The eastern China seas are some of the largest marginal seas in the world, where high primary productivity and phytoplankton blooms are often observed. However, little is known about their systematic variation of phytoplankton blooms on large spatial and long temporal scales due to the difficulty of monitoring bloom events by field measurement. In this study, we investigated the seasonal and interannual variability and long-term changes in phytoplankton blooms in the eastern China seas using a 14 yr (1998–2011) time series of satellite ocean colour data. To ensure a proper satellite dataset to figure out the bloom events, we validated and corrected the satellite-derived chlorophyll concentration (chl a) using extensive in situ datasets from two large cruises. The correlation coefficients between the satellite retrieval data and the in situ chl a on the logarithmic scale were 0.85 and 0.72 for the SeaWiFS and Aqua/MODIS data, respectively. Although satellites generally overestimate the chl a, especially in highly turbid waters, both the in situ and satellite data show that the overestimation of satellite-derived chl a has an upper limit value (10 μg L⁻¹), which can be used as a threshold for the identification of phytoplankton blooms to avoid the false blooms resulting from turbid waters. Taking 10 μg L⁻¹ as the threshold, we present the spatial-temporal variability of phytoplankton blooms in the eastern China seas over the past 14 yr. Most blooms occur in the Changjiang Estuary and along the coasts of Zhejiang, with a maximal frequency of 20% (about 73 days per year). The coasts of the northern Yellow Sea and the Bohai Sea also have high-frequency blooms (up to 20%). The blooms show significant seasonal variation, with most occurring in spring (April–June) and summer (July–September). The study also revealed a doubling in bloom intensity in the Yellow Sea and Bohai Sea during the past 14 yr. The nutrient supply in the eastern China seas might be a major controlling factor in bloom variation. The time series in situ nutrient datasets show that both the nitrate and phosphate concentrations increased more than twofold between 1998 and 2005 in the Yellow Sea. This might be the reason for the doubling of the bloom intensity index in the Yellow Sea and Bohai Sea. In contrast, there has been no significant long-term increase or decrease in the Changjiang Estuary, which might be regulated by the Changjiang River discharge. These results offer a foundation for the study of the influence of phytoplankton blooms on the carbon flux estimation and biogeochemical processes in the eastern China seas.

Compared to rivers in Europe and North America, Chinese rivers that discharge into oceans have different organic carbon (OC) transport characteristics. Out of the top 25 largest rivers worldwide, three (Changjiang, Huanghe, and Zhujiang rivers) are located in China, along with numerous small rivers. Thus, synthesized estimates of total riverine OC flux from Chinese rivers into marginal seas are critical but remain deficient. In this study, we developed relationships between riverine OC (dissolved OC, or DOC, and particulate OC, or POC) and basin characteristic variables (basin population density, precipitation, and riverine suspended sediment concentration) to estimate annual riverine DOC and POC fluxes during 1953-2016. The results showed that rivers in mainland China transported 9.63 Tg C of OC to the marginal seas in 2008, with 4.61 Tg C of DOC and 5.02 Tg C of POC. Of this transported OC, 14.28% DOC and 17.49% POC were transported by small southeastern rivers, whose drainage areas covered only 6.68% of the total. Because of intensifying human activities, DOC export increased but POC export decreased during 1953-2016. Additionally, basin population growth and reservoir water capacity were the major factors for increasing DOC flux and decreasing POC flux, respectively. Overall, the DOC/POC ratio increased for OC transport in Chinese rivers. Therefore, this study is important for understanding human-induced impacts on environmental change and the carbon cycle in marginal seas.

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS (Sea-viewing Wide Field-of-view Sensor) in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve climate change related studies, it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI (Ocean Colour Climate Change Initiative), which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the inter-mission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the SeaWiFS, MODIS (Medium-spectral Resolution Imaging Spectrometer), MERIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and OLCI (Ocean and Land Colour Instrument) with inter-mission differences corrected in this work. The fitness of the dataset on long-term Chla trend study was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. The results suggest that our dataset can be used for long-term series analysis and trend detection. We also provide the global trend map in Chla over 23 years (1998–2020) and present a significant positive global trend with 0.67% ± 0.37%/yr.

Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (R_rs) were used as inputs in our model. Compared with models that only use R_rs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R²) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved R_rs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.

This study assessed the effects of typhoons on sea surface pCO₂ and CO₂ flux in the northern South China Sea (SCS). During the passage of three major typhoons from May to August 2013, sea surface pCO₂, surface seawater temperature (SST), and other meteorological parameters were continuously measured on a moored buoy. Surface water in the region was a source of CO₂ to the atmosphere with large variations ranging from hours to months. SST was the primary factor controlling the variation of surface pCO₂ through most of the time period. Typhoons are seen to impact surface pCO₂ in three steps: first by cooling, thus decreasing surface pCO₂, and then by causing vertical mixing that brings up deep, high‐CO₂ water, and lastly triggering net uptake of CO₂ due to the nutrients brought up in this deep water. The typhoons of this study primarily impacted air‐sea CO₂ flux via increasing wind speeds. The mean CO₂ flux during a typhoon ranged from 3.6 to 5.4 times the pretyphoon mean flux. The magnitude of the CO₂ flux during typhoons was strongly inversely correlated with the typhoon center distance. The effect of typhoons accounted for 22% of the total CO₂ flux in the study period, during which typhoons occurred only 9% of the time. It was estimated that typhoons enhanced annual CO₂ efflux by 23–56% in the northern SCS during the last decade. As such, tropical cyclones may play a large and increasingly important role in controlling CO₂ fluxes in a warmer and stormier ocean of the future.