climate_ref_esmvaltool.diagnostics.tcre #

class TransientClimateResponseEmissions(ESMValToolDiagnostic):
    """
    Calculate the global mean Transient Climate Response to Cumulative CO2 Emissions.
    """

    name = "Transient Climate Response to Cumulative CO2 Emissions"
    slug = "transient-climate-response-emissions"
    base_recipe = "recipe_tcre.yml"

    variables = (
        "tas",
        "fco2antt",
    )
    data_requirements = (
        DataRequirement(
            source_type=SourceDatasetType.CMIP6,
            filters=(
                FacetFilter(
                    facets={
                        "variable_id": variables,
                        "experiment_id": "esm-1pctCO2",
                        "table_id": "Amon",
                    },
                ),
                FacetFilter(
                    facets={
                        "variable_id": "tas",
                        "experiment_id": "esm-piControl",
                        "table_id": "Amon",
                    },
                ),
            ),
            group_by=("source_id", "member_id", "grid_label"),
            constraints=(
                RequireContiguousTimerange(group_by=("instance_id",)),
                RequireOverlappingTimerange(group_by=("instance_id",)),
                RequireFacets("experiment_id", ("esm-1pctCO2", "esm-piControl")),
                RequireFacets("variable_id", variables),
                AddSupplementaryDataset.from_defaults("areacella", SourceDatasetType.CMIP6),
            ),
        ),
    )
    facets = ("grid_label", "member_id", "source_id", "region", "metric")
    # TODO: the ESMValTool diagnostic script does not save the data for the timeseries.
    series = tuple()

    @staticmethod
    def update_recipe(
        recipe: Recipe,
        input_files: dict[SourceDatasetType, pandas.DataFrame],
    ) -> None:
        """Update the recipe."""
        # Prepare updated datasets section in recipe. It contains three
        # datasets, "tas" and "fco2antt" for the "esm-1pctCO2" and just "tas"
        # for the "esm-piControl" experiment.
        recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
        tas_esm_1pctCO2 = next(
            ds for ds in recipe_variables["tas"]["additional_datasets"] if ds["exp"] == "esm-1pctCO2"
        )
        fco2antt_esm_1pctCO2 = next(
            ds for ds in recipe_variables["fco2antt"]["additional_datasets"] if ds["exp"] == "esm-1pctCO2"
        )
        tas_esm_piControl = next(
            ds for ds in recipe_variables["tas"]["additional_datasets"] if ds["exp"] == "esm-piControl"
        )
        tas_esm_piControl["timerange"] = tas_esm_1pctCO2["timerange"]

        recipe["diagnostics"]["tcre"]["variables"] = {
            "tas_esm-1pctCO2": {
                "short_name": "tas",
                "preprocessor": "global_annual_mean_anomaly",
                "additional_datasets": [tas_esm_1pctCO2],
            },
            "tas_esm-piControl": {
                "short_name": "tas",
                "preprocessor": "global_annual_mean_anomaly",
                "additional_datasets": [tas_esm_piControl],
            },
            "fco2antt": {
                "preprocessor": "global_cumulative_sum",
                "additional_datasets": [fco2antt_esm_1pctCO2],
            },
        }
        recipe["diagnostics"].pop("barplot")

        # Update descriptions.
        dataset = tas_esm_1pctCO2["dataset"]
        ensemble = tas_esm_1pctCO2["ensemble"]
        settings = recipe["diagnostics"]["tcre"]["scripts"]["calculate_tcre"]
        settings["caption"] = (
            settings["caption"].replace("MPI-ESM1-2-LR", dataset).replace("r1i1p1f1", ensemble)
        )
        settings["pyplot_kwargs"]["title"] = (
            settings["pyplot_kwargs"]["title"].replace("MPI-ESM1-2-LR", dataset).replace("r1i1p1f1", ensemble)
        )

    @staticmethod
    def format_result(
        result_dir: Path,
        execution_dataset: ExecutionDatasetCollection,
        metric_args: MetricBundleArgs,
        output_args: OutputBundleArgs,
    ) -> tuple[CMECMetric, CMECOutput]:
        """Format the result."""
        tcre_ds = xarray.open_dataset(result_dir / "work" / "tcre" / "calculate_tcre" / "tcre.nc")
        tcre = float(fillvalues_to_nan(tcre_ds["tcre"].values)[0])

        # Update the diagnostic bundle arguments with the computed diagnostics.
        metric_args[MetricCV.DIMENSIONS.value] = {
            "json_structure": [
                "region",
                "metric",
            ],
            "region": {"global": {}},
            "metric": {"tcre": {}},
        }
        metric_args[MetricCV.RESULTS.value] = {
            "global": {
                "tcre": tcre,
            },
        }
        return CMECMetric.model_validate(metric_args), CMECOutput.model_validate(output_args)