EGU22-9237
https://doi.org/10.5194/egusphere-egu22-9237
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using complex networks to predict abrupt changes in oscillatory systems

Noemie Ehstand1, Reik V. Donner2,3, Cristóbal López1, and Emilio Hernández-García1
Noemie Ehstand et al.
  • 1Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain (n.ehstand@ifisc.uib-csic.es)
  • 2Department of Water, Environment, Construction & Safety, Magdeburg–Stendal University of Applied Sciences, Magdeburg, Germany
  • 3Research Department IV – Complexity Science and Research Department I – Earth System Analysis, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany

Functional networks are powerful tools to study statistical interdependency structures in extended systems. They have been used to get insights into the structure and dynamics of complex systems in various areas of science. In particular, several studies have suggested the use of precursors based on percolation transitions in correlation networks to forecast El Niño events.

Our aim is to provide a better understanding of the potential of such percolation precursors for the prediction of episodic events in generic systems presenting chaotic oscillations. To this end, we study the behavior of the precursors in a spatially extended stochastic Vallis model, an asymmetric Lorenz-63 type model for the El Niño-Southern Oscillation (ENSO). Our results demonstrate the ability of the largest connected component of the network to anticipate abrupt changes associated with the system's oscillatory dynamics.

This research was conducted as part of the CAFE Innovative Training Network (http://www.cafes2se-itn.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844.

How to cite: Ehstand, N., Donner, R. V., López, C., and Hernández-García, E.: Using complex networks to predict abrupt changes in oscillatory systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9237, https://doi.org/10.5194/egusphere-egu22-9237, 2022.

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