Scientific cooperation to respond climate change in the South China Sea: The study of tides and sea level change

Based on the advanced wind, wave numerical model of the Laboratory of Geophysical Fluid Dynamics and Numerical Modeling (LAGFD), the current 3-D Princeton Ocean Model (POM), and collected data, Part I hindcasts the strongest 298 tropical cyclones (TCs) affecting the area 19°∼23°N, 113°∼118°E) in the South China Sea (SCS) from 1945 to 1995. It also provides the extreme parameters of wind, wave, current and sea level of the above region, and briefly analyzes the climate characteristics of SCS (15°∼27°N, 108°∼122°E). In part II, the strongest 211 TCs affecting the Wenchang area (16°∼23°N, 105°∼114°E) were hindcast. The marine environmental parameters of wind, wave, current and sea level at 35 points in the research area were provided. The present work puts forward the concept of the conditional extreme value. The conditional extreme values of the point (20°N, 112°E) were also given. The research provides basic data for ocean environmental research and engineering design in this region.

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Non Linear Internal Wave Dynamics in the South China Sea - Analysis of NCOM Surface Circulation and Sea Level

10.21236/ada502421 ◽

2009 ◽

Author(s):

Luca Centurioni ◽

Pearn P. Niiler ◽

Dong-Kyu Lee

Keyword(s):

South China Sea ◽

Internal Wave ◽

Sea Level ◽

South China ◽

The South China Sea ◽

Wave Dynamics ◽

China Sea ◽

Surface Circulation ◽

Linear Internal Wave ◽

And Sea Level

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Oxygen Isotope Records of Core 8KL of the South China Sea and Sea-level Change1

Acta Geologica Sinica - English Edition ◽

10.1111/j.1755-6724.1995.mp8002007.x ◽

2009 ◽

Vol 8 (2) ◽

pp. 209-219

Author(s):

Qian Jianxing

Keyword(s):

South China Sea ◽

Oxygen Isotope ◽

Sea Level ◽

South China ◽

The South China Sea ◽

The South ◽

China Sea ◽

And Sea Level

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Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Scientific Programming ◽

10.1155/2021/6618135 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Shanwei Liu ◽

Yue Jiao ◽

Qinting Sun ◽

Jinghui Jiang

Keyword(s):

South China Sea ◽

Sea Level ◽

South China ◽

The South China Sea ◽

Sea Level Changes ◽

The South ◽

Short Term ◽

Combined Model ◽

China Sea ◽

Level Change

The South China Sea is China’s largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea.

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