The role of in‐situ ocean data assimilation in ECMWF subseasonal forecasts of SST and MLD 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 ENSO, 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 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 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 rapidly shoals at the beginning of the forecast. These results emphasize how initialization and model biases influence the air‐sea interaction and the accuracy of subseasonal forecast in the tropical Pacific.This article is protected by copyright. All rights reserved.