diff --git a/docs/examples/parallel-computing-with-dask.ipynb b/docs/examples/parallel-computing-with-dask.ipynb
index ad3fb825..18ba7643 100644
--- a/docs/examples/parallel-computing-with-dask.ipynb
+++ b/docs/examples/parallel-computing-with-dask.ipynb
@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -674,10 +674,10 @@
        "    cmor_version:                    3.4.0\n",
        "    _NCProperties:                   version=2,netcdf=4.6.2,hdf5=1.10.5\n",
        "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time</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-a4f2c5e4-47c1-4f03-9779-2869da5368f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4f2c5e4-47c1-4f03-9779-2869da5368f9' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9f1c2603-1bad-43d5-a47e-13a634878b65' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f1c2603-1bad-43d5-a47e-13a634878b65' 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 ... 2014-12-...</div><input id='attrs-b47f1492-cd35-4198-b05e-0ec8f81b2db8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b47f1492-cd35-4198-b05e-0ec8f81b2db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1034aaaa-990e-4fbb-b877-25c8821a9e35' class='xr-var-data-in' type='checkbox'><label for='data-1034aaaa-990e-4fbb-b877-25c8821a9e35' 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><dt><span>_ChunkSizes :</span></dt><dd>1</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",
+       "    DODS_EXTRA.Unlimited_Dimension:  time</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-8886dd54-88a5-4ab3-be57-877fae008c45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8886dd54-88a5-4ab3-be57-877fae008c45' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-562741cc-89d8-4306-afab-6fddad59305d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-562741cc-89d8-4306-afab-6fddad59305d' 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 ... 2014-12-...</div><input id='attrs-d19fc366-77b4-49c2-8b1f-5fc628791b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d19fc366-77b4-49c2-8b1f-5fc628791b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b718cc84-4574-4b6f-8145-4fe8b4b144a6' class='xr-var-data-in' type='checkbox'><label for='data-b718cc84-4574-4b6f-8145-4fe8b4b144a6' 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><dt><span>_ChunkSizes :</span></dt><dd>1</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;2014-10-16T12:00:00.000000000&#x27;,\n",
        "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-b2288efe-2fc4-41c3-b1bc-a8b7cca7f73a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2288efe-2fc4-41c3-b1bc-a8b7cca7f73a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f421d0f-1811-4f72-a6ec-38b1b30e7b8f' class='xr-var-data-in' type='checkbox'><label for='data-4f421d0f-1811-4f72-a6ec-38b1b30e7b8f' 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([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-d766582c-8d06-4ed1-8bf8-96bdf563aa26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d766582c-8d06-4ed1-8bf8-96bdf563aa26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e65f0ec-ac32-4f3f-b711-31c8e0a2186e' class='xr-var-data-in' type='checkbox'><label for='data-6e65f0ec-ac32-4f3f-b711-31c8e0a2186e' 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([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
        "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
        "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
        "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
@@ -693,7 +693,7 @@
        "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
        "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
        "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-c1e00ba7-0a92-48f8-b2dc-70edebb0a18c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1e00ba7-0a92-48f8-b2dc-70edebb0a18c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cc695e9-2fe7-46a5-9202-080682f601c0' class='xr-var-data-in' type='checkbox'><label for='data-0cc695e9-2fe7-46a5-9202-080682f601c0' 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.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-d9d27582-fef0-40eb-ab43-0b5f68a1386a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d27582-fef0-40eb-ab43-0b5f68a1386a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41db6b98-e741-4d22-863c-ca9279e35822' class='xr-var-data-in' type='checkbox'><label for='data-41db6b98-e741-4d22-863c-ca9279e35822' 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.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
        "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
        "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
        "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
@@ -716,7 +716,7 @@
        "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
        "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
        "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e813e8c3-f4dc-49b9-8629-9758dd790634' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e813e8c3-f4dc-49b9-8629-9758dd790634' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4c2e7c8-2899-403c-89c6-15b84bcc6630' class='xr-var-data-in' type='checkbox'><label for='data-d4c2e7c8-2899-403c-89c6-15b84bcc6630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdecb9ec-cb0a-4dde-aa5b-f1e43518ca84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdecb9ec-cb0a-4dde-aa5b-f1e43518ca84' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-2e6d3813-44f5-43c3-b94f-b44bac8b30b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e6d3813-44f5-43c3-b94f-b44bac8b30b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70ed7849-ef8d-4b8f-8779-8a0df275f12a' class='xr-var-data-in' type='checkbox'><label for='data-70ed7849-ef8d-4b8f-8779-8a0df275f12a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6c730db-4c02-47d7-848c-27c27f00762d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6c730db-4c02-47d7-848c-27c27f00762d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9400a07-ec05-47d9-a95c-1fa392160975' class='xr-var-data-in' type='checkbox'><label for='data-a9400a07-ec05-47d9-a95c-1fa392160975' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76ef0e37-b05b-46ce-a81f-f8297916b1a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76ef0e37-b05b-46ce-a81f-f8297916b1a5' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-09c9b832-91a7-4dd0-88eb-4eda1e32d2ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09c9b832-91a7-4dd0-88eb-4eda1e32d2ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfad1946-34dd-40d3-865c-647be21222d4' class='xr-var-data-in' type='checkbox'><label for='data-bfad1946-34dd-40d3-865c-647be21222d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -789,7 +789,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></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'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-1be1ff9b-3175-4bbe-b73d-76a95ca6261d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1be1ff9b-3175-4bbe-b73d-76a95ca6261d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc4f4bdb-56c4-4c0d-8d3a-5047713afd43' class='xr-var-data-in' type='checkbox'><label for='data-cc4f4bdb-56c4-4c0d-8d3a-5047713afd43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></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'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-e5d51e3e-a870-48ab-bbf9-6a7943cd7001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5d51e3e-a870-48ab-bbf9-6a7943cd7001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1a67b54-0b32-4970-9172-68ffda19b086' class='xr-var-data-in' type='checkbox'><label