Use years from KCS recipe (#2223)

* use years from cfg for plots and reference period

* trailing whitespaces

* specified control period as flexible

* Remove write_plots option from global_matching diagnostic

* Minor automatic reformatting

Co-authored-by: Klaus Zimmermann <klaus.zimmermann@smhi.se>
Co-authored-by: Manuel Schlund <manuel.schlund@dlr.de>

doc/sphinx/source/recipes/recipe_kcs.rst

@@ -13,7 +13,7 @@ The original method created 8 resampled datasets:
 * 2 main scenarios: Moderate (M) and Warm (W) (Lenderink 2014 uses "G" instead of "M").
 * 2 'sub'scenarios: Relatively high (H) or low (L) changes in seasonal temperature and precipitation
 * 2 time horizons: Mid-century (MOC; 2050) and end-of-century (EOC; 2085)
-* Each scenario consists of changes calculated between 2 periods: Control (1981-2010) and future (variable).
+* Each scenario consists of changes calculated between 2 periods: Control (e.g. 1981-2010) and future (variable).
 
 The configuration settings for these resamples can be found in table 1 of Lenderink 2014's `supplementary data <https://iopscience.iop.org/1748-9326/9/11/115008/media/erl503687suppdata.pdf>`_.
 

esmvaltool/diag_scripts/kcs/global_matching.py

@@ -1,15 +1,19 @@
 """Align the target model with the CMIP ensemble."""
 import logging
-from pathlib import Path
 from itertools import product
+from pathlib import Path
 
 import matplotlib.pyplot as plt
 import pandas as pd
 import xarray as xr
 
-from esmvaltool.diag_scripts.shared import (get_diagnostic_filename,
-                                            get_plot_filename, run_diagnostic,
-                                            select_metadata, ProvenanceLogger)
+from esmvaltool.diag_scripts.shared import (
+    ProvenanceLogger,
+    get_diagnostic_filename,
+    get_plot_filename,
+    run_diagnostic,
+    select_metadata,
+)
 
 logger = logging.getLogger(Path(__file__).name)
 
@@ -53,9 +57,9 @@ def get_cmip_dt(metadata, year, percentile):
 def get_resampling_period(target_dts, cmip_dt):
     """Return 30-year time bounds of the resampling period.
 
-    This is the period for which the target model delta T
-    matches the cmip delta T for a specific year.
-    Uses a 30-year rolling window to get the best match.
+    This is the period for which the target model delta T matches the
+    cmip delta T for a specific year. Uses a 30-year rolling window to
+    get the best match.
     """
     target_dts = target_dts.rolling(time=30, center=True,
                                     min_periods=30).mean()
@@ -85,8 +89,8 @@ def _timeline(axes, yloc, interval):
 def make_plot(metadata, scenarios, cfg, provenance):
     """Make figure 3, left graph.
 
-    Multimodel values as line, reference value in black square,
-    steering variables in dark dots.
+    Multimodel values as line, reference value in black square, steering
+    variables in dark dots.
     """
     fig, axes = plt.subplots()
     for member in select_metadata(metadata, variable_group='tas_cmip'):

esmvaltool/diag_scripts/kcs/local_resampling.py

@@ -323,7 +323,7 @@ def select_final_subset(cfg, subsets, prov=None):
     return all_scenarios
 
 
-def _cmip_envelope(datasetlist, variable, target_year):
+def _cmip_envelope(datasetlist, variable, target_year, control_period):
     """Determine the change in <variable> PDF of each CMIP model.
 
     Note: using mf_dataset not possible due to different calendars.
@@ -333,7 +333,8 @@ def _cmip_envelope(datasetlist, variable, target_year):
     ancestors = []
     for data_dict in cmip:
         dataset = xr.open_dataset(data_dict['filename'])[variable]
-        control = dataset.sel(time=slice('1981', '2010'))
+        control = dataset.sel(time=slice(str(control_period[0]),
+                                         str(control_period[1])))
         future = dataset.sel(time=slice(str(target_year -
                                             15), str(target_year + 15)))
 
@@ -437,11 +438,14 @@ def make_plots(cfg, climates):
     # tas/pr and once to get the multimodel pdf of the quantile changes
     metadata = cfg['input_data'].values()
 
-    for year in [2050, 2085]:
+    years = np.unique([scenario['scenario_year']
+                       for scenario in cfg['scenarios'].values()])
+    for year in years:
         fig, subplots = plt.subplots(2, 2, figsize=(12, 8))
 
         for row, variable in zip(subplots, ['pr', 'tas']):
-            cmip, prov = _cmip_envelope(metadata, variable, year)
+            cmip, prov = _cmip_envelope(metadata, variable, year,
+                                        cfg['control_period'])
 
             for axes, season in zip(row, ['DJF', 'JJA']):
                 percentiles = cmip.percentile.values