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In progress: #4220

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130 changes: 49 additions & 81 deletions esmvaltool/diag_scripts/seaice/seaice_sensitivity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import iris
import matplotlib.pyplot as plt
import pandas as pd
import yaml
from matplotlib.colors import Normalize
from scipy import stats

Expand Down Expand Up @@ -37,13 +38,24 @@ def get_provenance_record(cfg, caption):
return record


def list_datasets(data):
"""Actually returns a set of datatsets, to avoid duplication."""
logger.debug("listing datasets")
datasets = set()
def create_categorised_dataset_dict(data):
"""Create a dictionary of datasets categorised into models and observations."""
logger.debug("Creating categorised dataset dictionary from %s", data)
# Initialise blank dictionary
dict = {
'models': {},
'observations': {},
}

# Iterate over the data
for element in data:
datasets.add(element["dataset"])
return datasets
if 'observation' in element:
if element['observation']:
dict['observations'][element['dataset']] = {}
else:
dict['models'][element['dataset']] = {}

return dict


def extract_cube(data, variable_group):
Expand Down Expand Up @@ -142,28 +154,6 @@ def write_obs_from_cfg(cfg):
obs_dict["notz_style"]["std_dev"] = notz_values["standard deviation"]
obs_dict["notz_style"]["plausible"] = notz_values["plausible range"]

# Add a blank dictionary for the Roach-style plot
obs_dict["roach_style"] = {}

# Add each observation point to the dictionary
roach_values = cfg["observations"]["annual trends (Roach-style plot)"]
for point in roach_values.keys():
obs_dict["roach_style"][point] = {}

# Add the individual values for the observation point
obs_dict["roach_style"][point]["annual_tas_trend"] = roach_values[
point
]["GMST trend"]
obs_dict["roach_style"][point]["annual_siconc_trend"] = roach_values[
point
]["SIA trend"]
obs_dict["roach_style"][point]["r_value"] = roach_values[point][
"Pearson CC of SIA over GMST"
]
obs_dict["roach_style"][point]["p_value"] = roach_values[point][
"significance of SIA over GMST"
]

return obs_dict


Expand Down Expand Up @@ -208,7 +198,7 @@ def write_dictionary_to_csv(cfg, model_dict, filename):
csv_filepath = f"{cfg['work_dir']}/{filename}.csv"

# Write the data to a csv file (via a Pandas DataFrame)
pd.DataFrame.from_dict(model_dict, orient="index").to_csv(csv_filepath)
pd.DataFrame.from_dict(model_dict, orient="index").to_csv(csv_filepath, index_label="Dataset")
logger.info("Wrote data to %s", csv_filepath)

# Create a provenance record for the csv file
Expand Down Expand Up @@ -349,54 +339,36 @@ def roach_style_plot_from_dict(data_dictionary, titles_dictionary, cfg):
if inner_dict["label"] == "to_label":
plt.annotate(dataset, xy=(x, y), xytext=(x + 0.01, y - 0.005))

# Read from observations dictionary
obs_years = titles_dictionary["obs"]["obs_period"]
obs_dict = titles_dictionary["obs"]["roach_style"]

# Add the observations
for point in obs_dict.keys():
# Get the values for the point
x = obs_dict[point]["annual_tas_trend"]
y = obs_dict[point]["annual_siconc_trend"]
r_corr = obs_dict[point]["r_value"]
p_val = obs_dict[point]["p_value"]

# Provide a default colour for the point if Pearson coefficient is missing
if r_corr is None:
r_corr = 0

# Provide a pattern for the point if the p-value is present and sufficiently large
if p_val is not None and p_val >= 0.05:
h = 5 * "/" # This is a hatch pattern
else:
h = None

