CMIP6_global_projections.ipynb

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    "# CMIP6 global projections\n",
    "\n",
    "Experimental showcase for getting CMIP6 climate data with [Intake-esm](https://intake-esm.readthedocs.io/en/latest/).  The following packages are used:\n",
    "\n",
    "- [Intake-esm](https://intake-esm.readthedocs.io/en/latest/): an intake plugin for parsing an Earth System Model (ESM) catalog and loading assets into xarray datasets.\n",
    "- [xarray](http://xarray.pydata.org/en/stable/) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun! ([Xarray Tutorial](https://xarray-contrib.github.io/xarray-tutorial/) / [Xarray | SciPy 2020](https://www.youtube.com/watch?v=mecN-Ph_-78&list=PLYx7XA2nY5Gde-6QO98KUJ9iL_WW4rgYf&index=4))\n",
    "- [ProPlot](https://proplot.readthedocs.io/en/latest/): a lightweight matplotlib wrapper for making beautiful, publication-quality graphics. \n",
    "(still in development)\n",
    "- [xESMF](https://xesmf.readthedocs.io/en/latest/): Universal Regridder for Geospatial Data (only for Linux and Mac, an alternative is the [interp](http://xarray.pydata.org/en/stable/interpolation.html#example) function from xarray)\n",
    "- [Dask](https://dask.org/) provides advanced parallelism for analytics ([jacobtomlinson/dask-video-tutorial-2020](https://github.com/jacobtomlinson/dask-video-tutorial-2020) / [Dask | SciPy 2020](https://www.youtube.com/watch?v=EybGGLbLipI&list=PLYx7XA2nY5Gde-6QO98KUJ9iL_WW4rgYf&index=6))\n",
    "\n",
    "## Climate analysis\n",
    "\n",
    "The purpose of this analysis will be to make a figure of the time series over the historical period, as well as projections for future scenarios.\n",
    "\n",
    "\n",
    "## Where is this running?\n",
    "\n",
    "This session is running on [binder.pangeo.io](https://binder.pangeo.io),\n",
    "a service designed and maintained by the [Pangeo community](https://pangeo.io) for scalable earth science.\n",
    "It is running on Google Cloud Platform using credits generously donated by Google to that Community.\n"
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   "source": [
    "# Import packages"
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     "text": [
      "/home/lalandmi/miniconda3/envs/intake_CMIP6_v0/lib/python3.8/site-packages/proplot/config.py:1454: ProPlotWarning: Rebuilding font cache.\n"
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   "source": [
    "import intake\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import proplot as plot # New plot library (https://proplot.readthedocs.io/en/latest/)\n",
    "plot.rc['savefig.dpi'] = 300 # 1200 is too big! #https://proplot.readthedocs.io/en/latest/basics.html#Creating-figures\n",
    "from scipy import stats\n",
    "import xesmf as xe # For regridding (https://xesmf.readthedocs.io/en/latest/)"
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    "# Make a Dask cluster for parallelization\n",
    "\n",
    "![](img/dask.png)\n",
    "\n",
    "Click on the Dask icon (image above) in the left toolbar. Click on the `+ NEW` button, then right-click on the LocalCluster that will appear below and click on `Inject Dask Client Connection Code`. You should have a cell similar to the one below. Otherwise you can just copy the `Scheduler Address` and replace it in the cell below."
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       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:35639</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
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       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
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       "  <li><b>Workers: </b>4</li>\n",
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       "  <li><b>Memory: </b>33.33 GB</li>\n",
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    "# from dask.distributed import Client\n",
    "\n",
    "# client = Client(\"tcp://127.0.0.1:35639\")\n",
    "# client"
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    "You can then display the `WORKERS`, `PROGRESS` and `TASK STREAM` for example to get an overview of how the tasks will be parallelized."
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   "source": [
    "# Get observations\n",
    "HadCRUT4 is a global temperature dataset, providing gridded temperature anomalies across the world as well as averages for the hemispheres and the globe as a whole. CRUTEM4 and HadSST3 are the land and ocean components of this overall dataset, respectively.\n",
    "\n",
    "These datasets have been developed by the Climatic Research Unit (University of East Anglia) in conjunction with the Hadley Centre (UK Met Office), apart from the sea surface temperature (SST) dataset which was developed solely by the Hadley Centre. These datasets will be updated at roughly monthly intervals into the future. Hemispheric and global averages as monthly and annual values are available as separate files.\n",
    "\n",
    "https://crudata.uea.ac.uk/cru/data/temperature/"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temperature_anomaly&#x27; (time: 2048, latitude: 36, longitude: 72)&gt;\n",
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       "  * latitude   (latitude) float32 -87.5 -82.5 -77.5 -72.5 ... 77.5 82.5 87.5\n",
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       "    long_name:         near_surface_temperature_anomaly\n",
       "    units:             K\n",
       "    reference_period:  [1961 1990]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temperature_anomaly'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2048</li><li><span class='xr-has-index'>latitude</span>: 36</li><li><span class='xr-has-index'>longitude</span>: 72</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f4b84257-7f0d-4ed5-81c9-303927b8f7ba' class='xr-array-in' type='checkbox' checked><label for='section-f4b84257-7f0d-4ed5-81c9-303927b8f7ba' 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>...</span></div><div class='xr-array-data'><pre>[5308416 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-729dbbe6-97c7-4c32-8389-5d6c16305898' class='xr-section-summary-in' type='checkbox'  checked><label for='section-729dbbe6-97c7-4c32-8389-5d6c16305898' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-7199cb98-82df-43ae-adf3-bac98f55aef7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7199cb98-82df-43ae-adf3-bac98f55aef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dbabf1b-ab52-4687-a018-edb9e0af8c3f' class='xr-var-data-in' type='checkbox'><label for='data-6dbabf1b-ab52-4687-a018-edb9e0af8c3f' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-177.5 -172.5 ... 172.5 177.5</div><input id='attrs-52723753-1729-4131-a2a2-d5133b85faf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52723753-1729-4131-a2a2-d5133b85faf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3c1aaba-e70c-4058-a7ad-94ee9947b7b6' class='xr-var-data-in' type='checkbox'><label for='data-f3c1aaba-e70c-4058-a7ad-94ee9947b7b6' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,\n",
       "       -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,\n",
       "        -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,\n",
       "        -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,\n",
       "          2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,\n",
       "         47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,\n",
       "         92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,\n",
       "        137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],\n",
       "      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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 2020-08-...</div><input id='attrs-be8f604e-fb8c-42da-a9b2-566c7bd28a4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be8f604e-fb8c-42da-a9b2-566c7bd28a4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3393dd1e-0206-4065-a826-98283fc6529d' class='xr-var-data-in' type='checkbox'><label for='data-3393dd1e-0206-4065-a826-98283fc6529d' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>start_year :</span></dt><dd>1850</dd><dt><span>end_year :</span></dt><dd>2020</dd><dt><span>start_month :</span></dt><dd>1</dd><dt><span>end_month :</span></dt><dd>8</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-16T12:00:00.000000000&#x27;, &#x27;2020-08-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-219d1e9a-0a27-4118-b1d5-eafcdd491fcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-219d1e9a-0a27-4118-b1d5-eafcdd491fcf' 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>long_name :</span></dt><dd>near_surface_temperature_anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>reference_period :</span></dt><dd>[1961 1990]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'temperature_anomaly' (time: 2048, latitude: 36, longitude: 72)>\n",
