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
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

Table 1.

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

[1]	Zhang L, Huang S X, Shen C, Shi W L 2011 Chin. Phys. B 20 129201 DOI: 10.1088/1674-1056/20/12/129201
[2]	Shen X Y, Qing T, Li X F 2013 Chin. Phys. B 22 94213 DOI: 10.1088/1674-1056/22/9/094213
[3]	Chave A D, Arrott M, Farcas C, Farcas E, Krueger I, Meisinger M, Orcutt J A, Vernon F L, Peach C, and S Oscar Oceans May 11–14, 2009 Bremen, Germany 1
[4]	Liu Y j, Qiu M, Liu C, Guo Z W 2017 Pers. Ubiquitous Comput. 21 55 DOI: 10.1007/s00779-016-0980-2
[5]	Owens D, Best M, Guillemot E, Jenkyns R Oceans B 2010 May 24–27 2010 Sydney, Australia 1
[6]	Sun X, Chen C, Dong J, Liu D, Hu G 2020 Knowl.-Based Syst. 196 105824 DOI: 10.1016/j.knosys.2020.105824
[7]	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
[12]	Charakopoulos A K, Katsouli G A, Karakasidis T E 2009 Physica A 495 436 DOI: 10.1016/j.physa.2017.12.027
[13]	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
[21]	Tsonis A A, Swanson K L, Wang G 2008 J. Clim. 21 2990 DOI: 10.1175/2007JCLI1907.1
[22]	Tsonis A A, Roebber P J 2004 Phys. A: Stat. Mech. Its Appl. 333 497 DOI: 10.1016/j.physa.2003.10.045
[23]	Yamasaki K, Gozolchiani A, Havlin S 2008 Phys. Review Letters 100 228501 DOI: 10.1103/PhysRevLett.100.228501
[24]	Tsonis A A, Swanson K L 2008 Phys. Rev. Lett. 100 228502 DOI: 10.1103/PhysRevLett.100.228502
[25]	Iglesias G, Kale D C, Liu Y 2015 The 5th International Workshop on Climate Informatics September 24–25, 2015 Boulder, USA https://www2.cisl.ucar.edu/sites/default/files/29
[26]	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
[28]	Wang Y H, Shen X R, Yang S Q, Gao Z K 2020 Europhys. Lett. 128 60005 DOI: 10.1209/0295-5075/128/60005
[29]	Feng Q Y, Henk A D 2017 Chaos: An Interdisciplinary J. Nonlinear Sci. 27 035801 DOI: 10.1063/1.4971784
[30]	Wiedermann M, Radebach A, Donges J F, Kurths J, Donner R V 2016 Geophys. Res. Lett. 43 7176 DOI: 10.1002/2016GL069119
[31]	Tsonis A A, Swanson K L, Roebber P J 2006 Bull. Am. Meteorological Soc. 87 585 DOI: 10.1175/BAMS-87-5-585
[32]	Zerenner T, Friederichs P, Lehnertz K, Hense A 2014 Chaos: An Interdisciplinary J. Nonlinear Sci. 24 023103 DOI: 10.1063/1.4870402
[33]	Zhe J, Li X F, Zhou Y S, Gao S T 2012 Chin. Phys. B 21 054215 DOI: 10.1088/1674-1056/21/5/054215
[34]	Smith N R 2000 Adv. Space Res. 25 1089 DOI: 10.1016/S0273-1177(99)00868-6
[35]	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
[36]	Freeman L C 1978 Soc. Netw. 1 215 DOI: 10.1016/0378-8733(78)90021-7
[37]	Duncan O D 1968 Am. Sociological Rev. 33 457 DOI: 10.2307/2091921
[38]	Huang J P, Kaz H, Amir S 1998 Geophys. Res. Lett. 25 2707 DOI: 10.1029/98GL01936
[39]	Fronczak A, Fronczak P, Hołyst J A 2007 Phys. Rev. E 76 061106 DOI: 10.1103/PhysRevE.76.061106

