Update time statistics functions

doc/sphinx/source/recipes/recipe_oceans.rst

@@ -286,7 +286,7 @@ For this reason, we recommend extracting a small number of specific layers in
 the preprocessor, using the `extract_layer` preprocessor.
 
 This script can not process NetCDFs with multiple time steps. Please use the
-`time_average` preprocessor to collapse the time dimension.
+`climate_statistics` preprocessor to collapse the time dimension.
 
 This diagnostic also includes the optional arguments, `threshold` and
 `thresholds`.
@@ -311,7 +311,7 @@ For a  Global 2D field:
   .. code-block:: yaml
 
       prep_map_1:
-	time_average:
+	climate_statistics:
 
 
 For a  regional 2D field:
@@ -324,7 +324,8 @@ For a  regional 2D field:
 	      end_longitude: 30.
 	      start_latitude: -80.
 	      end_latitude: 80.
-	    time_average:
+        climate_statistics:
+          operator: mean
 
 For a  Global 3D field at the surface and 10m depth:
 
@@ -335,7 +336,8 @@ For a  Global 3D field at the surface and 10m depth:
 	  extract_levels:
 	    levels: [0., 10.]
 	    scheme: linear_horizontal_extrapolate_vertical
-	  time_average:
+      climate_statistics:
+        operator: mean
 
 
 For a multi-model comparison mean of 2D global fields including contour thresholds.
@@ -344,7 +346,8 @@ For a multi-model comparison mean of 2D global fields including contour threshol
 
 	prep_map_4:
 	  custom_order: true
-	  time_average:
+      climate_statistics:
+        operator: mean
 	  regrid:
 	    target_grid: 1x1
 	    scheme: linear
@@ -390,7 +393,8 @@ An appropriate preprocessor for a 2D field would be:
   .. code-block:: yaml
 
 	prep_quad_map:
-	    time_average:
+        climate_statistics:
+            operator: mean
 
 and an example of an appropriate diagnostic section of the recipe would be:
 
@@ -440,7 +444,7 @@ This diagnostic also includes the optional arguments, `maps_range` and
 `diff_range` to manually define plot ranges. Both arguments are a list of two floats
 to set plot range minimun and maximum values respectively for Model and Observations
 maps (Top panels) and for the Model minus Observations panel (bottom left).
-Note that if input data have negative values the Model over Observations map 
+Note that if input data have negative values the Model over Observations map
 (bottom right) is not produced.
 
 The scatter plots plot the matched model coordinate on the x axis, and the
@@ -457,7 +461,8 @@ An appropriate preprocessor for a 3D+time field would be:
 	    extract_levels:
 	      levels:  [100., ]
 	      scheme: linear_extrap
-	    time_average:
+        climate_statistics:
+          operator: mean
 	    regrid:
 	      target_grid: 1x1
 	      scheme: linear
@@ -573,7 +578,8 @@ An appropriate preprocessor for a 3D+time field would be:
 
   .. code-block:: yaml
 
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_slice:
       latitude: [-50.,50.]
       longitude: 332.

environment.yml

@@ -6,7 +6,7 @@ channels:
 
 dependencies:
   # Python packages that cannot be installed from PyPI:
-  - esmvalcore>=2.0.0b1,<2.1
+  - esmvalcore>=2.0.0b2,<2.1
   # Non-Python dependencies
   - graphviz
   - cdo

esmvaltool/diag_scripts/ocean/diagnostic_maps.py

@@ -18,7 +18,8 @@
       extract_levels:
         levels:  [100., ]
          scheme: linear_extrap
-      time_average:
+      climate_statistics:
+        operator: mean
 
 
 Note that this recipe may not function on machines with no access to the

esmvaltool/diag_scripts/ocean/diagnostic_maps_quad.py

@@ -27,7 +27,8 @@
       extract_levels:
         levels:  [100., ]
         scheme: linear_extrap
-      time_average:
+      climate_statistics:
+        operator: mean
 