for='data-b1a67b54-0b32-4970-9172-68ffda19b086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -844,7 +844,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></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'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-a2faa97b-860a-4488-88c1-d958a7aa032e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2faa97b-860a-4488-88c1-d958a7aa032e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af15ea42-0f2e-42a4-967e-8dd66827669e' class='xr-var-data-in' type='checkbox'><label for='data-af15ea42-0f2e-42a4-967e-8dd66827669e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></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'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-1d887471-c39b-44c1-85b1-7d77384602b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d887471-c39b-44c1-85b1-7d77384602b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d6ecdc2-80ac-42ed-8617-1e9be7619bdb' class='xr-var-data-in' type='checkbox'><label for='data-0d6ecdc2-80ac-42ed-8617-1e9be7619bdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -899,7 +899,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-7b3ce5fe-c5d4-40f8-812c-f7d4f3e541f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b3ce5fe-c5d4-40f8-812c-f7d4f3e541f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e75a0411-4266-4a95-9ddd-e6bfb383c0ee' class='xr-var-data-in' type='checkbox'><label for='data-e75a0411-4266-4a95-9ddd-e6bfb383c0ee' 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>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-884f23c2-6735-4d70-80de-7eba75b392df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-884f23c2-6735-4d70-80de-7eba75b392df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f61b82f-55de-4e2d-99c1-1a3a64bfbb7e' class='xr-var-data-in' type='checkbox'><label for='data-0f61b82f-55de-4e2d-99c1-1a3a64bfbb7e' 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>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1013,7 +1013,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-549dd176-05af-454d-96ce-4b9aea95918e' class='xr-section-summary-in' type='checkbox'  ><label for='section-549dd176-05af-454d-96ce-4b9aea95918e' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0aca2458-c0cc-406f-b820-ef634815e0d0' class='xr-index-data-in' type='checkbox'/><label for='index-0aca2458-c0cc-406f-b820-ef634815e0d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;, &#x27;1850-02-15 00:00:00&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-42872cf2-1b47-4ef4-9f33-045316fab870' class='xr-section-summary-in' type='checkbox'  ><label for='section-42872cf2-1b47-4ef4-9f33-045316fab870' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1ff7703-1b56-4026-9868-e317df226fb4' class='xr-index-data-in' type='checkbox'/><label for='index-a1ff7703-1b56-4026-9868-e317df226fb4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;, &#x27;1850-02-15 00:00:00&#x27;,\n",
        "               &#x27;1850-03-16 12:00:00&#x27;, &#x27;1850-04-16 00:00:00&#x27;,\n",
        "               &#x27;1850-05-16 12:00:00&#x27;, &#x27;1850-06-16 00:00:00&#x27;,\n",
        "               &#x27;1850-07-16 12:00:00&#x27;, &#x27;1850-08-16 12:00:00&#x27;,\n",
@@ -1024,17 +1024,17 @@
        "               &#x27;2014-07-16 12:00:00&#x27;, &#x27;2014-08-16 12:00:00&#x27;,\n",
        "               &#x27;2014-09-16 00:00:00&#x27;, &#x27;2014-10-16 12:00:00&#x27;,\n",
        "               &#x27;2014-11-16 00:00:00&#x27;, &#x27;2014-12-16 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1980, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c294d84a-cdda-4c02-832a-ce0324cbdfe7' class='xr-index-data-in' type='checkbox'/><label for='index-c294d84a-cdda-4c02-832a-ce0324cbdfe7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,\n",
-       "               -80.0, -78.75,\n",
-       "              ...\n",
-       "               78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,\n",
-       "               88.75,   90.0],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cc142cf4-781f-46ba-ab31-8e2f8bd63c76' class='xr-index-data-in' type='checkbox'/><label for='index-cc142cf4-781f-46ba-ab31-8e2f8bd63c76' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,\n",
-       "               13.125,    15.0,  16.875,\n",
-       "              ...\n",
-       "               341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5,\n",
-       "              354.375,  356.25, 358.125],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98914db8-30bf-4e74-afca-b8ef3c3f55bc' class='xr-section-summary-in' type='checkbox'  ><label for='section-98914db8-30bf-4e74-afca-b8ef3c3f55bc' class='xr-section-summary' >Attributes: <span>(49)</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>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</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>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1980, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-03aceeab-cf5b-4230-9888-72af5bb521ba' class='xr-index-data-in' type='checkbox'/><label for='index-03aceeab-cf5b-4230-9888-72af5bb521ba' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
+       "       -78.75,\n",
+       "       ...\n",
+       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
+       "         90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9ded466-12e8-4757-a75d-03dcce034bed' class='xr-index-data-in' type='checkbox'/><label for='index-b9ded466-12e8-4757-a75d-03dcce034bed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
+       "          15.0,  16.875,\n",
+       "       ...\n",
+       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
+       "        356.25, 358.125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f074f29a-c31d-40ba-8a3a-764ff791bc8c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f074f29a-c31d-40ba-8a3a-764ff791bc8c' class='xr-section-summary' >Attributes: <span>(49)</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>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</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>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
        "aerosol: CLASSIC (v1.0)\n",
        "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
        "atmosChem: none\n",
@@ -1074,7 +1074,7 @@
