Repository: bird-house/finch (language: python)

Issue:
Allow ensemble processes to return the dataset with individual realizations
## Description
For climatedata.ca users have expressed interest of being able to calculate indicators for individual model runs.  This could potentially be done by calling a large number individual finch processes but in my mind the simplest method forward would be to add a `calculate_percentiles` flag (default = True) to the ensemble_utils calls here : https://github.com/bird-house/finch/blob/38b3e322c140c22a6795e686f62809daafe9dd7c/finch/processes/ensemble_utils.py#L346

ideally we could also make use of the `create_ensemble()`  realizations parameter to add simulation ids as the realizations coordinate https://xclim.readthedocs.io/en/stable/xclim.ensembles.html#xclim.ensembles._base.create_ensemble


Discussion / hints:
May I suggest an alternative?  Allow to pass `ensemble_percentiles="None"` or just to leave it empty.  This way we don't add another parameter and we don't get the ambiguous situation with both `ensemble_percentiles="10,50,90"` and `calculate_percentiles=False`.
Yes makes more sense
This is not as easy as it seems...
The valid information about the dataset is in the filename of the input and parsed by the `Dataset` class. However, from then on we only work with file lists. We either have to guess again this realization name from the final file names or try to pass the information along. I'm trying to hack some global attributes for the second idea, I'll let you know how it goes.

Otherwise, for the "long term goals we never have time to implement" category, I would add "use catalogs internally".
@aulemahal  if `create_ensemble()`  receives something like `realizations = [f.stem for f in filelist]`  I think that would satisfy the need for now ... basically would like to ensure some provenance info of which run is which

Fix patch:
diff --git a/CHANGES.rst b/CHANGES.rst
index 6eb300ff..6a0a16dc 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -2,10 +2,13 @@ Changes
 *******
 
 TBD (unreleased)
-===================
+================
 * Fixed iter_local when depth > 0 to avoid all files to be considered twice
 * Revised documentation configuration on ReadTheDocs to leverage Anaconda (Mambaforge)
 * Minor adjustments to dependency configurations
+* Update to xclim 0.40.0.
+* Passing an empty string to `ensemble_percentiles` in ensemble processes will return the merged un-reduced ensemble. The different members are listed along the `realization` coordinates through raw names allowing for basic distinction between the input members.
+* Removed configuration elements handling from `finch start`. One can still pass custom config files, but all configuration defaults are handled by `finch/default.cfg` and the WSGI function. `jinja2` is not a dependency anymore.
 
 0.10.0 (2022-11-04)
 ===================
diff --git a/Makefile b/Makefile
index 5628afb9..095dbef8 100644
--- a/Makefile
+++ b/Makefile
@@ -4,11 +4,6 @@ APP_NAME := finch
 
 WPS_URL = http://localhost:5000
 
-# Used in target refresh-notebooks to make it looks like the notebooks have
-# been refreshed from the production server below instead of from the local dev
-# instance so the notebooks can also be used as tutorial notebooks.
-OUTPUT_URL = https://pavics.ouranos.ca/wpsoutputs/finch
-
 SANITIZE_FILE := https://github.com/Ouranosinc/PAVICS-e2e-workflow-tests/raw/master/notebooks/output-sanitize.cfg
 
 # end of configuration
@@ -136,7 +131,7 @@ lint:
 .PHONY: refresh-notebooks
 refresh-notebooks:
 	@echo "Refresh all notebook outputs under docs/source/notebooks"
-	@bash -c 'for nb in $(CURDIR)/docs/source/notebooks/*.ipynb; do WPS_URL="$(WPS_URL)" jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output "$$nb" "$$nb"; sed -i "s@$(WPS_URL)/outputs/@$(OUTPUT_URL)/@g" "$$nb"; done; cd $(APP_ROOT)'
+	@bash -c 'for nb in $(CURDIR)/docs/source/notebooks/*.ipynb; do WPS_URL="$(WPS_URL)" jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output "$$nb" "$$nb"; done; cd $(APP_ROOT)'
 
