Modeling and analysis of the ocean dynamic with Gaussian complex network*Project supported by the National Natural Science Foundation of China (Grant Nos. U1706218, 61971388, and L1824025). (September 2020)

• Record Type: Journal Article
• Authors: Sun 孙, Xin 鑫; Yu 于, Yongbo 勇波; Yang 杨, Yuting 玉婷; Dong 董, Junyu 军宇; Böhm, Christian; Chen 陈, Xueen 学恩
• Is Part Of: Chinese physics B. Volume 29:Number 10(2020)
• Publication Date: 2020-09
• Subjects: complex networks -- ocean dynamic -- Gaussian mixture model -- physical processes; 89.75.Fb -- 05.45.Tp -- 64.60.aq; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1674-1056/aba27d
• Languages: English
• ISSNs: 1674-1056

Abstract

Abstract : The techniques for oceanographic observation have made great progress in both space-time coverage and quality, which make the observation data present some characteristics of big data. We explore the essence of global ocean dynamic via constructing a complex network with regard to sea surface temperature. The global ocean is divided into discrete regions to represent the nodes of the network. To understand the ocean dynamic behavior, we introduce the Gaussian mixture models to describe the nodes as limit-cycle oscillators. The interacting dynamical oscillators form the complex network that simulates the ocean as a stochastic system. Gaussian probability matching is suggested to measure the behavior similarity of regions. Complex network statistical characteristics of the network are analyzed in terms of degree distribution, clustering coefficient and betweenness. Experimental results show a pronounced sensitivity of network characteristics to the climatic anomaly in the oceanic circulation. Particularly, the betweenness reveals the main pathways to transfer thermal energy of El Niño–Southern oscillation. Our works provide new insights into the physical processes of ocean dynamic, as well as climate changes and ocean anomalies.