps:** The CTSM history includes an initial 0th timestep for each model simulation. This offset in the time dimension can cause challenges when analyzing and evaluating model data if not treated properly.\n",
+    "<br>*Don't worry too much about the details of these functions right now*\n",
+    "\n",
+    "**To Do:** You can change the 'variables' list below to add or remove variables that you are interested in plotting. Make sure to follow the same formatting. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f2a23fe-7273-4370-8f2c-fe3dfad5029e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -- read only these variables from the whole netcdf files\n",
+    "def preprocess (ds):\n",
+    "    variables = ['FCEV', 'FCTR', 'FGEV','FSH','GPP','FSA','FIRA','AR','HR','ELAI']\n",
+    "\n",
+    "    ds_new= ds[variables].isel(lndgrid=0)\n",
+    "    return ds_new\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3919e8c9-4c08-485f-9e01-8c42f6b5ddad",
+   "metadata": {},
+   "source": [
+    "Now we have created the functions needed to manipulate our datasets\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.4 List all the files we're going to open\n",
+    "The the 30-minute, high frequency data (**'h1' files**) are written out every day for NEON simulations. CTSM typically saves files as monthly averages (the 'h0' files) and many more variables are available at this frequency. \n",
+    "\n",
+    "To open all of the `*h1*`files in our `data_folder`, we use the `glob` python function. \n",
+    "\n",
+    "You will notice that **several python functions are combined in a single line of code** that runs through a `for` loop over defined simulation years (written as a list of strings).\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> If you're new to python it's dense, but efficient. This code is borrowed from Negin Sobhani. Sharing code is really helpful!\n",
+    "</div>\n",
+    "\n",
+    "**To Do:** You can change the years of data that you will read in below. Make sure that you do not add years where data are not available, though! You can check by listing the files in your `data_folder`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617ea958-499e-4dda-92da-8e531b152fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This list gives you control over the years of data to read in\n",
+    "years = [\"2018\",\"2019\",\"2020\",\"2021\"]  \n",
+    "\n",
+    "# Create an empty list of all the file names to extend\n",
+    "sim_files = []\n",
+    "for year in years:\n",
+    "    sim_files.extend(sorted(glob(join(data_folder,\"*h1.\"+year+\"*.nc\"))))\n",
+    "\n",
+    "print(\"All simulation files for all years: [\", len(sim_files), \"files]\")\n",
+    "print(sim_files[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d280416-00b4-431d-944a-bb2ca7b4cceb",
+   "metadata": {},
+   "source": [
+    "How many files are you going to read in?  What is the last day of the simulation you'll be looking at?\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.5 Read in the data\n",
+    "`.open_mfdataset` will open all of these data files and concatinate them into a single **xarray dataset**.\n",
+    "\n",
+    "There are lot of files so this can take a long time! Be patient, it will be done in < 1 minute. This can be done more quickly with dask, which parallelizes python code, but we do not use that here.\n",
+    "\n",
+    "1. `preprocess` will limit the number of variables we're reading in. This is an xarray feature that helps save time (and memory resources).\n",
+    "2. `fix_time_h1` corrects anoying features related to how CTSM history files handle time and is provided in `neon_utils.py`.\n",
+    "*Don't worry too much about the details of these functions right now*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aee5a387-f155-4fe8-a6e8-bfe2f95a2f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "print ('---------------------------')\n",
+    "print (\"Reading in data for \"+neon_site)\n",
+    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',preprocess=preprocess)\n",
+    "ds_ctsm = fix_time_h1 (ds_ctsm)\n",
+    "\n",
+    "end = time.time()\n",
+    "print(\"Reading all simulation files took:\", end-start, \"s.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05bc09ba-c7f3-4b37-bdd0-22a13d3f26ef",
+   "metadata": {},
+   "source": [
+    "#### Take a quick look at the dataset.\n",
+    "- What are your coodinate variables?\n",
+    "- How long is the time dimension?\n",
+    "- What variables are available to look at?\n",
+    "- What are the 'long names', or descriptions, of some of these variables? (HINT: try `ds_ctsm.GPP`)\n",
+    "- What other metadata are associated with this dataset? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ecd9859-fb98-470d-8cbf-fbeed70302f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6656c923-3ce1-4a20-a711-48b1188add9c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# 2. Manipulating data and making plots\n",
+    "The data frequency in the **h1 files** is 30 minutes. The below examples focus on different ways to average the time dimension of the data to visualize results.\n",
+    "\n",
+    "While several variables are read in, this example focuses on gross primary production (GPP). You can change the variable to look at other variables of intereset.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 2.1 Plotting the full time series\n",
+    "It's often helpful to visualize the raw data.\n",
+    "This is quick and easy if you:\n",
+    "- point your dataset `ds_ctsm`, \n",
+    "- select a variable `GPP` and use \n",
+    "- `.plot()` function that's built into xarray.  \n",
+    "\n",
+    "These three actions are combined in a single line of code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9038c557-6398-4c07-8d90-2d0a981376a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.GPP.plot() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c0287b-c569-4aa8-973e-599ee726aac4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "When looking at this figure:\n",
+    "- Do you see clear peaks for each year's growing season? \n",
+    "- What other trends are evident in the data? \n",
+    "- Are patterns similar for other variables?\n",
+    "\n",
+    "It can be challenging to interpret patterns and trends with this volume of data. Below are a few examples to summarize data in different ways\n",
+    "\n",
+    "---\n",
+    "## 2.2 Mean diel cycle\n",
+    "The high frequcncy data allows us to look at an average daily cycle of fluxes.  \n",
+    "\n",
+    "**Let's start by looking at just a few days of data.**\n",
+    "\n",
+    "We will subset the data by adding the following xarray features to our previous `ds_ctsm.GPP.plot()` command:\n",
+    "- `.isel` the *index select* on the coorinate variable, *time*, and: \n",
+    "- `slice` which let's us slice out a range of data, instead of a single time point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d8a9b13-545a-45f8-b838-0d43f9cbe03d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select the data range. Below selects a few days of July (Julian Day 181 to 185)\n",
+    "#  at 30 minute frequency (this means 48 times recorded / day, which is why the Julian Day is multiplied by 48)\n",
+    "first_time = (181*48)\n",
+    "last_time = (185*48)\n",
+    "\n",
+    "ds_ctsm.GPP.isel(time=slice(first_time,last_time)).plot() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397f4f42-ade5-4892-8cfe-a67aa4b26019",
+   "metadata": {},
+   "source": [
+    "*From this plot, you notice that at night GPP goes to zero.  That's why there are zero values in the middle of summer!*\n",
+    "\n",
+    "**You can also subset data to explore a single month.** \n",
+    "\n",
+    "Here, we look at data for all of July using the xarray `.where` function, which requires a: \n",
+    "  - logical to evaluate. In the below example, this is when the time dimension equals our selected month; and \n",
+    "  - 'dropping', or not using, the remaining data where the logical is false (optional). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64ca1dae-4c9c-468d-9e05-7849396d3bdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sel_month = 7\n",
+    "month_GPP = ds_ctsm['GPP'].where(ds_ctsm.time.dt.month == sel_month, drop=True)\n",
+    "print(\"month \"+str(sel_month)+\" has length = \"+str(len(month_GPP.time)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96c89720-c942-469c-a7bd-b41298b770a1",
+   "metadata": {},
+   "source": [
+    "Does the time dimension have the right length? (48 times/day * n days/month * n year of simulations) \n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> You can also do this other ways. For example, using the xarray select function `.sel` should produce idential results.\n",
+    "\n",
+    "> `month_GPP = ds_ctsm.GPP.sel(time=ds_ctsm.time.dt.month.isin([sel_month]))`    \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "**Calculate at the average diel cycle for a month.**\n",
+    "\n",
+    "We can accomplish this using the:\n",
+    "- `.groupby` function for combined \n",
+    "- `local_time` variable (which is made by adding the hour to a decimal minute for each day) and \n",
+    "- `.plot`, which again gives us a quck look at an xarray data array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31a77877-e131-479a-bbe9-eb8598c0359d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "month_GPP['local_time'] = month_GPP[\"time.hour\"]+month_GPP[\"time.minute\"]/60\n",
+    "month_GPP.groupby(month_GPP[\"local_time\"]).mean().plot() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb123684-8ffa-431d-9fcb-c40821087b96",
+   "metadata": {},
+   "source": [
+    "CTSM simulations are run with GMT, not local_time.\n",
+    "\n",
+    "We'll shift these results here, centering to local noon for now.\n",
+    "CPER is in the Mountain time zone (GMT-7), meaning we should:\n",
+    "- `.shift` -14 timesteps (one timestep = 30 minutes), repeating the \n",
+    "- `.groupby` function on our `local_time variable`, and then taking the \n",
+    "- `.mean` or `.std`\n",
+    "\n",
+    "You can layer all these steps into a sigle line of code!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1476b827-e20c-4016-b23d-84a238e2c533",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate mean and standard deviation \n",
+    "mean = month_GPP.shift(time=-14).groupby(month_GPP[\"local_time\"]).mean()\n",
+    "std = month_GPP.shift(time=-14).groupby(month_GPP[\"local_time\"]).std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4371bfd8-31c8-4700-b4fd-35bd219fb288",
+   "metadata": {},
+   "source": [
+    "**Now make the plots!**\n",
+    "Here we'll switch to plotting with matplotlib, loaded as `plt` for convenience, which has intuitive functions like:\n",
+    "- `.plot`(x, y, ...)\n",
+    "- `.fill_between`(x, y+z, y-z, ...) and plot control of features like\n",
+    "- `.xlabel`, `.ylabel`, `.title`, and `.grid`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db8af133-27ae-4223-beea-06907f33a2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(mean['local_time'], mean, marker = 'o')\n",
+    "plt.fill_between(mean['local_time'], mean+std,mean-std, alpha=0.3)\n",
+    "plt.xlabel('local_time (h)')\n",
+    "plt.ylabel(month_GPP.attrs['long_name']+\" (\"+month_GPP.attrs['units']+\")\")\n",
+    "plt.title(neon_site+\" diel mean, month=\"+str(sel_month), \n",
+    "          loc='left',fontweight='bold') \n",
+    "plt.grid() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c8d0d4c-05d5-42cd-9812-07728cd433ca",
+   "metadata": {},
+   "source": [
+    "**OK just for fun, lets go crazy by plotting mean diel cycles for each month of the year.**\n",
+    "\n",
+    "We can do this in a `for` loop with just a few lines of code by layering multiple functions into each line.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note:</b> conditionals in python use indent spacing to define what's happening inside the loop.  This helps make the code easier to read and write. See the example below.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf45bc8b-1222-4c00-b3d0-203c4ee8e577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in range(1,13):\n",
+    "    month_GPP = ds_ctsm.GPP.sel(time=ds_ctsm.time.dt.month.isin([m]))\n",
+    "    month_GPP['local_time'] = month_GPP[\"time.hour\"]+month_GPP[\"time.minute\"]/60\n",
+    "    month_GPP.shift(time=-14).groupby(month_GPP[\"local_time\"]).mean().plot(label=str(m))\n",
+    "\n",
+    "# Note with newer versions of xarray this generates a 'FutureWarning', which is being suppressed\n",
+    "\n",
+    "plt.title(neon_site+\" diel mean\", loc='left',fontweight='bold') \n",
+    "plt.legend(title='month') ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edee4cb6-82c5-4c09-897e-a71a1b22900e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "**Look at these results!**\n",
+    "- What months have the highest daily fluxes at this site?\n",
+    "- Why does the breadth of each curve change for different months?\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "## 2.3 Daily mean flux calculations\n",
+    "\n",
+    "The above examples have calculated the mean and not changed units (*currently gC/m^2/s*).  Since each day has the same number of timesteps (48) and we are plotting diel cycles, the averaging and native units work. However, it is helpful to consider how data are being grouped in other types of averages and whether you might need to adjust the units.  For example, you may want to calculate a weighted mean at times (e.g. each month has a different number of days) or change units to make them better reflect your calculations. **You can think about this as you are working** by printing the outputs of  calculations at the end of each code cell and thinking about their dimensions, attributes, etc.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note:</b> Weighted averages and units changes can be calculated several ways. \n",
+    "\n",
+    "For example, you can use `.resample` or `.groupby` in xarray  \n",
+    "    \n",
+    "For one-dimensional variables it is more computationally efficient to use these functions in a pandas dataframe. For simplicity in this example, we will keep using xarray.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "To calculate a daily mean we'll focus on: \n",
+    "- `GPP` as the variable of interest\n",
+    "- the `.resample` function in xarray, as this handles the time dimension most intuitively\n",
+    "- the daily time dimension, `time='D'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c8499b1-dc88-4319-9977-349dfeb0bb8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#-- Calculate daily average for every day \n",
+    "daily_GPP = ds_ctsm.GPP.resample(time='D').mean()\n",
+    "daily_GPP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d720f5c-33af-4b7f-802d-25d45d779625",
+   "metadata": {},
+   "source": [
+    "**Take a look at your new data array:**\n",
+    "- Do the values of the time coordinate seem appropriate?  \n",
+    "- Is the length of the time coordinate accurate?\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note:</b> Variable attributes were carried forward to this new xarray data array, but we can modify this to get more sensible results.\n",
+    "</div>\n",
+    "\n",
+    "Now we can modify the rate of the GPP fluxes from units of *per second* to units of *per day* and quickly `.plot()` results.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Be careful when changing values of variable!</b> \n",
+    "    \n",
+    "If you run a code block like the one below repeatedly, you'll modify your GPP values every time. To avoid this, you can recalculate `daily_GPP` at the start of this cell by uncommenting out the first line of the code, below. Even though it's a little slower, it may ultimately be safer.\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e58b1b2b-f808-48cf-9f37-4387e1e35933",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# daily_GPP = ds_ctsm.GPP.resample(time='D').mean()\n",
+    "\n",
+    "sec_per_day = 60. * 60. * 24.\n",
+    "daily_GPP = daily_GPP * sec_per_day\n",
+    "\n",
+    "# Then change the attirbute\n",
+    "daily_GPP.attrs['units'] = 'gC/m^2/day'\n",
+    "\n",
+    "# Now plot results\n",
+    "daily_GPP.plot() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48aacde3-216a-496b-946c-1232c88ec47e",
+   "metadata": {},
+   "source": [
+    "Notice how averaging makes the results look smoother. We can also more easily see the interannual variability in GPP that is simulated in different years.\n",
+    "\n",
+    "**What is the range in annual GPP that CTSM simulates at this NEON site?**\n",
+    "\n",
+    "We can assess this by creating a different plot: \n",
+    "- use the `.resample` function  \n",
+    "- adjust the time dimension to the start of the year, `time='AS'`\n",
+    "- convert the `.mean()` to an annual flux with appropriate units (**NOTE** this assumes that all years have 365 days) \n",
+    "- create a bar plot (only available as a Pandas series, not in xarray). Use `.to_series()` to convert to a pandas.Series and `.plot.bar()` to make a bar plot \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36cc1f07-c5b0-42a7-a24a-e7e4e203db65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(ds_ctsm.GPP.resample(time='AS').mean()*sec_per_day*365).to_series().plot.bar() ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed5cebc4-c690-4db4-83b2-dacf66a999ae",
+   "metadata": {},
+   "source": [
+    "**There's even more to think about here:**\n",
+    "- What could cause this kind of interannual variability in GPP?  \n",
+    "- Are similar patterns common across multiple NEON sites in the region?\n",
+    "- Where is this site and what kind of vegetation grows there? \n",
+    "- Are these annual GPP values realistic for this type of ecosystem? \n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34ec31fa-6c50-48e5-bbeb-20acb83084b4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 2.4 Annual climatology\n",
+    "\n",
+    "Sometimes it's helpful to look at the seaonal cycle.  Averaging across multiple years to generate a mean annual cycle is called a climatology. This example uses the `.groupby` function.\n",
+    "\n",
+    "We have already calculated the daily mean and will also need to calculate the daily standard deviation `.std` to understand how the variability in the **daily** fluxes contributes to seasonal variability across years.\n",
+    "\n",
+    "This will create two data arrays for the variable `GPP` (the mean and the standard deviation). We want to:\n",
+    "- use `.rename` to facilitate combining the data arrays\n",
+    "- combine the variable names and their array values into a dataset, `xr.Dataset`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5c36dd3-b71e-4d64-923d-4bf3e83a9cb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate mean and standard deviation for variables\n",
+    "climo_GPP_mean = daily_GPP.groupby(daily_GPP[\"time.dayofyear\"]).mean()\n",
+    "climo_GPP_std  = daily_GPP.groupby(daily_GPP[\"time.dayofyear\"]).std()\n",
+    "\n",
+    "## rename this variable for this data array\n",
+    "climo_GPP_std = climo_GPP_std.rename('std')\n",
+    "\n",
+    "# combine the data arrays into a dataset\n",
+    "climo_GPP = xr.Dataset({\"GPP\": climo_GPP_mean, \"std\": climo_GPP_std})\n",
+    "\n",
+    "# print a variable from the dataset\n",
+    "climo_GPP['GPP']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49453c40-a4dd-4b00-be30-30e35bd74a79",
+   "metadata": {},
+   "source": [
+    "Since we are already using daily values, the units are correct.\n",
+    "\n",
+    "You'll notice that using `.groupby` changes the time coordinates. Let's adjust these back to a datetime format using the `.rename` function again (here on a coordinate variable)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4aa749b-3bab-493e-9ba4-1922e6f0570b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename the coordinate variable\n",
+    "climo_GPP = climo_GPP.rename({'dayofyear': 'time'})\n",
+    "\n",
+    "#create a pandas dataframe of the same length\n",
+    "# It's true our climatology is the mean of simulated daily vales between 2018-2021, \n",
+    "#   but datetime objects are simpler to work with if you provide a year\n",
+    "time = pd.date_range(\"2020-01-01\", periods=366)\n",
+    "\n",
+    "#replace time coordinate values\n",
+    "climo_GPP['time'] = time \n",
+    "\n",
+    "# Check that it all worked\n",
+    "climo_GPP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e5b1d7-ca89-4124-8f27-bc7f64c16594",
+   "metadata": {},
+   "source": [
+    "**Now plot the data!** \n",
+    "We've done a lot of work. Let's:\n",
+    "1. Save this figure. We can do this in a new directory, `plot_dir`, under the `NEON_cases` directory.\n",
+    "2. Plot interanual variability around the daily means using matplotlib. Note that this code is a little more complex to make a nice looking figure, so don't worry about understanding the details now.  Instead, refer back to this as you become more familiar with analyses in python and plotting in matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce5c2abc-c5bd-46a1-b9c5-ee422f4ef566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make the figures directory if it's not already there\n",
+    "plot_dir = os.path.realpath(os.path.expanduser('~/scratch/NEON_cases/figures'))\n",
+    "if not os.path.isdir(plot_dir):\n",
+    "    os.makedirs(plot_dir, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d332e817-7067-45b3-bc45-9f2d9dc961ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now plot the climatological mean\n",
+    "f, ax = plt.subplots()\n",
+    "ax.plot(climo_GPP.time,climo_GPP.GPP)\n",
+    "\n",
+    "# Add shading around the mean +/- one sigma of daily mean\n",
+    "ax.fill_between(climo_GPP.time, (climo_GPP.GPP-climo_GPP['std']), \n",
+    "                                (climo_GPP.GPP+climo_GPP['std']), alpha=0.3)\n",
+    "\n",
+    "ax.set_ylabel('GPP ('+climo_GPP.GPP.attrs['units']+')',fontweight='bold')\n",
+    "ax.set_title(neon_site+\" annual climatology\", loc='left',fontweight='bold')\n",
+    "\n",
+    "\n",
+    "# format x axis for monthly dates\n",
+    "locator = mdates.MonthLocator()  # every month\n",
+    "# Specify the format - %b gives us Jan, Feb...\n",
+    "fmt = mdates.DateFormatter('%b')            \n",
+    "            \n",
+    "X=ax.xaxis\n",
+    "X.set_major_locator(locator)\n",
+    "X.set_major_formatter(fmt)\n",
+    "ax.grid()   ; \n",
+    "\n",
+    "# Save out a .pdf.  Other options include .png, .eps, ect.\n",
+    "print(' -- saving figure -- ')\n",
+    "plot_name = neon_site+'_'+'GPP_climatology.pdf'\n",
+    "plt.savefig (os.path.join(plot_dir,plot_name), \n",
+    "             dpi=300,bbox_inches='tight', format = 'pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "323c98cb-9445-49df-89eb-03d04497f1a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can check the image was created\n",
+    "!ls ~/scratch/NEON_cases/figures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "124c6fa1-2f6f-4a3c-ba1a-8f390c584efc",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Export postprocessed data\n",
+    "\n",
+    "Finally! We did a lot of work to generate this climatology. You want to save the data as a different file type to work with later (e.g. .csv) so that you don't need to rely on this python script. To do this: \n",
+    "1. Create a directory to save the file\n",
+    "2. Define the file name for our postprocessed data\n",
+    "3. Generate a pandas dataframe from the climotology (xarray) dataset using the `.to_dataframe` function  \n",
+    "4. Save the dataframe as a .csv file using the `.to_csv` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc395ddd-9c72-4334-8c92-c07ad28e7edb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make the figures directory if it's not already there\n",
+    "out_dir = os.path.realpath(os.path.expanduser('~/scratch/NEON_cases/postprocessed_data'))\n",
+    "if not os.path.isdir(out_dir):\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "    \n",
+    "# define the file name and directory\n",
+    "file_out = out_dir+'/'+neon_site+'_'+'GPP_climatology.csv'\n",
+    "\n",
+    "# Create a dataframe and write out the .csv file\n",
+    "climo_GPP.to_dataframe().to_csv(file_out)\n",
+    "print('wrote '+ file_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1900e97b-100f-4a88-9697-7d349ce2bf20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check, does this file exist.\n",
+    "!ls /scratch/${USER}/NEON_cases/postprocessed_data/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b6a0edb-015a-4fc1-bc07-aaeca72c8438",
+   "metadata": {},
+   "source": [
+    "#### Check the .csv file\n",
+    "- Is there much metadata associated with the .csv file?\n",
+    "- Will you be able to remember how you generated these data if you use them elsewhere or share them with colleagues?\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratulations!</b> \n",
+    "    \n",
+    "You've manipulated and plotted data from one site and one variable.  \n",
+    "    \n",
+    "What would you like to look at now?\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f11e37f-8301-4ba6-87b4-bf990912017f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/CTSMsp_NEON_fromScratch.ipynb b/notebooks/SinglePoint/ProjectExamples/CTSMsp_NEON_fromScratch.ipynb
new file mode 100644
index 0000000..e70b55a
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/CTSMsp_NEON_fromScratch.ipynb
@@ -0,0 +1,879 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CTSM-SP simulations at NEON sites\n",
+    "\n",
+    "### *run_neon* doesn't work for satelite phenology (SP cases), so we'll create these cases from *'scratch'*\n",
+    "\n",
+    "The `run_neon` script we've been using is very useful, but it simplifies a number of steps that users may want to take control of in their workflow.  We'll introduce how you can control aspects of your case setup and configuration here. For example, a deeper understanding of the model is need if your want to run a satelite phenology (SP) case an unsupported tower (non-NEON) site, a generic single point simulation, a regional domain, or for global case.\n",
+    "\n",
+    "If you're new to running CTSM, this is a somewhat advanced tutorial.  We're planning on doing all of our examples in the workshop using `run_neon`, so the information in this tutorial may not be needed. That said, it illustrates some important features of how to run the model, how to get initial condition files, and more.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## In this tutorial\n",
+    "\n",
+    "The tutorial has several components. Below you will find steps to: \n",
+    "1. Create, setup, build, and run a CTSM-SP case at a NEON site from scratch\n",
+    "2. Locate history files\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> 1. Set up and run a simulation</h1>\n",
+    "\n",
+    "### CTSM can be run in 4 simple steps.\n",
+    "\n",
+    "1. **Create** a new case\n",
+    "  - *This step sets up a new simulation. It is the most complicated of these four steps because it involves making choices to set up the model configuration*\n",
+    "\n",
+    "2. **Setup** the case \n",
+    "  - *This step configures the model so that it can compile*\n",
+    "\n",
+    "3. **Build** the executable\n",
+    "  - *This step compiles the model*\n",
+    "\n",
+    "4. **Submit** your run to the batch queue\n",
+    "  - *This step submits the model simulation*\n",
+    "  \n",
+    "**NOTE:** We'll also customine our case with xml and namelist changes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h1> 1.1 create a new case</h1>\n",
+    "\n",
+    "*Set up a new simulation*\n",
+    "***\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b>  If you haven't done this already, you'll want to create the following directory\n",
+    "\n",
+    "`mkdir ~/scratch/NEON_cases`\n",