 ## Sphinx targets
 
diff --git a/docs/source/notebooks/dap_subset.ipynb b/docs/source/notebooks/dap_subset.ipynb
index 6a31f9f9..2a4399da 100644
--- a/docs/source/notebooks/dap_subset.ipynb
+++ b/docs/source/notebooks/dap_subset.ipynb
@@ -15,7 +15,14 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:06.595739Z",
+     "iopub.status.busy": "2023-06-02T19:35:06.594965Z",
+     "iopub.status.idle": "2023-06-02T19:35:09.133584Z",
+     "shell.execute_reply": "2023-06-02T19:35:09.132717Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -35,7 +42,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:09.136501Z",
+     "iopub.status.busy": "2023-06-02T19:35:09.136227Z",
+     "iopub.status.idle": "2023-06-02T19:35:10.385485Z",
+     "shell.execute_reply": "2023-06-02T19:35:10.384778Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -428,15 +442,15 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</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-b41f1382-5543-474d-a6de-7f456bc5344b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b41f1382-5543-474d-a6de-7f456bc5344b' 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>: 6</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-baefe32b-59c2-4795-aee0-95f8091b5073' class='xr-section-summary-in' type='checkbox'  checked><label for='section-baefe32b-59c2-4795-aee0-95f8091b5073' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-f72b7b75-9317-4838-b993-a0ad64ec0c84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72b7b75-9317-4838-b993-a0ad64ec0c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f6af637-60bb-4e45-a3dc-adb04f626baf' class='xr-var-data-in' type='checkbox'><label for='data-2f6af637-60bb-4e45-a3dc-adb04f626baf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-8e5c836b-270f-47e6-93f3-9fe811d785ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e5c836b-270f-47e6-93f3-9fe811d785ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0983cb-ffa2-48f2-b5ee-4fd00d426a12' class='xr-var-data-in' type='checkbox'><label for='data-bb0983cb-ffa2-48f2-b5ee-4fd00d426a12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00 ... 2065-12-...</div><input id='attrs-bb2a9c58-978f-4be9-ad9b-c1e60219f6b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2a9c58-978f-4be9-ad9b-c1e60219f6b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64648cc7-5823-4f66-8c53-c051937fd3f4' class='xr-var-data-in' type='checkbox'><label for='data-64648cc7-5823-4f66-8c53-c051937fd3f4' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</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-9fa94ad7-a89b-4bfc-bd51-8ea82b5fd623' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fa94ad7-a89b-4bfc-bd51-8ea82b5fd623' 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>: 6</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-562e0543-ade5-4130-9fa1-06e41fba5b3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-562e0543-ade5-4130-9fa1-06e41fba5b3c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-6ddd02fd-a6fb-4700-9f74-5ee80d123db6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ddd02fd-a6fb-4700-9f74-5ee80d123db6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a3c82d7-3e59-4ae7-b78b-fc72d3223812' class='xr-var-data-in' type='checkbox'><label for='data-7a3c82d7-3e59-4ae7-b78b-fc72d3223812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-259f43d0-e562-4db8-bf50-d0681e989994' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-259f43d0-e562-4db8-bf50-d0681e989994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bc3e4a-8248-4159-ad2f-a5c6862d0d1d' class='xr-var-data-in' type='checkbox'><label for='data-b7bc3e4a-8248-4159-ad2f-a5c6862d0d1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00 ... 2065-12-...</div><input id='attrs-264479bf-9aad-4639-95f4-1454900f2933' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-264479bf-9aad-4639-95f4-1454900f2933' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec5d9e1c-4b0f-4c5a-befe-822a5950919e' class='xr-var-data-in' type='checkbox'><label for='data-ec5d9e1c-4b0f-4c5a-befe-822a5950919e' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2046, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2046, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2065, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe738302-d105-4fdf-a9ff-1c5971f07f8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe738302-d105-4fdf-a9ff-1c5971f07f8c' 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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ea5a1b4-5c24-4508-b2ad-63929b7aa12c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea5a1b4-5c24-4508-b2ad-63929b7aa12c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb79cc6-1985-40f5-a6a1-6e8e6ca879bc' class='xr-var-data-in' type='checkbox'><label for='data-dbb79cc6-1985-40f5-a6a1-6e8e6ca879bc' 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>[6 2]</dd></dl></div><div class='xr-var-data'><pre>[12 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75336db7-dc95-44c4-a0b7-508976d6cf51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75336db7-dc95-44c4-a0b7-508976d6cf51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e92b02-3ab6-46b4-b069-8976d1b63980' class='xr-var-data-in' type='checkbox'><label for='data-06e92b02-3ab6-46b4-b069-8976d1b63980' 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>[7 2]</dd></dl></div><div class='xr-var-data'><pre>[14 values with dtype=float64]</pre></div></li><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'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-845883f9-3f9a-4bcb-9619-54bbe3c085ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-845883f9-3f9a-4bcb-9619-54bbe3c085ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd6bc802-7ab4-4085-8444-6beb6f587eaf' class='xr-var-data-in' type='checkbox'><label for='data-bd6bc802-7ab4-4085-8444-6beb6f587eaf' 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>[7200    2]</dd></dl></div><div class='xr-var-data'><pre>[14400 values with dtype=object]</pre></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'>...</div><input id='attrs-d08bf4bd-3c7e-4346-bea0-0fc44a221811' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d08bf4bd-3c7e-4346-bea0-0fc44a221811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-107a67ad-c48e-467f-8824-397d2515b354' class='xr-var-data-in' type='checkbox'><label for='data-107a67ad-c48e-467f-8824-397d2515b354' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[302400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eee1a560-6bbd-4a31-a5bf-466672c503ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-eee1a560-6bbd-4a31-a5bf-466672c503ee' 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-1c329755-0424-4a3f-bd53-78f7bfdcca5a' class='xr-index-data-in' type='checkbox'/><label for='index-1c329755-0424-4a3f-bd53-78f7bfdcca5a' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25d7d1ff-3b56-4ff4-a0eb-75b20491d6d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25d7d1ff-3b56-4ff4-a0eb-75b20491d6d6' 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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28e3a7b7-4a84-4001-81c3-f19024d443c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28e3a7b7-4a84-4001-81c3-f19024d443c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142ff9ae-7450-4302-9998-6220982ea0cc' class='xr-var-data-in' type='checkbox'><label for='data-142ff9ae-7450-4302-9998-6220982ea0cc' 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>[6 2]</dd></dl></div><div class='xr-var-data'><pre>[12 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbc59090-e341-4e91-ad98-47542dd05be9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbc59090-e341-4e91-ad98-47542dd05be9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-073a431a-307f-465e-b5e3-0da9d5a99ead' class='xr-var-data-in' type='checkbox'><label for='data-073a431a-307f-465e-b5e3-0da9d5a99ead' 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>[7 2]</dd></dl></div><div class='xr-var-data'><pre>[14 values with dtype=float64]</pre></div></li><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'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-328e6500-3522-42ca-94a7-3e84bd9edbc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-328e6500-3522-42ca-94a7-3e84bd9edbc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba38360c-3158-417c-a177-ded364793ea6' class='xr-var-data-in' type='checkbox'><label for='data-ba38360c-3158-417c-a177-ded364793ea6' 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>[7200    2]</dd></dl></div><div class='xr-var-data'><pre>[14400 values with dtype=object]</pre></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'>...</div><input id='attrs-2b3abbe0-8bdd-45ae-8e9b-7df88d51263b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b3abbe0-8bdd-45ae-8e9b-7df88d51263b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e28b10ab-a630-4534-a31d-4fbf5e2dd858' class='xr-var-data-in' type='checkbox'><label for='data-e28b10ab-a630-4534-a31d-4fbf5e2dd858' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[302400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11d1cb59-7de8-4766-a71c-eeaa6ab08d47' class='xr-section-summary-in' type='checkbox'  ><label for='section-11d1cb59-7de8-4766-a71c-eeaa6ab08d47' 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-e8d4b3ec-a688-4109-ae44-9d567833859b' class='xr-index-data-in' type='checkbox'/><label for='index-e8d4b3ec-a688-4109-ae44-9d567833859b' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
        "              61.2315712],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-3cb02255-78a3-4642-91b4-14abc60698f0' class='xr-index-data-in' type='checkbox'/><label for='index-3cb02255-78a3-4642-91b4-14abc60698f0' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-e286b593-45ac-469b-8ef8-9766d4bd1c3c' class='xr-index-data-in' type='checkbox'/><label for='index-e286b593-45ac-469b-8ef8-9766d4bd1c3c' 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(CFTimeIndex([2046-01-01 12:00:00, 2046-01-02 12:00:00, 2046-01-03 12:00:00,\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-5701b896-059d-4678-9083-7570c9750ceb' class='xr-index-data-in' type='checkbox'/><label for='index-5701b896-059d-4678-9083-7570c9750ceb' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-d6a70b18-75c0-4299-85ae-c25c55db2499' class='xr-index-data-in' type='checkbox'/><label for='index-d6a70b18-75c0-4299-85ae-c25c55db2499' 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(CFTimeIndex([2046-01-01 12:00:00, 2046-01-02 12:00:00, 2046-01-03 12:00:00,\n",
        "             2046-01-04 12:00:00, 2046-01-05 12:00:00, 2046-01-06 12:00:00,\n",
        "             2046-01-07 12:00:00, 2046-01-08 12:00:00, 2046-01-09 12:00:00,\n",
        "             2046-01-10 12:00:00,\n",
@@ -445,7 +459,7 @@
        "             2065-12-24 12:00:00, 2065-12-25 12:00:00, 2065-12-26 12:00:00,\n",
        "             2065-12-27 12:00:00, 2065-12-28 12:00:00, 2065-12-29 12:00:00,\n",
        "             2065-12-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=7200, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46a9551b-d84f-4159-ad88-cee300605d9b' class='xr-section-summary-in' type='checkbox'  ><label for='section-46a9551b-d84f-4159-ad88-cee300605d9b' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "            dtype=&#x27;object&#x27;, length=7200, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d110f14-9a58-4f8e-877d-d13a74302b2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-7d110f14-9a58-4f8e-877d-d13a74302b2f' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -493,6 +507,12 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:10.419446Z",
+     "iopub.status.busy": "2023-06-02T19:35:10.419162Z",
+     "iopub.status.idle": "2023-06-02T19:35:10.431846Z",
+     "shell.execute_reply": "2023-06-02T19:35:10.431253Z"
+    },
     "tags": [
      "nbval-ignore-output"
     ]
@@ -542,7 +562,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:10.434305Z",
+     "iopub.status.busy": "2023-06-02T19:35:10.434108Z",
+     "iopub.status.idle": "2023-06-02T19:35:10.776440Z",
+     "shell.execute_reply": "2023-06-02T19:35:10.775411Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -928,7 +955,7 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</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-fc3194a3-6d20-4db3-bcc6-87d76a9e451f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc3194a3-6d20-4db3-bcc6-87d76a9e451f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 1</li><li><span>lat</span>: 6</li><li><span>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cf1ac8df-8f0c-422b-bdd0-6c6db7c1ac93' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf1ac8df-8f0c-422b-bdd0-6c6db7c1ac93' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3ed12567-f129-4732-a25d-a1dd44d77bc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ed12567-f129-4732-a25d-a1dd44d77bc7' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-5757fbc9-8350-4ae3-9592-ab71d84f400c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5757fbc9-8350-4ae3-9592-ab71d84f400c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1faa2bdd-2096-47b9-a4fb-26d2e333539b' class='xr-var-data-in' type='checkbox'><label for='data-1faa2bdd-2096-47b9-a4fb-26d2e333539b' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[42 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0562a1eb-44a8-4667-bb31-71150c677ae8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0562a1eb-44a8-4667-bb31-71150c677ae8' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-71fd6f00-ac34-4bf1-b297-41f2b921c598' class='xr-section-summary-in' type='checkbox'  ><label for='section-71fd6f00-ac34-4bf1-b297-41f2b921c598' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "    NCO:            4.0.9</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-ba5ea83d-650c-4736-bb17-b3868f4e4009' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba5ea83d-650c-4736-bb17-b3868f4e4009' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 1</li><li><span>lat</span>: 6</li><li><span>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8282a804-c7d4-49ff-ae1b-35b05929391f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8282a804-c7d4-49ff-ae1b-35b05929391f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-781fc9b5-652a-458d-84f7-34b38e8f112e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-781fc9b5-652a-458d-84f7-34b38e8f112e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-697c6e71-74ee-46b6-9627-9efaebcb09c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-697c6e71-74ee-46b6-9627-9efaebcb09c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e48c001-cd5a-4a49-a2e0-9bfe74ec0c3e' class='xr-var-data-in' type='checkbox'><label for='data-6e48c001-cd5a-4a49-a2e0-9bfe74ec0c3e' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[42 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-734aa8cb-7471-4379-95fb-9f4e1acb62ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-734aa8cb-7471-4379-95fb-9f4e1acb62ae' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-08bde251-f784-40b0-8039-5a674078b5f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-08bde251-f784-40b0-8039-5a674078b5f4' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -973,7 +1000,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:10.780253Z",
+     "iopub.status.busy": "2023-06-02T19:35:10.779708Z",
+     "iopub.status.idle": "2023-06-02T19:35:11.305639Z",
+     "shell.execute_reply": "2023-06-02T19:35:11.303384Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1362,11 +1396,11 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</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-5b4301f2-443b-4ae3-b33f-3f17335c85cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b4301f2-443b-4ae3-b33f-3f17335c85cc' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7c7acdb6-0b45-44f6-bd4d-08580afa5aa9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c7acdb6-0b45-44f6-bd4d-08580afa5aa9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-15213b38-b002-42d7-83df-0738b673e574' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15213b38-b002-42d7-83df-0738b673e574' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03ce64b-e414-4dff-b218-f020092e56ac' class='xr-var-data-in' type='checkbox'><label for='data-d03ce64b-e414-4dff-b218-f020092e56ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-deb07818-9927-40a9-a050-f9507a0380c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb07818-9927-40a9-a050-f9507a0380c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd133039-1ad3-44de-a54d-3c27c86343b9' class='xr-var-data-in' type='checkbox'><label for='data-dd133039-1ad3-44de-a54d-3c27c86343b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00</div><input id='attrs-765d2eae-940e-48e4-8707-877c0ec924a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-765d2eae-940e-48e4-8707-877c0ec924a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f93ccc9a-7521-4a3f-be57-4a84b2621b01' class='xr-var-data-in' type='checkbox'><label for='data-f93ccc9a-7521-4a3f-be57-4a84b2621b01' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d6c84ab-2ced-44d4-8852-f5f61630f322' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d6c84ab-2ced-44d4-8852-f5f61630f322' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-b2c7801c-c6a5-4502-b339-74878a727c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2c7801c-c6a5-4502-b339-74878a727c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0cedc8-6333-47fb-a8e2-c2dc28167ec4' class='xr-var-data-in' type='checkbox'><label for='data-bc0cedc8-6333-47fb-a8e2-c2dc28167ec4' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[42 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd4ddb62-c459-461a-96d9-2d7e63fd5723' class='xr-section-summary-in' type='checkbox'  ><label for='section-dd4ddb62-c459-461a-96d9-2d7e63fd5723' 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-c0e312ac-1df1-4ffa-b2c0-3dbf9bf60718' class='xr-index-data-in' type='checkbox'/><label for='index-c0e312ac-1df1-4ffa-b2c0-3dbf9bf60718' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
+       "    NCO:            4.0.9</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-c5f71d25-e4c3-45ff-bf12-8a10f617ef2e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5f71d25-e4c3-45ff-bf12-8a10f617ef2e' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0a7926a3-f650-4453-a0fb-651159063d6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a7926a3-f650-4453-a0fb-651159063d6f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-0b6cf803-eb80-4ffe-a618-4a81e92dc50a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b6cf803-eb80-4ffe-a618-4a81e92dc50a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3be09b41-82e3-4285-a3ea-519100a5ce64' class='xr-var-data-in' type='checkbox'><label for='data-3be09b41-82e3-4285-a3ea-519100a5ce64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-f2f965c6-9c7f-4756-80a6-af041a6e05f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2f965c6-9c7f-4756-80a6-af041a6e05f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92216859-609c-4d3c-b4ed-cc42d7da2043' class='xr-var-data-in' type='checkbox'><label for='data-92216859-609c-4d3c-b4ed-cc42d7da2043' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 12:00:00</div><input id='attrs-92bae632-2353-4ca4-99d4-d2bb0f80c742' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92bae632-2353-4ca4-99d4-d2bb0f80c742' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692888c6-21a0-4421-986e-cb382df3269e' class='xr-var-data-in' type='checkbox'><label for='data-692888c6-21a0-4421-986e-cb382df3269e' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffb63f6c-3782-4634-af84-014a2b5038b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffb63f6c-3782-4634-af84-014a2b5038b9' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-5bc9e2fc-4081-4f6a-8e2e-315b5f98cf6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bc9e2fc-4081-4f6a-8e2e-315b5f98cf6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cade6539-6e18-4cd4-a3a9-706d378f4f80' class='xr-var-data-in' type='checkbox'><label for='data-cade6539-6e18-4cd4-a3a9-706d378f4f80' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[42 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75f8b84e-6c7d-4a6f-bdc6-5fb30f1d3e14' class='xr-section-summary-in' type='checkbox'  ><label for='section-75f8b84e-6c7d-4a6f-bdc6-5fb30f1d3e14' 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-321cc72a-5ab6-48ed-99d5-9fba57566d82' class='xr-index-data-in' type='checkbox'/><label for='index-321cc72a-5ab6-48ed-99d5-9fba57566d82' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
        "              61.2315712],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-c22ce524-8cee-4125-8c9e-f85bb9bd2360' class='xr-index-data-in' type='checkbox'/><label for='index-c22ce524-8cee-4125-8c9e-f85bb9bd2360' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-50720130-9e36-4998-a3b1-f6f85c89ccb1' class='xr-index-data-in' type='checkbox'/><label for='index-50720130-9e36-4998-a3b1-f6f85c89ccb1' 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(CFTimeIndex([2046-01-01 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=1, calendar=&#x27;360_day&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99ab9ca7-9891-4a1e-a3e0-6158e6fc92a7' class='xr-section-summary-in' type='checkbox'  ><label for='section-99ab9ca7-9891-4a1e-a3e0-6158e6fc92a7' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-f6912cae-8427-458c-94c6-503b0904e7cd' class='xr-index-data-in' type='checkbox'/><label for='index-f6912cae-8427-458c-94c6-503b0904e7cd' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-89ff2881-2537-44f1-8d99-6116d16b74e9' class='xr-index-data-in' type='checkbox'/><label for='index-89ff2881-2537-44f1-8d99-6116d16b74e9' 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(CFTimeIndex([2046-01-01 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=1, calendar=&#x27;360_day&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c9316c1-b7b7-486b-a4e8-82c31da71101' class='xr-section-summary-in' type='checkbox'  ><label for='section-5c9316c1-b7b7-486b-a4e8-82c31da71101' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -1414,7 +1448,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:11.314483Z",
+     "iopub.status.busy": "2023-06-02T19:35:11.313780Z",
+     "iopub.status.idle": "2023-06-02T19:35:12.066308Z",
+     "shell.execute_reply": "2023-06-02T19:35:12.065672Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1804,13 +1845,13 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</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-3012e4e6-e55e-4306-bf1e-ff1e8f365395' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3012e4e6-e55e-4306-bf1e-ff1e8f365395' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li><li><span>time_1</span>: 1801</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9d3ac861-c5c1-41ed-a1c5-7d142331d283' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d3ac861-c5c1-41ed-a1c5-7d142331d283' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-4275d320-5467-47fe-9cea-9f3b65a62da1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4275d320-5467-47fe-9cea-9f3b65a62da1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0feec45d-0992-4e28-b6d2-00ef25b355a4' class='xr-var-data-in' type='checkbox'><label for='data-0feec45d-0992-4e28-b6d2-00ef25b355a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-967529e7-5cb1-483e-8e20-794938b8a5b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-967529e7-5cb1-483e-8e20-794938b8a5b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e34cc58-c07f-4878-96ca-df8bc60ae6df' class='xr-var-data-in' type='checkbox'><label for='data-3e34cc58-c07f-4878-96ca-df8bc60ae6df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-2e002fac-ebfd-4c02-be87-57a2626a677a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e002fac-ebfd-4c02-be87-57a2626a677a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10364726-f553-4dde-a4cb-5b6d43e28aff' class='xr-var-data-in' type='checkbox'><label for='data-10364726-f553-4dde-a4cb-5b6d43e28aff' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</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-55d09ac4-7577-44fc-974c-adcdf821c044' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55d09ac4-7577-44fc-974c-adcdf821c044' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li><li><span>time_1</span>: 1801</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a47f8045-d19c-4ebb-a9b4-8fb1b315c70a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a47f8045-d19c-4ebb-a9b4-8fb1b315c70a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-7684583c-b853-49c3-a64a-d814b7b2afee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7684583c-b853-49c3-a64a-d814b7b2afee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceff1fb9-9ed6-470b-aafa-4d64cfcc01a9' class='xr-var-data-in' type='checkbox'><label for='data-ceff1fb9-9ed6-470b-aafa-4d64cfcc01a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-e68b5a0b-e871-4c21-8b66-4b73e76aaeaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e68b5a0b-e871-4c21-8b66-4b73e76aaeaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1c9832-6486-40ae-86a8-dd070403fcd2' class='xr-var-data-in' type='checkbox'><label for='data-1b1c9832-6486-40ae-86a8-dd070403fcd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-062610f1-80b3-442b-a848-11a9a66aa572' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-062610f1-80b3-442b-a848-11a9a66aa572' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36e8d610-fb7e-4246-b29c-cb9b70bc8ad0' class='xr-var-data-in' type='checkbox'><label for='data-36e8d610-fb7e-4246-b29c-cb9b70bc8ad0' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2064, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfaffeba-ff27-4bdc-88f2-5c6b8a3a1472' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfaffeba-ff27-4bdc-88f2-5c6b8a3a1472' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time_1, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93496eb1-061e-43bb-8e8b-04682ca5c4a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93496eb1-061e-43bb-8e8b-04682ca5c4a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e801108c-1c34-4d15-946d-c570ea17b025' class='xr-var-data-in' type='checkbox'><label for='data-e801108c-1c34-4d15-946d-c570ea17b025' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[1801 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-931b594f-bdb2-4083-97c9-cfcb67565993' class='xr-section-summary-in' type='checkbox'  ><label for='section-931b594f-bdb2-4083-97c9-cfcb67565993' 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-fb2465cc-661e-4a38-8dd9-fa408480af9a' class='xr-index-data-in' type='checkbox'/><label for='index-fb2465cc-661e-4a38-8dd9-fa408480af9a' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-b795db7f-d321-4c9d-b2f3-26c58fc591d4' class='xr-index-data-in' type='checkbox'/><label for='index-b795db7f-d321-4c9d-b2f3-26c58fc591d4' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-b0c278bc-fd40-4816-a7be-18c2c5accb97' class='xr-index-data-in' type='checkbox'/><label for='index-b0c278bc-fd40-4816-a7be-18c2c5accb97' 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(CFTimeIndex([2060-01-01 12:00:00, 2060-01-02 12:00:00, 2060-01-03 12:00:00,\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7eaab541-8a26-42cf-8d38-ff73480a063e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7eaab541-8a26-42cf-8d38-ff73480a063e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time_1, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b5db0f5-91da-4a42-832e-4256afc2c2fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b5db0f5-91da-4a42-832e-4256afc2c2fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a962cd8e-3b15-48bc-a74c-af367fdfe9aa' class='xr-var-data-in' type='checkbox'><label for='data-a962cd8e-3b15-48bc-a74c-af367fdfe9aa' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[1801 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a52dba0-39cf-4eeb-9fc9-9f3db42913f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a52dba0-39cf-4eeb-9fc9-9f3db42913f9' 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-b24072cd-c259-46ac-8ef6-b0e25f830cb1' class='xr-index-data-in' type='checkbox'/><label for='index-b24072cd-c259-46ac-8ef6-b0e25f830cb1' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-2f631d61-ae4a-4876-957e-dcfa87adc516' class='xr-index-data-in' type='checkbox'/><label for='index-2f631d61-ae4a-4876-957e-dcfa87adc516' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-d25ac562-a906-4762-a025-6d47bdbd4f62' class='xr-index-data-in' type='checkbox'/><label for='index-d25ac562-a906-4762-a025-6d47bdbd4f62' 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(CFTimeIndex([2060-01-01 12:00:00, 2060-01-02 12:00:00, 2060-01-03 12:00:00,\n",
