2021 review

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm


author keywords: South China Sea; Nutrients; Climatological distributions; Predictive algorithm
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
14. Life Below Water (Web of Science; OpenAlex)
Source: Web Of Science
Added: July 6, 2021

Nutrients are typically the most important determinant of the productivity of marine ecosystems. Hence, nutrients have been an essential variable of ocean observations in the modern oceanography era. Understanding of marine ecosystems and biogeochemistry, however, is largely limited by the spatiotemporal coverage of nutrient data. Herein, we developed a novel algorithm based on a large observational dataset of nutrients and their relationship with water masses indexed by temperature and salinity in the South China Sea (SCS). The algorithm yielded errors of ≤ 1.3, ≤ 0.10 and ≤ 3.5 μmol L−1 for NO3– + NO2– (N + N), phosphate and silicate, respectively. It is then applied to reconstruct nutrient concentrations primarily using temperature and salinity data archived in the World Ocean Database and Argo database during 1940–2018. It increases the nutrient data to ~ 5 million, by ca. three orders of magnitude compared to direct measurements in the SCS. This allows for a full examination of the seasonal climatology of nutrients in the SCS. In summer, in the upper 200 m, nutrient concentrations in the northwest and southernmost SCS are higher than in the rest of the SCS. An overall reversed pattern is revealed in winter, when higher nutrient concentrations are found in the central basin rather than basin margins. The Kuroshio intrusion and the vertical displacement of the nutricline, driven by upwelling/downwelling induced by horizontal convergences/divergences at both meso- and basin scales, determine the spatial and seasonal variation of nutrients. Seasonally, the Kuroshio intrusion and vertical displacements of the nutricline tend to offset the spatial variation of nutrient concentrations in spring, while they show an additive effect in the fall.