       "[5308416 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 -87.5 -82.5 -77.5 -72.5 ... 77.5 82.5 87.5\n",
       "  * longitude  (longitude) float32 -177.5 -172.5 -167.5 ... 167.5 172.5 177.5\n",
       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2020-08-16T12:00:00\n",
       "Attributes:\n",
       "    long_name:         near_surface_temperature_anomaly\n",
       "    units:             K\n",
       "    reference_period:  [1961 1990]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = xr.open_dataset('data/HadCRUT.4.6.0.0.median.nc').temperature_anomaly\n",
    "obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get CMIP6 data\n",
    "This is made easier thanks to the [Intake-esm](https://intake-esm.readthedocs.io/en/latest/) package in combination with the [Pangeo](https://pangeo.io/) project and [Google Cloud](https://cloud.google.com/blog/products/data-analytics/new-climate-model-data-now-google-public-datasets) (still quite experimental)! Otherwise you have to download the data from https://esgf-node.llnl.gov/search/cmip6/ (official international database, but this is much more tedious).\n",
    "\n",
    "See the [Intake-esm tutorial](https://intake-esm.readthedocs.io/en/latest/notebooks/tutorial.html) for more informations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lalandmi/miniconda3/envs/intake_CMIP6_v0/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3417: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 4952 dataset(s) from 315272 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
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       "      <th>table_id</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>315272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the catalog of CMIP6 data\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(url)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 161 dataset(s) from 161 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>66</td>\n",
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       "</div>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select he first member of Near-Surface Air Temperature (tas) \n",
    "# for the historical and future scenarios exepriments\n",
    "\n",
    "cat = col.search(\n",
    "    activity_id=['CMIP', 'ScenarioMIP'],\n",
    "    experiment_id=['historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585'],\n",
    "    table_id=\"Amon\",\n",
    "    variable_id=\"tas\",\n",
    "    member_id=\"r1i1p1f1\"\n",
    ")\n",
    "\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP/AS-RCEC/TaiESM1/historical/r1i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>AWI</td>\n",
       "      <td>AWI-CM-1-1-MR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP/AWI/AWI-CM-1-1-MR/historical/r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>AWI</td>\n",
       "      <td>AWI-ESM-1-1-LR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP/AWI/AWI-ESM-1-1-LR/historical/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-CSM2-MR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP/BCC/BCC-CSM2-MR/historical/r1i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20181126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP/BCC/BCC-ESM1/historical/r1i1p1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20181214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/ScenarioMIP/NUIST/NESM3/ssp245/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>NUIST</td>\n",
       "      <td>NESM3</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/ScenarioMIP/NUIST/NESM3/ssp585/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/ScenarioMIP/THU/CIESM/ssp126/r1i1p1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/ScenarioMIP/THU/CIESM/ssp245/r1i1p1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/ScenarioMIP/THU/CIESM/ssp585/r1i1p1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     activity_id institution_id       source_id experiment_id member_id  \\\n",
       "0           CMIP        AS-RCEC         TaiESM1    historical  r1i1p1f1   \n",
       "1           CMIP            AWI   AWI-CM-1-1-MR    historical  r1i1p1f1   \n",
       "2           CMIP            AWI  AWI-ESM-1-1-LR    historical  r1i1p1f1   \n",
       "3           CMIP            BCC     BCC-CSM2-MR    historical  r1i1p1f1   \n",
       "4           CMIP            BCC        BCC-ESM1    historical  r1i1p1f1   \n",
       "..           ...            ...             ...           ...       ...   \n",
       "156  ScenarioMIP          NUIST           NESM3        ssp245  r1i1p1f1   \n",
       "157  ScenarioMIP          NUIST           NESM3        ssp585  r1i1p1f1   \n",
       "158  ScenarioMIP            THU           CIESM        ssp126  r1i1p1f1   \n",
       "159  ScenarioMIP            THU           CIESM        ssp245  r1i1p1f1   \n",
       "160  ScenarioMIP            THU           CIESM        ssp585  r1i1p1f1   \n",
       "\n",
       "    table_id variable_id grid_label  \\\n",
       "0       Amon         tas         gn   \n",
       "1       Amon         tas         gn   \n",
       "2       Amon         tas         gn   \n",
       "3       Amon         tas         gn   \n",
       "4       Amon         tas         gn   \n",
       "..       ...         ...        ...   \n",
       "156     Amon         tas         gn   \n",
       "157     Amon         tas         gn   \n",
       "158     Amon         tas         gr   \n",
       "159     Amon         tas         gr   \n",
       "160     Amon         tas         gr   \n",
       "\n",
       "                                                zstore  dcpp_init_year  \\\n",
       "0    gs://cmip6/CMIP/AS-RCEC/TaiESM1/historical/r1i...             NaN   \n",
       "1    gs://cmip6/CMIP/AWI/AWI-CM-1-1-MR/historical/r...             NaN   \n",
       "2    gs://cmip6/CMIP/AWI/AWI-ESM-1-1-LR/historical/...             NaN   \n",
       "3    gs://cmip6/CMIP/BCC/BCC-CSM2-MR/historical/r1i...             NaN   \n",
       "4    gs://cmip6/CMIP/BCC/BCC-ESM1/historical/r1i1p1...             NaN   \n",
       "..                                                 ...             ...   \n",
       "156  gs://cmip6/ScenarioMIP/NUIST/NESM3/ssp245/r1i1...             NaN   \n",
       "157  gs://cmip6/ScenarioMIP/NUIST/NESM3/ssp585/r1i1...             NaN   \n",
       "158  gs://cmip6/ScenarioMIP/THU/CIESM/ssp126/r1i1p1...             NaN   \n",
       "159  gs://cmip6/ScenarioMIP/THU/CIESM/ssp245/r1i1p1...             NaN   \n",
       "160  gs://cmip6/ScenarioMIP/THU/CIESM/ssp585/r1i1p1...             NaN   \n",
       "\n",
       "      version  \n",
       "0    20200623  \n",
       "1    20200720  \n",
       "2    20200212  \n",
       "3    20181126  \n",
       "4    20181214  \n",
       "..        ...  \n",
       "156  20190731  \n",
       "157  20190728  \n",
       "158  20200417  \n",
       "159  20200417  \n",
       "160  20200605  \n",
       "\n",
       "[161 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize it (no data is loaded yet)\n",
    "\n",
    "cat.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
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   "source": [
    "# Get the data in a dictionnary of lazy* Dataset\n",
    "# *http://xarray.pydata.org/en/stable/dask.html\n",
    "\n",
    "dset_dict = cat.to_dataset_dict(\n",
    "    zarr_kwargs={\"consolidated\": True, \"decode_times\": True}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series computation\n",
    "\n",
    "Let's calculate the time series for each of the models and experiments as well as the climatology over the reference period. The results will be stored in a DataFrame.\n",
    "\n",