        "    DODS_EXTRA.Unlimited_Dimension:  time"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1098,38 +1098,582 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "nanmean() got an unexpected keyword argument 'method'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m tas_daily_avg \u001b[39m=\u001b[39m (\n\u001b[0;32m----> 2\u001b[0m     ds2[\u001b[39m\"\u001b[39;49m\u001b[39mtas\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mgroupby(ds2\u001b[39m.\u001b[39;49mtime\u001b[39m.\u001b[39;49mdt\u001b[39m.\u001b[39;49mday)\u001b[39m.\u001b[39;49mmean(method\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcohorts\u001b[39;49m\u001b[39m\"\u001b[39;49m, engine\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mflox\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m )\n\u001b[1;32m      4\u001b[0m tas_daily_avg\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/_aggregations.py:5953\u001b[0m, in \u001b[0;36mDataArrayGroupByAggregations.mean\u001b[0;34m(self, dim, skipna, keep_attrs, **kwargs)\u001b[0m\n\u001b[1;32m   5944\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flox_reduce(\n\u001b[1;32m   5945\u001b[0m         func\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mmean\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   5946\u001b[0m         dim\u001b[39m=\u001b[39mdim,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5950\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   5951\u001b[0m     )\n\u001b[1;32m   5952\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 5953\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreduce(\n\u001b[1;32m   5954\u001b[0m         duck_array_ops\u001b[39m.\u001b[39;49mmean,\n\u001b[1;32m   5955\u001b[0m         dim\u001b[39m=\u001b[39;49mdim,\n\u001b[1;32m   5956\u001b[0m         skipna\u001b[39m=\u001b[39;49mskipna,\n\u001b[1;32m   5957\u001b[0m         keep_attrs\u001b[39m=\u001b[39;49mkeep_attrs,\n\u001b[1;32m   5958\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m   5959\u001b[0m     )\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/groupby.py:1202\u001b[0m, in \u001b[0;36mDataArrayGroupByBase.reduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, shortcut, **kwargs)\u001b[0m\n\u001b[1;32m   1191\u001b[0m     \u001b[39mreturn\u001b[39;00m ar\u001b[39m.\u001b[39mreduce(\n\u001b[1;32m   1192\u001b[0m         func\u001b[39m=\u001b[39mfunc,\n\u001b[1;32m   1193\u001b[0m         dim\u001b[39m=\u001b[39mdim,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1197\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   1198\u001b[0m     )\n\u001b[1;32m   1200\u001b[0m check_reduce_dims(dim, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdims)\n\u001b[0;32m-> 1202\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmap(reduce_array, shortcut\u001b[39m=\u001b[39;49mshortcut)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/groupby.py:1105\u001b[0m, in \u001b[0;36mDataArrayGroupByBase.map\u001b[0;34m(self, func, args, shortcut, **kwargs)\u001b[0m\n\u001b[1;32m   1103\u001b[0m grouped \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iter_grouped_shortcut() \u001b[39mif\u001b[39;00m shortcut \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iter_grouped()\n\u001b[1;32m   1104\u001b[0m applied \u001b[39m=\u001b[39m (maybe_wrap_array(arr, func(arr, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)) \u001b[39mfor\u001b[39;00m arr \u001b[39min\u001b[39;00m grouped)\n\u001b[0;32m-> 1105\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_combine(applied, shortcut\u001b[39m=\u001b[39;49mshortcut)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/groupby.py:1124\u001b[0m, in \u001b[0;36mDataArrayGroupByBase._combine\u001b[0;34m(self, applied, shortcut)\u001b[0m\n\u001b[1;32m   1122\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_combine\u001b[39m(\u001b[39mself\u001b[39m, applied, shortcut\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m   1123\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Recombine the applied objects like the original.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1124\u001b[0m     applied_example, applied \u001b[39m=\u001b[39m peek_at(applied)\n\u001b[1;32m   1125\u001b[0m     coord, dim, positions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_infer_concat_args(applied_example)\n\u001b[1;32m   1126\u001b[0m     \u001b[39mif\u001b[39;00m shortcut:\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/utils.py:188\u001b[0m, in \u001b[0;36mpeek_at\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Returns the first value from iterable, as well as a new iterator with\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[39mthe same content as the original iterable\u001b[39;00m\n\u001b[1;32m    186\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    187\u001b[0m gen \u001b[39m=\u001b[39m \u001b[39miter\u001b[39m(iterable)\n\u001b[0;32m--> 188\u001b[0m peek \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39;49m(gen)\n\u001b[1;32m    189\u001b[0m \u001b[39mreturn\u001b[39;00m peek, itertools\u001b[39m.\u001b[39mchain([peek], gen)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/groupby.py:1104\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1062\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Apply a function to each array in the group and concatenate them\u001b[39;00m\n\u001b[1;32m   1063\u001b[0m \u001b[39mtogether into a new array.\u001b[39;00m\n\u001b[1;32m   1064\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1101\u001b[0m \u001b[39m    The result of splitting, applying and combining this array.\u001b[39;00m\n\u001b[1;32m   1102\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1103\u001b[0m grouped \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iter_grouped_shortcut() \u001b[39mif\u001b[39;00m shortcut \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iter_grouped()\n\u001b[0;32m-> 1104\u001b[0m applied \u001b[39m=\u001b[39m (maybe_wrap_array(arr, func(arr, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)) \u001b[39mfor\u001b[39;00m arr \u001b[39min\u001b[39;00m grouped)\n\u001b[1;32m   1105\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_combine(applied, shortcut\u001b[39m=\u001b[39mshortcut)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/groupby.py:1191\u001b[0m, in \u001b[0;36mDataArrayGroupByBase.reduce.<locals>.reduce_array\u001b[0;34m(ar)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mreduce_array\u001b[39m(ar: DataArray) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataArray:\n\u001b[0;32m-> 1191\u001b[0m     \u001b[39mreturn\u001b[39;00m ar\u001b[39m.\u001b[39;49mreduce(\n\u001b[1;32m   1192\u001b[0m         func\u001b[39m=\u001b[39;49mfunc,\n\u001b[1;32m   1193\u001b[0m         dim\u001b[39m=\u001b[39;49mdim,\n\u001b[1;32m   1194\u001b[0m         axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m   1195\u001b[0m         keep_attrs\u001b[39m=\u001b[39;49mkeep_attrs,\n\u001b[1;32m   1196\u001b[0m         keepdims\u001b[39m=\u001b[39;49mkeepdims,\n\u001b[1;32m   1197\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m   1198\u001b[0m     )\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/variable.py:1962\u001b[0m, in \u001b[0;36mVariable.reduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m   1958\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(axis, \u001b[39mtuple\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(axis) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   1959\u001b[0m         \u001b[39m# unpack axis for the benefit of functions\u001b[39;00m\n\u001b[1;32m   1960\u001b[0m         \u001b[39m# like np.argmin which can't handle tuple arguments\u001b[39;00m\n\u001b[1;32m   1961\u001b[0m         axis \u001b[39m=\u001b[39m axis[\u001b[39m0\u001b[39m]\n\u001b[0;32m-> 1962\u001b[0m     data \u001b[39m=\u001b[39m func(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdata, axis\u001b[39m=\u001b[39;49maxis, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1963\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1964\u001b[0m     data \u001b[39m=\u001b[39m func(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/duck_array_ops.py:610\u001b[0m, in \u001b[0;36mmean\u001b[0;34m(array, axis, skipna, **kwargs)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[39mreturn\u001b[39;00m _to_pytimedelta(mean_timedeltas, unit\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mus\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39m+\u001b[39m offset\n\u001b[1;32m    609\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 610\u001b[0m     \u001b[39mreturn\u001b[39;00m _mean(array, axis\u001b[39m=\u001b[39;49maxis, skipna\u001b[39m=\u001b[39;49mskipna, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[0;32m~/miniconda3/envs/xcdat_dev/lib/python3.10/site-packages/xarray/core/duck_array_ops.py:377\u001b[0m, in \u001b[0;36m_create_nan_agg_method.