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

Strongly coupled data assimilation (SCDA), such as using atmospheric observations to update ocean analyses, is critical for properly initializing Earth System models predict subseasonal decadal timescales. We show that a Kalman filter with linear emulator of the dynamics can be used efficiently assimilate SCDA. A inverse model (LIM), trained on 25 years Climate Forecast Reanalysis gridded data, 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 control experiment where only SST assimilated LIM. The improvements enhance forecast skill leads at least 50 days. In contrast, extratropical Northern Hemisphere 2m air increase these experiments, despite reduction training