Implication of Errors in Space-Based Sea Surface Salinity Measurement on Ocean State Estimation: An Observing System Simulation Experiment for the Tropical Indian Ocean

This study investigates the permissible error limits in satellite-derived sea surface salinity (SSS) through Observing System Simulation Experiments (OSSEs). The OSSEs were conducted in the Indian Ocean, using a data-assimilative oceanic general circulation model with error in satellite-derived SSS ranging from 0.2 to 1.0 psu for the year 2014. Comparison with Argo SSS revealed a remarkable 31% improvement in model salinity when assimilating Aquarius SSS data. The OSSE experiments indicated a clear correlation between errors in model SSS and the range of error introduced into satellite SSS data. The impact of SSS errors (≥0.5 psu) was particularly noticeable in regions with high salinity variability, such as the equatorial region and the Bay of Bengal (BOB), which also experience a profound seasonal variability. This spatial and seasonal variability in salinity errors is extended to other ocean parameters, including mixed layer depth (MLD) and sonic layer depth (SLD). When error is introduced in satellite SSS data, temporal variability of these parameters is enhanced with highest impact during summer monsoon period. Notably, regions characterized by high SSS variability exhibited higher root mean square error (RMSE) values for SSS, MLD, and SLD. These findings underscore the critical importance of minimizing SSS errors, especially in regions with substantial salinity variability, to enhance the accuracy of ocean state and improve our understanding of ocean dynamics.