Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China

Abstract

A time series of satellite data on Chlorophyll- a concentration (Chl- a ) that used ocean color was studied to determine mechanisms of phytoplankton variation in recent decade in the Yellow Sea, China during 2003–2015. The variability patterns on seasonal and inter-annual oscillation periods were confirmed using the Empirical Orthogonal Function (EOF), and Morlet wavelet transform analyses, respectively. The first EOF mode for Chl- a was dominated by obvious spring and fall blooms in a spatial pattern that was related to the strong mixing of the water masses from the Yellow Sea Cold Warm Mass (YSCWM) and the Yellow Sea Warm Current (YSWC) in winter. The second EOF mode for Chl- a showed an opposite spatial pattern between the northern and southern regions. The temporal coefficient showed differences in the timing of blooms. On an inter-annual scale, Chl- a indicated variation at periods of 2–4 years during 2003–2015. Chl- a showed a significantly negative correlation with the sea surface temperature (r = -0.21, p<0.01), with time lags of 4 months (Chl- a ahead). Chl- a was coupled with El Niño Southern Oscillation (ENSO) events, with a positive correlation (r = 0.46, p<0.01) at a lag of 3–5 months (ENSO ahead). The present study demonstrated that the variation in phytoplankton biomass was controlled primarily by water mass seasonally, and it was influenced by ENSO events on an inter-annual scale.

Evolution of satellite derived chlorophyll-a trends in the Bohai and Yellow Seas during 2002–2018: Comparison between linear and nonlinear trends

“…From the linear trend analysis (obtained from MK test), an overall significant positive Chl-a trend was detected in the BYS during 2002-2018. This result is in agreement with previous studies, which also revealed positive Chl-a trends in the BYS or around its adjacent areas throughout similar periods (Kong et al, 2019;Liu et al, 2019;Xing et al, 2015;Zhang et al, 2017). The increased phytoplankton biomass in marginal seas was well documented and was likely related to enhanced coastal eutrophication (Boyce and Worm, 2015;Jickells, 1998;Marrari et al, 2016).…”

Using Geographical Overlaps to Track Temporal Changes in Species Interactions and Community Coexistence Instability

“…Consistent with the importance of regional variability in the Eastern Bering Sea highlighted in this study, regional climate effects have been observed in dense temperate European and North American forests, with cooler forest-floor temperatures attenuating species' responses to global warming via increased shading during the growing season (De Frenne et al, 2013). Similarly, regional changes in water masses, sea surface temperature, the El Niño-Southern Oscillation (ENSO), and the seasonal Northern Atlantic Oscillation (NAO) have driven phytoplankton variation and bottom-up trophic cascades in marine ecosystems (Liu et al, 2019). However, many other constraints, including biotic mutualism, trophic competition (Araújo et al, 2011) and other abiotic anthropogenic effects, such as habitat destruction (Colorado-Zuluaga, 2015), also regulate the geographically interactive dependency between species.…”

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

“…This study area belongs to the Southern Yellow Sea (119-123 • E, 32-37 • N), a part of the Yellow Sea in China. The Southern Yellow Sea is a semi-enclosed shallow sea with an average depth of 44 m (Figure 1) [31,32], covering an area of 309,000 km 2 . Influenced by the Yellow Sea Warm Current (YSWC), the Yellow Sea Cold Water Mass (YSCWM), the coastal current of the Yellow Sea, and the diluted current of the Yangtze River, the Southern Yellow Sea has complex hydrographic conditions [33].…”