Skip to content

climate_ref_esmvaltool.diagnostics.cloud_radiative_effects #

CloudRadiativeEffects #

Bases: ESMValToolDiagnostic

Plot climatologies and zonal mean profiles of cloud radiative effects (sw + lw) for a dataset.

Source code in packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/diagnostics/cloud_radiative_effects.py 
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
class CloudRadiativeEffects(ESMValToolDiagnostic):
    """
    Plot climatologies and zonal mean profiles of cloud radiative effects (sw + lw) for a dataset.
    """

    name = "Climatologies and zonal mean profiles of cloud radiative effects"
    slug = "cloud-radiative-effects"
    base_recipe = "ref/recipe_ref_cre.yml"

    variables = (
        "rlut",
        "rlutcs",
        "rsut",
        "rsutcs",
    )
    data_requirements = (
        DataRequirement(
            source_type=SourceDatasetType.CMIP6,
            filters=(
                FacetFilter(
                    facets={
                        "variable_id": variables,
                        "experiment_id": "historical",
                        "table_id": "Amon",
                    }
                ),
            ),
            group_by=("source_id", "member_id", "grid_label"),
            constraints=(
                RequireTimerange(
                    group_by=("instance_id",),
                    start=PartialDateTime(1996, 1),
                    end=PartialDateTime(2014, 12),
                ),
                RequireOverlappingTimerange(group_by=("instance_id",)),
                RequireFacets("variable_id", variables),
                AddSupplementaryDataset.from_defaults("areacella", SourceDatasetType.CMIP6),
            ),
        ),
        # TODO: Use CERES-EBAF, ESACCI-CLOUD, and ISCCP-FH from obs4MIPs once available.
    )

    facets = ()
    series = tuple(
        SeriesDefinition(
            file_pattern=f"plot_profiles/plot/variable_vs_lat_{var_name}_*.nc",
            sel={"dim0": 0},  # Select the model.
            dimensions={"variable_id": var_name, "statistic": "zonal mean"},
            values_name=var_name,
            index_name="lat",
            attributes=[],
        )
        for var_name in ["lwcre", "swcre"]
    ) + tuple(
        SeriesDefinition(
            file_pattern=f"plot_profiles/plot/variable_vs_lat_{var_name}_*.nc",
            sel={"dim0": i},  # Select the observation.
            dimensions={"variable_id": var_name, "statistic": "zonal mean", "reference_source_id": source_id},
            values_name=var_name,
            index_name="lat",
            attributes=[],
        )
        for var_name in ["lwcre", "swcre"]
        for i, source_id in enumerate(
            ["CERES-EBAF-Ed4.2", "ESACCI-CLOUD-AVHRR-AMPM-fv3.0", "ISCCP-FH"], start=1
        )
    )

    @staticmethod
    def update_recipe(recipe: Recipe, input_files: dict[SourceDatasetType, pandas.DataFrame]) -> None:
        """Update the recipe."""
        recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
        recipe_variables = {k: v for k, v in recipe_variables.items() if k != "areacella"}

        datasets = recipe_variables["rsut"]["additional_datasets"]
        for dataset in datasets:
            dataset.pop("timerange")
        recipe["datasets"] = datasets

update_recipe(recipe, input_files) staticmethod #

Update the recipe.

Source code in packages/climate-ref-esmvaltool/src/climate_ref_esmvaltool/diagnostics/cloud_radiative_effects.py 
86
87
88
89
90
91
92
93
94
95
@staticmethod
def update_recipe(recipe: Recipe, input_files: dict[SourceDatasetType, pandas.DataFrame]) -> None:
    """Update the recipe."""
    recipe_variables = dataframe_to_recipe(input_files[SourceDatasetType.CMIP6])
    recipe_variables = {k: v for k, v in recipe_variables.items() if k != "areacella"}

    datasets = recipe_variables["rsut"]["additional_datasets"]
    for dataset in datasets:
        dataset.pop("timerange")
    recipe["datasets"] = datasets