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   "metadata": {},
   "source": [
    "<img src=\"http://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png\" align=\"center\" width=\"30%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Xarray 7: Calculating ENSO with Xarray\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives \n",
    "\n",
    "- Load SST data from CESM2\n",
    "- Mask data using `.where()` method\n",
    "- Compute climatologies and anomalies using `.groupby()`\n",
    "- Use `.rolling()` to compute moving average\n",
    "- Compute, normalize, and plot the Niño 3.4 Index "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "\n",
    "\n",
    "| Concepts | Importance | Notes |\n",
    "| --- | --- | --- |\n",
    "| [Introduction to Xarray](./xarray.ipynb) | Necessary | |\n",
    "| [Computation and Masking](./computation-masking.ipynb) | Necessary | |\n",
    "\n",
    "\n",
    "\n",
    "- **Time to learn**: 20 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from pythia_datasets import DATASETS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Niño 3.4 Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this notebook, we are going to combine all concepts/topics we've covered so far to compute [Niño 3.4 Index](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) for the CESM2 submission for the [CMIP6 project](https://esgf-node.llnl.gov/projects/cmip6/). \n",
    "\n",
    "\n",
    "> Niño 3.4 (5N-5S, 170W-120W): The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more.\n",
    "\n",
    "> Nino X Index computation: (a) Compute area averaged total SST from Niño X region; (b) Compute monthly climatology (e.g., 1950-1979) for area averaged total SST from Niño X region, and subtract climatology from area averaged total SST time series to obtain anomalies; (c) Smooth the anomalies with a 5-month running mean; (d) Normalize the smoothed values by its standard deviation over the climatological period.\n",
    "\n",
    "\n",
    "![](https://www.ncdc.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)\n",
    "\n",
    "\n",
    "At the end of this notebook, you should be able to produce a plot that looks similar to this [Oceanic Niño Index plot](https://climatedataguide.ucar.edu/sites/default/files/styles/node_lightbox_display/public/key_figures/climate_data_set/indices_oni_2_2_lg.png?itok=61jS7Jz7):\n",
    "\n",
    "![](https://climatedataguide.ucar.edu/sites/default/files/styles/node_lightbox_display/public/key_figures/climate_data_set/indices_oni_2_2_lg.png?itok=61jS7Jz7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the sea surface temperature and areacello datasets and use Xarray's `merge` method to combine them into a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-stderr"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/knight/anaconda_aug22/envs/aug22_env/lib/python3.10/site-packages/xarray/conventions.py:521: SerializationWarning: variable 'tos' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 180, d2: 2, lat: 180, lon: 360)\n",
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       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
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       "    lon_bnds   (lon, d2) float64 0.0 1.0 1.0 2.0 2.0 ... 358.0 359.0 359.0 360.0\n",
       "    tos        (time, lat, lon) float32 ...\n",
       "    areacello  (lat, lon) float64 1.079e+08 1.079e+08 ... 1.079e+08 1.079e+08\n",
       "Attributes: (12/45)\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:  219000.0\n",
       "    case_id:                972\n",
       "    ...                     ...\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               Omon\n",
       "    tracking_id:            hdl:21.14100/2975ffd3-1d7b-47e3-961a-33f212ea4eb2\n",
       "    variable_id:            tos\n",
       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
       "    variant_label:          r11i1p1f1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2d4a7dd7-00ed-4c16-a8cb-398ebb9593fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d4a7dd7-00ed-4c16-a8cb-398ebb9593fa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 180</li><li><span>d2</span>: 2</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0e5ccb3b-e0b7-4f5e-a235-5f10c99ace0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e5ccb3b-e0b7-4f5e-a235-5f10c99ace0e' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-ed9867e5-903f-47e9-9ec3-f8deb537711d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed9867e5-903f-47e9-9ec3-f8deb537711d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8440d91d-12de-4da4-9b71-28dcef936cc7' class='xr-var-data-in' type='checkbox'><label for='data-8440d91d-12de-4da4-9b71-28dcef936cc7' 