Radiocarbon in the Maritime Air and Sea Surface Water of the South China Sea

Radiocarbon ◽  
2018 ◽  
Vol 61 (2) ◽  
pp. 461-472 ◽  
Author(s):  
Pan Gao ◽  
Liping Zhou ◽  
Kexin Liu ◽  
Xiaomei Xu
Keyword(s):  
Surface Water ◽  
South China Sea ◽  
South China ◽  
Dissolved Inorganic Carbon ◽  
The South China Sea ◽  
Sea Surface ◽  
Surface Seawater ◽  
The South ◽  
China Sea ◽  
Maritime Air

ABSTRACTRadiocarbon (14C) generated by the thermonuclear tests in the late 1950s to early 1960s has been used as a tracer to study atmospheric and oceanic circulations, carbon exchange between different reservoirs, and fossil fuel emissions. Here we report the first measurements of 14C in atmospheric CO2 of maritime air collected over the South China Sea (SCS) during July 2014. We also present 14C of the dissolved inorganic carbon (DIC) in the sea surface water in the same region. Most of the Δ14C values of the atmospheric CO2 vary in the range of 15.6±1.6‰– 22.0±1.6‰, indicating that the central SCS maritime air is well-mixed and consistent with the clean background air in the Northern Hemisphere. The 14C values of the DIC (DI14C) in the surface seawater vary between 28.3±2.5‰ and 40.6±2.7‰, mainly due to the lateral mixing between the SCS and western Pacific. The average surface seawater DI14C is 15.4 ± 5.1‰ higher than that of the maritime air 14CO2. The reversal of the sea–air Δ14C gradient occurred at ∼2000, marking the start of the upper ocean transferring bomb 14C back to the atmosphere in the SCS.

scholarly journals Purpleback Flying Squid Sthenoteuthis oualaniensis in the South China Sea: Growth, Resources and Association with the Environment

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Chunxu Zhao ◽  
Chunyan Shen ◽  
Andrew Bakun ◽  
Yunrong Yan ◽  
Bin Kang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Biological Characteristics ◽  
Trophic Levels ◽  
Sea Surface ◽  
Height Anomaly ◽  
The South ◽  
China Sea ◽  
Sthenoteuthis Oualaniensis

The purpleback flying squid (Ommastrephidae: Sthenoteuthis oualaniensis) is an important species at higher trophic levels of the regional marine ecosystem in the South China Sea (SCS), where it is considered to show the potential for fishery development. Accordingly, under increasing climatic and environmental changes, understanding the nature and importance of various factors that determine the spatial and temporal distribution and abundance of S. oualaniensis in the SCS is of great scientific and socio-economic interest. Using generalized additive model (GAM) methods, we analyzed the relationship between available environmental factors and catch per unit effort (CPUE) data of S. oualaniensis. The body size of S. oualaniensis in the SCS was relatively small (<19.4 cm), with a shorter lifespan than individuals in other seas. The biological characteristics indicate that S. oualaniensis in the SCS showed a positive allometric growth, and could be suitably described by the logistic growth equation. In our study, the sea areas with higher CPUE were mainly distributed at 10°–11° N, with a 27–28 °C sea surface temperature (SST) range, a sea surface height anomaly (SSHA) of −0.05–0.05 m, and chlorophyll-a concentration (Chl-a) higher than 0.18 μg/L. The SST was the most important factor in the GAM analysis and the best fitting GAM model explained 67.9% of the variance. Understanding the biological characteristics and habitat status of S. oualaniensis in the SCS will benefit the management of this resource.

Application of deep learning technique to the sea surface height prediction in the South China Sea

2021 ◽  
Vol 40 (7) ◽  
pp. 68-76
Author(s):  
Tao Song ◽  
Ningsheng Han ◽  
Yuhang Zhu ◽  
Zhongwei Li ◽  
Yineng Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China Sea ◽  
South China ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Learning Technique ◽  
Height Prediction

Dynamics of dissolved inorganic carbon in the South China Sea: A modeling study

2020 ◽  
Vol 186 ◽  
pp. 102367
Author(s):  
Chuanjun Du ◽  
Jianping Gan ◽  
Chiwing Rex Hui ◽  
Zhongming Lu ◽  
Xiaozheng Zhao ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Modeling Study

Large conversion rates of NO2 to HNO2 observed in air masses from the South China Sea: Evidence of strong production at sea surface?

2014 ◽  
Vol 41 (21) ◽  
pp. 7710-7715 ◽  
Author(s):  
Qiaozhi Zha ◽  
Likun Xue ◽  
Tao Wang ◽  
Zheng Xu ◽  
Chungpong Yeung ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
Air Masses ◽  
China Sea ◽  
Conversion Rates

scholarly journals Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

2019 ◽  
Vol 11 (8) ◽  
pp. 919 ◽  
Author(s):  
Ziyao Mu ◽  
Weimin Zhang ◽  
Pinqiang Wang ◽  
Huizan Wang ◽  
Xiaofeng Yang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Observation Data ◽  
The South ◽  
Surface Salinity ◽  
China Sea ◽  
Ocean Salinity

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.

Holocene hydrological and sea surface temperature changes in the northern coast of the South China Sea

2017 ◽  
Vol 135 ◽  
pp. 268-280 ◽  
Author(s):  
Mong-Sin Wu ◽  
Yongqiang Zong ◽  
Ka-Man Mok ◽  
Ka-Ming Cheung ◽  
Haixian Xiong ◽  
...  
Keyword(s):  
South China Sea ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Northern Coast ◽  
The South ◽  
Temperature Changes ◽  
China Sea
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