9. ESA CCI¶
9.1. Overview¶
Namelist for creating the 20 figures from Lauer et al. (2017) using the European Space Agency’s Climate Change Initiative (ESA CCI) data sets for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases (CO2). This namelist demonstrates the value of the ESA CCI data for model evaluation providing an overview on the possible applications of the new data to evaluating CMIP models.
9.2. Available namelists and diagnostics¶
Namelists are stored in nml/
- namelist_lauer17rse.xml
Diagnostics are stored in diag_scripts/
- aerosol_stations.ncl: comparison of ESA CCI aerosol with AeroNet and MODIS
- clouds.ncl: global maps of (multi-year) annual means including multi-model mean
- clouds_interannual.ncl: global maps of interannual variability of cloud properties
- clouds_ipcc.ncl: maps of multi-model mean bias and zonal averages
- clouds_taylor.ncl: taylor diagrams
- eyring13jgr_fig01.ncl: calculates seasonal cycles of zonally averaged total ozone columns.
- eyring13jgr_fig02.ncl: time series of area-weighted total ozone from 1960-2005 for the annual mean averaged over the global domain (90°S-90°N), Tropics (25°S-25°N), northern mid-latitudes (35°N-60°N), southern mid-latitudes (35°S-60°S), and the March and October mean averaged over the Arctic (60°N-90°N) and the Antarctic (60°S-90°S).
- eyring13jgr_fig04.ncl: climatological annual mean tropospheric ozone columns (geographical distribution).
- lc_ESACCI.py: ESA CCI land cover diagnostics including global maps of grass and cropland cover and of forest and shrub cover
- perfmetrics_grading.ncl: calculates grades according to a given metric with different options for normalization. It requires fields precalculated by perfmetrics_main.ncl (see Performance metrics for essential climate parameters).
- perfmetrics_grading_collect.ncl: collects results from metrics previously calculated by perfmetrics_grading.ncl and passes them to the plotting functions (see Performance metrics for essential climate parameters).
- perfmetrics_main.ncl: calculates and (optionally) plots annual/seasonal cycles, zonal means, lat-lon fields and time-lat-lon fields from input monthly 2-d or 3-d (“T2M”, “T3Ms”) data. The calculated fields can be also plotted as difference w.r.t. a given reference model. They are also used as input to calculate grading metrics (see perfmetrics_grading.ncl) (see Performance metrics for essential climate parameters).
- SeaIce_polcon.ncl: polar stereographic plots of sea ice concentration (= sea ice area fraction) and extent (grid cells with a sea ice concentration of at least 15%) for individual models or observational data sets, for Arctic and Antarctic regions with flexible paneling of the individual plots. The edges of sea ice extent can be highlighted via an optional red line.
- SeaIce_polcon_diff.ncl: polar stereographic plots of sea ice area concentration difference between individual models and reference data (e.g., an observational data set) for both Arctic and Antarctic with flexible paneling of the individual plots. All data are regridded to a common grid (1°x1°) before comparison.
- SeaIce_tsline.ncl: time series line plots of total sea ice area and extent (accumulated) for northern and southern hemispheres with optional multi-model mean and standard deviation. One value is used per model per year, either annual mean or the mean value of a selected month.
- sm_ESACCI.py: ESA CCI soil moisture diagnostics including global maps of temporal trend in soil moisture and percentile maps for soil moisture
- sst_ESACCI.py: ESA CCI SST diagnostics including global maps of absolute and relative SST bias and time series of mean SST for different ocean basins
- tsline.ncl: time line plots of annual means for spatial averages
- vpline.ncl: produces vertical profiles according to Eyring et al. (2006) Figure 5 upper panels following eyring06jgr_fig05.ncl.
