3,- 1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany (josef.ludescher@pik-potsdam.de)
- 2State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 3School of Systems Science, Beijing Normal University, Beijing, China
- 4Institute for Theoretical Physics, Justus Liebig University Giessen, Giessen, Germany
- 5International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Recently, we have developed two approaches (a climate network [1] and a complexity-based approach [2]) 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. These methods successfully forecasted the onset of an Eastern Pacific El Niño for 2023/24 and the subsequent record-breaking warming of 2024 [3]. 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 about 1 year in advance. Based on these approaches, in December 2024 we correctly forecasted with 91.4% probability the absence of an El Niño in 2025 [4]. With 69.6% probability, we predicted a neutral event as the most likely outcome for boreal winter 2025/26.
[1] Ludescher, J., Gozolchiani, A., Bogachev, M. I., Bunde, A., Havlin, S., Schellnhuber, H. J., (2013). Improved El Niño forecasting by cooperativity detection. Proc. Natl. Acad. Sci. U.S.A. 110(29), 11742.
[2] Meng, J., et al. (2020). Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier. Proc. Natl. Acad. Sci. U.S.A. 117(1), 177.
[3] Ludescher, J., Meng, J., Fan, J., Bunde, A., Schellnhuber, H. J., Very early warning of a moderate-to-strong El Niño in 2023, https://doi.org/10.48550/arXiv.2301.10763
[4] Ludescher, J., Meng, J., Fan, J., Bunde, A., Schellnhuber, H. J., Climate network and complexity approach predict neutral ENSO event for 2025, https://doi.org/10.48550/arXiv.2502.00643
How to cite: Ludescher, J., Meng, J., Fan, J., Bunde, A., and Schellnhuber, H. J.: Predicting ENSO dynamics with network & complexity analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14829, https://doi.org/10.5194/egusphere-egu26-14829, 2026.