<locals>.f\u001b[0;34m(values, axis, skipna, **kwargs)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[39mwith\u001b[39;00m warnings\u001b[39m.\u001b[39mcatch_warnings():\n\u001b[1;32m    376\u001b[0m         warnings\u001b[39m.\u001b[39mfilterwarnings(\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mAll-NaN slice encountered\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 377\u001b[0m         \u001b[39mreturn\u001b[39;00m func(values, axis\u001b[39m=\u001b[39;49maxis, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    378\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_duck_dask_array(values):\n",
-      "\u001b[0;31mTypeError\u001b[0m: nanmean() got an unexpected keyword argument 'method'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 995 ms, sys: 4.49 ms, total: 999 ms\n",
+      "Wall time: 997 ms\n"
      ]
+    },
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+       "  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-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\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",
+       ".xr-index-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",
+       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-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",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (day: 2, lat: 145, lon: 192)&gt;\n",
+       "dask.array&lt;transpose, shape=(2, 145, 192), dtype=float32, chunksize=(1, 145, 192), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
+       "    height   float64 ...\n",
+       "  * day      (day) int64 15 16\n",
+       "Attributes:\n",
+       "    standard_name:  air_temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    comment:        near-surface (usually, 2 meter) air temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   area: time: mean\n",
+       "    cell_measures:  area: areacella\n",
+       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
+       "    _ChunkSizes:    [  1 145 192]</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'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>day</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</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-fca7106f-7b16-4042-9dc3-4d51ffeb499a' class='xr-array-in' type='checkbox' checked><label for='section-fca7106f-7b16-4042-9dc3-4d51ffeb499a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 217.50 kiB </td>\n",
+       "                        <td> 108.75 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2, 145, 192) </td>\n",
+       "                        <td> (1, 145, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 22 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"195\" height=\"155\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"25\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"98\" />\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 25.064196541853565,15.064196541853565 25.064196541853565,105.68919654185356 10.0,90.625\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 145.06419654185356,15.064196541853565 25.064196541853565,15.064196541853565\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"105\" x2=\"145\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"145\" y1=\"15\" x2=\"145\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"25.064196541853565,15.064196541853565 145.06419654185356,15.064196541853565 145.06419654185356,105.68919654185356 25.064196541853565,105.68919654185356\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"85.064197\" y=\"125.689197\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
+       "  <text x=\"165.064197\" y=\"60.376697\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,165.064197,60.376697)\">145</text>\n",
+       "  <text x=\"7.532098\" y=\"118.157098\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.532098,118.157098)\">2</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-9998de30-5be2-4010-aec2-179191611280' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9998de30-5be2-4010-aec2-179191611280' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-8a56680f-de34-4adc-a6ea-30b245eda05f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a56680f-de34-4adc-a6ea-30b245eda05f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2896df06-6437-4ea4-8307-29d90f1f4c8e' class='xr-var-data-in' type='checkbox'><label for='data-2896df06-6437-4ea4-8307-29d90f1f4c8e' 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([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
+       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
+       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
+       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
+       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
+       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
+       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
+       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
+       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
+       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
+       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
+       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
+       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
+       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
+       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
+       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-1fad5871-1f93-4aa5-98f2-b2cc97fd1397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fad5871-1f93-4aa5-98f2-b2cc97fd1397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad0c66f7-784a-4bac-b6cc-6735490a2a8b' class='xr-var-data-in' type='checkbox'><label for='data-ad0c66f7-784a-4bac-b6cc-6735490a2a8b' 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.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
+       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
+       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
+       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
+       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
+       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
+       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
+       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
+       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
+       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
+       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
+       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
+       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
+       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
+       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
+       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
+       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
+       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
+       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
