3 Basic xarray - plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The xarray library has an inbuilt `plot` function that is a thin wrapper around the [matplotlib library](https://matplotlib.org).\n",
    "\n",
    "The [documentation of the xarray plot function is excellent](http://xarray.pydata.org/en/stable/plotting.html). Read it for an in-depth explanation of the power of the xarray plotting functionality. This notebook covers just enough to get started and make use of plots in subsequent tutorials.\n",
    "\n",
    "As xarray dataarrays contain all the associated metadata and coordinates for the data, it can create informative plots with very little effort."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step is to import xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to use a [jupyter \"magic\" function](https://ipython.readthedocs.io/en/stable/interactive/magics.html) that causes matplotlib figures to be displayed within the notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now open the same dataset as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xarray.open_dataset('http://dapds00.nci.org.au/thredds/dodsC/rr3/CMIP5/output1/CSIRO-BOM/ACCESS1-3/historical/mon/atmos/Amon/r1i1p1/latest/tas/tas_Amon_ACCESS1-3_historical_r1i1p1_185001-200512.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is the `xarray.DataArray` that has a `plot` method defined, it does not work on an `xarray.Dataset`. So to plot the first time step of `tas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbd6331e970>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas = ds.tas\n",
    "tas.isel(time=0).plot(size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operator chaining means it is possible to have multiple selection operators and add `.plot` to the end to visualise the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbd631faeb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas.isel(time=0).sel(lon=slice(20,160), lat=slice(-80,25)).plot(size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xarray will automatically guess the type of plot based on the dimensionality of the data being plotted. If passed too many dimensions `xarray` will default to displaying a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4534.,  13524.,  13211.,  18105.,  16757.,  18695.,  61065.,\n",
       "         77034., 220789.,  15286.]),\n",
       " array([205.5402 , 216.309  , 227.07782, 237.84662, 248.61542, 259.38422,\n",
       "        270.153  , 280.92184, 291.69064, 302.45944, 313.22824],\n",
       "       dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas.sel(time=slice('1871-01','1876-12'), lon=slice(20,160), lat=slice(-80,25)).plot(size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce the dimensionality enough and it will plot a line. For example, a time series of near surface temperatures in Brisbane (Note: this is selecting a single point, so `sel` requires either an exact location that exists in the data, or to specify `method` argument to tell it how to choose a location from the data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbd6208aee0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas.sel(time=slice('1871-01','1876-12')).sel(lat=-27.47, lon=153.03, method='nearest').plot(size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now open a new dataset from the same collection, this is the air temperature data but at all pressure levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_air = xarray.open_dataset('http://dapds00.nci.org.au/thredds/dodsC/rr3/CMIP5/output1/CSIRO-BOM/ACCESS1-3/historical/mon/atmos/Amon/r1i1p1/latest/ta/ta_Amon_ACCESS1-3_historical_r1i1p1_185001-189912.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 144, lon: 192, plev: 17, time: 600)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 1899-12-16T12:00:00\n",
       "  * plev       (plev) float64 1e+05 9.25e+04 8.5e+04 7e+04 ... 3e+03 2e+03 1e+03\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] 1850-01-01 1850-02-01 ... 1900-01-01\n",
       "    plev_bnds  (plev, bnds) float64 1.038e+05 9.625e+04 ... 1.5e+03 500.0\n",
       "    lat_bnds   (lat, bnds) float64 -90.0 -88.75 -88.75 ... 88.75 88.75 90.0\n",
       "    lon_bnds   (lon, bnds) float64 0.0 1.875 1.875 3.75 ... 358.1 358.1 360.0\n",
       "    ta         (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    institution:                     CSIRO (Commonwealth Scientific and Indus...\n",
       "    institute_id:                    CSIRO-BOM\n",
       "    experiment_id:                   historical\n",
       "    source:                          ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N...\n",
       "    model_id:                        ACCESS1.3\n",
       "    forcing:                         GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2,...\n",
       "    parent_experiment_id:            piControl\n",
       "    parent_experiment_rip:           r1i1p1\n",
       "    branch_time:                     90945.0\n",
       "    contact:                         The ACCESS wiki: http://wiki.csiro.au/co...\n",
       "    history:                         CMIP5 compliant file produced from raw A...\n",
