diff --git a/docs/source/notebooks/subset-user-input.ipynb b/docs/source/notebooks/subset-user-input.ipynb
index 37ad408b..29fa6765 100644
--- a/docs/source/notebooks/subset-user-input.ipynb
+++ b/docs/source/notebooks/subset-user-input.ipynb
@@ -98,7 +98,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0241691acb55499b9dc421493ef8e1af",
+       "model_id": "61087403e4cb4d7db3b118bd2adc40a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -245,7 +245,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "963956ad4cc648d790a41fdacd7b0313",
+       "model_id": "e14f68cad5bd452c982e17c4405b4b1a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -880,28 +880,50 @@
        "    license_type:         permissive\n",
        "    license:              https://open.canada.ca/en/open-government-licence-c...\n",
        "    attribution:          The authors provide this data under the Environment...\n",
-       "    citation:             Natural Resources Canada ANUSPLIN interpolated hist...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c9903e9a-021c-48cc-83f9-683d10e43e66' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9903e9a-021c-48cc-83f9-683d10e43e66' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 510</li><li><span class='xr-has-index'>lon</span>: 1068</li><li><span class='xr-has-index'>time</span>: 24837</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3005997c-1c26-4a53-bf9e-5636e15ddd06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3005997c-1c26-4a53-bf9e-5636e15ddd06' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.46 83.38 83.29 ... 41.12 41.04</div><input id='attrs-ddca425a-15a4-4b42-8c1d-65ddbfd20e18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddca425a-15a4-4b42-8c1d-65ddbfd20e18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5284a539-2577-4d74-b382-586a9f1d3ec9' class='xr-var-data-in' type='checkbox'><label for='data-5284a539-2577-4d74-b382-586a9f1d3ec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_ChunkSizes :</span></dt><dd>510</dd></dl></div><div class='xr-var-data'><pre>array([83.45833 , 83.375   , 83.291664, ..., 41.208332, 41.125   , 41.041668],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-141.0 -140.9 ... -52.13 -52.04</div><input id='attrs-0553d34f-d27f-4c79-b906-255d80730a6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0553d34f-d27f-4c79-b906-255d80730a6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd35da5a-f19c-4c4c-bc77-e7f3dd26721c' class='xr-var-data-in' type='checkbox'><label for='data-bd35da5a-f19c-4c4c-bc77-e7f3dd26721c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_ChunkSizes :</span></dt><dd>1068</dd></dl></div><div class='xr-var-data'><pre>array([-140.95833 , -140.875   , -140.79167 , ...,  -52.208336,  -52.125004,\n",
-       "        -52.04167 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2017-12-31</div><input id='attrs-fa7ab32e-6e8a-42da-a4af-b4c67a7f5329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa7ab32e-6e8a-42da-a4af-b4c67a7f5329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-093ade1c-fbdd-4ea1-b8fd-fe6521d28e9f' class='xr-var-data-in' type='checkbox'><label for='data-093ade1c-fbdd-4ea1-b8fd-fe6521d28e9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
+       "    citation:             Natural Resources Canada ANUSPLIN interpolated hist...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-75c42b7b-c23a-4c42-97d7-9f06e770e742' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75c42b7b-c23a-4c42-97d7-9f06e770e742' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 510</li><li><span class='xr-has-index'>lon</span>: 1068</li><li><span class='xr-has-index'>time</span>: 24837</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-72ae7707-3828-42c1-92b8-bb7786497f1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-72ae7707-3828-42c1-92b8-bb7786497f1e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.46 83.38 83.29 ... 41.12 41.04</div><input id='attrs-79d6d50b-7884-49cf-84c0-05d95d742145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79d6d50b-7884-49cf-84c0-05d95d742145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e18361e3-65c7-438b-8d5d-d16e2a6dface' class='xr-var-data-in' type='checkbox'><label for='data-e18361e3-65c7-438b-8d5d-d16e2a6dface' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_ChunkSizes :</span></dt><dd>510</dd></dl></div><div class='xr-var-data'><pre>array([83.45833 , 83.375   , 83.291664, ..., 41.208332, 41.125   , 41.041668],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-141.0 -140.9 ... -52.13 -52.04</div><input id='attrs-a1607602-074d-4b26-bb74-44204bf48bb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1607602-074d-4b26-bb74-44204bf48bb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0bff193-c418-4cf3-beae-9e8291c260dd' class='xr-var-data-in' type='checkbox'><label for='data-d0bff193-c418-4cf3-beae-9e8291c260dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_ChunkSizes :</span></dt><dd>1068</dd></dl></div><div class='xr-var-data'><pre>array([-140.95833 , -140.875   , -140.79167 , ...,  -52.208336,  -52.125004,\n",
