Understanding Biases in Indian Ocean Seasonal SST in CMIP6 Models

Abstract

AbstractThe latest generation of climate models continue to exhibit biases in their representation of climatological sea surface temperature (SST), affecting their ability to simulate climate variability including the Indian Ocean Dipole which is typically too strong. Here, we analyze the surface layer heat budget of the Indian Ocean to diagnose the processes leading to biases in climatological SST biases in 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, in comparison to a suite of observational and reanalysis products. In the western tropical Indian Ocean, we find that weaker than observed winds reduce the strength of surface currents leading to warm SST bias. In the south‐eastern tropical Indian Ocean, overly strong southeasterly winds are associated with overestimated coastal upwelling that increases cooling across CMIP6 models. In the Arabian Sea, overly strong surface winds increase latent heat loss and leads to cool SST biases. We also analyze other regions like the Bay of Bengal, where a persistent cool bias cannot be explained by seasonal heat budgets, and southern Indian Ocean, where overly strong surface winds are responsible for cool SST biases. These biases in surface processes are supported by intermodel relationships between relevant variables, thus explaining differences in simulating the climatological SSTs across the model ensemble. Our analysis suggests that biases in atmospheric processes in particular surface winds are a primary cause of biases in Indian Ocean SST. Reducing these biases would improve the simulation of climate variability in the Indian Ocean toward more reliable climate projections and predictions.