Exploring air–sea interactions in a regional weakly coupled data assimilation system: An observing system experiment for the 2023 Indian Ocean monsoon and Cyclone Biparjoy

We examined air–sea interactions in a weakly coupled ocean–atmosphere data assimilation (DA) system by performing an observing system experiment (OSE). The OSE is performed for the Arabian Sea monsoon onset event in 2023, which also coincides with the passage of the tropical cyclone Biparjoy. Results show that assimilating both oceanic and atmospheric data produces a better forecast than either experiment assimilating only oceanic or atmospheric data. This indicates that the coupled system provides improved initial conditions with more balanced oceanic and atmospheric states when assimilating all observations. However, we found that assimilating only atmospheric data improves both the ocean and atmosphere state forecasts, whereas assimilating only oceanic data improves the ocean state forecasts but degrades the atmospheric state forecasts, when compared with the free run. We found that this counterintuitive model performance is due to the potential misrepresentation of air–sea exchanges. First, errors in the cyclone path increase when atmospheric data are not assimilated. Then, assimilation of sea surface temperature (SST) leads to a warmer SST below the misplaced cyclone (with an offset of about 200 km), resulting in increased cyclone intensity. This misrepresentation of air–sea exchanges not only occurs during the cyclone passage but also persists throughout the early monsoon season. Since errors in one component can affect the other and result in positive feedback, leading to error growth, this study highlights an interesting effect on weakly coupled ocean–atmosphere DA systems when atmospheric observations are unavailable or limited, particularly during high-impact atmospheric events such as tropical cyclones.