Subseasonal Forecast Skill Improvement From Strongly Coupled Data Assimilation With a Linear Inverse Model

Abstract

Strongly coupled data assimilation (SCDA), such as using atmospheric observations to update ocean analyses, is critical for properly initializing Earth System models to predict subseasonal to decadal timescales. We show that a Kalman filter with a linear emulator of the coupled dynamics can be used to efficiently assimilate observations with SCDA. A linear inverse model (LIM), trained on 25 years of Climate Forecast System Reanalysis gridded data, is used to assimilate observations daily during an independent 7-year period. SCDA sea-surface temperature (SST) analysis errors are reduced over 20% in global-mean mean-squared error relative to a control experiment where only SST observations are assimilated with an SST LIM. The analysis improvements enhance forecast skill for leads of at least 50 days. In contrast, extratropical Northern Hemisphere 2 m air temperature forecast errors increase for coupled data assimilation in these experiments, despite reduction during the training period.