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 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.

Automated summary

The study shows that a Kalman filter with a linear emulator can efficiently assimilate strongly coupled data, improving the forecast skill of ocean analyses. The experiment results demonstrate that daily assimilation of observations using a linear inverse model reduces the analysis errors of sea-surface temperature by 20% compared to a control experiment. Additionally, it enhances the forecast skill for at least 50 days. However, the assimilation of coupled data leads to an increase in forecast errors of extratropical Northern Hemisphere 2 m air temperature.