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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<api.esmvaltool.diag_scripts.emergent_constraints.ecs_scatter>.

  • Script emergent_constraints/single_constraint.py

    See here<api.esmvaltool.diag_scripts.emergent_constraints.single_constraint>.

  • Script climate_metrics/psi.py

    See here<psi.py>.

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

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

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

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

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

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

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.