climate_ref_esmvaltool.diagnostics.ecs #

class EquilibriumClimateSensitivity(ESMValToolDiagnostic):
    """
    Calculate the global mean equilibrium climate sensitivity for a dataset.
    """

    name = "Equilibrium Climate Sensitivity"
    slug = "equilibrium-climate-sensitivity"
    base_recipe = "recipe_ecs.yml"

    variables = (
        "rlut",
        "rsdt",
        "rsut",
        "tas",
    )
    experiments = (
        "abrupt-4xCO2",
        "piControl",
    )
    data_requirements = (
        DataRequirement(
            source_type=SourceDatasetType.CMIP6,
            filters=(
                FacetFilter(
                    facets={
                        "variable_id": variables,
                        "experiment_id": experiments,
                        "table_id": "Amon",
                    },
                ),
            ),
            group_by=("source_id", "member_id", "grid_label"),
            constraints=(
                RequireContiguousTimerange(group_by=("instance_id",)),
                RequireOverlappingTimerange(group_by=("instance_id",)),
                RequireFacets(
                    "variable_id",
                    required_facets=variables,
                    group_by=("source_id", "member_id", "grid_label", "experiment_id"),
                ),
                RequireFacets(
                    "experiment_id",
                    required_facets=experiments,
                    group_by=("source_id", "member_id", "grid_label", "variable_id"),
                ),
                AddSupplementaryDataset.from_defaults("areacella", SourceDatasetType.CMIP6),
            ),
        ),
    )
    facets = ("grid_label", "member_id", "source_id", "region", "metric")
    series = (
        SeriesDefinition(
            file_pattern="ecs/calculate/ecs_regression_*.nc",
            dimensions={
                "statistic": ("global annual mean anomaly of rtnt vs tas"),
            },
            values_name="rtnt_anomaly",
            index_name="tas_anomaly",
            attributes=[],
        ),
    )

    @staticmethod
    def update_recipe(
        recipe: Recipe,
        input_files: dict[SourceDatasetType, pandas.DataFrame],
    ) -> None:
        """Update the recipe."""
        # Only run the diagnostic that computes ECS for a single model.
        recipe["diagnostics"] = {
            "ecs": {
                "description": "Calculate ECS.",
                "variables": {
                    "tas": {
                        "preprocessor": "spatial_mean",
                    },
                    "rtnt": {
                        "preprocessor": "spatial_mean",
                        "derive": True,
                    },
                },
                "scripts": {
                    "calculate": {
                        "script": "climate_metrics/ecs.py",
                        "calculate_mmm": False,
                    },
                },
            },
        }

        # Prepare updated datasets section in recipe. It contains two
        # datasets, one for the "abrupt-4xCO2" and one for the "piControl"
        # experiment.
        recipe_variables = dataframe_to_recipe(
            input_files[SourceDatasetType.CMIP6],
            equalize_timerange=True,
        )
        recipe["datasets"] = recipe_variables["tas"]["additional_datasets"]

        # Remove keys from the recipe that are only used for YAML anchors
        keys_to_remove = [
            "CMIP5_RTMT",
            "CMIP6_RTMT",
            "CMIP5_RTNT",
            "CMIP6_RTNT",
            "ECS_SCRIPT",
            "SCATTERPLOT",
        ]
        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."""
        ecs_ds = xarray.open_dataset(result_dir / "work" / "ecs" / "calculate" / "ecs.nc")
        ecs = float(fillvalues_to_nan(ecs_ds["ecs"].values)[0])
        lambda_ds = xarray.open_dataset(result_dir / "work" / "ecs" / "calculate" / "lambda.nc")
        lambda_ = float(fillvalues_to_nan(lambda_ds["lambda"].values)[0])

        # Update the diagnostic bundle arguments with the computed diagnostics.
        metric_args[MetricCV.DIMENSIONS.value] = {
            MetricCV.JSON_STRUCTURE.value: [
                "region",
                "metric",
            ],
            "region": {"global": {}},
            "metric": {"ecs": {}, "lambda": {}},
        }
        metric_args[MetricCV.RESULTS.value] = {
            "global": {
                "ecs": ecs,
                "lambda": lambda_,
            },
        }

        return CMECMetric.model_validate(metric_args), CMECOutput.model_validate(output_args)