        "             2060-01-04 12:00:00, 2060-01-05 12:00:00, 2060-01-06 12:00:00,\n",
        "             2060-01-07 12:00:00, 2060-01-08 12:00:00, 2060-01-09 12:00:00,\n",
        "             2060-01-10 12:00:00,\n",
@@ -1819,7 +1860,7 @@
        "             2064-12-24 12:00:00, 2064-12-25 12:00:00, 2064-12-26 12:00:00,\n",
        "             2064-12-27 12:00:00, 2064-12-28 12:00:00, 2064-12-29 12:00:00,\n",
        "             2064-12-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=1800, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58209113-9f43-4fc7-b201-cd0b07560ec0' class='xr-section-summary-in' type='checkbox'  ><label for='section-58209113-9f43-4fc7-b201-cd0b07560ec0' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "            dtype=&#x27;object&#x27;, length=1800, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00a183ef-c6ca-4591-9a67-40beea038164' class='xr-section-summary-in' type='checkbox'  ><label for='section-00a183ef-c6ca-4591-9a67-40beea038164' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -1864,6 +1905,12 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:12.070160Z",
+     "iopub.status.busy": "2023-06-02T19:35:12.068946Z",
+     "iopub.status.idle": "2023-06-02T19:35:12.077186Z",
+     "shell.execute_reply": "2023-06-02T19:35:12.076534Z"
+    },
     "tags": [
      "nbval-ignore-output"
     ]
@@ -1887,7 +1934,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:12.080332Z",
+     "iopub.status.busy": "2023-06-02T19:35:12.079814Z",
+     "iopub.status.idle": "2023-06-02T19:35:12.086850Z",
+     "shell.execute_reply": "2023-06-02T19:35:12.086175Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1934,7 +1988,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:12.089477Z",
+     "iopub.status.busy": "2023-06-02T19:35:12.089009Z",
+     "iopub.status.idle": "2023-06-02T19:35:12.709706Z",
+     "shell.execute_reply": "2023-06-02T19:35:12.709141Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -2323,13 +2384,13 @@
        "    Conventions:    CF-1.0\n",
        "    id:             pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da\n",
        "    history:        Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -...\n",
-       "    NCO:            4.0.9</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-d37df1eb-56c8-4aa6-b37c-46d63ed09deb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d37df1eb-56c8-4aa6-b37c-46d63ed09deb' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-16d84f84-9e5c-48fe-b31b-7b8ddedf7884' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16d84f84-9e5c-48fe-b31b-7b8ddedf7884' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-095ed20a-48cc-43f0-af47-8d45bf380b35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-095ed20a-48cc-43f0-af47-8d45bf380b35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b82f97ea-18d0-4ba9-9993-8863a0995172' class='xr-var-data-in' type='checkbox'><label for='data-b82f97ea-18d0-4ba9-9993-8863a0995172' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-352aaac5-e1da-4210-9421-571d3a96456b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-352aaac5-e1da-4210-9421-571d3a96456b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f299aad9-8e2c-4837-8d17-f2b50989f540' class='xr-var-data-in' type='checkbox'><label for='data-f299aad9-8e2c-4837-8d17-f2b50989f540' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-b226a24d-7f2f-4f9f-bc9a-d8309ad65706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b226a24d-7f2f-4f9f-bc9a-d8309ad65706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40a1824e-220e-4179-81e8-5e1a57db0247' class='xr-var-data-in' type='checkbox'><label for='data-40a1824e-220e-4179-81e8-5e1a57db0247' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    NCO:            4.0.9</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-43572e81-ea8d-4f8e-9f9f-b0d80e4e049b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43572e81-ea8d-4f8e-9f9f-b0d80e4e049b' 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>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>time</span>: 1800</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ae6ea39-4f5d-4f85-b224-08acf11872b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ae6ea39-4f5d-4f85-b224-08acf11872b8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-09c8a149-d67f-4712-9438-caabb8a86976' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09c8a149-d67f-4712-9438-caabb8a86976' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63cdbb63-026f-4e3a-a2ce-ade80d0d2069' class='xr-var-data-in' type='checkbox'><label for='data-63cdbb63-026f-4e3a-a2ce-ade80d0d2069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-3b0828da-7803-44c4-b8fb-4d28493bba1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b0828da-7803-44c4-b8fb-4d28493bba1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3258f59d-ae4f-4850-b1c4-6cf8d349d8fa' class='xr-var-data-in' type='checkbox'><label for='data-3258f59d-ae4f-4850-b1c4-6cf8d349d8fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 12:00:00 ... 2064-12-...</div><input id='attrs-27ea7ab4-386b-4b98-b786-5dc6a1008842' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27ea7ab4-386b-4b98-b786-5dc6a1008842' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c05a65c-23e6-4e73-bd2d-ead81a8c2e10' class='xr-var-data-in' type='checkbox'><label for='data-5c05a65c-23e6-4e73-bd2d-ead81a8c2e10' 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>_ChunkSizes :</span></dt><dd>7200</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2060, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
        "       cftime.Datetime360Day(2064, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da0f0ee9-6c34-4c6e-b908-4be312d2ad9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da0f0ee9-6c34-4c6e-b908-4be312d2ad9c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-94dc97e9-a9a0-424a-b5cf-fd9e54d6c8d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94dc97e9-a9a0-424a-b5cf-fd9e54d6c8d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ad43659-612d-44a4-ae4a-e97b6d454cc0' class='xr-var-data-in' type='checkbox'><label for='data-1ad43659-612d-44a4-ae4a-e97b6d454cc0' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[1800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2da4d2a-0af7-4d02-aa61-b1329e945f8b' class='xr-section-summary-in' type='checkbox'  ><label for='section-b2da4d2a-0af7-4d02-aa61-b1329e945f8b' 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-3a226e96-27a3-46be-8b49-56fcc19d2baa' class='xr-index-data-in' type='checkbox'/><label for='index-3a226e96-27a3-46be-8b49-56fcc19d2baa' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-7b09e6c7-a4f9-4470-ae29-3a6b9caaba76' class='xr-index-data-in' type='checkbox'/><label for='index-7b09e6c7-a4f9-4470-ae29-3a6b9caaba76' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-60cf4a97-e75b-487b-86c8-e7635c154eed' class='xr-index-data-in' type='checkbox'/><label for='index-60cf4a97-e75b-487b-86c8-e7635c154eed' 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(CFTimeIndex([2060-01-01 12:00:00, 2060-01-02 12:00:00, 2060-01-03 12:00:00,\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-226efd8d-d139-4e9c-adb6-23a25fbd06bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-226efd8d-d139-4e9c-adb6-23a25fbd06bf' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-427e121e-e0c8-411f-a6c0-8b228beea378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-427e121e-e0c8-411f-a6c0-8b228beea378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e850f02a-d2b1-4a9f-a559-df735cc12146' class='xr-var-data-in' type='checkbox'><label for='data-e850f02a-d2b1-4a9f-a559-df735cc12146' 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>original_name :</span></dt><dd>APRL+APRC</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;</dd><dt><span>_ChunkSizes :</span></dt><dd>[7200    6    7]</dd></dl></div><div class='xr-var-data'><pre>[1800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e947744-4f31-4f4d-884d-357bfb629ae8' class='xr-section-summary-in' type='checkbox'  ><label for='section-2e947744-4f31-4f4d-884d-357bfb629ae8' 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-23ecb446-2b3a-470e-9847-fcac8fcee533' class='xr-index-data-in' type='checkbox'/><label for='index-23ecb446-2b3a-470e-9847-fcac8fcee533' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-4d96eeb3-fc9d-4d2b-be7d-b9dee0234f2b' class='xr-index-data-in' type='checkbox'/><label for='index-4d96eeb3-fc9d-4d2b-be7d-b9dee0234f2b' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-ff474f57-a08a-4838-8885-ee2954ab1771' class='xr-index-data-in' type='checkbox'/><label for='index-ff474f57-a08a-4838-8885-ee2954ab1771' 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(CFTimeIndex([2060-01-01 12:00:00, 2060-01-02 12:00:00, 2060-01-03 12:00:00,\n",
        "             2060-01-04 12:00:00, 2060-01-05 12:00:00, 2060-01-06 12:00:00,\n",
        "             2060-01-07 12:00:00, 2060-01-08 12:00:00, 2060-01-09 12:00:00,\n",
        "             2060-01-10 12:00:00,\n",
@@ -2338,7 +2399,7 @@
        "             2064-12-24 12:00:00, 2064-12-25 12:00:00, 2064-12-26 12:00:00,\n",
        "             2064-12-27 12:00:00, 2064-12-28 12:00:00, 2064-12-29 12:00:00,\n",
        "             2064-12-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=1800, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f8a82fb-8549-46d4-8bb5-3f74c77b8a3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f8a82fb-8549-46d4-8bb5-3f74c77b8a3f' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "            dtype=&#x27;object&#x27;, length=1800, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e93e9ba3-fce7-4a15-953b-a6bc265313ec' class='xr-section-summary-in' type='checkbox'  ><label for='section-e93e9ba3-fce7-4a15-953b-a6bc265313ec' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>MIUB (University of Bonn, Bonn, Germany)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Stephanie Legutke (legutke@dkrz.de), Seung-Ki Min(skmin@uni-bonn.de);</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:42:37 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v pr http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/pr.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
@@ -2388,7 +2449,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:12.712704Z",
+     "iopub.status.busy": "2023-06-02T19:35:12.712477Z",
+     "iopub.status.idle": "2023-06-02T19:35:14.854114Z",
+     "shell.execute_reply": "2023-06-02T19:35:14.852342Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -2763,20 +2831,20 @@
        "  * lon      (lon) float64 288.8\n",
        "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "Attributes:\n",
-       "    units:          mm/day\n",
+       "    units:          mm d-1\n",
        "    cell_methods:   time: mean (interval: 30 minutes)\n",
-       "    history:        pr=max(0,pr) applied to raw data;\\n[2023-01-11 09:48:48] ...\n",
+       "    history:        pr=max(0,pr) applied to raw data;\\n[2023-06-02 15:35:13] ...\n",
        "    standard_name:  lwe_thickness_of_precipitation_amount\n",
-       "    long_name:      Average precipitation during wet days (sdii)\n",
-       "    description:    Annual simple daily intensity index (sdii) : annual avera...</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'>'sdii'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a54092c2-65b6-42d0-816d-f6d4b81401ae' class='xr-array-in' type='checkbox' checked><label for='section-a54092c2-65b6-42d0-816d-f6d4b81401ae' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[5 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-d713d53c-c2fb-464e-b58a-2da91b8bfd57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d713d53c-c2fb-464e-b58a-2da91b8bfd57' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-028554ca-1d12-4250-a860-9965c077b3a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-028554ca-1d12-4250-a860-9965c077b3a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f481855-9f14-4538-8901-ccab72ed10b7' class='xr-var-data-in' type='checkbox'><label for='data-7f481855-9f14-4538-8901-ccab72ed10b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-e10d5bb7-2533-4c4d-9ba1-6213c89fdfbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e10d5bb7-2533-4c4d-9ba1-6213c89fdfbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-704a6325-41a7-4fa2-bd4e-09fbeab89601' class='xr-var-data-in' type='checkbox'><label for='data-704a6325-41a7-4fa2-bd4e-09fbeab89601' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 00:00:00 ... 2064-01-...</div><input id='attrs-0d162b82-641a-4fda-84af-da8aa97730e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d162b82-641a-4fda-84af-da8aa97730e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64c47cd2-9190-4419-b292-9ef633f95356' class='xr-var-data-in' type='checkbox'><label for='data-64c47cd2-9190-4419-b292-9ef633f95356' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "    long_name:      Average precipitation during days with daily precipitatio...\n",
+       "    description:    Annual simple daily intensity index (sdii) or annual aver...</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'>'sdii'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e81486af-7391-49a9-8998-aa04b9b76149' class='xr-array-in' type='checkbox' checked><label for='section-e81486af-7391-49a9-8998-aa04b9b76149' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[5 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-f7f940fe-6e40-430b-abbd-ae2d5ed0a84b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7f940fe-6e40-430b-abbd-ae2d5ed0a84b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.39</div><input id='attrs-1c5e6ada-f125-4d6e-b920-dfd26dddb63d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c5e6ada-f125-4d6e-b920-dfd26dddb63d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86e0079e-9d24-4bee-a95e-bc696dac525b' class='xr-var-data-in' type='checkbox'><label for='data-86e0079e-9d24-4bee-a95e-bc696dac525b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([46.388557])</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'>288.8</div><input id='attrs-371b7504-b9d9-4edd-8a4c-b7daf98fd430' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-371b7504-b9d9-4edd-8a4c-b7daf98fd430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a7a7f26-dd84-418c-a34f-c67b36302cb7' class='xr-var-data-in' type='checkbox'><label for='data-6a7a7f26-dd84-418c-a34f-c67b36302cb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([288.75])</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'>object</div><div class='xr-var-preview xr-preview'>2060-01-01 00:00:00 ... 2064-01-...</div><input id='attrs-617b7171-6687-477f-97f5-14a1010ddc3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-617b7171-6687-477f-97f5-14a1010ddc3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a38dbb16-587a-4dc1-bcd6-d8db0a1ded92' class='xr-var-data-in' type='checkbox'><label for='data-a38dbb16-587a-4dc1-bcd6-d8db0a1ded92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2060, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2061, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2062, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-439f233b-0cc9-4eb8-9e3c-59c7839e800a' class='xr-section-summary-in' type='checkbox'  ><label for='section-439f233b-0cc9-4eb8-9e3c-59c7839e800a' 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-9446c241-fb34-4d0f-9e7b-3eb97cd385af' class='xr-index-data-in' type='checkbox'/><label for='index-9446c241-fb34-4d0f-9e7b-3eb97cd385af' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-2dae9707-bb2f-451e-9296-66b1d0f6abec' class='xr-index-data-in' type='checkbox'/><label for='index-2dae9707-bb2f-451e-9296-66b1d0f6abec' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-f4bbb4c2-75b0-4e3c-ba6d-d50990068a75' class='xr-index-data-in' type='checkbox'/><label for='index-f4bbb4c2-75b0-4e3c-ba6d-d50990068a75' 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(CFTimeIndex([2060-01-01 00:00:00, 2061-01-01 00:00:00, 2062-01-01 00:00:00,\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-187b2c12-2e42-44b3-8abb-7851523026e6' class='xr-section-summary-in' type='checkbox'  ><label for='section-187b2c12-2e42-44b3-8abb-7851523026e6' 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-fbc3e6c1-88f0-4c1c-bd89-29e26c0c41ef' class='xr-index-data-in' type='checkbox'/><label for='index-fbc3e6c1-88f0-4c1c-bd89-29e26c0c41ef' 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([46.38855743], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-c32b3664-78db-44b8-a1d1-41343a106327' class='xr-index-data-in' type='checkbox'/><label for='index-c32b3664-78db-44b8-a1d1-41343a106327' 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([288.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-e9962670-852e-4bd5-86e1-90dd3ae3b727' class='xr-index-data-in' type='checkbox'/><label for='index-e9962670-852e-4bd5-86e1-90dd3ae3b727' 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(CFTimeIndex([2060-01-01 00:00:00, 2061-01-01 00:00:00, 2062-01-01 00:00:00,\n",
        "             2063-01-01 00:00:00, 2064-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=5, calendar=&#x27;360_day&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d40fcd0-41c8-470d-acfc-d771bdf2956d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d40fcd0-41c8-470d-acfc-d771bdf2956d' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;\n",
-       "[2023-01-11 09:48:48] sdii: SDII(pr=pr, thresh=&#x27;1 mm/day&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.38.0</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>long_name :</span></dt><dd>Average precipitation during wet days (sdii)</dd><dt><span>description :</span></dt><dd>Annual simple daily intensity index (sdii) : annual average precipitation for days with daily precipitation over 1 mm/day. this indicator is also known as the &#x27;simple daily intensity index&#x27; (sdii).</dd></dl></div></li></ul></div></div>"
+       "            dtype=&#x27;object&#x27;, length=5, calendar=&#x27;360_day&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22bfec50-cc77-490c-aeca-666b4b1659b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22bfec50-cc77-490c-aeca-666b4b1659b4' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 30 minutes)</dd><dt><span>history :</span></dt><dd>pr=max(0,pr) applied to raw data;\n",
+       "[2023-06-02 15:35:13] sdii: SDII(pr=pr, thresh=&#x27;1 mm/day&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.40.0</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>long_name :</span></dt><dd>Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)</dd><dt><span>description :</span></dt><dd>Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'sdii' (time: 5, lat: 1, lon: 1)>\n",
@@ -2786,12 +2854,12 @@
        "  * lon      (lon) float64 288.8\n",
        "  * time     (time) object 2060-01-01 00:00:00 ... 2064-01-01 00:00:00\n",
        "Attributes:\n",
-       "    units:          mm/day\n",
+       "    units:          mm d-1\n",
        "    cell_methods:   time: mean (interval: 30 minutes)\n",
-       "    history:        pr=max(0,pr) applied to raw data;\\n[2023-01-11 09:48:48] ...\n",
+       "    history:        pr=max(0,pr) applied to raw data;\\n[2023-06-02 15:35:13] ...\n",
        "    standard_name:  lwe_thickness_of_precipitation_amount\n",
-       "    long_name:      Average precipitation during wet days (sdii)\n",
-       "    description:    Annual simple daily intensity index (sdii) : annual avera..."
+       "    long_name:      Average precipitation during days with daily precipitatio...\n",
+       "    description:    Annual simple daily intensity index (sdii) or annual aver..."
       ]
      },
      "execution_count": 10,
@@ -2830,7 +2898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/finch-usage.ipynb b/docs/source/notebooks/finch-usage.ipynb
index 66898e17..8ea70afe 100644
--- a/docs/source/notebooks/finch-usage.ipynb
+++ b/docs/source/notebooks/finch-usage.ipynb
@@ -12,7 +12,14 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:19.080375Z",
+     "iopub.status.busy": "2023-06-02T19:35:19.080144Z",
+     "iopub.status.idle": "2023-06-02T19:35:22.068125Z",
+     "shell.execute_reply": "2023-06-02T19:35:22.067392Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -37,7 +44,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:22.073512Z",
+     "iopub.status.busy": "2023-06-02T19:35:22.072057Z",
+     "iopub.status.idle": "2023-06-02T19:35:22.082037Z",
+     "shell.execute_reply": "2023-06-02T19:35:22.081268Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -46,7 +60,7 @@
       "Help on method frost_days in module birdy.client.base:\n",
       "\n",
       "frost_days(tasmin=None, thresh='0 degC', freq='YS', month=None, season=None, check_missing='any', missing_options=None, cf_compliance='warn', data_validation='raise', variable=None, output_name=None, output_format='netcdf', csv_precision=None, output_formats=None) method of birdy.client.base.WPSClient instance\n",
-      "    Number of days where daily minimum temperatures are below a threshold temperature.\n",
+      "    Number of days where the daily minimum temperature is below a given threshold.\n",
       "    \n",
       "    Parameters\n",
       "    ----------\n",
@@ -69,7 +83,7 @@
       "    data_validation : {'log', 'warn', 'raise'}string\n",
       "        Whether to log, warn or raise when inputs fail data validation checks.\n",
       "    variable : string\n",
-      "        Name of the variable in the NetCDF file.\n",
+      "        Name of the variable in the input files.\n",
       "    output_name : string\n",
       "        Filename of the output (no extension).\n",
       "    output_format : {'netcdf', 'csv'}string\n",
@@ -109,7 +123,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:22.125379Z",
+     "iopub.status.busy": "2023-06-02T19:35:22.125094Z",
+     "iopub.status.idle": "2023-06-02T19:35:25.696864Z",
+     "shell.execute_reply": "2023-06-02T19:35:25.696188Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tasmin = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/testdata/flyingpigeon/cmip3/tasmin.sresa2.miub_echo_g.run1.atm.da.nc\"\n",
@@ -119,14 +140,21 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:25.699734Z",
+     "iopub.status.busy": "2023-06-02T19:35:25.699500Z",
+     "iopub.status.idle": "2023-06-02T19:35:25.703765Z",
+     "shell.execute_reply": "2023-06-02T19:35:25.703159Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Process status:  ProcessSucceeded\n",
-      "Link to process output:  https://pavics.ouranos.ca/wpsoutputs/finch/dc8d9086-ad7d-11ed-a969-0242ac170008/frost_days_sres_a2_experiment_20460101_20650101.nc\n"
+      "Link to process output:  http://localhost:5000/outputs/9d475210-017c-11ee-b431-54ef33fc01a5/frost_days_sres_a2_experiment_20460101_20650101.nc\n"
      ]
     }
    ],
@@ -146,7 +174,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:25.706750Z",
+     "iopub.status.busy": "2023-06-02T19:35:25.706520Z",
+     "iopub.status.idle": "2023-06-02T19:35:26.104457Z",
+     "shell.execute_reply": "2023-06-02T19:35:26.103837Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -412,6 +447,11 @@
        "  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",
@@ -433,14 +473,16 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data {\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-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -450,13 +492,16 @@
        "\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-var-data,\n",
+       ".xr-index-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -494,7 +539,8 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -504,67 +550,19 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;frost_days&#x27; (time: 20, lat: 6, lon: 7)&gt;\n",
-       "array([[[122., 121., ...,  19.,   9.],\n",
-       "        [156., 168., ..., 113.,  93.],\n",
-       "        ...,\n",
-       "        [227., 227., ..., 194., 133.],\n",
-       "        [269., 277., ..., 178., 136.]],\n",
-       "\n",
-       "       [[114., 115., ...,  31.,  23.],\n",
-       "        [161., 161., ..., 106.,  89.],\n",
-       "        ...,\n",
-       "        [231., 225., ..., 189., 134.],\n",
-       "        [259., 258., ..., 186., 140.]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[122., 111., ...,   0.,   0.],\n",
-       "        [151., 153., ...,  80.,  43.],\n",
-       "        ...,\n",
-       "        [221., 220., ..., 140.,  94.],\n",
-       "        [246., 247., ..., 137., 107.]],\n",
-       "\n",
-       "       [[105.,  98., ...,   3.,   1.],\n",
-       "        [163., 172., ...,  91.,  59.],\n",
-       "        ...,\n",
-       "        [225., 225., ..., 161., 107.],\n",
-       "        [251., 250., ..., 156., 103.]]])\n",
+       "[840 values with dtype=float64]\n",
        "Coordinates:\n",
-       "    height   float64 2.0\n",
