Publications

Wang, J; Xu, JF; Yang, YJ; Lyv, YY; Luan, KF (2021). Seasonal and interannual variations of sea surface temperature and influencing factors in the Yangtze River Estuary. REGIONAL STUDIES IN MARINE SCIENCE, 45, 101827.

Abstract
This study explores seasonal and interannual variability in sea surface temperature (SST) in the Yangtze River Estuary, China based on remote sensing data from 1982 to 2017. The influences of climate and river flow on SST were explored using power spectrum and correlation analyses. The results show that SST had a warming trend of 0.48 degrees C per decade over the study period and exhibited significant oscillation periods of 10, 3.6, 2.4, and 1 years Seasonal SSTs showed warming trends, which were strongest in spring and weakest in autumn. Spatially, SSTs distinctly increased from the northwest to the southeast of the study area. This study represents that the runoff, air temperature, El Nino, and East Asian monsoon contribute to SST in the Yangtze River estuary. Monthly means indicate that higher runoff rates are associated with higher SSTs in the waters adjacent to the Yangtze River Estuary, and vice versa. River runoff, air temperature, and El Nino and East Asian monsoon events all influenced SST in the Yangtze River Estuary. After studying the correlation between long-term temperature and SST, it is found that temperature has forcing effect on SST, and overall trend is increasing. Heat transfer via ocean-atmosphere interactions has caused the SST in the Yangtze River Estuary to warm for many years. In addition, El Nino and monsoon intensities also affect SST; in El Nino years, the SST of the Yangtze River Estuary decreased but then increased abnormally in the following year. The meridional wind speed during the East Asian monsoon was negatively correlated with SST, while zonal wind speed was positively correlated, with meridional wind having the greater effect. This knowledge will allow more accurate predictions of local oceanography, with profound implications for fisheries, industry, and climate modeling. (C) 2021 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.rsma.2021.101827

ISSN:
2352-4855