       "    references:                      See http://wiki.csiro.au/confluence/disp...\n",
       "    initialization_method:           1\n",
       "    physics_version:                 1\n",
       "    tracking_id:                     eb0cfe7a-9e5d-4ce0-bb72-0c12b500a4d3\n",
       "    version_number:                  v20120413\n",
       "    product:                         output\n",
       "    experiment:                      historical\n",
       "    frequency:                       mon\n",
       "    creation_date:                   2012-03-23T01:31:12Z\n",
       "    Conventions:                     CF-1.4\n",
       "    project_id:                      CMIP5\n",
       "    table_id:                        Table Amon (01 February 2012) 01388cb450...\n",
       "    title:                           ACCESS1-3 model output prepared for CMIP...\n",
       "    parent_experiment:               pre-industrial control\n",
       "    modeling_realm:                  atmos\n",
       "    realization:                     1\n",
       "    cmor_version:                    2.8.0\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d810a8d4-e48f-4f1f-b027-703dca6e8513' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d810a8d4-e48f-4f1f-b027-703dca6e8513' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span class='xr-has-index'>plev</span>: 17</li><li><span class='xr-has-index'>time</span>: 600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e1f09118-26ff-4c5c-a968-4c2237219b9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1f09118-26ff-4c5c-a968-4c2237219b9a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 1899-12-...</div><input id='attrs-26ed769c-c692-4b51-ba9c-6b30b85f227f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26ed769c-c692-4b51-ba9c-6b30b85f227f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a3785c-7bbf-4771-835a-b6debdcdf531' class='xr-var-data-in' type='checkbox'><label for='data-89a3785c-7bbf-4771-835a-b6debdcdf531' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;1899-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1899-11-16T00:00:00.000000000&#x27;, &#x27;1899-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 2e+03 1e+03</div><input id='attrs-48a13bdd-4659-4c77-8c95-bea8976489be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48a13bdd-4659-4c77-8c95-bea8976489be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e35e7a-bc2f-4ef1-a881-0a79027d37cc' class='xr-var-data-in' type='checkbox'><label for='data-50e35e7a-bc2f-4ef1-a881-0a79027d37cc' 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>bounds :</span></dt><dd>plev_bnds</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n",
       "        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n",
       "         1000.])</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'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-6ae65a35-8248-44cc-9a76-1f34d18a55c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ae65a35-8248-44cc-9a76-1f34d18a55c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2447ae-5b22-4cb8-81c7-66a501441e54' class='xr-var-data-in' type='checkbox'><label for='data-0d2447ae-5b22-4cb8-81c7-66a501441e54' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
       "       -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
       "       -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
       "       -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
       "       -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
       "       -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
       "       -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
       "       -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
       "        -9.375,  -8.125,  -6.875,  -5.625,  -4.375,  -3.125,  -1.875,  -0.625,\n",
       "         0.625,   1.875,   3.125,   4.375,   5.625,   6.875,   8.125,   9.375,\n",
       "        10.625,  11.875,  13.125,  14.375,  15.625,  16.875,  18.125,  19.375,\n",
       "        20.625,  21.875,  23.125,  24.375,  25.625,  26.875,  28.125,  29.375,\n",
       "        30.625,  31.875,  33.125,  34.375,  35.625,  36.875,  38.125,  39.375,\n",
       "        40.625,  41.875,  43.125,  44.375,  45.625,  46.875,  48.125,  49.375,\n",
       "        50.625,  51.875,  53.125,  54.375,  55.625,  56.875,  58.125,  59.375,\n",
       "        60.625,  61.875,  63.125,  64.375,  65.625,  66.875,  68.125,  69.375,\n",
       "        70.625,  71.875,  73.125,  74.375,  75.625,  76.875,  78.125,  79.375,\n",
       "        80.625,  81.875,  83.125,  84.375,  85.625,  86.875,  88.125,  89.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-20aa5c8e-48af-4b81-9b60-e2cdcbf5cae3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20aa5c8e-48af-4b81-9b60-e2cdcbf5cae3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eef1d1c-b505-4475-ba22-1b1ed8122fa1' class='xr-var-data-in' type='checkbox'><label for='data-2eef1d1c-b505-4475-ba22-1b1ed8122fa1' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.9375,   2.8125,   4.6875,   6.5625,   8.4375,  10.3125,  12.1875,\n",