    "Note that all operations are actually not calculated here. Dask only creates calculation graphs under the hood to optimize the calculations in parallel while respecting the allocated memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScenarioMIP.CCCR-IITM.IITM-ESM.ssp585.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CAMS.CAMS-CSM1-0.ssp126.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CAMS.CAMS-CSM1-0.ssp245.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.NCC.NorESM2-LM.ssp245.Amon.gn KeyError raised (might not have the actual period: 2031-2030).\n",
      "ScenarioMIP.CAMS.CAMS-CSM1-0.ssp370.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CCCR-IITM.IITM-ESM.ssp245.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CCCR-IITM.IITM-ESM.ssp370.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.NCC.NorESM2-LM.ssp370.Amon.gn KeyError raised (might not have the actual period: 2091-2050).\n",
      "CMIP.AWI.AWI-CM-1-1-MR.historical.Amon.gn does not have all the period available!\n",
      "CMIP.E3SM-Project.E3SM-1-0.historical.Amon.gr does not have all the period available!\n",
      "ScenarioMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.ssp370.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.CAMS.CAMS-CSM1-0.ssp585.Amon.gn does not have all the period available!\n",
      "ScenarioMIP.NCC.NorESM2-LM.ssp126.Amon.gn KeyError raised (might not have the actual period: 2031-2020).\n",
      "ScenarioMIP.NCC.NorESM2-MM.ssp126.Amon.gn KeyError raised (might not have the actual period: 2071-2090).\n"
     ]
    }
   ],
   "source": [
    "period_future = slice('2015','2100')\n",
    "period_past = slice('1850','2014')\n",
    "\n",
    "n_months_future = (int(period_future.stop) - int(period_future.start) + 1) * 12\n",
    "n_months_past = (int(period_past.stop) - int(period_past.start) + 1) * 12\n",
    "\n",
    "columns = ['clim', 'historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585']\n",
    "df = pd.DataFrame(index=[], columns=columns)\n",
    "\n",
    "for key in list(dset_dict.keys())[:]:\n",
    "# for key in [\"ScenarioMIP.EC-Earth-Consortium.EC-Earth3-Veg.ssp126.Amon.gr\"]:\n",
    "    \n",
    "#     print(key)\n",
    "    \n",
    "    model = key.split('.')[2]\n",
    "    experiment = key.split('.')[3]\n",
    "    \n",
    "    # Get the dataset\n",
    "    ds = dset_dict[key]\n",
    "    \n",
    "    # Preformat data\n",
    "    if ('latitude' in ds.coords):\n",
    "        ds = ds.rename({'latitude': 'lat'})\n",
    "    if ('longitude' in ds.coords):\n",
    "        ds = ds.rename({'longitude': 'lon'})\n",
    "    if ('height' in ds.coords):\n",
    "        ds = ds.drop('height')\n",
    "    \n",
    "    try:\n",
    "        # Historical\n",
    "        if experiment == 'historical':\n",
    "            da = ds.sel(member_id='r1i1p1f1', time=period_past).tas - 273.15\n",
    "            \n",
    "        # Futur scenarios\n",
    "        elif experiment in ['ssp126', 'ssp245', 'ssp370', 'ssp585']:\n",
    "            da = ds.sel(member_id='r1i1p1f1', time=period_future).tas - 273.15\n",
    "            \n",
    "        try:\n",
    "            # Check time\n",
    "            if experiment == 'historical':\n",
    "                np.testing.assert_equal(n_months_past, da.time.size)\n",
    "            elif experiment in ['ssp126', 'ssp245', 'ssp370', 'ssp585']:\n",
    "                np.testing.assert_equal(n_months_future, da.time.size)            \n",
    "            \n",
    "            # Make global spatial average\n",
    "            weights = np.cos(np.deg2rad(da.lat))\n",
    "            da_weighted = da.weighted(weights)\n",
    "            global_mean = da_weighted.mean(dim=('lat', 'lon'))\n",
    "            \n",
    "            # Resample by years\n",
    "            global_mean_year = global_mean.groupby('time.year').mean('time')     \n",
    "            \n",
    "            # Store results\n",
    "            if model in df.index:\n",
    "                df[experiment][model] = global_mean_year\n",
    "            else:\n",
    "                temp = pd.DataFrame(index=[model], columns=columns)\n",
    "                temp[experiment][model] = global_mean_year\n",
    "                df = df.append(temp)\n",
    "                \n",
    "            # Compute climatology for reference period (to compute anomalies)\n",
    "            if experiment == 'historical':\n",
    "                clim = global_mean_year.sel(\n",
    "                    year=slice(str(obs.reference_period[0]), str(obs.reference_period[1]))\n",
    "                ).mean('year')\n",
    "                df.clim[model] = clim\n",
    "            \n",
    "        except AssertionError:\n",
    "            print(key + ' does not have all the period available!')\n",
    "            \n",
    "    except KeyError:\n",
    "        print(key + ' KeyError raised (might not have the actual period: '+str(ds['time.year'][0].values)+'-'+str(ds['time.year'][-1].values)+').')\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize computation graph\n",
    "https://docs.dask.org/en/latest/graphviz.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataArray selection\n",
    "`da = ds.sel(member_id='r1i1p1f1', time=period_past).tas - 273.15`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       ".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, 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;tas&#x27; (time: 1032, lat: 144, lon: 192)&gt;\n",
       "dask.array&lt;sub, shape=(1032, 144, 192), dtype=float32, chunksize=(516, 144, 192), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "  * time       (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "    member_id  &lt;U8 &#x27;r1i1p1f1&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7cff9355-affa-43a4-a173-4f696e53e300' class='xr-array-in' type='checkbox' checked><label for='section-7cff9355-affa-43a4-a173-4f696e53e300' 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=(516, 144, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 114.13 MB </td> <td> 57.07 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1032, 144, 192) </td> <td> (516, 144, 192) </td></tr>\n",
       "    <tr><th> Count </th><td> 9 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"172\" height=\"160\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"40\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"51\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"122\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"122\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 122.28685677722032,70.58823529411765 122.28685677722032,110.94027403716188 80.58823529411765,110.94027403716188\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"101.437546\" y=\"130.940274\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
       "  <text x=\"142.286857\" y=\"90.764255\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.286857,90.764255)\">144</text>\n",
       "  <text x=\"35.294118\" y=\"95.646156\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,95.646156)\">1032</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-fa76258c-236c-4a2b-9d5d-c1acf74e2931' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa76258c-236c-4a2b-9d5d-c1acf74e2931' class='xr-section-summary' >Coordinates: <span>(4)</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'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-269ebe08-aae0-4dcc-8066-18447e69f441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-269ebe08-aae0-4dcc-8066-18447e69f441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6104bd5-373d-4e0a-b446-ba940dd52932' class='xr-var-data-in' type='checkbox'><label for='data-c6104bd5-373d-4e0a-b446-ba940dd52932' 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([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
       "       -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
       "       -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
       "       -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
       "       -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
       "       -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
       "       -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
       "       -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
       "        -9.375,  -8.125,  -6.875,  -5.625,  -4.375,  -3.125,  -1.875,  -0.625,\n",
       "         0.625,   1.875,   3.125,   4.375,   5.625,   6.875,   8.125,   9.375,\n",
       "        10.625,  11.875,  13.125,  14.375,  15.625,  16.875,  18.125,  19.375,\n",
       "        20.625,  21.875,  23.125,  24.375,  25.625,  26.875,  28.125,  29.375,\n",
