Predicting ENSO dynamics with network and complexity analyses

Ludescher, J., Meng, J., Fan, J., Bunde, A., & Schellnhuber, H.J. ORCID: https://orcid.org/0000-0001-7453-4935 (2026). Predicting ENSO dynamics with network and complexity analyses. Chaos: An Interdisciplinary Journal of Nonlinear Science 36 (2) e023139. 10.1063/5.0307169.

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Abstract

The El Niño Southern Oscillation (ENSO) consists of El Niño, La Niña, and neutral events. Recently, we have developed two approaches (a climate network and a complexity-based approach) that allow forecasting the onset of El Niño events about 1 year in advance. The complexity-based approach additionally enables forecasting the magnitude of an upcoming El Niño event. Here, we propose the interannual relationship of the Oceanic Niño Index as an additional predictor for forecasting La Niña and neutral events. Combining the three approaches therefore enables probabilistic forecasting of all three phases of ENSO dynamics. Based on these approaches, in December 2024, we correctly forecasted the absence of an El Niño in 2025 (with 91.4% probability) and a resulting temporary decrease in the global mean temperature. With 69.6% probability, we predicted a neutral event as the most likely outcome.

Item Type:	Article
Uncontrolled Keywords:	Weather hazard, Oceanography, Oceans, Weather forecasting, Climatology, Network analysis, Information theory entropy, Regression analysis
Research Programs:	Directorate (DIR)
Depositing User:	Luke Kirwan
Date Deposited:	23 Feb 2026 08:21
Last Modified:	23 Feb 2026 08:21
URI:	https://pure.iiasa.ac.at/21340

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