Fig. 7: Seasonal phase locking of sea surface temperature anomalies in the Niño3.4 region for 1870–2014. Black dots give the monthly standard deviation of the observed Niño3.4 index for 1870–2014 (Rayner et al., 2003); blue lines give the standard deviations of the simulated Niño3.4 indices for each of the five historical ensemble members (hist1 to hist5). The range (min/max) spanned by the model results is shaded in light blue. All data have been linearly detrended and the seasonal cycle removed before computing the standard deviation.

4.4 Ocean

Spatial distributions of temperature and salinity biases at the surface and in the interior of the ocean for historical simulations are shown in Fig. 8. Most areas show a small cold bias of 1°C or less in sea surface temperature (SST). There is a pronounced cold bias in the North Atlantic, which is related to the too zonal pathway of the North Atlantic Current; this is a problem that is present in many CMIP climate models (e.g. Wang et al., 2014). If refining the horizontal resolution further to half of the local Rossby radius which for the long time periods of CMIP6 simulations is computationally prohibitive, this bias is largely reduced (Sein et al., 2017). Warm SST biases of up to 1.5°C can be found over the Kuroshio extension, west of Africa as well as very localized close to the equator west of South America, in the Irminger current, over the Labrador Sea, and in the Southern mid-latitudes in the Indian and Atlantic sector. Some of these biases are typical for climate models such as the cold bias over the North Atlantic subpolar gyre or the warm bias west of Africa. However, over the Southern Ocean no pronounced warm bias is found. This is in stark contrast to MPI-ESM-1.2, the climate model with the same atmospheric component but different ocean model (Müller et al., 2018, their Fig. 2b), and the E3SM model (Golaz et al., 2019, their Fig. 10c), while there are other CMIP models that represent Southern Ocean temperature well.
At the surface, most of the ocean exhibits a fresh bias. In many subtropical and tropical areas this bias amounts to 0.5 to 1 psu; it tends to be weaker in mid-latitude areas. Pronounced but localized salt biases of around 2 psu can be seen close to the coasts of the Eurasian Arctic, in and around the Gulf of Mexico, and in the Bay of Bengal. Smaller salinity biases of up to 0.3 psu can be found over the Southern Ocean and the Pacific warm pool. The general feature of a surface fresh bias in many regions is present also in other climate models such as the E3SM (Golaz et al., 2019), although the regional distribution is not necessarily the same. Features such as the Gulf of Mexico and Bay of Bengal salinity biases are in common with E3SM.
Many CMIP5 models that have coarse ocean resolution suffer from a warm bias at around 1000 m, which is especially strong in the Atlantic Ocean. Increase in the horizontal resolution leads to reduction of this bias, as pointed out by Rackow et al. (2019). Therefore, the performance of AWI-CM in Atlantic temperature is improved compared to other CMIP models. In the AWI-CM simulations discussed in this paper, the magnitude of the warm bias in the South Atlantic is similar to the one over most of the Pacific Ocean (Fig. 8b). The cold and fresh bias in the North Atlantic is related to the outflow and spreading of Mediterranean waters from the Strait of Gibraltar. The reasons for this bias and possible ways to reduce it are discussed in Rackow et al. (2019). The positive temperature and salinity biases in the Indian Ocean are most probably related to excessive supply of warm and salty water from the Red sea. Generally, the biases in temperature and salinity compensate each other in terms of density.
(a) (b)