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#

  • Preprocessor

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

  • Script climate_metrics/ecs.py

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

    • complex_gregory_plot, bool, optional (default: False): Plot complex Gregory plot (also add response for first sep_year years and last 150 - sep_year years, default: sep_year=20) if True.

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

    • read_external_file, str, optional: Read ECS and feedback parameters from external file. The path can be given relative to this diagnostic script or as absolute path.

    • savefig_kwargs, dict, optional: Keyword arguments for matplotlib.pyplot.savefig().

    • seaborn_settings, dict, optional: Options for seaborn.set_theme() (affects all plots).

    • sep_year, int, optional (default: 20): Year to separate regressions of complex Gregory plot. Only effective if complex_gregory_plot is True.

    • x_lim, list of float, optional (default: [1.5, 6.0]): Plot limits for X axis of Gregory regression plot (T).

    • y_lim, list of float, optional (default: [0.5, 3.5]): Plot limits for Y axis of Gregory regression plot (N).

  • Script climate_metrics/create_barplot.py

    • add_mean, str, optional: Add a bar representing the mean for each class.

    • label_attribute, str, optional: Cube attribute which is used as label for different input files.

    • order, list of str, optional: Specify the order of the different classes in the barplot by giving the label, makes most sense when combined with label_attribute.

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

    • savefig_kwargs, dict, optional: Keyword arguments for matplotlib.pyplot.savefig().

    • seaborn_settings, dict, optional: Options for seaborn.set_theme() (affects all plots).

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

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

    • subplots_kwargs, dict, optional: Keyword arguments for matplotlib.pyplot.subplots().

    • 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.

  • 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.set_theme() (affects all plots).

    • 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. 126 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).#

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