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Chin. Phys. B, 2020, Vol. 29(10): 108901    DOI: 10.1088/1674-1056/aba27d
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

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 Böhm(陈学恩)3, Xueen Chen4
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
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      Published:  05 October 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 Böhm(陈学恩), Xueen Chen 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

Clustering coefficient Distance Diameter
La Niña period MG-network 0.136 2.083 4
SG-network 0.102 2.905 8
Normal period MG-network 0.302 1.98 3
SG-network 0.132 2.07 4
El Niño period} MG-network 0.1585 8189 6
SG-network 0.261 1.809 4
Table 1.  

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

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