+       "        -52.04167 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2017-12-31</div><input id='attrs-39a201f0-980c-49e2-af29-d34106943665' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39a201f0-980c-49e2-af29-d34106943665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61a653d4-85c0-4661-91bc-51b50bbacba3' class='xr-var-data-in' type='checkbox'><label for='data-61a653d4-85c0-4661-91bc-51b50bbacba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2017-12-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2017-12-30T00:00:00.000000000&#x27;, &#x27;2017-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71f78a31-0fd7-4306-90f9-41b1aaae2edc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71f78a31-0fd7-4306-90f9-41b1aaae2edc' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-8e55668c-7b3f-4b92-b594-f24f61796bc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e55668c-7b3f-4b92-b594-f24f61796bc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-960eeb0f-8a78-4062-8ec3-d863e614f1e0' class='xr-var-data-in' type='checkbox'><label for='data-960eeb0f-8a78-4062-8ec3-d863e614f1e0' 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>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
-       "<tr>\n",
-       "<td>\n",
-       "<table>\n",
-       "  <thead>\n",
-       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 50.40 GiB </td> <td> 127.96 MiB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (24837, 510, 1068) </td> <td> (7390, 51, 89) </td></tr>\n",
-       "    <tr><th> Count </th><td> 481 Tasks </td><td> 480 Chunks </td></tr>\n",
-       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</td>\n",
-       "<td>\n",
-       "<svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f298365-85d5-42b2-a439-ee2a9a1c84a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f298365-85d5-42b2-a439-ee2a9a1c84a0' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-2f99dc93-a3d0-4a8b-9bb1-4f531f6b1131' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f99dc93-a3d0-4a8b-9bb1-4f531f6b1131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c635a05-eb79-450a-ae24-5e9e9a3432ea' class='xr-var-data-in' type='checkbox'><label for='data-1c635a05-eb79-450a-ae24-5e9e9a3432ea' 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>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 50.40 GiB </td>\n",
+       "                        <td> 127.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (24837, 510, 1068) </td>\n",
+       "                        <td> (7390, 51, 89) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 481 Tasks </td>\n",
+       "                        <td> 480 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
@@ -987,25 +1009,47 @@
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        "  <text x=\"35.294118\" y=\"84.473365\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,84.473365)\">24837</text>\n",
        "</svg>\n",
-       "</td>\n",
-       "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-eec9d805-f2cc-44fb-8c09-1274a6375a3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eec9d805-f2cc-44fb-8c09-1274a6375a3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-843ac624-35fd-4aa2-a57d-0413ca7a5949' class='xr-var-data-in' type='checkbox'><label for='data-843ac624-35fd-4aa2-a57d-0413ca7a5949' 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>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
-       "<tr>\n",
-       "<td>\n",
-       "<table>\n",
-       "  <thead>\n",
-       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 50.40 GiB </td> <td> 127.96 MiB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (24837, 510, 1068) </td> <td> (7390, 51, 89) </td></tr>\n",
-       "    <tr><th> Count </th><td> 481 Tasks </td><td> 480 Chunks </td></tr>\n",
-       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</td>\n",
-       "<td>\n",
-       "<svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-d84edeba-b164-4410-8727-e8fcd0797eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d84edeba-b164-4410-8727-e8fcd0797eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4b7f701-b7ce-4f8b-8256-ebe32b6ca749' class='xr-var-data-in' type='checkbox'><label for='data-b4b7f701-b7ce-4f8b-8256-ebe32b6ca749' 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>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 50.40 GiB </td>\n",
+       "                        <td> 127.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (24837, 510, 1068) </td>\n",
+       "                        <td> (7390, 51, 89) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 481 Tasks </td>\n",
+       "                        <td> 480 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
@@ -1091,25 +1135,47 @@
        "  <text x=\"134.164937\" y=\"85.177859\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,134.164937,85.177859)\">510</text>\n",
        "  <text x=\"35.294118\" y=\"84.473365\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,84.473365)\">24837</text>\n",
        "</svg>\n",
-       "</td>\n",
-       "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-fe96cdcc-2b85-4415-a213-cdf43dbbf466' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe96cdcc-2b85-4415-a213-cdf43dbbf466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1b8a037-adca-4684-b01b-49bc1362cc35' class='xr-var-data-in' type='checkbox'><label for='data-e1b8a037-adca-4684-b01b-49bc1362cc35' 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>mm s-1</dd><dt><span>long_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