+       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
+       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
+       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6a79b40-ea8f-49a3-acd2-f9d4b7a9a216' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6a79b40-ea8f-49a3-acd2-f9d4b7a9a216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8cbc06-5981-4b74-95b9-7b7035103f2a' class='xr-var-data-in' type='checkbox'><label for='data-8f8cbc06-5981-4b74-95b9-7b7035103f2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>15 16</div><input id='attrs-08296614-f141-4a30-bf83-032a29b96bbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08296614-f141-4a30-bf83-032a29b96bbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c163cbd-749a-4f54-bca6-ec3ee6ccd70d' class='xr-var-data-in' type='checkbox'><label for='data-9c163cbd-749a-4f54-bca6-ec3ee6ccd70d' 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><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([15, 16])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-850df224-6c52-4d07-a717-50bb27754ed0' class='xr-section-summary-in' type='checkbox'  ><label for='section-850df224-6c52-4d07-a717-50bb27754ed0' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04a847d4-0895-4592-9644-db6fa5527f56' class='xr-index-data-in' type='checkbox'/><label for='index-04a847d4-0895-4592-9644-db6fa5527f56' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
+       "       -78.75,\n",
+       "       ...\n",
+       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
+       "         90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1eeb71eb-5ee3-4531-a1f0-2c6037fe9b72' class='xr-index-data-in' type='checkbox'/><label for='index-1eeb71eb-5ee3-4531-a1f0-2c6037fe9b72' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
+       "          15.0,  16.875,\n",
+       "       ...\n",
+       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
+       "        356.25, 358.125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f046c14e-a8bc-4869-98f7-9f7848847002' class='xr-index-data-in' type='checkbox'/><label for='index-f046c14e-a8bc-4869-98f7-9f7848847002' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15, 16], dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c2866f9-177f-4615-bbc3-a12127b3b090' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c2866f9-177f-4615-bbc3-a12127b3b090' class='xr-section-summary' >Attributes: <span>(8)</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>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'tas' (day: 2, lat: 145, lon: 192)>\n",
+       "dask.array<transpose, shape=(2, 145, 192), dtype=float32, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
+       "    height   float64 ...\n",
+       "  * day      (day) int64 15 16\n",
+       "Attributes:\n",
+       "    standard_name:  air_temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    comment:        near-surface (usually, 2 meter) air temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   area: time: mean\n",
+       "    cell_measures:  area: areacella\n",
+       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
+       "    _ChunkSizes:    [  1 145 192]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "%%time\n",
     "tas_daily_avg = (\n",
     "    ds2[\"tas\"].groupby(ds2.time.dt.day).mean(method=\"cohorts\", engine=\"flox\")\n",
     ")\n",
     "tas_daily_avg\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 70.3 ms, sys: 7.09 ms, total: 77.4 ms\n",
+      "Wall time: 74.8 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "with xr.set_options(use_flox=False):\n",
+    "    tas_daily_avg = ds2[\"tas\"].groupby(ds2.time.dt.day).mean()\n",
+    "    tas_daily_avg\n"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -1141,7 +1685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1536,10 +2080,10 @@
        "    cmor_version:                    3.4.0\n",
        "    _NCProperties:                   version=2,netcdf=4.6.2,hdf5=1.10.5\n",
        "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time</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-3aeadf3d-15bd-466c-aee2-0234c6fef64d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3aeadf3d-15bd-466c-aee2-0234c6fef64d' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9bac9b1-03dc-443f-9037-1e1d0358d2b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9bac9b1-03dc-443f-9037-1e1d0358d2b6' 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 ... 2014-12-...</div><input id='attrs-554f198a-7a06-42d2-b53c-ef48179585ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-554f198a-7a06-42d2-b53c-ef48179585ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb372af-ae86-4010-8506-3ce3a626ae4c' class='xr-var-data-in' type='checkbox'><label for='data-cdb372af-ae86-4010-8506-3ce3a626ae4c' 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><dt><span>_ChunkSizes :</span></dt><dd>1</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",
+       "    DODS_EXTRA.Unlimited_Dimension:  time</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-18931fc0-3701-4907-bfb7-58cc709970f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18931fc0-3701-4907-bfb7-58cc709970f6' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3d0927b-ae65-4282-ae31-5ce5d824cb54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3d0927b-ae65-4282-ae31-5ce5d824cb54' 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 ... 2014-12-...</div><input id='attrs-72dceedd-ce90-4fbb-8c70-dbd3c372d831' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72dceedd-ce90-4fbb-8c70-dbd3c372d831' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a41b12c-d106-4b05-9757-169072acc636' class='xr-var-data-in' type='checkbox'><label for='data-1a41b12c-d106-4b05-9757-169072acc636' 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><dt><span>_ChunkSizes :</span></dt><dd>1</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;2014-10-16T12:00:00.000000000&#x27;,\n",
        "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-41939192-53a2-480e-a8a5-5225c123f7c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41939192-53a2-480e-a8a5-5225c123f7c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0f995a7-385b-4997-a631-27d51fde563f' class='xr-var-data-in' type='checkbox'><label for='data-e0f995a7-385b-4997-a631-27d51fde563f' 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([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-ef5ca1e3-1217-4d7a-af35-452f358efbd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef5ca1e3-1217-4d7a-af35-452f358efbd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29415332-982d-45f4-ac81-8b6315ce5982' class='xr-var-data-in' type='checkbox'><label for='data-29415332-982d-45f4-ac81-8b6315ce5982' 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([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
        "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
        "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
        "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
@@ -1555,7 +2099,7 @@
        "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
        "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