 This diagnostic also requires the ``exper_model``, ``exper_model`` and
 ``observational_dataset`` keys in the recipe::

esmvaltool/diag_scripts/ocean/diagnostic_model_vs_obs.py

@@ -30,7 +30,8 @@
       extract_levels:
         levels:  [100., ]
         scheme: linear_extrap
-      time_average:
+      climate_statistics:
+        operator: mean
       regrid:
         target_grid: 1x1
         scheme: linear

esmvaltool/diag_scripts/ocean/diagnostic_transects.py

@@ -14,7 +14,8 @@
 
   preprocessors:
     prep_transect:
-      time_average:
+      climate_statistics:
+        operator: mean
       extract_slice: # Atlantic Meridional Transect
         latitude: [-50.,50.]
         longitude: 332.

esmvaltool/diag_scripts/shared/_validation.py

@@ -2,7 +2,7 @@
 import os
 import logging
 import iris
-from esmvalcore.preprocessor import time_average
+from esmvalcore.preprocessor import climate_statistics
 from esmvaltool.diag_scripts.shared import select_metadata
 
 
@@ -53,12 +53,12 @@ def get_control_exper_obs(short_name, input_data, cfg, cmip_type):
 
 
 # apply supermeans: handy function that loads CONTROL, EXPERIMENT
-# and OBS (if any) files and applies time_average() to mean the cubes
+# and OBS (if any) files and applies climate_statistics() to mean the cubes
 def apply_supermeans(ctrl, exper, obs_list):
     """
     Apply supermeans on data components ie MEAN on time
 
-    This function is an extension of time_average() meant to ease the
+    This function is an extension of climate_statistics() meant to ease the
     time-meaning procedure when dealing with CONTROL, EXPERIMENT and OBS
     (if any) datasets.
     ctrl: dictionary of CONTROL dataset
@@ -71,14 +71,14 @@ def apply_supermeans(ctrl, exper, obs_list):
     exper_file = exper['filename']
     ctrl_cube = iris.load_cube(ctrl_file)
     exper_cube = iris.load_cube(exper_file)
-    ctrl_cube = time_average(ctrl_cube)
-    exper_cube = time_average(exper_cube)
+    ctrl_cube = climate_statistics(ctrl_cube)
+    exper_cube = climate_statistics(exper_cube)
     if obs_list:
         obs_cube_list = []
         for obs in obs_list:
             obs_file = obs['filename']
             obs_cube = iris.load_cube(obs_file)
-            obs_cube = time_average(obs_cube)
+            obs_cube = climate_statistics(obs_cube)
             obs_cube_list.append(obs_cube)
     else:
         obs_cube_list = None

esmvaltool/recipes/recipe_ocean_Landschuetzer2016.yml

@@ -34,7 +34,8 @@ preprocessors:
   # --------------------------------------------------
   # For a 2D global surface map
   prep_surface_map_2D:
-    time_average:
+    climate_statistics:
+      operator: mean
     regrid:
       target_grid: 2x2
       scheme: linear
@@ -110,8 +111,4 @@ diagnostics:
         script: ocean/diagnostic_maps.py
       Global_Ocean_model_vs_obs:
         script: ocean/diagnostic_model_vs_obs.py
-        observational_dataset: {dataset: Landschuetzer2016, project: OBS}
-
-
-
-
+        observational_dataset: {dataset: Landschutzer2016, project: OBS}

esmvaltool/recipes/recipe_ocean_amoc.yml

@@ -57,7 +57,8 @@ preprocessors:
       end_year: 2003
       end_month: 12
       end_day: 31
-    time_average:
+    climate_statistics:
+      operator: mean
 
 diagnostics:
   # --------------------------------------------------

esmvaltool/recipes/recipe_ocean_bgc.yml

@@ -89,40 +89,45 @@ preprocessors:
 
