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)