Articles | Volume 32, issue 2
https://doi.org/10.5194/npg-32-201-2025
https://doi.org/10.5194/npg-32-201-2025
Research article
 | 
01 Jul 2025
Research article |  | 01 Jul 2025

Explaining the high skill of reservoir computing methods in El Niño prediction

Francesco Guardamagna, Claudia Wieners, and Henk A. Dijkstra

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Cited articles

Barnston, A. G., Tippett, M. K., L'Heureux, M. L., Li, S., and DeWitt, D. G.: Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?, B. Am. Meteorol. Soc., 93, 631–651, 2012. a
Battisti, D. S. and Hirst, A. C.: Interannual Variability in a Tropical Atmosphere-Ocean Model: Influence of the Basic State, Ocean Geometry and Nonlinearity, J. Atmos. Sci., 46, 1687–1712, https://doi.org/10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2, 1989. a
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., and Monteleoni, C.: Machine Learning for the Physics of Climate, Nature Reviews Physics, 7, 6–20, https://doi.org/10.1038/s42254-024-00776-3, 2024. a
Copernicus Climate Change Service: ORAS5 global ocean reanalysis monthly data from 1958 to present, 2021. a, b
Duan, W. and Wei, C.: The “spring predictability barrier” for ENSO predictions and its possible mechanism: Results from a fully coupled model, Int. J. Climatol., 33, 1280–1292, https://doi.org/10.1002/joc.3513, 2013. a
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Short summary
Artificial intelligence (AI) has recently shown promising results in ENSO (El Niño–Southern Oscillation) forecasting, outperforming traditional models. Yet AI models deliver accurate predictions without showing the underlying mechanisms. Our study examines a specific AI model, the reservoir computer (RC). Our results show that the RC is less sensitive to initial perturbations than the traditional Zebiak–Cane (ZC) model. This reduced sensitivity can explain the RC's superior skills.
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