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The architecture of the climate network

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  • Tsonis, A.A.
  • Roebber, P.J.

Abstract

We consider climate as a network of many dynamical systems and apply ideas from graph theory to a global data set to study its collective behavior. We find that the network has properties of ‘small-world’ networks (Nature 393 (1999) 440). A detailed investigation of the coupling architecture of this network reveals that the overall dynamics emerge from the interaction of two interweaved subnetworks. One subnetwork operates in the tropics and the other at higher latitudes with the equatorial one acting as an agent that establishes links between the two hemispheres. Both subsystems are ‘small-world’ networks, but there are distinct differences between the two subsystems. The tropical one is an almost fully connected network, whereas the mid-latitude one is more like a scale-free network characterized by dominant super nodes, and multifractal properties. This unique architecture may lead to new insights not only about the dynamics of the climate system but of other spatially extended complex systems with a large number of degrees of freedom.

Suggested Citation

  • Tsonis, A.A. & Roebber, P.J., 2004. "The architecture of the climate network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 497-504.
    • Handle: RePEc:eee:phsmap:v:333:y:2004:i:c:p:497-504
    DOI: 10.1016/j.physa.2003.10.045
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    Cited by:

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    3. Ikeda, Nobutoshi, 2010. "Impact of initial lattice structures on networks generated by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3336-3347.
    4. Tsonis, Anastasios A. & Swanson, Kyle L. & Wang, Geli, 2008. "Estimating the clustering coefficient in scale-free networks on lattices with local spatial correlation structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5287-5294.
    5. Stefan Lange & Jonathan F Donges & Jan Volkholz & Jürgen Kurths, 2015. "Local Difference Measures between Complex Networks for Dynamical System Model Evaluation," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-28, April.
    6. Pranesh, Samana & Gupta, Sayan, 2023. "Explosive death transitions in complex networks of limit cycle and chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Ikeda, N., 2007. "Network formed by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 701-713.
    8. Nico Wunderling & Frederik Wolf & Obbe A. Tuinenburg & Arie Staal, 2022. "Network motifs shape distinct functioning of Earth’s moisture recycling hubs," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    9. Jun Meng & Jingfang Fan & Uma S. Bhatt & Jürgen Kurths, 2023. "Arctic weather variability and connectivity," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Pyko, Nikita S. & Pyko, Svetlana A. & Markelov, Oleg A. & Karimov, Artur I. & Butusov, Denis N. & Zolotukhin, Yaroslav V. & Uljanitski, Yuri D. & Bogachev, Mikhail I., 2018. "Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1054-1072.
    11. Whitney K. Huang & Daniel S. Cooley & Imme Ebert-Uphoff & Chen Chen & Snigdhansu Chatterjee, 2019. "New Exploratory Tools for Extremal Dependence: $$\chi $$ χ Networks and Annual Extremal Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 484-501, September.
    12. Somnath Mondal & Ashok K. Mishra & Ruby Leung & Benjamin Cook, 2023. "Global droughts connected by linkages between drought hubs," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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