Equilibrium climate sensitivity

Overview

Equilibrium climate sensitivity is defined as the change in global mean temperature as a result of a doubling of the atmospheric CO2 concentration compared to pre-industrial times after the climate system has reached a new equilibrium. This recipe uses a regression method based on Gregory et al. (2004) to calculate it for several CMIP models.

Available recipes and diagnostics

Recipes are stored in recipes/

  • recipe_ecs.yml

Diagnostics are stored in diag_scripts/

  • climate_metrics/ecs.py

  • climate_metrics/create_barplot.py

  • climate_metrics/create_scatterplot.py

User settings in recipe

  1. Preprocessor

    • area_statistics (operation: mean): Calculate global mean.

  2. Script climate_metrics/ecs.py

    • calculate_mmm, bool, optional (default: True): Calculate multi-model mean ECS.

    • output_attributes, dict, optional: Write additional attributes to all output netcdf files.

    • read_external_file, str, optional: Read ECS and net climate feedback parameter from external file. Can be given relative to the diagnostic script or as absolute path.

    • seaborn_settings, dict, optional: Options for seaborn’s set() method (affects all plots), see https://seaborn.pydata.org/generated/seaborn.set.html.

  3. Script climate_metrics/create_barplot.py

    • label_attribute, str, optional: Attribute of the cube which is used as label for the different input files in the barplot.

    • patterns, list of str, optional: Patterns to filter list of input files.

    • seaborn_settings, dict, optional: Options for seaborn’s set() method (affects all plots), see https://seaborn.pydata.org/generated/seaborn.set.html.

    • sort_ascending, bool, optional (default: False): Sort bars in ascending order.

    • sort_descending, bool, optional (default: False): Sort bars in descending order.

    • value_labels, bool, optional (default: False): Label bars with value of that bar.

    • y_range, list of float, optional: Range for the Y axis of the plot.

  4. Script climate_metrics/create_scatterplot.py

    • dataset_style, str, optional: Name of the style file (located in esmvaltool.diag_scripts.shared.plot.styles_python).

    • pattern, str, optional: Pattern to filter list of input files.

    • seaborn_settings, dict, optional: Options for seaborn’s set() method (affects all plots), see https://seaborn.pydata.org/generated/seaborn.set.html.

    • y_range, list of float, optional: Range for the Y axis of the plot.

Variables

  • rlut (atmos, monthly, longitude, latitude, time)

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

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

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

Observations and reformat scripts

None

References

  • Gregory, Jonathan M., et al. “A new method for diagnosing radiative forcing and climate sensitivity.” Geophysical research letters 31.3 (2004).

Example plots

../_images/CanESM2.png

Fig. 73 Scatterplot between TOA radiance and global mean surface temperature anomaly for 150 years of the abrupt 4x CO2 experiment including linear regression to calculate ECS for CanESM2 (CMIP5).