climate-proj-download-omo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Search and Load CMIP6 Projections in Omo\n",
    "\n",
    "Start by loading necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import zarr\n",
    "import gcsfs\n",
    "\n",
    "xr.set_options(display_style='html')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = 12, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load precipitation data\n",
    "\n",
    "Function to load precipitation data from a dataframe of projections for a given experiment id. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pr_data(pr_df, expt_id):\n",
    "    \"\"\"\n",
    "    Load precipitation data for given experiment ID and dataframe of projections.\n",
    "    \"\"\"\n",
    "    uri = df_pr[(df_pr.experiment_id == expt_id)].zstore.values[0]\n",
    "    gcs = gcsfs.GCSFileSystem(token='anon')\n",
    "    ds = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with the following:\n",
    "\n",
    "* **CMCC-CM2-SR5:** much wetter future with increasing precipitation from historical to mid-century to late-century. \n",
    "* **MIROC-ES2L:** slightly wetter but with some extreme dry years.\n",
    "* **IITM-ESM:** much drier future with decreasing precipitation from historical to mid-century to late-century. \n",
    "* **CNRM-ESM2-1:** slightly drier \n",
    "* **NESM3:** futures are  a mix of wetter and drier from the baseline depending on the scenario and timeframe. \n",
    "* **NorESM2-LM:** wetter and drier depending on scenario & timeframe, with more extreme wet/dry years\n",
    "\n",
    "Load zarr store of CMIP6 projections from google store and query for the projections above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# load zarr store to df\n",
    "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "hist\n",
      "5\n",
      "hist\n",
      "5\n",
      "hist\n",
      "9\n",
      "hist\n",
      "4\n",
      "hist\n",
      "6\n",
      "hist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 7 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 386,
       "width": 662
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in [\"CMCC-CM2-SR5\", \"MIROC-ES2L\", \"IITM-ESM\", \"CNRM-ESM2-1\", \"NESM3\", \"NorESM2-LM\"]:\n",
    "#     pr = esgf_search(activity_id='CMIP', table_id='day',\n",
    "#                      variable_id='pr', experiment_id='historical',\n",
    "#                      source_id=,\n",
    "#                      member_id=\"r10i1p1f1\")\n",
    "    \n",
    "    \n",
    "    # select single ensemble for each projection \n",
    "    df_pr = df[(df.table_id == 'day') & (df.variable_id == 'pr') & (df.source_id == item)]\n",
    "    if (item == \"MIROC-ES2L\") | (item == \"CNRM-ESM2-1\"):\n",
    "        df_pr = df_pr[df_pr.member_id == 'r1i1p1f2']\n",
    "    if (item == \"NESM3\") | (item == \"NorESM2-LM\"):\n",
    "        df_pr = df_pr[df_pr.member_id == 'r1i1p1f1']\n",
    "    print(len(df_pr.experiment_id))\n",
    "    \n",
    "    # \n",
    "    hist = load_pr_data(df_pr, 'historical') # sel(lat=slice(4, 10)).sel(lon=slice(33,39)).load()\n",
    "    ssp126 = load_pr_data(df_pr, 'ssp126')# .sel(lat=slice(4, 10)).sel(lon=slice(33,39)).load()\n",
    "    ssp245 = load_pr_data(df_pr, 'ssp245') #.sel(lat=slice(4, 10)).sel(lon=slice(33,39)).load()\n",
    "    ssp585 = load_pr_data(df_pr, 'ssp585') # .sel(lat=slice(4, 10)).sel(lon=slice(33,39)).load()\n",
    "    if len(df_pr[df_pr.experiment_id == 'ssp370'].member_id) > 0:\n",
    "        ssp370 = load_pr_data(df_pr, 'ssp370') # .sel(lat=slice(4, 10)).sel(lon=slice(33,39)).load()\n",
    "    \n",
    "    hist.pr.isel(time=2).sel(lat=slice(4, 10)).sel(lon=slice(32,39)).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-b294dc02818d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m33\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m39\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sel' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'shapely'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-db2fdec83bd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# import geopandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# import rioxarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0momo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'omo/subs1.shp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"epsg:32637\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0momo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'init'\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\"epsg:32637\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'shapely'"
     ]
    }
   ],
   "source": [
    "import geopandas \n",
    "from shapely.geometry import mapping \n",
    "omo = geopandas.read_file('omo/subs1.shp', crs=\"epsg:32637\")\n",
    "omo.crs = {'init' :\"epsg:32637\"}\n",
    "omo = omo.to_crs({'init': 'epsg:4326'})\n",
    "# omo = omo.to_crs(\"EPSG:4326\")\n",
    "plt.figure(figsize=(16,8))\n",