+    "    \n",
+    "<b>TIP:</b> If you're running on Cheyenne, creating a softlink to scratch in your home directory is helpful.  \n",
+    "    \n",
+    "You can create this with the following: `ln -s /glade/scratch/$USER ~/scratch`\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations. \n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud, you only have to do this once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cp -rp /opt/ncar/ctsm/ ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1.1 Navigate to the source code directory\n",
+    "\n",
+    "Your source code is in your home directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/cime/scripts\n",
+    "\n",
+    "# Change the 4-character NEON site below.\n",
+    "export neon_site=\"STEI\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1.1 Create a new case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then create a new case.\n",
+    "./create_newcase --case ~/scratch/NEON_cases/${neon_site}_sp_test \\\n",
+    "    --res CLM_USRDAT \\\n",
+    "    --compset I1PtClm51SpRs \\\n",
+    "    --output-root ~/scratch/NEON_cases/ \\\n",
+    "    --run-unsupported \\\n",
+    "    --user-mods-dir NEON/${neon_site} "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "The code above doesn't always render properly online.  Here's what the code actually says:\n",
+    "\n",
+    "```\n",
+    "./create_newcase --case ~/scratch/NEON_cases/${neon_site}_sp_test \\\n",
+    "    --res CLM_USRDAT \\\n",
+    "    --compset I1PtClm51SpRs \\\n",
+    "    --output-root ~/scratch/NEON_cases/ \\\n",
+    "    --run-unsupported \\\n",
+    "    --user-mods-dir NEON/${neon_site} \\\n",
+    "    --handle-preexisting-dirs r\n",
+    "```\n",
+    "***\n",
+    "### **./create_newcase**\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b> There is a lot of information that goes into creating a case.\n",
+    "\n",
+    "You can learn more about the options by typing <i>./create_newcase --help</i> on the the command line or in a new code cell.\n",
+    "\n",
+    "<b>We'll briefly go over some of the highlights here.</b>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Required arguments to create a new case\n",
+    "There are 3 Required arguments Needed to create a new case.  These include \n",
+    "1. `--case`, which specifies the *location* and *name* of the case being created\n",
+    "  - `~/scratch` = the alias to your scratch  directory\n",
+    "  - `NEON_cases` = the subdirectory we created to store your other cases\n",
+    "  - `${neon_site}_sp_test` = the name of the case you're creating\n",
+    "  - *Recommendation:* Use meaningful names, including model version, type of simulation, and any additional details to help you remember the configuration of this simulation\n",
+    "<br><br>\n",
+    "2. `--res` Defines the model *resolution*, or grid,\n",
+    "  - `CLM_USRDAT`, which is an option to get a case setup without having to define the grid resolution, yet. \n",
+    "  - In global cases, the land model is commonly run at a nominal 1 degree `f09_g17` or 2 degree `f19_g17` resolution \n",
+    "  - Using `./query_config --grids` provides a list of supported model resolutions\n",
+    "<br><br>  \n",
+    "3. `--compset` Defines the *component set* for your case, \n",
+    "  - The Component set specifies the default configuration for the case which includes:\n",
+    "    - Component models (e.g. active vs. data vs. stub), \n",
+    "    - Time period of simulations and forcing scenarios (e.g. 1850 vs 2000 vs. HIST) and \n",
+    "    - Physics options (e.g. CLM5.1 vs CLM5.0). \n",
+    "  - `I1PtClm51Bgc` is alias that actually describes a much longer set of components that are being used for this single point case. \n",
+    "  - All CLM-only compsets start with *\"I\"*.\n",
+    "    - Using `./query_config --compsets clm` provides examples of other CLM compsets\n",
+    "<br>\n",
+    "\n",
+    "*There are a few other **optional** flags used to create this new case that we'll briefly touch on here*\n",
+    "<br>\n",
+    "\n",
+    "4. `--output-root`, which specifies the *location* of your run directory\n",
+    "  - `~/scratch/NEON_cases` = the subdirectory we already created \n",
+    "<br> \n",
+    "\n",
+    "5. `--user-mods-dirs` sets up the configuration of the case with a user modification directory that defines the location of the site.\n",
+    "  - `NEON/FATES/${neon_site}` has files that format of the history files and a few other custom settings for your case. \n",
+    "  - This inludes setting some of .xml variables and name list settings correctly for a FATES simulation in a NEON single point case. \n",
+    "<br>\n",
+    "\n",
+    "6. `--run-unsupported` avoids error using compsets are not scientifically supported \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "\n",
+    "<b>NOTE:</b> You may notice an error about project codes when you create your case. The project code isn't important for these simulations. But you may need to change this if you're running on Cheyenne.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "***\n",
+    "\n",
+    "\n",
+    "### 1.1.2 Create a parallel BGC case for comparison\n",
+    "we'll set this up as an AD case to make things easier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "export base_case='KONZ'\n",
+    "cd ~/CTSM/tools/site_and_regional/\n",
+    "./run_neon.py --neon-sites $neon_site  \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --output-root ~/scratch/NEON_cases "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 1.2 Customize your SP case </h2>\n",
+    "\n",
+    "*Modifications for an SP case*\n",
+    "\n",
+    "### 1.2.1 Move to your SP case directory\n",
+    "\n",
+    "**Check differences in env_run with a To start with, we want to run an SP case, not with active biogeochemistry**\n",
+    "\n",
+    "These would be set up if you created a global sp compset, but we need to make changes for our single point case here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/${neon_site}_sp_test\n",
+    "diff env_run.xml ../${neon_site}.transient/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "What differences do you see between the env_run files?\n",
+    "\n",
+    "**Background**\n",
+    "- Most NEON sites we have 4 complete years of data to work with (2018-2021). \n",
+    "- run_neon and usermod_dirs handle setting up cases correctly for BGC cases.\n",
+    "- NOTE, there are other differences caused by the compset we used to create our NEON case related to transient CO2.  We'll ignore these for now.\n",
+    "- For an SP case, we want to run NEON sites for a bit to let soil water and temperatures the equilibrate before comparing to observations. Luckily, this only takes a few years.\n",
+    "- We can handle this with the following xml changes\n",
+    "\n",
+    "The code below may need to be customized for particular sites, but in general, we want to run for twice as many years as we have NEON observations.  CLM will just cycle over the meterological data that NEON provides.  This will allow the second cycle of our our simulation to end with the same dates over which we have NEON observations, making plotting easier later on. We also need to force a coldstart since there are no initial conditions to compare to.\n",
+    "\n",
+    "### 1.2.2 xlm changes to modify env_run options\n",
+    "xlm changes are one way you can control your case to customize the simulation\n",
+    "- We won't cover this here, but other tutorials cover more about xml changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./xmlchange CCSM_BGC=CO2A\n",
+    "./xmlchange DATM_CO2_TSERIES=20tr\n",
+    "./xmlchange CLM_NML_USE_CASE=2018-PD_transient\n",
+    "./xmlchange CLM_CO2_TYPE=diagnostic\n",
+    "\n",
+    "./xmlchange RUN_STARTDATE=2014-01-01 \n",
+    "./xmlchange STOP_OPTION=nyears\n",
+    "./xmlchange STOP_N=8  \n",
+    "./xmlchange DATM_YR_END=2021  #this is the last complete year of input data\n",
+    "./xmlchange CLM_FORCE_COLDSTART=on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check that it worked\n",
+    "diff env_run.xml ../${neon_site}.transient/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.3 Modify user_nl_clm\n",
+    "We also won't discuss namelist modificaiton here, but they are another way to customize your case.\n",
+    "\n",
+    "Specifically these namelist changes:\n",
+    "- set up a FATES-SP case.\n",
+    "- modifies history file output for an SP case.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove the old user_nl_clm file and create a new one\n",
+    "rm user_nl_clm\n",
+    "echo \"flanduse_timeseries = ' ' \" >> user_nl_clm \n",
+    "echo \"fsurdat = '/scratch/inputdata/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_${neon_site}_hist_78pfts_CMIP6_simyr2000_c230111.nc' \" >> user_nl_clm\n",
+    "echo \"hist_mfilt(2) = 48\" >> user_nl_clm\n",
+    "echo \"hist_nhtfrq(2) = 1\" >> user_nl_clm\n",
+    "echo \"hist_fincl2 = 'FCEV','FCTR','FGEV','FIRA','FSA','FSH','GPP',\n",
+    "  'H2OSOI','SNOW_DEPTH','TBOT','TSOI'\" >> user_nl_clm "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you're running in the cloud, we also have to point to the right surface dataset\n",
+    "# TODO, this should work for others if we don't get this onto /scratch/data\n",
+    "#echo \"fsurdat = '/scratch/wwieder/FATES_surfacedata/surfdata_1x1_NEON_${neon_site}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm\n",
+    "#echo \"fsurdat = '/scratch/data/NEONv2/misc_inputs/FATES_surfacedata/surfdata_1x1_NEON_${neon_site}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Setup and build your case\n",
+    "\n",
+    "*This step configures the model so that it can then compile* \n",
+    "\n",
+    "### 1.3.1 case.setup\n",
+    "The `./case.setup` script:\n",
+    "1. configures the model\n",
+    "2. creates files to modify input data and run options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h3> 1.3.2 Build the executable  </h3>\n",
+    "\n",
+    "*This step compiles the model*\n",
+    "\n",
+    "It also takes a long time, so be patient.\n",
+    "***\n",
+    "\n",
+    "The `./case.build` script:\n",
+    "1. Checks input data\n",
+    "2. Creates a build/run directory with model executable and namelists\n",
+    "\n",
+    "TODO: using qcmd -- ./case.build on cheyenne fails with wget errors of met data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "---\n",
+    "This takes some time, and will throw a bunch of errors... don't worry, just give it time\n",
+    "\n",
+    "\n",
+    "**When the build completes successfully you'll see a notice that the `MODEL BUILD HAS FINISHED SUCCESSFULLY`**\n",
+    "\n",
+    "\n",
+    "You can read on, but before executing any code blocks in the notebook **wait for the model to build.**\n",
+    "This can take a while.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> 1.4 Submit your case  </h3>\n",
+    "\n",
+    "Now you're ready to submit the case!\n",
+    "\n",
+    "*This step submits the model simulation*\n",
+    "- You'll also be downloading all the meterological data from NEON for your site, this also takes a little time and prints lots of information to the screen\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you submit a job, you will see confirmation that it successfully submitted:\n",
+    "\n",
+    "### Congratulations! \n",
+    "### You've created and submitted a single point NEON run from scratch.\n",
+    "\n",
+    "Next, you will probably want to check on the status of your jobs.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
+    "\n",
+    "<b>TIP:</b> This is dependent on the scheduler that you're using. \n",
+    "\n",
+    "<ul>\n",
+    "<li>Cheyenne uses <b>PBS</b> where status is checked with <i>qstat -u $USER</i></li>\n",
+    "<li>This is also enabled in the cloud for you, try it in the code block below</li></ul>\n",
+    "\n",
+    "If you want to stop the simulation, you can do so with qdel here (or on Cheyenne).\n",
+    "<ul>\n",
+    "<li> Find your Job ID after typing <i>qstat</i> </li>\n",
+    "<li> Type <i>qdel {Job ID}</i> </li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qstat -u $USER"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Once your jobs are complete (or show the 'C' state under the 'Use' column, which means complete), we can check the CaseStatus file to ensure there were no errors and it completed successfully.  To do this, we'll 'tail' the end of the CaseStatus file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tail  ~/scratch/NEON_cases/${neon_site}_sp_test/CaseStatus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should see several lines, with the middle one saying 'case.run success'.  \n",
+    "\n",
+    "Before that you'll see notifications about xml changes, case.setup, and case.submit, and case.run\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created a CTSM case for the NEON tower you selected.\n",
+    "\n",
+    "We'll build on these basics in additional tutorials to customize your simulations.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "****\n",
+    "# 2. Locate model history files\n",
+    "Your simulation will likely take some time to complete. The information\n",
+    "provided next shows where the model output will be located while the\n",
+    "model is running and once the simulation is complete. We also provide\n",
+    "files from a simulation that is already complete so that you can do the\n",
+    "next exercises before your simulation completes. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
+    "\n",
+    "<b>When your simulation is running</b> history files go to your scratch directory: \n",
+    "\n",
+    "<ul>\n",
+    "<li><i>~/scratch/NEON_cases/{CASE}</i> </li>\n",
+    "</ul>\n",
+    "\n",
+    "Within this directory you can find <i>/run</i> and <i>/bld</i> subdirectories.\n",
+    "\n",
+    "<b>When the simulation is complete</b>, a short-term archive directory is created, and history files are moved here: \n",
+    "\n",
+    "<ul>\n",
+    "<li><i>~/scratch/NEON_cases/<b>archive</b>/{CASE}/lnd/hist/</i> </li>\n",
+    "</ul>\n",
+    "\n",
+    "Note that files necessary to continue the run are left in the run directory: <i>~/scratch/NEON_cases/{CASE}/run</i>\n",
+    "</div>\n",
+    "\n",
+    "## 2.1 Run directory\n",
+    "*What's in your run directory?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases/${neon_site}_sp_test/run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Do you see any log or history files?*\n",
+    "- log files look like `lnd.log*`\n",
+    "- history files look like `${neon_site}.transient.clm2.h0.*.nc`\n",
+    "\n",
+    "You can keep running the cell above until you see log and history files, then the model is running.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 2.2 Archive directory\n",
+    "*What's in your archive directory?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases/archive/${neon_site}_sp_test/lnd/hist | head -5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can drop the `| head -5` part of the cell above if you want to see ALL the files that have been archived.\n",
+    "\n",
+    "If you don't see any history files your simulation is likely still running or in the queue (check using squeue or qstat). Check again before you leave today to see if your simulation completed and if the files were transferred to archive. Even if your run isn't finished, you can move on in this tutorial.\n",
+    "\n",
+    "If you'd like to visualize these results you can go back to tutorial Day0c and modify to point to these results from the neon_site you just ran."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "***\n",
+    "# 3. Create a Clone\n",
+    "<h4> This step is optional, but provides helpful information </h4>\n",
+    "    \n",
+    "Creating and building a new case is slow.  \n",
+    "\n",
+    "Cloning cases can speed this up!\n",
+    "\n",
+    "<h2>3.1 Create a clone </h2>\n",
+    "\n",
+    "- Clones use the same resolution, compset, and output root.\n",
+    "- Clones can also share the same executable (build), which can save time!\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\" markdown=\"1\">\n",
+    "\n",
+    "<b>WARNING:</b> If you're making code modifications be careful using clones that share the same build. The example below is a good way to run the same model configuration at different NEON sites.\n",
+    "\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Move back to your source code directory\n",
+    "cd ~/CTSM/cime/scripts\n",
+    "\n",
+    "# Change the 4-character new NEON site you want to run.\n",
+    "export neon_site2=\"TEAK\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then create a cloned case.\n",
+    "./create_clone --case ~/scratch/NEON_cases/${neon_site2}_sp_test \\\n",
+    "               --clone ~/scratch/NEON_cases/${neon_site}_sp_test \\\n",
+    "               --user-mods-dirs ~/CTSM/cime_config/usermods_dirs/NEON/FATES/${neon_site2} \\\n",
+    "               --keepexe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### **./create_clone**\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b> There is a lot of information that goes into creating a clone.\n",
+    "\n",
+    "You can learn more about the options by typing <i>./create_clone --help</i> on the the command line or in a new code cell.\n",
+    "\n",
+    "<b>We'll briefly go over some of the highlights here.</b>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "### Required arguments to create a clone\n",
+    "There are 2 required arguments needed to create a clone.  These include: \n",
+    "1. `--case`, this is the path and name of the new case your cloning.\n",
+    "<br><br>\n",
+    "2. `--clone`, this is the path and name of the existing case you want to clone.\n",
+    "<br><br>  \n",
+    "### Additional information we provided here were: \n",
+    "3. `--user-mods-dirs` as before, this sets up the configuration of the case with a user modification directory that defines the location of the new site we're running, but it requires the full path to the `user-mods-dir` you want to use.\n",
+    "<br><br>  \n",
+    "4. `--keepexe` Point to original build from the case being cloned.\n",
+    "\n",
+    "---\n",
+    "We'll also create a BGC case clone for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "./run_neon.py --neon-sites $neon_site2  \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --output-root ~/scratch/NEON_cases "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 3.2 Customize the clone? </h2>\n",
+    "\n",
+    "*Because you created a clone and point to an existing build we can skip the `case.setup` and `case.build` steps* \n",
+    "***\n",
+    "\n",
+    "### 3.2.1 Move to your case directory and compare env_run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/${neon_site2}_sp_test\n",
+    "diff env_run.xml ../${neon_site2}.transient/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Make xlm changes\n",
+    "In the example above the teak data record only starts in 2019, meaning we have 3 years of meterological data to cycle through"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./xmlchange RUN_STARTDATE=2016-01-01 \n",
+    "./xmlchange STOP_OPTION=nyears\n",
+    "./xmlchange STOP_N=6  \n",
+    "./xmlchange DATM_YR_END=2021  #this is the last complete year of input data\n",
+    "./xmlchange CLM_NML_USE_CASE=2018-PD_transient\n",
+    "# Check that chagnes look as expected from the other sp case\n",
+    "diff env_run.xml ../${neon_site}_sp_test/env_run.xml\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.3 Copy user_nl_clm\n",
+    "We can just copy the user_nl_clm into our cloned case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp ../${neon_site}_sp_test/user_nl_clm .\n",
+    "echo \"fsurdat = '/scratch/inputdata/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_${neon_site2}_hist_78pfts_CMIP6_simyr2000_c230111.nc' \" >> user_nl_clm\n",
+    "\n",
+    "# If you're running in the cloud, we also have to point to the right surface dataset\n",
+    "\n",
+    "# TODO, this should work for others if we don't get this onto /scratch/data\n",
+    "#echo \"fsurdat = '/scratch/wwieder/FATES_surfacedata/surfdata_1x1_NEON_${neon_site2}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm\n",
+    "#echo \"fsurdat = '/scratch/data/NEONv2/misc_inputs/FATES_surfacedata/surfdata_1x1_NEON_${neon_site2}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 3.3 Submit the case </h2>\n",
+    "\n",
+    "*Because you created a clone and point to an existing build we can skip the `case.setup` and `case.build` steps* \n",
+    "***\n",
+    "\n",
+    "- As before, you'll be downloading all the meterological data, which takes a little time and prints lots of information to the screen\n",
+    "***\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created and run a clone Fof CLM- for the NEON tower you selected.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "You can track progress on your run using steps outlined in part #2 of this tutorial.\n",
+    "You can also work on visualizing your results for different sites using visualization code provided\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're developing this tutorial:\n",
+    "Before saving and pushing this code to github go to `Kernel` and `Restart kernel and clear all outputs...`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls -lrt ~/scratch/NEON_cases/${neon_site2}_sp_test/run/ | tail -10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/Clone_Case.ipynb b/notebooks/SinglePoint/ProjectExamples/Clone_Case.ipynb
new file mode 100644
index 0000000..ab39109
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/Clone_Case.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Tutorial 1c: Cloning a NEON case\n",
+    "\n",
+    "**This is an optional tutorial** it's a little bit more advanced, but it will help you think about how to modify the model configuration to run new sites or model experiments.  \n",
+    "\n",
+    "The `run_neon` script we used in the [introductory tutorial 1b](1b_NEON_Simulation_Intro.ipynb) created and ran a base case as well as a `.transient` case. \n",
+    "\n",
+    "You'll recall that building the case was pretty time consuming. Luckily, if you aren't changing the model code, you can run from this base case without rebuilding multiple times. \n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## In this tutorial\n",
+    "\n",
+    "The tutorial has several components. Below you will find steps to: \n",
+    "1. Create a clone for a new NEON site from the base case you already built.\n",
+    "2. Introduce ways to customize the configuration of your cases.\n",
+    "\n",
+    "*Extra credit* \n",
+    "\n",
+    "3. Create another clone for an experiment you're hoping to do.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b>  This tutorial assumes that you've done your homework! \n",
+    "    \n",
+    "If you haven't downloaded CTSM from the GitHub repository you need to go back to: \n",
+    "<ul>\n",
+    "    <li><b>1b_NEON_Simulation_Tutorial</b> </li> \n",
+    "</ul>\n",
+    "    \n",
+    "Do these first!\n",
+    "\n",
+    "    \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login, you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> 1. Create a clone </h1>\n",
+    "\n",
+    "CTSM cases can be cloned to save time and create model experiments for different sites or different configurations that are otherwise identical to each other.   \n",
+    "\n",
+    "### 1.1.1 Set up environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations. \n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud, you only have to do this once and it should be set up for you already"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cp -rp /opt/ncar/ctsm/ ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1.2 Navigate to your source code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Create a clone with `run_neon.py`\n",
+    "We'll just use run_neon again, but point to the **base case** you created in that `1b` tutorial.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "    \n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following code will:\n",
+    "- create a clone, \n",
+    "- download input data from NEON\n",
+    "- submit the simulation\n",
+    "\n",
+    "As before, it takes a little bit for all this to happen, but you'll notice it's MUCH faster since we don't have to build the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "export base_case='KONZ'   # should match the base case you created in 1b\n",
+    "export new_case='CPER'    # the new site you want to run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --overwrite"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two new flags are being used here:\n",
+    "- `--base-case` points to base case that's already been built, letting you run your new simulation more quickly.  \n",
+    "- `--overwrite` will let you overwrite an existing case.  \n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> \n",
+    "    \n",
+    "- Creating clones while pointing to a `--base-case` saves time by sharing the built model executable.\n",
+    "- Creating clones like this also requires that your base-case and new-case are the same type of simulation, `.transient` simulations in this example.  \n",
+    "- It also assumes that you're <b>not making modifications to the model source code </b> that would require you to rebuild a case.  \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "You can check to make sure things are running as expected. On the command line of a terminal window you can enter (HINT, you'll likely have to define `new_case` in the terminal window, as above in the notebook: \n",
+    "> `ls ~/scratch/NEON_cases/$new_case.transient/run` **or** \n",
+    "\n",
+    ">`qstat -u $USER` **or**\n",
+    "\n",
+    ">`tail  ~/scratch/NEON_cases/${new_case}.transient/CaseStatus`\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "How are our simulations different?  let's take a quick look to see.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "# 2. Introduction to controling case configuration\n",
+    "\n",
+    "There are a few places that summarize differences in simulations.  These files include:\n",
+    "- `env_run.xlm`, which are variables that can be changed to configure your case;  \n",
+    "- `lnd_in`, which cannot be modified directly, but that gets information from user_nl_clm (and provides addition control over the way the land model is configured in your simulation);  \n",
+    "- `datm.streams.xml`, which also cannot be modified directly, but modified with user_datm_streams to point to different data atmosphere streams.\n",
+    "\n",
+    "The easiest way to do this is by differencing the same files in each case directory with the `diff` command. \n",
+    "\n",
+    "## 2.1 `env_run.xml` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases\n",
+    "diff $new_case.transient/env_run.xml $base_case.transient/env_run.xml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What are the differences between these two cases**, based on their `env_run.xlm` files?\n",
+    "\n",
+    "These differences are expected, because NEON cases are set up with `usermod_dirs` that control some of these .xml variables.\n",
+    "\n",
+    "You can look at particlar changes for your $new_case below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat ~/CTSM/cime_config/usermods_dirs/NEON/$new_case/shell_commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We won't really go into usermod directories here, but they are a useful way to modify the way cases are configured for different model experiments.\n",
+    "\n",
+    "usermod_dirs were used when you created your NEON cases with run_neon.  \n",
+    "- The shell_command and user_nl_* files are copied from `~/CTSM/cime_config/usermods_dirs/NEON` into your case directory.  \n",
+    "- Shell commands are executed after the case is created.\n",
