Subseasonal Forecast Skill Improvement From Strongly Coupled Data Assimilation With a Linear Inverse Model. Issue 11 (6th June 2022)

• Record Type: Journal Article
• Authors: Hakim, Gregory J.; Snyder, Chris; Penny, Stephen G.; Newman, Matthew
• Is Part Of: Geophysical research letters. Volume 49:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-06
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2022GL097996
• Languages: English
• ISSNs: 0094-8276

Abstract

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. Plain Language Summary: Using observations to consistently initialize a forecast is very difficult with coupled Earth system models due to their enormous computational demand. Here we show that a simplified model can be used to improve forecasts through this initialization process. In particular, we show that this approach allows observations of the atmosphere to be used to estimate sea‐surface temperature (SST) and to improve forecasts of SST when compared to an approach that uses only observations of SST. This suggests the potential to improve SST forecasts one to 2 months in the future using such an approach. Key Points: We provide proof‐of‐concept results that low‐dimensional Earth System emulators are useful for testing coupled data assimilation approaches Strongly coupled data assimilation improves analysis and forecast skill on subseasonal timescales relative to the weakly coupled case