    "ax = plt.axes()\n",
    "hist.pr.isel(time=150).sel(lat=slice(4, 10)).sel(lon=slice(32,39)).plot(ax=ax)\n",
    "omo.plot(ax = ax, alpha = 0.8, facecolor = 'none', lw = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 3, lon: 3, time: 31390)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 4.737 6.632 8.526\n",
       "    lat_bnds   (lat, bnds) float64 ...\n",
       "  * lon        (lon) float64 32.5 35.0 37.5\n",
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       "    time_bnds  (time, bnds) object ...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
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       "Attributes:\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               ScenarioMIP\n",
       "    branch_method:             Hybrid-restart from year 2015-01-01 of historical\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     60225.0\n",
       "    cmor_version:              3.5.0\n",
       "    contact:                   Please send any requests or bug reports to nor...\n",
       "    creation_date:             2019-12-30T11:12:30Z\n",
       "    data_specs_version:        01.00.31\n",
       "    experiment:                update of RCP4.5 based on SSP2\n",
       "    experiment_id:             ssp245\n",
       "    external_variables:        areacella\n",
       "    forcing_index:             1\n",
       "    frequency:                 day\n",
       "    further_info_url:          https://furtherinfo.es-doc.org/CMIP6.NCC.NorES...\n",
       "    grid:                      finite-volume grid with 1.9x2.5 degree lat/lon...\n",
       "    grid_label:                gn\n",
       "    history:                   2019-12-30T11:12:30Z ; CMOR rewrote data to be...\n",
       "    initialization_index:      1\n",
       "    institution:               NorESM Climate modeling Consortium consisting ...\n",
       "    institution_id:            NCC\n",
       "    license:                   CMIP6 model data produced by NCC is licensed u...\n",
       "    mip_era:                   CMIP6\n",
       "    model_id:                  NorESM2-LM\n",
       "    nominal_resolution:        250 km\n",
       "    parent_activity_id:        CMIP\n",
       "    parent_experiment_id:      historical\n",
       "    parent_mip_era:            CMIP6\n",
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       "    parent_variant_label:      r1i1p1f1\n",
       "    physics_index:             1\n",
       "    product:                   model-output\n",
       "    realization_index:         1\n",
       "    realm:                     atmos\n",
       "    run_variant:               N/A\n",
       "    source:                    NorESM2-LM (2017): \\naerosol: OsloAero\\natmos:...\n",
       "    source_id:                 NorESM2-LM\n",
       "    source_type:               AOGCM AER BGC\n",
       "    status:                    2020-06-24;created; by gcs.cmip6.ldeo@gmail.com\n",
       "    sub_experiment:            none\n",
       "    sub_experiment_id:         none\n",
       "    table_id:                  day\n",
       "    table_info:                Creation Date:(24 July 2019) MD5:0bb394a356ef9...\n",
       "    title:                     NorESM2-LM output prepared for CMIP6\n",
       "    tracking_id:               hdl:21.14100/031d86ee-6584-49a8-9040-4afdf11ed...\n",
       "    variable_id:               pr\n",
       "    variant_label:             r1i1p1f1</pre><div class='xr-wrap' hidden><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-688375c6-7cc0-41ed-8670-39a0df48c6a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-688375c6-7cc0-41ed-8670-39a0df48c6a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 3</li><li><span class='xr-has-index'>lon</span>: 3</li><li><span class='xr-has-index'>time</span>: 31390</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-924c195f-b88c-4e89-b519-bdc8f71d6401' class='xr-section-summary-in' type='checkbox'  checked><label for='section-924c195f-b88c-4e89-b519-bdc8f71d6401' class='xr-section-summary' >Coordinates: <span>(6)</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'>4.737 6.632 8.526</div><input id='attrs-c85825c7-ff9a-4111-a09b-b93d7d255bc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c85825c7-ff9a-4111-a09b-b93d7d255bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8618e11e-3af6-40bc-b386-5c690d717072' class='xr-var-data-in' type='checkbox'><label for='data-8618e11e-3af6-40bc-b386-5c690d717072' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([4.736842, 6.631579, 8.526316])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-efbd164d-b310-4702-bd0d-cbc6b11df5f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efbd164d-b310-4702-bd0d-cbc6b11df5f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a116c28-1b81-42b8-bd98-e89cb3a7cc9d' class='xr-var-data-in' type='checkbox'><label for='data-0a116c28-1b81-42b8-bd98-e89cb3a7cc9d' 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([[3.789474, 5.684211],\n",
