scholarly journals Decadal warming of coastal China Seas and coupling with winter monsoon and currents

2013 ◽  
Vol 40 (23) ◽  
pp. 6288-6292 ◽  
Author(s):  
L.-Y. Oey ◽  
M.-C. Chang ◽  
Y.-L. Chang ◽  
Y.-C. Lin ◽  
F.-H. Xu
Keyword(s):  
Winter Monsoon ◽  
China Seas ◽  
Coastal China

scholarly journals Regional and Urban-Scale Environmental Influences of Oceanic DMS Emissions over Coastal China Seas

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 849 ◽  
Author(s):  
Shanshan Li ◽  
Yan Zhang ◽  
Junri Zhao ◽  
Golam Sarwar ◽  
Shengqian Zhou ◽  
...  
Keyword(s):  
Bohai Sea ◽  
Regional Scale ◽  
The Yellow Sea ◽  
East China ◽  
China Seas ◽  
The East China Sea ◽  
Different Seasons ◽  
Fine Mode ◽  
Atmospheric Sulfur ◽  
Coastal China

Marine biogenic dimethyl sulfide (DMS) is an important natural source of sulfur in the atmosphere, which may play an important role in air quality. In this study, the WRF-CMAQ model is employed to assess the impact of DMS on the atmospheric environment at the regional scale of eastern coastal China and urban scale of Shanghai in 2017. A national scale database of DMS concentration in seawater is established based on the historical DMS measurements in the Yellow Sea, the Bohai Sea and the East China Sea in different seasons during 2009~2017. Results indicate that the sea-to-air emission flux of DMS varies greatly in different seasons, with the highest in summer, followed by spring and autumn, and the lowest in winter. The annual DMS emissions from the Yellow Sea, the Bohai Sea and the East China Sea are 0.008, 0.059, and 0.15 Tg S a−1, respectively. At the regional scale, DMS emissions increase atmospheric sulfur dioxide (SO2) and sulfate (SO42−) concentrations over the East China seas by a maximum of 8% in summer and a minimum of 2% in winter, respectively. At the urban scale, the addition of DMS emissions increase the SO2 and SO42− levels by 2% and 5%, respectively, and reduce ozone (O3) in the air of Shanghai by 1.5%~2.5%. DMS emissions increase fine-mode ammonium particle concentration distribution by 4% and 5%, and fine-mode nss-SO42− concentration distributions by 4% and 9% in the urban and marine air, respectively. Our results indicate that although anthropogenic sources are still the dominant contributor of atmospheric sulfur burden in China, biogenic DMS emissions source cannot be ignored.

Impacts of and adaptation to inter-decadal marine climate change in coastal China seas

2015 ◽  
Vol 36 (11) ◽  
pp. 3770-3780 ◽  
Author(s):  
Rongshuo Cai ◽  
Hongjian Tan ◽  
Qinghua Qi
Keyword(s):  
Climate Change ◽  
China Seas ◽  
Marine Climate ◽  
Coastal China

scholarly journals Accounting for Seasonal Growth in Per-Recruit Analyses: A Case Study of Four Commercial Fish in Coastal China Seas

2021 ◽  
Vol 8 ◽  
Author(s):  
Yiwen Liu ◽  
Chongliang Zhang ◽  
Binduo Xu ◽  
Ying Xue ◽  
Yiping Ren ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Stock Assessment ◽  
Growth Patterns ◽  
Data Availability ◽  
China Seas ◽  
Seasonal Growth ◽  
Length Frequency ◽  
Commercial Fish ◽  
Coastal China ◽  
Length Frequency Analysis

Biological reference points (BRPs) derived from per-recruit analyses are commonly used in inferring stock status and serve as the target or threshold in fisheries management. However, the estimation of BRPs may be impacted by the variability in life history processes, and particularly, individual growth rates often display substantial seasonal oscillations but are seldomly considered in per-recruit analyses. Using four commercial fish species Lophius litulon, Saurida elongata, Hexagrammos otakii, and Larimichthys polyactis in coastal China Seas as examples, this study examined the effects of seasonal growth variability on per-recruit analyses and on the estimation of BRPs. We developed an individual-based modeling framework to simulate growth patterns with and without variations at the seasonal and the individual levels and adopted two common assessment methods, age-based analysis and length-frequency analysis, to estimate growth parameters regarding data availability in data-rich or data-poor fisheries, respectively. We found that ignoring seasonality could lead to substantial errors in the estimation of BRPs for the small-size species H. otakii and L. polyactis in our evaluation; when seasonal growth was considered, the estimation could be largely improved. Length-frequency analysis might yield considerably less reliable estimations than age-based method. The time of year when fast growth occurs determines positive or negative bias in estimation, and the amplitude of seasonal growth determines the degree of biases. In general, ignoring the seasonality of growth when there is can lead to underestimated growth parameter K and trigger biases that propagate in stock assessment and management, whereas incorporating seasonality falsely in assessment when there is no seasonal variation will have little influences on the estimation of BRPs. This study contributes to demonstrate the risk of ignoring seasonality in stock assessment and the approaches accounting for seasonal variability in fishery management.

scholarly journals Species diversity of Ascidian in the coastal China Seas

1995 ◽  
Vol 03 (4) ◽  
pp. 201-205
Author(s):  
Zheng Chengxing ◽  
Keyword(s):  
Species Diversity ◽  
China Seas ◽  
Coastal China

scholarly journals Particulate and Dissolved Black Carbon in Coastal China Seas: Spatiotemporal Variations, Dynamics, and Potential Implications

2020 ◽  
Vol 55 (1) ◽  
pp. 788-796
Author(s):  
Yin Fang ◽  
Yingjun Chen ◽  
Guopei Huang ◽  
Limin Hu ◽  
Chongguo Tian ◽  
...  
Keyword(s):  
Black Carbon ◽  
China Seas ◽  
Spatiotemporal Variations ◽  
Coastal China

Heat budget responses of the eastern China seas to global warming in a coupled atmosphere-ocean model

2019 ◽  
Vol 79 (2) ◽  
pp. 109-126
Author(s):  
D Tian ◽  
J Su ◽  
F Zhou ◽  
B Mayer ◽  
D Sein ◽  
...  
Keyword(s):  
Global Warming ◽  
Heat Budget ◽  
Eastern China ◽  
Ocean Model ◽  
China Seas ◽  
Eastern China Seas

Molecular Identification of Stranded Cetaceans in Coastal China

2019 ◽  
Vol 45 (5) ◽  
pp. 525-532
Author(s):  
Ting Li ◽  
Hao Wu ◽  
Caiwen Wu ◽  
Guang Yang ◽  
Bingyao Chen
Keyword(s):  
Molecular Identification ◽  
Coastal China
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