-       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
+       "    height   float64 ...\n",
        "  * lat      (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon      (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          days\n",
        "    cell_methods:   time: minimum (interval: 30 minutes) time: sum over days\n",
        "    history:        tas=max(195,tas) applied to raw data; min of 194.73 detec...\n",
        "    standard_name:  days_with_air_temperature_below_threshold\n",
-       "    long_name:      Number of frost days (tmin &lt; 0 degc)\n",
-       "    description:    Annual number of days with minimum daily temperature belo...</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'>'frost_days'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1d37d7c7-4efe-4fa8-a029-e5aeb6ada42c' class='xr-array-in' type='checkbox' checked><label for='section-1d37d7c7-4efe-4fa8-a029-e5aeb6ada42c' 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>122.0 121.0 98.0 47.0 33.0 19.0 ... 249.0 229.0 193.0 156.0 103.0</span></div><div class='xr-array-data'><pre>array([[[122., 121., ...,  19.,   9.],\n",
-       "        [156., 168., ..., 113.,  93.],\n",
-       "        ...,\n",
-       "        [227., 227., ..., 194., 133.],\n",
-       "        [269., 277., ..., 178., 136.]],\n",
-       "\n",
-       "       [[114., 115., ...,  31.,  23.],\n",
-       "        [161., 161., ..., 106.,  89.],\n",
-       "        ...,\n",
-       "        [231., 225., ..., 189., 134.],\n",
-       "        [259., 258., ..., 186., 140.]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[122., 111., ...,   0.,   0.],\n",
-       "        [151., 153., ...,  80.,  43.],\n",
-       "        ...,\n",
-       "        [221., 220., ..., 140.,  94.],\n",
-       "        [246., 247., ..., 137., 107.]],\n",
-       "\n",
-       "       [[105.,  98., ...,   3.,   1.],\n",
-       "        [163., 172., ...,  91.,  59.],\n",
-       "        ...,\n",
-       "        [225., 225., ..., 161., 107.],\n",
-       "        [251., 250., ..., 156., 103.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-2c7de322-6a7a-492d-a303-c41a13d4a63c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c7de322-6a7a-492d-a303-c41a13d4a63c' 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>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-0b4f681a-11bf-4a32-bfc6-870a85362c7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b4f681a-11bf-4a32-bfc6-870a85362c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c63e57e-ded8-444e-834c-d08454e384f1' class='xr-var-data-in' type='checkbox'><label for='data-4c63e57e-ded8-444e-834c-d08454e384f1' 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>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-2f1f7a8a-72ee-4cab-9537-98ec1141b0e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f1f7a8a-72ee-4cab-9537-98ec1141b0e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6944a508-bb64-4c2b-87b3-830649cdb241' class='xr-var-data-in' type='checkbox'><label for='data-6944a508-bb64-4c2b-87b3-830649cdb241' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "    long_name:      Number of days where the daily minimum temperature is bel...\n",
+       "    description:    Annual number of days where the daily minimum temperature...</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'>'frost_days'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cda1d62e-cf03-4276-89b6-521b7fd6a4b2' class='xr-array-in' type='checkbox' checked><label for='section-cda1d62e-cf03-4276-89b6-521b7fd6a4b2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[840 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-cfe78aa6-3f1a-4f34-bcf2-bae5fe6e7c68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cfe78aa6-3f1a-4f34-bcf2-bae5fe6e7c68' 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>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-f4d8e3b9-7a39-4619-b3cc-3f53e8a89d17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d8e3b9-7a39-4619-b3cc-3f53e8a89d17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d05a58e-ebf6-491e-bbfa-47bb43eb7ec6' class='xr-var-data-in' type='checkbox'><label for='data-3d05a58e-ebf6-491e-bbfa-47bb43eb7ec6' 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>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-2ab6ac19-b721-4760-9411-3f6b3889c593' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ab6ac19-b721-4760-9411-3f6b3889c593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0604f2b-9b87-4c1e-819c-833753e38bf2' class='xr-var-data-in' type='checkbox'><label for='data-c0604f2b-9b87-4c1e-819c-833753e38bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-77c43e6f-d48d-4f84-8b73-4138b438a9b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77c43e6f-d48d-4f84-8b73-4138b438a9b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173f0b47-4a5c-47d2-815a-cee43bba7669' class='xr-var-data-in' type='checkbox'><label for='data-173f0b47-4a5c-47d2-815a-cee43bba7669' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-eb223e50-0e3c-4e28-8698-113cdcbc4d96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb223e50-0e3c-4e28-8698-113cdcbc4d96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd445c88-cef8-4e88-bd13-deca09e120c1' class='xr-var-data-in' type='checkbox'><label for='data-cd445c88-cef8-4e88-bd13-deca09e120c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -584,48 +582,33 @@
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-6b26222a-6598-4c40-bd30-eff84b1e86da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b26222a-6598-4c40-bd30-eff84b1e86da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3490379-adb2-482a-b157-28c12c3756a5' class='xr-var-data-in' type='checkbox'><label for='data-b3490379-adb2-482a-b157-28c12c3756a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-e6f740dd-35f3-4a67-b5c6-89da7538a553' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6f740dd-35f3-4a67-b5c6-89da7538a553' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01520f73-d144-4607-8c1b-3a60c2fd55c1' class='xr-var-data-in' type='checkbox'><label for='data-01520f73-d144-4607-8c1b-3a60c2fd55c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90299a54-91d0-45c3-a90b-0df6dc28322c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90299a54-91d0-45c3-a90b-0df6dc28322c' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
-       "[2023-02-15 22:12:41] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div></li></ul></div></div>"
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df2b18fe-3101-4068-8468-96d79854bcec' class='xr-section-summary-in' type='checkbox'  ><label for='section-df2b18fe-3101-4068-8468-96d79854bcec' 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-00eb9011-f949-49ac-bc48-d397e9dfbd5e' class='xr-index-data-in' type='checkbox'/><label for='index-00eb9011-f949-49ac-bc48-d397e9dfbd5e' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
+       "              61.2315712],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-7e40bac8-2719-440b-9fb0-f7321ea7380f' class='xr-index-data-in' type='checkbox'/><label for='index-7e40bac8-2719-440b-9fb0-f7321ea7380f' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-2ccd40b1-2a47-41f8-8630-c46a396ae8ad' class='xr-index-data-in' type='checkbox'/><label for='index-2ccd40b1-2a47-41f8-8630-c46a396ae8ad' 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(CFTimeIndex([2046-01-01 00:00:00, 2047-01-01 00:00:00, 2048-01-01 00:00:00,\n",
+       "             2049-01-01 00:00:00, 2050-01-01 00:00:00, 2051-01-01 00:00:00,\n",
+       "             2052-01-01 00:00:00, 2053-01-01 00:00:00, 2054-01-01 00:00:00,\n",
+       "             2055-01-01 00:00:00, 2056-01-01 00:00:00, 2057-01-01 00:00:00,\n",
+       "             2058-01-01 00:00:00, 2059-01-01 00:00:00, 2060-01-01 00:00:00,\n",
+       "             2061-01-01 00:00:00, 2062-01-01 00:00:00, 2063-01-01 00:00:00,\n",
+       "             2064-01-01 00:00:00, 2065-01-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=20, calendar=&#x27;360_day&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b5147af-4f74-4f5b-a56f-b58d1db6cb81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b5147af-4f74-4f5b-a56f-b58d1db6cb81' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days</dd><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
+       "[2023-06-02 15:35:24] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.40.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of days where the daily minimum temperature is below 0 degc</dd><dt><span>description :</span></dt><dd>Annual number of days where the daily minimum temperature is below 0 degc.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'frost_days' (time: 20, lat: 6, lon: 7)>\n",
-       "array([[[122., 121., ...,  19.,   9.],\n",
-       "        [156., 168., ..., 113.,  93.],\n",
-       "        ...,\n",
-       "        [227., 227., ..., 194., 133.],\n",
-       "        [269., 277., ..., 178., 136.]],\n",
-       "\n",
-       "       [[114., 115., ...,  31.,  23.],\n",
-       "        [161., 161., ..., 106.,  89.],\n",
-       "        ...,\n",
-       "        [231., 225., ..., 189., 134.],\n",
-       "        [259., 258., ..., 186., 140.]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[122., 111., ...,   0.,   0.],\n",
-       "        [151., 153., ...,  80.,  43.],\n",
-       "        ...,\n",
-       "        [221., 220., ..., 140.,  94.],\n",
-       "        [246., 247., ..., 137., 107.]],\n",
-       "\n",
-       "       [[105.,  98., ...,   3.,   1.],\n",
-       "        [163., 172., ...,  91.,  59.],\n",
-       "        ...,\n",
-       "        [225., 225., ..., 161., 107.],\n",
-       "        [251., 250., ..., 156., 103.]]])\n",
+       "[840 values with dtype=float64]\n",
        "Coordinates:\n",
        "    height   float64 ...\n",
-       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat      (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon      (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time     (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Attributes:\n",
        "    units:          days\n",
        "    cell_methods:   time: minimum (interval: 30 minutes) time: sum over days\n",
        "    history:        tas=max(195,tas) applied to raw data; min of 194.73 detec...\n",
        "    standard_name:  days_with_air_temperature_below_threshold\n",
-       "    long_name:      Number of frost days (tmin < 0 degc)\n",
-       "    description:    Annual number of days with minimum daily temperature belo..."
+       "    long_name:      Number of days where the daily minimum temperature is bel...\n",
+       "    description:    Annual number of days where the daily minimum temperature..."
       ]
      },
      "execution_count": 5,
@@ -654,15 +637,15 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-02T19:35:26.107282Z",
+     "iopub.status.busy": "2023-06-02T19:35:26.107063Z",
+     "iopub.status.idle": "2023-06-02T19:35:26.146267Z",
+     "shell.execute_reply": "2023-06-02T19:35:26.145675Z"
+    }
+   },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmpcv9f6kmj/frost_days_sres_a2_experiment_20460101_20650101.nc.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -927,6 +910,11 @@
        "  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",
@@ -948,14 +936,16 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data {\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-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -965,13 +955,16 @@
        "\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-var-data,\n",
+       ".xr-index-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -1009,7 +1002,8 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -1019,14 +1013,14 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:     (time: 20, lat: 6, lon: 7)\n",
+       "Dimensions:     (lat: 6, lon: 7, time: 20)\n",
        "Coordinates:\n",
-       "    height      float64 2.0\n",
-       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
+       "    height      float64 ...\n",
        "  * lat         (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon         (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Data variables:\n",
-       "    frost_days  (time, lat, lon) timedelta64[ns] 122 days 121 days ... 103 days\n",
+       "    frost_days  (time, lat, lon) timedelta64[ns] ...\n",
        "Attributes: (12/20)\n",
        "    comment:                  Spinup: restart files from end of experiment 20...\n",
        "    title:                    MIUB  model output prepared for IPCC Fourth Ass...\n",
@@ -1040,7 +1034,7 @@
        "    NCO:                      4.0.9\n",
        "    climateindex_package_id:  https://github.com/Ouranosinc/xclim\n",
        "    product:                  derived climate index\n",
-       "    institute_id:             CCCS</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-02570067-b87f-4604-a510-9038964875ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02570067-b87f-4604-a510-9038964875ca' 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>: 20</li><li><span class='xr-has-index'>lat</span>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f596bc92-db0b-4690-80dc-798bb583fd6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f596bc92-db0b-4690-80dc-798bb583fd6d' 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>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-0a6e38a8-822f-4e09-89d0-e840f67778e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a6e38a8-822f-4e09-89d0-e840f67778e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-886b92ad-fa1f-4c16-9931-79d2cae3070c' class='xr-var-data-in' type='checkbox'><label for='data-886b92ad-fa1f-4c16-9931-79d2cae3070c' 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>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-f68f339a-2859-4925-8d86-c7a212f00142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f68f339a-2859-4925-8d86-c7a212f00142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d196c281-bfce-46a0-8060-95f27ef71475' class='xr-var-data-in' type='checkbox'><label for='data-d196c281-bfce-46a0-8060-95f27ef71475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
+       "    institute_id:             CCCS</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-fd84a1b1-cf6e-416d-a93b-0240cfdfb65e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd84a1b1-cf6e-416d-a93b-0240cfdfb65e' 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>: 6</li><li><span class='xr-has-index'>lon</span>: 7</li><li><span class='xr-has-index'>time</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5fc821ee-3f90-4454-9347-e819450c7895' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5fc821ee-3f90-4454-9347-e819450c7895' 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>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-5c67c375-db3b-4c62-81a5-df767c47e7ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c67c375-db3b-4c62-81a5-df767c47e7ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffd919b-6da5-4b9c-a58c-687ee90cbb95' class='xr-var-data-in' type='checkbox'><label for='data-0ffd919b-6da5-4b9c-a58c-687ee90cbb95' 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>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-d4667860-11cc-416b-9ac2-15791f1f4331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4667860-11cc-416b-9ac2-15791f1f4331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-504059b0-5747-4acc-b51e-6358a03cce1e' class='xr-var-data-in' type='checkbox'><label for='data-504059b0-5747-4acc-b51e-6358a03cce1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-57ec8e97-9ee1-4d58-9ba8-5d0095e16060' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ec8e97-9ee1-4d58-9ba8-5d0095e16060' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da9c9f48-6c04-444b-b48f-220b19b620f6' class='xr-var-data-in' type='checkbox'><label for='data-da9c9f48-6c04-444b-b48f-220b19b620f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</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'>object</div><div class='xr-var-preview xr-preview'>2046-01-01 00:00:00 ... 2065-01-...</div><input id='attrs-916947c4-50e2-4b4c-81c7-2cbfe7f9ea29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-916947c4-50e2-4b4c-81c7-2cbfe7f9ea29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e90bf9de-be56-426a-8a35-46cabb03e99b' class='xr-var-data-in' type='checkbox'><label for='data-e90bf9de-be56-426a-8a35-46cabb03e99b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2046, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2047, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2048, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2049, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
@@ -1060,58 +1054,27 @@
        "       cftime.Datetime360Day(2063, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2064, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
        "       cftime.Datetime360Day(2065, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>42.68 46.39 50.1 53.81 57.52 61.23</div><input id='attrs-fdc98aa4-a093-44fc-9ccc-385f876a815c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdc98aa4-a093-44fc-9ccc-385f876a815c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e62a06c-da38-4c08-8cc5-7e4a0cfe1756' class='xr-var-data-in' type='checkbox'><label for='data-0e62a06c-da38-4c08-8cc5-7e4a0cfe1756' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>6</dd></dl></div><div class='xr-var-data'><pre>array([42.677605, 46.388557, 50.099453, 53.810272, 57.520992, 61.231571])</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'>281.2 285.0 288.8 ... 300.0 303.8</div><input id='attrs-061f2c8f-1434-4d6a-8975-34c037aa6c84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-061f2c8f-1434-4d6a-8975-34c037aa6c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de6f4ffe-332f-4077-9bf9-7f8abe32573c' class='xr-var-data-in' type='checkbox'><label for='data-de6f4ffe-332f-4077-9bf9-7f8abe32573c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>7</dd></dl></div><div class='xr-var-data'><pre>array([281.25, 285.  , 288.75, 292.5 , 296.25, 300.  , 303.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15a8a9f0-9a10-48a6-98fd-76c69d18eabc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15a8a9f0-9a10-48a6-98fd-76c69d18eabc' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>frost_days</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfa41d34-cf94-490b-9eb2-86004661de93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfa41d34-cf94-490b-9eb2-86004661de93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eddc808-a6c7-4a2d-b0d1-6a86872e0d91' class='xr-var-data-in' type='checkbox'><label for='data-2eddc808-a6c7-4a2d-b0d1-6a86872e0d91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
-       "[2023-02-15 22:12:41] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.37.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of frost days (tmin &lt; 0 degc)</dd><dt><span>description :</span></dt><dd>Annual number of days with minimum daily temperature below 0 degc.</dd></dl></div><div class='xr-var-data'><pre>array([[[10540800000000000, 10454400000000000, ...,  1641600000000000,\n",
-       "           777600000000000],\n",
-       "        [13478400000000000, 14515200000000000, ...,  9763200000000000,\n",
-       "          8035200000000000],\n",
-       "        ...,\n",
-       "        [19612800000000000, 19612800000000000, ..., 16761600000000000,\n",
-       "         11491200000000000],\n",
-       "        [23241600000000000, 23932800000000000, ..., 15379200000000000,\n",
-       "         11750400000000000]],\n",
-       "\n",
-       "       [[ 9849600000000000,  9936000000000000, ...,  2678400000000000,\n",
-       "          1987200000000000],\n",
-       "        [13910400000000000, 13910400000000000, ...,  9158400000000000,\n",
-       "          7689600000000000],\n",
-       "        ...,\n",
-       "        [19958400000000000, 19440000000000000, ..., 16329600000000000,\n",
-       "         11577600000000000],\n",
-       "        [22377600000000000, 22291200000000000, ..., 16070400000000000,\n",
-       "         12096000000000000]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[10540800000000000,  9590400000000000, ...,                 0,\n",
-       "                         0],\n",
-       "        [13046400000000000, 13219200000000000, ...,  6912000000000000,\n",
-       "          3715200000000000],\n",
-       "        ...,\n",
-       "        [19094400000000000, 19008000000000000, ..., 12096000000000000,\n",
-       "          8121600000000000],\n",
-       "        [21254400000000000, 21340800000000000, ..., 11836800000000000,\n",
-       "          9244800000000000]],\n",
-       "\n",
-       "       [[ 9072000000000000,  8467200000000000, ...,   259200000000000,\n",
-       "            86400000000000],\n",
-       "        [14083200000000000, 14860800000000000, ...,  7862400000000000,\n",
-       "          5097600000000000],\n",
-       "        ...,\n",
-       "        [19440000000000000, 19440000000000000, ..., 13910400000000000,\n",
-       "          9244800000000000],\n",
-       "        [21686400000000000, 21600000000000000, ..., 13478400000000000,\n",
-       "          8899200000000000]]], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4b344a5-826e-4d46-9edf-7194294123fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-d4b344a5-826e-4d46-9edf-7194294123fc' class='xr-section-summary' >Attributes: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Services (CCCS)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Canadian Centre for Climate Services</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:43:58 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v tasmin http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/tasmin.sresa2.miub_echo_g.run1.atm.da.nc\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e50840fe-bde5-444a-8e04-c942a818d7f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e50840fe-bde5-444a-8e04-c942a818d7f0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>frost_days</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28e20893-8ab1-4c89-9977-a84a2bfbcdd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28e20893-8ab1-4c89-9977-a84a2bfbcdd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68df0170-1e5c-4520-9220-c0cf292ded9f' class='xr-var-data-in' type='checkbox'><label for='data-68df0170-1e5c-4520-9220-c0cf292ded9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: minimum (interval: 30 minutes) time: sum over days</dd><dt><span>history :</span></dt><dd>tas=max(195,tas) applied to raw data; min of 194.73 detected;\n",
+       "[2023-06-02 15:35:24] frost_days: FROST_DAYS(tasmin=tasmin, thresh=&#x27;0 degC&#x27;, freq=&#x27;YS&#x27;) with options check_missing=any - xclim version: 0.40.0</dd><dt><span>standard_name :</span></dt><dd>days_with_air_temperature_below_threshold</dd><dt><span>long_name :</span></dt><dd>Number of days where the daily minimum temperature is below 0 degc</dd><dt><span>description :</span></dt><dd>Annual number of days where the daily minimum temperature is below 0 degc.</dd></dl></div><div class='xr-var-data'><pre>[840 values with dtype=timedelta64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab9bf37a-58e7-4979-99a3-b50783c638d1' class='xr-section-summary-in' type='checkbox'  ><label for='section-ab9bf37a-58e7-4979-99a3-b50783c638d1' 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-50f9568b-598f-458e-89cc-5e3088e24a0a' class='xr-index-data-in' type='checkbox'/><label for='index-50f9568b-598f-458e-89cc-5e3088e24a0a' 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([42.67760468, 46.38855743, 50.09945297, 53.81027222, 57.52099228,\n",
+       "              61.2315712],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-ddd02f8b-95bf-45dd-9a4d-bfe8fb727237' class='xr-index-data-in' type='checkbox'/><label for='index-ddd02f8b-95bf-45dd-9a4d-bfe8fb727237' 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([281.25, 285.0, 288.75, 292.5, 296.25, 300.0, 303.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><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-4ac758c8-1c55-47cb-b119-e2195c1e5a34' class='xr-index-data-in' type='checkbox'/><label for='index-4ac758c8-1c55-47cb-b119-e2195c1e5a34' 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(CFTimeIndex([2046-01-01 00:00:00, 2047-01-01 00:00:00, 2048-01-01 00:00:00,\n",
+       "             2049-01-01 00:00:00, 2050-01-01 00:00:00, 2051-01-01 00:00:00,\n",
+       "             2052-01-01 00:00:00, 2053-01-01 00:00:00, 2054-01-01 00:00:00,\n",
+       "             2055-01-01 00:00:00, 2056-01-01 00:00:00, 2057-01-01 00:00:00,\n",
+       "             2058-01-01 00:00:00, 2059-01-01 00:00:00, 2060-01-01 00:00:00,\n",
+       "             2061-01-01 00:00:00, 2062-01-01 00:00:00, 2063-01-01 00:00:00,\n",
+       "             2064-01-01 00:00:00, 2065-01-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=20, calendar=&#x27;360_day&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1026f239-51a7-47db-b593-e0df7a4703fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-1026f239-51a7-47db-b593-e0df7a4703fe' class='xr-section-summary' >Attributes: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Spinup: restart files from end of experiment 20C3M (corresponding realisation).  Anthropogenic (GHGs, sulfate aerosol direct and first indirect effects) and natural forcing (solar irradiance and volcanic activity implemented through varying solar constant) are used for the 20C3M simulations.</dd><dt><span>title :</span></dt><dd>MIUB  model output prepared for IPCC Fourth Assessment SRES A2 experiment</dd><dt><span>cmor_version :</span></dt><dd>0.96</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Services (CCCS)</dd><dt><span>source :</span></dt><dd>ECHO-G(1999): atmosphere: ECHAM4 (T30L19) with partial sea ice with flux aggregation and continental ice sheet discharge incl. latent heat; ocean: HOPE-G (T42+equator refinement/L20); sea ice: HOPE-G</dd><dt><span>contact :</span></dt><dd>Canadian Centre for Climate Services</dd><dt><span>references :</span></dt><dd>ECHAM4: E. Roeckner et al., 1996, The atmospheric general circulation model ECHAM4, MPI Report No. 218; HOPE-G: Legutke and Maier-Reimer, 1999, Climatology of the HOPE-G Global Ocean General Circulation Model, DKRZ Techn. Report 21; ECHO-G: Min et al., 2004, Climatology and internal variability in a 1000-year control simulation with the coupled climate model ECHO-G, Tellus A, in print.</dd><dt><span>experiment_id :</span></dt><dd>SRES A2 experiment</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>directory :</span></dt><dd>/ipcc/sresa2/atm/da/</dd><dt><span>table_id :</span></dt><dd>Table A2 (17 November 2004)</dd><dt><span>calendar :</span></dt><dd>360_day</dd><dt><span>project_id :</span></dt><dd>IPCC Fourth Assessment</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>id :</span></dt><dd>pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da</dd><dt><span>history :</span></dt><dd>Mon Aug  1 11:43:58 2011: ncks -4 -L 7 -d lat,42.0,64.0 -d lon,278.0,306.0 -d time,1950-01-01 00:00:00,2070-01-01 00:00:00 -v tasmin http://davidhuard:patate@esgcet.llnl.gov/dap/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -o /home/david/projects/ingenieurs/data/cmip3/tasmin.sresa2.miub_echo_g.run1.atm.da.nc\n",
