1750,2000,2500,3000,3500,4000,4500,5000,5500 thetao_Omon_remapped2D.nc thetao_Omon_remapped3D.nc

A2 Reference observation data and computation of objective performance indices

As reference data for the computation of the objective performance indices, various observation and reanalysis data are selected: For the following atmospheric variables, the ERA-40 reanalysis data are used: 2 m temperature (t2m), 10 m u wind component (u10m), 10 m v wind component (v10m), 500 hPa geopotential height (z500), and 300 hPa u component (u300). This is augmented by the following data: CERES for top of atmosphere outgoing longwave radiation (TOA, Loeb et al., 2012), GPCP for precipitation (pr, Huffman et al., 2009), MODIS for total cloud cover (tcc, Platnick et al., 2003), and OSISAF for sea ice concentration (sic, Tonboe et al., 2016). For the ocean, Polar Science Center Hydrographic Climatology (PHC, updated from Steele et al., 2001) is used as a reference for both potential temperature and salinity.
The absolute error is computed for each grid cell and averaged over different regions. For the atmosphere the different regions are Arctic (60–90°N), northern mid-latitudes (30–60°N), tropics (30°S–30°N), southern mid-latitudes (30–60°S), Antarctic (60–90°S), and global. For the ocean the domain is split into the major ocean basins: Arctic Ocean, North Atlantic Ocean, North Pacific Ocean, Indian Ocean, South Atlantic Ocean, South Pacific Ocean, Southern Ocean. Like for the atmosphere, the global ocean is also considered globally in addition. The mean absolute error is computed for each season: for the atmosphere for the four seasons DJF, MAM, JJA, SON, and for the ocean for two seasons DJF and JJA. For the ocean, model data are vertically interpolated to the z-levels of the PHC. Errors are computed for each z-level of the climatology and averaged over the levels. Then the error is normalized with the mean absolute error averaged over a set of CMIP5 models. By doing this, the performance of our new CMIP6 model can be compared objectively using the performance of CMIP5 models in terms of agreement with observation data. A performance index of 1 indicates that the model performs as well as the average of the CMIP5 models; a performance index of smaller than 1 (larger than 1) indicates a better (worse) performance.