IPCC AR6 Chapter 3 (selected figures)

Overview

This recipe collects selected diagnostics used in IPCC AR6 WGI Chapter 3: Human influence on the climate system (Eyring et al., 2021). Plots from IPCC AR6 can be readily reproduced and compared to previous versions. The aim is to be able to start with what was available now the next time allowing us to focus on developing more innovative analysis methods rather than constantly having to “re-invent the wheel”.

Processing of CMIP3 models currently works only in serial mode, due to an issue in the input data still under investigation. To run the recipe for Fig 3.42a and Fig. 3.43 set “max_parallel_tasks: 1” in the config-user.yml file.

The plots are produced collecting the diagnostics from individual recipes. The following figures from Eyring et al. (2021) can currently be reproduced:

• Figure 3.3 a,b,c,d: Surface Air Temperature - Model Bias

• Figure 3.4: Anomaly Of Near-Surface Air Temperature

• Figure 3.5: Temporal Variability Of Near-Surface Air Temperature

• Figure 3.9: Anomaly Of Near-Surface Air Temperature - Attribution

• Figure 3.13: Precipitation - Model Bias

• Figure 3.15: Precipitation Anomaly

• Figure 3.19: Speed-Up Of Zonal Mean Wind

• Figure 3.42: Relative Model Performance

• Figure 3.43: Correlation Pattern

To reproduce Fig. 3.9 you need the shapefile of the AR6 reference regions (Iturbide et al., 2020). Please download the file IPCC-WGI-reference-regions-v4_shapefile.zip, unzip and store it in <auxiliary_data_dir>/IPCC-regions/ (the auxiliary_data_dir is defined in the config-user.yml file).

Available recipes and diagnostics

Recipes are stored in esmvaltool/recipes/ipccwg1ar6ch3/

• recipe_ipccwg1ar6ch3_atmosphere.yml

• recipe_ipccwg1ar6ch3_fig_3_9.yml

• recipe_ipccwg1ar6ch3_fig_3_19.yml

• recipe_ipccwg1ar6ch3_fig_3_42_a.yml

• recipe_ipccwg1ar6ch3_fig_3_42_b.yml

• recipe_ipccwg1ar6ch3_fig_3_43.yml

Diagnostics are stored in esmvaltool/diag_scripts/

Fig. 3.3:

• ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl: See here:.

• ipcc_ar6/model_bias.ncl

Fig. 3.4:

• ipcc_ar6/tas_anom.ncl

• ipcc_ar6/tsline_collect.ncl

Fig. 3.5:

• ipcc_ar6/zonal_st_dev.ncl

Fig. 3.9:

• ipcc_ar6/tas_anom_damip.ncl

Fig. 3.13:

• ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl: See here:.

• ipcc_ar6/model_bias.ncl

Fig. 3.15:

• ipcc_ar6/precip_anom.ncl

Fig. 3.19:

• ipcc_ar6/zonal_westerly_winds.ncl

Fig. 3.42:

• perfmetrics/main.ncl

• perfmetrics/collect.ncl

Fig. 3.43:

• ipcc_ar6/corr_pattern.ncl

• ipcc_ar6/corr_pattern_collect.ncl

User settings in recipe

1. Script ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl

   See here.

2. Script ipcc_ar6/model_bias.ncl

   Optional settings (scripts)

   • plot_abs_diff: additionally also plot absolute differences (true, false)

   • plot_rel_diff: additionally also plot relative differences (true, false)

   • plot_rms_diff: additionally also plot root mean square differences (true, false)

   • 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 dataset

   Color tables

   • variable “tas” and “tos”: diag_scripts/shared/plot/rgb/ipcc-ar6_temperature_div.rgb, diag_scripts/shared/plot/rgb/ipcc-ar6_temperature_10.rgb, diag_scripts/shared/plot/rgb/ipcc-ar6_temperature_seq.rgb

