2022
DOI: 10.5194/essd-2022-236
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Reconstructing ocean subsurface salinity at high resolution using a machine learning approach

Abstract: Abstract. A gridded ocean subsurface salinity dataset with global coverage is useful for research on climate change and its variability. Here, we explore a machine learning approach to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (0–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) fiel… Show more

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