+    "- This makes is easier set up cases in a consistent, repeatable way.\n",
+    "**Pretty slick software engineering!**\n",
+    "\n",
+    "Some NEON sites have additional changes, notably for sites with gaps in the NEON input data.  These sites have shorter run times (than the 2018-2021 defaults).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat ~/CTSM/cime_config/usermods_dirs/NEON/MOAB/shell_commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These .xml changes set up high-level control over how your simulation is run.  What are some of the specifics related to the land model?  We can see this by looking at our `lnd_in` file.\n",
+    "\n",
+    "## 2.2 `lnd_in` \n",
+    "\n",
+    "**What are the differences between these two cases**, based on their `lnd_in` files?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff $new_case.transient/CaseDocs/lnd_in $base_case.transient/CaseDocs/lnd_in "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<style> \n",
+    "table td, table th, table tr {text-align:left !important;}\n",
+    "</style>\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>REMEMBER:</b>\n",
+    "    \n",
+    "- The <i>lnd_in</i> file provides a high level summary of all the name list chagnes and files that are being used by CLM. \n",
+    "- It can be found in the CaseDocs directory, or in your run directory. \n",
+    "- You cannot directly modifiy the <i>lnd_in file</i>, instead users can modify <i>user_nl_clm</i>.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "**Initial conditions dataset:** `finidat`\n",
+    "  - These are initial conditions files that we created by spinning up the model.  \n",
+    "  - Spin up requires starting the model from bare ground conditions (we call it a *coldstart*).\n",
+    "  - Spin up takes a few hundred years of simulations so that ecosystem carbon and nitrogen pools acheive steady state conditions (e.g. average net ecosystem exchange equals zero).  \n",
+    "  - Since this takes a long time, we provide initial conditions for you to start from.\n",
+    "  - **This also means that if you change model parameterizations, input data, or anything else you ahve to spin up the model again!** \n",
+    "\n",
+    "**Surface dataset:** `fsurdat`\n",
+    "  - The surface datasets describe what vegetation is growing in a grid cell, characteristics of soil physical properties, and much more information about what the land surface 'looks like' to the model.  \n",
+    "  - We modifed these the default surface dataset for each NEON simulation with information about the dominant plant functional type (PFT) and soil properties, based on NEON measurements. \n",
+    "  - This could likely be further refined, but it's a step towards making the model look more like the real world ecosystems we are trying to simulate.\n",
+    "\n",
+    "\n",
+    "There are other differences in the build, but this is basically just reflecting the different case directories for the two NEON cases.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "The `lnd_in` files are controlled by `user_nl_clm`.  Let's see how these are different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff $new_case.transient/user_nl_clm $base_case.transient/user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the only difference here are initial conditions, but that's because we used environmental variables to get the right surface dataset\n",
+    "\n",
+    "If you open one of the `user_nl_clm` files you'll see:\n",
+    "```\n",
+    "fsurdat = \"$DIN_LOC_ROOT/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_${NEONSITE}_hist_78pfts_CMIP6_simyr2000_c230111.nc\"\n",
+    "```\n",
+    "\n",
+    "We saw this already by `diff`ing the `env_run.xml files`, above, but now we'll use `.xmlquery` to see how these are different in each case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "echo moving to base_case directory\n",
+    "cd ~/scratch/NEON_cases/$base_case.transient/\n",
+    "./xmlquery NEONSITE\n",
+    "\n",
+    "echo moving to new_case directory\n",
+    "cd ~/scratch/NEON_cases/$new_case.transient/\n",
+    "./xmlquery NEONSITE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also see what **parameter file** is being used for your case.  Since we haven't changed this, the model just points to the default CTSM5.1 parameter file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat ~/scratch/NEON_cases/$base_case.transient/CaseDocs/lnd_in | grep paramfile\n",
+    "cat ~/scratch/NEON_cases/$new_case.transient/CaseDocs/lnd_in | grep paramfile\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The land model is using site specific initial conditions and surface data for each NEON site.  How else are our simulations different?  \n",
+    "\n",
+    "## 2.3 `datm.streams.xml` \n",
+    "\n",
+    "**What are the differences between these two cases**, based on their `datm.streams.xml` files?\n",
+    "\n",
+    "The answer here isn't very interesting... the two cases likely point to different input data reflecting local meterology at each site.  It's still helpful to know about how these files are set up.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/\n",
+    "cat $new_case.transient/CaseDocs/datm.streams.xml | head -20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Which aspects of this file could be changed for a different site?*\n",
+    "\n",
+    "You can see check with this code *(HINT: you'll have to paste it in the command line on into a code cell).*\n",
+    "\n",
+    "> ```\n",
+    "> diff $new_case.transient/CaseDocs/datm.streams.xml         $base_case.transient/CaseDocs/datm.streams.xml \n",
+    "> ```\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "<b>REMEMBER:</b> \n",
+    "\n",
+    "- The <i>datm.stream.xml</i> file points to all of the atmospheric boundary conditions (input data) that are being read in for a case. \n",
+    "- Like your <i>lnd_in</i> files, it can be found in the CaseDocs directory, or in your run directory. \n",
+    "- You cannot directly modifiy this file, instead users can modify <i>user_nl_datm_streams</i>.  \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You have now cloned a CTSM case to run a simulation at a new NEON tower site, check that yoe can locate the history files from this site and try to plot up some of these data for these new results.\n",
+    "</div>\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Create an experimental clone:\n",
+    "\n",
+    "<h4> This step is optional, but provides helpful information that you may use in your own workflow </h4>\n",
+    "\n",
+    "So far, everything we've done has been *out of the box* looking at different NEON sites, but without changing anything in the underlying model code.  You may want to do model experiments where you alter the vegetation growing at a site, modify some of the model parameters, modify namelist settings, change the input data, or even alter model code.  We'll get into how do make these changes later, but for now we'll get a test case set up.\n",
+    "\n",
+    "Since we're already run an out of the box case for KONZ, we can create a paired experimental case at the same site.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "<b>RECOMMENDATION:</b> use a short, descriptive name for your experiment, it will help you down the road.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "This example below just builds on what you've already been doing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "export base_case='KONZ'   # should match the base case you created in 1b\n",
+    "export new_case='KONZ'    # the new site you want to run\n",
+    "\n",
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --overwrite \\\n",
+    "   --experiment test1 \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two new flags are being used here:\n",
+    "- `--experiment` just appends the case name for the experiment\n",
+    "- `--setup-only` will create the case, but not submit it.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\" markdown='1'>\n",
+    "\n",
+    "<b>WARNING:</b> Because we're also using the `--base-case` flag, we won't have to rebuild our experimental case.  This may not be advisable if you're modifying model code.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "At this point your experimental case has been created.  \n",
+    "- What is the case name?\n",
+    "- Can you navigate to your case directory?\n",
+    "- Are there any differences between this experimental case and the base case you already ran?\n",
+    "- Can you find the datm input data for your case?\n",
+    "- Is the model going to use an initial conditions file?\n",
+    "- Where is the surface dataset that's being used?\n",
+    "- Can you find the parameter file for your case?\n",
+    "\n",
+    "**Extra Credit**\n",
+    "- What changes may you like to make to the model in this new experimental case?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/FATES_NEON_fromScratch.ipynb b/notebooks/SinglePoint/ProjectExamples/FATES_NEON_fromScratch.ipynb
new file mode 100644
index 0000000..6b63220
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/FATES_NEON_fromScratch.ipynb
@@ -0,0 +1,865 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FATES simulations at NEON sites\n",
+    "\n",
+    "### *run_neon* doesn't work for FATES cases, so we'll create these cases from *'scratch'*\n",
+    "\n",
+    "The `run_neon` script we've been using is very useful, but it simplifies a number of steps that users may want to take control of in their workflow.  We'll introduce how you can control aspects of your case setup and configuration here. For example, a deeper understanding of the model is need if your want to run FATES case, an unsupported tower (non-NEON) site, a generic single point simulation, a regional domain, or for global case.\n",
+    "\n",
+    "If you're new to running CTSM, this is a somewhat advanced tutorial.  We're planning on doing all of our examples in the workshop using `run_neon`, so the information in this tutorial may not be needed. That said, it illustrates some important features of how to run the model, how to get initial condition files, and more.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## In this tutorial\n",
+    "\n",
+    "The tutorial has several components. Below you will find steps to: \n",
+    "1. Create, setup, build, and run a CTSM-FATES-SP case at a NEON site from scratch\n",
+    "2. Locate history files\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> 1. Set up and run a simulation</h1>\n",
+    "\n",
+    "### CTSM can be run in 4 simple steps.\n",
+    "\n",
+    "1. **Create** a new case\n",
+    "  - *This step sets up a new simulation. It is the most complicated of these four steps because it involves making choices to set up the model configuration*\n",
+    "\n",
+    "2. **Setup** the case \n",
+    "  - *This step configures the model so that it can compile*\n",
+    "\n",
+    "3. **Build** the executable\n",
+    "  - *This step compiles the model*\n",
+    "\n",
+    "4. **Submit** your run to the batch queue\n",
+    "  - *This step submits the model simulation*\n",
+    "  \n",
+    "**NOTE:** We'll also customine our case with xml and namelist changes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h1> 1.1 create a new case</h1>\n",
+    "\n",
+    "*Set up a new simulation*\n",
+    "***\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b>  If you haven't done this already, you'll want to create the following directory\n",
+    "\n",
+    "`mkdir ~/scratch/NEON_cases`\n",
+    "    \n",
+    "<b>TIP:</b> If you're running on Cheyenne, creating a softlink to scratch in your home directory is helpful.  \n",
+    "    \n",
+    "You can create this with the following: `ln -s /glade/scratch/$USER ~/scratch`\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations. \n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud, you only have to do this once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cp -rp /opt/ncar/ctsm/ ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1.1 Navigate to the source code directory\n",
+    "\n",
+    "Your source code is in your home directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/cime/scripts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1.1 Create a new case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "export neon_site=\"STEI\"\n",
+    "# then create a new case.\n",
+    "./create_newcase --case ~/scratch/NEON_cases/${neon_site}_FATESsp_test \\\n",
+    "    --res CLM_USRDAT \\\n",
+    "    --compset I1PtClm51Fates \\\n",
+    "    --output-root ~/scratch/NEON_cases/ \\\n",
+    "    --run-unsupported \\\n",
+    "    --user-mods-dir NEON/FATES/${neon_site} \\\n",
+    "    --handle-preexisting-dirs r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "The code above doesn't always render properly online.  Here's what the code actually says:\n",
+    "\n",
+    "```\n",
+    "./create_newcase --case ~/scratch/NEON_cases/${neon_site}_FATESsp_test \\\n",
+    "    --res CLM_USRDAT \\\n",
+    "    --compset I1PtClm51Fates \\\n",
+    "    --output-root ~/scratch/NEON_cases/ \\\n",
+    "    --run-unsupported \\\n",
+    "    --user-mods-dir NEON/FATES/${neon_site} \\\n",
+    "    --handle-preexisting-dirs r\n",
+    "```\n",
+    "***\n",
+    "### **./create_newcase**\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b> There is a lot of information that goes into creating a case.\n",
+    "\n",
+    "You can learn more about the options by typing <i>./create_newcase --help</i> on the the command line or in a new code cell.\n",
+    "\n",
+    "<b>We'll briefly go over some of the highlights here.</b>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Required arguments to create a new case\n",
+    "There are 3 Required arguments Needed to create a new case.  These include \n",
+    "1. `--case`, which specifies the *location* and *name* of the case being created\n",
+    "  - `~/scratch` = the alias to your scratch  directory\n",
+    "  - `NEON_cases` = the subdirectory we created to store your other cases\n",
+    "  - `${neon_site}_FATESsp_test` = the name of the case you're creating\n",
+    "  - *Recommendation:* Use meaningful names, including model version, type of simulation, and any additional details to help you remember the configuration of this simulation\n",
+    "<br><br>\n",
+    "2. `--res` Defines the model *resolution*, or grid,\n",
+    "  - `CLM_USRDAT`, which is an option to get a case setup without having to define the grid resolution, yet. \n",
+    "  - In global cases, the land model is commonly run at a nominal 1 degree `f09_g17` or 2 degree `f19_g17` resolution \n",
+    "  - Using `./query_config --grids` provides a list of supported model resolutions\n",
+    "<br><br>  \n",
+    "3. `--compset` Defines the *component set* for your case, \n",
+    "  - The Component set specifies the default configuration for the case which includes:\n",
+    "    - Component models (e.g. active vs. data vs. stub), \n",
+    "    - Time period of simulations and forcing scenarios (e.g. 1850 vs 2000 vs. HIST) and \n",
+    "    - Physics options (e.g. CLM5.1 vs CLM5.0). \n",
+    "  - `I1PtClm51Fates` is alias that actually describes a much longer set of components that are being used for this single point case. \n",
+    "  - All CLM-only compsets start with *\"I\"*.\n",
+    "    - Using `./query_config --compsets clm` provides examples of other CLM compsets\n",
+    "<br>\n",
+    "\n",
+    "*There are a few other **optional** flags used to create this new case that we'll briefly touch on here*\n",
+    "<br>\n",
+    "\n",
+    "4. `--output-root`, which specifies the *location* of your run directory\n",
+    "  - `~/scratch/NEON_cases` = the subdirectory we already created \n",
+    "<br> \n",
+    "\n",
+    "5. `--user-mods-dirs` sets up the configuration of the case with a user modification directory that defines the location of the site.\n",
+    "  - `NEON/FATES/${neon_site}` has files that format of the history files and a few other custom settings for your case. \n",
+    "  - This inludes setting some of .xml variables and name list settings correctly for a FATES simulation in a NEON single point case. \n",
+    "<br>\n",
+    "\n",
+    "6. `--run-unsupported` avoids error using compsets are not scientifically supported \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "\n",
+    "<b>NOTE:</b> You may notice an error about project codes when you create your case. The project code isn't important for these simulations. But you may need to change this if you're running on Cheyenne.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "***\n",
+    "\n",
+    "\n",
+    "### 1.1.2 Create a parallel non-FATES case for comparison\n",
+    "we'll set this up as an AD case to make things easier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional/\n",
+    "./run_neon.py --neon-sites $neon_site  \\\n",
+    "    --output-root ~/scratch/NEON_cases \\\n",
+    "    --setup-only \\\n",
+    "    --overwrite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 1.2 Customize your case </h2>\n",
+    "\n",
+    "*Modifications for a FATESsp case*\n",
+    "\n",
+    "### 1.2.1 Move to your case directory\n",
+    "\n",
+    "**Check differences in env_run with a To start with, we want to run a FATESsp case, not with active biogeochemistry**\n",
+    "\n",
+    "These would be set up if you created a global fates sp compset, but we need to make changes for our single point case here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/${neon_site}_FATESsp_test\n",
+    "diff env_run.xml ../${neon_site}.transient/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, this doesn't always render properly online:\n",
+    "\n",
+    "```\n",
+    "cd ~/scratch/NEON_cases/${neon_site}_FATESsp_test\n",
+    "diff env_run.xml ../${neon_site}.transient/env_run.xml\n",
+    "```\n",
+    "\n",
+    "What differences do you see between the env_run files?\n",
+    "\n",
+    "**Background**\n",
+    "- Most NEON sites we have 4 complete years of data to work with (2018-2021). \n",
+    "- run_neon and usermod_dirs handle setting up cases correctly for non-FATES cases.\n",
+    "- NOTE, there are other differences caused by the compset we used to create our NEON case related to transient CO2.  We'll ignore these for now.\n",
+    "- For an SP case, we want to run NEON sites for a bit to let soil water and temperatures the equilibrate before comparing to observations. Luckily, this only takes a few years.\n",
+    "- We can handle this with the following xml changes\n",
+    "\n",
+    "The code below may need to be customized for particular sites, but in general, we want to run for twice as many years as we have NEON observations.  CLM will just cycle over the meterological data that NEON provides.  This will allow the second cycle of our our simulation to end with the same dates over which we have NEON observations, making plotting easier later on. We also need to force a coldstart since there are no initial conditions to compare to.\n",
+    "\n",
+    "### 1.2.2 xlm changes to modify env_run options\n",
+    "xlm changes are one way you can control your case to customize the simulation\n",
+    "- We won't cover this here, but other tutorials cover more about xml changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./xmlchange RUN_STARTDATE=2014-01-01 \n",
+    "./xmlchange STOP_OPTION=nyears\n",
+    "./xmlchange STOP_N=8  \n",
+    "./xmlchange DATM_YR_END=2021  #this is the last complete year of input data\n",
+    "./xmlchange CLM_FORCE_COLDSTART=on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.3 Modify user_nl_clm\n",
+    "We also won't discuss namelist modificaiton here, but they are another way to customize your case.\n",
+    "\n",
+    "Specifically these namelist changes:\n",
+    "- set up a FATES-SP case.\n",
+    "- modifies history file output for an SP case.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#user_nl_clm changes\n",
+    "echo \"use_fates_sp = .true.\" >> user_nl_clm\n",
+    "echo \"soil_decomp_method = 'None'\" >> user_nl_clm\n",
+    "echo \"use_lch4 = .false.\" >> user_nl_clm\n",
+    "echo \"fates_spitfire_mode = 0\" >> user_nl_clm\n",
+    "echo \"use_fates_fixed_biogeog = .true.\" >> user_nl_clm\n",
+    "echo \"use_fates_nocomp = .true.\" >> user_nl_clm\n",
+    "echo \"hist_fincl2 = 'FCEV','FCTR','FGEV','FIRA','FSA','FSH',\n",
+    " 'FATES_GPP','FATES_GPP_PF','H2OSOI','SNOW_DEPTH','TBOT','TSOI'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you're running in the cloud, we also have to point to the right surface dataset\n",
+    "# TODO, this should work for others if we don't get this onto /scratch/data\n",
+    "#echo \"fsurdat = '/scratch/wwieder/FATES_surfacedata/surfdata_1x1_NEON_${neon_site}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm\n",
+    "echo \"fsurdat = '/scratch/data/NEONv2/misc_inputs/FATES_surfacedata/surfdata_1x1_NEON_${neon_site}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Setup and build your case\n",
+    "\n",
+    "*This step configures the model so that it can then compile* \n",
+    "\n",
+    "### 1.3.1 case.setup\n",
+    "The `./case.setup` script:\n",
+    "1. configures the model\n",
+    "2. creates files to modify input data and run options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h3> 1.3.2 Build the executable  </h3>\n",
+    "\n",
+    "*This step compiles the model*\n",
+    "\n",
+    "It also takes a long time, so be patient.\n",
+    "***\n",
+    "\n",
+    "The `./case.build` script:\n",
+    "1. Checks input data\n",
+    "2. Creates a build/run directory with model executable and namelists\n",
+    "\n",
+    "TODO: using qcmd -- ./case.build on cheyenne fails with wget errors of met data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "---\n",
+    "This takes some time, and will throw a bunch of errors... don't worry, just give it time\n",
+    "\n",
+    "\n",
+    "**When the build completes successfully you'll see a notice that the `MODEL BUILD HAS FINISHED SUCCESSFULLY`**\n",
+    "\n",
+    "\n",
+    "You can read on, but before executing any code blocks in the notebook **wait for the model to build.**\n",
+    "This can take a while.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> 1.4 Submit your case  </h3>\n",
+    "\n",
+    "Now you're ready to submit the case!\n",
+    "\n",
+    "*This step submits the model simulation*\n",
+    "- You'll also be downloading all the meterological data from NEON for your site, this also takes a little time and prints lots of information to the screen\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you submit a job, you will see confirmation that it successfully submitted:\n",
+    "\n",
+    "### Congratulations! \n",
+    "### You've created and submitted a single point NEON run from scratch.\n",
+    "\n",
+    "Next, you will probably want to check on the status of your jobs.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
+    "\n",
+    "<b>TIP:</b> This is dependent on the scheduler that you're using. \n",
+    "\n",
+    "<ul>\n",
+    "<li>Cheyenne uses <b>PBS</b> where status is checked with <i>qstat -u $USER</i></li>\n",
+    "<li>This is also enabled in the cloud for you, try it in the code block below</li></ul>\n",
+    "\n",
+    "If you want to stop the simulation, you can do so with qdel here (or on Cheyenne).\n",
+    "<ul>\n",
+    "<li> Find your Job ID after typing <i>qstat</i> </li>\n",
+    "<li> Type <i>qdel {Job ID}</i> </li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qstat -u $USER"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Once your jobs are complete (or show the 'C' state under the 'Use' column, which means complete), we can check the CaseStatus file to ensure there were no errors and it completed successfully.  To do this, we'll 'tail' the end of the CaseStatus file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tail  ~/scratch/NEON_cases/${neon_site}_FATESsp_test/CaseStatus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should see several lines, with the middle one saying 'case.run success'.  \n",
+    "\n",
+    "Before that you'll see notifications about xml changes, case.setup, and case.submit, and case.run\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created a CTSM case for the NEON tower you selected.\n",
+    "\n",
+    "We'll build on these basics in additional tutorials to customize your simulations.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "****\n",
+    "# 2. Locate model history files\n",
+    "Your simulation will likely take some time to complete. The information\n",
+    "provided next shows where the model output will be located while the\n",
+    "model is running and once the simulation is complete. We also provide\n",
+    "files from a simulation that is already complete so that you can do the\n",
+    "next exercises before your simulation completes. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
+    "\n",
+    "<b>When your simulation is running</b> history files go to your scratch directory: \n",
+    "\n",
+    "<ul>\n",
+    "<li><i>~/scratch/NEON_cases/{CASE}</i> </li>\n",
+    "</ul>\n",
+    "\n",
+    "Within this directory you can find <i>/run</i> and <i>/bld</i> subdirectories.\n",
+    "\n",
+    "<b>When the simulation is complete</b>, a short-term archive directory is created, and history files are moved here: \n",
+    "\n",
+    "<ul>\n",
+    "<li><i>~/scratch/NEON_cases/<b>archive</b>/{CASE}/lnd/hist/</i> </li>\n",
+    "</ul>\n",
+    "\n",
+    "Note that files necessary to continue the run are left in the run directory: <i>~/scratch/NEON_cases/{CASE}/run</i>\n",
+    "</div>\n",
+    "\n",
+    "## 2.1 Run directory\n",
+    "*What's in your run directory?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases/${neon_site}_FATESsp_test/run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Do you see any log or history files?*\n",
+    "- log files look like `lnd.log*`\n",
+    "- history files look like `${neon_site}.transient.clm2.h0.*.nc`\n",
+    "\n",
+    "You can keep running the cell above until you see log and history files, then the model is running.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 2.2 Archive directory\n",
+    "*What's in your archive directory?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls ~/scratch/NEON_cases/archive/${neon_site}_FATESsp_test/lnd/hist | head -5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can drop the `| head -5` part of the cell above if you want to see ALL the files that have been archived.\n",
+    "\n",
+    "If you don't see any history files your simulation is likely still running or in the queue (check using squeue or qstat). Check again before you leave today to see if your simulation completed and if the files were transferred to archive. Even if your run isn't finished, you can move on in this tutorial.\n",
+    "\n",
+    "If you'd like to visualize these results you can go back to tutorial Day0c and modify to point to these results from the neon_site you just ran."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "***\n",
+    "# 3. Create a Clone\n",
+    "<h4> This step is optional, but provides helpful information </h4>\n",
+    "    \n",
+    "Creating and building a new case is slow.  \n",
+    "\n",
+    "Cloning cases can speed this up!\n",
+    "\n",
+    "<h2>3.1 Create a clone </h2>\n",
+    "\n",
+    "- Clones use the same resolution, compset, and output root.\n",
+    "- Clones can also share the same executable (build), which can save time!\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\" markdown=\"1\">\n",
+    "\n",
+    "<b>WARNING:</b> If you're making code modifications be careful using clones that share the same build. The example below is a good way to run the same model configuration at different NEON sites.\n",
+    "\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Move back to your source code directory\n",