       "        30.625,  31.875,  33.125,  34.375,  35.625,  36.875,  38.125,  39.375,\n",
       "        40.625,  41.875,  43.125,  44.375,  45.625,  46.875,  48.125,  49.375,\n",
       "        50.625,  51.875,  53.125,  54.375,  55.625,  56.875,  58.125,  59.375,\n",
       "        60.625,  61.875,  63.125,  64.375,  65.625,  66.875,  68.125,  69.375,\n",
       "        70.625,  71.875,  73.125,  74.375,  75.625,  76.875,  78.125,  79.375,\n",
       "        80.625,  81.875,  83.125,  84.375,  85.625,  86.875,  88.125,  89.375])</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'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-085d1db0-6689-4247-8db9-ba03451dcac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-085d1db0-6689-4247-8db9-ba03451dcac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d91ab6c6-3dfe-4005-8333-ab08e8ad4c5c' class='xr-var-data-in' type='checkbox'><label for='data-d91ab6c6-3dfe-4005-8333-ab08e8ad4c5c' 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([  0.9375,   2.8125,   4.6875,   6.5625,   8.4375,  10.3125,  12.1875,\n",
       "        14.0625,  15.9375,  17.8125,  19.6875,  21.5625,  23.4375,  25.3125,\n",
       "        27.1875,  29.0625,  30.9375,  32.8125,  34.6875,  36.5625,  38.4375,\n",
       "        40.3125,  42.1875,  44.0625,  45.9375,  47.8125,  49.6875,  51.5625,\n",
       "        53.4375,  55.3125,  57.1875,  59.0625,  60.9375,  62.8125,  64.6875,\n",
       "        66.5625,  68.4375,  70.3125,  72.1875,  74.0625,  75.9375,  77.8125,\n",
       "        79.6875,  81.5625,  83.4375,  85.3125,  87.1875,  89.0625,  90.9375,\n",
       "        92.8125,  94.6875,  96.5625,  98.4375, 100.3125, 102.1875, 104.0625,\n",
       "       105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
       "       119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
       "       132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
       "       145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
       "       158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
       "       171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
       "       184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
       "       197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
       "       210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
       "       224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
       "       237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
       "       250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
       "       263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
       "       276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
       "       289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
       "       302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
       "       315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
       "       329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
       "       342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
       "       355.3125, 357.1875, 359.0625])</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'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-12cb27c3-b8be-4b0e-bd04-dde5f4e9f61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12cb27c3-b8be-4b0e-bd04-dde5f4e9f61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13b1bbe9-3031-4056-ba12-4602649af07d' class='xr-var-data-in' type='checkbox'><label for='data-13b1bbe9-3031-4056-ba12-4602649af07d' 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([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-761745f4-5de0-4d7e-8f66-ba640726af5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-761745f4-5de0-4d7e-8f66-ba640726af5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e1d3d9-632d-4b03-a498-4fc23c5ed8bf' class='xr-var-data-in' type='checkbox'><label for='data-e0e1d3d9-632d-4b03-a498-4fc23c5ed8bf' 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(&#x27;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa06f410-09c7-43f0-8d8f-5aa7c9458f2f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa06f410-09c7-43f0-8d8f-5aa7c9458f2f' 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 'tas' (time: 1032, lat: 144, lon: 192)>\n",
       "dask.array<sub, shape=(1032, 144, 192), dtype=float32, chunksize=(516, 144, 192), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "  * time       (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "    member_id  <U8 'r1i1p1f1'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.data.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global Mean\n",
    "`global_mean = da_weighted.mean(dim=('lat', 'lon'))`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\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, 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 (time: 1032)&gt;\n",
       "dask.array&lt;truediv, shape=(1032,), dtype=float64, chunksize=(516,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "    member_id  &lt;U8 &#x27;r1i1p1f1&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1f9fb94c-6184-4794-a4ff-1ea8a017efeb' class='xr-array-in' type='checkbox' checked><label for='section-1f9fb94c-6184-4794-a4ff-1ea8a017efeb' 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=(516,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 8.26 kB </td> <td> 4.13 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1032,) </td> <td> (516,) </td></tr>\n",
       "    <tr><th> Count </th><td> 34 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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=\"60\" y1=\"0\" x2=\"60\" 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:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1032</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-cb56326d-500d-4107-b211-ceda8168a734' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb56326d-500d-4107-b211-ceda8168a734' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-215c84d7-90de-478c-be28-0c00e81f9187' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215c84d7-90de-478c-be28-0c00e81f9187' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3b7613f-a289-48ad-992d-7a5f913d30bc' class='xr-var-data-in' type='checkbox'><label for='data-a3b7613f-a289-48ad-992d-7a5f913d30bc' 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([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-3608f1f2-2fc4-4347-8274-b7bee03c8461' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3608f1f2-2fc4-4347-8274-b7bee03c8461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f54328b-cd28-445f-b792-ff0ce2f570dd' class='xr-var-data-in' type='checkbox'><label for='data-2f54328b-cd28-445f-b792-ff0ce2f570dd' 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(&#x27;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f0002da-15ff-4f97-bb02-0be61ec43cca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4f0002da-15ff-4f97-bb02-0be61ec43cca' 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 (time: 1032)>\n",
       "dask.array<truediv, shape=(1032,), dtype=float64, chunksize=(516,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "    member_id  <U8 'r1i1p1f1'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_mean.data.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>clim</th>\n",
       "      <th>historical</th>\n",
       "      <th>ssp126</th>\n",
       "      <th>ssp245</th>\n",
       "      <th>ssp370</th>\n",
