Quantifying progress across different CMIP phases

Overview

The recipe recipe_bock20jgr.yml generates figures to quantify the progress across different CMIP phases.

Note

The current recipe uses a horizontal 5x5 grid for figure 10, while the original plot in the paper shows a 2x2 grid. This is solely done for computational reasons (running the recipe with a 2x2 grid for figure 10 takes considerably more time than running it with a 5x5 grid) and can be easily changed in the preprocessor section of the recipe if necessary.

Available recipes and diagnostics

Recipes are stored in recipes/bock20jgr

  • recipe_bock20jgr_fig_1-4.yml

  • recipe_bock20jgr_fig_6-7.yml

  • recipe_bock20jgr_fig_8-10.yml

Diagnostics are stored in diag_scripts/

Fig. 1:

  • bock20jgr/tsline.ncl: timeseries of global mean surface temperature anomalies

Fig. 2:

  • bock20jgr/tsline_collect.ncl: collect different timeseries from tsline.ncl to compare different models ensembles

Fig. 3 and 4:

  • bock20jgr/model_bias.ncl: global maps of the multi-model mean and the multi-model mean bias

Fig. 6:

  • perfmetrics/main.ncl

  • perfmetrics/collect.ncl

Fig. 7:

  • bock20jgr/corr_pattern.ncl: calculate pattern correlation

  • bock20jgr/corr_pattern_collect.ncl: create pattern correlation plot

Fig. 8:

  • climate_metrics/ecs.py

  • climate_metrics/create_barplot.py

Fig. 9:

  • clouds/clouds_ipcc.ncl

Fig. 10:

  • climate_metrics/feedback_parameters.py

User settings in recipe

  1. Script tsline.ncl

    Required settings (scripts)

    • styleset: as in diag_scripts/shared/plot/style.ncl functions

    Optional settings (scripts)

    • time_avg: type of time average (currently only “yearly” and “monthly” are available).

    • ts_anomaly: calculates anomalies with respect to the defined reference period; for each gird point by removing the mean for the given calendar month (requiring at least 50% of the data to be non-missing)

    • ref_start: start year of reference period for anomalies

    • ref_end: end year of reference period for anomalies

    • ref_value: if true, right panel with mean values is attached

    • ref_mask: if true, model fields will be masked by reference fields

    • region: name of domain

    • plot_units: variable unit for plotting

    • y_min: set min of y-axis

    • y_max: set max of y-axis

    • mean_nh_sh: if true, calculate first NH and SH mean

    • volcanoes: if true, lines of main volcanic eruptions will be added

    • header: if true, use region name as header

    • write_stat: if true, write multi-model statistics to nc-file

    Required settings (variables)

    none

    • Optional settings (variables)

    none

  2. Script tsline_collect.ncl

    Required settings (scripts)

    • styleset: as in diag_scripts/shared/plot/style.ncl functions

    Optional settings (scripts)

    • time_avg: type of time average (currently only “yearly” and “monthly” are available).

    • ts_anomaly: calculates anomalies with respect to the defined period

    • ref_start: start year of reference period for anomalies

    • ref_end: end year of reference period for anomalies

    • region: name of domain

    • plot_units: variable unit for plotting

    • y_min: set min of y-axis

    • y_max: set max of y-axis

    • order: order in which experiments should be plotted

    • header: if true, region name as header

    • stat_shading: if true: shading of statistic range

    • ref_shading: if true: shading of reference period

    Required settings (variables)

    none

    • Optional settings (variables)

    none

  3. Script model_bias.ncl

    Required settings (scripts)

    none

    Optional settings (scripts)

    • projection: map projection, e.g., Mollweide, Mercator

    • timemean: time averaging, i.e. “seasonalclim” (DJF, MAM, JJA, SON), “annualclim” (annual mean)

    • Required settings (variables)*

    • reference_dataset: name of reference datatset

    Optional settings (variables)

    • long_name: description of variable

    Color tables

    • variable “tas”: diag_scripts/shared/plot/rgb/ipcc-ar6_temperature_div.rgb,

    • variable “pr-mmday”: diag_scripts/shared/plots/rgb/ipcc-ar6_precipitation_seq.rgb diag_scripts/shared/plot/rgb/ipcc-ar6_precipitation_div.rgb

  4. Script perfmetrics_main.ncl

    See here.

  5. Script perfmetrics_collect.ncl

    See here.

  6. Script corr_pattern.ncl

    Required settings (scripts)

    none

    Optional settings (scripts)

    • plot_median

    Required settings (variables)

    • reference_dataset

    Optional settings (variables)

    • alternative_dataset

  7. Script corr_pattern_collect.ncl

    Required settings (scripts)

    none

    Optional settings (scripts)

    • diag_order

    Color tables

    • diag_scripts/shared/plot/rgb/ipcc-ar6_line_03.rgb

  8. Script ecs.py

    See here.

  9. Script create_barplot.py

    See here.

  10. Script clouds_ipcc.ncl

    See here.

  11. Script feedback_parameters.py

    Required settings (scripts)

    none

    Optional settings (scripts)

    • calculate_mmm: bool (default: True). Calculate multi-model means.

    • only_consider_mmm: bool (default: False). Only consider multi-model mean dataset. This automatically sets calculate_mmm to True. For large multi-dimensional datasets, this might significantly reduce the computation time if only the multi-model mean dataset is relevant.