+    "cd ~/CTSM/cime/scripts\n",
+    "\n",
+    "# Change the 4-character new NEON site you want to run.\n",
+    "export neon_site2=\"TEAK\"\n",
+    "\n",
+    "# then create a cloned case.\n",
+    "./create_clone --case ~/scratch/NEON_cases/${neon_site2}_FATESsp_test \\\n",
+    "               --clone ~/scratch/NEON_cases/${neon_site}_FATESsp_test \\\n",
+    "               --user-mods-dirs ~/CTSM/cime_config/usermods_dirs/NEON/FATES/${neon_site2} \\\n",
+    "               --keepexe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, the code above doesn't always render properly online.  Here's what we're doing:\n",
+    "\n",
+    "```\n",
+    "./create_clone --case ~/scratch/NEON_cases/${neon_site2}_FATESsp_test \\\n",
+    "               --clone ~/scratch/NEON_cases/${neon_site}_FATESsp_test \\\n",
+    "               --user-mods-dirs ~/CTSM/cime_config/usermods_dirs/NEON/FATES/${neon_site2} \\\n",
+    "               --keepexe\n",
+    "```\n",
+    "\n",
+    "***\n",
+    "\n",
+    "### **./create_clone**\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b> There is a lot of information that goes into creating a clone.\n",
+    "\n",
+    "You can learn more about the options by typing <i>./create_clone --help</i> on the the command line or in a new code cell.\n",
+    "\n",
+    "<b>We'll briefly go over some of the highlights here.</b>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "### Required arguments to create a clone\n",
+    "There are 2 required arguments needed to create a clone.  These include: \n",
+    "1. `--case`, this is the path and name of the new case your cloning.\n",
+    "<br><br>\n",
+    "2. `--clone`, this is the path and name of the existing case you want to clone.\n",
+    "<br><br>  \n",
+    "### Additional information we provided here were: \n",
+    "3. `--user-mods-dirs` as before, this sets up the configuration of the case with a user modification directory that defines the location of the new site we're running, but it requires the full path to the `user-mods-dir` you want to use.\n",
+    "<br><br>  \n",
+    "4. `--keepexe` Point to original build from the case being cloned.\n",
+    "\n",
+    "---\n",
+    "We'll also create a non-FATES case clone for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "./run_neon.py --neon-sites $neon_site2  \\\n",
+    "    --output-root ~/scratch/NEON_cases \\\n",
+    "    --setup-only \\\n",
+    "    --overwrite"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 3.2 Customize the clone? </h2>\n",
+    "\n",
+    "*Because you created a clone and point to an existing build we can skip the `case.setup` and `case.build` steps* \n",
+    "***\n",
+    "\n",
+    "### 3.2.1 Move to your case directory and compare env_run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/${neon_site2}_FATESsp_test\n",
+    "diff env_run.xml ../${neon_site2}.transient/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Make xlm changes\n",
+    "In the example above the teak data record only starts in 2019, meaning we have 3 years of meterological data to cycle through"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./xmlchange RUN_STARTDATE=2016-01-01 \n",
+    "./xmlchange STOP_OPTION=nyears\n",
+    "./xmlchange STOP_N=6  \n",
+    "./xmlchange DATM_YR_END=2021  #this is the last complete year of input data\n",
+    "./xmlchange CLM_FORCE_COLDSTART=on\n",
+    "\n",
+    "# Check that chagnes look as expected from the other FATESsp case\n",
+    "diff env_run.xml ../${neon_site}_FATESsp_test/env_run.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.3 Copy user_nl_clm\n",
+    "We can just copy the user_nl_clm into our cloned case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp ../${neon_site}_FATESsp_test/user_nl_clm .\n",
+    "# If you're running in the cloud, we also have to point to the right surface dataset\n",
+    "\n",
+    "# TODO, this should work for others if we don't get this onto /scratch/data\n",
+    "#echo \"fsurdat = '/scratch/wwieder/FATES_surfacedata/surfdata_1x1_NEON_${neon_site2}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm\n",
+    "echo \"fsurdat = '/scratch/data/NEONv2/misc_inputs/FATES_surfacedata/surfdata_1x1_NEON_${neon_site2}_hist_16pfts_Irrig_CMIP6_simyr2000_c230120.nc'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "<h2> 3.3 Submit the case </h2>\n",
+    "\n",
+    "*Because you created a clone and point to an existing build we can skip the `case.setup` and `case.build` steps* \n",
+    "***\n",
+    "\n",
+    "- As before, you'll be downloading all the meterological data, which takes a little time and prints lots of information to the screen\n",
+    "***\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created and run a clone Fof CLM- for the NEON tower you selected.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "You can track progress on your run using steps outlined in part #2 of this tutorial.\n",
+    "You can also work on visualizing your results for different sites using visualization code provided\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're developing this tutorial:\n",
+    "Before saving and pushing this code to github go to `Kernel` and `Restart kernel and clear all outputs...`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls -lrt ~/scratch/NEON_cases/${neon_site2}_FATESsp_test/run/ | tail -10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/Plot_flux_climatology.ipynb b/notebooks/SinglePoint/ProjectExamples/Plot_flux_climatology.ipynb
new file mode 100644
index 0000000..4f5ae20
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/Plot_flux_climatology.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a1542d31-3794-4108-8544-2becd146ba72",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Create Climatology Plots for NEON sites\n",
+    "\n",
+    "This notebook creates an annual climatology of observed and simulated fluxes. \n",
+    "\n",
+    "Examples can be seen in Fig 3 of the [NCAR-NEON system overview paper](https://doi.org/10.5194/egusphere-2023-271). \n",
+    "\n",
+    "##### Author: Negin Sobhani negins@ucar.edu [@negin513](https://github.com/negin513)\n",
+    "- Modified by: Will Wieder (wwieder@ucar.edu ; [@wwieder](https://github.com/wwieder)) and Teagan King (tking@ucar.edu ; [@TeaganKing](https://github.com/teaganking))\n",
+    "##### Last revised: 2023-05-31\n",
+    " \n",
+    "<figure>\n",
+    "    <img src=\"../../images/TREE_climatology_tseries_allvars.png\"\n",
+    "         alt=\"Tree Haven Flux Climatology\"\n",
+    "         width=\"500\" height=\"600\">\n",
+    "    <figcaption>\n",
+    "        <i>Mean daily flux climatology at the NEON Treehaven site (TREE) in Wisconsin.</i>\n",
+    "    </figcaption>\n",
+    "</figure>\n",
+    "\n",
+    "_______\n",
+    "\n",
+    "This notebook includes scripts for:\n",
+    "\n",
+    "1. Reading evaluation (NEON) and model (CTSM) data for all neon sites\n",
+    "2. Making climatology figures, including time-series with standard deviation as shaded regions \n",
+    "\n",
+    "This code works more efficiently using dask.  \n",
+    "- Using 16 dask workers it will take ~45 minutes to create plots for all NEON sites.\n",
+    "- The code below does not use dask, but works well enough for individual sites, although it can overload memory, especially if you save your plots to disk.\n",
+    "- It will take over 1.5 minutes just to read in the model data, which has 365 files / year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10ab2a31-cbf5-4178-88a4-dc35d7707136",
+   "metadata": {},
+   "source": [
+    "# Imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68ce59c2-2769-431d-b565-880c82cf34f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from glob import glob\n",
+    "from os.path import join\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "import calendar\n",
+    "import tqdm\n",
+    "import cftime\n",
+    "\n",
+    "from neon_utils import download_eval_files\n",
+    "from neon_utils import fix_time_h1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d75ba391-9350-4daf-8550-38af54eb26df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('xarray '+xr.__version__) # was working with 2023.5.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3fa2e7d-b590-4cac-b2de-4acff9309f72",
+   "metadata": {},
+   "source": [
+    "## User defined options\n",
+    "Modify the directory structures below for your own cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feecdae6-4df7-466e-9693-e7b28179fcd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neon_sites = ['ABBY'] # list of the sites you want to plot\n",
+    "case = '.transient'   # this should be the rest of your case name for each neon_site \n",
+    "                      # (e.g. \"site.transient\" or \"site.experiment.transient\")\n",
+    "save_switch = True    # set to false to save time and memory.\n",
+    "years = [\"2018\",\"2019\",\"2020\",\"2021\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "183e2011-73ec-451d-8ecc-edfe3b9e6ac5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make the figures directory if it's not already there\n",
+    "plot_dir = '~/scratch/NEON_cases/figures'\n",
+    "plot_dir = os.path.realpath(os.path.expanduser(plot_dir))\n",
+    "if not os.path.isdir(plot_dir):\n",
+    "    print (\"figures directory does not exist... creating it now!\")\n",
+    "    os.makedirs(plot_dir, exist_ok=True)\n",
+    "else:\n",
+    "    print(\"figures directory already exists\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c27b7001-8e43-481c-a0a2-4dbce71d87ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make the eval_files directory if it's not already there\n",
+    "eval_dir = '~/scratch/NEON_cases/eval_files'\n",
+    "eval_dir = os.path.realpath(os.path.expanduser(eval_dir))\n",
+    "if not os.path.isdir(eval_dir):\n",
+    "    print (\"eval directory does not exist... creating it now!\")\n",
+    "    os.makedirs(eval_dir, exist_ok=True)\n",
+    "else:\n",
+    "    print(\"eval directory already exists\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dd59976-8014-4821-9623-20d230173100",
+   "metadata": {},
+   "source": [
+    "### Download NEON Evaluation data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8016ab40-52a9-4b4a-8146-e7529672a545",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> This is slow and only has to be done once (assuming NEON eval files have not been updated).\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4c927c3-5519-4284-939c-db58e777aa6e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for neon_site in neon_sites:\n",
+    "    site_dir = eval_dir+'/'+neon_site\n",
+    "    site_dir = os.path.realpath(os.path.expanduser(site_dir))\n",
+    "    if not os.path.isdir(site_dir):\n",
+    "        download_eval_files(neon_site,eval_dir)\n",
+    "    else:\n",
+    "        print(site_dir +\" exists\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90ec7dbb-609d-4033-905b-00ab54bd9db4",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "## Define Useful Functions and Objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ce9d25f-ffcd-45c8-988b-63a7ea60d83e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def shaded_tseries( df_daily, df_daily_std, var, ax, color1= '#e28743',color2='#1d657e'):\n",
+    "    \n",
+    "    plot_var = var.obs_var\n",
+    "    sim_var = var.sim_var\n",
+    "    plot_var_desc = var.long_name\n",
+    "    plot_var_unit = var.unit\n",
+    "    \n",
+    "    ax.plot ( df_daily.time, df_daily[sim_var], marker = 'o' , linestyle ='dashed', color = color2, label=\"CTSM\", alpha = 0.9)\n",
+    "    ax.plot ( df_daily.time, df_daily[plot_var], marker = 'o' , color = color1,label=\"NEON\", alpha = 0.9)\n",
+    "    \n",
+    "    ax.fill_between(df_daily.time, \n",
+    "                    df_daily[plot_var]-df_daily_std[plot_var], \n",
+    "                    df_daily[plot_var]+df_daily_std[plot_var] ,alpha=0.15, color = color1)\n",
+    "    ax.fill_between(df_daily.time, \n",
+    "                    df_daily[sim_var]-df_daily_std[sim_var], \n",
+    "                    df_daily[sim_var]+df_daily_std[sim_var] ,alpha=0.15, color = color2)\n",
+    "\n",
+    "    ax.set_xlabel('Time', fontsize=17)\n",
+    "    ax.set_ylabel(plot_var_desc+\" [\"+plot_var_unit+\"]\", fontsize=17)\n",
+    "    ax.margins(x=0.02)\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "def climatology_tseries_allvars_fig3 (fig,  df_daily, df_daily_std, all_vars, plot_dir, color1= '#e28743',color2='#1d657e' , save_switch=False):\n",
+    "        panel_labels = [\"(a)\", \"(b)\", \"(c)\", \"(d)\", \"(e)\"]\n",
+    "\n",
+    "        axes = fig.subplots(nrows=5, ncols=1)\n",
+    "        axe = axes.ravel()\n",
+    "\n",
+    "        for index, var in enumerate(all_vars):\n",
+    "            ax = axe[index]\n",
+    "            \n",
+    "            shaded_tseries ( df_daily, df_daily_std, var, ax,color1,color2)\n",
+    "            \n",
+    "            ax.text(.025,0.90,panel_labels[index],\n",
+    "                horizontalalignment='left',\n",
+    "                transform=ax.transAxes, fontweight='bold',fontsize=19)\n",
+    "\n",
+    "\n",
+    "            # Set the locator for boxplots\n",
+    "            locator = mdates.MonthLocator()  # every month\n",
+    "            \n",
+    "            # Specify the format - %b gives us Jan, Feb...\n",
+    "            fmt = mdates.DateFormatter('%b')            \n",
+    "\n",
+    "            if index == 0:\n",
+    "                ax.text(.5,1.03,'NEON site : '+neon_site + ' [2018-2021]',\n",
+    "                horizontalalignment='center',\n",
+    "                transform=ax.transAxes, fontweight='bold',fontsize=19)\n",
+    "                ax.legend(fontsize = 17)\n",
+    "\n",
+    "\n",
+    "            ax.tick_params(axis='both', which='both', labelsize=17,width=1,length=7)\n",
+    "            ax.tick_params(axis='x',direction=\"in\", length = 7)\n",
+    "            ax.yaxis.set_ticks_position('both')\n",
+    "            ax.tick_params(axis='y',direction=\"out\", length = 7)\n",
+    "            \n",
+    "            X=ax.xaxis\n",
+    "            X.set_major_locator(locator)\n",
+    "            X.set_major_formatter(fmt)\n",
+    "            \n",
+    "            ax.get_yaxis().set_label_coords(-0.05,0.5)\n",
+    "\n",
+    "            if index == 5:\n",
+    "                X = plt.gca().xaxis\n",
+    "                X.set_major_locator(locator)\n",
+    "                X.set_major_formatter(fmt)\n",
+    "\n",
+    "        ax.set_xlabel('Month', fontsize=17)\n",
+    "        fig.subplots_adjust(wspace=0, hspace=0)\n",
+    "\n",
+    "        if save_switch:\n",
+    "            \n",
+    "            plot_name = neon_site+'_'+'climatology_tseries'+'_'+'allvars.png'\n",
+    "            plot_dir1 = os.path.join(plot_dir, 'climatology_tseries_final', 'png')\n",
+    "            if not os.path.isdir(plot_dir1):\n",
+    "                os.makedirs(plot_dir1, exist_ok=True)            \n",
+    "            \n",
+    "            print ('Saving '+ os.path.join(plot_dir1,plot_name))\n",
+    "            plt.savefig (os.path.join(plot_dir1,plot_name), dpi=600,bbox_inches='tight') \n",
+    "            \n",
+    "                    \n",
+    "            plot_name = neon_site+'_'+'climatology_tseries'+'_'+'allvars.pdf'\n",
+    "            plot_dir2 = os.path.join(plot_dir, 'climatology_tseries_final', 'pdf')\n",
+    "            if not os.path.isdir(plot_dir2):\n",
+    "                os.makedirs(plot_dir2, exist_ok=True)            \n",
+    "\n",
+    "            \n",
+    "            print ('Saving '+ os.path.join(plot_dir2,plot_name))\n",
+    "            plt.savefig (os.path.join(plot_dir2,plot_name), dpi=600,bbox_inches='tight', format = 'pdf')  \n",
+    "        else:\n",
+    "            plt.show()\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "167472fe-4915-4790-bc9e-c17fa6bc0e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PlotVariable ():\n",
+    "  def __init__(self, short_name, long_name, unit):\n",
+    "    self.short_name = short_name\n",
+    "    self.long_name = long_name\n",
+    "    self.unit = unit\n",
+    "    self.obs_var = short_name\n",
+    "    self.sim_var = 'sim_'+short_name\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de6fa84e-b33a-44a1-aed3-c4c44e9d81c1",
+   "metadata": {},
+   "source": [
+    "## Define variables to create plots\n",
+    "\n",
+    "Create a list of variables for the plots. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c51ea8d8-6d00-49a9-9594-83d52410ed95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_vars= [] \n",
+    "failed_sites = [] \n",
+    "\n",
+    "plot_var = 'Rnet'\n",
+    "sim_var = 'sim_'+plot_var\n",
+    "plot_var_desc = \"Net Radiation\"\n",
+    "plot_var_unit= \"W m⁻²\"\n",
+    "this_var = PlotVariable(plot_var, plot_var_desc, plot_var_unit)\n",
+    "all_vars.append(this_var)\n",
+    "\n",
+    "plot_var = 'FSH'\n",
+    "sim_var = 'sim_'+plot_var\n",
+    "plot_var_desc = 'Sensible Heat Flux'\n",
+    "plot_var_unit= \"W m⁻²\"\n",
+    "this_var = PlotVariable(plot_var, plot_var_desc, plot_var_unit)\n",
+    "all_vars.append(this_var)\n",
+    "\n",
+    "plot_var = 'EFLX_LH_TOT'\n",
+    "sim_var = 'sim_'+plot_var\n",
+    "plot_var_desc = \"Latent Heat Flux\"\n",
+    "plot_var_unit= \"W m⁻²\"\n",
+    "this_var = PlotVariable(plot_var, plot_var_desc, plot_var_unit)\n",
+    "all_vars.append(this_var)\n",
+    "\n",
+    "plot_var = 'GPP'\n",
+    "sim_var = 'sim_'+plot_var\n",
+    "plot_var_desc = \"Gross Primary Production\"\n",
+    "plot_var_unit= \"gC m⁻² day⁻¹\"\n",
+    "this_var = PlotVariable(plot_var, plot_var_desc, plot_var_unit)\n",
+    "all_vars.append(this_var)\n",
+    "\n",
+    "plot_var = 'NEE'\n",
+    "sim_var = 'sim_'+plot_var\n",
+    "plot_var_desc = \"Net Ecosystem Exchange\"\n",
+    "plot_var_unit= \"gC m⁻² day⁻¹\"\n",
+    "this_var = PlotVariable(plot_var, plot_var_desc, plot_var_unit)\n",
+    "all_vars.append(this_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe596ba-3fd7-46f1-b1ca-e3ab79709354",
+   "metadata": {},
+   "source": [
+    "---------------------------\n",
+    "## Make Climatology Figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c913bb51-ab6d-4f78-8a10-2792dd5575aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read only these variables from the whole netcdf files\n",
+    "def preprocess (ds):\n",
+    "    variables = ['FCEV', 'FCTR', 'FGEV','FSH','GPP','FSA','FIRA','AR','HR','ELAI']\n",
+    "\n",
+    "    ds_new= ds[variables]\n",
+    "    return ds_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d206a7cf-895b-4269-9008-e2c644e9a932",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set some defaults for our figures\n",
+    "plt.rcParams[\"font.weight\"] = \"bold\"\n",
+    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
+    "font = {'weight' : 'bold',\n",
+    "        'size'   : 15} \n",
+    "matplotlib.rc('font', **font)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dd6342a-689f-4385-90e8-4a96f6e30056",
+   "metadata": {},
+   "source": [
+    "Loop through the list of sites and make plots for all of them.\n",
+    "If you want to save your plots change `save_switch = True`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "543a4bc6-9bac-4485-a9bf-063bf93a80b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for neon_site in neon_sites:\n",
+    "    try: \n",
+    "        start_site = time.time()\n",
+    "\n",
+    "        print ('---------------------------')\n",
+    "        print (\"Making plots for \"+neon_site)\n",
+    "        sim_files =[]\n",
+    "        for year in years:\n",
+    "            sim_path = \"~/scratch/NEON_cases/archive/\"+neon_site+case+\"/lnd/hist/\"\n",
+    "            sim_path = os.path.realpath(os.path.expanduser(sim_path))\n",
+    "            sim_files.extend(sorted(glob(join(sim_path,neon_site+case+\".clm2.h1.\"+year+\"*.nc\"))))\n",
+    "\n",
+    "        print(sim_path)\n",
+    "        print(\"All simulation files for all years: [\", len(sim_files), \"files]\")\n",
+    "        start = time.time()\n",
+    "\n",
+    "        ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',\n",
+    "                                    parallel=True,preprocess=preprocess)\n",
+    "        ds_ctsm = fix_time_h1(ds_ctsm)\n",
+    "\n",
+    "        end = time.time()\n",
+    "        print(\"Reading all simulation files took:\", end-start, \"s.\")\n",
+    "\n",
+    "        eval_files = []\n",
+    "        for year in years:\n",
+    "            eval_files.extend(sorted(glob(join(site_dir,neon_site+\"_eval_\"+year+\"*.nc\"))))\n",
+    "\n",
+    "        print(site_dir)\n",
+    "        print (\"All evaluation files for all years: [\", len(eval_files), \"files]\")\n",
+    "\n",
+    "        start = time.time()\n",
+    "\n",
+    "        ds_eval = xr.open_mfdataset(eval_files, decode_times=True, combine='by_coords')\n",
+    "\n",
+    "        end = time.time()\n",
+    "        print(\"Reading all observation files took:\", end-start, \"s.\")\n",
+    "\n",
+    "        print (\"Processing data...\")\n",
+    "        # Convert CTSM data to a Pandas Dataframe for easier handling:\n",
+    "        ctsm_vars = ['FCEV', 'FCTR', 'FGEV','FSH','GPP','FSA','FIRA','AR','HR','ELAI']\n",
+    "\n",
+    "        df_ctsm = pd.DataFrame({'time':ds_ctsm.time})\n",
+    "        df_ctsm['time'] = pd.to_datetime(df_ctsm['time'],format= '%Y-%m-%d %H:%M:%S' )\n",
+    "\n",
+    "        for var in tqdm.tqdm(ctsm_vars):\n",
+    "            sim_var_name = \"sim_\"+var\n",
+    "            field = np.ravel ( ds_ctsm[var])     \n",
+    "            df_ctsm[sim_var_name]=field\n",
+    "            # Shift simulation data by one\n",
+    "            df_ctsm[sim_var_name]=df_ctsm[sim_var_name].shift(-1).values\n",
+    "\n",
+    "        # Convert NEON data to a Pandas Dataframe for easier handling:\n",
+    "        eval_vars = ['NEE','FSH','EFLX_LH_TOT','GPP','Rnet']\n",
+    "\n",
+    "        df_all = pd.DataFrame({'time':ds_eval.time})\n",
+    "\n",
+    "        for var in eval_vars:\n",
+    "            field = np.ravel (ds_eval[var])\n",
+    "            df_all[var]=field\n",
+    "            \n",
+    "        # Merge two pandas dataframe on time\n",
+    "        df_all=df_all.merge(df_ctsm.set_index('time'), on='time', how='left')\n",
+    "\n",
+    "        clm_var = 'sim_EFLX_LH_TOT'\n",
+    "        # Latent Heat Flux:\n",
+    "        # EFLX_LH_TOT = FCEV + FCTR +FGEV\n",
+    "        df_all [clm_var] = df_all['sim_FCEV']+ df_all['sim_FCTR']+ df_all['sim_FGEV']\n",
+    "\n",
+    "        clm_var = 'sim_Rnet'\n",
+    "        # Net Radiation:\n",
+    "        # Rnet = FSA-FIRA\n",
+    "        df_all [clm_var] = df_all ['sim_FSA']-df_all['sim_FIRA']\n",
+    "\n",
+    "        clm_var = 'sim_NEE'\n",
+    "        # Net Ecosystem Exchange\n",
+    "        # NEE = GPP - (AR+HR)\n",
+    "        # The signs are opposite so we calculated negative NEE\n",
+    "        df_all [clm_var] = -(df_all ['sim_GPP']-(df_all['sim_AR']+df_all['sim_HR']))\n",
+    "\n",
+    "        # Convert NEE units from  umolm-2s-1 to gC/m2/s\n",
+    "        df_all ['NEE']= df_all ['NEE']*(12.01/1000000)\n",
+    "        df_all ['GPP']= df_all ['GPP']*(12.01/1000000)\n",
+    "        \n",
+    "        # Convert gC/m2/s to gC/m2/day\n",
+    "        df_all ['NEE']= df_all['NEE']*60*60*24\n",
+    "        df_all ['sim_NEE']= df_all['sim_NEE']*60*60*24\n",
+    "\n",
+    "        df_all ['GPP']= df_all['GPP']*60*60*24\n",
+    "        df_all ['sim_GPP']= df_all['sim_GPP']*60*60*24\n",
+    "\n",
+    "        # Extract year, month, day, hour information from time\n",
+    "        df_all['year'] = df_all['time'].dt.year\n",
+    "        df_all['month'] = df_all['time'].dt.month\n",
+    "        df_all['day'] = df_all['time'].dt.day\n",
+    "        df_all['hour'] = df_all['time'].dt.hour\n",
+    "\n",
+    "        # Calculate daily average for every day \n",
+    "        df_daily_allyears = df_all.groupby(['year','month','day']).mean().reset_index()\n",
+    "        df_daily_allyears['time']=pd.to_datetime(df_daily_allyears[[\"year\",\"month\", \"day\"]])\n",
+    "        \n",
+    "        # Calculate average of daily averages for all years\n",
+    "        df_daily = df_daily_allyears.groupby(['month','day']).mean().reset_index()\n",
+    "        df_daily['year']='2020'\n",
+    "        df_daily['time']=pd.to_datetime(df_daily[[\"year\",\"month\", \"day\"]])\n",
+    "        \n",
+    "        # Calculate Standard Deviation of daily averages over years\n",
+    "        df_daily_std = df_daily_allyears.groupby(['month','day']).std().reset_index()\n",
+    "        df_daily_std['time'] = df_daily['time']\n",
+    "\n",
+    "        df_daily['site']=neon_site\n",
+    "\n",
+    "        print (\"Making climatology plots...\")\n",
+    "        color1 = '#e28743'\n",
+    "        color2 = '#1d657e'\n",
+    "\n",
+    "        #========================================================================\n",
+    "        fig = plt.figure(num=None, figsize=(27, 37),  facecolor='w', edgecolor='k')\n",
+    "        climatology_tseries_allvars_fig3( fig, df_daily, df_daily_std, all_vars, plot_dir, color1,color2, save_switch)\n",
+    "        \n",
+    "        end_site = time.time()\n",
+    "        print(\"Making these plots for \"+neon_site+\" took : \", end_site-start_site, \"s.\")\n",
+    "\n",
+    "    except Exception as e: \n",
+    "        print (e)\n",
+    "        print ('THIS SITE FAILED:', neon_site)\n",
+    "        failed_sites.append(neon_site)\n",
+    "        pass\n",
+    "\n",
+    "print (\"Making plots for \", len(failed_sites), \"sites failed : \")\n",
+    "print (*failed_sites, sep=\" \\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd34a7c4-7f02-46c3-aa24-9ceb2d81fb82",
+   "metadata": {},
+   "source": [
+    "#### This code takes some time to run (over 1.5 minutes just to read in the daily model data!)\n",
+    "It's a good idea to confirm that you're opening the number of files you're expecting.  This should include:\n",
+    "- Daily simulation data from CLM for n years +\n",
+    "- Monthly evaluation files from NEON for n years\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**After your plot comes up, what do you see?**  \n",
+    "- How does the CLM simulation look compared to the NEON observations?\n",
+    "- If net radiation doesn't look good, it suggests there's an issue with energy balance and albedo.\n",
+    "- If sensible and latent heat fluxes are off, what could this suggest?\n",
+    "- Does the timing and magnitude of GPP fluxes seem sensible in the both model AND observations?\n",
+    "- What about the timing and magnitude of NEE? Remember, in CLM we're making steady state assumptions about the long-term net C flux. Does this assumption necessarily hold in real world ecosystems?\n",
+    "\n",
+    "**Where can I even start looking for clues?**\n",
+    "- Does the simulated LAI at your site look reasonable?\n",
+    "  - This can be done with monthly (h0) files, but daily (h1) may be more instructive.\n",
+    "- How does the forcing data look? \n",
+    "  - Specifically, try looking at precipitation (from NEON input data), or the sum of RAIN and SNOW (from model history files)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e397596-9bbb-4863-a480-0a5ac2527dc0",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratualtions:</b> \n",
+    "    \n",
+    "You've done the easy part!\n",
+    "\n",
+    "Diagnosing sources of potential biases is both an art and a science.  \n",
+    "\n",
+    "Please ask for help if you're going to dive into this, and also let us know what you find!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd25b86a-175c-4b4c-86ba-21aef66b1dc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM-FATESsp.ipynb b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM-FATESsp.ipynb