       "        14.0625,  15.9375,  17.8125,  19.6875,  21.5625,  23.4375,  25.3125,\n",
       "        27.1875,  29.0625,  30.9375,  32.8125,  34.6875,  36.5625,  38.4375,\n",
       "        40.3125,  42.1875,  44.0625,  45.9375,  47.8125,  49.6875,  51.5625,\n",
       "        53.4375,  55.3125,  57.1875,  59.0625,  60.9375,  62.8125,  64.6875,\n",
       "        66.5625,  68.4375,  70.3125,  72.1875,  74.0625,  75.9375,  77.8125,\n",
       "        79.6875,  81.5625,  83.4375,  85.3125,  87.1875,  89.0625,  90.9375,\n",
       "        92.8125,  94.6875,  96.5625,  98.4375, 100.3125, 102.1875, 104.0625,\n",
       "       105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
       "       119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
       "       132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
       "       145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
       "       158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
       "       171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
       "       184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
       "       197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
       "       210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
       "       224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
       "       237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
       "       250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
       "       263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
       "       276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
       "       289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
       "       302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
       "       315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
       "       329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
       "       342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
       "       355.3125, 357.1875, 359.0625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a75bce5d-cab4-49c6-8666-34d033e88b15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a75bce5d-cab4-49c6-8666-34d033e88b15' 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, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6a778a6-5bfe-4f1c-be79-9f143063ed9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6a778a6-5bfe-4f1c-be79-9f143063ed9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d4e95d5-c238-4640-b24c-da79832bc8cb' class='xr-var-data-in' type='checkbox'><label for='data-4d4e95d5-c238-4640-b24c-da79832bc8cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;1850-01-01T00:00:00.000000000&#x27;, &#x27;1850-02-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-01T00:00:00.000000000&#x27;, &#x27;1850-03-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-03-01T00:00:00.000000000&#x27;, &#x27;1850-04-01T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;1899-10-01T00:00:00.000000000&#x27;, &#x27;1899-11-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1899-11-01T00:00:00.000000000&#x27;, &#x27;1899-12-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1899-12-01T00:00:00.000000000&#x27;, &#x27;1900-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev_bnds</span></div><div class='xr-var-dims'>(plev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2010d7a5-9a37-4cd1-ad3f-bb3aab90e2a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2010d7a5-9a37-4cd1-ad3f-bb3aab90e2a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfc1556e-37f7-4ed9-bef6-b9179838e00a' class='xr-var-data-in' type='checkbox'><label for='data-dfc1556e-37f7-4ed9-bef6-b9179838e00a' 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([[103750.,  96250.],\n",
       "       [ 96250.,  88750.],\n",
       "       [ 88750.,  77500.],\n",
       "       [ 77500.,  65000.],\n",
       "       [ 65000.,  55000.],\n",
       "       [ 55000.,  45000.],\n",
       "       [ 45000.,  35000.],\n",
       "       [ 35000.,  27500.],\n",
       "       [ 27500.,  22500.],\n",
       "       [ 22500.,  17500.],\n",
       "       [ 17500.,  12500.],\n",
       "       [ 12500.,   8500.],\n",
       "       [  8500.,   6000.],\n",
       "       [  6000.,   4000.],\n",
       "       [  4000.,   2500.],\n",
       "       [  2500.,   1500.],\n",
       "       [  1500.,    500.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6783720-ec44-4958-8274-70e5401a0c2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6783720-ec44-4958-8274-70e5401a0c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6f328b6-d47b-475c-8a69-22a40581623a' class='xr-var-data-in' type='checkbox'><label for='data-a6f328b6-d47b-475c-8a69-22a40581623a' 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([[-90.  , -88.75],\n",
       "       [-88.75, -87.5 ],\n",
       "       [-87.5 , -86.25],\n",
       "       ...,\n",
       "       [ 86.25,  87.5 ],\n",
       "       [ 87.5 ,  88.75],\n",