       "      <th>ssp585</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-LR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TaiESM1</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM-1-2-HAM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCESS-CM2</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-g3</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3-Veg</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDL-CM4</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIO-ESM-2-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIESM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCESS-ESM1-5</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIOST-ESM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRI-ESM2-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2-WACCM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCC-CSM2-MR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM5-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KACE-1-0-G</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-HR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-MM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIROC6</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWI-ESM-1-1-LR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2-WACCM-FV2</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CanESM5</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM4-8</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-LM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NESM3</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GISS-E2-1-G-CC</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDL-ESM4</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GISS-E2-1-G</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3-Veg-LR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCC-ESM1</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCC-CM2-SR5</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IITM-ESM</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAS-ESM2-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GISS-E2-1-H</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-f3-L</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCM-UA-1-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPSL-CM6A-LR</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWI-CM-1-1-MR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMS-CSM1-0</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E3SM-1-1-ECA</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E3SM-1-1</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAM0-UNICON</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorCPM1</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2-FV2</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCC-CM2-HR4</th>\n",
       "      <td>&lt;xarray.DataArray ()&gt;\\ndask.array&lt;mean_agg-agg...</td>\n",
       "      <td>[&lt;xarray.DataArray ()&gt;\\ndask.array&lt;getitem, sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               clim  \\\n",
       "MPI-ESM1-2-LR     <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "TaiESM1           <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "MPI-ESM-1-2-HAM   <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "ACCESS-CM2        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "FGOALS-g3         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "EC-Earth3-Veg     <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "EC-Earth3         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "GFDL-CM4          <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "FIO-ESM-2-0       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CIESM             <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "ACCESS-ESM1-5     <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "KIOST-ESM         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "MRI-ESM2-0        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CESM2-WACCM       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "BCC-CSM2-MR       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "INM-CM5-0         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "KACE-1-0-G        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "MPI-ESM1-2-HR     <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "NorESM2-MM        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "MIROC6            <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "AWI-ESM-1-1-LR    <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CESM2-WACCM-FV2   <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CanESM5           <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "INM-CM4-8         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "NorESM2-LM        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "NESM3             <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "GISS-E2-1-G-CC    <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "GFDL-ESM4         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "GISS-E2-1-G       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "EC-Earth3-Veg-LR  <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "BCC-ESM1          <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CMCC-CM2-SR5      <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "IITM-ESM          <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CAS-ESM2-0        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "GISS-E2-1-H       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "FGOALS-f3-L       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "MCM-UA-1-0        <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "IPSL-CM6A-LR      <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CESM2             <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "AWI-CM-1-1-MR                                                   NaN   \n",
       "CAMS-CSM1-0       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "E3SM-1-1-ECA      <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "E3SM-1-1          <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "SAM0-UNICON       <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "NorCPM1           <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CESM2-FV2         <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "CMCC-CM2-HR4      <xarray.DataArray ()>\\ndask.array<mean_agg-agg...   \n",
       "\n",
       "                                                         historical  \\\n",
       "MPI-ESM1-2-LR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "TaiESM1           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM-1-2-HAM   [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "ACCESS-CM2        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FGOALS-g3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3-Veg     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GFDL-CM4          [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FIO-ESM-2-0       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CIESM             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "ACCESS-ESM1-5     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KIOST-ESM         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MRI-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-WACCM       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "BCC-CSM2-MR       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM5-0         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KACE-1-0-G        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM1-2-HR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-MM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MIROC6            [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "AWI-ESM-1-1-LR    [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-WACCM-FV2   [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CanESM5           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM4-8         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-LM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NESM3             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G-CC    [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GFDL-ESM4         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3-Veg-LR  [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "BCC-ESM1          [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CMCC-CM2-SR5      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "IITM-ESM          [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CAS-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-H       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FGOALS-f3-L       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MCM-UA-1-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "IPSL-CM6A-LR      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "AWI-CM-1-1-MR                                                   NaN   \n",
       "CAMS-CSM1-0       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "E3SM-1-1-ECA      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "E3SM-1-1          [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "SAM0-UNICON       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorCPM1           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-FV2         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CMCC-CM2-HR4      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "\n",