        "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-ac5ac2eb-f39d-4c2e-9a60-23ed909a82a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac5ac2eb-f39d-4c2e-9a60-23ed909a82a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7e4d6df-8289-4efa-aa55-6f1aab409fda' class='xr-var-data-in' type='checkbox'><label for='data-d7e4d6df-8289-4efa-aa55-6f1aab409fda' 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.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-802abdae-93f6-4aaa-ae5e-d71af51f9a73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-802abdae-93f6-4aaa-ae5e-d71af51f9a73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-579e096c-efe7-42ed-bbaa-2564867a687b' class='xr-var-data-in' type='checkbox'><label for='data-579e096c-efe7-42ed-bbaa-2564867a687b' 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.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
        "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
        "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
        "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
@@ -1578,7 +2122,7 @@
        "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
        "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
        "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7050673-ad64-4d3e-a002-3c73798e54af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7050673-ad64-4d3e-a002-3c73798e54af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57c1e06d-2bf0-427c-9ab8-7f27c8bc8e67' class='xr-var-data-in' type='checkbox'><label for='data-57c1e06d-2bf0-427c-9ab8-7f27c8bc8e67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-202b8799-a3fb-4d46-b1e3-427f09a07595' class='xr-section-summary-in' type='checkbox'  checked><label for='section-202b8799-a3fb-4d46-b1e3-427f09a07595' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-ef820a27-b75a-4126-b799-e08cfbc2e1f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef820a27-b75a-4126-b799-e08cfbc2e1f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4e4121e-b2c9-49d4-88fc-5b8c12bb7184' class='xr-var-data-in' type='checkbox'><label for='data-b4e4121e-b2c9-49d4-88fc-5b8c12bb7184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e3a86fe-541e-46c6-b618-c59487730ce7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3a86fe-541e-46c6-b618-c59487730ce7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e8fd897-5e8b-43c0-9f89-18aa421e6eb1' class='xr-var-data-in' type='checkbox'><label for='data-7e8fd897-5e8b-43c0-9f89-18aa421e6eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a131ac8-e003-4680-bf8d-6feea83f3797' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a131ac8-e003-4680-bf8d-6feea83f3797' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-b66964f3-4404-4746-897d-31ffd68a22e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b66964f3-4404-4746-897d-31ffd68a22e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7ae6fd7-cbcd-4c21-a485-146676de1861' class='xr-var-data-in' type='checkbox'><label for='data-c7ae6fd7-cbcd-4c21-a485-146676de1861' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1633,7 +2177,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></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'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-29d3f0ed-b8c8-40af-b679-cf2cf9a2dce0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29d3f0ed-b8c8-40af-b679-cf2cf9a2dce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38346021-3488-4a7f-a44f-39b50efdf0ad' class='xr-var-data-in' type='checkbox'><label for='data-38346021-3488-4a7f-a44f-39b50efdf0ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></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'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-7985564c-b7dd-477e-8bdd-42585308173c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7985564c-b7dd-477e-8bdd-42585308173c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a50a3f7-863b-4dc7-be85-11a7a329439c' class='xr-var-data-in' type='checkbox'><label for='data-2a50a3f7-863b-4dc7-be85-11a7a329439c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1688,7 +2232,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></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'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-65bf35f2-1bba-472f-88be-d78524d26744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65bf35f2-1bba-472f-88be-d78524d26744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e26d1898-6cf2-4746-a0b0-bf06149cae73' class='xr-var-data-in' type='checkbox'><label for='data-e26d1898-6cf2-4746-a0b0-bf06149cae73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></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'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-397d66d3-11e6-40ae-9ce1-383883b244ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-397d66d3-11e6-40ae-9ce1-383883b244ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a10d484a-3357-46ed-8660-ea90b6c8364e' class='xr-var-data-in' type='checkbox'><label for='data-a10d484a-3357-46ed-8660-ea90b6c8364e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1743,7 +2287,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1695, 122, 162), meta=np.ndarray&gt;</div><input id='attrs-53850bc6-a2c8-4ba4-b02d-d1d83cba2608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53850bc6-a2c8-4ba4-b02d-d1d83cba2608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-431d4c3a-8806-484a-8bfb-6ac7788fb303' class='xr-var-data-in' type='checkbox'><label for='data-431d4c3a-8806-484a-8bfb-6ac7788fb303' 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>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1695, 122, 162), meta=np.ndarray&gt;</div><input id='attrs-243bdb48-a0e2-4f2b-8eb8-791d82475a81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-243bdb48-a0e2-4f2b-8eb8-791d82475a81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cee708b-b0dc-4217-a5e1-e239141ff974' class='xr-var-data-in' type='checkbox'><label for='data-7cee708b-b0dc-4217-a5e1-e239141ff974' 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>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1827,7 +2371,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb9f9522-820a-4aad-91f0-4cce225c4346' class='xr-section-summary-in' type='checkbox'  ><label for='section-bb9f9522-820a-4aad-91f0-4cce225c4346' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-45116bfe-f222-46a8-9992-033eb91c17b2' class='xr-index-data-in' type='checkbox'/><label for='index-45116bfe-f222-46a8-9992-033eb91c17b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;, &#x27;1850-02-15 00:00:00&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-27437ba7-b589-4967-bcee-13c06c1f1571' class='xr-section-summary-in' type='checkbox'  ><label for='section-27437ba7-b589-4967-bcee-13c06c1f1571' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c87d5ca-b20d-4977-8bfb-73986ea716d1' class='xr-index-data-in' type='checkbox'/><label for='index-6c87d5ca-b20d-4977-8bfb-73986ea716d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;, &#x27;1850-02-15 00:00:00&#x27;,\n",
        "               &#x27;1850-03-16 12:00:00&#x27;, &#x27;1850-04-16 00:00:00&#x27;,\n",
        "               &#x27;1850-05-16 12:00:00&#x27;, &#x27;1850-06-16 00:00:00&#x27;,\n",
        "               &#x27;1850-07-16 12:00:00&#x27;, &#x27;1850-08-16 12:00:00&#x27;,\n",
@@ -1838,17 +2382,17 @@
        "               &#x27;2014-07-16 12:00:00&#x27;, &#x27;2014-08-16 12:00:00&#x27;,\n",
        "               &#x27;2014-09-16 00:00:00&#x27;, &#x27;2014-10-16 12:00:00&#x27;,\n",