new file mode 100644
index 0000000..c518a3a
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM-FATESsp.ipynb
@@ -0,0 +1,556 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51f96e25-587b-4108-a7d9-34666f7a50e6",
+   "metadata": {},
+   "source": [
+    "# Quick plots - CTSM-FATESsp\n",
+    "## Quickly look at various output from FATESsp\n",
+    "\n",
+    "This tutorial is an introduction to [xarray](https://docs.xarray.dev/en/stable/user-guide/terminology.html) and [matplotlib](https://matplotlib.org/stable/index.html). There is plenty more information to be found at the documentation for these libraries.\n",
+    "\n",
+    "This tutorial can be be run either on data from cases that you ran earlier, or can be run on pre-staged data.\n",
+    "\n",
+    "In this tutorial you will find steps and instructions to:\n",
+    "\n",
+    "1. Load python libraries\n",
+    "2. Locate history files\n",
+    "3. Read in history files\n",
+    "4. Make plots for variables including soil moisture and GPP\n",
+    "\n",
+    "------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7edf783f-9442-4a1b-ab7c-e9455be3ac5b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 1. Load Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33b035fb-f248-4e16-aeca-21b89815fe20",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.1 Load Python Libraries\n",
+    "We always start by loading in the libraries we're going to use for the script.  There are more libraries being loaded here than we'll likely use, but this list is a good one to get started for most of your plotting needs.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87882ecf-c37c-4b01-bfb4-24e56d671777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from glob import glob\n",
+    "from os.path import join\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "from neon_utils import fix_time_h1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3039504-ae5d-4653-acac-44a402cef436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# It's helpful to document the version of some tools that are quickly changing\n",
+    "print('xarray '+xr.__version__) # was working with 2023.5.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db2ec48-9370-422a-a4e4-893bc39906e2",
+   "metadata": {},
+   "source": [
+    "## 1.2 Point to history files \n",
+    "\n",
+    "### 1.2.1 Where are my simulation results?\n",
+    "After your simulations finish, history files are all saved in your `/scratch/NEON_cases/archive/` directory.\n",
+    "\n",
+    "We can print the cases we have to look at using bash magic, `%%bash` or `!` which turns the python cell block below into a bash cell.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acf88bc1-a2ab-4ae5-bfcb-232cc5d6ad82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "ls ~/scratch/NEON_cases/archive/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf652c-e127-4cb8-89cb-a4423b29f2b4",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> you can accomplish the same thing with the following.\n",
+    "\n",
+    "> `!ls ~/scratch/NEON_cases/archive/`\n",
+    "    \n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> if you prefer to look at example data instead of your own data, you can read in data located at `/scratch/data/NEONv2/hist`. We'll go over this in the next section.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.2 Point to the directory with history files \n",
+    "**We'll set the following:**\n",
+    "- site to look at\n",
+    "- path to our archive directory\n",
+    "- directory with input data (where history files are found)  \n",
+    "\n",
+    "By doing this more generally, it makes the script easier to modify for different sites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e3bee8-fa6b-4c37-ae55-f62151640598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neon_site = 'STEI'  # NEON site we're going to look at\n",
+    "\n",
+    "# If you would like to look at your own data, set the path to your archive directory\n",
+    "archive = '~/scratch/NEON_cases/archive' # Path to archive directory\n",
+    "\n",
+    "# If you would like to look at example data, set the path to this archive directory\n",
+    "archive = '/scratch/wwieder/NEON_cases/archive'  #TODO: COMMENT OUT!\n",
+    "\n",
+    "# This expands the shortcut we used above\n",
+    "archive = os.path.realpath(os.path.expanduser(archive)) \n",
+    "\n",
+    "# Identify path to the data folder\n",
+    "data_folder = archive+'/'+neon_site+'_FATESsp_test/lnd/hist'\n",
+    "data_folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d25741-5010-496f-b61a-f15e1c135989",
+   "metadata": {},
+   "source": [
+    "**Is this the path for input data, `data_folder`, correct?** \n",
+    "\n",
+    "*HINT:* You can check in the terminal window or using bash magic.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.3 Create some functions we'll use when opening the data\n",
+    "1. `preprocess` will limit the number of variables we're reading in. This is an xarray feature that helps save time (and memory resources).\n",
+    "2. `fix_time_h1` corrects annoying features related to how CTSM history files handle time and is provided as part of `neon_utils.py`.\n",
+    "\n",
+    "*Don't worry too much about the details of these functions right now.*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f2a23fe-7273-4370-8f2c-fe3dfad5029e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read only these variables from the netcdf files\n",
+    "def preprocess_some (ds):\n",
+    "    variables = ['FCEV', 'FCTR', 'FGEV','FSH','GPP','FSA','FIRA','AR','HR','ELAI']\n",
+    "    ds_new= ds[variables].isel(lndgrid=0)\n",
+    "    return ds_new\n",
+    "\n",
+    "# Read all these variables from the netcdf files\n",
+    "def preprocess_all (ds):\n",
+    "    ds_new= ds.isel(lndgrid=0)\n",
+    "    return ds_new"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3919e8c9-4c08-485f-9e01-8c42f6b5ddad",
+   "metadata": {},
+   "source": [
+    "Now we have created the functions needed to manipulate our datasets.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.4 List all the files we're going to open\n",
+    "The the 30-minute, high frequency history output (**'h1' files**) are written out every day in for NEON cases. \n",
+    "\n",
+    "To open all of these files we're going to need to know their names.  This can be done if we:\n",
+    "- Create an empty list `[]` of simulation files that is\n",
+    "- `.extend`ed with a \n",
+    "- `sorted` list of files generted with the \n",
+    "- `glob` function in python of the \n",
+    "- `*h1*`files in our `data_folder` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617ea958-499e-4dda-92da-8e531b152fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This list gives you control over the years of data to read in\n",
+    "# We're just going to look at one year of data\n",
+    "years = [\"2019\"]  \n",
+    "\n",
+    "# Create an empty list of all the file names to extend\n",
+    "sim_files = []\n",
+    "for year in years:\n",
+    "    sim_files.extend(sorted(glob(join(data_folder,\"*h1.\"+year+\"*.nc\"))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8919fcde-de2e-43d3-a9a5-7439b6d592e1",
+   "metadata": {},
+   "source": [
+    "How many files are you going have to read in?  What is the last day of the simulation you'll be looking at?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c591bd39-6875-4d8f-acb9-67943cef8418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Total number of simulation files: \", len(sim_files), \"files\")\n",
+    "print(\"Last simulation file:\", sim_files[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d280416-00b4-431d-944a-bb2ca7b4cceb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### 1.2.5 Read in the data\n",
+    "`xr.open_mfdataset` will open all of these data files and concatinate them into a single **xarray dataset**.\n",
+    "\n",
+    "We are going to also going use or `preprocess` and `fix_time` functions in this step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aee5a387-f155-4fe8-a6e8-bfe2f95a2f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "print (\"Reading in data for \"+neon_site)\n",
+    "\n",
+    "# Just reading *some* of the data here, could use preprocess_all instead.\n",
+    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',\n",
+    "                            preprocess=preprocess_all)\n",
+    "ds_ctsm = fix_time_h1(ds_ctsm)\n",
+    "\n",
+    "end = time.time()\n",
+    "print(\"Reading all simulation files took:\", end-start, \"s.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbad64f5-88ef-402b-8fdd-3610fde4766d",
+   "metadata": {},
+   "source": [
+    "### Print the dataset you're working with ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cda5d25-ce4b-419c-8dd5-e44f248e7604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed2dde75-b0eb-4aa3-998d-9858c2c9ad75",
+   "metadata": {},
+   "source": [
+    "#### Take a quick look at the dataset.\n",
+    "- What are your coodinate variables?\n",
+    "- How long is the time dimensions?\n",
+    "- What variables do we have to look at?\n",
+    "- What are the long names of some of these variables? (HINT: try `ds_ctsm.FATES_GPP`)\n",
+    "- What other metadata are associated with this dataset? \n",
+    "\n",
+    "---\n",
+    "Let's start plotting!\n",
+    "#### What do GPP fluxes look like in this FATES_SP run?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2d24312-72a1-466f-9489-cc969a550e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.FATES_GPP.plot(); "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f14e87a9-f617-4f4e-bc54-e59198cc23d8",
+   "metadata": {},
+   "source": [
+    "Let's repeat this, but look at daily mean fluxes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "327d0322-5b47-41d6-838a-7d0d2a8cc408",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert from kgC/m2/s to daily flux (gC/m2/d)\n",
+    "spd = 24 * 60 * 60\n",
+    "((ds_ctsm.FATES_GPP.resample(time='D').mean())*spd*1e3).plot()\n",
+    "plt.ylabel('GPP (gC/m2/d)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff79b808-d0b8-4061-9f73-3e1879116011",
+   "metadata": {},
+   "source": [
+    "#### The `FATES_GPP` variable actually includes several PFTs on the surface dataset.  \n",
+    "This is different from how single point *\"Big Leaf\"* CLM simulations are done by default, which only have a single PFT on the surface data.\n",
+    "\n",
+    "We can look at each of the PFTs from our FATES-SP run below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "414f49f9-86f1-4401-8208-609f73fe5971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp = ((ds_ctsm.FATES_GPP_PF.resample(time='D').mean())*spd*1e3)\n",
+    "temp.plot(hue='fates_levpft') \n",
+    "plt.ylabel('FATES GPP (gC/m2/d)') ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eb333ec-3c77-4681-a135-b3cf1f733999",
+   "metadata": {},
+   "source": [
+    "You can find out what PFTs this corresponds to with the following commnads\n",
+    "\n",
+    "```\n",
+    "cat ~/scratch/NEON_cases/STEI_FATESsp_test/run/lnd_in | grep fates_paramfile\n",
+    "ncdump -v fates_pftname <path to the fates_paramfile from above>\n",
+    "```\n",
+    "\n",
+    "For the STEI site, it looks like FATES PFTs # 2, 6, & 11.\n",
+    "You'll have to go to the FATES github  \n",
+    "This corresponds to:\n",
+    "- needleleaf_evergreen_extratrop_tree\n",
+    "- broadleaf_colddecid_extratrop_tree\n",
+    "- cool_c3_grass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fff0b9d5-16ad-4010-b432-23d53cadd17d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp.sel(fates_levpft=[2,6,11]).plot(hue='fates_levpft') \n",
+    "plt.ylabel('FATES GPP (gC/m2/d)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cfaa9c3-7ae6-43b2-acd7-72d0c2e9af6c",
+   "metadata": {},
+   "source": [
+    "This raises questions about why the GPP for certain PFTs is so much lower?  \n",
+    "Units are per m2, so it's not that the grass makes up a smaller fraction of the total grid area.\n",
+    "- Per unit leaf area, is the photosynthetic capacity of grasses lower?\n",
+    "  - A number of parameter control photosynthetic capacity but vcmax is one important place to start\n",
+    "  - HINT to get started try\n",
+    "     >cat ~/scratch/NEON_cases/STEI_FATESsp_test/run/lnd_in | grep fates_paramfile\n",
+    "     \n",
+    "     >ncdump -v fates_leaf_vcmax25top `<path to the fates_paramfile from above>`\n",
+    "\n",
+    "- Does this have to do with canopy scaling?  That is, does the grass PFT just have a lower LAI?\n",
+    "  - You can look at this by printing the LAI for each corresponding PFT on the CLM surface dataset.\n",
+    "  - HINT to get started try:\n",
+    "    > cat ~/scratch/NEON_cases/STEI_FATESsp_test/run/lnd_in | grep fsurdat\n",
+    "   \n",
+    "    > ncdump -v MONTHLY_LAI,PCT_NAT_PFT `<path to the surface dataset from above>`\n",
+    "   \n",
+    "Note: the CLM PFT indexes are different from what FATES uses.\n",
+    "\n",
+    "Moreover, LAI on the surface dataset is kind of hard to interpret, as they are monthly values dimensioned [time x PFT].  \n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>CHALLENGE</b> \n",
+    "    \n",
+    "Can you write a few lines of code to open the surface dataset and plot the monthly PFT values for the PFTs represented in your FATES-SP case?\n",
+    "    \n",
+    "</div>\n",
+    "\n",
+    "It's also helpful to look at the simulated energy budget.\n",
+    "- Does net radiation, sensible heat flux, and latent heat flux seem OK?\n",
+    "- How do we compare fluxes from multiple PFTs to flux tower measurements that integrate fluxes across their entire footprint?\n",
+    "- We have information on the gridcell weighted mean fluxes on our h1 files, but can you write out these fluxes at a PFT level?\n",
+    "- Would it be helpful to have a results from a *Big Leaf* CLM simulation to compare to?  \n",
+    "\n",
+    "There's a lot to start looking into here! As you can see, this quickly gets complicated to investigate.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d8f5cfe-d449-4518-9007-c27e451898d6",
+   "metadata": {},
+   "source": [
+    "#### Make a contour plot of soil moisture over time with depth on the y axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878ee93c-086d-467d-b701-60d399235308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.H2OSOI.plot(robust=True, y='levsoi')\n",
+    "plt.gca().invert_yaxis();\n",
+    "plt.title(neon_site);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc12258b-d735-4498-86a5-36244053e14b",
+   "metadata": {},
+   "source": [
+    "### This example plots: \n",
+    "- vertical profiles of soil moisture \n",
+    "- for one time step\n",
+    "- over the top 4m of soil \n",
+    "- with depth on the y axis, and reversed so deeper soil levels are at the bottom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8990cb0-3a57-425c-a9fa-80f02a9e2d02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.H2OSOI.isel(time=(181*48)).plot(y='levsoi',marker='o',ylim=(0,4))\n",
+    "plt.gca().invert_yaxis() \n",
+    "plt.suptitle(neon_site);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df902a6-0cda-4588-9437-6b3ae8f23d8e",
+   "metadata": {},
+   "source": [
+    "It seems odd that surface soil layers are wetter than deeper ones.\n",
+    "\n",
+    "### This example plots a time series of soil moisture \n",
+    "- for a single soil level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8e0fa84-458d-4df1-aae2-8fbb38b118ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.H2OSOI.isel(levsoi=4).plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b6a0edb-015a-4fc1-bc07-aaeca72c8438",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratulations:</b> \n",
+    "    \n",
+    "You've quickly looked at some of the monthly output from CLM-FATESsp run.\n",
+    "\n",
+    "What other sites or variables would you like to look at?\n",
+    "\n",
+    "Give it a shot, you can make lots of plots quickly with all this data!\n",
+    "    \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d2c2fb4-3b0d-4502-beb9-b069ad70fc0a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_h0.ipynb b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_h0.ipynb
new file mode 100644
index 0000000..dd17804
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_h0.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51f96e25-587b-4108-a7d9-34666f7a50e6",
+   "metadata": {},
+   "source": [
+    "# Quick plots - CTSM - Monthly \n",
+    "## Simple way to quickly look at monthly history files (*h0*).  \n",
+    "\n",
+    "This tutorial is an introduction to [xarray](https://docs.xarray.dev/en/stable/user-guide/terminology.html) and [matplotlib](https://matplotlib.org/stable/index.html). There is plenty more information to be found at the documentation for these libraries.\n",
+    "\n",
+    "This tutorial can be be run either on data from cases that you ran earlier, or can be run on pre-staged data.\n",
+    "\n",
+    "In this tutorial you will find steps and instructions to:\n",
+    "\n",
+    "1. Load python libraries needed for plotting\n",
+    "2. Locate monthly history files\n",
+    "3. Load datasets with xarray\n",
+    "4. Plot the data\n",
+    "------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7edf783f-9442-4a1b-ab7c-e9455be3ac5b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 1. Load Datasets\n",
+    "\n",
+    "## 1.1 Load Python Libraries\n",
+    "We always start by loading in the libraries we're going to use for the script. There are more libraries being loaded here than we'll likely use, but this list is a good one to get started for most of your plotting needs.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87882ecf-c37c-4b01-bfb4-24e56d671777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from glob import glob\n",
+    "from os.path import join\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "from neon_utils import fix_time_h0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3039504-ae5d-4653-acac-44a402cef436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# It's helpful to document the version of some tools that are quickly changing\n",
+    "print('xarray '+xr.__version__) # was working with 2023.5.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db2ec48-9370-422a-a4e4-893bc39906e2",
+   "metadata": {},
+   "source": [
+    "## 1.2 Point to history files \n",
+    "\n",
+    "### 1.2.1 Where are my simulation results?\n",
+    "After your simulations finish, history files are all saved in your `/scratch/NEON_cases/archive/` directory\n",
+    "\n",
+    "We can print the cases we have to look at using bash magic, `%%bash` or `!` which turns the python cell block below into a bash cell.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acf88bc1-a2ab-4ae5-bfcb-232cc5d6ad82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "ls ~/scratch/NEON_cases/archive/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf652c-e127-4cb8-89cb-a4423b29f2b4",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> you can accomplish the same thing with the following.\n",
+    "\n",
+    "> `!ls ~/scratch/NEON_cases/archive/`\n",
+    "    \n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> if you prefer to look at example data instead of your own data, you can read in data located at `/scratch/data/NEONv2/hist`. We'll go over this in the next section.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.2 Point to the data folder with history files \n",
+    "**We'll set the following:**\n",
+    "- site to look at\n",
+    "- path to our archive directory\n",
+    "- directory with input data (where history files are found)\n",
+    "\n",
+    "\n",
+    "By doing this more generally, it makes the script easier to modify for different sites."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a673c3d7-6ba5-4f60-9ff6-72370648c1ee",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> you can accomplish the same thing with the following.\n",
+    "\n",
+    "> `!ls ~/scratch/NEON_cases/archive/`\n",
+    "    \n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.2 Point to the data folder with history files \n",
+    "**We'll set the following:**\n",
+    "- site to look at\n",
+    "- path to our archive directory\n",
+    "- directory with input data (where history files are found)  \n",
+    "\n",
+    "By doing this more generally, it makes the script easier to modify for different sites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e3bee8-fa6b-4c37-ae55-f62151640598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neon_site = 'ABBY'  # NEON site we're going to look at\n",
+    "archive = '~/scratch/NEON_cases/archive' # Path to archive directory\n",
+    "\n",
+    "# If you prefer to look at example data, you can uncomment the following line\n",
+    "# archive = '~/../../scratch/data/NEONv2/hist'\n",
+    "\n",
+    "# This unpacks the and expands the shortcut, '~', we used above\n",
+    "archive = os.path.realpath(os.path.expanduser(archive)) \n",
+    "\n",
+    "# Create a path to the data folder\n",
+    "data_folder = archive+'/'+neon_site+'.transient/lnd/hist'\n",
+    "\n",
+    "data_folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d25741-5010-496f-b61a-f15e1c135989",
+   "metadata": {},
+   "source": [
+    "**Is this path for input data, `data_folder`, correct?** \n",
+    "\n",
+    "*HINT:* You can check in the terminal window or use bash magic (`%%bash`) and then list the contents of `data_folder` with `ls` in the same cell.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dea12afa-a949-4743-9f69-436da46ecd31",
+   "metadata": {},
+   "source": [
+    "### 1.2.3 Create some functions we'll use when opening the data\n",
+    "1. `preprocess` will limit the number of variables we're reading in. This is an xarray feature that helps save time (and memory resources).\n",
+    "2. `fix_time` corrects annoying features related to how CTSM history files handle time and is provided in `neon_utils.py`.\n",
+    "\n",
+    "*Don't worry too much about the details of these functions right now*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f2a23fe-7273-4370-8f2c-fe3dfad5029e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read all variables from the netcdf files\n",
+    "# This just drops an unused coordinate variable (lndgrid) from the dataset\n",
+    "def preprocess_all (ds):\n",
+    "    ds_new= ds.isel(lndgrid=0) \n",
+    "    return ds_new\n",
+    "\n",
+    "# Read some of the variables from the netcdf files, \n",
+    "# This will make things faster, but requires you to list the variables you want to look at\n",
+    "def preprocess_some (ds):\n",
+    "    variables = ['H2OSOI', 'TSOI']\n",
+    "    ds_new= ds[variables].isel(lndgrid=0)\n",
+    "    return ds_new"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3919e8c9-4c08-485f-9e01-8c42f6b5ddad",
+   "metadata": {},
+   "source": [
+    "Now we have created the functions needed to manipulate our datasets.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 1.2.4 List all the files we're going to open\n",
+    "The monthly history output (**'h0' files**) are written out for NEON cases. \n",
+    "\n",
+    "To open all of these files we're going to need to know their names.  This can be done if we:\n",
+    "- Create an empty list `[]` of simulation files that is\n",
+    "- `.extend`ed with a \n",
+    "- `sorted` list of files generated with the \n",
+    "- `glob` function in python of the \n",
+    "- `*h0*`files in our `data_folder` \n",
+    "\n",
+    "You'll notice that **all of this gets combined in a single line of code** that runs through a \n",
+    "- `for` loop over defined simulation years (written as a list of strings)\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> If you're new to python it's dense, but efficient.  I actually borrowed a bunch this code from a colleague, Negin Sobhani, who's good at python! Sharing code is really helpful. \n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617ea958-499e-4dda-92da-8e531b152fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an empty list of all the file names to extend\n",
+    "sim_files = []\n",
+    "\n",
+    "# If you want to choose a few particular years, you can use this loop\n",
+    "# years = [\"2021\", \"2022\"]\n",
+    "# for year in years:\n",
+    "#    sim_files.extend(sorted(glob(join(data_folder,\"*h0.\"+year+\"*.nc\"))))\n",
+    "\n",
+    "sim_files.extend(sorted(glob(join(data_folder,\"*h0.*.nc\"))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50be9689-f985-413a-bb30-4c49e257a204",
+   "metadata": {},
+   "source": [
+    "How many files are you going have to read in?  What is the last day of the simulation you'll be looking at?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e76ac53d-9f1e-4479-b471-7ca2eccab8a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Total number of simulation files: \", len(sim_files), \"files\")\n",
+    "print(\"Last simulation file:\", sim_files[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d280416-00b4-431d-944a-bb2ca7b4cceb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### 1.2.5 Read in the data\n",
+    "`xr.open_mfdataset` will open all of these data files and concatenate them into a single **xarray dataset**.\n",
+    "\n",
+    "We are also going to use the `preprocess` and `fix_time` functions in this step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aee5a387-f155-4fe8-a6e8-bfe2f95a2f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "print (\"Reading in data for \"+neon_site)\n",
+    "\n",
+    "# Just reading *some* of the data here, could use preprocess_all instead.\n",
+    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',\n",
+    "                            preprocess=preprocess_some)\n",
+    "ds_ctsm = fix_time_h0(ds_ctsm)\n",
+    "\n",
+    "end = time.time()\n",
+    "print(\"Reading all simulation files took:\", end-start, \"s.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbad64f5-88ef-402b-8fdd-3610fde4766d",
+   "metadata": {},
+   "source": [
+    "### Print the dataset you're working with ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cda5d25-ce4b-419c-8dd5-e44f248e7604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2be98dc-b543-419c-a2cf-653adfc0b536",
+   "metadata": {},
+   "source": [
+    "#### Take a quick look at the dataset.\n",
+    "- What are your coodinate variables?\n",
+    "- How long is the time dimensions?\n",
+    "- What variables do we have to look at?\n",
+    "- What are the long names of some of these variables? (HINT: try `ds_ctsm.TSOI`)\n",
+    "- What are other metadata are associated with this dataset? \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d081822-98b6-41e9-a364-0471a35b71d3",
+   "metadata": {},
+   "source": [
+    "### Let's start plotting!\n",
+    "\n",
+    "### Make a contour plot of soil moisture over time with depth on the y-axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878ee93c-086d-467d-b701-60d399235308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot H2OSOI\n",
+    "ds_ctsm.H2OSOI.plot(robust=True, y='levsoi')\n",
+    "# Invert the y-axis\n",
+    "plt.gca().invert_yaxis()\n",
+    "# Add the title\n",
+    "plt.title(neon_site)\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc12258b-d735-4498-86a5-36244053e14b",
+   "metadata": {},
+   "source": [
+    "### Plot vertical profiles of soil moisture \n",
+    "This example is looking at one month of data over the top 2m of soil. Depth is on the y axis, and the plot is reversed so deeper soil levels are at the bottom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8990cb0-3a57-425c-a9fa-80f02a9e2d02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot H20SOI at time 5; marker determines style of points\n",
+    "ds_ctsm.H2OSOI.isel(time=5).plot(y='levsoi',marker='o',ylim=(0,2))\n",