       "                                                             ssp126  \\\n",
       "MPI-ESM1-2-LR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "TaiESM1                                                         NaN   \n",
       "MPI-ESM-1-2-HAM                                                 NaN   \n",
       "ACCESS-CM2        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FGOALS-g3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3-Veg     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GFDL-CM4                                                        NaN   \n",
       "FIO-ESM-2-0       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CIESM             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "ACCESS-ESM1-5     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KIOST-ESM         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MRI-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-WACCM       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "BCC-CSM2-MR       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM5-0         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KACE-1-0-G        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM1-2-HR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-MM                                                      NaN   \n",
       "MIROC6            [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "AWI-ESM-1-1-LR                                                  NaN   \n",
       "CESM2-WACCM-FV2                                                 NaN   \n",
       "CanESM5           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM4-8         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-LM                                                      NaN   \n",
       "NESM3             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G-CC                                                  NaN   \n",
       "GFDL-ESM4         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G                                                     NaN   \n",
       "EC-Earth3-Veg-LR                                                NaN   \n",
       "BCC-ESM1                                                        NaN   \n",
       "CMCC-CM2-SR5      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "IITM-ESM                                                        NaN   \n",
       "CAS-ESM2-0                                                      NaN   \n",
       "GISS-E2-1-H                                                     NaN   \n",
       "FGOALS-f3-L       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MCM-UA-1-0                                                      NaN   \n",
       "IPSL-CM6A-LR      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2                                                           NaN   \n",
       "AWI-CM-1-1-MR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CAMS-CSM1-0                                                     NaN   \n",
       "E3SM-1-1-ECA                                                    NaN   \n",
       "E3SM-1-1                                                        NaN   \n",
       "SAM0-UNICON                                                     NaN   \n",
       "NorCPM1                                                         NaN   \n",
       "CESM2-FV2                                                       NaN   \n",
       "CMCC-CM2-HR4                                                    NaN   \n",
       "\n",
       "                                                             ssp245  \\\n",
       "MPI-ESM1-2-LR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "TaiESM1                                                         NaN   \n",
       "MPI-ESM-1-2-HAM                                                 NaN   \n",
       "ACCESS-CM2        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FGOALS-g3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3-Veg     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GFDL-CM4          [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FIO-ESM-2-0       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CIESM             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "ACCESS-ESM1-5     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KIOST-ESM         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MRI-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-WACCM       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "BCC-CSM2-MR       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM5-0         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KACE-1-0-G        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM1-2-HR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-MM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MIROC6            [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "AWI-ESM-1-1-LR                                                  NaN   \n",
       "CESM2-WACCM-FV2                                                 NaN   \n",
       "CanESM5           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM4-8         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-LM                                                      NaN   \n",
       "NESM3             [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G-CC                                                  NaN   \n",
       "GFDL-ESM4         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G                                                     NaN   \n",
       "EC-Earth3-Veg-LR                                                NaN   \n",
       "BCC-ESM1                                                        NaN   \n",
       "CMCC-CM2-SR5      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "IITM-ESM                                                        NaN   \n",
       "CAS-ESM2-0                                                      NaN   \n",
       "GISS-E2-1-H                                                     NaN   \n",
       "FGOALS-f3-L       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MCM-UA-1-0                                                      NaN   \n",
       "IPSL-CM6A-LR      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2                                                           NaN   \n",
       "AWI-CM-1-1-MR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CAMS-CSM1-0                                                     NaN   \n",
       "E3SM-1-1-ECA                                                    NaN   \n",
       "E3SM-1-1                                                        NaN   \n",
       "SAM0-UNICON                                                     NaN   \n",
       "NorCPM1                                                         NaN   \n",
       "CESM2-FV2                                                       NaN   \n",
       "CMCC-CM2-HR4                                                    NaN   \n",
       "\n",
       "                                                             ssp370  \\\n",
       "MPI-ESM1-2-LR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "TaiESM1           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM-1-2-HAM                                                 NaN   \n",
       "ACCESS-CM2        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "FGOALS-g3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3-Veg     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "EC-Earth3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GFDL-CM4                                                        NaN   \n",
       "FIO-ESM-2-0                                                     NaN   \n",
       "CIESM                                                           NaN   \n",
       "ACCESS-ESM1-5     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KIOST-ESM                                                       NaN   \n",
       "MRI-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2-WACCM       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "BCC-CSM2-MR       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM5-0         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "KACE-1-0-G        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MPI-ESM1-2-HR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-MM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MIROC6            [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "AWI-ESM-1-1-LR                                                  NaN   \n",
