scholarly journals Feasibility of reconstructing the basin–scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea

2020 ◽  
Author(s):  
Guizhi Wang ◽  
Samuel S. P. Shen ◽  
Yao Chen ◽  
Yan Bai ◽  
Huan Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
South China Sea ◽  
Partial Pressure ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
High Pco2 ◽  
The South ◽  
China Sea ◽  
Basin Scale

Abstract. Sea surface partial pressure of CO2 (pCO2) data with high spatial-temporal resolution are important in studying the global carbon cycle and assessing the oceanic carbon uptake capacity. However, the observed sea surface pCO2 data are usually limited in spatial and temporal coverage, especially in marginal seas. This study provides an approach to reconstruct the complete sea surface pCO2 field in the South China Sea (SCS) with a grid resolution of 0.5º × 0.5º over the period of 2000–2017 using both remote-sensing derived pCO2 and observed pCO2. Empirical orthogonal functions (EOFs) were computed from the remote sensing derived pCO2. Then, a multilinear regression was applied to the observed pCO2 as the response variable with the EOFs as the explanatory variables. EOF1 explains the general spatial pattern of pCO2 in the SCS. EOF2 shows the pattern influenced by the Pearl River plume on the northern shelf and slope. EOF3 is consistent with the pattern influenced by coastal upwelling along the north coast of the SCS. The reconstructions always agree with observations. When pCO2 observations cover a sufficiently large area, the reconstructed fields successfully display a pattern of relatively high pCO2 in the mid-and-southern basin. The rate of sea surface pCO2 increase in the SCS is 2.383 μatm per year based on the spatial average of the reconstructed pCO2 over the period of 2000–2017. All the data for this paper are openly and freely available at PANGAEA under the link https://doi.pangaea.de/10.1594/PANGAEA.921210 (Wang et al., 2020).

scholarly journals Feasibility of reconstructing the summer basin-scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea

2021 ◽  
Vol 13 (3) ◽  
pp. 1403-1417
Author(s):  
Guizhi Wang ◽  
Samuel S. P. Shen ◽  
Yao Chen ◽  
Yan Bai ◽  
Huan Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
South China Sea ◽  
Partial Pressure ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
High Pco2 ◽  
The South ◽  
China Sea

Abstract. Sea surface partial pressure of CO2 (pCO2) data with a high spatiotemporal resolution are important in studying the global carbon cycle and assessing the oceanic carbon uptake. However, the observed sea surface pCO2 data are usually limited in spatial and temporal coverage, especially in marginal seas. This study provides an approach to reconstruct the complete sea surface pCO2 field in the South China Sea (SCS) with a grid resolution of 0.5∘×0.5∘ over the period of 2000–2017 using both remote-sensing-derived pCO2 and observed underway pCO2, among which the gridded underway pCO2 data in 2004, 2005, and 2006 are presented for the first time. Empirical orthogonal functions (EOFs) were computed from the remote-sensing-derived pCO2. Then, a multilinear regression was applied to the observed pCO2 as the response variable with the EOFs as the explanatory variables. EOF1 explains the general spatial pattern of pCO2 in the SCS. EOF2 shows the pattern influenced by the Pearl River plume on the northern shelf and slope. EOF3 is consistent with the pattern influenced by coastal upwelling along the northern coast of the SCS. When pCO2 observations cover a sufficiently large area, the reconstructed fields successfully display a pattern of relatively high pCO2 in the mid and southern basin. The rate of sea surface pCO2 increase in the SCS is 2.4±0.8 µatm yr−1 based on the spatial average of the reconstructed pCO2 over the period of 2000–2017. This is consistent with the temporal trends at Station SEATS (SouthEast Asia Time-series Study; 18∘ N, 116∘ E) in the northern basin of the SCS and at Station ALOHA (A Long-Term Oligotrophic Habitat Assessment; 22∘45′ N, 158∘ W) in the North Pacific. We validated our reconstruction with a leave-one-out cross-validation approach, which yields the root-mean-square error (RMSE) in the range of 2.4–5.2 µatm, smaller than the spatial standard deviation of our reconstructed data and much smaller than the spatial standard deviation of the observed underway data. The RMSE between the reconstructed summer pCO2 and the observed underway pCO2 is no larger than 31.7 µatm, in contrast to (a) the RMSE from 12.8 to 89.0 µatm between the remote-sensing-derived pCO2 and the underway data and (b) the RMSE from 32.6 to 44.5 µatm between the neural-network-produced pCO2 and the underway data. The difference between the reconstructed pCO2 and those calculated from observations at Station SEATS is in the range from −7 to 10 µatm. These comparison results indicate the reliability of our reconstruction method and output. All the data for this paper are openly and freely available at PANGAEA under the link https://doi.org/10.1594/PANGAEA.921210 (Wang et al., 2020).

scholarly journals A reduction in the sea surface warming rate in the South China Sea during 1999–2010

2021 ◽  
Author(s):  
Guo-Qing Jiang ◽  
Qinjian Jin ◽  
Jun Wei ◽  
Paola Malanotte-Rizzoli ◽  
Arnold L. Gordon ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Winter Monsoon ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
Surface Warming ◽  
China Sea ◽  
Basin Scale ◽  
Warming Rate

AbstractThe South China Sea (SCS) experienced a significant reduction in warming rate (− 0.01 °C decade−1, $$p>0.10$$ p > 0.10 ) during 1999–2010 following an accelerated and unprecedented warming (+ 0.15 °C decade−1, $$p<0.01$$ p < 0.01 ) in the last three decades (1970–1998). However, most global climate models of the CMIP5 RCP4.5 scenario failed to capture this SCS warming slowdown. In this study, we identify two drivers through numerical simulations by using a regional high-resolution, ocean–atmosphere coupled climate model: the major variance (75%) in the sea surface warming slowdown could be explained by the strengthened winter monsoon over the SCS, and the minor variance (12%) could be explained by the changes in the upper ocean circulations. The winter monsoon over the SCS is likely linked to the La Niña-like SST pattern in the eastern tropical Pacific, which strengthens the Walker circulation and results in anticyclonic circulation over the northwestern Pacific. This enhanced winter monsoon is the atmospheric driver that slows down the SCS basin-scale warming, while the largest reduction of the warming rate occurs in the northern SCS that can be attributed to the oceanic throughflow via the Luzon Strait. These findings could have important implications for future climate projections over the SCS and adjacent oceans.

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

Sub-basin scale inhomogeneity of mantle in the South China Sea revealed by magnesium isotopes

2020 ◽  
Author(s):  
Yuan Zhong ◽  
Guo-Liang Zhang ◽  
Qi-Zhen Jin ◽  
Fang Huang ◽  
Xiao-Jun Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
The South ◽  
Magnesium Isotopes ◽  
China Sea ◽  
Basin Scale ◽  
Scale Inhomogeneity

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
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