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></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2001, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2002, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2003, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2004, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2005, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2006, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2011, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2013, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-c1b0cfc6-0ee5-4c62-bcaf-0a28be5be659' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1b0cfc6-0ee5-4c62-bcaf-0a28be5be659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8025f1cd-245a-4382-8b17-f0556183d047' class='xr-var-data-in' type='checkbox'><label for='data-8025f1cd-245a-4382-8b17-f0556183d047' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-97493318-14b4-493c-918c-57e60f5680f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97493318-14b4-493c-918c-57e60f5680f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22fb451a-358d-42a3-8d1a-0e92a9ba3d44' class='xr-var-data-in' type='checkbox'><label for='data-22fb451a-358d-42a3-8d1a-0e92a9ba3d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddec9731-fe64-458a-b267-5cce3efb7437' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddec9731-fe64-458a-b267-5cce3efb7437' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf62f1b0-7b51-4282-9ed2-f19c5cc0e0dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf62f1b0-7b51-4282-9ed2-f19c5cc0e0dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cb24a6c-b334-46cc-ae31-278966cbbba4' class='xr-var-data-in' type='checkbox'><label for='data-9cb24a6c-b334-46cc-ae31-278966cbbba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, d2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fcf577c6-8d9f-4330-a040-729079313c62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcf577c6-8d9f-4330-a040-729079313c62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de988bb-a885-42a0-9d3a-23b5ec4e0141' class='xr-var-data-in' type='checkbox'><label for='data-0de988bb-a885-42a0-9d3a-23b5ec4e0141' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[-90., -89.],\n",
       "       [-89., -88.],\n",
       "       [-88., -87.],\n",
       "       ...,\n",
       "       [ 87.,  88.],\n",
       "       [ 88.,  89.],\n",
       "       [ 89.,  90.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, d2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2aa0730-1298-4e0a-8cbd-53664f206070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2aa0730-1298-4e0a-8cbd-53664f206070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7c78490-9bd7-47b0-aa89-17307e942768' class='xr-var-data-in' type='checkbox'><label for='data-e7c78490-9bd7-47b0-aa89-17307e942768' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,   1.],\n",
       "       [  1.,   2.],\n",
       "       [  2.,   3.],\n",
       "       ...,\n",
       "       [357., 358.],\n",
       "       [358., 359.],\n",
       "       [359., 360.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-780d70b5-dcec-449a-a47a-0542a2fcb64e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-780d70b5-dcec-449a-a47a-0542a2fcb64e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf729257-bdd5-40bc-ba85-714c653ed569' class='xr-var-data-in' type='checkbox'><label for='data-bf729257-bdd5-40bc-ba85-714c653ed569' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.</dd><dt><span>description :</span></dt><dd>This may differ from &quot;surface temperature&quot; in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tos</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>mipTable :</span></dt><dd>Omon</dd><dt><span>out_name :</span></dt><dd>tos</dd><dt><span>prov :</span></dt><dd>Omon ((isd.003))</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>standard_name :</span></dt><dd>sea_surface_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>Sea Surface Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>variable_id :</span></dt><dd>tos</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f645b588-0d5d-47b2-af6d-44c7a9ee70bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f645b588-0d5d-47b2-af6d-44c7a9ee70bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f8c3fcc-4f3e-44ef-a501-f0eef905d964' class='xr-var-data-in' type='checkbox'><label for='data-0f8c3fcc-4f3e-44ef-a501-f0eef905d964' 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>TAREA</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report ocean variables and variables which are requested as used on the model ocean grid (e.g. hfsso, which is a downward heat flux from the atmosphere interpolated onto the ocean grid). 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>areacello</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Ocean Variables</dd><dt><span>mipTable :</span></dt><dd>Ofx</dd><dt><span>out_name :</span></dt><dd>areacello</dd><dt><span>prov :</span></dt><dd>Ofx ((isd.003))</dd><dt><span>realm :</span></dt><dd>ocean</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 Ocean 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>areacello</dd></dl></div><div class='xr-var-data'><pre>array([[1.078982e+08, 1.078982e+08, 1.078982e+08, ..., 1.078982e+08,\n",