       "CESM2-WACCM-FV2                                                 NaN   \n",
       "CanESM5           [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "INM-CM4-8         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "NorESM2-LM                                                      NaN   \n",
       "NESM3                                                           NaN   \n",
       "GISS-E2-1-G-CC                                                  NaN   \n",
       "GFDL-ESM4         [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "GISS-E2-1-G                                                     NaN   \n",
       "EC-Earth3-Veg-LR                                                NaN   \n",
       "BCC-ESM1                                                        NaN   \n",
       "CMCC-CM2-SR5      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "IITM-ESM                                                        NaN   \n",
       "CAS-ESM2-0                                                      NaN   \n",
       "GISS-E2-1-H                                                     NaN   \n",
       "FGOALS-f3-L       [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "MCM-UA-1-0                                                      NaN   \n",
       "IPSL-CM6A-LR      [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CESM2                                                           NaN   \n",
       "AWI-CM-1-1-MR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...   \n",
       "CAMS-CSM1-0                                                     NaN   \n",
       "E3SM-1-1-ECA                                                    NaN   \n",
       "E3SM-1-1                                                        NaN   \n",
       "SAM0-UNICON                                                     NaN   \n",
       "NorCPM1                                                         NaN   \n",
       "CESM2-FV2                                                       NaN   \n",
       "CMCC-CM2-HR4                                                    NaN   \n",
       "\n",
       "                                                             ssp585  \n",
       "MPI-ESM1-2-LR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "TaiESM1           [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "MPI-ESM-1-2-HAM                                                 NaN  \n",
       "ACCESS-CM2        [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "FGOALS-g3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "EC-Earth3-Veg     [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "EC-Earth3         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "GFDL-CM4          [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "FIO-ESM-2-0       [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "CIESM             [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "ACCESS-ESM1-5     [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "KIOST-ESM         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "MRI-ESM2-0        [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "CESM2-WACCM       [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "BCC-CSM2-MR       [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "INM-CM5-0         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "KACE-1-0-G        [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "MPI-ESM1-2-HR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "NorESM2-MM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "MIROC6            [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "AWI-ESM-1-1-LR                                                  NaN  \n",
       "CESM2-WACCM-FV2                                                 NaN  \n",
       "CanESM5           [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "INM-CM4-8         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "NorESM2-LM        [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "NESM3             [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "GISS-E2-1-G-CC                                                  NaN  \n",
       "GFDL-ESM4         [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "GISS-E2-1-G                                                     NaN  \n",
       "EC-Earth3-Veg-LR                                                NaN  \n",
       "BCC-ESM1                                                        NaN  \n",
       "CMCC-CM2-SR5      [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "IITM-ESM                                                        NaN  \n",
       "CAS-ESM2-0                                                      NaN  \n",
       "GISS-E2-1-H                                                     NaN  \n",
       "FGOALS-f3-L       [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "MCM-UA-1-0                                                      NaN  \n",
       "IPSL-CM6A-LR      [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "CESM2                                                           NaN  \n",
       "AWI-CM-1-1-MR     [<xarray.DataArray ()>\\ndask.array<getitem, sh...  \n",
       "CAMS-CSM1-0                                                     NaN  \n",
       "E3SM-1-1-ECA                                                    NaN  \n",
       "E3SM-1-1                                                        NaN  \n",
       "SAM0-UNICON                                                     NaN  \n",
       "NorCPM1                                                         NaN  \n",
       "CESM2-FV2                                                       NaN  \n",
       "CMCC-CM2-HR4                                                    NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removes models for which climatology is not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['clim'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenate experiments and make computation (only now!)\n",
    "See this link for more information about Dask computation: https://distributed.dask.org/en/latest/manage-computation.html\n",
    "\n",
    "Here we use `.load()` that will load the result into memory and implicitly will make `.compute()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['clim', 'historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clim = xr.concat(df.clim, pd.Index(df.clim.index, name='model')).load()\n",
    "hist = xr.concat(df.historical, pd.Index(df.historical.index, name='model')).load()\n",
    "ssp126 = xr.concat(df.ssp126.dropna(), pd.Index(df.ssp126.dropna().index, name='model')).load()\n",
    "ssp245 = xr.concat(df.ssp245.dropna(), pd.Index(df.ssp245.dropna().index, name='model')).load()\n",
    "ssp370 = xr.concat(df.ssp370.dropna(), pd.Index(df.ssp370.dropna().index, name='model')).load()\n",
    "ssp585 = xr.concat(df.ssp585.dropna(), pd.Index(df.ssp585.dropna().index, name='model')).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (model: 46)&gt;\n",
       "array([13.87020245, 13.82319117, 13.73772413, 13.88897115, 14.36591005,\n",
       "       14.21326342, 13.92392082, 12.83517225, 13.78927017, 14.91435557,\n",
       "       14.57684957, 13.22148063, 13.92401491, 14.08595617, 14.92909787,\n",
       "       13.49433167, 14.05719622, 14.35037603, 13.89940171, 15.45186582,\n",
       "       13.366908  , 14.31972509, 13.98865349, 13.69428617, 14.49884066,\n",
       "       13.84148213, 13.95459678, 13.55283392, 13.91119852, 13.88932422,\n",
       "       14.77249746, 14.55118139, 14.56346181, 13.90757797, 14.46397448,\n",
       "       13.18554759, 13.82625451, 13.37410164, 14.2404776 , 14.17193894,\n",
       "       13.49546511, 13.57470208, 13.18953518, 12.75870012, 14.15510653,\n",
       "       14.57580774])\n",
       "Coordinates:\n",
       "    member_id  &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "  * model      (model) object &#x27;MPI-ESM1-2-LR&#x27; &#x27;TaiESM1&#x27; ... &#x27;CMCC-CM2-HR4&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>model</span>: 46</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cc4ed546-8554-4e1a-ba39-9de0a136643f' class='xr-array-in' type='checkbox' checked><label for='section-cc4ed546-8554-4e1a-ba39-9de0a136643f' 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>13.87 13.82 13.74 13.89 14.37 14.21 ... 13.57 13.19 12.76 14.16 14.58</span></div><div class='xr-array-data'><pre>array([13.87020245, 13.82319117, 13.73772413, 13.88897115, 14.36591005,\n",