-       "<tr>\n",
-       "<td>\n",
-       "<table>\n",
-       "  <thead>\n",
-       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 50.40 GiB </td> <td> 127.96 MiB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (24837, 510, 1068) </td> <td> (7390, 51, 89) </td></tr>\n",
-       "    <tr><th> Count </th><td> 481 Tasks </td><td> 480 Chunks </td></tr>\n",
-       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</td>\n",
-       "<td>\n",
-       "<svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</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=(7390, 51, 89), meta=np.ndarray&gt;</div><input id='attrs-dfd2ac2b-0410-4153-9652-bb54aea02eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd2ac2b-0410-4153-9652-bb54aea02eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3286e5cd-9376-4937-8bd3-785dd417a040' class='xr-var-data-in' type='checkbox'><label for='data-3286e5cd-9376-4937-8bd3-785dd417a040' 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>mm s-1</dd><dt><span>long_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  510 1068]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 50.40 GiB </td>\n",
+       "                        <td> 127.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (24837, 510, 1068) </td>\n",
+       "                        <td> (7390, 51, 89) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 481 Tasks </td>\n",
+       "                        <td> 480 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"164\" height=\"149\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
@@ -1195,9 +1261,9 @@
        "  <text x=\"134.164937\" y=\"85.177859\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,134.164937,85.177859)\">510</text>\n",
        "  <text x=\"35.294118\" y=\"84.473365\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,84.473365)\">24837</text>\n",
        "</svg>\n",
-       "</td>\n",
-       "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-07d3400b-3e41-49d6-ba2d-e106c9cc102e' class='xr-section-summary-in' type='checkbox'  ><label for='section-07d3400b-3e41-49d6-ba2d-e106c9cc102e' class='xr-section-summary' >Attributes: <span>(15)</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-1.5</dd><dt><span>title :</span></dt><dd>NRCAN ANUSPLIN daily gridded dataset : version 2</dd><dt><span>history :</span></dt><dd>Fri Jan 25 14:11:15 2019 : Convert from original format to NetCDF</dd><dt><span>institute_id :</span></dt><dd>NRCAN</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>abstract :</span></dt><dd>Gridded daily observational dataset produced by Natural Resources Canada (NRCan), available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. The version 2 dataset was updated in its entirety compared with version 1 and is not a simple extension of version 1 with additional years of data. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) implementation of the trivariate thin plate splines interpolation method with latitude, longitude and elevation as predictors. </dd><dt><span>dataset_description :</span></dt><dd>https://cfs.nrcan.gc.ca/projects/3/1</dd><dt><span>type :</span></dt><dd>gridded_obs</dd><dt><span>institute :</span></dt><dd>Natural Resources Canada : Canadian Forest Service</dd><dt><span>dataset_id :</span></dt><dd>NRCAN_anusplin_daily_v2</dd><dt><span>version :</span></dt><dd>2.0</dd><dt><span>license_type :</span></dt><dd>permissive</dd><dt><span>license :</span></dt><dd>https://open.canada.ca/en/open-government-licence-canada</dd><dt><span>attribution :</span></dt><dd>The authors provide this data under the Environment and Climate Change Canada Data Server End Use License and demand that any references to or derivative works based on this dataset be cited using the provided citation information</dd><dt><span>citation :</span></dt><dd>Natural Resources Canada ANUSPLIN interpolated historical climate model from McKenney, D.W., M.F. Hutchinson, P. Papadol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R.F. Hopkinson, D. Price, and T. Owen, 2011. Customized Spatial Climate Models for North America. Bull. Amer. Meteor. Soc., 92, 1611-1622, https://doi.org/10.1175/2011BAMS3132.1</dd></dl></div></li></ul></div></div>"
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aaa539f8-ef09-49a0-80e0-7dfb0ca50389' class='xr-section-summary-in' type='checkbox'  ><label for='section-aaa539f8-ef09-49a0-80e0-7dfb0ca50389' class='xr-section-summary' >Attributes: <span>(15)</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-1.5</dd><dt><span>title :</span></dt><dd>NRCAN ANUSPLIN daily gridded dataset : version 2</dd><dt><span>history :</span></dt><dd>Fri Jan 25 14:11:15 2019 : Convert from original format to NetCDF</dd><dt><span>institute_id :</span></dt><dd>NRCAN</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>abstract :</span></dt><dd>Gridded daily observational dataset produced by Natural Resources Canada (NRCan), available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. The version 2 dataset was updated in its entirety compared with version 1 and is not a simple extension of version 1 with additional years of data. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) implementation of the trivariate thin plate splines interpolation method with latitude, longitude and elevation as predictors. </dd><dt><span>dataset_description :</span></dt><dd>https://cfs.nrcan.gc.ca/projects/3/1</dd><dt><span>type :</span></dt><dd>gridded_obs</dd><dt><span>institute :</span></dt><dd>Natural Resources Canada : Canadian Forest Service</dd><dt><span>dataset_id :</span></dt><dd>NRCAN_anusplin_daily_v2</dd><dt><span>version :</span></dt><dd>2.0</dd><dt><span>license_type :</span></dt><dd>permissive</dd><dt><span>license :</span></dt><dd>https://open.canada.ca/en/open-government-licence-canada</dd><dt><span>attribution :</span></dt><dd>The authors provide this data under the Environment and Climate Change Canada Data Server End Use License and demand that any references to or derivative works based on this dataset be cited using the provided citation information</dd><dt><span>citation :</span></dt><dd>Natural Resources Canada ANUSPLIN interpolated historical climate model from McKenney, D.W., M.F. Hutchinson, P. Papadol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R.F. Hopkinson, D. Price, and T. Owen, 2011. Customized Spatial Climate Models for North America. Bull. Amer. Meteor. Soc., 92, 1611-1622, https://doi.org/10.1175/2011BAMS3132.1</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1272,7 +1338,7 @@
     {
      "data": {
       "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/0dfbc373-ce29-11eb-95d5-8962d32ec48d/nrcan_v2_sub.ncml'"
+       "'https://pavics.ouranos.ca/wpsoutputs/0677460a-1b1e-11ec-8812-55fea68da9e0/nrcan_v2_sub.ncml'"
       ]
      },
      "execution_count": 16,
@@ -1312,7 +1378,7 @@
     {
      "data": {
       "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/12300a73-ce29-11eb-a763-8962d32ec48d/nrcan_v2_sub.ncml'"
+       "'https://pavics.ouranos.ca/wpsoutputs/0ab2e94f-1b1e-11ec-85dd-55fea68da9e0/nrcan_v2_sub.ncml'"
       ]
      },
      "execution_count": 18,
@@ -1344,15 +1410,15 @@
      "text": [
       "Help on method heat_wave_frequency in module birdy.client.base:\n",
       "\n",
-      "heat_wave_frequency(tasmin=None, tasmax=None, check_missing='any', cf_compliance='warn', data_validation='raise', thresh_tasmin='22.0 degC', thresh_tasmax='30 degC', window=3, freq='YS', missing_options=None, variable=None) method of birdy.client.base.WPSClient instance\n",
+      "heat_wave_frequency(tasmin=None, tasmax=None, thresh_tasmin='22.0 degC', thresh_tasmax='30 degC', window=3, freq='YS', check_missing='any', missing_options=None, cf_compliance='warn', data_validation='raise', variable=None) method of birdy.client.base.WPSClient instance\n",
       "    Number of heat waves over a given period. A heat wave is defined as an event where the minimum and maximum daily temperature both exceeds specific thresholds over a minimum number of days.\n",
       "    \n",
       "    Parameters\n",
       "    ----------\n",
       "    tasmin : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/x-ogc-dods`\n",
-      "        NetCDF Files or archive (tar/zip) containing netCDF files.\n",
+      "        NetCDF Files or archive (tar/zip) containing netCDF files. Minimum surface temperature.\n",
       "    tasmax : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/x-ogc-dods`\n",
-      "        NetCDF Files or archive (tar/zip) containing netCDF files.\n",
+      "        NetCDF Files or archive (tar/zip) containing netCDF files. Maximum surface temperature.\n",
       "    thresh_tasmin : string\n",
       "        The minimum temperature threshold needed to trigger a heatwave event.\n",
       "    thresh_tasmax : string\n",
@@ -1418,7 +1484,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading to /tmp/tmpttfhv15r/out.nc.\n"
+      "Downloading to /tmp/tmpc3d7imo7/out.nc.\n"
      ]
     }
    ],
@@ -1443,7 +1509,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f7eeb720150>"
+       "<matplotlib.collections.QuadMesh at 0x7f0b8a25af10>"
       ]
      },
      "execution_count": 23,
@@ -1452,7 +1518,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1497,14 +1563,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output_url =  https://pavics.ouranos.ca/wpsoutputs/16490462-ce29-11eb-a975-8962d32ec48d/out.nc\n",
-      "output_path =  wps_outputs/16490462-ce29-11eb-a975-8962d32ec48d/out.nc\n"
+      "output_url =  https://pavics.ouranos.ca/wpsoutputs/0eca243a-1b1e-11ec-ad67-55fea68da9e0/out.nc\n",
+      "output_path =  wps_outputs/0eca243a-1b1e-11ec-ad67-55fea68da9e0/out.nc\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'https://pavics.ouranos.ca:8083/thredds/wms/birdhouse/wps_outputs/16490462-ce29-11eb-a975-8962d32ec48d/out.nc'"
+       "'https://pavics.ouranos.ca:8083/thredds/wms/birdhouse/wps_outputs/0eca243a-1b1e-11ec-ad67-55fea68da9e0/out.nc'"
       ]
      },
      "execution_count": 24,
@@ -1538,7 +1604,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0aba01decc9648018a4a315a8fe1b083",
+       "model_id": "4e3e681377544cf196d48de4150fcbb0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1579,7 +1645,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1607,4 +1673,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file