        "Model raw diagnostic output;  At 21:54:41 on 06/22/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements [2006-4-5 21:36:54] cdscan -x /var/www/html/ipcc4/sresa2/miub_echo_g/pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da.xml -d pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm.da --exclude=plev --ignore-open-error --var-locate ps,ps_.* -f /tmp/scan/list.pcmdi.ipcc4.miub_echo_g.sresa2.run1.atm ...</dd><dt><span>NCO :</span></dt><dd>4.0.9</dd><dt><span>climateindex_package_id :</span></dt><dd>https://github.com/Ouranosinc/xclim</dd><dt><span>product :</span></dt><dd>derived climate index</dd><dt><span>institute_id :</span></dt><dd>CCCS</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:     (time: 20, lat: 6, lon: 7)\n",
+       "Dimensions:     (lat: 6, lon: 7, time: 20)\n",
        "Coordinates:\n",
        "    height      float64 ...\n",
-       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "  * lat         (lat) float64 42.68 46.39 50.1 53.81 57.52 61.23\n",
        "  * lon         (lon) float64 281.2 285.0 288.8 292.5 296.2 300.0 303.8\n",
+       "  * time        (time) object 2046-01-01 00:00:00 ... 2065-01-01 00:00:00\n",
        "Data variables:\n",
        "    frost_days  (time, lat, lon) timedelta64[ns] ...\n",
        "Attributes: (12/20)\n",
@@ -1159,7 +1122,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/subset.ipynb b/docs/source/notebooks/subset.ipynb
deleted file mode 100644
index 67a8a925..00000000
--- a/docs/source/notebooks/subset.ipynb
+++ /dev/null
@@ -1,2018 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5ef8b9ad",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:48.471991Z",
-     "iopub.status.busy": "2023-02-17T21:34:48.471492Z",
-     "iopub.status.idle": "2023-02-17T21:34:48.486983Z",
-     "shell.execute_reply": "2023-02-17T21:34:48.484701Z"
-    },
-    "nbsphinx": "hidden"
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66b3223f",
-   "metadata": {},
-   "source": [
-    "# Spatial subsets and averages\n",
-    "\n",
-    "Finch includes processes to subset and average gridded data. Subsetting processes return a rectangular portion of the original grid. This portion can either be defined as a bounding box over geographic coordinates (longitude, latitude), or as a polygon outline.\n",
-    "\n",
-    "When subsetting a polygon or over a curvilinear grid, the subset usually does not align with the grid dimensions. In this case, the subset functions will return data over a rectangular array, but mask grid cells that fall outside the subset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8f4fd0c3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:48.491789Z",
-     "iopub.status.busy": "2023-02-17T21:34:48.491257Z",
-     "iopub.status.idle": "2023-02-17T21:34:50.677205Z",
-     "shell.execute_reply": "2023-02-17T21:34:50.676184Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"USE_PYGEOS\"] = \"0\"  # force use Shapely with GeoPandas\n",
-    "\n",
-    "import xarray as xr\n",
-    "from birdy import WPSClient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "500631a5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:50.685381Z",
-     "iopub.status.busy": "2023-02-17T21:34:50.684206Z",
-     "iopub.status.idle": "2023-02-17T21:34:52.990814Z",
-     "shell.execute_reply": "2023-02-17T21:34:52.989539Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pavics_url = \"https://pavics.ouranos.ca/twitcher/ows/proxy/finch/wps\"\n",
-    "url = os.environ.get(\"WPS_URL\", pavics_url)\n",
-    "verify_ssl = True if \"DISABLE_VERIFY_SSL\" not in os.environ else False\n",
-    "wps = WPSClient(url, verify=verify_ssl)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81f66ddd",
-   "metadata": {},
-   "source": [
-    "For the examples in this notebook, we are using a projection of the annual mean temperature over Québec."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f7a203aa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:52.996628Z",
-     "iopub.status.busy": "2023-02-17T21:34:52.996290Z",
-     "iopub.status.idle": "2023-02-17T21:34:53.000864Z",
-     "shell.execute_reply": "2023-02-17T21:34:52.999783Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "tg_mean_url = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.3/NorESM1-M_rcp85_tg_mean_annual.nc\"  # TEST_USE_PROD_DATA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1322a378",
-   "metadata": {},
-   "source": [
-    "## Bounding box subsetting\n",
-    "\n",
-    "The `subset_bbox` process returns the grid portion within longitude and latitude bounds. Western hemisphere longitudes can be given as negative numbers or positive ones, no matter what range the dataset uses (longitudes are either in `[-180, 180[`  or `[0, 360[`).\n",
-    "\n",
-    "The box chosen here is over the city of Montréal and its surroundings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eee118da",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:53.005029Z",
-     "iopub.status.busy": "2023-02-17T21:34:53.004566Z",
-     "iopub.status.idle": "2023-02-17T21:34:54.284026Z",
-     "shell.execute_reply": "2023-02-17T21:34:54.283004Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "resp = wps.subset_bbox(\n",
-    "    tg_mean_url,\n",
-    "    lon0=-74.2,\n",
-    "    lon1=-73.3,\n",
-    "    lat0=45.25,\n",
-    "    lat1=45.80,\n",
-    "    variable=[\"tg_mean_annual\"],\n",
-    "    start_date=\"1990\",\n",
-    "    end_date=\"2020\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "961b3e52",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:54.289298Z",
-     "iopub.status.busy": "2023-02-17T21:34:54.288766Z",
-     "iopub.status.idle": "2023-02-17T21:34:55.601965Z",
-     "shell.execute_reply": "2023-02-17T21:34:55.600974Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmppp8xwm_h/noresm1_m_rcp85_tg_mean_annual_sub.nc.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_mtl = resp.get(asobj=True).output\n",
-    "ds_mtl.tg_mean_annual.isel(time=0).plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d7d0f71",
-   "metadata": {},
-   "source": [
-    "## Polygon subsetting\n",
-    "\n",
-    "The subset processes of finch also support subsetting from a polygon input. Polygons are passed as GeoJSON or zipped-ShapeFiles files. They can be either local or remote.\n",
-    "\n",
-    "In the next example, we retrieve a dataset of polygons from a geospatial data server. Communication with this server uses the Web Feature Services (WFS) standard, an Open Geospatial Consortium (OGC) protocol.\n",
-    "\n",
-    "We then inspect and select the polygon using GeoPandas, write it down in a local file and send the subsetting request to finch.\n",
-    "\n",
-    "The subsetting methods are provided under-the-hood by `clisops`. Note that the grid cells returned are those whose centroid lies within the polygon. If the polygon overlays a corner of a grid cell, it will not be included in the subset, or will be masked. Thus, this algorithm might not be appropriate for small or intricate polygons relative to the underlying grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e2bc916c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:55.608843Z",
-     "iopub.status.busy": "2023-02-17T21:34:55.606734Z",
-     "iopub.status.idle": "2023-02-17T21:34:55.859573Z",
-     "shell.execute_reply": "2023-02-17T21:34:55.857484Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import geopandas as gpd\n",
-    "from owslib.wfs import WebFeatureService\n",
-    "\n",
-    "# Connect to GeoServer WFS service.\n",
-    "wfs_url = \"https://pavics.ouranos.ca/geoserver/wfs\"  # TEST_USE_PROD_DATA\n",
-    "wfs = WebFeatureService(wfs_url, version=\"1.1.0\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57fa6bbb",
-   "metadata": {},
-   "source": [
-    "### Downloading and handling polygons locally\n",
-    "\n",
-    "This first example shows how to download a collection of polygons from the GeoServer using WFS and `owslib`. The polygon selection is done with `geopandas`, locally, allowing for more polygon processing if needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "288f4067",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:55.870269Z",
-     "iopub.status.busy": "2023-02-17T21:34:55.869703Z",
-     "iopub.status.idle": "2023-02-17T21:34:58.634410Z",
-     "shell.execute_reply": "2023-02-17T21:34:58.633389Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>RES_NM_REG</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>MULTIPOLYGON (((-63.55940 54.98840, -63.56340 ...</td>\n",
-       "      <td>Côte-Nord</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>MULTIPOLYGON (((-73.88440 45.54370, -73.88480 ...</td>\n",
-       "      <td>Laval</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>MULTIPOLYGON (((-75.04050 47.76270, -75.02440 ...</td>\n",
-       "      <td>Laurentides</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>MULTIPOLYGON (((-72.96110 46.09000, -72.95640 ...</td>\n",
-       "      <td>Montérégie</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>MULTIPOLYGON (((-75.98530 47.77010, -75.98530 ...</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>MULTIPOLYGON (((-71.06890 45.92750, -71.06890 ...</td>\n",
-       "      <td>Estrie</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>MULTIPOLYGON (((-75.52060 47.84720, -75.52070 ...</td>\n",
-       "      <td>Outaouais</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>MULTIPOLYGON (((-74.67630 48.99960, -74.62850 ...</td>\n",
-       "      <td>Mauricie</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>MULTIPOLYGON (((-63.52630 48.92920, -63.49910 ...</td>\n",
-       "      <td>Gaspésie--Îles-de-la-Madeleine</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>MULTIPOLYGON (((-70.02990 52.83260, -70.02570 ...</td>\n",
-       "      <td>Saguenay--Lac-Saint-Jean</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>MULTIPOLYGON (((-74.35830 47.65910, -74.35070 ...</td>\n",
-       "      <td>Lanaudière</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>MULTIPOLYGON (((-72.06680 46.56980, -72.06560 ...</td>\n",
-       "      <td>Centre-du-Québec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>MULTIPOLYGON (((-70.07300 47.32270, -70.07290 ...</td>\n",
-       "      <td>Chaudière-Appalaches</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>MULTIPOLYGON (((-78.01450 55.18750, -78.01530 ...</td>\n",
-       "      <td>Nord-du-Québec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>MULTIPOLYGON (((-69.80550 48.14330, -69.80180 ...</td>\n",
-       "      <td>Capitale-Nationale</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>MULTIPOLYGON (((-66.83520 48.98690, -66.83240 ...</td>\n",
-       "      <td>Bas-Saint-Laurent</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>MULTIPOLYGON (((-73.70930 45.54320, -73.70870 ...</td>\n",
-       "      <td>Montréal</td>\n",
-       "    </tr>\n",
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-       "                                             geometry  \\\n",
-       "0   MULTIPOLYGON (((-63.55940 54.98840, -63.56340 ...   \n",
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-       "2   MULTIPOLYGON (((-75.04050 47.76270, -75.02440 ...   \n",
-       "3   MULTIPOLYGON (((-72.96110 46.09000, -72.95640 ...   \n",
-       "4   MULTIPOLYGON (((-75.98530 47.77010, -75.98530 ...   \n",
-       "5   MULTIPOLYGON (((-71.06890 45.92750, -71.06890 ...   \n",
-       "6   MULTIPOLYGON (((-75.52060 47.84720, -75.52070 ...   \n",
-       "7   MULTIPOLYGON (((-74.67630 48.99960, -74.62850 ...   \n",
-       "8   MULTIPOLYGON (((-63.52630 48.92920, -63.49910 ...   \n",
-       "9   MULTIPOLYGON (((-70.02990 52.83260, -70.02570 ...   \n",
-       "10  MULTIPOLYGON (((-74.35830 47.65910, -74.35070 ...   \n",
-       "11  MULTIPOLYGON (((-72.06680 46.56980, -72.06560 ...   \n",
-       "12  MULTIPOLYGON (((-70.07300 47.32270, -70.07290 ...   \n",
-       "13  MULTIPOLYGON (((-78.01450 55.18750, -78.01530 ...   \n",
-       "14  MULTIPOLYGON (((-69.80550 48.14330, -69.80180 ...   \n",
-       "15  MULTIPOLYGON (((-66.83520 48.98690, -66.83240 ...   \n",
-       "16  MULTIPOLYGON (((-73.70930 45.54320, -73.70870 ...   \n",
-       "\n",
-       "                        RES_NM_REG  \n",
-       "0                        Côte-Nord  \n",
-       "1                            Laval  \n",
-       "2                      Laurentides  \n",
-       "3                       Montérégie  \n",
-       "4            Abitibi-Témiscamingue  \n",
-       "5                           Estrie  \n",
-       "6                        Outaouais  \n",
-       "7                         Mauricie  \n",
-       "8   Gaspésie--Îles-de-la-Madeleine  \n",
-       "9         Saguenay--Lac-Saint-Jean  \n",
-       "10                      Lanaudière  \n",
-       "11                Centre-du-Québec  \n",
-       "12            Chaudière-Appalaches  \n",
-       "13                  Nord-du-Québec  \n",
-       "14              Capitale-Nationale  \n",
-       "15               Bas-Saint-Laurent  \n",
-       "16                        Montréal  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the json as a binary stream\n",
-    "# Here we select Quebec's MRCs polygons\n",
-    "# We select only a few properties to be returned.\n",
-    "data = wfs.getfeature(\n",
-    "    typename=\"public:quebec_admin_boundaries\",\n",
-    "    # bbox=(-93.1, 41.1, -75.0, 49.6),\n",
-    "    outputFormat=\"json\",\n",
-    "    propertyname=[\"the_geom\", \"RES_NM_REG\"],\n",
-    ")\n",
-    "# Load into a GeoDataFrame by reading the json on-the-fly\n",
-    "all_shapes = gpd.GeoDataFrame.from_features(json.load(data))\n",
-    "all_shapes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "22746b90",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:58.638947Z",
-     "iopub.status.busy": "2023-02-17T21:34:58.638406Z",
-     "iopub.status.idle": "2023-02-17T21:34:58.684687Z",
-     "shell.execute_reply": "2023-02-17T21:34:58.683254Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>RES_NM_REG</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>MULTIPOLYGON (((-73.88440 45.54370, -73.88480 ...</td>\n",
-       "      <td>Laval</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>MULTIPOLYGON (((-72.96110 46.09000, -72.95640 ...</td>\n",
-       "      <td>Montérégie</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>MULTIPOLYGON (((-73.70930 45.54320, -73.70870 ...</td>\n",
-       "      <td>Montréal</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                             geometry  RES_NM_REG\n",
-       "1   MULTIPOLYGON (((-73.88440 45.54370, -73.88480 ...       Laval\n",
-       "3   MULTIPOLYGON (((-72.96110 46.09000, -72.95640 ...  Montérégie\n",
-       "16  MULTIPOLYGON (((-73.70930 45.54320, -73.70870 ...    Montréal"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Select 3 regions around the city of Montréal\n",
-    "poly_mtl = all_shapes[all_shapes.RES_NM_REG.isin([\"Montérégie\", \"Montréal\", \"Laval\"])]\n",
-    "poly_mtl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "da924370",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:58.690321Z",
-     "iopub.status.busy": "2023-02-17T21:34:58.689980Z",
-     "iopub.status.idle": "2023-02-17T21:34:58.769284Z",
-     "shell.execute_reply": "2023-02-17T21:34:58.768165Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Write to file\n",
-    "from tempfile import TemporaryDirectory\n",
-    "\n",
-    "tmp_dir = TemporaryDirectory()\n",
-    "poly_file = Path(tmp_dir.name) / \"mtl_raw.geojson\"\n",
-    "poly_mtl.to_file(poly_file, driver=\"GeoJSON\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf042c5b",
-   "metadata": {},
-   "source": [
-    "Now that we have a (multipart) polygon, we can call the WPS subset process. When passing a local file, birdy requires the path to be absolute, our use of a `Path` object makes this easy. Before subsetting, the three polygons of our GeoJSON will be merged as one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b9f968d5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:34:58.775780Z",
-     "iopub.status.busy": "2023-02-17T21:34:58.775435Z",
-     "iopub.status.idle": "2023-02-17T21:35:00.189913Z",
-     "shell.execute_reply": "2023-02-17T21:35:00.187645Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "resp = wps.subset_polygon(\n",
-    "    tg_mean_url,\n",
-    "    poly_file.absolute(),\n",
-    "    variable=[\"tg_mean_annual\"],\n",
-    "    start_date=\"2020-01-01\",\n",
-    "    end_date=\"2050-01-01\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ea7a2700",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:00.195807Z",
-     "iopub.status.busy": "2023-02-17T21:35:00.195129Z",
-     "iopub.status.idle": "2023-02-17T21:35:00.928299Z",
-     "shell.execute_reply": "2023-02-17T21:35:00.927422Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmpeknygmsi/noresm1_m_rcp85_tg_mean_annual_sub.nc.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_mtl = resp.get(asobj=True).output\n",
-    "ds_mtl.tg_mean_annual.isel(time=0).plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "502eff39",
-   "metadata": {},
-   "source": [
-    "## Averaging over polygons\n",
-    "\n",
-    "Finch also provides an `average_subset` process. The call is similar, but it instead averages the data over each polygon in the GeoJSON. In this case, in opposition to `subset_shape`, the intersection between polygon and grid cells is exact: partial overlaps between the polygon and grid cell outline are considered, as well as holes within the polygon itself.\n",
-    "\n",
-    "The computation of these overlaps can be time-consuming, especially when the polygon is defined at high resolution. In turn, long execution time can cause time-outs on the server side. As the second example below demonstrates, a strategy to speed up execution is to simplify the polygon shape.\n",
-    "\n",
-    "\n",
-    "### Basic averaging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2955da6e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:00.939753Z",
-     "iopub.status.busy": "2023-02-17T21:35:00.939359Z",
-     "iopub.status.idle": "2023-02-17T21:35:03.212831Z",
-     "shell.execute_reply": "2023-02-17T21:35:03.211389Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Basic example, let's simply reuse the same geojson:\n",
-    "resp = wps.average_polygon(\n",
-    "    tg_mean_url,\n",
-    "    poly_file.absolute(),\n",
-    "    variable=[\"tg_mean_annual\"],\n",
-    "    start_date=\"2020-01-01\",\n",
-    "    end_date=\"2050-01-01\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "6fec7d59",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:03.218421Z",
-     "iopub.status.busy": "2023-02-17T21:35:03.217653Z",
-     "iopub.status.idle": "2023-02-17T21:35:03.890767Z",
-     "shell.execute_reply": "2023-02-17T21:35:03.889890Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmpwvrcy2aq/noresm1_m_rcp85_tg_mean_annual_avg.nc.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_avgmtl = resp.get(asobj=True).output\n",
-    "ds_avgmtl.tg_mean_annual.plot(hue=\"geom\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "88713a7e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:03.895242Z",
-     "iopub.status.busy": "2023-02-17T21:35:03.894919Z",
-     "iopub.status.idle": "2023-02-17T21:35:03.919284Z",
-     "shell.execute_reply": "2023-02-17T21:35:03.917157Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
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-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
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-       "  stroke: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:         (time: 31, geom: 3)\n",
-       "Coordinates:\n",
-       "  * time            (time) datetime64[ns] 2020-01-01 2021-01-01 ... 2050-01-01\n",
-       "    lon             (geom) float64 -73.73 -73.3 -73.69\n",
-       "    lat             (geom) float64 45.61 45.39 45.51\n",
-       "  * geom            (geom) int64 0 1 2\n",
-       "    RES_NM_REG      (geom) object &#x27;Laval&#x27; &#x27;Montérégie&#x27; &#x27;Montréal&#x27;\n",
-       "Data variables:\n",
-       "    tg_mean_annual  (time, geom) float32 280.6 280.5 280.9 ... 282.8 282.6 283.0\n",
-       "Attributes:\n",
-       "    Conventions:     CF-1.5\n",
-       "    title:           NorESM1-M model output prepared for CMIP5 historical\n",
-       "    history:         2011-06-01T01:20:15Z CMOR rewrote data to comply with CF...\n",
-       "    institution:     Norwegian Climate Centre\n",
-       "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
-       "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative</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-f8c6b292-b0d2-434a-b051-6bf9f706f83d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8c6b292-b0d2-434a-b051-6bf9f706f83d' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c81bc981-7911-4a41-916b-4f79ae6a5de7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c81bc981-7911-4a41-916b-4f79ae6a5de7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-01-01 ... 2050-01-01</div><input id='attrs-d8d9e969-750a-411c-a266-bc0e23fbd0b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8d9e969-750a-411c-a266-bc0e23fbd0b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf496c6b-3ac1-4a79-bdfd-0f9ff0b39c0e' class='xr-var-data-in' type='checkbox'><label for='data-bf496c6b-3ac1-4a79-bdfd-0f9ff0b39c0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2048-01-01T00:00:00.000000000&#x27;, &#x27;2049-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ce6f340-da22-498e-9b32-9fbc0c3701e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ce6f340-da22-498e-9b32-9fbc0c3701e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ff30d1-3eb4-4707-a570-2cee6684c008' class='xr-var-data-in' type='checkbox'><label for='data-50ff30d1-3eb4-4707-a570-2cee6684c008' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-73.725043, -73.298828, -73.694512])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a827ce3-2d83-4513-b689-7bbc96cbfe5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a827ce3-2d83-4513-b689-7bbc96cbfe5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58eed329-2d09-4139-a64e-14aa307c8f16' class='xr-var-data-in' type='checkbox'><label for='data-58eed329-2d09-4139-a64e-14aa307c8f16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([45.605235, 45.386636, 45.507791])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-490fc0ce-d86a-4fb1-b06b-9627053ff2ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-490fc0ce-d86a-4fb1-b06b-9627053ff2ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01ed49e9-3444-426e-a4de-a9b68aedeb44' class='xr-var-data-in' type='checkbox'><label for='data-01ed49e9-3444-426e-a4de-a9b68aedeb44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RES_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c27b7d2-a59f-4cd3-b74c-5cdbcb4b7aa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c27b7d2-a59f-4cd3-b74c-5cdbcb4b7aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6162a27f-e599-40a5-81a0-b056f4c9bd27' class='xr-var-data-in' type='checkbox'><label for='data-6162a27f-e599-40a5-81a0-b056f4c9bd27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Laval&#x27;, &#x27;Montérégie&#x27;, &#x27;Montréal&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f49c0e58-8d4c-4d6e-96f9-1495bc104ea9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f49c0e58-8d4c-4d6e-96f9-1495bc104ea9' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02543d42-d436-4483-9abf-31e460720c3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02543d42-d436-4483-9abf-31e460720c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a78d791d-fa1b-4560-ae0c-354300d80e6c' class='xr-var-data-in' type='checkbox'><label for='data-a78d791d-fa1b-4560-ae0c-354300d80e6c' 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>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[280.6498 , 280.49625, 280.9249 ],\n",
-       "       [280.5494 , 280.34033, 280.8113 ],\n",
-       "       [280.88556, 280.74432, 281.14438],\n",
-       "       [282.2344 , 282.1285 , 282.4849 ],\n",
-       "       [282.48773, 282.36823, 282.74252],\n",
-       "       [281.3926 , 281.20505, 281.6528 ],\n",
-       "       [281.129  , 280.93726, 281.38806],\n",
-       "       [281.78577, 281.63022, 282.03702],\n",
-       "       [280.35156, 280.13522, 280.61203],\n",
-       "       [281.88196, 281.7724 , 282.14142],\n",
-       "       [281.73917, 281.58585, 281.99152],\n",
-       "       [281.31744, 281.14227, 281.57944],\n",
-       "       [281.84085, 281.77213, 282.12177],\n",
-       "       [281.681  , 281.50186, 281.9397 ],\n",
-       "       [281.05508, 280.8483 , 281.3095 ],\n",
-       "       [282.4441 , 282.31662, 282.71655],\n",
-       "       [280.84964, 280.6876 , 281.10904],\n",
-       "       [283.32886, 283.20706, 283.58582],\n",
-       "       [281.92303, 281.7775 , 282.18195],\n",
-       "       [281.35193, 281.1851 , 281.626  ],\n",
-       "       [282.82816, 282.74075, 283.10184],\n",
-       "       [281.52286, 281.3698 , 281.79434],\n",
-       "       [282.38895, 282.2307 , 282.65845],\n",
-       "       [281.63315, 281.48697, 281.9097 ],\n",
-       "       [281.38593, 281.183  , 281.65228],\n",
-       "       [282.08182, 281.85495, 282.35034],\n",
-       "       [282.4905 , 282.32047, 282.75424],\n",
-       "       [282.05273, 281.84796, 282.31708],\n",
-       "       [282.09875, 281.92465, 282.37344],\n",
-       "       [283.1577 , 282.97922, 283.41135],\n",
-       "       [282.7805 , 282.59613, 283.04648]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3e9fc21-e9c8-4051-9379-1ce19a39d354' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3e9fc21-e9c8-4051-9379-1ce19a39d354' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
-       "2016-01-19T05:16:03: Interpolate to nrcan_livneh grid.\n",
-       "2016-02-10T09:50:15: Bias correction using nrcan_livneh.</dd><dt><span>institution :</span></dt><dd>Norwegian Climate Centre</dd><dt><span>source :</span></dt><dd>NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r112, f19L26);  ocean: MICOM (MICOM-noresm-ver1_cmip5-r112, gx1v6L53);  sea ice: CICE (CICE4-noresm-ver1_cmip5-r112);  land: CLM (CLM4-noresm-ver1_cmip5-r112)\n",
