Generating Climate Patterns from CMIP6 Models#

Overview#

The recipe recipe_climate_patterns generates climate patterns from CMIP6 model datasets.

Note

The regrid setting in the recipe is set to a 2.5x3.75 grid. This is done to match the current resolution in the IMOGEN-JULES model, but can be adjusted with no issues for a finer/coarser patterns grid.

Available recipes and diagnostics#

Recipes are stored in esmvaltool/recipes/

  • recipe_climate_patterns.yml

Diagnostics are stored in esmvaltool/diag_scripts/climate_patterns/

  • climate_patterns.py: generates climate patterns from input datasets

  • sub_functions.py: set of sub functions to assist with driving scripts

  • plotting.py: contains all plotting functions for driving scripts

User settings in recipe#

  1. Script climate_patterns.py

    Required settings for script

    None

    Optional settings for script

    • jules_mode: output jules-specific var names + .nc files

    • parallelise: parallelise over models or not

    • area: calculate the patterns globally, or over land only

    Required settings for variables

    • short_name

    • additional_datasets

    Optional settings for variables

    None

    Required settings for preprocessor

    • monthly_statistics: converts data to mean monthly data

    Optional settings for preprocessor

    • regrid: regrids data

Variables#

  1. Script climate_patterns.py

  • tasmax (atmos, monthly, longitude latitude time)

  • tasmin (atmos, monthly, longitude latitude time)

  • tas (atmos, monthly, longitude latitude time)

  • huss (atmos, monthly, longitude latitude time)

  • pr (atmos, monthly, longitude latitude time)

  • sfcWind (atmos, monthly, longitude latitude time)

  • ps (atmos, monthly, longitude latitude time)

  • rsds (atmos, monthly, longitude latitude time)

  • rlds (atmos, monthly, longitude latitude time)

Observations and reformat scripts#

None

References#

  • Huntingford, C., Cox, P. An analogue model to derive additional climate change scenarios from existing GCM simulations. Climate Dynamics 16, 575–586 (2000). https://doi.org/10.1007/s003820000067

  • Mathison, C. T. et al. A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME). EGUsphere [preprint], (2024). https://doi.org/10.5194/egusphere-2023-2932

Example plots#

../_images/patterns.png

Fig. 40 Patterns generated for CMIP6 models, gridded view. Patterns are shown per variable, for the month of January.#