        "               &#x27;2014-11-16 00:00:00&#x27;, &#x27;2014-12-16 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1980, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-70157703-b326-43e3-b6c8-979f9b7622a7' class='xr-index-data-in' type='checkbox'/><label for='index-70157703-b326-43e3-b6c8-979f9b7622a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,\n",
-       "               -80.0, -78.75,\n",
-       "              ...\n",
-       "               78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,\n",
-       "               88.75,   90.0],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-855b8b6f-847c-4daa-965a-eab6d29d3051' class='xr-index-data-in' type='checkbox'/><label for='index-855b8b6f-847c-4daa-965a-eab6d29d3051' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,\n",
-       "               13.125,    15.0,  16.875,\n",
-       "              ...\n",
-       "               341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5,\n",
-       "              354.375,  356.25, 358.125],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71519f8a-4790-4cad-897d-df3ea5773518' class='xr-section-summary-in' type='checkbox'  ><label for='section-71519f8a-4790-4cad-897d-df3ea5773518' class='xr-section-summary' >Attributes: <span>(49)</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>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</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>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1980, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eafc3a0a-79d5-48ec-a14b-184c96ca5a3f' class='xr-index-data-in' type='checkbox'/><label for='index-eafc3a0a-79d5-48ec-a14b-184c96ca5a3f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
+       "       -78.75,\n",
+       "       ...\n",
+       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
+       "         90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e646847b-0179-479b-8b3e-5abfc55cc5e1' class='xr-index-data-in' type='checkbox'/><label for='index-e646847b-0179-479b-8b3e-5abfc55cc5e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
+       "          15.0,  16.875,\n",
+       "       ...\n",
+       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
+       "        356.25, 358.125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c03c91f-3ebf-441b-9203-e9f43abde456' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c03c91f-3ebf-441b-9203-e9f43abde456' class='xr-section-summary' >Attributes: <span>(49)</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>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</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>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
        "aerosol: CLASSIC (v1.0)\n",
        "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
        "atmosChem: none\n",
@@ -1888,7 +2432,7 @@
        "    DODS_EXTRA.Unlimited_Dimension:  time"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1901,7 +2445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1910,7 +2454,7 @@
        "Frozen({'time': (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 'bnds': (2,), 'lat': (145,), 'lon': (192,)})"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1942,9 +2486,9 @@
    "source": [
     "## How to Use Dask with xCDAT\n",
     "\n",
-    "Many core xCDAT APIs inherit Xarray's Dask support by operating on Xarray Dataset objects and making calls to parallelized Xarray Dataset APIs.\n",
+    "Many core xCDAT APIs inherit Xarray's Dask support by operating on `xarray.Dataset` objects and making calls to parallelized Xarray APIs.\n",
     "\n",
-    "The only thing xCDAT users have to do is **chunk the dataset** and **call any of the parallelizable xCDAT APIs**.\n"
+    "xCDAT users just need **chunk the xarray.Dataset object** before **calling any of the parallelizable xCDAT APIs**.\n"
    ]
   },
   {
@@ -1988,7 +2532,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Performance Metrics\n"
+    "### Performance Metrics\n",
+    "\n",
+    "Factors\n",
+    "\n",
+    "- Data size\n",
+    "  - 100 MB - 1 GB\n",
+    "  - 1 GB - 10 GB\n",
+    "  - 10 GB - 25 GB\n",
+    "  - 25GB - 50 GB\n",
+    "  - 50 GB - 100 GB\n",
+    "  - 100 GB+\n",
+    "- Workflow type\n",
+    "  - Sequential\n",
+    "  - Parallel\n",
+    "- Operation\n",
+    "  - Spatial averaging\n",
+    "    - `ds.spatial.average()`\n",
+    "    - `cdutil.averager(TransientVariable, axis=\"XY\")`\n",
+    "  - Temporal averaging - average\n",
+    "    - `ds.temporal.average()`\n",
+    "    - `cdutil.averager(TransientVariable, axis=\"T\")`\n",
+    "  - Temporal averaging - group average\n",
+    "    - `ds.temporal.group_average()`\n",
+    "    - `cdutil.SEASONALCYCLE()`\n",
+    "    - `cdutil.ANNUALCYCLE()`\n",
+    "    - `cdutil.<DJF|MAM|JJA|SON>()`\n",
+    "    - `cdutil.<JAN|FEB|...|DEC>()`\n",
+    "  - Temporal averaging - climatology\n",
+    "    - `ds.temporal.climatology()`\n",
+    "    - `cdutil.ANNUALCYCLE.climatology()`\n",
+    "    - `cdutil.SEASONALCYCLE.climatology()`\n",
+    "    - `cdutil.<DJF|MAM|JJA|SON>().climatology()`\n",
+    "    - `cdutil.<JAN|FEB|...|DEC>().climatology()`\n",
+    "  - Temporal averaging - departures\n",
+    "    - `ds.temporal.departures()`\n",
+    "    - `cdutil.ANNUALCYCLE.departures()`\n",
+    "    - `cdutil.SEASONALCYCLE.departures()`\n",
+    "    - `cdutil.<DJF|MAM|JJA|SON>().departures()`\n",
+    "    - `cdutil.<JAN|FEB|...|DEC>().departures()`\n"
    ]
   },
   {
@@ -2006,17 +2588,16 @@
    "source": [
     "### Are there cases where xCDAT loads Dask arrays into memory?\n",
     "\n",
-    "xCDAT loads Dask arrays into memory when performing operations or computations using\n",
-    "multi-dimensional arrays, specifically coordinate bounds . As of `xarray=2023.5.0`,\n",
-    "`xarray can't set arrays with multiple array indices to dask yet`, so these arrays must\n",
-    "be loaded into memory.\n",
+    "As of `xarray=2023.5.0`, Xarray does not support updating/setting multi-dimensional dask\n",
+    "arrays. The followin error is raised if this is attempted: `xarray can't set arrays with multiple array indices to dask yet`.\n",
     "\n",
-    "xCDAT loads coordinate bounds into memory in the following APIs during specific\n",
-    "situations:\n",
+    "As a workaround, xCDAT loads coordinate bounds into memory if they are multi-dimensional\n",
+    "Dask arrays before performing operations or computations. This loading occurs in the\n",
+    "following APIs:\n",
     "\n",
     "- `xcdat.axis.swap_lon_axis`\n",
     "  - swapping longitude axis orientation\n",
-    "  - aligning longitude bounds to 360 axis\n",
+    "  - aligning longitude bounds to (0, 360) axis\n",
     "- `xarray.Dataset.spatial.average`\n",
     "  - generating weights using lat/lon coordinate bounds\n",
     "  - swapping longitude axis orientation\n",
@@ -2048,7 +2629,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.3"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/examples/parallel-computing-with-dask.py b/docs/examples/parallel-computing-with-dask.py
new file mode 100644
index 00000000..1c8c7cfc
--- /dev/null
+++ b/docs/examples/parallel-computing-with-dask.py
@@ -0,0 +1,145 @@
+# %%
+import timeit
+from typing import List, Tuple
+
+import cdms2
+import cdutil
+import numpy as np
+import xarray as xr
+import xcdat as xc
+
+
+# %%
+def get_runtime(
+    setup: str, stmt: str, repeat: int = 5, number: int = 1
+) -> Tuple[float, float]:
+    """Get the runtime for a code statement using timeit.
