Climate Scenarios

Working with climate projection data, especially when combining historical and projection experiments, can often be complex. To streamline data selection and avoid manual preprocessing steps, CLIX allows you to select files based on Data Reference Syntax (DRS) elements defined in a YAML configuration file, using the --input argument.

Defining Input with a YAML File

Within the YAML file, you must first specify a root_directory where your simulation data is stored. Following this, you set a DRS standard (supported standards can be listed with pyku.drs.list_drs_standards). Finally, you define various DRS facets and their values to select one or multiple experiments or variables.

Facets can be specified as direct key: value pairs or as key: [list, of, values].

Note

If a facet is not explicitly provided in the YAML file, it defaults to a wildcard character (e.g., version: "*"). This is useful when multiple versions need to be considered.

Below is an example YAML configuration for selecting regional climate projection data from the CMIP5 era:

---

root_dir: /path/to/CORDEX/data/cordex/
standard: cordex
facets:
    product: output
    CORDEX_domain: EUR-11
    institute_id: CLMcom
    driving_model_id: CCCma-CanESM2
    driving_model_ensemble_member: r1i1p1
    driving_experiment_name:
        - historical
        - rcp85
    model_id: CLMcom-CCLM4-8-17
    rcm_version_id: v1
    frequency: day
    version: v20171121
    variable_name: tas

In this example, a single model is selected across two RCP scenarios. All specified facets are evaluated against the cordex standard. The selected NetCDF files are then loaded into a dataframe and merged into a single time series, beginning with the historical simulation files and extending with the RCP8.5 scenario files.

Applying Climate Indicators to Climate Scenarios

To produce a single time series for a full simulation run (combining historical and scenario data) and apply a climate indicator, use the CLIX command with your YAML configuration:

clixrun --input config_CORDEX-CMIP5.yaml --frequency year txge25

This command will result in a yearly time series of summer days (txge25).

Calculating Climate Change Response

Combining input files specified via a YAML configuration with the --anomaly argument is powerful for computing the change in a climate indicator between two distinct time periods.

The following example demonstrates how to compute the change in the diurnal temperature range using the CORDEX-CMIP6 standard and its facets:

---

root_dir: /path/to/CORDEX-CMIP6/
standard: cordex_cmip6
facets:
  project_id: CORDEX-CMIP6
  activity_id: DD
  domain_id: EUR-12
  institution_id: CLMcom-DWD
  driving_source_id: MPI-ESM1-2-HR
  driving_variant_label: r1i1p1f1
  driving_experiment_id:
    - historical
    - ssp370
  source_id: ICON-CLM-202407-1-1
  version_realization: v1-r1
  frequency: day
  version: v20240920
  variable_name:
    - tasmax
    - tasmin

clixrun --input config_CORDEX-CMIP6.yaml --frequency season \
--ref_date_range 19710101 20001231 --date_range 20710101 21001231 \
--anomaly abs dtr

This command will output the absolute change of the diurnal temperature range for the end of the century (2071-2100) compared to the reference period of 1971-2000.

Tip

To determine the necessary facet names for standards not explicitly shown in the examples, use the pyku.drs.get_facets_from_file_parent and pyku.drs.get_facets_from_file_stem functions. These functions require a sample file that adheres to a standard listed in pyku.drs.list_drs_standards.