       "       14.21326342, 13.92392082, 12.83517225, 13.78927017, 14.91435557,\n",
       "       14.57684957, 13.22148063, 13.92401491, 14.08595617, 14.92909787,\n",
       "       13.49433167, 14.05719622, 14.35037603, 13.89940171, 15.45186582,\n",
       "       13.366908  , 14.31972509, 13.98865349, 13.69428617, 14.49884066,\n",
       "       13.84148213, 13.95459678, 13.55283392, 13.91119852, 13.88932422,\n",
       "       14.77249746, 14.55118139, 14.56346181, 13.90757797, 14.46397448,\n",
       "       13.18554759, 13.82625451, 13.37410164, 14.2404776 , 14.17193894,\n",
       "       13.49546511, 13.57470208, 13.18953518, 12.75870012, 14.15510653,\n",
       "       14.57580774])</pre></div></div></li><li class='xr-section-item'><input id='section-333a92e1-f43c-4e3a-a53d-294376a6c512' class='xr-section-summary-in' type='checkbox'  checked><label for='section-333a92e1-f43c-4e3a-a53d-294376a6c512' 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>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-8cc5bc6d-5c7c-4a9b-9e2b-0c7e79e96c04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8cc5bc6d-5c7c-4a9b-9e2b-0c7e79e96c04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-330e7c91-f067-455e-b754-b8189cb424ac' class='xr-var-data-in' type='checkbox'><label for='data-330e7c91-f067-455e-b754-b8189cb424ac' 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(&#x27;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;MPI-ESM1-2-LR&#x27; ... &#x27;CMCC-CM2-HR4&#x27;</div><input id='attrs-6cd1cc2f-f937-41d9-86f1-69c2727ddc1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cd1cc2f-f937-41d9-86f1-69c2727ddc1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b5a59d-1bff-412d-9aad-26b69b93375e' class='xr-var-data-in' type='checkbox'><label for='data-24b5a59d-1bff-412d-9aad-26b69b93375e' 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([&#x27;MPI-ESM1-2-LR&#x27;, &#x27;TaiESM1&#x27;, &#x27;MPI-ESM-1-2-HAM&#x27;, &#x27;ACCESS-CM2&#x27;,\n",
       "       &#x27;FGOALS-g3&#x27;, &#x27;EC-Earth3-Veg&#x27;, &#x27;EC-Earth3&#x27;, &#x27;GFDL-CM4&#x27;, &#x27;FIO-ESM-2-0&#x27;,\n",
       "       &#x27;CIESM&#x27;, &#x27;ACCESS-ESM1-5&#x27;, &#x27;KIOST-ESM&#x27;, &#x27;MRI-ESM2-0&#x27;, &#x27;CESM2-WACCM&#x27;,\n",
       "       &#x27;BCC-CSM2-MR&#x27;, &#x27;INM-CM5-0&#x27;, &#x27;KACE-1-0-G&#x27;, &#x27;MPI-ESM1-2-HR&#x27;, &#x27;NorESM2-MM&#x27;,\n",
       "       &#x27;MIROC6&#x27;, &#x27;AWI-ESM-1-1-LR&#x27;, &#x27;CESM2-WACCM-FV2&#x27;, &#x27;CanESM5&#x27;, &#x27;INM-CM4-8&#x27;,\n",
       "       &#x27;NorESM2-LM&#x27;, &#x27;NESM3&#x27;, &#x27;GISS-E2-1-G-CC&#x27;, &#x27;GFDL-ESM4&#x27;, &#x27;GISS-E2-1-G&#x27;,\n",
       "       &#x27;EC-Earth3-Veg-LR&#x27;, &#x27;BCC-ESM1&#x27;, &#x27;CMCC-CM2-SR5&#x27;, &#x27;IITM-ESM&#x27;,\n",
       "       &#x27;CAS-ESM2-0&#x27;, &#x27;GISS-E2-1-H&#x27;, &#x27;FGOALS-f3-L&#x27;, &#x27;MCM-UA-1-0&#x27;,\n",
       "       &#x27;IPSL-CM6A-LR&#x27;, &#x27;CESM2&#x27;, &#x27;CAMS-CSM1-0&#x27;, &#x27;E3SM-1-1-ECA&#x27;, &#x27;E3SM-1-1&#x27;,\n",
       "       &#x27;SAM0-UNICON&#x27;, &#x27;NorCPM1&#x27;, &#x27;CESM2-FV2&#x27;, &#x27;CMCC-CM2-HR4&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54bb0df5-2115-4342-978c-2a4328566dde' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54bb0df5-2115-4342-978c-2a4328566dde' 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 (model: 46)>\n",
       "array([13.87020245, 13.82319117, 13.73772413, 13.88897115, 14.36591005,\n",
       "       14.21326342, 13.92392082, 12.83517225, 13.78927017, 14.91435557,\n",
       "       14.57684957, 13.22148063, 13.92401491, 14.08595617, 14.92909787,\n",
       "       13.49433167, 14.05719622, 14.35037603, 13.89940171, 15.45186582,\n",
       "       13.366908  , 14.31972509, 13.98865349, 13.69428617, 14.49884066,\n",
       "       13.84148213, 13.95459678, 13.55283392, 13.91119852, 13.88932422,\n",
       "       14.77249746, 14.55118139, 14.56346181, 13.90757797, 14.46397448,\n",
       "       13.18554759, 13.82625451, 13.37410164, 14.2404776 , 14.17193894,\n",
       "       13.49546511, 13.57470208, 13.18953518, 12.75870012, 14.15510653,\n",
       "       14.57580774])\n",
       "Coordinates:\n",
       "    member_id  <U8 'r1i1p1f1'\n",
       "  * model      (model) object 'MPI-ESM1-2-LR' 'TaiESM1' ... 'CMCC-CM2-HR4'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = (hist - clim.load())\n",
    "ssp126 = (ssp126 - clim.where(clim.model == ssp126.model))\n",
    "ssp245 = (ssp245 - clim.where(clim.model == ssp245.model))\n",
    "ssp370 = (ssp370 - clim.where(clim.model == ssp370.model))\n",
    "ssp585 = (ssp585 - clim.where(clim.model == ssp585.model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make ensemble median and quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_median_quantile(da):\n",
    "    da_median = da.median('model').compute()\n",
    "    da_quantile = da.quantile([0.25, 0.75], dim='model').compute()\n",
    "    \n",
    "    return da_median, da_quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_median, hist_quantile = compute_median_quantile(hist)\n",
    "ssp126_median, ssp126_quantile = compute_median_quantile(ssp126)\n",
    "ssp245_median, ssp245_quantile = compute_median_quantile(ssp245)\n",
    "ssp370_median, ssp370_quantile = compute_median_quantile(ssp370)\n",
    "ssp585_median, ssp585_quantile = compute_median_quantile(ssp585)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssp = [ssp126, ssp245, ssp370, ssp585]\n",
    "ssp_median = [ssp126_median, ssp245_median, ssp370_median, ssp585_median]\n",
    "ssp_quantile = [ssp126_quantile, ssp245_quantile, ssp370_quantile, ssp585_quantile]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make global mean of obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "coslat = np.cos(np.deg2rad(obs.latitude)).where(~obs.isnull())\n",
    "weights = coslat / coslat.sum(dim=('latitude', 'longitude'))\n",
    "obs_weighted = (obs * weights).sum(dim=('latitude', 'longitude'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_median = obs_weighted.groupby('time.year').mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 666.639x368.333 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 666
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.subplots(aspect=2, axwidth=6)\n",
    "\n",
    "ymin = -1.5\n",
    "ymax = 6.5\n",
    "\n",
    "# Models past\n",
    "h_past = ax.plot(hist_median, shadedata=hist_quantile, \n",
    "                 label='historical ('+str(hist.model.size)+')', color='gray9')\n",
    "\n",
    "# Obs\n",
    "h_obs = ax.plot(obs_median, \n",
    "                label='HadCRUT4', color='gray9', linestyle='--')\n",
    "\n",
    "# Models future\n",
    "colors = ['indigo9', 'blue5', 'orange5', 'red6']\n",
    "scenarios = columns[2:]\n",
    "h_future = [None]*len(scenarios)\n",
    "\n",
    "for i, scenario in enumerate(scenarios):\n",
    "    h_future[i] = ax.plot(ssp_median[i], shadedata=ssp_quantile[i], \n",
    "                          label=scenario+' ('+str(ssp[i].model.size)+')', color=colors[i])\n",
    "\n",
    "# Lines\n",
    "ax.vlines(2014.5, ymin, ymax, linewidth=2.5)\n",
    "ax.hlines(0, 1850, 2100, linewidth=0.7, color='gray8')\n",
    "\n",
    "# Legend / Format\n",
    "ax.legend(\n",
    "    [h_past[0], h_obs, h_future[0][0], h_future[1][0], h_future[2][0], h_future[3][0]], \n",
    "    loc='ul', frame=True, ncols=2\n",
    ")\n",
    "\n",
    "ax.format(\n",
    "    title='GMST anomalies (relative to '+str(obs.reference_period[0])+'–'+str(obs.reference_period[1])+')',\n",
    "    ylabel='Near-Surface Air Temperature [°C]', ylocator=1, yminorlocator=1.00001, ylim=(ymin,ymax),\n",
    "    xlabel='Year', xminorlocator=10, xlocator=20\n",
    ")\n",
    "\n",
    "filename = 'GMST_anomalies'\n",
    "for ext in ['jpg', 'png', 'pdf']:\n",
    "    fig.save('img/'+filename+'.'+ext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close Dask client\n",
    "Click on `SHUTDOWN` on the Dask left panel or run the cell bellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:intake_CMIP6_v0] *",
   "language": "python",
   "name": "conda-env-intake_CMIP6_v0-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}