       "        1.078982e+08, 1.078982e+08],\n",
       "       [3.236618e+08, 3.236618e+08, 3.236618e+08, ..., 3.236618e+08,\n",
       "        3.236618e+08, 3.236618e+08],\n",
       "       [5.393268e+08, 5.393268e+08, 5.393268e+08, ..., 5.393268e+08,\n",
       "        5.393268e+08, 5.393268e+08],\n",
       "       ...,\n",
       "       [5.393268e+08, 5.393268e+08, 5.393268e+08, ..., 5.393268e+08,\n",
       "        5.393268e+08, 5.393268e+08],\n",
       "       [3.236618e+08, 3.236618e+08, 3.236618e+08, ..., 3.236618e+08,\n",
       "        3.236618e+08, 3.236618e+08],\n",
       "       [1.078982e+08, 1.078982e+08, 1.078982e+08, ..., 1.078982e+08,\n",
       "        1.078982e+08, 1.078982e+08]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1489a610-a5e8-41de-bb97-0ca8121fcc30' class='xr-section-summary-in' type='checkbox'  ><label for='section-1489a610-a5e8-41de-bb97-0ca8121fcc30' class='xr-section-summary' >Attributes: <span>(45)</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>219000.0</dd><dt><span>case_id :</span></dt><dd>972</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.011</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-04-02T04:44:58Z</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>areacello</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.r11i1p1f1</dd><dt><span>grid :</span></dt><dd>ocean data regridded from native gx1v7 displaced pole grid (384x320 latxlon) to 180x360 latxlon using conservative regridding</dd><dt><span>grid_label :</span></dt><dd>gr</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>1x1 degree</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>11</dd><dt><span>realm :</span></dt><dd>ocean</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>Omon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/2975ffd3-1d7b-47e3-961a-33f212ea4eb2</dd><dt><span>variable_id :</span></dt><dd>tos</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)</dd><dt><span>variant_label :</span></dt><dd>r11i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 180, d2: 2, lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * time       (time) object 2000-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "Dimensions without coordinates: d2\n",
       "Data variables:\n",
       "    time_bnds  (time, d2) object ...\n",
       "    lat_bnds   (lat, d2) float64 ...\n",
       "    lon_bnds   (lon, d2) float64 ...\n",
       "    tos        (time, lat, lon) float32 ...\n",
       "    areacello  (lat, lon) float64 ...\n",
       "Attributes: (12/45)\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:  219000.0\n",
       "    case_id:                972\n",
       "    ...                     ...\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               Omon\n",
       "    tracking_id:            hdl:21.14100/2975ffd3-1d7b-47e3-961a-33f212ea4eb2\n",
       "    variable_id:            tos\n",
       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
       "    variant_label:          r11i1p1f1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = DATASETS.fetch('CESM2_sst_data.nc')\n",
    "data = xr.open_dataset(filepath)\n",
    "filepath2 = DATASETS.fetch('CESM2_grid_variables.nc')\n",
    "areacello = xr.open_dataset(filepath2).areacello\n",
    "ds = xr.merge([data, areacello])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tos&#x27; ()&gt;\n",
       "array(-1.89237607)\n",
       "Coordinates:\n",
       "    time     object 2000-01-15 12:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tos'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-888b053a-2166-41f9-bfed-cd4aec9e7343' class='xr-array-in' type='checkbox' checked><label for='section-888b053a-2166-41f9-bfed-cd4aec9e7343' 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>-1.892</span></div><div class='xr-array-data'><pre>array(-1.89237607)</pre></div></div></li><li class='xr-section-item'><input id='section-87d51a0e-7c85-4fc5-ae1b-3e000e05ebb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87d51a0e-7c85-4fc5-ae1b-3e000e05ebb0' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-15 12:00:00</div><input id='attrs-1393147c-cc21-4250-9eff-30fa476b733f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1393147c-cc21-4250-9eff-30fa476b733f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0830ba8d-c5c6-44b8-81f1-493bd61f83cb' class='xr-var-data-in' type='checkbox'><label for='data-0830ba8d-c5c6-44b8-81f1-493bd61f83cb' 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></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4feeb10-76c2-45b6-8db4-d800df6ff830' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b4feeb10-76c2-45b6-8db4-d800df6ff830' 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 'tos' ()>\n",
       "array(-1.89237607)\n",
       "Coordinates:\n",
