Outcomes from UGROW-IO: Forecast errors in the Eastern Indian Ocean across lead times

UGROW is an ECMWF cross-departmental project focused on Understanding systematic error GROWth from hours to seasons ahead. The forecast errors over the Indian Ocean is one of the focus themes (UGROW-IO), which assesses the lead-time-dependent evolution of forecast errors in the Indian Ocean, which happens to be the eastern pole of the Indian Ocean Dipole Index.
Seasonal forecasts rapidly develop an easterly surface wind bias in the Equatorial Eastern Indian Ocean (EEIO) during boreal summer, visible already at week 1. This bias amplifies with time via coupled feedbacks, and it eventually manifests in a cold SST bias, as one of the prominent errors at the seasonal time scales in SEAS5.
The EEIO exhibits two regimes: a warm pool regime or convective regime, characterized by a local negative wind-SST coupled feedback which limits warming, and a cold-tongue regime, characterized by a non-local positive wind-SST-thermocline feedback which enhances and maintains the cold SST. The error in the seasonal forecasts suggest that in the model the cold-tongue regime dominates, while the wind-SST coupling in the warm regime is very weak compared to observations.
We have studied the dependency of this error to ocean and atmosphere initial conditions, ocean and atmospheric resolution, and different model cycles. We conclude that there are two fundamental and independent sources of errors that lead to the SST errors in seasonal forecast. The first one is of atmospheric nature and is largely related with too stable easterly circulation present in the equatorial Indian Ocean, further characterized by the lack of response of the local winds to local surface heating in the EEIO. This induces an easterly bias which leaves the model state predominantly in a state with a shallow thermocline and cold SSTs in the EEIO. The second error is of oceanic origin, associated with a too shallow thermocline, which enhances the SST errors arising from errors in the wind. Ocean initial conditions, which depend on both the quality of the assimilation and the ocean model, play an important role in this context. Nevertheless, the version of the ocean model used for the forecast can also play a non-negligible role at the seasonal time scales, by amplifying or damping the subsurface errors in the initial conditions due to the strength of the atmosphere-ocean coupling in this region.