class TransientClimateResponse(ESMValToolDiagnostic):
"""
Calculate the global mean transient climate response for a dataset.
"""
name = "Transient Climate Response"
slug = "transient-climate-response"
base_recipe = "recipe_tcr.yml"
experiments = (
"1pctCO2",
"piControl",
)
data_requirements = (
(
DataRequirement(
source_type=SourceDatasetType.CMIP6,
filters=(
FacetFilter(
facets={
"variable_id": ("tas",),
"experiment_id": "1pctCO2",
"table_id": "Amon",
},
),
),
group_by=("source_id", "member_id", "grid_label"),
constraints=(
AddParentDataset.from_defaults(SourceDatasetType.CMIP6),
RequireContiguousTimerange(group_by=("instance_id",)),
AddSupplementaryDataset.from_defaults("areacella", SourceDatasetType.CMIP6),
),
),
),
(
DataRequirement(
source_type=SourceDatasetType.CMIP7,
filters=(
FacetFilter(
facets={
"branded_variable": "tas_tavg-h2m-hxy-u",
"experiment_id": experiments,
"frequency": "mon",
"region": "glb",
},
),
),
group_by=("source_id", "variant_label", "grid_label"),
constraints=(
RequireContiguousTimerange(group_by=("instance_id",)),
RequireFacets("experiment_id", experiments),
AddSupplementaryDataset.from_defaults("areacella", SourceDatasetType.CMIP7),
),
),
),
)
facets = ("grid_label", "member_id", "variant_label", "source_id", "region", "metric")
series = (
SeriesDefinition(
file_pattern="tcr/calculate/{source_id}*.nc",
dimensions={
"statistic": "global annual mean tas anomaly relative to linear fit of piControl run",
},
values_name="tas_anomaly",
index_name="time",
attributes=[],
),
)
files = (
FileDefinition(
file_pattern="plots/tcr/calculate/*.png",
dimensions={
"statistic": "global annual mean tas anomaly relative to linear fit of piControl run",
},
),
FileDefinition(
file_pattern="work/tcr/calculate/tcr.nc",
dimensions={"metric": "tcr"},
),
)
test_data_spec = TestDataSpecification(
test_cases=(
TestCase(
name="cmip6",
description="Test with CMIP6 data.",
requests=(
CMIP6Request(
slug="cmip6",
facets={
"experiment_id": ["1pctCO2", "piControl"],
"source_id": "CanESM5",
"variable_id": ["areacella", "tas"],
"frequency": ["fx", "mon"],
},
remove_ensembles=True,
),
),
),
TestCase(
name="cmip7",
description="Test with CMIP7 data.",
requests=(
CMIP7Request(
slug="cmip7",
facets={
"experiment_id": ["1pctCO2", "piControl"],
"source_id": "CanESM5",
"variable_id": ["areacella", "tas"],
"branded_variable": [
"areacella_ti-u-hxy-u",
"tas_tavg-h2m-hxy-u",
],
"variant_label": "r1i1p1f1",
"frequency": ["fx", "mon"],
"region": "glb",
},
remove_ensembles=True,
),
),
),
)
)
@staticmethod
def update_recipe(
recipe: Recipe,
input_files: dict[SourceDatasetType, pandas.DataFrame],
) -> None:
"""Update the recipe."""
# Only run the diagnostic that computes TCR for a single model.
recipe["diagnostics"] = {
"tcr": {
"description": "Calculate TCR.",
"variables": {
"tas": {
"preprocessor": "spatial_mean",
},
},
"scripts": {
"calculate": {
"script": "climate_metrics/tcr.py",
"calculate_mmm": False,
},
},
},
}
# Prepare updated datasets section in recipe. It contains two
# datasets, one for the "1pctCO2" and one for the "piControl"
# experiment.
cmip_source = get_cmip_source_type(input_files)
df = input_files[cmip_source]
recipe["datasets"] = get_child_and_parent_dataset(
df[df.variable_id == "tas"],
parent_experiment="piControl",
child_duration_in_years=140,
parent_offset_in_years=0,
parent_duration_in_years=140,
)
# Remove keys from the recipe that are only used for YAML anchors
keys_to_remove = [
"TCR",
"SCATTERPLOT",
"VAR_SETTING",
]
for key in keys_to_remove:
recipe.pop(key, None)
@staticmethod
def format_result(
result_dir: Path,
execution_dataset: ExecutionDatasetCollection,
metric_args: MetricBundleArgs,
output_args: OutputBundleArgs,
) -> tuple[CMECMetric, CMECOutput]:
"""Format the result."""
tcr_ds = xarray.open_dataset(result_dir / "work" / "tcr" / "calculate" / "tcr.nc")
tcr = float(fillvalues_to_nan(tcr_ds["tcr"].values)[0])
# Update the diagnostic bundle arguments with the computed diagnostics.
metric_args[MetricCV.DIMENSIONS.value] = {
"json_structure": [
"region",
"metric",
],
"region": {"global": {}},
"metric": {"tcr": {}},
}
metric_args[MetricCV.RESULTS.value] = {
"global": {
"tcr": tcr,
},
}
return CMECMetric.model_validate(metric_args), CMECOutput.model_validate(output_args)