# Plot the point only if both x and y values are provided
if x is not None and y is not None:
plt.scatter(
x,
y,
marker="s",
s=150,
c=[r_corr],
hatch=h,
cmap=cmap,
norm=norm,
zorder=0,
edgecolors="black",
)
# # Provide a default colour for the point if Pearson coefficient is missing
# if r_corr is None:
# r_corr = 0
#
# # Provide a pattern for the point if the p-value is present and sufficiently large
# if p_val is not None and p_val >= 0.05:
# h = 5 * "/" # This is a hatch pattern
# else:
# h = None
#
# # Plot the point only if both x and y values are provided
# if x is not None and y is not None:
# plt.scatter(
# x,
# y,
# marker="s",
# s=150,
# c=[r_corr],
# hatch=h,
# cmap=cmap,
# norm=norm,
# zorder=0,
# edgecolors="black",
# )

# Add a colour bar
plt.colorbar(label="Pearson correlation coefficient")

# Create caption based on whether observational temp trend is present
if obs_dict["first point"]["annual_tas_trend"] is not None:
caption = (
"Decadal trends of sea ice area and global mean temperature."
f"Observations from {obs_years} are plotted as squares."
)
else:
caption = "Decadal trends of sea ice area and global mean temperature."
# Create caption
caption = "Decadal trends of sea ice area and global mean temperature."

# Save the figure (also closes it)
save_figure(
Expand All @@ -419,22 +391,18 @@ def main(cfg):
titles_and_obs_dict = create_titles_dict(input_data, cfg)
logger.debug("Titles and observations dictionary: %s", titles_and_obs_dict)

# Initialize blank data dictionary to send to plotting codes later
data_dict = {}

# Get list of datasets from cfg
logger.info("Listing datasets in the data")
datasets = list_datasets(input_data)
dataset_dict = create_categorised_dataset_dict(input_data)
data_dict = dataset_dict['models']


# Iterate over each dataset
for dataset in datasets:
for dataset in data_dict.keys():
# Select only data from that dataset
logger.debug("Selecting data from %s", dataset)
selection = select_metadata(input_data, dataset=dataset)

# Add the dataset to the dictionary with a blank inner dictionary
data_dict[dataset] = {}

# Add an entry to determine labelling in plots
if "label_dataset" in selection[0]:
data_dict[dataset]["label"] = "to_label"
Expand Down
107 changes: 57 additions & 50 deletions esmvaltool/recipes/recipe_seaice_sensitivity.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -21,24 +21,33 @@ documentation:
maintainer:
- parsons_naomi

defaults: &defaults {ensemble: r1i1p1f1, exp: historical, grid: gn, project: CMIP6}
datasets:
- {<<: *defaults, dataset: HadGEM3-GC31-LL, institute: MOHC, ensemble: r1i1p1f3, label_dataset: True}
- {<<: *defaults, dataset: UKESM1-0-LL, institute: MOHC, ensemble: r1i1p1f2, label_dataset: True}
- {<<: *defaults, dataset: ACCESS-CM2, institute: CSIRO-ARCCSS}
- {<<: *defaults, dataset: ACCESS-ESM1-5, institute: CSIRO}
- {<<: *defaults, dataset: BCC-CSM2-MR, institute: BCC}
- {<<: *defaults, dataset: CAMS-CSM1-0, institute: CAMS}
- {<<: *defaults, dataset: CanESM5, institute: CCCma}
- {<<: *defaults, dataset: CESM2, institute: NCAR}
- {<<: *defaults, dataset: CESM2-WACCM, institute: NCAR}
- {<<: *defaults, dataset: CESM2-WACCM-FV2, institute: NCAR}
- {<<: *defaults, dataset: FIO-ESM-2-0, institute: FIO-QLNM}
- {<<: *defaults, dataset: MIROC6, institute: MIROC}
- {<<: *defaults, dataset: MPI-ESM-1-2-HAM, institute: HAMMOZ-Consortium}
- {<<: *defaults, dataset: MPI-ESM1-2-HR, institute: MPI-M}
- {<<: *defaults, dataset: MPI-ESM1-2-LR, institute: MPI-M}
- {<<: *defaults, dataset: MRI-ESM2-0, institute: MRI}
model_defaults: &model_defaults { ensemble: r1i1p1f1, exp: historical, grid: gn, project: CMIP6}
model_datasets: &model_datasets
- {<<: *model_defaults, dataset: HadGEM3-GC31-LL, institute: MOHC, ensemble: r1i1p1f3, label_dataset: True}
- {<<: *model_defaults, dataset: UKESM1-0-LL, institute: MOHC, ensemble: r1i1p1f2, label_dataset: True}
- {<<: *model_defaults, dataset: ACCESS-CM2, institute: CSIRO-ARCCSS}
- {<<: *model_defaults, dataset: ACCESS-ESM1-5, institute: CSIRO}
- {<<: *model_defaults, dataset: BCC-CSM2-MR, institute: BCC}
- {<<: *model_defaults, dataset: CAMS-CSM1-0, institute: CAMS}
- {<<: *model_defaults, dataset: CanESM5, institute: CCCma}
- {<<: *model_defaults, dataset: CESM2, institute: NCAR}
- {<<: *model_defaults, dataset: CESM2-WACCM, institute: NCAR}
- {<<: *model_defaults, dataset: CESM2-WACCM-FV2, institute: NCAR}
- {<<: *model_defaults, dataset: FIO-ESM-2-0, institute: FIO-QLNM}
- {<<: *model_defaults, dataset: MIROC6, institute: MIROC}
- {<<: *model_defaults, dataset: MPI-ESM-1-2-HAM, institute: HAMMOZ-Consortium}
- {<<: *model_defaults, dataset: MPI-ESM1-2-HR, institute: MPI-M}
- {<<: *model_defaults, dataset: MPI-ESM1-2-LR, institute: MPI-M}
- {<<: *model_defaults, dataset: MRI-ESM2-0, institute: MRI}