-       "30-day moving window 50-bins quantile mapping with detrending.</dd><dt><span>redistribution :</span></dt><dd>Redistribution prohibited. For internal use only.</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:         (time: 31, geom: 3)\n",
-       "Coordinates:\n",
-       "  * time            (time) datetime64[ns] 2020-01-01 2021-01-01 ... 2050-01-01\n",
-       "    lon             (geom) float64 ...\n",
-       "    lat             (geom) float64 ...\n",
-       "  * geom            (geom) int64 0 1 2\n",
-       "    RES_NM_REG      (geom) object ...\n",
-       "Data variables:\n",
-       "    tg_mean_annual  (time, geom) float32 ...\n",
-       "Attributes:\n",
-       "    Conventions:     CF-1.5\n",
-       "    title:           NorESM1-M model output prepared for CMIP5 historical\n",
-       "    history:         2011-06-01T01:20:15Z CMOR rewrote data to comply with CF...\n",
-       "    institution:     Norwegian Climate Centre\n",
-       "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
-       "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_avgmtl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b406a18",
-   "metadata": {},
-   "source": [
-    "### High-resolution polygons\n",
-    "\n",
-    "The pavics finch server limits the uploads to 3 MB, so our polygon file is limited to that size. In any case, a 3 MB polygon is already quite large and will take a long time to process. Here, we download other polygons (the municipalities of Abitibi) and simplify it _before_ sending it to finch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "023b40ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:03.924245Z",
-     "iopub.status.busy": "2023-02-17T21:35:03.923426Z",
-     "iopub.status.idle": "2023-02-17T21:35:05.222451Z",
-     "shell.execute_reply": "2023-02-17T21:35:05.221615Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>MRS_NO_IND</th>\n",
-       "      <th>MRS_DE_IND</th>\n",
-       "      <th>MRS_CO_MRC</th>\n",
-       "      <th>MRS_NM_MRC</th>\n",
-       "      <th>MRS_CO_REG</th>\n",
-       "      <th>MRS_NM_REG</th>\n",
-       "      <th>MRS_CO_REF</th>\n",
-       "      <th>MRS_CO_VER</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>MULTIPOLYGON (((-75.52070 48.69780, -75.52070 ...</td>\n",
-       "      <td>50 02 0030 000</td>\n",
-       "      <td>MRC (incluant les territoires autochtones)</td>\n",
-       "      <td>89</td>\n",
-       "      <td>La Vallée-de-l'Or</td>\n",
-       "      <td>08</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>MULTIPOLYGON (((-78.64320 48.71980, -78.64320 ...</td>\n",
-       "      <td>50 02 0010 000</td>\n",
-       "      <td>Municipalité régionale de comté</td>\n",
-       "      <td>87</td>\n",
-       "      <td>Abitibi-Ouest</td>\n",
-       "      <td>08</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>MULTIPOLYGON (((-77.10490 48.78100, -77.10480 ...</td>\n",
-       "      <td>50 02 0030 000</td>\n",
-       "      <td>MRC (incluant les territoires autochtones)</td>\n",
-       "      <td>88</td>\n",
-       "      <td>Abitibi</td>\n",
-       "      <td>08</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>MULTIPOLYGON (((-77.45440 47.53800, -77.45430 ...</td>\n",
-       "      <td>50 02 0030 000</td>\n",
-       "      <td>MRC (incluant les territoires autochtones)</td>\n",
-       "      <td>85</td>\n",
-       "      <td>Témiscamingue</td>\n",
-       "      <td>08</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>MULTIPOLYGON (((-79.34070 47.85150, -79.38870 ...</td>\n",
-       "      <td>50 02 0040 000</td>\n",
-       "      <td>Municipalité régionale de comté géographique</td>\n",
-       "      <td>86</td>\n",
-       "      <td>Rouyn-Noranda</td>\n",
-       "      <td>08</td>\n",
-       "      <td>Abitibi-Témiscamingue</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>MULTIPOLYGON (((-75.52060 47.84720, -75.52070 ...</td>\n",
-       "      <td>50 02 0030 000</td>\n",
-       "      <td>MRC (incluant les territoires autochtones)</td>\n",
-       "      <td>83</td>\n",
-       "      <td>La Vallée-de-la-Gatineau</td>\n",
-       "      <td>07</td>\n",
-       "      <td>Outaouais</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>MULTIPOLYGON (((-77.91790 47.26940, -77.90750 ...</td>\n",
-       "      <td>50 02 0010 000</td>\n",
-       "      <td>Municipalité régionale de comté</td>\n",
-       "      <td>84</td>\n",
-       "      <td>Pontiac</td>\n",
-       "      <td>07</td>\n",
-       "      <td>Outaouais</td>\n",
-       "      <td>SDA20K</td>\n",
-       "      <td>V2021-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            geometry      MRS_NO_IND  \\\n",
-       "0  MULTIPOLYGON (((-75.52070 48.69780, -75.52070 ...  50 02 0030 000   \n",
-       "1  MULTIPOLYGON (((-78.64320 48.71980, -78.64320 ...  50 02 0010 000   \n",
-       "2  MULTIPOLYGON (((-77.10490 48.78100, -77.10480 ...  50 02 0030 000   \n",
-       "3  MULTIPOLYGON (((-77.45440 47.53800, -77.45430 ...  50 02 0030 000   \n",
-       "4  MULTIPOLYGON (((-79.34070 47.85150, -79.38870 ...  50 02 0040 000   \n",
-       "5  MULTIPOLYGON (((-75.52060 47.84720, -75.52070 ...  50 02 0030 000   \n",
-       "6  MULTIPOLYGON (((-77.91790 47.26940, -77.90750 ...  50 02 0010 000   \n",
-       "\n",
-       "                                     MRS_DE_IND MRS_CO_MRC  \\\n",
-       "0    MRC (incluant les territoires autochtones)         89   \n",
-       "1               Municipalité régionale de comté         87   \n",
-       "2    MRC (incluant les territoires autochtones)         88   \n",
-       "3    MRC (incluant les territoires autochtones)         85   \n",
-       "4  Municipalité régionale de comté géographique         86   \n",
-       "5    MRC (incluant les territoires autochtones)         83   \n",
-       "6               Municipalité régionale de comté         84   \n",
-       "\n",
-       "                 MRS_NM_MRC MRS_CO_REG             MRS_NM_REG MRS_CO_REF  \\\n",
-       "0         La Vallée-de-l'Or         08  Abitibi-Témiscamingue     SDA20K   \n",
-       "1             Abitibi-Ouest         08  Abitibi-Témiscamingue     SDA20K   \n",
-       "2                   Abitibi         08  Abitibi-Témiscamingue     SDA20K   \n",
-       "3             Témiscamingue         08  Abitibi-Témiscamingue     SDA20K   \n",
-       "4             Rouyn-Noranda         08  Abitibi-Témiscamingue     SDA20K   \n",
-       "5  La Vallée-de-la-Gatineau         07              Outaouais     SDA20K   \n",
-       "6                   Pontiac         07              Outaouais     SDA20K   \n",
-       "\n",
-       "  MRS_CO_VER  \n",
-       "0   V2021-02  \n",
-       "1   V2021-02  \n",
-       "2   V2021-02  \n",
-       "3   V2021-02  \n",
-       "4   V2021-02  \n",
-       "5   V2021-02  \n",
-       "6   V2021-02  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = wfs.getfeature(\n",
-    "    typename=\"public:quebec_mrc_boundaries\",\n",
-    "    # This bbox represents our input data's coverage.\n",
-    "    # Polygons *touching* that zone will be returned in the response.\n",
-    "    bbox=(-79.5, 46.7, -76.3, 48.5),\n",
-    "    outputFormat=\"JSON\",\n",
-    ")\n",
-    "\n",
-    "# Directly read into a GeoDataFrame\n",
-    "all_shapes = gpd.GeoDataFrame.from_features(json.load(data))\n",
-    "all_shapes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "e976d3e5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:05.232559Z",
-     "iopub.status.busy": "2023-02-17T21:35:05.232150Z",
-     "iopub.status.idle": "2023-02-17T21:35:05.455538Z",
-     "shell.execute_reply": "2023-02-17T21:35:05.454658Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_shapes.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "a9dde0db",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:05.463788Z",
-     "iopub.status.busy": "2023-02-17T21:35:05.463493Z",
-     "iopub.status.idle": "2023-02-17T21:35:05.692308Z",
-     "shell.execute_reply": "2023-02-17T21:35:05.691445Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Create a Dataframe with simplified polygons.\n",
-    "# The buffer(0) call repairs polygons that might have been broken by the simplification.\n",
-    "poly_simple = all_shapes.copy()\n",
-    "poly_simple[\"geometry\"] = all_shapes.simplify(\n",
-    "    tolerance=0.01, preserve_topology=True\n",
-    ").buffer(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "08e3483f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:05.698096Z",
-     "iopub.status.busy": "2023-02-17T21:35:05.697649Z",
-     "iopub.status.idle": "2023-02-17T21:35:06.659107Z",
-     "shell.execute_reply": "2023-02-17T21:35:06.657949Z"
-    },
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Write polygons to geojson files.\n",
-    "poly_file = Path(tmp_dir.name) / \"mrc_simp.geojson\"\n",
-    "poly_simple.to_file(poly_file, driver=\"GeoJSON\")\n",
-    "\n",
-    "# Write the unsimplfied version for comparison only\n",
-    "all_shapes.to_file(Path(tmp_dir.name) / \"mrc_raw.geojson\", driver=\"GeoJSON\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9623b938",
-   "metadata": {
-    "tags": [
-     "nbval-ignore-output"
-    ]
-   },
-   "source": [
-    "The simplified version is way smaller than the raw. Finch will not accept inputs larger than 3 Mo (raises \"BrokenPipe\" error). But even with ~2 Mo, the process is to slow and might time out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "81b47e25",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:06.664731Z",
-     "iopub.status.busy": "2023-02-17T21:35:06.664131Z",
-     "iopub.status.idle": "2023-02-17T21:35:06.674960Z",
-     "shell.execute_reply": "2023-02-17T21:35:06.672880Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mrc_raw.geojson      3105 KB\n",
-      "mrc_simp.geojson     16 KB\n",
-      "mtl_raw.geojson      95 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "for file in sorted(Path(tmp_dir.name).iterdir()):\n",
-    "    print(f\"{file.name:20s} {file.stat().st_size / 2**10:.0f} KB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "db34519a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:06.681947Z",
-     "iopub.status.busy": "2023-02-17T21:35:06.681247Z",
-     "iopub.status.idle": "2023-02-17T21:35:08.573281Z",
-     "shell.execute_reply": "2023-02-17T21:35:08.572397Z"
-    },
-    "tags": [
-     "nbval-skip"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "resp = wps.average_polygon(\n",
-    "    resource=tg_mean_url,\n",
-    "    shape=poly_file.absolute(),\n",
-    "    start_date=\"2020-01-01\",\n",
-    "    end_date=\"2050-01-01\",\n",
-    "    tolerance=-1,  # deactivate finch's tolerance, since we already simplified the shape\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "46026a4b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:08.578498Z",
-     "iopub.status.busy": "2023-02-17T21:35:08.577629Z",
-     "iopub.status.idle": "2023-02-17T21:35:08.917363Z",
-     "shell.execute_reply": "2023-02-17T21:35:08.916569Z"
-    },
-    "tags": [
-     "nbval-skip"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmpcmqayxoj/noresm1_m_rcp85_tg_mean_annual_avg.nc.\n"
-     ]
-    },
-    {
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-       "Dimensions:         (time: 31, geom: 7)\n",
-       "Coordinates:\n",
-       "  * time            (time) datetime64[ns] 2020-01-01 2021-01-01 ... 2050-01-01\n",
-       "    lon             (geom) float64 -76.76 -79.11 -77.93 ... -78.87 -76.16 -77.09\n",
-       "    lat             (geom) float64 48.15 48.75 48.67 47.13 48.13 46.91 46.43\n",
-       "  * geom            (geom) int64 0 1 2 3 4 5 6\n",
-       "    MRS_NO_IND      (geom) object &#x27;50 02 0030 000&#x27; ... &#x27;50 02 0010 000&#x27;\n",
-       "    MRS_DE_IND      (geom) object &#x27;MRC (incluant les territoires autochtones)...\n",
-       "    MRS_CO_MRC      (geom) object &#x27;89&#x27; &#x27;87&#x27; &#x27;88&#x27; &#x27;85&#x27; &#x27;86&#x27; &#x27;83&#x27; &#x27;84&#x27;\n",
-       "    MRS_NM_MRC      (geom) object &quot;La Vallée-de-l&#x27;Or&quot; ... &#x27;Pontiac&#x27;\n",
-       "    MRS_CO_REG      (geom) object &#x27;08&#x27; &#x27;08&#x27; &#x27;08&#x27; &#x27;08&#x27; &#x27;08&#x27; &#x27;07&#x27; &#x27;07&#x27;\n",
-       "    MRS_NM_REG      (geom) object &#x27;Abitibi-Témiscamingue&#x27; ... &#x27;Outaouais&#x27;\n",
-       "    MRS_CO_REF      (geom) object &#x27;SDA20K&#x27; &#x27;SDA20K&#x27; ... &#x27;SDA20K&#x27; &#x27;SDA20K&#x27;\n",
-       "    MRS_CO_VER      (geom) object &#x27;V2021-02&#x27; &#x27;V2021-02&#x27; ... &#x27;V2021-02&#x27;\n",
-       "Data variables:\n",
-       "    tg_mean_annual  (time, geom) float32 275.5 275.3 275.3 ... 278.2 279.2 280.1\n",
-       "Attributes:\n",
-       "    Conventions:     CF-1.5\n",
-       "    title:           NorESM1-M model output prepared for CMIP5 historical\n",
-       "    history:         2011-06-01T01:20:15Z CMOR rewrote data to comply with CF...\n",
-       "    institution:     Norwegian Climate Centre\n",
-       "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
-       "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative</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-fb69e1f5-37fb-40ec-acea-9f9db80a4cf6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb69e1f5-37fb-40ec-acea-9f9db80a4cf6' 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>: 31</li><li><span class='xr-has-index'>geom</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-005f8e4f-f225-4897-a807-46dd5de2759c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-005f8e4f-f225-4897-a807-46dd5de2759c' class='xr-section-summary' >Coordinates: <span>(12)</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'>2020-01-01 ... 2050-01-01</div><input id='attrs-1d1310a9-6f76-43ea-95b5-95961279fbed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d1310a9-6f76-43ea-95b5-95961279fbed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ac18ddf-c070-4254-aac2-e0f5438bde85' class='xr-var-data-in' type='checkbox'><label for='data-2ac18ddf-c070-4254-aac2-e0f5438bde85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2048-01-01T00:00:00.000000000&#x27;, &#x27;2049-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2050-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db1fbaa9-7029-4096-a5ea-ec77fd3e0954' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db1fbaa9-7029-4096-a5ea-ec77fd3e0954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-046cf1f1-fe23-4c23-b48b-88a4508e6edc' class='xr-var-data-in' type='checkbox'><label for='data-046cf1f1-fe23-4c23-b48b-88a4508e6edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-76.756984, -79.111026, -77.932891, -78.559943, -78.87371 , -76.160389,\n",
-       "       -77.094422])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cdb58ba3-c6f9-44cb-980e-8b050316d0b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdb58ba3-c6f9-44cb-980e-8b050316d0b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6996132-fd5e-474b-bebb-9eb2dc255f6b' class='xr-var-data-in' type='checkbox'><label for='data-a6996132-fd5e-474b-bebb-9eb2dc255f6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([48.152176, 48.747089, 48.673527, 47.129865, 48.132749, 46.906679,\n",
-       "       46.426215])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-9481c508-2b60-48c4-9f4a-f640902704b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9481c508-2b60-48c4-9f4a-f640902704b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a246d36-ed4c-48fd-a2d0-69c74ddba086' class='xr-var-data-in' type='checkbox'><label for='data-2a246d36-ed4c-48fd-a2d0-69c74ddba086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NO_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2de936f-2ffa-414c-840a-cc44cdef9950' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2de936f-2ffa-414c-840a-cc44cdef9950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c510dcb2-a794-417f-94ed-00d6a8fcbb0b' class='xr-var-data-in' type='checkbox'><label for='data-c510dcb2-a794-417f-94ed-00d6a8fcbb0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0030 000&#x27;,\n",
-       "       &#x27;50 02 0040 000&#x27;, &#x27;50 02 0030 000&#x27;, &#x27;50 02 0010 000&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_DE_IND</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-39c03e46-2886-436c-bddb-dc08692322d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39c03e46-2886-436c-bddb-dc08692322d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5def7175-680e-431c-8bb1-16da551371e0' class='xr-var-data-in' type='checkbox'><label for='data-5def7175-680e-431c-8bb1-16da551371e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
-       "       &#x27;Municipalité régionale de comté&#x27;,\n",
-       "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
-       "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
-       "       &#x27;Municipalité régionale de comté géographique&#x27;,\n",
-       "       &#x27;MRC (incluant les territoires autochtones)&#x27;,\n",
-       "       &#x27;Municipalité régionale de comté&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-681ba0dd-bb8b-4330-a595-cfd1fca46c5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-681ba0dd-bb8b-4330-a595-cfd1fca46c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd80433d-f33b-40ea-b976-c95c156bb2ce' class='xr-var-data-in' type='checkbox'><label for='data-fd80433d-f33b-40ea-b976-c95c156bb2ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;89&#x27;, &#x27;87&#x27;, &#x27;88&#x27;, &#x27;85&#x27;, &#x27;86&#x27;, &#x27;83&#x27;, &#x27;84&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_MRC</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a0adf27-c331-499d-9055-f25eec44d85d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a0adf27-c331-499d-9055-f25eec44d85d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb74652d-a2a0-462a-a8f6-4ab0fee59ef5' class='xr-var-data-in' type='checkbox'><label for='data-bb74652d-a2a0-462a-a8f6-4ab0fee59ef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&quot;La Vallée-de-l&#x27;Or&quot;, &#x27;Abitibi-Ouest&#x27;, &#x27;Abitibi&#x27;, &#x27;Témiscamingue&#x27;,\n",
-       "       &#x27;Rouyn-Noranda&#x27;, &#x27;La Vallée-de-la-Gatineau&#x27;, &#x27;Pontiac&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36f70c0a-32ce-46a2-8758-ca7557ca8367' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36f70c0a-32ce-46a2-8758-ca7557ca8367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fd4d708-38db-4fa6-b2b1-6730ba48272e' class='xr-var-data-in' type='checkbox'><label for='data-4fd4d708-38db-4fa6-b2b1-6730ba48272e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;08&#x27;, &#x27;07&#x27;, &#x27;07&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_NM_REG</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-146984fa-28d3-462d-8394-25c31e10e75c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-146984fa-28d3-462d-8394-25c31e10e75c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0645023a-2988-4872-aa43-384131d6617a' class='xr-var-data-in' type='checkbox'><label for='data-0645023a-2988-4872-aa43-384131d6617a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Abitibi-Témiscamingue&#x27;, &#x27;Abitibi-Témiscamingue&#x27;,\n",
-       "       &#x27;Abitibi-Témiscamingue&#x27;, &#x27;Abitibi-Témiscamingue&#x27;,\n",
-       "       &#x27;Abitibi-Témiscamingue&#x27;, &#x27;Outaouais&#x27;, &#x27;Outaouais&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_REF</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8518a4ca-96fb-4b7f-b2e6-ecd48c7b698f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8518a4ca-96fb-4b7f-b2e6-ecd48c7b698f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e7c6c78-ba82-4d4f-a1a2-cc276aa1c8fa' class='xr-var-data-in' type='checkbox'><label for='data-8e7c6c78-ba82-4d4f-a1a2-cc276aa1c8fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;, &#x27;SDA20K&#x27;],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MRS_CO_VER</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca4cee3d-a6d1-448c-9a63-9d91a6bb0d7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca4cee3d-a6d1-448c-9a63-9d91a6bb0d7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6827166-c183-43b9-b0a3-9fe5c5c95b2a' class='xr-var-data-in' type='checkbox'><label for='data-f6827166-c183-43b9-b0a3-9fe5c5c95b2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;, &#x27;V2021-02&#x27;,\n",
-       "       &#x27;V2021-02&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b38ea1d2-8d4f-4c39-9fb5-5c827c0a461d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b38ea1d2-8d4f-4c39-9fb5-5c827c0a461d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tg_mean_annual</span></div><div class='xr-var-dims'>(time, geom)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55d859ca-b079-47bc-9edc-a7e33b7394a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55d859ca-b079-47bc-9edc-a7e33b7394a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd7f9860-dc77-4b63-a21e-9e3068e4608f' class='xr-var-data-in' type='checkbox'><label for='data-cd7f9860-dc77-4b63-a21e-9e3068e4608f' 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>cell_methods :</span></dt><dd> time: mean within days time: mean within days time: mean over days</dd><dt><span>xclim_history :</span></dt><dd>[2021-08-11 09:17:14] tg_mean_annual: agriclimat.TG_MEAN_ANNUAL(tas=&lt;array&gt;, freq=&#x27;YS&#x27;) - xclim version: 0.27.5-beta.</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean daily mean temperature</dd><dt><span>description :</span></dt><dd>Annual mean of daily mean temperature.</dd><dt><span>long_name_fr :</span></dt><dd>Moyenne de la température journalière moyenne</dd><dt><span>description_fr :</span></dt><dd>Moyenne annuelle de la température journalière moyenne</dd><dt><span>comment_fr :</span></dt><dd></dd><dt><span>_ChunkSizes :</span></dt><dd>[60 40 32]</dd></dl></div><div class='xr-var-data'><pre>array([[275.5102 , 275.30804, 275.27954, ..., 276.0464 , 276.98886, 277.89856],\n",
-       "       [275.6804 , 275.53677, 275.47397, ..., 276.31674, 277.07428, 277.89096],\n",
-       "       [276.14392, 275.95065, 275.94858, ..., 276.68753, 277.50842, 278.35504],\n",
-       "       ...,\n",
-       "       [276.94772, 276.69022, 276.65686, ..., 277.5143 , 278.46194, 279.40155],\n",
-       "       [278.16354, 277.92688, 277.89392, ..., 278.71936, 279.70706, 280.51218],\n",
-       "       [277.5508 , 277.4545 , 277.33646, ..., 278.22455, 279.19547, 280.14343]],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-062b3dea-fec8-4571-928c-f0927624cefa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-062b3dea-fec8-4571-928c-f0927624cefa' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NorESM1-M model output prepared for CMIP5 historical</dd><dt><span>history :</span></dt><dd>2011-06-01T01:20:15Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n",
-       "2016-01-19T05:16:03: Interpolate to nrcan_livneh grid.\n",
-       "2016-02-10T09:50:15: Bias correction using nrcan_livneh.</dd><dt><span>institution :</span></dt><dd>Norwegian Climate Centre</dd><dt><span>source :</span></dt><dd>NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r112, f19L26);  ocean: MICOM (MICOM-noresm-ver1_cmip5-r112, gx1v6L53);  sea ice: CICE (CICE4-noresm-ver1_cmip5-r112);  land: CLM (CLM4-noresm-ver1_cmip5-r112)\n",
-       "30-day moving window 50-bins quantile mapping with detrending.</dd><dt><span>redistribution :</span></dt><dd>Redistribution prohibited. For internal use only.</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:         (time: 31, geom: 7)\n",
-       "Coordinates:\n",
-       "  * time            (time) datetime64[ns] 2020-01-01 2021-01-01 ... 2050-01-01\n",
-       "    lon             (geom) float64 ...\n",
-       "    lat             (geom) float64 ...\n",
-       "  * geom            (geom) int64 0 1 2 3 4 5 6\n",
-       "    MRS_NO_IND      (geom) object ...\n",
-       "    MRS_DE_IND      (geom) object ...\n",
-       "    MRS_CO_MRC      (geom) object ...\n",
-       "    MRS_NM_MRC      (geom) object ...\n",
-       "    MRS_CO_REG      (geom) object ...\n",
-       "    MRS_NM_REG      (geom) object ...\n",
-       "    MRS_CO_REF      (geom) object ...\n",
-       "    MRS_CO_VER      (geom) object ...\n",
-       "Data variables:\n",
-       "    tg_mean_annual  (time, geom) float32 ...\n",
-       "Attributes:\n",
-       "    Conventions:     CF-1.5\n",
-       "    title:           NorESM1-M model output prepared for CMIP5 historical\n",
-       "    history:         2011-06-01T01:20:15Z CMOR rewrote data to comply with CF...\n",
-       "    institution:     Norwegian Climate Centre\n",
-       "    source:          NorESM1-M 2011  atmosphere: CAM-Oslo (CAM4-Oslo-noresm-v...\n",
-       "    redistribution:  Redistribution prohibited. For internal use only.\n",
-       "    regrid_method:   conservative"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "avg = resp.get(asobj=True).output\n",
-    "avg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "db5267fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-17T21:35:08.921266Z",
-     "iopub.status.busy": "2023-02-17T21:35:08.920866Z",
-     "iopub.status.idle": "2023-02-17T21:35:08.926423Z",
-     "shell.execute_reply": "2023-02-17T21:35:08.925774Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "tmp_dir.cleanup()\n",
-    "del tmp_dir"
-   ]
-  }
- ],
- "metadata": {
-  "celltoolbar": "Edit Metadata",
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/environment-docs.yml b/environment-docs.yml
index c0ca69ed..45b7faec 100644
--- a/environment-docs.yml
+++ b/environment-docs.yml
@@ -12,4 +12,4 @@ dependencies:
   - sphinx >=4.0
   - sphinxcontrib-bibtex
   - unidecode
-  - xclim =0.38  # remember to match xclim version in requirements_docs.txt as well
+  - xclim =0.40  # remember to match xclim version in requirements_docs.txt as well
diff --git a/environment.yml b/environment.yml
index ab752bf8..4e15d9ac 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,12 +10,11 @@ dependencies:
   - dask
   - distributed
   - geopandas
-  - jinja2
+  - h5netcdf
   - netcdf4
   - numpy
   - pandas
   - parse
-  - pint <0.20  # xclim < 0.39 is broken by pint >= 0.20
   - psutil
   - python-slugify
   - pywps >=4.5.1
@@ -25,6 +24,6 @@ dependencies:
   - sentry-sdk
   - siphon
   - unidecode
-  - xarray >=0.18.2
-  - xclim =0.38  # remember to match xclim version in requirements_docs.txt as well
-  - xesmf >=0.6.2
+  - xarray >=2023.01.0
+  - xclim =0.40  # remember to match xclim version in requirements_docs.txt as well
+  - xesmf >=0.7
diff --git a/finch/cli.py b/finch/cli.py
index d1707ca3..69c61277 100644
--- a/finch/cli.py
+++ b/finch/cli.py
@@ -11,7 +11,6 @@
 
