The role of in situ ocean data assimilation in ECMWF subseasonal forecasts of sea-surface temperature and mixed-layer depth over the tropical Pacific ocean

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Nino-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. We found that assimilating in situ ocean observations leads to smaller sea-surface temperature biases in subseasonal forecasts over the tropical Pacific. However, the benefits of initializing subseasonal forecasts with ocean data assimilation are mostly lost for mixed-layer depth over the tropical Pacific, due to model biases and initialization shock.image