+
+    This function takes the lowest performance value for each sample.
+
+    According to `Timer.repeat()`:
+
+        "In a typical case, the lowest value gives a lower bound for how fast
+        your machine can run the given code snippet; higher values in the result
+        vector are typically not caused by variability in Python's speed, but by
+        other processes interfering with your timing accuracy. So the min() of
+        the result is probably the only number you should be interested in."
+
+    Source: https://github.com/python/cpython/blob/2587b9f64eefde803a5e0b050171ad5f6654f31b/Lib/timeit.py#L193-L203
+
+    `timeit` is more accurate than `time.time` because:
+
+      - it repeats the tests many times to eliminate the influence of other
+        tasks on your machine, such as disk flushing and OS scheduling.
+      - it disables the garbage collector to prevent that process from skewing
+        the results by scheduling a collection run at an inopportune moment.
+      - it picks the most accurate timer for your OS, ``time.time`` or
+        ``time.clock`` in Python 2 and ``time.perf_counter()`` on Python 3.
+        See ``timeit.default_timer``.
+
+    Source: https://stackoverflow.com/questions/17579357/time-time-vs-timeit-timeit
+
+    Parameters
+    ----------
+    setup : str
+        The setup code (e.g,. imports).
+    stmt : str
+        The statement to measure performance on (e.g., API calls).
+    repeat : int, optional
+        Number of samples to take, by default 5.
+    number : int, optional
+        Number of times to repeat the statement for each sample, by default 1.
+
+    Returns
+    -------
+    float
+        The average minimum runtime out of all of the samples.
+
+    Example
+    -------
+
+    ``setup`` example:
+
+    >>> DIR = (
+    >>>    "/p/css03/esgf_publish/CMIP6/CMIP/MOHC/HadGEM3-GC31-MM/historical/r2i1p1f3"
+    >>>    "/day/ta/gn/v20191218"
+    >>> )
+    >>> setup = (
+    >>>    "import xcdat as xc\n"
+    >>>    f"ds = xc.open_mfdataset(f'{DIR}/*.nc', chunks='auto')\n"
+    >>> )
+
+    ``stmt` example:
+
+    >>> ds.spatial.average('ta')
+    """
+    runtimes: List[float] = timeit.repeat(
+        setup=setup,
+        stmt=stmt,
+        repeat=repeat,
+        number=number,
+    )
+
+    min = np.around(np.min(runtimes), decimals=6)
+
+    return min
+
+
+# %% STATIC VARIABLES
+# -------------------
+DIR = (
+    "/p/css03/esgf_publish/CMIP6/CMIP/MOHC/HadGEM3-GC31-MM/historical/r2i1p1f3"
+    "/day/ta/gn/v20191218"
+)
+VAR = "ta"
+
+# xCDAT API Calls for `get_runtime`
+XC_SPATIAL_AVG_STMT = f"ds.spatial.average('{VAR}')"
+XC_TEMP_AVG_STMT = f"ds.temporal.average('{VAR}', weighted=True)"
+XC_CLIMATOLOGY_STMT = f"ds.temporal.climatology('{VAR}', freq='month', weighted=True)"
+XC_DEPARTURES_STMT = f"ds.temporal.depatures('{VAR}', freq='month', weighted=True)"
+
+
+# %% xCDAT SERIAL
+# -------------------
+# Dataset object stored here for inspection. It is not actually used in the
+# performance benchmarking.
+# ds_serial = xc.open_mfdataset(f"{DIR}/*.nc", chunks="auto")
+
+XC_SERIAL_SETUP = (
+    "import xcdat as xc\n" f"ds = xc.open_mfdataset(f'{DIR}/*.nc', chunks=None)\n"
+)
+serial_results = {
+    "spatial_avg": get_runtime(XC_SERIAL_SETUP, XC_SPATIAL_AVG_STMT, repeat=5),
+    "temporal_avg": get_runtime(XC_SERIAL_SETUP, XC_TEMP_AVG_STMT, repeat=5),
+    "climatology": get_runtime(XC_SERIAL_SETUP, XC_CLIMATOLOGY_STMT, repeat=5),
+    "departures": get_runtime(XC_SERIAL_SETUP, XC_DEPARTURES_STMT, repeat=5),
+}
+
+# %% xCDAT Parallel
+# ---------------------
+# 245 GB -> 125MB chunk sizes
+# Dataset object stored here for inspection. It is not actually used in the
+# performance benchmarking.
+# ds_parallel = xc.open_mfdataset(f"{DIR}/*.nc", chunks="auto")
+XC_PARALLEL_SETUP = (
+    "import xcdat as xc\n" f"ds = xc.open_mfdataset(f'{DIR}/*.nc', chunks='auto')\n"
+)
+parallel_results = {
+    "spatial_avg": get_runtime(XC_PARALLEL_SETUP, XC_SPATIAL_AVG_STMT, repeat=5),
+    "temporal_avg": get_runtime(XC_PARALLEL_SETUP, XC_TEMP_AVG_STMT, repeat=5),
+    "climatology": get_runtime(XC_PARALLEL_SETUP, XC_CLIMATOLOGY_STMT, repeat=5),
+    "departures": get_runtime(XC_PARALLEL_SETUP, XC_DEPARTURES_STMT, repeat=5),
+}
+
+# %% CDMS2 (serial)
+# ---------------------
+C_SPATIAL_AVG_STMT = f"cdutil.averager(t_var, axis='xy')"
+C_TEMP_AVG_STMT = f"cdutil.averager(t_var, axis='t')"
+C_CLIMATOLOGY_STMT = f"cdutil.ANNUALCYCLE.climatoloy(t_var)"
+C_DEPARTURES_STMT = f"cdutil.ANNUALCYCLE.depatures(t_var)"
+
+ds_cdms = cdms2(DIR)
+t_var = ds_cdms(VAR)
+
+# %%