   • variable “pr”: diag_scripts/shared/plots/rgb/ipcc-ar6_precipitation_seq.rgb, diag_scripts/shared/plot/rgb/ipcc-ar6_precipitation_10.rgb

   • variable “sos”: diag_scripts/shared/plot/rgb/ipcc-ar6_misc_seq_1.rgb, diag_scripts/shared/plot/rgb/ipcc-ar6_misc_div.rgb

3. Script ipcc_ar6/tas_anom.ncl

   Required settings for script

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

   Optional settings for script

   • blending: if true, calculates blended surface temperature

   • 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

   • header: if true, region name as header

   • volcanoes: if true, adds volcanoes to the plot

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

   Optional settings for variables

   • reference_dataset: reference dataset; REQUIRED when calculating anomalies

   Color tables

   • e.g. diag_scripts/shared/plot/styles/cmip5.style

4. Script ipcc_ar6/tas_anom_damip.ncl

   Required settings for script

   • start_year: start year in figure

   • end_year: end year in figure

   • panels: list of variable blocks for each panel

   Optional settings for script

   • ref_start: start year of reference period for anomalies

   • ref_end: end year of reference period for anomalies

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

   • plot_units: variable unit for plotting

   • y-min: set min of y-axis

   • y-max: set max of y-axis

   • header: title for each panel

   • title: name of region as part of filename

   • legend: set labels for optional output of a legend in an extra file

5. Script ipcc_ar6/tsline_collect.ncl

   Optional settings for script

   • blending: if true, then var=”gmst” otherwise “gsat”

   • 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

   • stat_shading: if true: shading of statistic range

   • ref_shading: if true: shading of reference period

   Optional settings for variables

   • reference_dataset: reference dataset; REQUIRED when calculating anomalies

6. Script ipcc_ar6/zonal_st_dev.ncl

   Required settings for script

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

   Optional settings for script

   • plot_legend: if true, plot legend will be plotted

   • plot_units: variable unit for plotting

   • multi_model_mean: if true, multi-model mean and uncertaintiy will be plotted

   Optional settings for variables

   • reference_dataset: reference dataset; REQUIRED when calculating anomalies

7. Script ipcc_ar6/precip_anom.ncl

   Required settings for script

   • panels: list of variables plotted in each panel

   • start_year: start of time coordinate

   • end_year: end of time coordinate

   Optional settings for script

   • anomaly: true if anomaly should be calculated

   • ref_start: start year of reference period for anomalies

   • ref_end: end year of reference period for anomalies

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

   • region: name of domain

   • plot_units: variable unit for plotting

   • header: if true, region name as header

   • stat: statistics for multi model nc-file (MinMax,5-95,10-90)

   • y_min: set min of y-axis

   • y_max: set max of y-axis

8. Script ipcc_ar6/zonal_westerly_winds.ncl

   Optional settings for variables

   • reference_dataset: reference dataset; REQUIRED when calculating anomalies

   Optional settings for script

   • e13fig12_start_year: year when the climatology calculation starts (default: start_year of var)

   • e13fig12_end_year: year when the climatology calculation ends (default: end_year of var)

   • e13fig12_multimean: multimodel mean (default: False)

   • e13fig12_exp_MMM: name of the experiments for the MMM (required if @e13fig12_multimean = True)

   • e13fig12_season: season (default: ANN)

9. Script perfmetrics/perfmetrics_main.ncl

   See here.

10. Script perfmetrics/perfmetrics_collect.ncl

    See here.