       "       [ 88.75,  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, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f3f3d6f-168b-4162-9b76-af12d58a4a5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f3f3d6f-168b-4162-9b76-af12d58a4a5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba35c2d-a9da-4a7c-a1ae-c9be0792af6b' class='xr-var-data-in' type='checkbox'><label for='data-6ba35c2d-a9da-4a7c-a1ae-c9be0792af6b' 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([[  0.   ,   1.875],\n",
       "       [  1.875,   3.75 ],\n",
       "       [  3.75 ,   5.625],\n",
       "       ...,\n",
       "       [354.375, 356.25 ],\n",
       "       [356.25 , 358.125],\n",
       "       [358.125, 360.   ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ta</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d583872d-c50c-426e-992c-e7218d9d6c61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d583872d-c50c-426e-992c-e7218d9d6c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26cfc68d-9b0c-4a84-a687-5e6a068cb8d8' class='xr-var-data-in' type='checkbox'><label for='data-26cfc68d-9b0c-4a84-a687-5e6a068cb8d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2012-03-23T01:31:12Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20). 2012-03-23T01:31:12Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_ACCESS1-3_historical_r0i0p0.nc areacella: areacella_fx_ACCESS1-3_historical_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>[282009600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fa1ccf7-95d4-4c09-b963-3b6ce792bc75' class='xr-section-summary-in' type='checkbox'  ><label for='section-6fa1ccf7-95d4-4c09-b963-3b6ce792bc75' class='xr-section-summary' >Attributes: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>institution :</span></dt><dd>CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia), and BOM (Bureau of Meteorology, Australia)</dd><dt><span>institute_id :</span></dt><dd>CSIRO-BOM</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>source :</span></dt><dd>ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N96 grid-point, 1.875 degrees EW x approx 1.25 degree NS, 38 levels); ocean: NOAA/GFDL MOM4p1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S, 50 levels); sea ice: CICE4.1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S); land: CABLE1.0 (1.875 degree EW x 1.25 degree NS, 6 levels</dd><dt><span>model_id :</span></dt><dd>ACCESS1.3</dd><dt><span>forcing :</span></dt><dd>GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_experiment_rip :</span></dt><dd>r1i1p1</dd><dt><span>branch_time :</span></dt><dd>90945.0</dd><dt><span>contact :</span></dt><dd>The ACCESS wiki: http://wiki.csiro.au/confluence/display/ACCESS/Home. Contact Tony.Hirst@csiro.au regarding the ACCESS coupled climate model. Contact Peter.Uhe@csiro.au regarding ACCESS coupled climate model CMIP5 datasets.</dd><dt><span>history :</span></dt><dd>CMIP5 compliant file produced from raw ACCESS model output using the ACCESS Post-Processor and CMOR2. 2012-03-23T01:31:12Z CMOR rewrote data to comply with CF standards and CMIP5 requirements. Fri Apr 13 09:55:47 2012: forcing attribute modified to correct value Fri Apr 13 12:29:39 2012: corrected model_id from ACCESS1-3 to ACCESS1.3 Thu May  3 15:23:33 2012: updated version number to v20120413.</dd><dt><span>references :</span></dt><dd>See http://wiki.csiro.au/confluence/display/ACCESS/ACCESS+Publications</dd><dt><span>initialization_method :</span></dt><dd>1</dd><dt><span>physics_version :</span></dt><dd>1</dd><dt><span>tracking_id :</span></dt><dd>eb0cfe7a-9e5d-4ce0-bb72-0c12b500a4d3</dd><dt><span>version_number :</span></dt><dd>v20120413</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>creation_date :</span></dt><dd>2012-03-23T01:31:12Z</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>project_id :</span></dt><dd>CMIP5</dd><dt><span>table_id :</span></dt><dd>Table Amon (01 February 2012) 01388cb4507c2f05326b711b09604e7e</dd><dt><span>title :</span></dt><dd>ACCESS1-3 model output prepared for CMIP5 historical</dd><dt><span>parent_experiment :</span></dt><dd>pre-industrial control</dd><dt><span>modeling_realm :</span></dt><dd>atmos</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>cmor_version :</span></dt><dd>2.8.0</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 144, lon: 192, plev: 17, time: 600)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 1899-12-16T12:00:00\n",
       "  * plev       (plev) float64 1e+05 9.25e+04 8.5e+04 7e+04 ... 3e+03 2e+03 1e+03\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] ...\n",
       "    plev_bnds  (plev, bnds) float64 ...\n",
       "    lat_bnds   (lat, bnds) float64 ...\n",
       "    lon_bnds   (lon, bnds) float64 ...\n",
       "    ta         (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    institution:                     CSIRO (Commonwealth Scientific and Indus...\n",
       "    institute_id:                    CSIRO-BOM\n",
       "    experiment_id:                   historical\n",
       "    source:                          ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N...\n",
       "    model_id:                        ACCESS1.3\n",
       "    forcing:                         GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2,...\n",
       "    parent_experiment_id:            piControl\n",
