search_and_load_with_esgf_opendap.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Search and Load CMIP6 Data via ESGF / OPeNDAP\n",
    "\n",
    "This notebooks shows how to search and load data via [Earth System Grid Federation](https://esgf.llnl.gov/) infrastructure. This infrastructure works great and is the foundation of the CMIP6 distribution system.\n",
    "\n",
    "The main technologies used here are the [ESGF search API](https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API), used to figure out what data we want, and [OPeNDAP](https://www.opendap.org/), a remote data access protocol over HTTP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "xr.set_options(display_style='html')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search using ESGF API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "from __future__ import print_function\n",
    "import requests\n",
    "import xml.etree.ElementTree as ET\n",
    "import numpy\n",
    "\n",
    "# Author: Unknown\n",
    "# I got the original version from a word document published by ESGF\n",
    "# https://docs.google.com/document/d/1pxz1Kd3JHfFp8vR2JCVBfApbsHmbUQQstifhGNdc6U0/edit?usp=sharing\n",
    "\n",
    "# API AT: https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API#results-pagination\n",
    "\n",
    "def esgf_search(server=\"https://esgf-node.llnl.gov/esg-search/search\",\n",
    "                files_type=\"OPENDAP\", local_node=True, project=\"CMIP6\",\n",
    "                verbose=False, format=\"application%2Fsolr%2Bjson\",\n",
    "                use_csrf=False, **search):\n",
    "    client = requests.session()\n",
    "    payload = search\n",
    "    payload[\"project\"] = project\n",
    "    payload[\"type\"]= \"File\"\n",
    "    if local_node:\n",
    "        payload[\"distrib\"] = \"false\"\n",
    "    if use_csrf:\n",
    "        client.get(server)\n",
    "        if 'csrftoken' in client.cookies:\n",
    "            # Django 1.6 and up\n",
    "            csrftoken = client.cookies['csrftoken']\n",
    "        else:\n",
    "            # older versions\n",
    "            csrftoken = client.cookies['csrf']\n",
    "        payload[\"csrfmiddlewaretoken\"] = csrftoken\n",
    "\n",
    "    payload[\"format\"] = format\n",
    "\n",
    "    offset = 0\n",
    "    numFound = 10000\n",
    "    all_files = []\n",
    "    files_type = files_type.upper()\n",
    "    while offset < numFound:\n",
    "        payload[\"offset\"] = offset\n",
    "        url_keys = [] \n",
    "        for k in payload:\n",
    "            url_keys += [\"{}={}\".format(k, payload[k])]\n",
    "\n",
    "        url = \"{}/?{}\".format(server, \"&\".join(url_keys))\n",
    "        print(url)\n",
    "        r = client.get(url)\n",
    "        r.raise_for_status()\n",
    "        resp = r.json()[\"response\"]\n",
    "        numFound = int(resp[\"numFound\"])\n",
    "        resp = resp[\"docs\"]\n",
    "        offset += len(resp)\n",
    "        for d in resp:\n",
    "            if verbose:\n",
    "                for k in d:\n",
    "                    print(\"{}: {}\".format(k,d[k]))\n",
    "            url = d[\"url\"]\n",
    "            for f in d[\"url\"]:\n",
    "                sp = f.split(\"|\")\n",
    "                if sp[-1] == files_type:\n",
    "                    all_files.append(sp[0].split(\".html\")[0])\n",
    "    return sorted(all_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&table_id=Amon&variable_id=tas&experiment_id=historical&institution_id=NCAR&source_id=CESM2&member_id=r10i1p1f1&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=0\n"
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     "data": {
      "text/plain": [
       "['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc']"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "result = esgf_search(activity_id='CMIP', table_id='Amon', variable_id='tas', experiment_id='historical',\n",
    "                  institution_id=\"NCAR\", source_id=\"CESM2\", member_id=\"r10i1p1f1\")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data with Xarray\n",
    "\n",
    "These are OPeNDAP endpoints. Xarray, together with the netCDF4 python library, allow lazy loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rpa/Code/xarray/xarray/conventions.py:494: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-663bbbd0-bb58-4e25-9192-22a04a05886e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-663bbbd0-bb58-4e25-9192-22a04a05886e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3efb21c0-ab10-4ffd-a420-abd01dcc5f06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3efb21c0-ab10-4ffd-a420-abd01dcc5f06' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-05276061-3ed7-495b-9481-6402ecd913a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05276061-3ed7-495b-9481-6402ecd913a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b28d8a3-2f74-44d1-a9b9-bc751cc2e219' class='xr-var-data-in' type='checkbox'><label for='data-0b28d8a3-2f74-44d1-a9b9-bc751cc2e219' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>_ChunkSizes :</span></dt><dd>288</dd></dl></div><pre class='xr-var-data'>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></li><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'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-ee266fd5-b5aa-4679-9aa2-d786e1da8f32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee266fd5-b5aa-4679-9aa2-d786e1da8f32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9db753f-b4f4-4ae3-b964-3bbc81dbf202' class='xr-var-data-in' type='checkbox'><label