11. Script ipcc_ar6/corr_pattern.ncl

    Required settings for variables

    • reference_dataset: name of reference observation

    Optional settings for variables

    • alternative_dataset: name of alternative observations

12. Script ipcc_ar6/corr_pattern_collect.ncl

    Optional settings for script

    • diag_order: give order of plotting variables on the x-axis

    • labels: List of labels for each variable on the x-axis

    • model_spread: if True, model spread is shaded

    • plot_median: if True, median is plotted

    • project_order: give order of projects

Variables

• et (land, monthly mean, longitude latitude time)

• fgco2 (ocean, monthly mean, longitude latitude time)

• gpp (land, monthly mean, longitude latitude time)

• hfds (land, monthly mean, longitude latitude time)

• hus (land, monthly mean, longitude latitude level time)

• lai (land, monthly mean, longitude latitude time)

• lwcre (atmos, monthly mean, longitude latitude time)

• nbp (land, monthly mean, longitude latitude time)

• pr (atmos, monthly mean, longitude latitude time)

• psl (atmos, monthly mean, longitude latitude time)

• rlds (atmos, monthly mean, longitude latitude time)

• rlus (atmos, monthly mean, longitude latitude time)

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

• rsds (atmos, monthly mean, longitude latitude time)

• rsus (atmos, monthly mean, longitude latitude time)

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

• sm (land, monthly mean, longitude latitude time)

• sic (seaice, monthly mean, longitude latitude time)

• siconc (seaice, monthly mean, longitude latitude time)

• swcre (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)

• ua (atmos, monthly mean, longitude latitude level time)

• va (atmos, monthly mean, longitude latitude level time)

• zg (atmos, monthly mean, longitude latitude level time)

Observations and reformat scripts

• AIRS (hus - obs4MIPs)

• ATSR (tos - obs4MIPs)

• BerkeleyEarth (tasa - esmvaltool/cmorizers/data/formatters/datasets/berkeleyearth.py)

• CERES-EBAF (rlds, rlus, rlut, rlutcs, rsds, rsus, rsut, rsutcs - obs4MIPs)

• CRU (pr - esmvaltool/cmorizers/data/formatters/datasets/cru.py)

• ESACCI-SOILMOISTURE (sm - esmvaltool/cmorizers/data/formatters/datasets /esacci_soilmoisture.py)

• ESACCI-SST (ts - esmvaltool/cmorizers/data/formatters/datasets/esacci_sst.py)

• ERA5 (hus, psl, ta, tas, ua, va, zg - ERA5 data can be used via the native6 project)

• ERA-Interim (hfds - cmorizers/data/formatters/datasets/era_interim.py)

• FLUXCOM (gpp - cmorizers/data/formatters/datasets/fluxcom.py)

• GHCN (pr - esmvaltool/cmorizers/data/formatters/datasets/ghcn.ncl)

• GPCP-SG (pr - obs4MIPs)

• HadCRUT5 (tasa - esmvaltool/cmorizers/data/formatters/datasets/hadcrut5.py)

• HadISST (sic, tos, ts - esmvaltool/cmorizers/data/formatters/datasets/hadisst.ncl)

• JMA-TRANSCOM (fgco2, nbp - esmvaltool/cmorizers/data/formatters/datasets/jma_transcom.py)

• JRA-55 (psl - ana4MIPs)

• Kadow2020 (tasa - esmvaltool/cmorizers/data/formatters/datasets/kadow2020.py)

• LandFlux-EVAL (et - esmvaltool/cmorizers/data/formatters/datasets/landflux_eval.py)

• Landschuetzer2016 (fgco2 - esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2016.py)

• LAI3g (lai - esmvaltool/cmorizers/data/formatters/datasets/lai3g.py)

• MTE (gpp - esmvaltool/cmorizers/data/formatters/datasets/mte.py)

• NCEP-NCAR-R1 (ta, tas, ua, va, zg - esmvaltool/cmorizers/data/formatters/datasets/ncep_ncar_r1.py)

• NOAAGlobalTemp (tasa - esmvaltool/cmorizers/data/formatters/datasets/noaaglobaltemp.py)

References

• Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis , M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge Universiy Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423-552, doi: 10.1017/9781009157896.005.

Example plots

Fig. 125 Figure 3.3: Annual mean near-surface (2 m) air temperature (°C) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged, with respect to the climatology from ERA5. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where >=66% of models show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on sign of change.

