General model evaluation#
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
Diagnostics are stored in diag_scripts/monitor/
multi_datasets.py: Monitoring diagnostic to show multiple datasets in one plot (incl. biases).
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
--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.
rasterize: false in the recipe to disable
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).
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).
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.