+    "# Invert y-axis\n",
+    "plt.gca().invert_yaxis()\n",
+    "# Add suptitle above plot title with time slice\n",
+    "plt.suptitle(neon_site)\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfc54627-1614-428f-b418-8379dd1ef0ee",
+   "metadata": {},
+   "source": [
+    "It seems odd that surface soil layers are wetter than deeper ones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df902a6-0cda-4588-9437-6b3ae8f23d8e",
+   "metadata": {},
+   "source": [
+    "### Plot a time series of soil moisture for a single soil level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8e0fa84-458d-4df1-aae2-8fbb38b118ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot H2OSOI at soil level 4 with dot markers at each point\n",
+    "ds_ctsm.H2OSOI.isel(levsoi=4).plot(marker='o')\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b6a0edb-015a-4fc1-bc07-aaeca72c8438",
+   "metadata": {},
+   "source": [
+    "#### \n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratualtions:</b> \n",
+    "    \n",
+    "You've now looked at some of the monthly output from CLM.\n",
+    "\n",
+    "What other sites or variables would you like to look at?\n",
+    "\n",
+    "Give it a shot, you can make lots of plots with all this data!\n",
+    "    \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d2c2fb4-3b0d-4502-beb9-b069ad70fc0a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_spinup.ipynb b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_spinup.ipynb
new file mode 100644
index 0000000..a2c9411
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/QuickPlot_CTSM_spinup.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51f96e25-587b-4108-a7d9-34666f7a50e6",
+   "metadata": {},
+   "source": [
+    "# Quick plots - CTSM - Spinup \n",
+    "## This notebook provides a method to check if spinup simulations have reached steady state.  \n",
+    "\n",
+    "In this tutorial you will find steps and instructions to:\n",
+    "\n",
+    "1. Load python libraries\n",
+    "2. Locate history files\n",
+    "3. Read in preprocesed data\n",
+    "4. Plot total ecosystem C over time\n",
+    "5. Determine if spinup simulations have reached steady state\n",
+    "\n",
+    "------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7edf783f-9442-4a1b-ab7c-e9455be3ac5b",
+   "metadata": {},
+   "source": [
+    "# 1. Load Datasets\n",
+    "\n",
+    "## 1.1 Load Python Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87882ecf-c37c-4b01-bfb4-24e56d671777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import nc_time_axis\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from glob import glob\n",
+    "from os.path import join\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "from neon_utils import fix_time_h0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "295e5476-c8b3-4a4d-ba7a-566def668673",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note:</b> If you are having difficulties importing `nc_time_axis`, run the following line once, then comment it out and restart the kernel by clicking on Kernel --> Restart Kernel in the top left panel in order to ensure the package is installed.  \n",
+    "    \n",
+    "> `pip install nc-time-axis`\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3039504-ae5d-4653-acac-44a402cef436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('xarray '+xr.__version__) # was working with 2023.5.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00d6e2bc-d747-41ee-a560-38bc3366fc8d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 1.2 Locate and read in history files "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db2ec48-9370-422a-a4e4-893bc39906e2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.2.1 Where are my simulation results?\n",
+    "After your simulations finish, history files are all saved in your `/scratch/NEON_cases/archive/` directory\n",
+    "\n",
+    "We can print the cases available to look at using bash magic, `%%bash` or `!` which turns the python cell block below into a bash cell.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acf88bc1-a2ab-4ae5-bfcb-232cc5d6ad82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "ls ~/scratch/NEON_cases/archive/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf652c-e127-4cb8-89cb-a4423b29f2b4",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> you can accomplish the same thing with the following code:\n",
+    "\n",
+    "> `!ls ~/scratch/NEON_cases/archive/`\n",
+    "    \n",
+    "</div>\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "472907c6-e236-4358-934a-df62fda235c9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.2.2 Point to the directory with history files \n",
+    "**We'll set the following:**\n",
+    "- site and experiment to look at\n",
+    "- path to our archive directory\n",
+    "- directory with input data (where history files are found)   \n",
+    "\n",
+    "By doing this more generally, it makes the script easier to modify for different sites."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd3302b7-e809-4dc6-aaab-ea1ad2d3aafe",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note</b> You can use data that you generated in other tutorials, or you can look at the example data. If you'd like to use example data, please uncomment the example archive directory line below.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e3bee8-fa6b-4c37-ae55-f62151640598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neon_site = 'WOOD'  # NEON site we're going to look at\n",
+    "experiment = '.BTRAN.ad' # Can set to ad, postad, or transient\n",
+    "\n",
+    "# If you would like to look at your own data, set the path to your archive directory\n",
+    "archive = '~/scratch/NEON_cases/archive'\n",
+    "\n",
+    "# If you would like to look at example data, set the path to this archive directory:\n",
+    "# archive = '/scratch/wwieder/NEON_cases/archive'\n",
+    "\n",
+    "# This expands the shortcut '~' used above\n",
+    "archive = os.path.realpath(os.path.expanduser(archive)) \n",
+    "\n",
+    "# Create a path to the data folder\n",
+    "data_folder = archive+'/'+neon_site+experiment+'/lnd/hist'\n",
+    "data_folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892d909c-8412-4b7b-93c2-94067adfad48",
+   "metadata": {},
+   "source": [
+    "**Is this the path for input data, `data_folder`, correct?** \n",
+    "\n",
+    "*HINT:* You can check in the terminal window or using bash magic.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d25741-5010-496f-b61a-f15e1c135989",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.2.3 Create some functions we'll use when opening the data\n",
+    "`preprocess_all` will limit the number of coordinate variables we're reading in. This is an xarray feature that helps save time (and memory resources)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f2a23fe-7273-4370-8f2c-fe3dfad5029e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define function to preprocess all variables from the netcdf files\n",
+    "# This just drops an unused coordinate variable (lndgrid) from the dataset\n",
+    "def preprocess_all (ds):\n",
+    "    ds_new= ds.isel(lndgrid=0) \n",
+    "    return ds_new"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3919e8c9-4c08-485f-9e01-8c42f6b5ddad",
+   "metadata": {},
+   "source": [
+    "Now we have created the functions needed to manipulate our datasets\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adcff485-79f9-4b32-af43-ed07ea189b2b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.2.4 List all the files we're going to open\n",
+    "The the monthly history output (**'h0' files**) are written out for NEON cases. \n",
+    "\n",
+    "To open all of these files we're going to need to know their names.  This can be done if we:\n",
+    "- Create an empty list `[]` of simulation files that is\n",
+    "- `.extend`ed with a \n",
+    "- `sorted` list of files generted with the \n",
+    "- `glob` function in python of the \n",
+    "- `*h0*`files in our `data_folder` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617ea958-499e-4dda-92da-8e531b152fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an empty list of all the file names to extend\n",
+    "sim_files = []\n",
+    "sim_files.extend(sorted(glob(join(data_folder,\"*h0.*.nc\"))))\n",
+    "\n",
+    "print(\"All simulation files for all years: [\", len(sim_files), \"files]\")\n",
+    "print(sim_files[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "566f28f0-8a9b-4e04-9a1c-3052c70175bb",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d280416-00b4-431d-944a-bb2ca7b4cceb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### 1.2.5 Read in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aee5a387-f155-4fe8-a6e8-bfe2f95a2f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "print (\"Reading in data for \"+neon_site)\n",
+    "\n",
+    "ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, combine='by_coords',\n",
+    "                            preprocess=preprocess_all)\n",
+    "\n",
+    "end = time.time()\n",
+    "print(\"Reading all simulation files took:\", end-start, \"s.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbad64f5-88ef-402b-8fdd-3610fde4766d",
+   "metadata": {},
+   "source": [
+    "### 1.2.6 Print the dataset you're working with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cda5d25-ce4b-419c-8dd5-e44f248e7604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10b5969d-f298-4c6f-a554-cfa4993602e2",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2be98dc-b543-419c-a2cf-653adfc0b536",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 1.3 Plot total ecosystem C over time\n",
+    "We can visually check to see if the simulation achieved steady-state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878ee93c-086d-467d-b701-60d399235308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ctsm.TOTECOSYSC.plot()\n",
+    "plt.title(neon_site+experiment) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df902a6-0cda-4588-9437-6b3ae8f23d8e",
+   "metadata": {},
+   "source": [
+    "**LOOK**\n",
+    "- Does the model seem to have achieved steady state?\n",
+    "- Why are there periodic oscillations in the total ecosystem C pools?\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratulations:</b> \n",
+    "    \n",
+    "You've quickly looked at total ecosystem carbon stocks to check for steady state. \n",
+    "    \n",
+    "Are there other variables would you like to look at?\n",
+    "    \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d2c2fb4-3b0d-4502-beb9-b069ad70fc0a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/customizeCase_PRISM.ipynb b/notebooks/SinglePoint/ProjectExamples/customizeCase_PRISM.ipynb
new file mode 100644
index 0000000..9fe9f52
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/customizeCase_PRISM.ipynb
@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Customize your case: PRISM precipitation\n",
+    "We set up a special way to run_neon with PRISM precipitation data.\n",
+    "\n",
+    "**This is an optional tutorial** it's a little bit more advanced, but it will help you think about how to modify the model configuration to run new sites or model experiments.  \n",
+    "\n",
+    "The `run_neon` script we used in the [introductory tutorial 1b](1b_NEON_Simulation_Intro.ipynb) created and ran a base case as well as a `.transient` case.   Here we'll also be adding an experimental `.PRISM.transient` case that reads in an alternative precipitation dataset from PRISM. \n",
+    "\n",
+    "---\n",
+    "\n",
+    "## In this tutorial\n",
+    "\n",
+    "The tutorial has several components. Below you will find steps to: \n",
+    "1. Set up and run a simulation\n",
+    "\n",
+    "*Extra credit* \n",
+    "\n",
+    "2. Create another clone for an experiment you're hoping to do."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login, you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "***\n",
+    "\n",
+    "#### Set up your environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations.\n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp -r /opt/ncar/ctsm ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> 1. Create a new case </h1>\n",
+    "\n",
+    "## 1.1 Navigate to your source code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Create a new case with `run_neon.py`\n",
+    "\n",
+    "\n",
+    "The following code will:\n",
+    "- create a new case\n",
+    "- make a few modifications\n",
+    "- submit the simulation\n",
+    "\n",
+    "**REMEMBER** PRISM cases can only be run for NEON sites in the lower 48 states (HI, AK, and PR won't work)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "\n",
+    "export neon_site='KONZ'   # This should likely be a site you've already run.\n",
+    "export experiment='PRISM'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $neon_site  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --experiment $experiment \\\n",
+    "   --setup-only \\\n",
+    "   --overwrite \\\n",
+    "   --prism"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since this doesn't always render correctly on websites, here the code from above\n",
+    "\n",
+    "```\n",
+    "./run_neon.py --neon-sites $neon_site  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --experiment $experiment \\\n",
+    "   --setup-only \\\n",
+    "   --overwrite \\\n",
+    "   --prism\n",
+    "```\n",
+    "Two new flags are being used here:\n",
+    "- `--experiment` adds an additional string to our case name\n",
+    "- `--setup-only` creates the case without running it\n",
+    "- `--overwrite` will let you overwrite an existing case.  \n",
+    "- `--prism` configures the case to use PRISM precipitation data  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "# 2. Introduction to controling case configuration\n",
+    "\n",
+    "## 2.1 Setup and Build\n",
+    "First, we'll setup and build our case manually\n",
+    "\n",
+    "NOTE: As before, this takes some time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/${neon_site}.${experiment}.transient\n",
+    "./case.setup\n",
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 What's different with my case?\n",
+    "XML files are used to control how cases are configured \n",
+    "\n",
+    "Before digging in too we can start to see how our PRISM.transient case is different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff env_run.xml ../${neon_site}.PRISM.transient/env_run.xml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are the datm.streams for the PRISM.transient case, we'll just look at the end of the file\n",
+    "cat CaseDocs/datm.streams.xml | tail -26"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**What are the differences between these two cases** based on their `env_run.xlm` and `datm.streams.xml` files?\n",
+    "\n",
+    "Where does the KONZ.transient case get it's precipitation data from?\n",
+    "\n",
+    "These .xml changes set up high-level control over how your simulation is run.  What are some of the specifics related to the land model?  We can see this by looking at our `lnd_in` file.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 2.2 `lnd_in` \n",
+    "\n",
+    "**What are the differences between these two cases**, based on their `lnd_in` files?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | grep finidat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is not an initial conditions file from a PRISM simulation. We'll have to change this.\n",
+    "\n",
+    "<style> \n",
+    "table td, table th, table tr {text-align:left !important;}\n",
+    "</style>\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>REMEMBER:</b>\n",
+    "    \n",
+    "- The <i>lnd_in</i> file provides a high level summary of all the name list chagnes and files that are being used by CLM. \n",
+    "- It can be found in the CaseDocs directory, or in your run directory. \n",
+    "- You cannot directly modifiy the <i>lnd_in file</i>, instead users can modify <i>user_nl_clm</i>.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "**Initial conditions dataset:** `finidat`\n",
+    "  - These are initial conditions files that we created by spinning up the model.  \n",
+    "  - Spin up requires starting the model from bare ground conditions (we call it a *coldstart*).\n",
+    "  - Spin up takes a few hundred years of simulations so that ecosystem carbon and nitrogen pools acheive steady state conditions (e.g. average net ecosystem exchange equals zero).  \n",
+    "  - Since this takes a long time, we provide initial conditions for you to start from.\n",
+    "  - **This also means that if you change model parameterizations, input data, or anything else you ahve to spin up the model again!** \n",
+    "\n",
+    "---\n",
+    "\n",
+    "The `lnd_in` files are controlled by `user_nl_clm`.  We have to modify our user_nl_clm to point to the right initial conditions data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3 Submit the case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "echo \"finidat='/scratch/data/NEONv2/PRISM.restart/$neon_site.postad.clm2.r.0318-01-01-00000.nc'\" >> user_nl_clm\n",
+    "\n",
+    "#Then check it worked as intended\n",
+    "./preview_namelists\n",
+    "\n",
+    "cat CaseDocs/lnd_in | grep finidat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check to see taht your case is running \n",
+    "qstat -u $USER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseStatus | tail -10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Create an experimental clone:\n",
+    "\n",
+    "<h4> This step is optional, but provides helpful information that you may use in your own workflow </h4>\n",
+    "\n",
+    "So far, everything we've done has been *out of the box* looking at different NEON sites, but without changing anything in the underlying model code.  You may want to do model experiments where you alter the vegetation growing at a site, modify some of the model parameters, modify namelist settings, change the input data, or even alter model code.  We'll get into how do make these changes later, but for now we'll get a test case set up.\n",
+    "\n",
+    "Since we're already run an out of the box case for KONZ, we can create a paired experimental case at the same site.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" markdown='1'>\n",
+    "<b>RECOMMENDATION:</b> use a short, descriptive name for your experiment, it will help you down the road.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "This example below just builds on what you've already been doing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "export new_case='CPER'    # the new site you want to run\n",
+    "\n",
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases/ \\\n",
+    "   --base-case ~/scratch/NEON_cases/$neon_site.$experiment.transient \\\n",
+    "   --experiment $experiment \\\n",
+    "   --setup-only \\\n",
+    "   --overwrite \\\n",
+    "   --prism"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\" markdown='1'>\n",
+    "\n",
+    "<b>WARNING:</b> Because we're also using the `--base-case` flag, we won't have to rebuild our experimental case.  This may not be advisable if you're modifying model code.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "At this point your experimental case has been created.  \n",
+    "- What is the case name?\n",
+    "- Can you navigate to your case directory?\n",
+    "- Are there any differences between this experimental case and the base case you already ran?\n",
+    "- Can you find the datm input data for your case?\n",
+    "- Is the model going to use an initial conditions file?\n",
+    "- Where is the surface dataset that's being used?\n",
+    "- Can you find the parameter file for your case?\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Now we'll point to the correct restart file and submit the case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.transient\n",
+    "echo \"finidat='/scratch/data/NEONv2/PRISM.restart/$new_case.postad.clm2.r.0318-01-01-00000.nc'\" >> user_nl_clm\n",
+    "\n",
+    "#Then check it worked as intended\n",
+    "./preview_namelists\n",
+    "\n",
+    "cat CaseDocs/lnd_in | grep finidat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | grep finidat\n",
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created and run an experimental case that uses PRISM precipitation data instead of NEON precipitation\n",
+    "    .\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/customizeCase_modelFeatures.ipynb b/notebooks/SinglePoint/ProjectExamples/customizeCase_modelFeatures.ipynb
new file mode 100644
index 0000000..431f970
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/customizeCase_modelFeatures.ipynb
@@ -0,0 +1,840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Customize your case: Controling model features\n",
+    "## *Customizing your case with namelist changes & spinning up the model*\n",
+    "\n",
+    "**This is an optional tutorial** and provides some additional guidance for creating an experimental clone for a NEON case in CTSM and spinning up the model.\n",
+    "\n",
+    "We're going to move kind of quickly through some material that's already covered in the tutorial on [cloning a NEON case](./1c_NEON_Clone_Case.ipynb). It may be helpful to refer back to this tutorial if you have questions or want more information about creating an experimental clone.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "---\n",
+    "## In this tutorial\n",
+    "\n",
+    "1. Create a new base case for a NEON site that you've already run.\n",
+    "2. Customize your case with namelist changes.\n",
+    "3. Learn a bit more about spinup. \n",
+    "\n",
+    "---\n",
+    "### First let's start with a bit of background.\n",
+    "CLM has different features that can be turned on or off with namelist changes.  Some of these features represent alternative hypotheses about how different aspects of how the land works. The different options have alternative model structure and parameterizations for particular processes that are controlled with namelist flags. These include:\n",
+    "- FATES vs. 'BigLeaf' vegetation\n",
+    "- Medlyn vs. Ball-Berry stomatal conductance\n",
+    "- CENTURY vs. MIMICS soil biogeochemistry \n",
+    "- Different soil moisture stress formulations (below)\n",
+    "- an lots more\n",
+    "\n",
+    "*Why would you want to use this?*\n",
+    "\n",
+    "We noticed that soil moisture profiles in CLM have an odd feature where soil moisture is higher in surface soils than in the sub surface.  Why would this happen?\n",
+    "![moisture plot](../../images/WOOD_H2OSOI.png)\n",
+    "\n",
+    "Plant hydraulic stress (or **PHS**) was introduced in CLM5.  You can [learn more about PHS in this paper](https://doi.org/10.1029/2018MS001500), where Daniel Kennedy and co-authors mentioned hydraulic redistribution of soil water by plant roots. This happens in the real world too and is a pretty neat feature of PHS. Could hydraulic redistribution also be keeping surface soils in CLM too wet? To address this question we'll do a simple model experiment where we turn off PHS. This will revert back to the **BTRAN** formulation of soil moisture stress that was used in previous versions of CLM. \n",
+    "\n",
+    "We can turn of PHS with a simple namelist modification, but since this will change answers in the model, we also need to spin up the model again to generate new initial conditions with PHS off. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login, you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Set up environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations. \n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud, you only have to do this once.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cp -rp /opt/ncar/ctsm/ ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Create a new case.\n",
+    "Because we're going to modify namelist options that will change answers in CLM we have to run this case through an accelerated decompostion (AD) and postAD spinup.  This will generate new initial conditions for our experimental case. We'll learn more about spinup in part 3 of this tutorial.\n",
+    "\n",
+    "Doing an AD spinup requires a new base case to be created so the model is configured correctly.  This means we have to create and build a new case for both AD and postAD simulations.  This process is streamlined with the `run_neon` script and the use of usermod directories we created for NEON cases.  \n",
+    "\n",
+    "## 1.1 Navigate to your source code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Create a new base case.\n",
+    "You can modify the new case for the site you'd like to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If needed, change the 4-character NEON site below.\n",
+    "export new_case='WOOD'    # the new site you want to run \n",
+    "export experiment='BTRAN'    # the experimental name of your case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then run_neon with the following flags:\n",
+    "- `--experiment $experiment` \n",
+    "- `--run-type ad` \n",
+    "- `--setup-only` \n",
+    "\n",
+    "Since we're creating an experimental case that will change answers in the model we should run a full AD and postAD spinup.\n",
+    "We'll go over specifics related to experiment and run-type later in this tutorial.\n",
+    "\n",
+    "In our experimental case we'll be using the BTRAN formulation of soil moisture stress. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>HINT:</b> It's helpful to have descriptive experiment names. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then run_neon with experiment and setup-only flags\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type ad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.1 Build the base case\n",
+    "You may have noticed a warning in the code block above.  We'll go ahead and build the new base case above.  As before this takes some time so be patient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.2 Overwrite the *site.experiment.ad* case, now using the correct build\n",
+    "- move back to your source code\n",
+    "- run_neon again "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$new_case \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type ad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may notice the following was printed from creating an experimental clone\n",
+    "```\n",
+    "WARNING: CLM is starting up from a cold state\n",
+    "```\n",
+    "This is good, since we're going to be starting the model from a cold state, or bare ground with the AD spinup.\n",
+    "\n",
+    "### 1.2.3 Move to the case directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.ad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Customizing your case: Namelist changes\n",
+    "The configuration of CLM can be customized via namelist modifications. \n",
+    "\n",
+    "These changes made using namelist files like `user_nl_clm`\n",
+    "\n",
+    "## 2.1 Getting familiar with namelist files\n",
+    "`user_nl_<model>` files are created in the case directory after setting up your case with `./case.setup`\n",
+    "  - Note: This was already done for us since we cloned a case with run_neon\n",
+    "    \n",
+    "#### Take a look!\n",
+    "  - What other user `user_nl_<model>` files do you already have in this case directory?\n",
+    "  - What kind of information is already included in a `user_nl_clm` file?\n",
+    "\n",
+    "We can explore these files on the comand line using an editor, by opening them directly in jupyter hub, or by simply printing the contents of the files to the screen here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ls user_nl_*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cat user_nl_clm | tail -18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compset that you created your case with sets up initial, or default namelist options. These can be found in `CaseDocs/lnd_in`\n",
