Modeling and analysis of the ocean dynamic with Gaussian complex network

doi:10.1088/1674-1056/aba27d

中国物理B ›› 2020, Vol. 29 ›› Issue (10): 108901-.doi: 10.1088/1674-1056/aba27d

Xin Sun(孙鑫)¹, Yongbo Yu(于勇波)¹, Yuting Yang(杨玉婷)¹, Junyu Dong(董军宇)¹^,²^,†(), Christian Böhm(陈学恩)³, Xueen Chen⁴

收稿日期:2020-04-17 修回日期:2020-06-19 接受日期:2020-07-03 出版日期:2020-10-05 发布日期:2020-10-05
通讯作者: Junyu Dong(董军宇)

Modeling and analysis of the ocean dynamic with Gaussian complex network

Xin Sun(孙鑫)¹, Yongbo Yu(于勇波)¹, Yuting Yang(杨玉婷)¹, Junyu Dong(董军宇)^1,2,†, Christian B\"ohm³, and Xueen Chen(陈学恩)⁴

¹ Department of Computer Science and Technology, Ocean University of China, Qingdao 266000, China
² Frontiers Science Center for Deep Ocean Multispheres and Earth System, Qingdao 266000, China
³ Institut für Informatik, Ludwig Maximilian University of Munich, Munich 80331-81929, Germany
⁴ College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266000, China

Received:2020-04-17 Revised:2020-06-19 Accepted:2020-07-03 Online:2020-10-05 Published:2020-10-05
Contact: ^†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).

摘要/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.

Key words: complex networks, ocean dynamic, Gaussian mixture model, physical processes

中图分类号: (Structures and organization in complex systems)

89.75.Fb

05.45.Tp (Time series analysis) 64.60.aq (Networks)

Xin Sun(孙鑫), Yongbo Yu(于勇波), Yuting Yang(杨玉婷), Junyu Dong(董军宇), Christian Böhm(陈学恩), Xueen Chen. [J]. 中国物理B, 2020, 29(10): 108901-.

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[J]. Chin. Phys. B, 2020, 29(10): 108901-.

图/表 8

参考文献 39

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		Clustering coefficient	Distance	Diameter
La Niña period	MG-network	0.136	2.083	4
La Niña period	SG-network	0.102	2.905	8
Normal period	MG-network	0.302	1.98	3
Normal period	SG-network	0.132	2.07	4
El Niño period}	MG-network	0.1585	8189	6
El Niño period}	SG-network	0.261	1.809	4

Modeling and analysis of the ocean dynamic with Gaussian complex network

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