https://doi.org/10.1140/epjp/s13360-025-06638-w
Regular Article
Unravelling the complexity of the El Niño climate phenomena: a data-driven eigenvalue statistics and network approach
Department of Physics and Astrophysics, University of Delhi, 110007, Delhi, India
Received:
5
October
2024
Accepted:
8
July
2025
Published online:
1
August
2025
We use a strategy combining two methods (Network theory and Eigenvalue statistics) to explore the El Niño climate phenomenon. After adequate filtering of the tropical Pacific Ocean data, we make the climate networks via correlation matrices of temperature anomaly time series and compute the topological properties of the threshold networks yearly. These properties decrease during the indicator years and increase during El Niño, hence can be used as indicators. We observe that the nodes near the tropics and the equator are important, as these nodes remain connected while most other nodes get disconnected during the indicator years. Next, we calculate the Shannon entropy and IPR to show that the most localised eigenmodes are the smallest and second-smallest. These small eigenvalues can be used as indicators as they increase and decrease with the indicator years and El Niño episodes. Analysing eigenvectors corresponding to the smallest and second-smallest eigenvalues, we find localised peaks at the same geographical locations, complementing the important and highly interacting locations found by increasing the threshold in networks. Exploring La Niña episodes for the small eigenmodes reveals localised peaks near the equator for the strong episodes, and the important locations are different from those of El Niño. We use the eigenvalue density distribution and find that the largest eigenvalue and the number of eigenvalues outside the random matrix bounds (outliers) increase in the El Niño and decrease sharply for strong La Niña. Thus, integrating these two approaches opens an innovative way to research climate.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
