Salinity is not only an essential physical property of seawater but also a characteristic of water masses in the ocean. Moreover, it is a crucial indicator of changes in the global water cycle.  However, the availability of high-resolution spatial and temporal salinity data is often restricted due to inadequate coverage of direct observations. To tackle this challenge, we developed a machine learning algorithm based on a combination of remote sensing data and a large cruise observation-based dataset and reconstructed sea surface salinity (SSS) in the entire China Seas with a spatial resolution of 0.05º×0.05º over the period of 2000-2020. This new dataset was then used to examine  the spatial and temporal variation patterns in the China Seas. To do so, we employed the Eigen Microstates Approach to reveal  key factors that regulate the SSS  in different subregions of the China Seas. These analysis indicates that the SSS gradient in the China Seas has been on the rise, primarily attributable to to the accelerated effect of large scale hydrological circulation. We further suggest that the El Niño-Southern Oscillation (ENSO) is a significant driver of the SSS changes in the China Seas.
 

sea suface salinity