+    "\n",
+    "<style> \n",
+    "table td, table th, table tr {text-align:left !important;}\n",
+    "</style>\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Important:</b>   Don’t modify the lnd_in namelist file directly. Instead, make changes in user_nl_clm.\n",
+    "</div>\n",
+    "\n",
+    "## 2.2 Looking deeper at namelist options \n",
+    "All CLM namelist options are defined in the `lnd_in` file\n",
+    "\n",
+    "We can explore this file on the comand line using an editor, by opening it directly in jupyter hub, or by simply printing the contents of the files to the screen here.  \n",
+    "- **NOTE:** This file is huge, so we'll just look at the first 65 lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | head -65"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's a lot to dig into here! So we'll just stick with a few highlights.\n",
+    "\n",
+    "#### Take a look\n",
+    "**See what printed to in the cell above and answer the following questions**\n",
+    "- `finidat`: What initial conditions file are you using? \n",
+    "- `fsurdat`: What surface dataset are you using?\n",
+    "- `paramfile`: What parameter file are you using?\n",
+    "- `spinup_state`: What is your spinup state?\n",
+    "- `use_hydrstress`: Are you using plant hydraulic stress?\n",
+    "\n",
+    "Does this make sense?  Answers are in the hidden cell below.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "- `finidat = ' '`  There are no initial conditions because this is an AD run\n",
+    "- `fsurdat = \".../surfdata_1x1_NEON_WOOD_hist_78pfts_CMIP6_simyr2000_c230111.nc\"` This the surface dataset that's been modified for our NEON site.\n",
+    "- `paramfile = '.../ctsm51_params.c211112.nc'` is the default parameter file for CTSM5.1  \n",
+    "- `spinup_state = 2`, which is used for accelerated decomposition, or AD mode\n",
+    "- `use_hydrstress = .true.`, because plant hydraulic stress is on by default in CTSM5.1 \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "***\n",
+    "\n",
+    "- Additional information about customizing CTSM's configurations, including namelist modifications, are available in the [CTSM users guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/customizing-the-clm-configuration.html?highlight=namelist)\n",
+    "Namelist changes can also be used to modify variables on history file output. Simple modification of history file output DOES NOT require spinning up the model.\n",
+    "- A list of all the [CTSM history fields are available here](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_nofates.html)\n",
+    "- A list of all the [CTSM-FATES history fields are available here](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_fates.html)\n",
+    "\n",
+    "## 2.3 Turn off plant hydraulic stress\n",
+    "\n",
+    "For this experiment we're going turn plant hydraulic stress off. We can change user_nl_clm using an editor, by opening it directly in jupyter hub, or with the following comand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "echo \"use_hydrstress = .false.\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let make sure this worked.  We can:  \n",
+    "- preview namelists;\n",
+    "- check the  `lnd_in` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## now make sure it worked \n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | head -65"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've turned off PHS in simulation \n",
+    "\n",
+    "## 2.4 Start the AD spinup run\n",
+    "We already build the model, and when making namelist or .xml changes we don't have to rebuild our case, so now we're ready to submit the case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now our case is submitted!  Spinup, or model initialization from a cold start, actually takes a little bit of time.  Now that our AD case is running, we can briefly cover spinup.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# 3 More details on spinup\n",
+    "\n",
+    "We make make steady state assumptions about *initial* state of ecosystem properties like temperature\n",
+    "water, snow, ice, carbon & nitrogen.  This is the *equilibrium* state of the model, given the forcing\n",
+    "data. In model experiments  we make modifications to namelist settings, the parameter files, surface dataset, or underlying model code we also need to generate new initial conditions. \n",
+    "\n",
+    "In runs with active biogoechemistry, like these NEON simulations, this means we need to get the ecosystem carbon and nigrogen pools with long turnover times into steady state. The turnover and decomposition of these slow pools is mathamatically accelerated in our AD (accelerated decomposition) case.  This means we:\n",
+    "- Accelerate turnover of wood, litter and soil pools.\n",
+    "- Accelerate advection and diffusion terms too\n",
+    "- In CLM5 and CTSM5.1 this is calculated as a function of latitude so that spinup is more accelerated in high latitude regions.\n",
+    "\n",
+    "During spinup, we just cycle over several years of input data.  For most NEON sites we cycle over four years of data from 2018-2021. More information about spinup is available in the [CLM user's guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/running-special-cases/Spinning-up-the-biogeochemistry-BGC-spinup.html?highlight=spinup) \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>NOTE:</b> for runs without active biogeochemistry (called satelite shenology, or SP simulations), we still need to equilibrate the model. This is much faster and doesn't require accelerated decomposition.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For most temperate systems < 200 years of AD simulations seems adequate, but in colder ecosystems (like NEON sites in Alaska) this can take much longer because cold temperatures slow the turnover of \n",
+    "soil organic matter.  We have this set up already in the usermod directories that are used to configure your NEON case with `run_neon`. We can look to see if total ecosystem carbon pools (the sum of all vegetation, litter and soil C stocks) look to be equilibrated after 200 years.\n",
+    "\n",
+    "![ad_spinup](../../images/WOOD.BTRAN.ad_spin.png)\n",
+    "\n",
+    "#### LOOK\n",
+    "- Does the model seem to have achieved steady state?\n",
+    "- Why are there periodic oscillations in the total ecosystem C pools?\n",
+    "\n",
+    "This plot was generated using the [QuickPlot_CTSM_spinup](QuickPlot_CTSM_spinup.ipynb) notebook, which you can modify for your own site or simulation.\n",
+    "\n",
+    "After your AD simulation is finished, we have to take the model out of AD mode.  We call these postAD simulations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 postAD simulations\n",
+    "After the model has reached steady-state we have to take it out of AD mode, which requires another ~100 years of simulations.  We also have to create and build a new base case for postAD runs.\n",
+    "\n",
+    "### 3.1.1 Create a new base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "# This is a new base case that we'll create (and maybe use later for other sites)\n",
+    "export base_case='YELL'    \n",
+    "# should match the neon site case you did the AD run with\n",
+    "export new_case='WOOD'   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./run_neon.py --neon-sites $base_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --overwrite \\\n",
+    "   --run-type postad \\\n",
+    "   --setup-only "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll notice an error here if there's no ad run for our postad base_case.  This is fine. \n",
+    "\n",
+    "### 3.1.2 Build the base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$base_case\n",
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.3 Create the site.experiment.postad case from the built base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type postad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.4 make the same namelist changes to turn off PHS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.postad\n",
+    "echo \"use_hydrstress = .false.\" >> user_nl_clm\n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the lnd_in file\n",
+    "cat CaseDocs/lnd_in | grep use_hydrstress"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Stop and check!**\n",
+    "- How are the `lnd_in` files different in our ad and postad cases?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.ad/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the postad case has: \n",
+    "- `finidat = 'WOOD.BTRAN.ad...'` initial conditions\n",
+    "- `spinup_state = 0` meaning we're out of ad mode now\n",
+    "- different builds\n",
+    "\n",
+    "\n",
+    "### 3.1.5 Submit the postad simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Have a look to see if 100 years is enough to get pools to steady state? \n",
+    "\n",
+    "![postad](../../images/WOOD.BTRAN.postad.png)\n",
+    "\n",
+    "#### LOOK\n",
+    "- In postad simulations ecosystem carbon and nitrogen are much larger than in the ad case.  \n",
+    "- Does the model seem to have achieved steady state?\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 3.2 Transient simulations\n",
+    "Now we're ready to go ahead with the transient simulation\n",
+    "\n",
+    "### 3.2.1 Create an experimental transient case\n",
+    "- your `base_case` can now be a built transient case (here KONZ from our first tutorials)\n",
+    "- `--run-from-postad` let's us get initial conditions files from our experiment.postad case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "export base_case='KONZ'   # this should be a built, transient case\n",
+    "export new_case='WOOD'    # the new site you want to run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --experiment $experiment \\\n",
+    "   --overwrite \\\n",
+    "   --run-type transient \\\n",
+    "   --setup-only  \\\n",
+    "   --run-from-postad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Namelist changes to turn off PHS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.transient\n",
+    "echo \"use_hydrstress = .false.\" >> user_nl_clm\n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the lnd_in file\n",
+    "cat CaseDocs/lnd_in | grep use_hydrstress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How are the lnd_in fies different?\n",
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.postad/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're running: \n",
+    "- a transient simulation (transient CO2, nitrogen deposition, etc\n",
+    "- with different restart files, not from the postad run we already did\n",
+    "- with different builds\n",
+    "\n",
+    "### 3.2.3 Submit the transient case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Run a control case\n",
+    "If you haven't done it already, we should likely just run a control case for a transient case out of the box (with PHS on).\n",
+    "Lucking this is really easy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "    --output-root ~/scratch/NEON_cases \\\n",
+    "    --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "    --overwrite\n",
+    "\n",
+    "# we'll just go ahead and submit this case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Double check\n",
+    "It's a good idea to make sure cases have expected differences.  \n",
+    "\n",
+    "See if this is true for our control and experimental transient cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.transient\n",
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.transient/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created and run a full spinup for an experimental case of CLM at the NEON tower you selected!\n",
+    "    \n",
+    "Now we need to see how the simulations are different.\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're developing this tutorial:\n",
+    "Before saving and pushing this code to github go to `Kernel` and `Restart kernel and clear all outputs...`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/customizeCase_parameterModifications.ipynb b/notebooks/SinglePoint/ProjectExamples/customizeCase_parameterModifications.ipynb
new file mode 100644
index 0000000..dfcb867
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/customizeCase_parameterModifications.ipynb
@@ -0,0 +1,800 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Customize your case: Parameter modifications\n",
+    "## *Customizing your case with namelist changes & spinning up the model*\n",
+    "\n",
+    "**This is an optional tutorial** that provides some additional guidance for creating an experimental clone for a NEON case in CTSM and spinning up the model.\n",
+    "\n",
+    "We're going to move kind of quickly through some material that's already covered in the tutorial on [cloning a NEON case](./1c_NEON_Clone_Case.ipynb).  We're also going to quickly overview spinning up the model, but there's additional information in the tutorial on [customizing model features](./customizeCase_modelFeatures.ipynb)It may be helpful to refer back to these tutorials if you have questions or want more information about creating an experimental clone or spinup.\n",
+    "\n",
+    "Finally, this tutorial assumes that you've modified the CLM parameter file.  We provided an example notebook for [modifying parameters](./modifyParameterFile.ipynb).  **This should be done before running the rest of this notebook.**\n",
+    "<br>\n",
+    "\n",
+    "---\n",
+    "## In this tutorial\n",
+    "\n",
+    "1. Create a new base case for a NEON site that you've already run.\n",
+    "2. Point to a modified parameter file in user_nl_clm.\n",
+    "3. Spinup the model. \n",
+    "\n",
+    "---\n",
+    "### First let's start with a bit of background.\n",
+    "\n",
+    "Parameter files are read into CLM.  We provide a default parameter file for your cases, but this can be modified by pointing to a new parameter file in user_nl_clm.  \n",
+    "\n",
+    "Since changing parameters in the model will change answers, we also need to spin up the model to generate new initial conditions files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>NOTICE:</b> If you're running this notebook through the NCAR JupyterHub login, you need to be on a Cheyenne login node (NOT Casper).  \n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "### Set up your environment\n",
+    "It is important in order to have all the tools and packages you need to run simulations. \n",
+    "\n",
+    "The following code **is needed** if you're running in CESM-lab in the cloud, but should already be set up correctly for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cp -rp /opt/ncar/ctsm/ ~/CTSM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*The code below **is not** needed in the cloud*.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "   \n",
+    "<b>TIP:</b> <i>If you're running on Cheyenne</i>, you may need to uncomment the the following two lines of code. This will set up your conda environment correctly.\n",
+    "\n",
+    "<b>This is not required if your running CESM-Lab in the cloud.</b>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#module purge\n",
+    "#module load conda ncarenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Create a new case.\n",
+    "Because we're going to modify namelist options that will change answers in CLM we have to run this case through an accelerated decompostion (AD) and postAD spinup.  This will generate new initial conditions for our experimental case. We'll learn more about spinup in part 3 of this tutorial.\n",
+    "\n",
+    "Doing an AD spinup requires a new base case to be created so the model is configured correctly.  This means we have to create and build a new case for both AD and postAD simulations.  This process is streamlined with the `run_neon` script and the use of usermod directories we created for NEON cases.  \n",
+    "\n",
+    "## 1.1 Navigate to your source code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Create a new base case.\n",
+    "You can modify the new case for the site you'd like to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If needed, change the 4-character NEON site below.\n",
+    "export new_case='HARV'    # the new site you want to run \n",
+    "export experiment='foliarCN-30'    # this is name of your experimenal case "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then run_neon with the following flags:\n",
+    "- `--experiment $experiment` \n",
+    "- `--run-type ad` \n",
+    "- `--setup-only` \n",
+    "\n",
+    "Since we're creating an experimental case that will change answers in the model we should run a full AD and postAD spinup.\n",
+    "We'll go over specifics related to experiment and run-type later in this tutorial.\n",
+    "\n",
+    "In our experimental case we'll be using the BTRAN formulation of soil moisture stress. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "\n",
+    "<b>HINT:</b> It's helpful to have descriptive experiment names. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then run_neon with experiment and setup-only flags\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type ad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.1 Build the base case\n",
+    "You may have noticed a warning in the code block above.  We'll go ahead and build the new base case above.  \n",
+    "\n",
+    "*As before this takes some time so be patient.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.2 Overwrite the *site.experiment.ad* case, now using the correct build\n",
+    "- move back to your source code\n",
+    "- run_neon again "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$new_case \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type ad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may notice the following was printed from creating an experimental clone\n",
+    "```\n",
+    "WARNING: CLM is starting up from a cold state\n",
+    "```\n",
+    "This is good, since we're going to be starting the model from a cold state, or bare ground with the AD spinup.\n",
+    "\n",
+    "### 1.2.3 Move to the case directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.ad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Customizing your case: Namelist changes\n",
+    "The configuration of CLM can be customized via namelist modifications. \n",
+    "\n",
+    "These changes made using namelist files like `user_nl_clm`\n",
+    "\n",
+    "## 2.1 Getting familiar with namelist files\n",
+    "`user_nl_<model>` files are created in the case directory after setting up your case with `./case.setup`\n",
+    "  - Note: This was already done for us since we cloned a case with run_neon\n",
+    "    \n",
+    "#### Take a look!\n",
+    "  - What other user `user_nl_<model>` files do you already have in this case directory?\n",
+    "  - What kind of information is already included in a `user_nl_clm` file?\n",
+    "\n",
+    "We can explore these files on the comand line using an editor, by opening them directly in jupyter hub, or by simply printing the contents of the files to the screen here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ls user_nl_*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cat user_nl_clm | tail -18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compset that you created your case with sets up initial, or default namelist options. These can be found in `CaseDocs/lnd_in`\n",
+    "\n",
+    "<style> \n",
+    "table td, table th, table tr {text-align:left !important;}\n",
+    "</style>\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Important:</b>   Don’t modify the lnd_in namelist file directly. Instead, make changes in user_nl_clm.\n",
+    "</div>\n",
+    "\n",
+    "## 2.2 Looking deeper at namelist options \n",
+    "All CLM namelist options are defined in the `lnd_in` file\n",
+    "\n",
+    "We can explore this file on the comand line using an editor, by opening it directly in jupyter hub, or by simply printing the contents of the files to the screen here.  \n",
+    "- **NOTE:** This file is huge, so we'll just look at the first 65 lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | head -40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's a lot to dig into here! So we'll just stick with a few highlights.\n",
+    "\n",
+    "#### Take a look\n",
+    "**See what printed to in the cell above and answer the following questions**\n",
+    "- `finidat`: What initial conditions file are you using? \n",
+    "- `fsurdat`: What surface dataset are you using?\n",
+    "- `paramfile`: What parameter file are you using?\n",
+    "- `spinup_state`: What is your spinup state?\n",
+    "\n",
+    "Does this make sense?  Answers are in the hidden cell below.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "- `finidat = ' '`  There are no initial conditions because this is an AD run\n",
+    "- `fsurdat = \".../surfdata_1x1_NEON_WOOD_hist_78pfts_CMIP6_simyr2000_c230111.nc\"` This the surface dataset that's been modified for our NEON site.\n",
+    "- `paramfile = '.../ctsm51_params.c211112.nc'` is the default parameter file for CTSM5.1  \n",
+    "- `spinup_state = 2`, which is used for accelerated decomposition, or AD mode\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "\n",
+    "- Additional information about customizing CTSM's configurations, including namelist modifications, are available in the [CTSM users guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/customizing-the-clm-configuration.html?highlight=namelist)\n",
+    "Namelist changes can also be used to modify variables on history file output. Simple modification of history file output DOES NOT require spinning up the model.\n",
+    "- A list of all the [CTSM history fields are available here](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_nofates.html)\n",
+    "- A list of all the [CTSM-FATES history fields are available here](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/setting-up-and-running-a-case/master_list_fates.html)\n",
+    "\n",
+    "## 2.3 Point to your modified parameter file\n",
+    "\n",
+    "For this experiment we're changing the foliar C:N ratio for temperate deciduous forests.  Do do this we have to point to our modified parameter file. We can change user_nl_clm using an editor, by opening it directly in jupyter hub, or with the following comand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the path to the modified parameter file you created\n",
+    "\n",
+    "# If you're running on cheyenne, uncomment the line below\n",
+    "#echo \"paramfile = '/glade/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm\n",
+    "\n",
+    "# If you're running with CESM-Lab in the cloud, uncomment the line below\n",
+    "echo \"paramfile = '/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let make sure this worked.  We can:  \n",
+    "- preview namelists;\n",
+    "- check the  `lnd_in` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## now make sure it worked \n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat CaseDocs/lnd_in | head -40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've changes ethe parameter file that's being used for our simulation.\n",
+    "\n",
+    "## 2.4 Start the AD spinup run\n",
+    "We already build the model, and when making namelist or .xml changes we don't have to rebuild our case, so now we're ready to submit the case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now our case is submitted!  Spinup, or model initialization from a cold start, actually takes a little bit of time.  Check that your AD case is running "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qstat -u $USER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tail  ~/scratch/NEON_cases/$new_case.$experiment.ad/CaseStatus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 postAD simulations\n",
+    "After the model has reached steady-state we have to take it out of AD mode, which requires another ~100 years of simulations.  We also have to create and build a new base case for postAD runs.\n",
+    "\n",
+    "### 3.1.1 Create a new base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "# This is a new base case that we'll create (and maybe use later for other experiments)\n",
+    "export base_case='YELL'    \n",
+    "# should match the neon site case you did the AD run with\n",
+    "export new_case='HARV'   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./run_neon.py --neon-sites $base_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --overwrite \\\n",
+    "   --run-type postad \\\n",
+    "   --setup-only "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll notice an error here if there's no ad run for our postad base_case.  This is fine. \n",
+    "\n",
+    "### 3.1.2 Build the base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$base_case\n",
+    "qcmd -- ./case.build"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.3 Create the site.experiment.postad case from the built base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --overwrite \\\n",
+    "   --experiment $experiment \\\n",
+    "   --run-type postad \\\n",
+    "   --setup-only  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.4 make the same namelist changes to point to your new parameter file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.postad\n",
+    "\n",
+    "# This is the path to the modified parameter file you created\n",
+    "\n",
+    "# If you're running on cheyenne, uncomment the line below\n",
+    "#echo \"paramfile = '/glade/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm\n",
+    "\n",
+    "# If you're running with CESM-Lab in the cloud, uncomment the line below\n",
+    "echo \"paramfile = '/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm\n",
+    "\n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the lnd_in file\n",
+    "cat CaseDocs/lnd_in | grep paramfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Stop and check!**\n",
+    "- How are the `lnd_in` files different in our ad and postad cases?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.ad/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the postad case has: \n",
+    "- `finidat = 'HARV.foliarCN-30.ad...'` initial conditions\n",
+    "- `spinup_state = 0` meaning we're out of ad mode now\n",
+    "- different builds\n",
+    "\n",
+    "\n",
+    "### 3.1.5 Submit the postad simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Transient simulations\n",
+    "Now we're ready to go ahead with the transient simulation\n",
+    "\n",
+    "### 3.2.1 Create an experimental transient case\n",
+    "- your `base_case` can now be a built transient case (here KONZ from our first tutorials)\n",
+    "- `--run-from-postad` let's us get initial conditions files from our experiment.postad case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the 4-character NEON site below.\n",
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "export base_case='KONZ'   # this should be a built, transient case\n",
+    "export new_case='HARV'    # the new site you want to run\n",
+    "\n",
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "   --output-root ~/scratch/NEON_cases \\\n",
+    "   --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "   --experiment $experiment \\\n",
+    "   --overwrite \\\n",
+    "   --run-type transient \\\n",
+    "   --setup-only  \\\n",
+    "   --run-from-postad\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Namelist changes to point to your new parameter file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.$experiment.transient\n",
+    "\n",
+    "# This is the path to the modified parameter file you created\n",
+    "\n",
+    "# If you're running on cheyenne, uncomment the line below\n",
+    "#echo \"paramfile = '/glade/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm\n",
+    "\n",
+    "# If you're running with CESM-Lab in the cloud, uncomment the line below\n",
