Ocean data assimilation for the initialization of seasonal prediction with the Community Earth System Model

Abstract

Seasonal prediction highly depends on its initialization scheme. Currently, the operational utilization of advanced data assimilation methods in the oceanic initialization is still relatively less and its benefit is far from being fully unveiled. In this study, the Community Earth System Model (CESM) is configured with an Ensemble Adjustment Kalman Filter (EAKF) to initialize the seasonal prediction. The performance of the established prediction system is assessed by comparing it with a benchmark prediction system, which is based on the same model but with a nudging scheme. Results show that the assimilation of only oceanic observations can not only effectively constrain the oceanic variables, but also improves the atmospheric variables through coupling processes, which helps to produce accurate and compatible initial conditions. With the initial conditions, the current prediction system is better than the benchmark prediction system and can produce skillful predictions for sea surface temperature, ocean heat content, air temperature at 2 m, precipitation and major climate variabilities including El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The current prediction system also shows better ensemble consistency and less prediction drifts, which could contribute to the improvement of prediction skill.