[1]	Influential nodes identification in complex networks based on global and local information Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚). Chin. Phys. B, 2020, 29(8): 088903.
[2]	Identifying influential spreaders in complex networks based on entropy weight method and gravity law Xiao-Li Yan(闫小丽), Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni(倪顺江). Chin. Phys. B, 2020, 29(4): 048902.
[3]	Pyramid scheme model for consumption rebate frauds Yong Shi(石勇), Bo Li(李博), Wen Long(龙文). Chin. Phys. B, 2019, 28(7): 078901.
[4]	Theoretical analyses of stock correlations affected by subprime crisis and total assets: Network properties and corresponding physical mechanisms Shi-Zhao Zhu(朱世钊), Yu-Qing Wang(王玉青), Bing-Hong Wang(汪秉宏). Chin. Phys. B, 2019, 28(10): 108901.
[5]	Coordinated chaos control of urban expressway based on synchronization of complex networks Ming-bao Pang(庞明宝), Yu-man Huang(黄玉满). Chin. Phys. B, 2018, 27(11): 118902.
[6]	Detecting overlapping communities based on vital nodes in complex networks Xingyuan Wang(王兴元), Yu Wang(王宇), Xiaomeng Qin(秦小蒙), Rui Li(李睿), Justine Eustace. Chin. Phys. B, 2018, 27(10): 100504.
[7]	Dominant phase-advanced driving analysis of self-sustained oscillations in biological networks Zhi-gang Zheng(郑志刚), Yu Qian(钱郁). Chin. Phys. B, 2018, 27(1): 018901.
[8]	Ranking important nodes in complex networks by simulated annealing Yu Sun(孙昱), Pei-Yang Yao(姚佩阳), Lu-Jun Wan(万路军), Jian Shen(申健), Yun Zhong(钟赟). Chin. Phys. B, 2017, 26(2): 020201.
[9]	Empirical topological investigation of practical supply chains based on complex networks Hao Liao(廖好), Jing Shen(沈婧), Xing-Tong Wu(吴兴桐), Bo-Kui Chen(陈博奎), Mingyang Zhou(周明洋). Chin. Phys. B, 2017, 26(11): 110505.
[10]	An improved genetic algorithm with dynamic topology Kai-Quan Cai(蔡开泉), Yan-Wu Tang(唐焱武), Xue-Jun Zhang(张学军), Xiang-Min Guan(管祥民). Chin. Phys. B, 2016, 25(12): 128904.
[11]	Subtle role of latency for information diffusion in online social networks Fei Xiong(熊菲), Xi-Meng Wang(王夕萌), Jun-Jun Cheng(程军军). Chin. Phys. B, 2016, 25(10): 108904.
[12]	Synchronization of Markovian jumping complex networks with event-triggered control Shao Hao-Yu, Hu Ai-Hua, Liu Dan. Chin. Phys. B, 2015, 24(9): 098902.
[13]	Load-redistribution strategy based on time-varying load against cascading failure of complex network Liu Jun, Xiong Qing-Yu, Shi Xin, Wang Kai, Shi Wei-Ren. Chin. Phys. B, 2015, 24(7): 076401.
[14]	Degree distribution and robustness of cooperativecommunication network with scale-free model Wang Jian-Rong, Wang Jian-Ping, He Zhen, Xu Hai-Tao. Chin. Phys. B, 2015, 24(6): 060101.
[15]	Identifying influential nodes based on graph signal processing in complex networks Zhao Jia, Yu Li, Li Jing-Ru, Zhou Peng. Chin. Phys. B, 2015, 24(5): 058904.

[1]	CHEN GUANG-HUA, MA GUO-BIN, YANG YING-HU. INVESTIGATION OF GROWTH CHARACTERISTICS OF MONOCRYSTALLINE C₆₀ FILMS IN HELIUM GAS ENVIRONMENT[J]. Acta Phys. Sin. (Overseas Edition), 1997, 6(2): 125 -129 .
[2]	Luo Ying, Ma Ben-Kun, Duan Su-Qing, Zhao Xian-Geng, Wang Li-Min. Effects of a donor on the bond property of quantum-dot molecules[J]. Chin. Phys., 2004, 13(6): 942 -947 .
[3]	Yu Hua-Ling, Wang Zhi-Guo, Peng Ju. Phase coherence of the electron and hole in a ferromagnetic film in proximity with a superconductor[J]. Chin. Phys. B, 2008, 17(12): 4627 -4634 .
[4]	Zheng You-Jin, Li Yan-Tao, Zhou Lin, Jia Xiao-Peng, Ma Hong-An. Synthesis of industrial diamonds using FeNi alloy powder as catalyst[J]. Chin. Phys. B, 2008, 17(12): 4665 -4668 .
[5]	He Ji-Fang, Niu Zhi-Chuan, Chang Xiu-Ying, Ni Hai-Qiao, Zhu Yan, Li Mi-Feng, Shang Xiang-Jun. Molecular beam epitaxy growth of GaAs on an offcut Ge substrate[J]. Chin. Phys. B, 2011, 20(1): 18102 -018102 .
[6]	Lin Fang, Bao Jing-Dong. Environment-dependent continuous time random walk[J]. Chin. Phys. B, 2011, 20(4): 40502 -040502 .
[7]	Li Chun-Yan, Wang Jiang-Bin, Wang Yi-Qian. Microstructure and photocatalytic activity of titanium dioxide nanoparticles[J]. Chin. Phys. B, 2012, 21(9): 98102 -098102 .
[8]	Hong You-Li, Zhang Kai, Wang Zhi-Li, Zhu Zhong-Zhu, Zhao Xue-Jiao, Huang Wan-Xia, Yuan Qing-Xi, Zhu Pei-Ping, Wu Zi-Yu. Reconstructing a complex field from a series of its near-field diffraction patterns[J]. Chin. Phys. B, 2012, 21(10): 104202 .
[9]	Niu Peng-Bin, Wang Qiang, Nie Yi-Hang. Transport through artificial single-molecule magnets: Spin-pair state sequential tunneling and Kondo effects[J]. Chin. Phys. B, 2013, 22(2): 27307 -027307 .
[10]	Lai Ling-Ling, Cheng Rong-Jun, Li Zhi-Peng, Ge Hong-Xia. The KdV-Burgers equation in modified speed gradient continuum model[J]. Chin. Phys. B, 2013, 22(6): 60511 -060511 .

Viewed

Full text

Abstract

Cited

Shared

Discussed

Metrics
Related Articles