+    "echo \"paramfile = '/scratch/$USER/NEON_cases/modified_inputs/ctsm51_params.c211112_tdf_leafcn30.nc'\" >> user_nl_clm\n",
+    "\n",
+    "./preview_namelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the lnd_in file\n",
+    "cat CaseDocs/lnd_in | grep paramfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How are the lnd_in fies different?\n",
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.postad/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're running: \n",
+    "- a transient simulation (transient CO2, nitrogen deposition, etc)\n",
+    "- with different restart files, not from the postad run that are prestaged from default NEON cases\n",
+    "- with different builds\n",
+    "\n",
+    "### 3.2.3 Submit the transient case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "./case.submit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Run a control case\n",
+    "If you haven't done it already, we should likely just run a control case for a transient case out of the box (with PHS on).\n",
+    "Lucking this is really easy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/CTSM/tools/site_and_regional\n",
+    "\n",
+    "# then run_neon\n",
+    "./run_neon.py --neon-sites $new_case  \\\n",
+    "           --output-root ~/scratch/NEON_cases \\\n",
+    "           --base-case ~/scratch/NEON_cases/$base_case \\\n",
+    "           --overwrite\n",
+    "\n",
+    "# we'll just go ahead and submit this case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Double check\n",
+    "It's a good idea to make sure cases have expected differences.  \n",
+    "\n",
+    "See if this is true for our control and experimental transient cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd ~/scratch/NEON_cases/$new_case.transient\n",
+    "diff CaseDocs/lnd_in ../$new_case.$experiment.transient/CaseDocs/lnd_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "<strong>Congratulations!</strong> \n",
+    "    \n",
+    "You've created and run a full spinup for an experimental case of CLM at the NEON tower you selected!\n",
+    "    \n",
+    "Now we need to see how the simulations are different.\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're developing this tutorial:\n",
+    "Before saving and pushing this code to github go to `Kernel` and `Restart kernel and clear all outputs...`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/modifyParameterFile.ipynb b/notebooks/SinglePoint/ProjectExamples/modifyParameterFile.ipynb
new file mode 100644
index 0000000..ffcf730
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/modifyParameterFile.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "febd337f-52eb-44f3-aa20-b41ee0881a14",
+   "metadata": {},
+   "source": [
+    "# Modify Parameter File\n",
+    "\n",
+    "- This notebook is designed to open the default CLM parameter file and modify one or several fields.  \n",
+    "- It's easier to run if you've made a local copy of the parameter file somewhere in your working directory.\n",
+    "- You can find the parameter file by looking at your `lnd_in` file from a case directory on the command line.\n",
+    "    > e.g. `cat ~/scratch/NEON_cases/KONZ.transient/run/lnd_in | grep paramfile`\n",
+    "- Then I created a copy of this parameter file here (~/scratch/NEON_cases/.)\n",
+    "    > on Cheyenne: `cp /glade/p/cesmdata/cseg/inputdata/lnd/clm2/paramdata/ctsm51_params.c211112.nc ~/scratch/NEON_cases/.`\n",
+    "    \n",
+    "**In cloud we can make a local copy to work with**  *NOTE* since this is a python notebook our bash commands will look a little different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb79aaae-1b36-4e28-b30c-8dd4040e2a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! mkdir /scratch/${USER}/NEON_cases/modified_inputs/  # Directory to save the file\n",
+    "! cp /scratch/inputdata/lnd/clm2/paramdata/ctsm51_params.c211112.nc /scratch/${USER}/NEON_cases/modified_inputs/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b52316f-598c-447d-b4b2-0f2c86dcc3ff",
+   "metadata": {},
+   "source": [
+    "This tutorial has 3 parts:\n",
+    "1. Open the parameter file\n",
+    "2. Modify a global parameter\n",
+    "3. Modify a pft specific parameter\n",
+    "\n",
+    "Created by Will Wieder Nov 2021\n",
+    "Modified for NEON example May 2023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7fd46ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load libraries\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import netCDF4\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64d35274-3aac-4223-acb2-9e9560df0290",
+   "metadata": {},
+   "source": [
+    "## 1. Open the parameter file\n",
+    "As mentioned above, I've alreay copied the file to a local directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94f9f43a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathin = '~/scratch/NEON_cases/modified_inputs/'\n",
+    "basefile = pathin + 'ctsm51_params.c211112.nc'\n",
+    "p = xr.open_dataset(basefile,decode_times=False) \n",
+    "p.var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f37291c-051a-4823-981d-32dfa7b9cb4f",
+   "metadata": {},
+   "source": [
+    "### CLM has > 400 parameters!\n",
+    "It's too much for us to go over here. Hopefully between the [user's guide and tech note](https://escomp.github.io/ctsm-docs/versions/master/html/index.html) in combination with the [model code](https://escomp.github.html) you can find what you need to regarding parameters.\n",
+    "\n",
+    "For now, we'll just look at how to change a few parameters as an example.\n",
+    "\n",
+    "You'll notice some parameters are dimensioned by plant functional type (pft) while others are global.  The global parameters are easier to modify, so we'll to that first.\n",
+    "\n",
+    "## 2. Changing global parameters\n",
+    "For example, the parameter `rf_cwdl2` is used in the soil biogeogeochemistry code.\n",
+    "- Can you find out what the default values for this parameter is?\n",
+    "- Can you understand what it controls in the model?\n",
+    "- Can you find where it's used in the model code?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b6200c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.rf_cwdl2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb55a32-6d03-40a4-a02a-75be4ba7c4a0",
+   "metadata": {},
+   "source": [
+    "Let's imagine that you studied wood decomposition at a NEON site and have found that a 75% of wood decomposition is respired to the atmosphere. We can create a new parameter file, change the values for `rf_cwdl2`, and save the new file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "685cee30-63da-4602-89ac-9354a9d72633",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2 = p.copy(deep=True)       # create a copy of the parameter file to modify\n",
+    "p2['rf_cwdl2'].values = 0.75 # change the values of our parameter\n",
+    "p2.rf_cwdl2                  # check that it worked as intended"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d831173c-d887-49ba-8a58-05177b175dcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now save the file\n",
+    "p2.to_netcdf(pathin + 'ctsm51_params.c211112_high_rf_CWD.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "909c983b-bcb9-43d8-8ae7-34b1f69461dc",
+   "metadata": {},
+   "source": [
+    "You'll notice this is a non-standard way to modify parameter files in CTSM.  It's really just designed for quick experiments:\n",
+    "- I like to make the name descriptive so I remember what I changed.\n",
+    "- One disadvantage here is that we didn't append the attribues of the .nc file to record this change.\n",
+    "- There are lots of other more efficient ways to do this for parameter perturbation experiments, that we'll eventually cover in future tutorials.\n",
+    "\n",
+    "## 3. Modify a pft specific parameter\n",
+    "For this example, let's assume you looked at leaf traits that were measured at for your favorite NEON site and realized that the values being used in CLM in global simulations are not representative of local site conditions.  Here we can do this for temperate deciduous forests at Steigerwald (STEI).  \n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>HINT:</b>  It's a good idea to check what PFT is actually on the surface dataset for the site you're running.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Let's see how PFTs are named in the parameter file, this will help us change values for particular PFTs more accurately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca1c0875-1b8e-4307-9bc6-c8a25b7a8373",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.pftname.values[0:16]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2566f2c8-128f-4014-bdb0-d466a1a5b5ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now we'll create an index for this PFT and print the foliar C:N ratio that's used in CLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8625bc0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mypft = 'broadleaf_deciduous_temperate_tree'\n",
+    "ix    = np.array([mypft in str(n) for n in p.pftname.values])  #index vector for mypft\n",
+    "param = 'leafcn'\n",
+    "m = p[param].values\n",
+    "print(m[ix])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4bf84b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now we can change this value with something that's hypothetically been observed.\n",
+    "new_leafcn = 30\n",
+    "p3 = p.copy(deep=True)\n",
+    "p3[param].values[ix]= new_leafcn\n",
+    "p3[param][0:16]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b30ae43-b2e2-4532-b80c-4530886066ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now save the file\n",
+    "p3.to_netcdf(pathin + 'ctsm51_params.c211112_tdf_leafcn30.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e59bda4-2a62-4c3f-9b59-d48a855128cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratulations:</b> \n",
+    "    \n",
+    "You can now modify parameter files!  \n",
+    "Remember, to run with one of these you'll have to spin up the model first.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "You can see an example of running an experimental case with one of these parameter files in the `customizeCase_parameterModifications` notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d323df8-6da8-4a8c-82ce-e351678037ce",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/ProjectExamples/modifySurfdataFile.ipynb b/notebooks/SinglePoint/ProjectExamples/modifySurfdataFile.ipynb
new file mode 100644
index 0000000..3e96bc5
--- /dev/null
+++ b/notebooks/SinglePoint/ProjectExamples/modifySurfdataFile.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "12b3b107-e777-4d51-97bb-25958ae7763e",
+   "metadata": {},
+   "source": [
+    "# Modify Surfacedataset File\n",
+    "- This notebook is designed to open and modify one or several fields on a NEON surface dataset.  \n",
+    "- It's easier to run if you've made a local copy of the surface dataset somewhere in your working directory.\n",
+    "- You can find the surface dataset by looking at your `lnd_in` file from a case directory on the command line.\n",
+    "    > e.g. `cat ~/scratch/NEON_cases/KONZ.transient/run/lnd_in | grep fsurdat`\n",
+    "- Then I created a copy of this parameter file here (~/scratch/NEON_cases/modified_inputs/.)\n",
+    "    > on Cheyenne: `cp /glade/p/cesmdata/cseg/inputdata/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_KONZ_hist_78pfts_CMIP6_simyr2000_c230111.nc \n",
+    "~/scratch/NEON_cases/modified_inputs.`\n",
+    "\n",
+    "The most common thing you may want to change on the surface dataset is the plant functional type for a site, or the leaf area index (if you're running an satelite phenology (SP) case.\n",
+    "\n",
+    "- You can see what the default pft is using the following code on the command line\n",
+    "\n",
+    "> on Cheyenne: `ncdump -v PCT_NAT_PFT /glade/p/cesmdata/cseg/inputdata/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_KONZ_hist_78pfts_CMIP6_simyr2000_c230111.nc`\n",
+    "\n",
+    "**In cloud we can make a local copy to work with**  *NOTE* since this is a python notebook our bash commands will look a little different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc132977-4caf-459e-9f88-5ab7fad6a51c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! cat ~/scratch/NEON_cases/KONZ.transient/run/lnd_in | grep fsurdat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5f393f2-88d8-4616-b1cd-3e7fcc83c49a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! mkdir /scratch/${USER}/NEON_cases/modified_inputs/  # Directory to save the file\n",
+    "! cp /scratch/inputdata/lnd/clm2/surfdata_map/NEON/surfdata_1x1_NEON_KONZ_hist_78pfts_CMIP6_simyr2000_c230111.nc /scratch/${USER}/NEON_cases/modified_inputs/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0680fe-ea38-4615-bf60-b7e41d35abb5",
+   "metadata": {},
+   "source": [
+    "This tutorial has 3 parts:\n",
+    "1. Open the surface dataset\n",
+    "2. Change the dominant plant functional type (PFT).\n",
+    "3. Modify leaf area index (LAI) for the dominant PFT (for SP cases only).\n",
+    "\n",
+    "Created by Will Wieder May 2023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7fd46ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load libraries\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import netCDF4\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64d35274-3aac-4223-acb2-9e9560df0290",
+   "metadata": {},
+   "source": [
+    "## 1. Open the surface dataset\n",
+    "As mentioned above, I've alreay copied the file to a local directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94f9f43a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathin = '~/scratch/NEON_cases/modified_inputs/'\n",
+    "basefile = pathin + 'surfdata_1x1_NEON_KONZ_hist_78pfts_CMIP6_simyr2000_c230111.nc'\n",
+    "s = xr.open_dataset(basefile,decode_times=False) \n",
+    "s.var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f37291c-051a-4823-981d-32dfa7b9cb4f",
+   "metadata": {},
+   "source": [
+    "## Lots of data goes into the surface dataset.\n",
+    "\n",
+    "It's too much for us to go over here. Hopefully between the [user's guide and tech note](https://escomp.github.io/ctsm-docs/versions/master/html/index.html) find what you need to regarding surface data.\n",
+    "\n",
+    "In the NEON cases we set a dominant PFT for each site and modified soil properties to reflect NEON megapit measurements.\n",
+    "The real ecosystems at NEON sites, however, have multiple plant functional types represented in the tower footprint or region.  For example, mixed deciduous and evergreen forests, grassland - forest savannahs, mixed grasslands and woody shrublands, or a mix of C3 and C4 grasses.  Users can set the PFT mixtures to reflect local conditions, although this makes it difficult to understand individual PFT's contributions to the gridcell weighted fluxes we typically look at.\n",
+    "\n",
+    "For now, we'll just look at how to change the single dominant PFT at a site.\n",
+    "\n",
+    "\n",
+    "## 2. Changing the dominant PFT\n",
+    "For example, the surface datset  `PCT_NAT_PFT` defines the percent plant functional type on the natural vegetated landunit.\n",
+    "- What is the dominant PFT index at KONZ?\n",
+    "- What PFT does this correspond to in the model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b6200c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s.PCT_NAT_PFT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb55a32-6d03-40a4-a02a-75be4ba7c4a0",
+   "metadata": {},
+   "source": [
+    "Let's imagine that you studied plant composition at the KONZ site and found a there are a number of C3 and C4 grasses in the tower footprint.  We can modify the surface dataset to see what a C3 grass (pft 13) simulation looks like at KONZ.\n",
+    "\n",
+    "To do thsi, we'll create a new surface data file, change values for `PCT_NAT_PFT`, and save the new file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "685cee30-63da-4602-89ac-9354a9d72633",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s2 = s.copy(deep=True)         # create a copy of the surface dataset to modify\n",
+    "s2['PCT_NAT_PFT'][[13]] = 100. # set C3 grasses to 100\n",
+    "s2['PCT_NAT_PFT'][[14]] = 0.   # set C4 grasses to 0\n",
+    "s2['PCT_NAT_PFT'].values              # check that it worked as intended"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d831173c-d887-49ba-8a58-05177b175dcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now save the file\n",
+    "s2.to_netcdf(pathin + 'surfdata_1x1_NEON_KONZ_hist_78pfts_CMIP6_simyr2000_c230111_C3grass.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "909c983b-bcb9-43d8-8ae7-34b1f69461dc",
+   "metadata": {},
+   "source": [
+    "You'll notice this is a non-standard way to modify surface dataset in in CTSM.  It's really just designed for quick experiments:\n",
+    "- I like to make the name descriptive so I remember what I changed.\n",
+    "\n",
+    "## 3. Modify LAI for a PFT (SP simulations only)\n",
+    "For this example, let's assume you derived a climatology of LAI from NEON or remote sensing data products.  This would be a huge help, because the values being used in CLM in global simulations are likely not representative of local  conditions at NEON sites. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "\n",
+    "<b>HINT:</b>  It's a good idea to check what PFT is actually on the surface dataset for the site you're running.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Let's see what LAI looks like "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c414a1e-9d1e-4c57-b330-b0a05738d3fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s2.PCT_NAT_PFT.isel(natpft=slice(1,15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1748fd8d-de04-468f-bc56-b9355639a970",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is a hacky way to look at the LAI timeseries and identify the dominant PFT at a site\n",
+    "NATPFT = s2.PCT_NAT_PFT.isel(natpft=slice(1,15)).rename({\"natpft\":'lsmpft'})\n",
+    "(s2.MONTHLY_LAI.isel(lsmpft=slice(1,15))*NATPFT/100).plot(hue='lsmpft') ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00c122d4-ce58-4ec7-8115-6e7fefc2e132",
+   "metadata": {},
+   "source": [
+    "If you want to modify the LAI of your PFT we can discuss different ways to do this, but it would be really helpful to have NEON data that can help inform this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e59bda4-2a62-4c3f-9b59-d48a855128cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "<b>Congratulations:</b> \n",
+    "    \n",
+    "You can now modify parameter files!  \n",
+    "Remember, to run with one of these you'll have to spin up the model first.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f008a109-ea90-4b27-8d47-844364ead6c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SinglePoint/neon_utils.py b/notebooks/SinglePoint/neon_utils.py
new file mode 100644
index 0000000..03b37bb
--- /dev/null
+++ b/notebooks/SinglePoint/neon_utils.py
@@ -0,0 +1,264 @@
+from glob import glob
+from os.path import join, expanduser
+import time
+import xarray as xr
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import requests
+import pandas as pd
+import os
+import matplotlib.colors as colors
+import calendar
+import tqdm
+import cftime
+
+def preprocess (ds):
+    variables = [
+        'TSOI',
+        'H2OSOI'
+    ]
+    ds_new= ds[variables]
+    return ds_new
+
+
+#--this just drops an unused coordinate variable (lndgrid) from the dataset
+def preprocess_all (ds):
+    ds_new= ds.isel(lndgrid=0) 
+    return ds_new
+
+
+# -- fix timestamp on monthly CTSM files
+def fix_time_h0 (ds):
+    nsteps = len(ds.time)
+    yr0 = ds['time.year'][0].values
+    month0 = ds['time.month'][0].values - 1 
+    day0 = ds['time.day'][0].values 
+
+    date = cftime.datetime(yr0,month0,day0).isoformat() 
+    ds['time'] = xr.cftime_range(date, periods=nsteps, freq='M')
+    ds['time']= ds['time'].dt.strftime("%Y-%m").astype("datetime64[ns]")
+    return ds
+
+# -- fix timestamp on CTSM 30 minute h1 files so they can be matched with eval files
+def fix_time_h1 (ds):
+    '''
+    fix time formatting with reading multiple cesm files.
+    '''
+    nsteps = len(ds.time)
+    yr0 = ds['time.year'][0].values
+    month0 = ds['time.month'][0].values
+    day0 = ds['time.day'][0].values
+
+    date = cftime.datetime(yr0,month0,day0).isoformat() 
+    ds['time'] = xr.cftime_range(date, periods=nsteps, freq='30min')
+    ds['time']= ds['time'].dt.strftime("%Y-%m-%d %H:%M:%S").astype("datetime64[ns]")
+    return ds
+
+def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
+    new_cmap = colors.LinearSegmentedColormap.from_list(
+        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
+        cmap(np.linspace(minval, maxval, n)))
+    return new_cmap
+
+
+def quick_soil_profile(sim_path, case_name, var, year):
+    """              
+    Function for quick visualization of soil profile vs. time
+    Args:            
+        sim_path (str):   
+            path where the simulation files exist
+        case_name (str) : 
+            CTSM case name
+        var (str):
+            variable to create the plot for
+        year (int):
+            simulation year for plot
+    """
+    plt.rcParams["font.weight"] = "bold"    
+    plt.rcParams["axes.labelweight"] = "bold"
+    font = {'weight' : 'bold',
+                'size'   : 15} 
+    matplotlib.rc('font', **font)
+    
+    sim_files = sorted(glob(join(sim_path,case_name+".h1."+year.__str__()+"*.nc")))
+    print("All Simulation files: [", len(sim_files), "files]")
+    
+    start = time.time()
+    ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, preprocess=preprocess, combine='by_coords',parallel=True)
+    end = time.time()
+    
+    print("Reading all simulation files [", len(sim_files), "files] took:", end-start, "s.")
+        
+    if var=='TSOI':
+        ds_ctsm[var].isel(levgrnd=(slice(0,9))).plot(x="time",yincrease=False, robust=True,cmap='YlOrRd',figsize=(15, 5))
+    elif var=='H2OSOI':
+        ds_ctsm[var].isel(levsoi=(slice(0,15))).plot(x="time",yincrease=False, robust=True,cmap='viridis',figsize=(15, 5))
+    else:
+        print ('Please choose either TSOI or H2OSOI for plotting.')
+        
+        
+def plot_soil_profile_timeseries(sim_path, neon_site, case_name, var, year):
+    """              
+    Function for quick visualization of soil profile vs. time
+    Args:            
+        sim_path (str):   
+            path where the simulation files exist
+        case_name (str) : 
+            CTSM case name
+        var (str):
+            variable to create the plot for
+        year (int):
+            simulation year for plot
+    """
+    
+    time_0 = time.time()
+    plt.rcParams["font.weight"] = "bold"    
+    plt.rcParams["axes.labelweight"] = "bold"
+    font = {'weight' : 'bold',
+                'size'   : 15} 
+    matplotlib.rc('font', **font)
+    
+    sim_files = sorted(glob(join(sim_path,case_name+".h1."+year.__str__()+"*.nc")))
+    print("All Simulation files: [", len(sim_files), "files]")
+    
+    start = time.time()
+    #ds_ctsm = xr.open_mfdataset(sim_files, decode_times=True, preprocess=preprocess, combine='by_coords',parallel=True)
+    
+    ds_all = []
+    for f in tqdm.tqdm(sim_files):
+        ds_tmp = xr.open_dataset(f,drop_variables=['ZSOI','DZSOI','WATSAT','SUCSAT','BSW','HKSAT','ZLAKE','DZLAKE','PCT_SAND','PCT_CLAY'])
+        ds_all.append(ds_tmp.isel(time = 24))
+    
+    ds_ctsm = xr.concat (ds_all,dim='time')
+    
+    end = time.time()
+    
+    print("Reading all simulation files [", len(sim_files), "files] took:", end-start, "s.")
+        
+    if var=='TSOI':
+        #ds_ctsm[var].isel(levgrnd=(slice(0,9))).plot(x="time",yincrease=False, robust=True,cmap='YlOrRd',figsize=(15, 5))
+        
+        tsoi = ds_ctsm[var].isel(levgrnd=(slice(0,9)))
+        x= tsoi.time.values
+        y= -tsoi.levgrnd.values
+        plot_var =  tsoi[:,:,0].values.transpose()
+        plot_var = plot_var-273.15
+        
+        cmap = 'YlOrRd'
+        var_name = 'Soil Temperature'
+        var_unit = '[\u00B0C]'
+        
+    elif var=='H2OSOI':
+        
+        h2o_soi = ds_ctsm[var].isel(levsoi=(slice(0,15)))
+        x= h2o_soi.time.values
+        y= -h2o_soi.levsoi.values
+        plot_var =  h2o_soi[:,:,0].values.transpose()
+        
+        #cmap = 'viridis'
+        var_name = 'Soil Moisture'
+        var_unit = '[mm3/mm3]'
+        #ds_ctsm[var].isel(levsoi=(slice(0,15))).plot(x="time",yincrease=False, robust=True,cmap='viridis',figsize=(15, 5))
+        cmap = plt.get_cmap('gist_earth_r')
+        cmap = truncate_colormap(cmap, 0.15, 0.9)
+        
+    else:
+        print ('Please choose either TSOI or H2OSOI for plotting.')
+    
+    
+    X, Y = np.meshgrid(x, y)
+    fig= plt.figure(num=None, figsize=(15,5),  facecolor='w', edgecolor='k')
+
+    ax = plt.gca()
+    cs = ax.contourf(X, Y, plot_var,cmap=cmap,extend="both")
+    plt.xticks(rotation=30)
+    plt.ylabel('Soil Depth [m]')
+    plt.xlabel('Time')
+    plt.title ('Time-Series of '+ var_name +' Profile at '+neon_site,fontweight="bold")
+    cbar = fig.colorbar(cs, ax=ax, shrink=0.9)
+    y_label = var_name +' '+var_unit
+    cbar.ax.set_ylabel(y_label)
+
+    #plt.show()
+    time_1 = time.time()
+    print("Making this plot took ", time_1-time_0, "s.")
+                     
+                     
+def download_file(url, fname):
+    """              
+    Function to download a file.
+    Args:            
+        url (str):   
+            url of the file for downloading
+        fname (str) : 
+            file name to save the downloaded file.
+    """              
+    response = requests.get(url)
+                     
+    with open(fname, 'wb') as f:
+        f.write(response.content)
+                     
+    #-- Check if download status_code
+    if response.status_code == 200:
+        print('Download finished successfully for', fname,'.')
+    elif response.status_code == 404:
+        print('File '+fname+'was not available on the neon server:'+ url) 
+        
+def list_neon_eval_files(neon_site):                               
+    """              
+    A function to download neon listing.csv file
+    and parse it to find all eval files for the specified
+    neon tower site .
+    
+    Args:
+        neon_site (str):
+            4 character name of your neon site
+    """
+    # -- download listing.csv
+    listing_file = 'listing.csv'
+    url = "https://storage.neonscience.org/neon-ncar/listing.csv"
+    download_file(url, listing_file)
+    
+    # -- find eval files
+    df = pd.read_csv(listing_file)
+    df = df[df['object'].str.contains(neon_site+"_eval")]
+    
+    dict_out = dict(zip(df['object'],df['last_modified']))
+    return dict_out  
+        
+def download_eval_files (neon_site, eval_dir, year="all"):
+    """              
+    A function to download all eval files for the specified
+    neon tower site .
+    
+    Args:
+        neon_site (str):
+            4 character name of your neon site
+        eval_dir (str):
+            directory where you want your evaluation files
+    """
+
+    if (year=="all"):
+        print ("Downloading all available evaluation files for "+neon_site+".")
+    else:
+        print ("Downloading evaluation files for "+neon_site+" for year "+year+".")
+
+    #-- create directory if it does not exist
+    if not os.path.isdir(eval_dir):
+        os.mkdir(eval_dir)
+    
+    site_eval_dir = os.path.join(eval_dir,neon_site)
+
+    #-- create directory for the site if it does not exist
+    if not os.path.isdir(site_eval_dir):
+        os.mkdir(site_eval_dir)
+
+    #-- get all available eval file names
+    file_time = list_neon_eval_files(neon_site)
+
+    for key, value in file_time.items():
+        fname = key.rsplit('/',1)[1]
+        if year=="all" or year in fname:
+                fname_out = os.path.join(site_eval_dir, fname)
+                download_file(key, fname_out)