Calculates equilibrium climate sensitivity (ECS) versus
- S index, D index and lower tropospheric mixing index (LTMI); similar to fig. 5 from Sherwood et al. (2014)
- southern ITCZ index and tropical mid-tropospheric humidity asymmetry index; similar to fig. 2 and 4 from Tian (2015)
- covariance of shortwave cloud reflection (Brient and Schneider, 2016)
- climatological Hadley cell extent (Lipat et al., 2017)
- temperature variability metric; similar to fig. 2 from Cox et al. (2018)
- total cloud fraction difference between tropics and mid-latitudes; similar to fig. 3 from Volodin (2008)
- response of marine boundary layer cloud (MBLC) fraction changes to sea surface temperature (SST); similar to fig. 3 of Zhai et al. (2015)
- Cloud shallowness index (Brient et al., 2016)
- 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):
- save script given at the end of this recipe as ecs_cmip.cdl
- run command: ncgen -o ecs_cmip.nc ecs_cmip.cdl
- copy ecs_cmip.nc to directory given by diag_script_info@ecs_file (e.g. $diag_scripts/emergent_constraints/ecs_cmip.nc)
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)
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 :ref:`here<api.esmvaltool.diag_scripts.emergent_constraints.ecs_scatter>`.
Script emergent_constraints/single_constraint.py
See :ref:`here<api.esmvaltool.diag_scripts.emergent_constraints.single_constraint>`.
Script climate_metrics/psi.py
See :ref:`here<psi.py>`.
- 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)
Note
- Obs4mips data can be used directly without any preprocessing.
- 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
- 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.
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.