Remote Sensing of Sea Surface Salinity Variability in the South China Sea

2020 ◽  
Vol 125 (12) ◽  
Author(s):  
Daling Li Yi ◽  
Oleg Melnichenko ◽  
Peter Hacker ◽  
James Potemra
Keyword(s):  
Remote Sensing ◽  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The South ◽  
Surface Salinity ◽  
China Sea ◽  
Salinity Variability

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.

Diatom-based reconstruction of summer sea-surface salinity in the South China Sea over the last 15 000 years

Boreas ◽  
2013 ◽  
Vol 43 (1) ◽  
pp. 208-219 ◽  
Author(s):  
Hui Jiang ◽  
Mads F. Knudsen ◽  
Marit-Solveig Seidenkrantz ◽  
Meixun Zhao ◽  
Longbin Sha ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The South ◽  
Surface Salinity ◽  
China Sea

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 Expression of hydrocarbon on sea surface and its remote sensing detection: Taking the South China Sea area as an example

2011 ◽  
Vol 38 (1) ◽  
pp. 116-121 ◽  
Author(s):  
Liu Yang ◽  
Shao Yun ◽  
Yu Wuyi ◽  
Qi Xiaoping ◽  
Tian Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Sea Area

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).

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