       "       [5.684211, 7.578947],\n",
       "       [7.578947, 9.473684]])</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'>32.5 35.0 37.5</div><input id='attrs-62473018-0e76-46e8-b722-0544b8114006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62473018-0e76-46e8-b722-0544b8114006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1269a327-f3f6-42c1-897a-a8ae46de0c41' class='xr-var-data-in' type='checkbox'><label for='data-1269a327-f3f6-42c1-897a-a8ae46de0c41' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([32.5, 35. , 37.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59997d5f-b42f-4bd4-a8ea-27843d234858' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59997d5f-b42f-4bd4-a8ea-27843d234858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d5799e-a008-4dc1-9d8e-83712173be16' class='xr-var-data-in' type='checkbox'><label for='data-a8d5799e-a008-4dc1-9d8e-83712173be16' 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([[31.25, 33.75],\n",
       "       [33.75, 36.25],\n",
       "       [36.25, 38.75]])</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'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-957e19a3-17c3-46c2-a59a-1b9c5150cdb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-957e19a3-17c3-46c2-a59a-1b9c5150cdb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7292314d-92d6-46a2-92ee-dcc65c24d33c' class='xr-var-data-in' type='checkbox'><label for='data-7292314d-92d6-46a2-92ee-dcc65c24d33c' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 1, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0d75b06-3040-44be-8160-2a7fa9adac5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0d75b06-3040-44be-8160-2a7fa9adac5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a4e37fb-7f83-45e7-b642-bf45c4156575' class='xr-var-data-in' type='checkbox'><label for='data-2a4e37fb-7f83-45e7-b642-bf45c4156575' 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.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2015, 1, 2, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2015, 1, 2, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2015, 1, 3, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2015, 1, 3, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2015, 1, 4, 0, 0, 0, 0)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2100, 12, 29, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2100, 12, 30, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2100, 12, 30, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2100, 12, 31, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2100, 12, 31, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e1330d2-c640-46bf-9425-7d2140e7a2fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e1330d2-c640-46bf-9425-7d2140e7a2fc' class='xr-section-summary' >Data variables: <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>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-72d077f8-e4df-450b-b71d-34550040de85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72d077f8-e4df-450b-b71d-34550040de85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fef3df16-5d3c-4860-9f9f-a9b330029405' class='xr-var-data-in' type='checkbox'><label for='data-fef3df16-5d3c-4860-9f9f-a9b330029405' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>includes both liquid and solid phases</dd><dt><span>history :</span></dt><dd>2019-12-30T11:12:30Z altered by CMOR: Converted type from &#x27;d&#x27; to &#x27;f&#x27;.</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>original_name :</span></dt><dd>PRECT</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>[282510 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3baa882c-6ab9-4802-8c61-ad495f9279ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-3baa882c-6ab9-4802-8c61-ad495f9279ee' class='xr-section-summary' >Attributes: <span>(50)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>branch_method :</span></dt><dd>Hybrid-restart from year 2015-01-01 of historical</dd><dt><span>branch_time :</span></dt><dd>0.0</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60225.0</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>contact :</span></dt><dd>Please send any requests or bug reports to noresm-ncc@met.no.</dd><dt><span>creation_date :</span></dt><dd>2019-12-30T11:12:30Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>update of RCP4.5 based on SSP2</dd><dt><span>experiment_id :</span></dt><dd>ssp245</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCC.NorESM2-LM.ssp245.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>finite-volume grid with 1.9x2.5 degree lat/lon resolution</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-12-30T11:12:30Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway</dd><dt><span>institution_id :</span></dt><dd>NCC</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_id :</span></dt><dd>NorESM2-LM</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>NorESM2-LM</dd><dt><span>parent_sub_experiment_id :</span></dt><dd>none</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>N/A</dd><dt><span>source :</span></dt><dd>NorESM2-LM (2017): \n",
