Abstract
The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds.
Significance Statement
We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.
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