Emergent constraints for equilibrium climate sensitivity

Overview

Calculates equilibrium climate sensitivity (ECS) versus

  1. S index, D index and lower tropospheric mixing index (LTMI); similar to fig. 5 from Sherwood et al. (2014)

  2. southern ITCZ index and tropical mid-tropospheric humidity asymmetry index; similar to fig. 2 and 4 from Tian (2015)

  3. covariance of shortwave cloud reflection (Brient and Schneider, 2016)

  4. climatological Hadley cell extent (Lipat et al., 2017)

  5. temperature variability metric; similar to fig. 2 from Cox et al. (2018)

  6. total cloud fraction difference between tropics and mid-latitudes; similar to fig. 3 from Volodin (2008)

  7. response of marine boundary layer cloud (MBLC) fraction changes to sea surface temperature (SST); similar to fig. 3 of Zhai et al. (2015)

  8. Cloud shallowness index (Brient et al., 2016)

  9. Error in vertically-resolved tropospheric zonal average relative humidity (Su et al., 2014)

The results are displayed as scatterplots.

Note

The recipe recipe_ecs_scatter.yml requires pre-calulation of the equilibrium climate sensitivites (ECS) for all models. The ECS values are calculated with recipe_ecs.yml. The netcdf file containing the ECS values (path and filename) is specified by diag_script_info@ecs_file. Alternatively, the netcdf file containing the ECS values can be generated with the cdl-script $diag_scripts/emergent_constraints/ecs_cmip.cdl (recommended method):

  1. save script given at the end of this recipe as ecs_cmip.cdl

  2. run command: ncgen -o ecs_cmip.nc ecs_cmip.cdl

  3. copy ecs_cmip.nc to directory given by diag_script_info@ecs_file (e.g. $diag_scripts/emergent_constraints/ecs_cmip.nc)

Available recipes and diagnostics

Recipes are stored in recipes/

  • recipe_ecs_scatter.yml

  • recipe_ecs_constraints.yml

Diagnostics are stored in diag_scripts

  • emergent_constraints/ecs_scatter.ncl: calculate emergent constraints for ECS

  • emergent_constraints/ecs_scatter.py: calculate further emergent constraints for ECS

  • emergent_constraints/single_constraint.py: create scatterplots for emergent constraints

  • climate_metrics/psi.py: calculate temperature variabililty metric (Cox et al., 2018)

User settings in recipe

  • Script emergent_constraints/ecs_scatter.ncl

    Required settings (scripts)

    • diag: emergent constraint to calculate (“itczidx”, “humidx”, “ltmi”, “covrefl”, “shhc”, “sherwood_d”, “sherwood_s”)

    • ecs_file: path and filename of netCDF containing precalculated ECS values (see note above)

    Optional settings (scripts)

    • calcmm: calculate multi-model mean (True, False)

    • legend_outside: plot legend outside of scatterplots (True, False)

    • output_diag_only: Only write netcdf files for X axis (True) or write all plots (False)

    • output_models_only: Only write models (no reference datasets) to netcdf files (True, False)

    • output_attributes: Additonal attributes for all output netcdf files

    • predef_minmax: use predefined internal min/max values for axes (True, False)

    • styleset: “CMIP5” (if not set, diagnostic will create a color table and symbols for plotting)

    • suffix: string to add to output filenames (e.g.”cmip3”)

    Required settings (variables)

    • reference_dataset: name of reference data set

    Optional settings (variables)

    none

    Color tables

    none

  • Script emergent_constraints/ecs_scatter.py

    See here.

  • Script emergent_constraints/single_constraint.py

    See here.

  • Script climate_metrics/psi.py

    See here.

Variables

  • cl (atmos, monthly mean, longitude latitude level time)

  • clt (atmos, monthly mean, longitude latitude time)

  • pr (atmos, monthly mean, longitude latitude time)

  • hur (atmos, monthly mean, longitude latitude level time)

  • hus (atmos, monthly mean, longitude latitude level time)

  • rsdt (atmos, monthly mean, longitude latitude time)

  • rsut (atmos, monthly mean, longitude latitude time)

  • rsutcs (atmos, monthly mean, longitude latitude time)

  • rtnt or rtmt (atmos, monthly mean, longitude latitude time)

  • ta (atmos, monthly mean, longitude latitude level time)

  • tas (atmos, monthly mean, longitude latitude time)

  • tasa (atmos, monthly mean, longitude latitude time)

  • tos (atmos, monthly mean, longitude latitude time)

  • ts (atmos, monthly mean, longitude latitude time)

  • va (atmos, monthly mean, longitude latitude level time)

  • wap (atmos, monthly mean, longitude latitude level time)

  • zg (atmos, monthly mean, longitude latitude time)

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.

  • AIRS (obs4mips): hus, husStderr

  • AIRS-2-0 (obs4mips): hur

  • CERES-EBAF (obs4mips): rsdt, rsut, rsutcs

  • ERA-Interim (OBS6): hur, ta, va, wap

  • GPCP-SG (obs4mips): pr

  • HadCRUT4 (OBS): tasa

  • HadISST (OBS): ts

  • MLS-AURA (OBS6): hur

  • TRMM-L3 (obs4mips): pr, prStderr

References

  • Brient, F., and T. Schneider, J. Climate, 29, 5821-5835, doi:10.1175/JCLIM-D-15-0897.1, 2016.

  • Brient et al., Clim. Dyn., 47, doi:10.1007/s00382-015-2846-0, 2016.

  • Cox et al., Nature, 553, doi:10.1038/nature25450, 2018.

  • Gregory et al., Geophys. Res. Lett., 31, doi:10.1029/2003GL018747, 2004.

  • Lipat et al., Geophys. Res. Lett., 44, 5739-5748, doi:10.1002/2017GL73151, 2017.

  • Sherwood et al., nature, 505, 37-42, doi:10.1038/nature12829, 2014.

  • Su, et al., J. Geophys. Res. Atmos., 119, doi:10.1002/2014JD021642, 2014.

  • Tian, Geophys. Res. Lett., 42, 4133-4141, doi:10.1002/2015GL064119, 2015.

  • Volodin, Izvestiya, Atmospheric and Oceanic Physics, 44, 288-299, doi:10.1134/S0001433808030043, 2008.

  • Zhai, et al., Geophys. Res. Lett., 42, doi:10.1002/2015GL065911, 2015.

Example plots

../_images/ltmi.png

Fig. 82 Lower tropospheric mixing index (LTMI; Sherwood et al., 2014) vs. equilibrium climate sensitivity from CMIP5 models.

../_images/shhc.png

Fig. 83 Climatological Hadley cell extent (Lipat et al., 2017) vs. equilibrium climate sensitivity from CMIP5 models.

../_images/humidx.png

Fig. 84 Tropical mid-tropospheric humidity asymmetry index (Tian, 2015) vs. equilibrium climate sensitivity from CMIP5 models.

../_images/itczidx.png

Fig. 85 Southern ITCZ index (Tian, 2015) vs. equilibrium climate sensitivity from CMIP5 models.

../_images/covrefl.png

Fig. 86 Covariance of shortwave cloud reflection (Brient and Schneider, 2016) vs. equilibrium climate sensitivity from CMIP5 models.

../_images/volodin.png

Fig. 87 Difference in total cloud fraction between tropics (28°S - 28°N) and Southern midlatitudes (56°S - 36°S) (Volodin, 2008) vs. equilibrium climate sensitivity from CMIP5 models.