Prediction of Long-term Variation of Ocean Circulation Based on Grey Correlation Clustering

Abstract

In order to study the change of circulation in the South China Sea, the author puts forward the research on the distribution and change of water masses in the South China Sea based on grey correlation clustering. Based on the wod21 temperature and salt observation data of the South China Sea from 1966 to 2013, this paper uses the grey correlation clustering algorithm to divide the water masses in the whole sea area and 5° ×5° small areas and analyzes the distribution, temperature, salt properties, and seasonal changes of each water mass in the South China Sea combined with the systematic clustering method and T-S point clustering map. The experimental results show that in the vertical division, the South China Sea water mass is divided into five layers: surface water, subsurface water, sub-middle water, middle water, and deep water. The deep water in the South China Sea is mainly distributed in the basin with a depth of 900 m. The temperature value is lower than 5.5°C and the salinity range is 34.30–34.70. The properties and changes of water masses obtained in this paper are consistent with the existing conclusions, which shows that the grey correlation clustering algorithm is efficient and accurate in the division of water masses in the South China Sea.