Recipe - Clouds - evaluate cloud climatologies from CMIP models

Implementation of [Clouds diagnostics recipe ](https://docs.esmvaltool.org/en/latest/recipes/recipe_clouds.html)

Variables referencing each missing dataset:

CALIPSO-ICECLOUD:
variable `cli` in: recipe_lauer22jclim_fig3-4_zonal.yml, recipe_lauer22jclim_fig5_lifrac.yml

CLARA-AVHRR: (not used as reference_dataset in any recipe)

CLOUDSAT-L2:
variable `clw` in: recipe_lauer22jclim_fig3-4_zonal.yml, recipe_lauer22jclim_fig5_lifrac.yml

ERA5:
variable `cl` in: recipe_lauer22jclim_fig3-4_zonal.yml
variable `ta` in: recipe_lauer22jclim_fig5_lifrac.yml
variable `ts` in: recipe_lauer22jclim_fig8_dyn.yml
variable `wap` in: recipe_lauer22jclim_fig8_dyn.yml

ERA-Interim:
variable `tas` in: recipe_clouds_bias.yml

ESACCI-WATERVAPOUR:
variable `prw` in: recipe_lauer22jclim_fig1_clim.yml, recipe_lauer22jclim_fig1_clim_amip.yml, recipe_lauer22jclim_fig2_taylor.yml, recipe_lauer22jclim_fig2_taylor_amip.yml, recipe_lauer22jclim_fig6_interannual.yml, recipe_lauer22jclim_fig7_seas.yml

MAC-LWP: (not used as reference_dataset in any recipe)

UWisc:
variable `lwp` in: recipe_lauer13jclim.yml

Start with the linked Clouds diagnostics recipe and inspect the listed recipe_lauer22jclim_*.yml, recipe_clouds_bias.yml, and recipe_lauer13jclim.yml files. Trace each missing reference dataset and variable, then run the affected recipes to confirm that the cloud climatologies evaluate successfully for all listed datasets.

Written by the indexing model from the issue text.