9.3. User settings¶
User setting files (cfg files) are stored in nml/cfg_lauer17rse/
- cfg_aerosol_stations_AERONET.ncl
- cfg_carbon_line_3030.ncl
- cfg_carbon_line_3060.ncl
- cfg_carbon_line_6030.ncl
- cfg_carbon_line_h.ncl
- cfg_clouds_err.ncl
- cfg_clouds_interannual_esa.ncl
- cfg_clouds_ipcc.ncl
- cfg_clouds.ncl
- cfg_clouds_taylor_esa.ncl
- cfg_clouds_taylor_esa-sic.ncl
- cfg_dummy.conf
- cfg_esacci_vpline.ncl
- cfg_eyring13jgr_fig01.ncl
- cfg_eyring13jgr_fig01_NIWA.ncl
- cfg_eyring13jgr_fig02.ncl
- cfg_eyring13jgr_fig04.ncl
- cfg_lc_ESACCI.py
- cfg_perfmetrics_grading_collect.ncl
- cfg_perfmetrics_grading_RMSD_200_glob.ncl
- cfg_perfmetrics_grading_RMSD_400_glob.ncl
- cfg_perfmetrics_grading_RMSD_500_glob.ncl
- cfg_perfmetrics_grading_RMSD_850_glob.ncl
- cfg_perfmetrics_grading_RMSD_all_glob_aero.ncl
- cfg_perfmetrics_grading_RMSD_all_glob.ncl
- cfg_perfmetrics_grading_RMSD_all_glob_sm.ncl
- cfg_perfmetrics_grading_RMSD_all_glob_toz.ncl
- cfg_perfmetrics_grading_RMSD_all_glob_ts.ncl
- cfg_perfmetrics_grading_RMSD_all_glob_xco2.ncl
- cfg_perfmetrics_grading_RMSD_all_NHpolar_sic.ncl
- cfg_perfmetrics_grading_RMSD_all_SHpolar_sic.ncl
- cfg_perfmetrics_grading_RMSD_all_SHpolar_toz.ncl
- cfg_perfmetrics_latlon_annualclim_all_glob_aerosol.ncl
- cfg_perfmetrics_latlon_annualclim_all_glob.ncl
- cfg_SeaIce_NH.ncl
- cfg_SeaIce_SH.ncl
- cfg_sm_ESACCI.py
- cfg_sst_ESACCI_fig3.py
- cfg_sst_ESACCI_fig4.py
9.4. Variables¶
- abs550aer
- clt, cltStderr
- grassNcropFrac
- hus
- LW_CRE
- od550aer, od550aerStderr
- od550lt1aer
- od870aer, od870aerStderr
- pr
- rlut, rsut
- shrubNtreeFrac
- sic
- sm, smStderr
- SW_CRE
- ta
- tas
- tos
- toz, tozStderr
- tro3prof
- ts, tsStderr
- ua, va
- xco2, xco2Stderr
- zg
9.5. Observations and reformat scripts¶
Note: (1) obs4mips data can be used directly without any preprocessing; (2) see headers of reformat scripts for non-obs4mips data for download instructions.
- AIRS (hus): obs4mips
- BDBP (tro3prof): reformat_scripts/obs/reformat_obs_BDBP.ncl
- CERES-EBAF (LW_CRE, rlut, rsut, SW_CRE): obs4mips
- CLARA-A2 (clt): contact ESMValtool development team
- ERA-Interim (hus, ta, tas, ua, va, zg): reformat_scripts/obs/reformat_obs_ERA-Interim.ncl, reformat_scripts/obs/reformat_obs_ERA-Interim_surffluxes.ncl
- ESACCI-AEROSOL (abs550aer, od550aer, od550aerStderr, od550lt1aer, od870aer, od870aerStder): reformat_scripts/obs/reformat_obs_ESACCI-AEROSOL.ncl
- ESACCI-CLOUD (clt, cltStderr): reformat_scripts/obs/reformat_obs_ESACCI-CLOUD.ncl
- ESACCI-GHG (xco2, xco2Stderr): reformat_scripts/obs/reformat_obs_ESACCI-GHG.csh
- ESACCI-LANDCOVER (grassNcropFrac, shrubNtreeFrac): reformat_scripts/obs/reformat_obs_ESACCI-LANDCOVER.py
- ESACCI-OZONE (toz, tozStderr, tro3prof): reformat_scripts/obs/reformat_obs_ESACCI-OZONE.ncl, reformat_scripts/obs/reformat_obs_ESACCI-OZONE_LP.ncl
- ESACCI-SIC (sic): reformat_scripts/obs/reformat_obs_ESACCI-sic.ncl
- ESACCI-SOILMOISTURE (sm, smStderr): reformat_scripts/obs/reformat_obs_ESACCI-SOILMOISTURE.ncl
- ESACCI-SST (tos, ts, tsStderr): reformat_scripts/obs/reformat_obs_ESACCI-SST.ncl
- GPCP-SG (pr): obs4mips
- HadISST (ts): reformat_scripts/obs/reformat_obs_HadISST.ncl
- MODIS-L3-C6 (clt, od550aer): reformat_scripts/obs/reformat_obs_MODIS-L3-C6.ncl
- NCEP (ta, tas, ua, va, zg): reformat_scripts/obs/reformat_obs_NCEP.ncl
- NIWA (toz): reformat_scripts/obs/reformat_obs_NIWA.ncl
- NSIDC-NT (sic): reformat_scripts/obs/reformat_obs_NSIDC.ncl
- PATMOS (clt): contact ESMValtool development team
9.6. References¶
- Lauer, A., V. Eyring, M. Righi, M. Buchwitz, P. Defourny, M. Evaldsson, P. Friedlingstein, R. de Jeuf, G. de Leeuw, A. Loew, C. J. Merchant, B. Müller, T. Popp, M. Reuter, S. Sandven, D. Senftleben, M. Stengel, M. Van Roozendael, S. Wenzel, and U. Willén: Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool, Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2017.01.007, 2017.