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

    • seaborn_settings: dict. Options for seaborn.set() (affects all plots).

Variables

  • clt (atmos, monthly, longitude latitude time)

  • hus (atmos, monthly, longitude latitude lev time)

  • pr (atmos, monthly, longitude latitude time)

  • psl (atmos, monthly, longitude latitude time)

  • rlut (atmos, monthly, longitude latitude time)

  • rsdt (atmos, monthly, longitude latitude time)

  • rsut (atmos, monthly, longitude latitude time)

  • rtmt (atmos, monthly, longitude latitude time)

  • rlutcs (atmos, monthly, longitude latitude time)

  • rsutcs (atmos, monthly, longitude latitude time)

  • ta (atmos, monthly, longitude latitude lev time)

  • tas (atmos, monthly, longitude latitude time)

  • ts (atmos, monthly, longitude latitude time)

  • ua (atmos, monthly, longitude latitude lev time)

  • va (atmos, monthly, longitude latitude lev time)

  • zg (atmos, monthly, longitude latitude time)

Observations and reformat scripts

  • AIRS (obs4MIPs) - specific humidity

  • CERES-EBAF (obs4MIPs) - CERES TOA radiation fluxes (used for calculation of cloud forcing)

  • ERA-Interim - reanalysis of surface temperature, sea surface pressure

    Reformat script: recipes/cmorizers/recipe_era5.yml

  • ERA5 - reanalysis of surface temperature

    Reformat script: recipes/cmorizers/recipe_era5.yml

  • ESACCI-CLOUD - total cloud cover

    Reformat script: cmorizers/data/formatters/datasets/esacci_cloud.ncl

  • ESACCI-SST - sea surface temperature

    Reformat script: cmorizers/data/formatters/datasets/esacci_sst.py

  • GHCN - Global Historical Climatology Network-Monthly gridded land precipitation

    Reformat script: cmorizers/data/formatters/datasets/ghcn.ncl

  • GPCP-SG (obs4MIPs) - Global Precipitation Climatology Project total precipitation

  • HadCRUT4 - surface temperature anomalies

    Reformat script: cmorizers/data/formatters/datasets/hadcrut4.ncl

  • HadISST - surface temperature

    Reformat script: cmorizers/data/formatters/datasets/hadisst.ncl

  • JRA-55 (ana4mips) - reanalysis of sea surface pressure

  • NCEP-NCAR-R1 - reanalysis of surface temperature

    Reformat script: cmorizers/data/formatters/datasets/ncep_ncar_r1.py

  • PATMOS-x - total cloud cover

    Reformat script: cmorizers/data/formatters/datasets/patmos_x.ncl

References

  • Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Predoi, V., Meehl, G., Roberts, M., and Eyring, V.: Quantifying progress across different CMIP phases with the ESMValTool, Journal of Geophysical Research: Atmospheres, 125, e2019JD032321. https://doi.org/10.1029/2019JD032321

  • Copernicus Climate Change Service (C3S), 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, edited, Copernicus Climate Change Service Climate Data Store (CDS). https://cds.climate.copernicus.eu/cdsapp#!/home

  • Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason and M. Rummukainen, 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

  • Morice, C. P., Kennedy, J. J., Rayner, N. A., & Jones, P., 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set, Journal of Geophysical Research, 117, D08101. https://doi.org/10.1029/2011JD017187

Example plots

../_images/tas_Global_CMIP6_historical_anom_1850-2014.png

Fig. 41 Observed and simulated time series of the anomalies in annual and global mean surface temperature. All anomalies are differences from the 1850-1900 time mean of each individual time series (Fig. 1).

../_images/tas_Global_multimodel_anom_1850-2017.png

Fig. 42 Observed and simulated time series of the anomalies in annual and global mean surface temperature as in Figure 1; all anomalies are calculated by subtracting the 1850-1900 time mean from the time series. Displayed are the multimodel means of all three CMIP ensembles with shaded range of the respective standard deviation. In black the HadCRUT4 data set (HadCRUT4; Morice et al., 2012). Gray shading shows the 5% to 95% confidence interval of the combined effects of all the uncertainties described in the HadCRUT4 error model (measurement and sampling, bias, and coverage uncertainties) (Morice et al., 2012) (Fig. 2).

../_images/model_bias_tas_annual_CMIP6.png

Fig. 43 Annual mean near‐surface (2 m) air temperature (°C). (a) Multimodel (ensemble) mean constructed with one realization of CMIP6 historical experiments for the period 1995-2014. Multimodel‐mean bias of (b) CMIP6 (1995-2014) compared to the corresponding time period of the climatology from ERA5 (Copernicus Climate Change Service (C3S), 2017). (Fig. 3)

../_images/ta850-global_to_swcre-global_RMSD.png

Fig. 44 Relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP3, CMIP5, and CMIP6 simulations (1980-1999) compared to observational data sets (Table 5). A relative performance is displayed, with blue shading being better and red shading worse than the median RMSD of all model results of all ensembles. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and the alternative data set (upper left triangle) which are marked in Table 5. White boxes are used when data are not available for a given model and variable (Fig. 6).

../_images/patterncor.png

Fig. 45 Centered pattern correlations between models and observations for the annual mean climatology over the period 1980–1999 (Fig. 7).