Modeling and analysis of the ocean dynamic with Gaussian complex network

doi:10.1088/1674-1056/aba27d

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.

Keywords: complex networks ocean dynamic Gaussian mixture model physical processes

Received: 17 April 2020
Revised: 19 June 2020
Accepted manuscript online: 03 July 2020

PACS:	89.75.Fb	(Structures and organization in complex systems)
	05.45.Tp	(Time series analysis)
	64.60.aq	(Networks)

Corresponding Authors: ^†Corresponding author. E-mail: dongjunyu@ouc.edu.cn

About author:

†Corresponding author. E-mail: dongjunyu@ouc.edu.cn

* Project supported by the National Natural Science Foundation of China (Grant Nos. U1706218, 61971388, and L1824025).

Cite this article:

Xin Sun(孙鑫), Yongbo Yu(于勇波), Yuting Yang(杨玉婷), Junyu Dong(董军宇)†, Christian B\"ohm, and Xueen Chen(陈学恩) Modeling and analysis of the ocean dynamic with Gaussian complex network 2020 Chin. Phys. B 29 108901

Fig. 1.

Process of complex network modeling based on surface sea temperature (SST).

Fig. 2.

Degree distribution of nodes in (left) SG-network (right) MG-network.

Fig. 3.

Degree logD distributions with geographic locations in the global ocean: (a) SG-network, (b) MG-network.

Fig. 4.

Degree distributions in the northern hemisphere.

Fig. 5.

Clustering coefficient distribution of the network model at geographic location.

Fig. 6.

Betweenness distribution of the network model at geographic location.

Fig. 7.

Scatter plots of betweenness against degree of SG-network

Table 1.

Average clustering coefficient, distance and diameter for the networks constructed by Gaussian approach in three periods.

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