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Identifying causal gateways and mediators in complex spatio-temporal systems

Author

Listed:
  • Jakob Runge

    (Potsdam Institute for Climate Impact Research
    Humboldt University)

  • Vladimir Petoukhov

    (Potsdam Institute for Climate Impact Research)

  • Jonathan F. Donges

    (Potsdam Institute for Climate Impact Research
    Stockholm Resilience Centre, Stockholm University)

  • Jaroslav Hlinka

    (Institute of Computer Science, Academy of Sciences of the Czech Republic)

  • Nikola Jajcay

    (Institute of Computer Science, Academy of Sciences of the Czech Republic
    Charles University)

  • Martin Vejmelka

    (Institute of Computer Science, Academy of Sciences of the Czech Republic)

  • David Hartman

    (Institute of Computer Science, Academy of Sciences of the Czech Republic
    Computer Science Institute, Charles University)

  • Norbert Marwan

    (Potsdam Institute for Climate Impact Research)

  • Milan Paluš

    (Institute of Computer Science, Academy of Sciences of the Czech Republic)

  • Jürgen Kurths

    (Potsdam Institute for Climate Impact Research
    Humboldt University
    Institute for Complex Systems and Mathematical Biology, University of Aberdeen
    Nizhny Novgorod State University)

Abstract

Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Earth’s climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel network measures based on causal effect theory that go beyond standard complex network tools by distinguishing direct from indirect pathways. Applied to a data set of atmospheric dynamics, the method identifies several strongly uplifting regions acting as major gateways of perturbations spreading in the atmosphere. Additionally, the method provides a stricter statistical approach to pathways of atmospheric teleconnections, yielding insights into the Pacific–Indian Ocean interaction relevant for monsoonal dynamics. Also for neuroscience or power grids, the novel causal interaction perspective provides a complementary approach to simulations or experiments for understanding the functioning of complex spatio-temporal systems with potential applications in increasing their resilience to shocks or extreme events.

Suggested Citation

  • Jakob Runge & Vladimir Petoukhov & Jonathan F. Donges & Jaroslav Hlinka & Nikola Jajcay & Martin Vejmelka & David Hartman & Norbert Marwan & Milan Paluš & Jürgen Kurths, 2015. "Identifying causal gateways and mediators in complex spatio-temporal systems," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9502
    DOI: 10.1038/ncomms9502
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    Citations

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    Cited by:

    1. Jajcay, Nikola, 2018. "Spatial and temporal scales of atmospheric dynamics," Thesis Commons ar8ks, Center for Open Science.
    2. Ramos, Antônio M.T. & Casagrande, Helder L. & Macau, Elbert E.N., 2020. "Investigation on the high-order approximation of the entropy bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Kunxiaojia Yuan & Fa Li & Gavin McNicol & Min Chen & Alison Hoyt & Sara Knox & William J. Riley & Robert Jackson & Qing Zhu, 2024. "Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity," Nature Climate Change, Nature, vol. 14(3), pages 282-288, March.
    4. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
    5. Lucile Ricard & Fabrizio Falasca & Jakob Runge & Athanasios Nenes, 2024. "network-based constraint to evaluate climate sensitivity," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    6. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Albert C. Yang & Chung-Kang Peng & Norden E. Huang, 2022. "Reply To: Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition," Nature Communications, Nature, vol. 13(1), pages 1-3, December.

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