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    Publication Date: 2022-10-26
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yu, L. Variability and uncertainty of satellite sea surface salinity in the subpolar North Atlantic (2010-2019). Remote Sensing, 12(13), (2020): 2092, doi:10.3390/rs12132092.
    Description: Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at mid-high latitudes has yet to be established. Here, four SSS products derived from two satellite missions were evaluated in the subpolar North Atlantic Ocean in reference to two in situ gridded products. Harmonic analysis of annual and semiannual cycles in in situ products revealed that seasonal variations of SSS are dominated by an annual cycle, with a maximum in March and a minimum in September. The annual amplitudes are larger (〉0.3 practical salinity scale (pss)) in the western basin where surface waters are colder and fresher, and weaker (~0.06 pss) in the eastern basin where surface waters are warmer and saltier. Satellite SSS products have difficulty producing the right annual cycle, particularly in the Labrador/Irminger seas where the SSS seasonality is dictated by the influx of Arctic low-salinity waters along the boundary currents. The study also found that there are basin-scale, time-varying drifts in the decade-long SMOS data records, which need to be corrected before the datasets can be used for studying climate variability of SSS
    Description: This research was funded by NASA Ocean Salinity Science Team (OSST) activities through Grant 80NSSC18K1335.
    Keywords: Sea surface salinity ; Subpolar North Atlantic ; SMAP ; SMOS ; Argo ; Harmonic analysis ; Seasonal and interannual variations
    Repository Name: Woods Hole Open Access Server
    Type: Article
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