Fig. 126 Figure 3.4a: Observed and simulated time series of the anomalies in annual and global mean surface air temperature (GSAT). All anomalies are differences from the 1850-1900 time-mean of each individual time series. The reference period 1850-1900 is indicated by grey shading. (a) Single simulations from CMIP6 models (thin lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Office Hadley Centre/Climatic Research Unit dataset (HadCRUT5), and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5 observational data mask. Vertical lines indicate large historical volcanic eruptions. Inset: GSAT for each model over the reference period, not masked to any observations.

Fig. 127 Figure 3.4b: Observed and simulated time series of the anomalies in annual and global mean surface air temperature (GSAT). All anomalies are differences from the 1850-1900 time-mean of each individual time series. The reference period 1850-1900 is indicated by grey shading. (b) Multi-model means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth, National Oceanic and Atmospheric Administration NOAAGlobalTemp and Kadow et al. (2020). Masking was done as in (a). CMIP6 historical simulations were extended with SSP2-4.5 simulations for the period 2015-2020 and CMIP5 simulations were extended with RCP4.5 simulations for the period 2006-2020. All available ensemble members were used. The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850-2020).

Fig. 128 Figure 3.5: The standard deviation of annually averaged zonal-mean near-surface air temperature. This is shown for four detrended observed temperature datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp and Kadow et al. (2020), for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one ensemble member per model, 65 years) (after Jones et al., 2013). For line colours see the legend of Figure 3.4. Additionally, the multi-model mean (red) and standard deviation (grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend.

Fig. 129 Figure 3.9: Global, land and ocean annual mean near-surface air temperature anomalies in CMIP6 models and observations. Timeseries are shown for CMIP6 historical anthropogenic and natural (brown) natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentiles) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data mask. Temperature anomalies are shown relative to 1950-2010 for Antarctica and relative to 1850-1900 for other continents. CMIP6 historical simulations are expanded by the SSP2-4.5 scenario simulations. All available ensemble members were used. Regions are defined by Iturbide et al. (2020).

Fig. 130 Figure 3.13: Annual-mean precipitation rate (mm day-1) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and precipitation analysis from the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the precipitation analysis from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay indicates regions with robust signal, where >=66% of models show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change.

Fig. 131 Figure 3.15: Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c-f) Evolution of global and zonal average annual mean precipitation (mm day-1) over areas of land where there are observations, expressed relative to the base period of 1961-1990, simulated by CMIP6 models (one ensemble member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading shows the 5-95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from Zhang et al. (2007), data from the Global Precipitation Climatology Product (GPCP L3 version 2.3, Adler et al. (2003)) and from the Climate Research Unit (CRU TS4.02, Harris et al. (2014)). Also plotted are boxplots showing interquartile and 5-95% ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day-1) of GHCN version 2 for the years 1950-2014 over land areas used to compute the plots.

Fig. 132 Figure 3.19: Long-term mean (thin black contours) and linear trend (colour) of zonal mean December-January-February zonal winds from 1985 to 2014 in the Southern Hemisphere. The figure shows (a) ERA5 and (b) the CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified from Eyring et al. (2013), their Figure 12.

Fig. 133 Figure 3.42a: Relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980-1999) compared to observational datasets. A relative performance measure is displayed, with blue shading indicating better and red shading indicating worse performance than the median error of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and an additional data set (upper left triangle). Reference/additional datasets are from top to bottom in (a): ERA5/NCEP, GPCP-SG/GHCN, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55/ERA5, ESACCI-SST/HadISST, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, AIRS/ERA5, ERA5/NCEP. White boxes are used when data are not available for a given model and variable. Figure is updated and expanded from Bock et al. (2020).

Fig. 134 Figure 3.43 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980-1999. Results are shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5) are shown (solid grey circles) if available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4° in longitude and 5° latitude.