       "aerosol: OsloAero\n",
       "atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb)\n",
       "atmosChem: OsloChemSimp\n",
       "land: CLM\n",
       "landIce: CISM\n",
       "ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data])\n",
       "ocnBgchem: HAMOCC\n",
       "seaIce: CICE</dd><dt><span>source_id :</span></dt><dd>NorESM2-LM</dd><dt><span>source_type :</span></dt><dd>AOGCM AER BGC</dd><dt><span>status :</span></dt><dd>2020-06-24;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>table_info :</span></dt><dd>Creation Date:(24 July 2019) MD5:0bb394a356ef9d214d027f1aca45853e</dd><dt><span>title :</span></dt><dd>NorESM2-LM output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/031d86ee-6584-49a8-9040-4afdf11edb72\n",
       "hdl:21.14100/eebb049c-1cd4-4b59-a874-9dbef17edf81\n",
       "hdl:21.14100/75ee6eef-9838-44bb-929d-5ca50fd01876\n",
       "hdl:21.14100/a1ab6498-079a-4980-9716-a2076fda6e36\n",
       "hdl:21.14100/fb917790-5425-41cf-a81c-8d3a5a8eb8ba\n",
       "hdl:21.14100/c7c88e4d-7b0e-4bc6-a892-f2e5c77dd0ab\n",
       "hdl:21.14100/e8b46784-69bf-4ca2-8e37-0307b2158c61\n",
       "hdl:21.14100/c0d0acf9-d1cd-4dba-bb04-365e4a6a770e\n",
       "hdl:21.14100/520c81a9-d67a-4b59-a2bd-318a840fd045</dd><dt><span>variable_id :</span></dt><dd>pr</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 3, lon: 3, time: 31390)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 4.737 6.632 8.526\n",
       "    lat_bnds   (lat, bnds) float64 ...\n",
       "  * lon        (lon) float64 32.5 35.0 37.5\n",
       "    lon_bnds   (lon, bnds) float64 ...\n",
       "  * time       (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "    time_bnds  (time, bnds) object ...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    pr         (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               ScenarioMIP\n",
       "    branch_method:             Hybrid-restart from year 2015-01-01 of historical\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     60225.0\n",
       "    cmor_version:              3.5.0\n",
       "    contact:                   Please send any requests or bug reports to nor...\n",
       "    creation_date:             2019-12-30T11:12:30Z\n",
       "    data_specs_version:        01.00.31\n",
       "    experiment:                update of RCP4.5 based on SSP2\n",
       "    experiment_id:             ssp245\n",
       "    external_variables:        areacella\n",
       "    forcing_index:             1\n",
       "    frequency:                 day\n",
       "    further_info_url:          https://furtherinfo.es-doc.org/CMIP6.NCC.NorES...\n",
       "    grid:                      finite-volume grid with 1.9x2.5 degree lat/lon...\n",
       "    grid_label:                gn\n",
       "    history:                   2019-12-30T11:12:30Z ; CMOR rewrote data to be...\n",
       "    initialization_index:      1\n",
       "    institution:               NorESM Climate modeling Consortium consisting ...\n",
       "    institution_id:            NCC\n",
       "    license:                   CMIP6 model data produced by NCC is licensed u...\n",
       "    mip_era:                   CMIP6\n",
       "    model_id:                  NorESM2-LM\n",
       "    nominal_resolution:        250 km\n",
       "    parent_activity_id:        CMIP\n",
       "    parent_experiment_id:      historical\n",
       "    parent_mip_era:            CMIP6\n",
       "    parent_source_id:          NorESM2-LM\n",
       "    parent_sub_experiment_id:  none\n",
       "    parent_time_units:         days since 1850-01-01\n",
       "    parent_variant_label:      r1i1p1f1\n",
       "    physics_index:             1\n",
       "    product:                   model-output\n",
       "    realization_index:         1\n",
       "    realm:                     atmos\n",
       "    run_variant:               N/A\n",
       "    source:                    NorESM2-LM (2017): \\naerosol: OsloAero\\natmos:...\n",
       "    source_id:                 NorESM2-LM\n",
       "    source_type:               AOGCM AER BGC\n",
       "    status:                    2020-06-24;created; by gcs.cmip6.ldeo@gmail.com\n",
       "    sub_experiment:            none\n",
       "    sub_experiment_id:         none\n",
       "    table_id:                  day\n",
       "    table_info:                Creation Date:(24 July 2019) MD5:0bb394a356ef9...\n",
       "    title:                     NorESM2-LM output prepared for CMIP6\n",
       "    tracking_id:               hdl:21.14100/031d86ee-6584-49a8-9040-4afdf11ed...\n",
       "    variable_id:               pr\n",
       "    variant_label:             r1i1p1f1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssp245.sel(lat=slice(4, 10)).sel(lon=slice(32,39))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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