for='data-d9db753f-b4f4-4ae3-b964-3bbc81dbf202' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>_ChunkSizes :</span></dt><dd>192</dd></dl></div><pre class='xr-var-data'>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-15 12:00:00</div><input id='attrs-5b0e7a4a-8c69-47f0-b858-42277878d98e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b0e7a4a-8c69-47f0-b858-42277878d98e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4727daaf-ea31-4c9e-9fa0-3c6ba339665c' class='xr-var-data-in' type='checkbox'><label for='data-4727daaf-ea31-4c9e-9fa0-3c6ba339665c' 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>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, 2, 15),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, 4, 45),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, 5, 74), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, 5, 288),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, 1, 319),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, 3, 349)], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-1925132f-8970-45c2-9da3-82f4f0ab3392' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1925132f-8970-45c2-9da3-82f4f0ab3392' class='xr-section-summary' >Data variables: <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>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 2), meta=np.ndarray&gt;</div><input id='attrs-b305b164-c13a-48f8-9f5f-082ee7ed94e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b305b164-c13a-48f8-9f5f-082ee7ed94e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8a75fb-095c-4628-a79a-eec0ac00cff3' class='xr-var-data-in' type='checkbox'><label for='data-2e8a75fb-095c-4628-a79a-eec0ac00cff3' 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>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><pre class='xr-var-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> 31.68 kB </td> <td> 9.60 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (600, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 12 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
       "  <line x1=\"0\" y1=\"72\" x2=\"25\" y2=\"72\" />\n",
       "  <line x1=\"0\" y1=\"109\" x2=\"25\" y2=\"109\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.000000,0.000000 25.412617,0.000000 25.412617,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 192, 2), meta=np.ndarray&gt;</div><input id='attrs-84054537-6456-4690-805b-fa55bdf68305' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84054537-6456-4690-805b-fa55bdf68305' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f325c17-c733-472e-b65f-ded950f9ab7c' class='xr-var-data-in' type='checkbox'><label for='data-2f325c17-c733-472e-b65f-ded950f9ab7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><pre class='xr-var-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> 6.08 MB </td> <td> 1.84 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 192, 2) </td> <td> (600, 192, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 20 Tasks </td><td> 4 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=\"156\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"129.013868\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"126.000852\" y=\"89.801051\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,89.801051)\">192</text>\n",
       "  <text x=\"35.294118\" y=\"93.719750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,93.719750)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 288, 2), meta=np.ndarray&gt;</div><input id='attrs-4f9d6f77-5cc7-4a5e-a2e2-327791ca8f2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f9d6f77-5cc7-4a5e-a2e2-327791ca8f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e267278f-843b-4669-9437-ee7ff2504738' class='xr-var-data-in' type='checkbox'><label for='data-e267278f-843b-4669-9437-ee7ff2504738' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>[288   2]</dd></dl></div><pre class='xr-var-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> 9.12 MB </td> <td> 2.76 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 288, 2) </td> <td> (600, 288, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 20 Tasks </td><td> 4 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=\"156\" height=\"161\" 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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       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-abe5499a-7639-4dcf-bb16-ebe29d13cd95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abe5499a-7639-4dcf-bb16-ebe29d13cd95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f94a584-28f8-48df-94b5-5e3f90a85451' class='xr-var-data-in' type='checkbox'><label for='data-6f94a584-28f8-48df-94b5-5e3f90a85451' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 192 288]</dd></dl></div><pre class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 12 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-6b0d8c51-c5d0-4206-a97f-f24cbe36de8e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b0d8c51-c5d0-4206-a97f-f24cbe36de8e' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>306600.0</dd><dt><span>case_id :</span></dt><dd>24</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.010</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-12T06:39:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/e47b79db-3925-45a7-9c0a-6799c2f1e8ae</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 1980)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnd) object dask.array<chunksize=(600, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, lat, nbnd) float64 dask.array<chunksize=(600, 192, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, lon, nbnd) float64 dask.array<chunksize=(600, 288, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 dask.array<chunksize=(600, 192, 288), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            674885.0\n",