obs_defaults: &obs_defaults { project: OBS, observation: True, tier: 2}
tasa_obs: &tasa_obs
- { <<: *obs_defaults, dataset: GISTEMP, type: ground, version: v4 }
- { <<: *obs_defaults, dataset: HadCRUT5, type: ground, version: 5.0.1.0-analysis }
- { <<: *obs_defaults, dataset: NOAAGlobalTemp, type: ground, version: v5.0.0, mip: Amon }

arctic_si_obs: &arctic_si_obs
- { <<: *obs_defaults, dataset: OSI-450-nh, type: reanaly, version: v3, mip: OImon, supplementary_variables: [{short_name: areacello, mip: fx}]}

preprocessors:
extract_test_period:
Expand Down Expand Up @@ -112,9 +121,24 @@ diagnostics:
siconc:
preprocessor: pp_arctic_sept_sea_ice
mip: SImon
additional_datasets:
*model_datasets
tas:
preprocessor: pp_avg_ann_global_temp
mip: Amon
additional_datasets:
*model_datasets
si_obs:
short_name: siconc
preprocessor: pp_arctic_sept_sea_ice
additional_datasets:
*arctic_si_obs
tasa_obs:
short_name: tasa
preprocessor: pp_avg_ann_global_temp
mip: Amon
additional_datasets:
*tasa_obs
scripts:
sea_ice_sensitivity_script:
script: seaice/seaice_sensitivity.py
Expand All @@ -124,22 +148,6 @@ diagnostics:
mean: -4.01
standard deviation: 0.32
plausible range: 1.28
annual trends (Roach-style plot):
first point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST:
second point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST:
third point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST:


antarctic:
Expand All @@ -148,9 +156,24 @@ diagnostics:
siconc:
preprocessor: pp_antarctic_avg_ann_sea_ice
mip: SImon
additional_datasets:
*model_datasets
tas:
preprocessor: pp_avg_ann_global_temp
mip: Amon
additional_datasets:
*model_datasets
# si_obs:
# short_name: siconc
# preprocessor: pp_antarctic_avg_ann_sea_ice
# additional_datasets:
# *antarctic_si_obs
tasa_obs:
short_name: tasa
preprocessor: pp_avg_ann_global_temp
mip: Amon
additional_datasets:
*tasa_obs
scripts:
sea_ice_sensitivity_script:
script: seaice/seaice_sensitivity.py
Expand All @@ -160,19 +183,3 @@ diagnostics:
mean:
standard deviation:
plausible range:
annual trends (Roach-style plot):
first point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST:
second point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST:
third point:
GMST trend:
SIA trend:
Pearson CC of SIA over GMST:
significance of SIA over GMST: