General model evaluation

Overview

These recipes and diagnostics provide a basic climate model evaluation with observational data. This is especially useful to get an overview of the performance of a simulation. The diagnostics used here allow plotting arbitrary preprocessor output, i.e., arbitrary variables from arbitrary datasets.

Available recipes and diagnostics

Recipes are stored in recipes/model_evaluation

• recipe_model_evaluation_basics.yml

• recipe_model_evaluation_clouds_clim.yml

• recipe_model_evaluation_clouds_cycles.yml

• recipe_model_evaluation_precip_zonal.yml

Diagnostics are stored in diag_scripts/monitor/

• multi_datasets.py: Monitoring diagnostic to show multiple datasets in one plot (incl. biases).

User settings

It is recommended to use a vector graphic file type (e.g., SVG) for the output format when running this recipe, i.e., run the recipe with the command line option --output_file_type=svg or use output_file_type: svg in your User configuration file. Note that map and profile plots are rasterized by default. Use rasterize: false in the recipe to disable this.

Recipe settings

A list of all possible configuration options that can be specified in the recipe is given for each diagnostic individually (see links given for the available diagnostics in the previous section).

Variables

Any, but the variables’ number of dimensions should match the ones expected by each diagnostic (see links given for the available diagnostics in the previous section).

Example plots

Global climatology of 2m near-surface air temperature.

Global climatology of the shortwave cloud radiative effect (SWCRE).

Time series of the global mean top-of-the-atmosphere net radiative flux.

Zonal mean precipitation.

Annual cycle of Southern Ocean total cloud cover.