Lamb Weathertypes#

Overview#

A diagnostic to calculate Lamb weathertypes over a given region. Furthermore, correlations between weathertypes and precipitation patterns over a given area can be calculated and ‘combined’ or ‘simplified’ weathertypes can be derived. Additionally, mean fields, as well as anomalies and standard deviations can be plotted.

Available recipes and diagnostics#

Recipes are stored in esmvaltool/recipes/

  • recipe_weathertyping.yml

Diagnostics are stored in esmvaltool/diag_scripts/weathertyping/

  • weathertyping.py: calculate lamb and simplified WT, plot mean, anomalies and std for each WT for psl, tas, pr

User settings in recipe#

  1. weathertyping.py

    Required settings for script

    Optional settings for script

    • correlation_threshold: correlation threshold for selecting similar WT pairs, only needed if automatic_slwt==True and predefined_slwt==False. default=0.9

    • rmse_threshold: rmse threshold for selecting similar WT pairs, only needed if automatic_slwt==True and predefined_slwt==False. default=0.002

    • plotting: if true, create plots of means, anomalies and std for psl, tas, prcp

    • automatic_slwt: if true, automatically combine WT with similar precipitation patterns over specified area (via thresholds of correlation and rmse OR via predefined_slwt)

    • predefined_slwt: dictionary of mappings between weathertypes

Note

predefined_slwt can be a dictionary where keys are slwt and the values are arrays of lwt OR where keys are lwt and values are slwt

Required settings for variables

Optional settings for variables

Required settings for preprocessor

Optional settings for preprocessor

Color tables

Variables#

  • psl (atmos, day, time longitude latitude)

  • tas (atmos, day, time longitude latitude)

  • tp (atmos, day, time longitude latitude)

  • pr (atmos, day, time longitude latitude)

Observations and reformat scripts#

Note: (1) obs4MIPs data can be used directly without any preprocessing; (2) see headers of reformat scripts for non-obs4MIPs data for download instructions.

This recipe currently only works with the following reanalysis and observation datasets:

  • E-OBS: European Climate Assessment & Dataset gridded daily precipitation sum

  • ERA5: ECMWF reanalysis

References#

  • Maraun, D., Truhetz, H., & Schaffer, A. (2021). Regional climate model biases, their dependence on synoptic circulation biases and the potential for bias adjustment: A process-oriented evaluation of the Austrian regional climate projections. Journal of Geophysical Research: Atmospheres, 126, e2020JD032824. https://doi.org/10.1029/2020JD032824

  • Jones, P.D., Hulme, M. and Briffa, K.R. (1993), A comparison of Lamb circulation types with an objective classification scheme. Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606

Example plots#

../_images/lwt_1_ERA5__psl_mean_1958-2014.png

Fig. 222 PSL mean map of Lamb WT 1 for ERA5.#

../_images/lwt_1_TaiESM1_r1i1p1f1_psl_mean_1950-2014.png

Fig. 223 PSL mean map of Lamb WT 1 for TaiESM1.#

../_images/slwt_EOBS_4_ERA5__psl_mean_1958-2014.png

Fig. 224 PSL mean map of slwt_EOBS 4 for ERA5 (in this case combined Lamb WT 24 and 23).#

../_images/correlation_matrix_E-OBS_1958-2014.png

Fig. 225 Heatmap of correlation values for Lamb WTs 1-27.#

../_images/ERA5__lwt_rel_occurrence_1958-2014.png

Fig. 226 Stackplot of seasonal relative occurrences of each WT for ERA5.#