       "    branch_time_in_parent:           306600.0\n",
       "    case_id:                         24\n",
       "    cesm_casename:                   b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                         cesm_cmip6@ucar.edu\n",
       "    creation_date:                   2019-03-12T06:39:18Z\n",
       "    data_specs_version:              01.00.29\n",
       "    experiment:                      Simulation of recent past (1850 to 2014)...\n",
       "    experiment_id:                   historical\n",
       "    external_variables:              areacella\n",
       "    forcing_index:                   1\n",
       "    frequency:                       mon\n",
       "    further_info_url:                https://furtherinfo.es-doc.org/CMIP6.NCA...\n",
       "    grid:                            native 0.9x1.25 finite volume grid (192x...\n",
       "    grid_label:                      gn\n",
       "    initialization_index:            1\n",
       "    institution:                     National Center for Atmospheric Research...\n",
       "    institution_id:                  NCAR\n",
       "    license:                         CMIP6 model data produced by <The Nation...\n",
       "    mip_era:                         CMIP6\n",
       "    model_doi_url:                   https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:              100 km\n",
       "    parent_activity_id:              CMIP\n",
       "    parent_experiment_id:            piControl\n",
       "    parent_mip_era:                  CMIP6\n",
       "    parent_source_id:                CESM2\n",
       "    parent_time_units:               days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:            r1i1p1f1\n",
       "    physics_index:                   1\n",
       "    product:                         model-output\n",
       "    realization_index:               10\n",
       "    realm:                           atmos\n",
       "    source:                          CESM2 (2017): atmosphere: CAM6 (0.9x1.25...\n",
       "    source_id:                       CESM2\n",
       "    source_type:                     AOGCM BGC\n",
       "    sub_experiment:                  none\n",
       "    sub_experiment_id:               none\n",
       "    table_id:                        Amon\n",
       "    tracking_id:                     hdl:21.14100/e47b79db-3925-45a7-9c0a-679...\n",
       "    variable_id:                     tas\n",
       "    variant_info:                    CMIP6 20th century experiments (1850-201...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are mulitple sources of the same data--need to pick one\n",
    "files_to_open = result[-4:]\n",
    "\n",
    "ds = xr.open_mfdataset(files_to_open, combine='by_coords')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a map from a specific date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x11f86dc88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 402
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.tas.sel(time='1950-01').squeeze().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a timeseries of global-average surface air temperature. For this we need the area weighting factor for each gridpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=0\n",
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=10\n",
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=20\n"
     ]
    },
    {
     "name": "stderr",
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      "/Users/rpa/Code/xarray/xarray/conventions.py:494: SerializationWarning: variable 'areacella' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7eb22ea0-29a7-48dc-b305-577a19113d7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7eb22ea0-29a7-48dc-b305-577a19113d7a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d86f0767-8405-419e-a7dc-6e1dcb3de5cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d86f0767-8405-419e-a7dc-6e1dcb3de5cc' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-ce422c33-0805-4793-be87-c9fa94b8a8fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce422c33-0805-4793-be87-c9fa94b8a8fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5795e33a-57f7-4c2a-8f26-344841a062e6' class='xr-var-data-in' type='checkbox'><label for='data-5795e33a-57f7-4c2a-8f26-344841a062e6' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>_ChunkSizes :</span></dt><dd>192</dd></dl></div><pre class='xr-var-data'>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></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'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-a71a8619-c2fc-4d7d-a82e-9bdd1dbaca4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a71a8619-c2fc-4d7d-a82e-9bdd1dbaca4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efd9192-aa92-4f94-bf34-e47e40e52a0f' class='xr-var-data-in' type='checkbox'><label for='data-7efd9192-aa92-4f94-bf34-e47e40e52a0f' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>_ChunkSizes :</span></dt><dd>288</dd></dl></div><pre class='xr-var-data'>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-3ad2df80-909a-4fa2-914d-9e012c0b88ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ad2df80-909a-4fa2-914d-9e012c0b88ce' class='xr-section-summary' >Data variables: <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>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0074a43e-dc4b-46e6-8d30-27ac07484861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0074a43e-dc4b-46e6-8d30-27ac07484861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad232baa-fcb2-411d-904b-bddb1815f73f' class='xr-var-data-in' type='checkbox'><label for='data-ad232baa-fcb2-411d-904b-bddb1815f73f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><pre class='xr-var-data'>array([[-90.      , -89.528796],\n",
