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Demonstrates improved salinity modeling in ocean models using satellite observations, indicating enhanced accuracy.
In recent years, more and more satellite SSS products become available for improving salinity simulation in ocean models. This study introduces the implementation of satellite SSS as Dirichlet surface haline boundary condition (SHBC) in general ocean model. Using the Hybrid Coordinate Ocean Model, three numerical experiments covering the period 2019 on a quasi-global domain were performed with Dirichlet SHBC, Nudging and virtual salt flux, respectively. The effectiveness of the different SHBCs was assessed by comparisons with Argo observations and intercomparisons with the GLORYS12 reanalysis. The results demonstrate that the novel Dirichlet SHBC is able to constrain the modelled salinity in the upper 100 m depth better than other SHBCs. In particular, with respect to the Argo salinity, the Dirichlet SHBC is comparable to the GLORYS12 reanalysis in the upper 30 m depth in the Pacific Ocean. The sensitivity analysis on the observation error of the satellite SSS suggests that the proposed Dirichlet SHBC is optimal where the actual error of the satellite SSS relative to Argo is less than 0.8 PSU which can be reached by most SSS products. Furthermore, the novel Dirichlet SHBC has positive effects on mixed layer depth but is helpless for seawater temperature. This study offers a new perspective on the integration of high-accuracy satellite observations and ocean numerical models.
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Zhang et al. (2026) studied this question.
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