Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 32, issue 2
Articles | Volume 32, issue 2
https://doi.org/10.5194/npg-32-201-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-201-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 32, issue 2
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

Viewed

Total article views: 789 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
589 142 58 789 44 61
  • HTML: 589
  • PDF: 142
  • XML: 58
  • Total: 789
  • BibTeX: 44
  • EndNote: 61
Views and downloads (calculated since 20 Nov 2024)
Cumulative views and downloads (calculated since 20 Nov 2024)

Viewed (geographical distribution)

Total article views: 789 (including HTML, PDF, and XML) Thereof 765 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 07 Aug 2025
Download
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.
Read more
Share