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
Journal Article
Overview
abstract
Abstract; 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 of the ocean–atmosphere state (fit to the assimilated data) than either experiment assimilating only oceanic or atmospheric data. This indicates that the coupled system provides more consistent initial conditions, yielding more balanced oceanic and atmospheric states when assimilating all the data. 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 (coupled model simulation without assimilating any data). We found that this counterintuitive model performance is due to the systematic 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 cyclone path offset of about 200 km relative to IBTrACS data), 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 data are unavailable or limited in space and time, particularly during high‐impact atmospheric events such as tropical cyclones.