 import click
 import psutil
-from jinja2 import Environment, PackageLoader
 from pywps import configuration
 
 from . import wsgi
@@ -20,17 +19,6 @@
 
 CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"])
 
-template_env = Environment(loader=PackageLoader("finch", "templates"), autoescape=True)
-
-
-def write_user_config(**kwargs):  # noqa: D103
-    config_templ = template_env.get_template("pywps.cfg")
-    rendered_config = config_templ.render(**kwargs)
-    config_file = os.path.abspath(os.path.join(os.path.curdir, ".custom.cfg"))
-    with open(config_file, "w") as fp:
-        fp.write(rendered_config)
-    return config_file
-
 
 def get_host():  # noqa: D103
     url = configuration.get_config_value("server", "url")
@@ -126,89 +114,22 @@ def stop():
     "--bind-host",
     "-b",
     metavar="IP-ADDRESS",
-    default="127.0.0.1",
     help="IP address used to bind service.",
 )
 @click.option("--daemon", "-d", is_flag=True, help="run in daemon mode.")
-@click.option(
-    "--hostname",
-    metavar="HOSTNAME",
-    default="localhost",
-    help="hostname in PyWPS configuration.",
-)
-@click.option(
-    "--port", metavar="PORT", default="5000", help="port in PyWPS configuration."
-)
-@click.option(
-    "--maxsingleinputsize",
-    default="200mb",
-    help="maxsingleinputsize in PyWPS configuration.",
-)
-@click.option(
-    "--maxprocesses",
-    metavar="INT",
-    default="10",
-    help="maxprocesses in PyWPS configuration.",
-)
-@click.option(
-    "--parallelprocesses",
-    metavar="INT",
-    default="2",
-    help="parallelprocesses in PyWPS configuration.",
-)
-@click.option(
-    "--log-level",
-    metavar="LEVEL",
-    default="INFO",
-    help="log level in PyWPS configuration.",
-)
-@click.option(
-    "--log-file",
-    metavar="PATH",
-    default="pywps.log",
-    help="log file in PyWPS configuration.",
-)
-@click.option(
-    "--database",
-    default="sqlite:///pywps-logs.sqlite",
-    help="database in PyWPS configuration",
-)
-def start(
-    config,
-    bind_host,
-    daemon,
-    hostname,
-    port,
-    maxsingleinputsize,
-    maxprocesses,
-    parallelprocesses,
-    log_level,
-    log_file,
-    database,
-):
+def start(config, bind_host, daemon):
     """Start PyWPS service.
 
