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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": null,
   "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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {},
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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-8b69fc9f-5547-4264-825d-3dff57224366' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b69fc9f-5547-4264-825d-3dff57224366' 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-4a3ad9ea-7de1-477b-a484-0a29f11de9dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a3ad9ea-7de1-477b-a484-0a29f11de9dd' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-e0279040-89a8-4335-95e6-febb058fd233' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0279040-89a8-4335-95e6-febb058fd233' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8110aff-78b0-4822-a531-c9ff04598818' class='xr-var-data-in' type='checkbox'><label for='data-f8110aff-78b0-4822-a531-c9ff04598818' 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-02293cfd-dc2a-4dba-affc-6d45c3e59a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02293cfd-dc2a-4dba-affc-6d45c3e59a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fed3627-7b01-4eeb-98dd-b9389ccbb39d' class='xr-var-data-in' type='checkbox'><label for='data-9fed3627-7b01-4eeb-98dd-b9389ccbb39d' 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'>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-d6908530-cc9a-418d-b61f-57fcc24cdc3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6908530-cc9a-418d-b61f-57fcc24cdc3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db1f527c-65ab-426e-b504-6ef6cd5a68ff' class='xr-var-data-in' type='checkbox'><label for='data-db1f527c-65ab-426e-b504-6ef6cd5a68ff' 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-d78ae067-2838-4fb6-9dfb-1e16d81aad48' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d78ae067-2838-4fb6-9dfb-1e16d81aad48' 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-c1db97e3-55d3-4a2d-b9c2-eaf34880649b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1db97e3-55d3-4a2d-b9c2-eaf34880649b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab7c097-f114-46c2-a9ee-40b3a4dc4627' class='xr-var-data-in' type='checkbox'><label for='data-eab7c097-f114-46c2-a9ee-40b3a4dc4627' 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",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <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-b4783e79-3ceb-4c0f-bb78-1fd9f8f9f58f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4783e79-3ceb-4c0f-bb78-1fd9f8f9f58f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b67a7ca-4547-4bcd-87ef-a587ae9268be' class='xr-var-data-in' type='checkbox'><label for='data-9b67a7ca-4547-4bcd-87ef-a587ae9268be' 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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       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <polygon points=\"80.588235,70.588235 106.000852,70.588235 106.000852,109.013868 80.588235,109.013868\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\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-1cfcc732-d61a-4edd-b45e-296188c55548' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cfcc732-d61a-4edd-b45e-296188c55548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c4fdecb-018a-40df-ad6a-513f26addce8' class='xr-var-data-in' type='checkbox'><label for='data-0c4fdecb-018a-40df-ad6a-513f26addce8' 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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       "  <line x1=\"52\" y1=\"42\" x2=\"52\" y2=\"83\" />\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"111\" 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-35aa8c60-f650-476f-a1c5-a474b2d31834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35aa8c60-f650-476f-a1c5-a474b2d31834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-063ad3d3-5390-456b-8b63-1e3d2fdb441c' class='xr-var-data-in' type='checkbox'><label for='data-063ad3d3-5390-456b-8b63-1e3d2fdb441c' 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> Shape </th><td> (1980, 192, 288) </td> <td> (600, 192, 288) </td></tr>\n",
       "    <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-695465ed-6a10-4c10-9769-0872c6603bd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-695465ed-6a10-4c10-9769-0872c6603bd6' 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",
       "  * 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",
       "  * 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 0x11dd89908>"
      ]
     },
     "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-c9d2c922-64bc-4485-8904-421f6aff73d2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9d2c922-64bc-4485-8904-421f6aff73d2' 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-432540f0-501d-4a99-84a9-c7590e4105b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-432540f0-501d-4a99-84a9-c7590e4105b5' 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-eb949d52-c2fd-43d5-ad91-59de31c30e03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb949d52-c2fd-43d5-ad91-59de31c30e03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-471c75bf-0eee-4b88-94b9-ed4e0e157751' class='xr-var-data-in' type='checkbox'><label for='data-471c75bf-0eee-4b88-94b9-ed4e0e157751' 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-0f5a8426-1956-4da6-a112-d871b58cddf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f5a8426-1956-4da6-a112-d871b58cddf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeb7bbd8-634c-4fea-bc58-c7e9e1e924fe' class='xr-var-data-in' type='checkbox'><label for='data-aeb7bbd8-634c-4fea-bc58-c7e9e1e924fe' 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-ff6ae4d5-7131-4ec7-8a8d-f41a63c05fc9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff6ae4d5-7131-4ec7-8a8d-f41a63c05fc9' 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-7af16beb-de97-4d5f-8627-90283bab66f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7af16beb-de97-4d5f-8627-90283bab66f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-489cd5d8-eca1-4e6a-809f-385dcc214af3' class='xr-var-data-in' type='checkbox'><label for='data-489cd5d8-eca1-4e6a-809f-385dcc214af3' 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-52be258b-b461-47e5-ac2a-347e77f0a07c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52be258b-b461-47e5-ac2a-347e77f0a07c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-852b7dcc-83fb-47a8-9e52-c9beba2d18d1' class='xr-var-data-in' type='checkbox'><label for='data-852b7dcc-83fb-47a8-9e52-c9beba2d18d1' 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-c2ba7df6-283e-4bb4-9fb1-ffd54cc80023' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2ba7df6-283e-4bb4-9fb1-ffd54cc80023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8212684e-6180-4378-9c1a-72f518d07dc2' class='xr-var-data-in' type='checkbox'><label for='data-8212684e-6180-4378-9c1a-72f518d07dc2' 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-f80c96f9-85a3-4c88-9dc2-9182ed7d46d5' class='xr-section-summary-in' type='checkbox'  ><label for='section-f80c96f9-85a3-4c88-9dc2-9182ed7d46d5' 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-592768bf-c9d8-4866-b29f-7fa5b2f1682d' class='xr-array-in' type='checkbox' ><label for='section-592768bf-c9d8-4866-b29f-7fa5b2f1682d' 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",
       "<table>\n",
       "  <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-378aa39b-290b-453f-a243-438b2d5d8dce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-378aa39b-290b-453f-a243-438b2d5d8dce' 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-03031538-af75-4d9a-aa4d-22a62a895f30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03031538-af75-4d9a-aa4d-22a62a895f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-015d1caa-6196-4727-8051-939d126333f5' class='xr-var-data-in' type='checkbox'><label for='data-015d1caa-6196-4727-8051-939d126333f5' 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-06b576a3-34b2-4725-b071-02a0142d5fe6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06b576a3-34b2-4725-b071-02a0142d5fe6' 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 4.93 s, sys: 6.73 s, total: 11.7 s\n",
      "Wall time: 1min 35s\n"
     ]
    },
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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-310a7bd5-b8f7-4eee-973b-30bf0ac79555' class='xr-array-in' type='checkbox' ><label for='section-310a7bd5-b8f7-4eee-973b-30bf0ac79555' 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-291bf2b6-b79b-4c21-828c-051da82243e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-291bf2b6-b79b-4c21-828c-051da82243e5' 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-5209ef75-8800-4eab-90d3-015aa1dad455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5209ef75-8800-4eab-90d3-015aa1dad455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-553e67d7-2e91-46ac-876c-7718ac0cf5a3' class='xr-var-data-in' type='checkbox'><label for='data-553e67d7-2e91-46ac-876c-7718ac0cf5a3' 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-5e17c03f-fad1-45b8-bd33-a8a390406e99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e17c03f-fad1-45b8-bd33-a8a390406e99' 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": null,
   "metadata": {},
   "outputs": [],
   "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": [
    "ds.nbytes / 1e6 / 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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