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
1Department of Computer Science and Technology, Ocean University of China, Qingdao 266000, China 2Frontiers Science Center for Deep Ocean Multispheres and Earth System, Qingdao 266000, China 3Institut für Informatik, Ludwig Maximilian University of Munich, Munich 80331-81929, Germany 4College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266000, China
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.
* 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
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.
Pradhan R, Aygun R S, Maskey M, Ramachandran R, Cecil D J 2017 IEEE Trans. Image Process. 27 692 DOI: 10.1109/TIP.2017.2766358
[8]
Havlin S, Kenett D Y, Jacob E B, Bunde A, Cohen R 2012 Eur. Phys. J. Spec. Top. 214 273 DOI: 10.1140/epjst/e2012-01695-x
[9]
Wu Y, Shang Y, Chen M et al. 2008 IEEE Trans. Circuits Syst. 214 1335 DOI: 10.1063/1.2939136
[10]
Tominski C, Donges J F, Nocke T 2011 15th International Conference on Information Visualisation July 13–15, 2011 London, UK 298 305 DOI: 10.1109/IV.2011.12
[11]
Donges J F, Zou Y, Marwan N, Kurths J 2009 Eur. Phys. J. Spec. Top. 174 157 DOI: 10.1140/epjst/e2009-01098-2
Sun X, Song Z, Dong J, Yu Y, Plant C, Böhm C 2019 Thirty-Third AAAI Conference on Artificial Intelligence January 29–31, 2019 Hawaii, USA 5041 5048 DOI: 10.1609/aaai.v33i01.33015041
[14]
Steinhaeuser K, Chawla N V, Ganguly A R 2009 Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data June 28, 2009 Paris, France 23 31 DOI: 10.1145/1882471.1882476
[15]
Donges J F, Petrova I, Loew A, Marwan N, Kurths J 2015 Clim. Dynamics 45 2407 DOI: 10.1007/s00382-015-2479-3
[16]
Meng J, Fan J F, Ashkenazy Y, Havlin S 2017 Chaos: An Interdisciplinary J. Nonlinear Sci. 27 035807 DOI: 10.1063/1.4975766
[17]
Donges J F, Zou Y, Marwan N, Kurths J 2009 Eur. Phys. J. Special Topics 174 157 DOI: 10.1140/epjst/e2009-01098-2
[18]
Boers N, Bookhagen B, Marwan N, Kurths J, Marengo J 2013 Geophys. Res. Lett. 40 4386 DOI: 10.1002/grl.50681
[19]
Boers N, Bookhagen B, Barbosa H M J, Marwan N, Kurths J, Marengo J A 2014 Nat. Commun. 5 5199 DOI: 10.1038/ncomms6199
[20]
Josef L, Avi G, Mikhail I B, Armin B, Shlomo H, Hans J S 2014 Proc. Natl. Acad. Sci. USA 111 2064 DOI: 10.1073/pnas.1323058111
Fan J F, J M, Chen X S, Ashkenazy Y, Havlin S 2016 Sci. Chin. Phys. Mech. Astron. 60 010531 DOI: 10.1007/s11433-016-0362-2
[27]
Ford J D, Tilleard S E, Lea B F, Araos M, Biesbroek R, Lesnikowski A C, MacDonald G K, Hsu A, Chen C, Bizikova L 2016 Proc. Natl. Acad. Sci. USA 113 10729 DOI: 10.1073/pnas.1614023113
Bohm C, Pryakhin A, Schubert M 2006 Proceedings of the 22nd International Conference on Data Engineering April 3–7, 2006 Atlanta, GA, USA 9 DOI: 10.1109/ICDE.2006.159
Characteristics of vapor based on complex networks in China Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
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