Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018
1
First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China
2
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
3
Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yukiharu Hisaki and Jorge Vazquez
Remote Sens. 2021, 13(4), 811; https://doi.org/10.3390/rs13040811
Received: 9 December 2020 / Revised: 12 February 2021 / Accepted: 20 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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.
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Keywords:
sea surface salinity; sub-annual variability; annual cycle; interannual variability; intercomparison; SMOS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Liu, H.; Wei, Z. Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018. Remote Sens. 2021, 13, 811. https://doi.org/10.3390/rs13040811
AMA Style
Liu H, Wei Z. Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018. Remote Sensing. 2021; 13(4):811. https://doi.org/10.3390/rs13040811
Chicago/Turabian StyleLiu, Hao; Wei, Zexun. 2021. "Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018" Remote Sens. 13, no. 4: 811. https://doi.org/10.3390/rs13040811
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