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.