   # For a 2D global surface map
   prep_surface_map_2D:
-    time_average:
+    climate_statistics:
+      operator: mean
 
   # For a 3D global surface map
   prep_surface_map_3D:
     extract_levels:
       levels:  [0., ]
       scheme: linear_horizontal_extrapolate_vertical
-    time_average:
+    climate_statistics:
+      operator: mean
 
   prep_surface_map_regrid_3D:
     custom_order: true
     extract_levels:
       levels:  [0., ]
       scheme: linear_horizontal_extrapolate_vertical
-    time_average:
+    climate_statistics:
+      operator: mean
     regrid:
       target_grid: 1x1
       scheme: linear
 
 
   prep_global_profile:
-    annual_mean:
+    annual_statistics:
+      operator: mean
     area_statistics:
       operator: mean
 
   prep_global_profile_decadal:
-    annual_mean:
-       decadal: true
+    decadal_statistics:
+      operator: mean
     area_statistics:
       operator: mean
 
   prep_transect_AMT: # Atlantic Meridional Transect (28W)
     custom_order: true
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_region:
       start_longitude: 320.
       end_longitude: 345.
@@ -138,7 +143,8 @@ preprocessors:
 #   prep_depth_integration:
 #     depth_integration:
 # #      new_units: kg m-2 # need to specify in advance, as cf_units has strange behaviour.
-#     time_average:
+#     climate_statistics:
+#      operator: mean
 #
 #   # 2D map global depth integration time series maps
 #   prep_depth_integration_timeseries:

esmvaltool/recipes/recipe_ocean_example.yml

@@ -141,14 +141,16 @@ preprocessors:
   # Map preprocessors - 2D fields
   # --------------------------------------------------
   prep_map_1: # For Global 2D fields
-    time_average:
+    climate_statistics:
+      operator: mean
 
   prep_map_2: # For Global 2D fields with regridding
     custom_order: true
     regrid:
       target_grid: 1x1
       scheme: linear
-    time_average:
+    climate_statistics:
+      operator: mean
     multi_model_statistics:
       span: overlap
       statistics: [mean ]
@@ -159,7 +161,8 @@ preprocessors:
     extract_levels:
       levels: [0., 10., 100., 1000.,]
       scheme: linear_horizontal_extrapolate_vertical
-    time_average:
+    climate_statistics:
+      operator: mean
     regrid:
       target_grid: 1x1
       scheme: linear
@@ -174,7 +177,8 @@ preprocessors:
       end_longitude: 30.
       start_latitude: -80.
       end_latitude: 80.
-    time_average:
+    climate_statistics:
+      operator: mean
     regrid:
       target_grid: 1x1
       scheme: linear
@@ -187,7 +191,8 @@ preprocessors:
     extract_levels:
       levels: [0., 10., 100., 1000.,]
       scheme: linear_horizontal_extrapolate_vertical
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_region:
       start_longitude: -80.
       end_longitude: 30.
@@ -204,20 +209,23 @@ preprocessors:
   # Transects preprocessors
   # --------------------------------------------------
   prep_transect_1: # For extracting along a trajectory
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_trajectory:
       latitudes: [-5., 5.,]
       longitudes: [332.,332.]
       number_points: 4              # VERY SLOW with high numbers!
 
   prep_transect_2: # For extracting a transect
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_transect: # Pacific Equator
       latitude: 0.
       longitude: [120., 300.]
 
   prep_transect_3: # For extracting a transect
-    time_average:
+    climate_statistics:
+      operator: mean
     extract_transect: # Atlantic Meridional Transect
       latitude: [-50.,50.]
       longitude: 332.
@@ -247,7 +255,8 @@ preprocessors:
   # --------------------------------------------------
   prep_depth_integration_1: # For a 2D map global profile
     depth_integration:
-    time_average:
+    climate_statistics:
+      operator: mean
 
 
 diagnostics:

esmvaltool/recipes/recipe_ocean_quadmap.yml

@@ -34,7 +34,8 @@ preprocessors:
       end_year: 2003
       end_month: 12
       end_day: 31
-    time_average:
+    climate_statistics:
+      operator: mean
     regrid:
       target_grid: 1x1
       scheme: linear

meta.yaml

@@ -48,7 +48,7 @@ requirements:
     - cython
     - eofs
     - esmpy
-    - esmvalcore>=2.0.0b1,<2.1
+    - esmvalcore>=2.0.0b2,<2.1
     - jinja2
     - matplotlib
     - nc-time-axis

setup.py

@@ -28,7 +28,7 @@
         'cython',
         'jinja2',
         'eofs',
-        'esmvalcore>=2.0.0b1,<2.1',
+        'esmvalcore>=2.0.0b2,<2.1',
         'fiona',
         'matplotlib<3',
         'nc-time-axis',  # needed by iris.plot