       "       [-89.528796, -88.586387],\n",
       "       [-88.586387, -87.643979],\n",
       "       ...,\n",
       "       [ 87.643979,  88.586387],\n",
       "       [ 88.586387,  89.528796],\n",
       "       [ 89.528796,  90.      ]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96fa1fa1-6d89-44f3-835f-1bbad7f6a0ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96fa1fa1-6d89-44f3-835f-1bbad7f6a0ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f1e48d4-6f8e-4eca-805a-bcb87c2f28c2' class='xr-var-data-in' type='checkbox'><label for='data-4f1e48d4-6f8e-4eca-805a-bcb87c2f28c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>[288   2]</dd></dl></div><pre class='xr-var-data'>array([[ -0.625,   0.625],\n",
       "       [  0.625,   1.875],\n",
       "       [  1.875,   3.125],\n",
       "       ...,\n",
       "       [355.625, 356.875],\n",
       "       [356.875, 358.125],\n",
       "       [358.125, 359.375]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>areacella</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-018b16bf-b000-43f6-9d31-5af091a77d5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-018b16bf-b000-43f6-9d31-5af091a77d5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0473145f-6b61-4b1d-831c-6bcd665060cb' class='xr-var-data-in' type='checkbox'><label for='data-0473145f-6b61-4b1d-831c-6bcd665060cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>_ChunkSizes :</span></dt><dd>[192 288]</dd></dl></div><pre class='xr-var-data'>array([[2.994837e+07, 2.994837e+07, 2.994837e+07, ..., 2.994837e+07,\n",
       "        2.994837e+07, 2.994837e+07],\n",
       "       [2.395748e+08, 2.395748e+08, 2.395748e+08, ..., 2.395748e+08,\n",
       "        2.395748e+08, 2.395748e+08],\n",
       "       [4.790848e+08, 4.790848e+08, 4.790848e+08, ..., 4.790848e+08,\n",
       "        4.790848e+08, 4.790848e+08],\n",
       "       ...,\n",
       "       [4.790848e+08, 4.790848e+08, 4.790848e+08, ..., 4.790848e+08,\n",
       "        4.790848e+08, 4.790848e+08],\n",
       "       [2.395748e+08, 2.395748e+08, 2.395748e+08, ..., 2.395748e+08,\n",
       "        2.395748e+08, 2.395748e+08],\n",
       "       [2.994837e+07, 2.994837e+07, 2.994837e+07, ..., 2.994837e+07,\n",
       "        2.994837e+07, 2.994837e+07]], dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-5e6bee6b-fc24-4b88-8084-3de470166b3e' class='xr-section-summary-in' type='checkbox'  ><label for='section-5e6bee6b-fc24-4b88-8084-3de470166b3e' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>306600.0</dd><dt><span>case_id :</span></dt><dd>24</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.010</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-12T06:40:59Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>fx</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/54fe4682-51a1-43ca-a7d5-66b7bca3f8c8</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    lat_bnds   (lat, nbnd) float64 ...\n",
       "    lon_bnds   (lon, nbnd) float64 ...\n",
       "    areacella  (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_time_in_parent:  306600.0\n",
       "    case_id:                24\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-03-12T06:40:59Z\n",
       "    data_specs_version:     01.00.29\n",
       "    experiment:             Simulation of recent past (1850 to 2014). Impose ...\n",
       "    experiment_id:          historical\n",
       "    forcing_index:          1\n",
       "    frequency:              fx\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    grid:                   native 0.9x1.25 finite volume grid (192x288 latxlon)\n",
       "    grid_label:             gn\n",
       "    initialization_index:   1\n",
       "    institution:            National Center for Atmospheric Research, Climate...\n",
       "    institution_id:         NCAR\n",
       "    license:                CMIP6 model data produced by <The National Center...\n",
       "    mip_era:                CMIP6\n",
       "    model_doi_url:          https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:     100 km\n",
       "    parent_activity_id:     CMIP\n",
       "    parent_experiment_id:   piControl\n",
       "    parent_mip_era:         CMIP6\n",
       "    parent_source_id:       CESM2\n",
       "    parent_time_units:      days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:   r1i1p1f1\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      10\n",
       "    realm:                  atmos land\n",
       "    source:                 CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite v...\n",
       "    source_id:              CESM2\n",
       "    source_type:            AOGCM BGC\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               fx\n",
       "    tracking_id:            hdl:21.14100/54fe4682-51a1-43ca-a7d5-66b7bca3f8c8\n",
