Phytoplankton bloom and sea surface cooling induced by Category 5 Typhoon Megi in the South China Sea: direct multi-satellite observations

Typhoon Kalmaegi (2014) in the South China Sea (SCS) is simulated using a fully coupled atmosphere–ocean–wave model (COAWST). A set of sensitivity experiments are conducted to investigate the effects of different model coupling combinations on the typhoon simulation. Model results are validated by employing in-situ data at four locations in the SCS, and best-track and satellite data. Correlation and root-mean-square difference are used to assess the simulation quality. A skill score system is defined from these two statistical criteria to evaluate the performance of model experiments relative to a baseline. Atmosphere–ocean feedback is crucial for accurate simulations. Our baseline experiment successfully reconstructs the atmospheric and oceanic conditions during Typhoon Kalmaegi. Typhoon-induced sea surface cooling that weakens the system due to less heat and moisture availability is captured best in a Regional Ocean Modeling System (ROMS)-coupled run. The Simulated Wave Nearshore (SWAN)-coupled run has demonstrated the ability to estimate sea surface roughness better. Intense winds lead to a larger surface roughness where more heat and momentum are exchanged, while the rougher surface causes more friction, slowing down surface winds. From our experiments, we show that these intricate interactions require a fully coupled Weather Research and Forecasting (WRF)–ROMS–SWAN model to best reproduce the environment during a typhoon.

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Modulation of Typhoon-Induced Sea Surface Cooling by Preexisting Eddies in the South China Sea

Water ◽

10.3390/w13050653 ◽

2021 ◽

Vol 13 (5) ◽

pp. 653

Author(s):

Jianmin Yu ◽

Sheng Lin ◽

Yue Jiang ◽

Yuntao Wang

Keyword(s):

South China Sea ◽

South China ◽

The South China Sea ◽

Sea Surface ◽

Surface Cooling ◽

China Sea ◽

Sea Surface Cooling ◽

The Right ◽

Anticyclonic Eddies ◽

Typhoon Tracks

The interactions between mesoscale eddies and typhoons are important for understanding the oceanic environment, but large variance is identified in each case because of the complex underlying dynamics. Fifteen-year datasets of typhoon tracks and eddy tracks in the South China Sea (SCS) are employed to comprehensively determine the influence of preexisting eddies on typhoon-induced sea surface cooling (SSC). Typhoons with high wind speeds and slow translation speeds induce large SSC in summer and autumn, when more than 80% of typhoons occur during a year. The relative locations of typhoons and eddies are used to classify their distributions, and four groups are identified, with typhoons traversing to the left or right of cyclonic or anticyclonic eddies. Generally, cyclonic eddies (CEs) located to the right of a typhoon track can result in a large cooling core, but anticyclonic eddies (AEs) can interrupt the cooling band along the right side of typhoon tracks. The recovery from typhoon-induced SSC takes longer than 15 days, though preexisting AEs can induce a rapid rebound after reaching the minimum sea surface temperature (SST). In addition, the dependence of SSCs on a typhoon’s features, such as wind speed and translation speed, are amplified (reduced) by CEs (AEs). The enhancement of typhoon-induced local SSC by CEs is counterbalanced by the suppression of SSC by AEs; thus, the overall impacts of CEs and AEs on typhoon-induced local SSC are relatively weak in the SCS.

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

Water ◽

10.3390/w13010065 ◽

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.

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

Acta Oceanologica Sinica ◽

10.1007/s13131-021-1735-0 ◽

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

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

Geophysical Research Letters ◽

10.1002/2014gl061429 ◽

2014 ◽

Vol 41 (21) ◽

pp. 7710-7715 ◽

Cited By ~ 15

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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The correlation between the 37k index and sea surface temperatures in the warm boundary: The South China Sea

Geochimica et Cosmochimica Acta ◽

10.1016/s0016-7037(97)00280-9 ◽

1997 ◽

Vol 61 (22) ◽

pp. 4789-4797 ◽

Cited By ~ 93

Author(s):

Carles Pelejero ◽

Joan O. Grimalt

Keyword(s):

South China Sea ◽

South China ◽

The South China Sea ◽

Sea Surface ◽

Sea Surface Temperatures ◽

The South ◽

China Sea ◽

Surface Temperatures

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

Remote Sensing ◽

10.3390/rs11080919 ◽

2019 ◽

Vol 11 (8) ◽

pp. 919 ◽

Cited By ~ 2

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.

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