Abstract

Monthly sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration during 1997–2018 in the Northern South China Sea (NSCS) were gap-filled using the Data INterpolating Empirical Orthogonal Functions (DINEOFs) approach. Good accuracy of the reconstructed data was verified based on an independent cross-validation data set. Significant spatial heterogeneities and seasonal variations were observed in the cloud-free SST and Chl-a data. An apparent decreasing trend in SST was found along the coast of Guangdong, east of Hainan Island, and in the Taiwan Strait with an average decreasing rate amounting to 0.01 °C per year. Increase in SST was observed in Beibu Gulf and the deep offshore region in the NSCS at an average rate as high as 0.009 °C per year. An increasing trend in Chl-a was detected in the northern Beibu Gulf, near the mouth of the Pearl River estuary, and around Shantou with an average rate of 0.005 mg <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot \text{m}^{-3}$ </tex-math></inline-formula> per year. EOF analysis of SST and Chl-a time series showed that the first three modes of both SST and Chl-a can explain most of their variances with accumulative contributions of 99.1% and 82.3%, respectively. The effects of surface net heat flux (NHF), Oceanic Niño Index (ONI), wind speed, and river flux on the first three modes of SST and Chl-a were quantitatively elucidated. Factors that affected the temporal and spatial variations of Chl-a and SST in the NSCS were qualitatively discussed, including El Niño-Southern Oscillation (ENSO), monsoon, upwelling, typhoon, internal waves, and Kuroshio intrusion.

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