Coupled climate models such as those in the Coupled Model
Intercomparison Project Phase 6 (CMIP6) suffer from biases in sea
surface temperature (SST) which affect the representation of impactful
climate phenomena. We investigate the surface layer heat budget to
diagnose the processes leading to monthly climatological SST biases in
historical simulation of 20 CMIP6 models. Our findings indicate that
SST biases are closely linked to biases in surface winds, although the
mechanisms leading to these biases vary across different regions. In
the west tropical Indian Ocean, weaker than observed winds reduce
surface currents which lead to warm SST biases. Over the Arabian Sea,
overly strong surface winds contribute to strong latent heat loss and
cool SST biases. In In the southeast tropical Indian Ocean, a cool
bias is likely caused by stronger winds, which drive higher levels of
offshore heat transport and upwelling compared to observations. These
relationships are consistent across models, shown by intermodel
relationships across different variables, which help to explain biases
in SST.

Previous works showed Pacific Ocean SST impacts the strength of Indian
Ocean variability in climate models, indicating that SST bias may
affect interannual variability. To explore the effect of removing SST
biases in the tropical Pacific Ocean, we conduct SST restoring
experiments using the coupled ACCESS-CM2 model. Initial results
indicate that restoring to observed climatology (no interannual
variability) reduces Indian Ocean SST relative to the control,
particularly around the maritime continent. We aim to find what impact
Pacific Ocean SST biases have on both seasonal cycle and interannual
variability of the Indian ocean.

Date & Time: Wed Jun 21 2023 11:00:00 GMT+0200 (Central European Summer Time)
Room: salle du LOCEAN, couloir 45-55, 4ème étage, pièce 417