       "    time     object 2000-01-15 12:00:00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "ds.tos.isel(time=0).min()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the first time slice to make sure the data looks okay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.Robinson(central_longitude=180))\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "ds.tos.isel(time=0).plot(\n",
    "    ax=ax, transform=ccrs.PlateCarree(), vmin=-2, vmax=30, cbar_kwargs={'shrink': 0.5}, cmap='coolwarm'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the Niño 3.4 region \n",
    "\n",
    "There are a couple ways we could selecting the Niño 3.4 region:\n",
    "\n",
    "1. Use `sel()` or `isel()`\n",
    "2. Use `where()` and select all vlaues within the bounds of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1: `sel`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos_nino34 = ds.sel(lat=slice(-5, 5), lon=slice(190, 240))\n",
    "tos_nino34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2: `where`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos_nino34 = ds.where((ds.lat<5) & (ds.lat>-5) & (ds.lon>190) & (ds.lon<240), drop=True)\n",
    "tos_nino34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the selected region to make sure we are doing the right thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.Robinson(central_longitude=180))\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "tos_nino34.tos.isel(time=0).plot(\n",
    "    ax=ax, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink': 0.5}, vmin=-2, vmax=30, cmap='coolwarm'\n",
    ")\n",
    "ax.set_extent((120, 300, 10, -10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compute monthly climatology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Compute the monthly climo for area averaged total SST from Niño 3.4 region, and subtract climatology from area averaged total SST time series to obtain anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = tos_nino34.tos.groupby('time.month')\n",
    "tos_nino34_anom = gb - gb.mean(dim='time')\n",
    "index_nino34 = tos_nino34_anom.weighted(tos_nino34.areacello).mean(dim=['lat', 'lon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Smooth the anomalies with a 5-month running mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_nino34_rolling_mean = index_nino34.rolling(time=5,center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_nino34.plot(size=8)\n",
    "index_nino34_rolling_mean.plot()\n",
    "plt.legend(['anomaly', '5-month running mean anomaly']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize the smoothed values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll normalize the values by dividing the rolling mean by the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_dev = tos_nino34.tos.std()\n",
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_index_nino34_rolling_mean = index_nino34_rolling_mean / std_dev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the computed Niño 3.4 index. \n",
    "We'll highlight values in excess of 0.5, roughly corresponding to El Niño (warm) / La Niña (cold) events.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.fill_between(\n",
    "    normalized_index_nino34_rolling_mean.time.data,\n",
    "    normalized_index_nino34_rolling_mean.where(\n",
    "        normalized_index_nino34_rolling_mean >= 0.4\n",
    "    ).data,\n",
    "    0.4,\n",
    "    color='red',\n",
    "    alpha=0.9,\n",
    ")\n",
    "plt.fill_between(\n",
    "    normalized_index_nino34_rolling_mean.time.data,\n",
    "    normalized_index_nino34_rolling_mean.where(\n",
    "        normalized_index_nino34_rolling_mean <= -0.4\n",
    "    ).data,\n",
    "    -0.4,\n",
    "    color='blue',\n",
    "    alpha=0.9,\n",
    ")\n",
    "\n",
    "normalized_index_nino34_rolling_mean.plot(color='black')\n",
    "plt.axhline(0, color='black', lw=0.5)\n",
    "plt.axhline(0.4, color='black', linewidth=0.5, linestyle='dotted')\n",
    "plt.axhline(-0.4, color='black', linewidth=0.5, linestyle='dotted')\n",
    "plt.title('Niño 3.4 Index');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Summary \n",
    "\n",
    "- We have applied a variety of Xarray's selection, grouping, and statistical functions to compute and visualize an important climate index.\n",
    "\n",
    "### What's next?\n",
    "\n",
    "In the next notebook, we will use Xarray to analyze and visualize NWP model output in another common format, GRIB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources and References\n",
    "\n",
    "- [Niño 3.4 Index](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni)\n",
    "- [Matplotlib's `fill_between` method](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.fill_between.html)\n",
    "- [Matplotlib's `axhline` method](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html) (see also its analogous `axvline` method) \n"
   ]
  }
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