Portrait plot#

Overview#

Portrait plots are a flexible way to visualize performance metrics for multiple datasets and up to four references. In this recipe recipe_portrait_CMIP.yml the normalized Root Mean Squared Deviation (RMSD) of global mean seasonal climatologies is calculated for a selection of CMIP models. In the example recipe, for each variable up to two observation based datasets are used as reference. See Variables and Datasets for complete list of references. The recipe uses preprocessor functions (distance metrics, global mean, climate statistics) to calculate a scalar metric for each combination of dataset, variable and reference, which is plotted by the portrait_plot.py diagnostic script.

User settings in recipe#

By default cells are plotted for combinations of short_name, dataset, project and split, where split is an optional extra_facet for variables. However, this can be customized using the x_by, y_by, group_by and split_by script settings. For a complete and detailed list of settings, see the diagnostic documentation. While this allows very flexible use for any kind of data, there are some limitations as well: The grouping (subplots) and normalization is always applied along the x-axis. With default settings this means normalizing all metrics for each variable and grouping all datasets by project.

To plot distance metrics like RMSE, pearson R, bias etc. the distance_metric preprocessor or custom diagnostics can be used.

Variables and Datasets#

Note

The recipe generally works for any variable that is preprocessed correctly. To use different preprocessors or reference datasets it could be useful to create different variable groups and link them with the same extra_facet like variable_name. See recipe for examples. Listed below are the variables used to produce the example figure.

The following list shows which observational dataset is used as reference for each variable in this recipe. All variables are atmospheric monthly means. For 3D variables the selected pressure level is specified in parentheses.

  • clt (Ref1: ESACCI-CLOUD, Ref2: PATMOS-x)

  • pr (Ref1: GPCP-V2.2)

  • rlut, rsut (Ref1: CERES-EBAF)

  • tas (Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)

  • ts (Ref1: ESACCI-SST, Ref2: HadISST)

  • ua (200 hPa, Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)

  • zg (500 hPa, Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)

References#

  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, Performance metrics for climate models, J. Geophys. Res., 113, D06104, doi: 10.1029/2007JD008972 (2008).

  • Righi, M., Eyring, V., Klinger, C., Frank, F., Gottschaldt, K.-D., Jöckel, P., and Cionni, I.: Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations, Geosci. Model Dev., 8, 733, doi: 10.5194/gmd-8-733-2015 (2015).

Example plots#

../_images/portrait_plot.png

Fig. 2 Relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of CMIP5 and CMIP6 simulations. A relative performance is displayed, with blue shading indicating better and red shading indicating worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set.#