       "    variable_id:            areacella\n",
       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
       "    variant_label:          r10i1p1f1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_area = esgf_search(variable_id='areacella', activity_id='CMIP',\n",
    "                         experiment_id='historical', institution_id=\"NCAR\", source_id=\"CESM2\")\n",
    "ds_area = xr.open_dataset(files_area[0])\n",
    "ds_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "</style><div class='xr-wrap'><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>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f8646c1b-6f40-4389-bad6-82c33d07b289' class='xr-array-in' type='checkbox' ><label for='section-f8646c1b-6f40-4389-bad6-82c33d07b289' 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=(600,), meta=np.ndarray&gt;</span></div><pre class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 7.92 kB </td> <td> 2.40 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980,) </td> <td> (600,) </td></tr>\n",
       "    <tr><th> Count </th><td> 38 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></pre></div></li><li class='xr-section-item'><input id='section-14c7c401-1f57-438d-873a-2e48ca1cb236' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14c7c401-1f57-438d-873a-2e48ca1cb236' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-15 12:00:00</div><input id='attrs-93816e33-86ab-42ec-8971-386847679935' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93816e33-86ab-42ec-8971-386847679935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34908fcf-0a76-43d5-bb73-3dda0174798b' class='xr-var-data-in' type='checkbox'><label for='data-34908fcf-0a76-43d5-bb73-3dda0174798b' 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>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, 2, 15),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, 4, 45),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, 5, 74), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, 5, 288),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, 1, 319),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, 3, 349)], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-70b3e78c-eafb-401a-bee3-ab48adfe1284' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70b3e78c-eafb-401a-bee3-ab48adfe1284' 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: 1980)>\n",
       "dask.array<truediv, shape=(1980,), dtype=float32, chunksize=(600,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_area = ds_area.areacella.sum(dim=['lon', 'lat'])\n",
    "ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area\n",
    "ta_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.67 s, sys: 4.42 s, total: 8.08 s\n",
      "Wall time: 45.4 s\n"
     ]
    },
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       "  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: #fcfcfc;\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: #efefef;\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: #555;\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: #fff !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: #fff;\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><div class='xr-wrap'><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>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fa8f04a5-e405-4ce2-afe9-62d2eaf11511' class='xr-array-in' type='checkbox' ><label for='section-fa8f04a5-e405-4ce2-afe9-62d2eaf11511' 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>284.99948 285.23215 285.85364 ... 288.54376 287.61884 287.06284</span></div><pre class='xr-array-data'>array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
       "       287.06284], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-0501aa98-af52-4a5c-8c04-4c234a9c9174' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0501aa98-af52-4a5c-8c04-4c234a9c9174' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-15 12:00:00</div><input id='attrs-8e85b89b-cbfb-45ca-a759-c06b50e0c24d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e85b89b-cbfb-45ca-a759-c06b50e0c24d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76bb6b7c-bf90-46e5-b7ae-62ce4bfa7ad9' class='xr-var-data-in' type='checkbox'><label for='data-76bb6b7c-bf90-46e5-b7ae-62ce4bfa7ad9' 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>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, 2, 15),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, 4, 45),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, 5, 74), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, 5, 288),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, 1, 319),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, 3, 349)], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-e7905278-46e1-414d-92dd-5573b36da7fb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7905278-46e1-414d-92dd-5573b36da7fb' 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: 1980)>\n",
       "array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
       "       287.06284], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time ta_timeseries.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Global Mean Surface Air Temperature')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta_timeseries.plot(label='monthly')\n",
    "ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')\n",
    "plt.legend()\n",
    "plt.title('Global Mean Surface Air Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}