       "    parent_experiment_rip:           r1i1p1\n",
       "    branch_time:                     90945.0\n",
       "    contact:                         The ACCESS wiki: http://wiki.csiro.au/co...\n",
       "    history:                         CMIP5 compliant file produced from raw A...\n",
       "    references:                      See http://wiki.csiro.au/confluence/disp...\n",
       "    initialization_method:           1\n",
       "    physics_version:                 1\n",
       "    tracking_id:                     eb0cfe7a-9e5d-4ce0-bb72-0c12b500a4d3\n",
       "    version_number:                  v20120413\n",
       "    product:                         output\n",
       "    experiment:                      historical\n",
       "    frequency:                       mon\n",
       "    creation_date:                   2012-03-23T01:31:12Z\n",
       "    Conventions:                     CF-1.4\n",
       "    project_id:                      CMIP5\n",
       "    table_id:                        Table Amon (01 February 2012) 01388cb450...\n",
       "    title:                           ACCESS1-3 model output prepared for CMIP...\n",
       "    parent_experiment:               pre-industrial control\n",
       "    modeling_realm:                  atmos\n",
       "    realization:                     1\n",
       "    cmor_version:                    2.8.0\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_air"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select out the air temperature data variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ta&#x27; (time: 600, plev: 17, lat: 144, lon: 192)&gt;\n",
       "[282009600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00 ... 1899-12-16T12:00:00\n",
       "  * plev     (plev) float64 1e+05 9.25e+04 8.5e+04 7e+04 ... 3e+03 2e+03 1e+03\n",
       "  * lat      (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon      (lon) float64 0.9375 2.812 4.688 6.562 ... 353.4 355.3 357.2 359.1\n",
       "Attributes:\n",
       "    standard_name:     air_temperature\n",
       "    long_name:         Air Temperature\n",
       "    units:             K\n",
       "    cell_methods:      time: mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2012-03-23T01:31:12Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'ta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 600</li><li><span class='xr-has-index'>plev</span>: 17</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-22e0eb67-3720-4245-941a-a5aada20e53e' class='xr-array-in' type='checkbox' checked><label for='section-22e0eb67-3720-4245-941a-a5aada20e53e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[282009600 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-2aa5cb40-13c2-4514-9f78-46b688feb2b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2aa5cb40-13c2-4514-9f78-46b688feb2b5' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 1899-12-...</div><input id='attrs-305d1ed5-ba6e-4c9b-8fd1-669b8ac37744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-305d1ed5-ba6e-4c9b-8fd1-669b8ac37744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac9b154f-084b-47fa-823f-ed704812a24a' class='xr-var-data-in' type='checkbox'><label for='data-ac9b154f-084b-47fa-823f-ed704812a24a' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;1899-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1899-11-16T00:00:00.000000000&#x27;, &#x27;1899-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 2e+03 1e+03</div><input id='attrs-db011ab6-e1cc-4fe3-af87-dc4c220fed0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db011ab6-e1cc-4fe3-af87-dc4c220fed0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5de49c5-6641-40cb-97a5-b890b0b69f11' class='xr-var-data-in' type='checkbox'><label for='data-d5de49c5-6641-40cb-97a5-b890b0b69f11' 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>bounds :</span></dt><dd>plev_bnds</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n",
       "        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n",
       "         1000.])</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'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-2387d19b-fe3d-4fa7-83d0-92b881a924f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2387d19b-fe3d-4fa7-83d0-92b881a924f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c1ccd37-f3dd-41e1-903a-72c58a43f65f' class='xr-var-data-in' type='checkbox'><label for='data-3c1ccd37-f3dd-41e1-903a-72c58a43f65f' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
       "       -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
       "       -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
       "       -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
       "       -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
       "       -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
       "       -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
       "       -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
       "        -9.375,  -8.125,  -6.875,  -5.625,  -4.375,  -3.125,  -1.875,  -0.625,\n",
       "         0.625,   1.875,   3.125,   4.375,   5.625,   6.875,   8.125,   9.375,\n",
