Add ESMValTool Equilibrium Climate Sensitivity recipe

changelog/51.feature.md

@@ -0,0 +1 @@
+Added Equilibrium Climate Sensitivity (ECS) to the ESMValTool metrics package.

packages/ref-metrics-esmvaltool/src/ref_metrics_esmvaltool/__init__.py

@@ -2,15 +2,12 @@
 Rapid evaluating CMIP data with ESMValTool.
 """
 
-import importlib.metadata
-
 from ref_core.providers import MetricsProvider
 
-from ref_metrics_esmvaltool.example import GlobalMeanTimeseries
-
-__version__ = importlib.metadata.version("ref_metrics_esmvaltool")
-__core_version__ = importlib.metadata.version("ref_core")
+from ref_metrics_esmvaltool._version import __version__
+from ref_metrics_esmvaltool.metrics import EquilibriumClimateSensitivity, GlobalMeanTimeseries
 
 # Initialise the metrics manager and register the example metric
 provider = MetricsProvider("ESMValTool", __version__)
 provider.register(GlobalMeanTimeseries())
+provider.register(EquilibriumClimateSensitivity())

packages/ref-metrics-esmvaltool/src/ref_metrics_esmvaltool/_version.py

@@ -0,0 +1,3 @@
+import importlib
+
+__version__ = importlib.metadata.version("ref_metrics_esmvaltool")

packages/ref-metrics-esmvaltool/src/ref_metrics_esmvaltool/metrics/__init__.py

@@ -0,0 +1,9 @@
+"""ESMValTool metrics."""
+
+from ref_metrics_esmvaltool.metrics.ecs import EquilibriumClimateSensitivity
+from ref_metrics_esmvaltool.metrics.example import GlobalMeanTimeseries
+
+__all__ = [
+    "EquilibriumClimateSensitivity",
+    "GlobalMeanTimeseries",
+]

packages/ref-metrics-esmvaltool/src/ref_metrics_esmvaltool/metrics/base.py

@@ -0,0 +1,68 @@
+from abc import abstractmethod
+from pathlib import Path
+from typing import ClassVar
+
+import pandas
+from ref_core.datasets import SourceDatasetType
+from ref_core.metrics import Metric, MetricExecutionDefinition, MetricResult
+
+from ref_metrics_esmvaltool.recipe import load_recipe, run_recipe
+from ref_metrics_esmvaltool.types import OutputBundle, Recipe
+
+
+class ESMValToolMetric(Metric):
+    """ESMValTool Metric base class."""
+
+    base_recipe: ClassVar
+
+    @staticmethod
+    @abstractmethod
+    def update_recipe(recipe: Recipe, input_files: pandas.DataFrame) -> None:
+        """
+        Update the base recipe for the run.
+
+        Parameters
+        ----------
+        recipe:
+            The base recipe to update.
+        input_files:
+            The dataframe describing the input files.
+
+        """
+
+    @staticmethod
+    @abstractmethod
+    def format_result(result_dir: Path) -> OutputBundle:
+        """
+        Create a CMEC output bundle for the results.
+
+        Parameters
+        ----------
+        result_dir
+            Directory containing results from an ESMValTool run.
+
+        Returns
+        -------
+            A CMEC output bundle.
+        """
+
+    def run(self, definition: MetricExecutionDefinition) -> MetricResult:
+        """
+        Run a metric
+
+        Parameters
+        ----------
+        definition
+            A description of the information needed for this execution of the metric
+
+        Returns
+        -------
+        :
+            The result of running the metric.
+        """
+        input_files = definition.metric_dataset[SourceDatasetType.CMIP6].datasets
+        recipe = load_recipe(self.base_recipe)
+        self.update_recipe(recipe, input_files)
+        result_dir = run_recipe(recipe, definition)
+        output_bundle = self.format_result(result_dir)
+        return MetricResult.build_from_output_bundle(definition, output_bundle)

packages/ref-metrics-esmvaltool/src/ref_metrics_esmvaltool/metrics/ecs.py

@@ -0,0 +1,126 @@
+from pathlib import Path
+
+import pandas
+import xarray
+from ref_core.datasets import FacetFilter, SourceDatasetType
+from ref_core.metrics import DataRequirement
+
+from ref_metrics_esmvaltool._version import __version__
+from ref_metrics_esmvaltool.metrics.base import ESMValToolMetric
+from ref_metrics_esmvaltool.recipe import dataframe_to_recipe
+from ref_metrics_esmvaltool.types import OutputBundle, Recipe
+
+
+class EquilibriumClimateSensitivity(ESMValToolMetric):
+    """
+    Calculate the global mean equilibrium climate sensitivity for a dataset.
+    """
+
+    name = "Equilibrium Climate Sensitivity"
+    slug = "esmvaltool-equilibrium-climate-sensitivity"
+    base_recipe = "recipe_ecs.yml"
+
+    data_requirements = (
+        DataRequirement(
+            source_type=SourceDatasetType.CMIP6,
+            filters=(
+                FacetFilter(
+                    facets={
+                        "variable_id": (
+                            "rlut",
+                            "rsdt",
+                            "rsut",
+                            "tas",
+                        ),
+                        "experiment_id": (
+                            "abrupt-4xCO2",
+                            "piControl",
+                        ),
+                    },
+                ),
+            ),
+            # TODO: Select only datasets that have both experiments and all four variables
+            # TODO: Select only datasets that have a contiguous, shared timerange
+            # TODO: Add cell areas to the groups
+            # constraints=(AddCellAreas(),),
+            group_by=("source_id", "variant_label"),
+        ),
+    )
+
+    @staticmethod
+    def update_recipe(recipe: Recipe, input_files: pandas.DataFrame) -> None:
+        """Update the recipe."""
+        # Only run the diagnostic that computes ECS for a single model.
+        recipe["diagnostics"] = {
+            "cmip6": {
+                "description": "Calculate ECS.",
+                "variables": {
+                    "tas": {
+                        "preprocessor": "spatial_mean",
+                    },
+                    "rtnt": {
+                        "preprocessor": "spatial_mean",
+                        "derive": True,
+                    },
+                },
+                "scripts": {
+                    "ecs": {
+                        "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)
+
+        # Select a timerange covered by all datasets.
+        start_times, end_times = [], []
+        for variable in recipe_variables.values():
+            for dataset in variable["additional_datasets"]:
+                start, end = dataset["timerange"].split("/")
+                start_times.append(start)
+                end_times.append(end)
+        timerange = f"{max(start_times)}/{min(end_times)}"
+
+        datasets = recipe_variables["tas"]["additional_datasets"]
+        for dataset in datasets:
+            dataset["timerange"] = timerange
+
+        recipe["datasets"] = datasets
+
+    @staticmethod
+    def format_result(result_dir: Path) -> OutputBundle:
+        """Format the result."""
+        ecs_file = result_dir / "work/cmip6/ecs/ecs.nc"
+        ecs = xarray.open_dataset(ecs_file)
+
+        source_id = ecs.dataset.values[0].decode("utf-8")
+        cmec_output = {
+            "DIMENSIONS": {
+                "dimensions": {
+                    "source_id": {source_id: {}},
+                    "region": {"global": {}},
+                    "variable": {"ecs": {}},
+                },
+                "json_structure": [
+                    "model",
+                    "region",
+                    "statistic",
+                ],
+            },
+            # Is the schema tracked?
+            "SCHEMA": {
+                "name": "CMEC-REF",
+                "package": "ref_metrics_esmvaltool",
+                "version": __version__,
+            },
+            "RESULTS": {
+                source_id: {"global": {"ecs": ecs.ecs.values[0]}},
+            },
+        }
+
+        return cmec_output