Quantifying progress across different CMIP phases¶
Overview¶
The recipe recipe_bock20jgr.yml generates figures to quantify the progress across different CMIP phases.
Available recipes and diagnostics¶
Recipes are stored in recipes/
recipe_bock20jgr.yml
Diagnostics are stored in diag_scripts/bock20jgr/
tsline.ncl: timeseries of global mean surface temperature anomalies
tsline_collect.ncl: collect different timeseries from tsline.ncl to compare different models ensembles
model_bias.ncl: global maps of the multi-model mean and the multi-model mean bias
User settings in recipe¶
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
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
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
Variables¶
tas (atmos, monthly mean, longitude latitude time)
pr (atmos, monthly mean, longitude latitude time)
Observations and reformat scripts¶
HadCRUT4 - surface temperature anomalies
Reformat script: cmorizers/obs/cmorize_obs_hadcrut4.ncl
ERA5 - reanalysis of surface temperature
Reformat script: recipes/cmorizers/recipe_era5.yml
GPCP-SG (obs4mips) - Global Precipitation Climatology Project total precipitation
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¶
Fig. 36 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. 37 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. 38 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).¶