       "        10.625,  11.875,  13.125,  14.375,  15.625,  16.875,  18.125,  19.375,\n",
       "        20.625,  21.875,  23.125,  24.375,  25.625,  26.875,  28.125,  29.375,\n",
       "        30.625,  31.875,  33.125,  34.375,  35.625,  36.875,  38.125,  39.375,\n",
       "        40.625,  41.875,  43.125,  44.375,  45.625,  46.875,  48.125,  49.375,\n",
       "        50.625,  51.875,  53.125,  54.375,  55.625,  56.875,  58.125,  59.375,\n",
       "        60.625,  61.875,  63.125,  64.375,  65.625,  66.875,  68.125,  69.375,\n",
       "        70.625,  71.875,  73.125,  74.375,  75.625,  76.875,  78.125,  79.375,\n",
       "        80.625,  81.875,  83.125,  84.375,  85.625,  86.875,  88.125,  89.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-c75091d2-8436-4431-a61f-705b64660a8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c75091d2-8436-4431-a61f-705b64660a8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bd7f876-04c2-4eb6-a81f-02e1539fa219' class='xr-var-data-in' type='checkbox'><label for='data-1bd7f876-04c2-4eb6-a81f-02e1539fa219' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.9375,   2.8125,   4.6875,   6.5625,   8.4375,  10.3125,  12.1875,\n",
       "        14.0625,  15.9375,  17.8125,  19.6875,  21.5625,  23.4375,  25.3125,\n",
       "        27.1875,  29.0625,  30.9375,  32.8125,  34.6875,  36.5625,  38.4375,\n",
       "        40.3125,  42.1875,  44.0625,  45.9375,  47.8125,  49.6875,  51.5625,\n",
       "        53.4375,  55.3125,  57.1875,  59.0625,  60.9375,  62.8125,  64.6875,\n",
       "        66.5625,  68.4375,  70.3125,  72.1875,  74.0625,  75.9375,  77.8125,\n",
       "        79.6875,  81.5625,  83.4375,  85.3125,  87.1875,  89.0625,  90.9375,\n",
       "        92.8125,  94.6875,  96.5625,  98.4375, 100.3125, 102.1875, 104.0625,\n",
       "       105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
       "       119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
       "       132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
       "       145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
       "       158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
       "       171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
       "       184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
       "       197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
       "       210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
       "       224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
       "       237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
       "       250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
       "       263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
       "       276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
       "       289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
       "       302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
       "       315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
       "       329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
       "       342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
       "       355.3125, 357.1875, 359.0625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a524337-fe36-4453-ac31-3a36f22042ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a524337-fe36-4453-ac31-3a36f22042ba' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2012-03-23T01:31:12Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20). 2012-03-23T01:31:12Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_ACCESS1-3_historical_r0i0p0.nc areacella: areacella_fx_ACCESS1-3_historical_r0i0p0.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'ta' (time: 600, plev: 17, lat: 144, lon: 192)>\n",
       "[282009600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00 ... 1899-12-16T12:00:00\n",
       "  * plev     (plev) float64 1e+05 9.25e+04 8.5e+04 7e+04 ... 3e+03 2e+03 1e+03\n",
       "  * lat      (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon      (lon) float64 0.9375 2.812 4.688 6.562 ... 353.4 355.3 357.2 359.1\n",
       "Attributes:\n",
       "    standard_name:     air_temperature\n",
       "    long_name:         Air Temperature\n",
       "    units:             K\n",
       "    cell_methods:      time: mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2012-03-23T01:31:12Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ta = ds_air.ta\n",
    "ta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting out the first time index and the longitude corresponding to the Himalayas and plot gives a vertical profile of the atmosphere from pole to pole. Note the `yincrease=False` option was used for the plot to invert the y-axis as pressure decreases with height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbd6201e9a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta.isel(time=0).sel(lon=86.93, method='nearest').plot(size=6, yincrease=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-20.10]",
   "language": "python",
   "name": "conda-env-analysis3-20.10-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}