     This service is by default available at http://localhost:5000/wps
+    The default configuration is from finch/default.cfg
     """
     if os.path.exists(PID_FILE):
         click.echo(f'PID file exists: "{PID_FILE}". Service still running?')
         os._exit(0)
     cfgfiles = []
-    cfgfiles.append(
-        write_user_config(
-            wps_hostname=hostname,
-            wps_port=port,
-            wps_maxsingleinputsize=maxsingleinputsize,
-            wps_maxprocesses=maxprocesses,
-            wps_parallelprocesses=parallelprocesses,
-            wps_log_level=log_level,
-            wps_log_file=log_file,
-            wps_database=database,
-        )
-    )
     if config:
         cfgfiles.append(config)
-    app = wsgi.create_app(cfgfiles)
+    app = wsgi.create_app(cfgfiles)  # Will add default.cfg to the config
     # let's start the service ...
     # See:
     # * https://github.com/geopython/pywps-flask/blob/master/demo.py
diff --git a/finch/default.cfg b/finch/default.cfg
index 15c09674..b239c54c 100644
--- a/finch/default.cfg
+++ b/finch/default.cfg
@@ -8,7 +8,7 @@ provider_url=https://finch.readthedocs.org/en/latest/
 
 [server]
 url = http://localhost:5000/wps
-outputurl = http://localhost:5000/outputs
+outputurl = http://localhost:5000/outputs/
 allowedinputpaths = /
 maxsingleinputsize = 200mb
 maxprocesses = 10
diff --git a/finch/processes/__init__.py b/finch/processes/__init__.py
index 6d4a4ad3..9952cf79 100644
--- a/finch/processes/__init__.py
+++ b/finch/processes/__init__.py
@@ -37,7 +37,12 @@ def filter_func(elem):
             ind.realm in realms
             and ind.identifier is not None
             and name not in exclude
-            and ind.identifier.upper() == ind._registry_id  # official indicator
+            and (
+                ind.identifier.upper() == ind._registry_id  # official indicator
+                or ind._registry_id.startswith(
+                    "xclim.core.indicator"
+                )  # oups. Bug for discharge_distribution_fit in xclim 0.40
+            )
         )
 
     out = dict(filter(filter_func, xclim_registry.items()))
diff --git a/finch/processes/ensemble_utils.py b/finch/processes/ensemble_utils.py
index dd585446..78065607 100644
--- a/finch/processes/ensemble_utils.py
+++ b/finch/processes/ensemble_utils.py
@@ -12,7 +12,7 @@
 import xarray as xr
 from pandas.api.types import is_numeric_dtype
 from parse import parse
-from pywps import FORMATS, ComplexInput, Process, configuration
+from pywps import FORMATS, ComplexInput, Process
 from pywps.app.exceptions import ProcessError
 from pywps.exceptions import InvalidParameterValue
 from siphon.catalog import TDSCatalog
@@ -348,7 +348,9 @@ def make_file_groups(files_list: List[Path], variables: set) -> List[Dict[str, P
 def make_ensemble(
     files: List[Path], percentiles: List[int], average_dims: Optional[Tuple[str]] = None
 ) -> None:  # noqa: D103
-    ensemble = ensembles.create_ensemble(files)
+    ensemble = ensembles.create_ensemble(
+        files, realizations=[file.stem for file in files]
+    )
     # make sure we have data starting in 1950
     ensemble = ensemble.sel(time=(ensemble.time.dt.year >= 1950))
 
@@ -361,7 +363,12 @@ def make_ensemble(
     if average_dims is not None:
         ensemble = ensemble.mean(dim=average_dims)
 
-    ensemble_percentiles = ensembles.ensemble_percentiles(ensemble, values=percentiles)
+    if percentiles:
+        ensemble_percentiles = ensembles.ensemble_percentiles(
+            ensemble, values=percentiles
+        )
+    else:
+        ensemble_percentiles = ensemble
 
     # Doy data converted previously is converted back.
     for v in ensemble_percentiles.data_vars:
@@ -374,7 +381,7 @@ def make_ensemble(
     # a best effort at working around what looks like a bug in either xclim or xarray.
     # The xarray documentation mentions: 'this method can be necessary when working
     # with many file objects on disk.'
-    ensemble_percentiles.load()
+    # ensemble_percentiles.load()
 
     return ensemble_percentiles
 
@@ -516,7 +523,11 @@ def ensemble_common_handler(
     if not convert_to_csv:
         del process.status_percentage_steps["convert_to_csv"]
     percentiles_string = request.inputs["ensemble_percentiles"][0].data
-    ensemble_percentiles = [int(p.strip()) for p in percentiles_string.split(",")]
+    ensemble_percentiles = (
+        [int(p.strip()) for p in percentiles_string.split(",")]
+        if percentiles_string != "None"
+        else []
+    )
 
     write_log(
         process,
@@ -618,6 +629,13 @@ def ensemble_common_handler(
 
     process.set_workdir(str(base_work_dir))
 
+    if "realization" in ensembles[0].dims and len(scenarios) > 1:
+        # For non-reducing calls with multiple scenarios, remove the scenario information from the member name.
+        for scen, ds in zip(scenarios, ensembles):
+            ds["realization"] = [
+                real.replace(scen, "") for real in ds.realization.values
+            ]
+
     ensemble = xr.concat(
         ensembles, dim=xr.DataArray(scenarios, dims=("scenario",), name="scenario")
     )
diff --git a/finch/processes/wps_base.py b/finch/processes/wps_base.py
index d6c474d0..77898b6e 100644
--- a/finch/processes/wps_base.py
+++ b/finch/processes/wps_base.py
@@ -154,7 +154,7 @@ def convert_xclim_inputs_to_pywps(
     # Only for generic types
     data_types = {
         InputKind.BOOL: "boolean",
-        InputKind.QUANTITY_STR: "string",
+        InputKind.QUANTIFIED: "string",
         InputKind.NUMBER: "integer",
         InputKind.NUMBER_SEQUENCE: "integer",
         InputKind.STRING: "string",
diff --git a/finch/processes/wpsio.py b/finch/processes/wpsio.py
index f8740419..e7c85d24 100644
--- a/finch/processes/wpsio.py
+++ b/finch/processes/wpsio.py
@@ -218,7 +218,9 @@ def get_ensemble_inputs(novar=False):
     "Ensemble percentiles",
     abstract=(
         "Ensemble percentiles to calculate for input climate simulations. "
-        "Accepts a comma separated list of integers."
+        "Accepts a comma separated list of integers. An empty string will "
+        "disable the ensemble reduction and the output will have all members "
+        "along the 'realization' dimension, using the input filenames as coordinates."
     ),
     data_type="string",
     default="10,50,90",
diff --git a/finch/templates/pywps.cfg b/finch/templates/pywps.cfg
deleted file mode 100644
index 531d6e81..00000000
--- a/finch/templates/pywps.cfg
+++ /dev/null
@@ -1,27 +0,0 @@
-[server]
-{% if wps_url %}
-url = {{ wps_url }}
-{% else %}
-url = http://{{ wps_hostname }}:{{ wps_port }}/wps
-{% endif %}
-{% if wps_outputurl %}
-outputurl = {{ wps_outputurl }}
-{% else %}
-outputurl = http://{{ wps_hostname }}:{{ wps_port }}/outputs
-{% endif %}
-allowedinputpaths = /
-maxsingleinputsize = {{ wps_maxsingleinputsize|default('200mb') }}
-maxprocesses = {{ wps_maxprocesses|default('10') }}
-parallelprocesses = {{ wps_parallelprocesses|default('2') }}
-{% if wps_outputpath %}
-outputpath= {{ wps_outputpath }}
-{% endif %}
-{% if wps_workdir %}
-workdir={{ wps_workdir }}
-{% endif %}
-
-[logging]
-level = {{ wps_log_level|default('INFO') }}
-file = {{ wps_log_file|default('pywps.log') }}
-database = {{ wps_database|default('sqlite:///pywps-logs.sqlite') }}
-format = [%(asctime)s] [%(levelname)s] line=%(lineno)s module=%(module)s %(message)s
diff --git a/requirements.txt b/requirements.txt
index 0c11f2d2..ccb65223 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,11 @@
 cftime
 pywps>=4.5.1
-xclim==0.38
-pint<=0.19.2  # xclim < 0.39 breaks with pint 0.20
+xclim==0.40
 xarray>=0.18.2
 pandas
 xesmf>=0.6.2
 clisops>=0.9.3
 geopandas
-jinja2
 click
 psutil
 bottleneck
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 901ed293..4b99abfe 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -14,6 +14,5 @@ jupyter_client
 # Changing dependencies above this comment will create merge conflicts when updating the cookiecutter template with cruft. Add extra requirements below this line.
 birdhouse-birdy>=0.8.1
 geojson
-h5netcdf
 matplotlib
 pre-commit
diff --git a/requirements_docs.txt b/requirements_docs.txt
index fec5074b..c6ec9412 100644
--- a/requirements_docs.txt
+++ b/requirements_docs.txt
@@ -2,8 +2,7 @@ pywps>=4.5.1
 sphinx>=4.0
 nbsphinx
 ipython
-xclim==0.38
-pint<0.20  # xclim < 0.39 breaks with pint 0.20
+xclim==0.40
 matplotlib
 birdhouse-birdy>=0.8.1
 unidecode


Test patch:
diff --git a/tests/test_wps_ensemble.py b/tests/test_wps_ensemble.py
index 53858037..fdebe652 100644
--- a/tests/test_wps_ensemble.py
+++ b/tests/test_wps_ensemble.py
@@ -70,6 +70,52 @@ def test_ensemble_heatwave_frequency_grid_point(client):
     assert len(ds.attrs["source_datasets"].split("\n")) == 4
 
 
+def test_ensemble_heatwave_frequency_grid_point_no_perc(client):
+    # --- given ---
+    identifier = "ensemble_grid_point_heat_wave_frequency"
+    inputs = [
+        wps_literal_input("lat", "46"),
+        wps_literal_input("lon", "-72.8"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
+        wps_literal_input("thresh_tasmin", "22.0 degC"),
+        wps_literal_input("thresh_tasmax", "30 degC"),
+        wps_literal_input("window", "3"),
+        wps_literal_input("freq", "MS"),
+        wps_literal_input("ensemble_percentiles", ""),
+        wps_literal_input("output_format", "netcdf"),
+        wps_literal_input("output_name", "testens"),
+    ]
+
+    # --- when ---
+    outputs = execute_process(client, identifier, inputs)
+
+    # --- then ---
+    assert len(outputs) == 1
+    assert Path(outputs[0]).stem.startswith("testens_46_000_72_800_rcp45")
+    ds = open_dataset(outputs[0])
+    dims = dict(ds.dims)
+    assert dims == {
+        "region": 1,
+        "time": 4,  # there are roughly 4 months in the test datasets
+        "scenario": 1,
+        "realization": 2,
+    }
+
+    ensemble_variables = {k: v for k, v in ds.data_vars.items()}
+    assert sorted(ensemble_variables) == ["heat_wave_frequency"]
+    for var in ensemble_variables.values():
+        variable_dims = {d: s for d, s in zip(var.dims, var.shape)}
+        assert variable_dims == {
+            "region": 1,
+            "time": 4,
+            "scenario": 1,
+            "realization": 2,
+        }
+
+    assert len(ds.attrs["source_datasets"].split("\n")) == 4
+
+
 def test_ensemble_dded_grid_point_multiscenario(client):
     # --- given ---
     identifier = "ensemble_grid_point_degree_days_exceedance_date"
@@ -109,6 +155,49 @@ def test_ensemble_dded_grid_point_multiscenario(client):
         assert variable_dims == {"region": 1, "time": 4, "scenario": 2}
 
 
+def test_ensemble_dded_grid_point_multiscenario_noperc(client):
+    # --- given ---
+    identifier = "ensemble_grid_point_degree_days_exceedance_date"
+    inputs = [
+        wps_literal_input("lat", "46"),
+        wps_literal_input("lon", "-72.8"),
+        wps_literal_input("scenario", "rcp26"),
+        wps_literal_input("scenario", "rcp45"),
+        wps_literal_input("dataset", "test_subset"),
+        wps_literal_input("thresh", "-5 degC"),
+        wps_literal_input("sum_thresh", "30 K days"),
+        wps_literal_input("op", ">"),
+        wps_literal_input("freq", "MS"),
+        wps_literal_input("ensemble_percentiles", ""),
+        wps_literal_input("output_format", "netcdf"),
+    ]
+
+    # --- when ---
+    outputs = execute_process(client, identifier, inputs)
+
+    # --- then ---
+    assert len(outputs) == 1
+    ds = open_dataset(outputs[0])
+    dims = dict(ds.dims)
+    assert dims == {
+        "region": 1,
+        "time": 4,  # there are roughly 4 months in the test datasets
+        "scenario": 2,
+        "realization": 2,
+    }
+
+    ensemble_variables = {k: v for k, v in ds.data_vars.items()}
+    assert sorted(ensemble_variables) == ["degree_days_exceedance_date"]
+    for var in ensemble_variables.values():
+        variable_dims = dict(zip(var.dims, var.shape))
+        assert variable_dims == {
+            "region": 1,
+            "time": 4,
+            "scenario": 2,
+            "realization": 2,
+        }
+
+
 def test_ensemble_heatwave_frequency_bbox(client):
     # --- given ---
     identifier = "ensemble_bbox_heat_wave_frequency"
diff --git a/tests/test_wps_xclim_indices.py b/tests/test_wps_xclim_indices.py
index 1819e3dc..8bc2b145 100644
--- a/tests/test_wps_xclim_indices.py
+++ b/tests/test_wps_xclim_indices.py
@@ -210,10 +210,10 @@ def test_missing_options(client, netcdf_datasets):
 
 def test_stats_process(client, netcdf_datasets):
     """Test stats and the capacity to choose the variable."""
-    identifier = "stats"
+    identifier = "discharge_stats"
 
     inputs = [
-        wps_input_file("da", netcdf_datasets["pr_discharge"]),
+        wps_input_file("discharge", netcdf_datasets["pr_discharge"]),
         wps_literal_input("freq", "YS"),
         wps_literal_input("op", "max"),
         wps_literal_input("season", "JJA"),
@@ -227,7 +227,7 @@ def test_stats_process(client, netcdf_datasets):
 def test_freqanalysis_process(client, netcdf_datasets):
     identifier = "freq_analysis"
     inputs = [
-        wps_input_file("da", netcdf_datasets["discharge"]),
+        wps_input_file("discharge", netcdf_datasets["discharge"]),
         wps_literal_input("t", "2"),
         wps_literal_input("t", "50"),
         wps_literal_input("freq", "YS"),
@@ -242,11 +242,11 @@ def test_freqanalysis_process(client, netcdf_datasets):
 
 
 class TestFitProcess:
-    identifier = "fit"
+    identifier = "discharge_distribution_fit"
 
     def test_simple(self, client, netcdf_datasets):
         inputs = [
-            wps_input_file("da", netcdf_datasets["discharge"]),
+            wps_input_file("discharge", netcdf_datasets["discharge"]),
             wps_literal_input("dist", "norm"),
         ]
         outputs = execute_process(client, self.identifier, inputs)
@@ -256,7 +256,7 @@ def test_simple(self, client, netcdf_datasets):
     def test_nan(self, client, q_series, tmp_path):
         q_series([333, 145, 203, 109, 430, 230, np.nan]).to_netcdf(tmp_path / "q.nc")
         inputs = [
-            wps_input_file("da", tmp_path / "q.nc"),
+            wps_input_file("discharge", tmp_path / "q.nc"),
             wps_literal_input("dist", "norm"),
         ]
         outputs = execute_process(client, self.identifier, inputs)