Abstract
The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.
Highlights
The ensemble-averaged Sea surface salinity (SSS) derived from ten products (Figure 1) shows an overall consistent pattern with surface freshwater fluxes [48,49]: salty surface waters mostly occur within subtropical gyres due to excessive evaporation, and fresh waters are located over tropical and subpolar regions (Figure 1a) due to overwhelming precipitation or river discharge
We calculate the standard deviation (STD) of the total SSS variations over 2011–2018 from the ensemble mean fields and each product
Maps, we found that the global mean ECA CCI SSS maps show the best resemblance to the ensemble mean fields on the sub-annual and interannual time scales
Summary
Academic Editors: Yukiharu Hisaki and Jorge Vazquez. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Sea surface salinity (SSS) is widely used as an indicator for monitoring the hydrological cycle [1–4], oceanic processes (such as sea-level changes [5], instability waves [6,7], and Rossby waves [8]), and climate variability [9–15]. An accurate representation of SSS, especially its variations, is highly dependent on observational data. Salinity observations have undergone dramatic changes over recent decades. Before the 2000s, salinity observations were generally obtained from research vessels and moorings. These observations were generally sparse in both space and time. After 2004–2005, hundreds of Argo profilers were released [16]. Argo profilers covered only one 3◦ grid every 10 days [17]
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