Enhancing the predictability limits of ENSO with physics-guided deep echo state networks
ID：1630
Oral Presentation
2027-01-15 09:00 （China Standard Time）
Session：Session 18 - Oceanography Meets Network Science, Complexity, and AI
Abstract
The El Niño–Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet the mechanisms limiting its
long-lead predictability remain unclear. Here we develop a physics-guided Deep Echo State Network (DESN) that operates
on physically interpretable climate modes selected from the extended recharge oscillator (XRO) framework. DESN achieves
skillful Niño 3.4 predictions up to 16–20 months ahead with minimal computational cost. Mechanistic experiments show that
extended predictability arises from nonlinear coupling between warm water volume and inter-basin climate modes. Error-growth
analysis further indicates a finite ENSO predictability horizon of approximately 30 months. These results demonstrate that
physics-guided reservoir computing provides an efficient and interpretable framework for diagnosing and predicting ENSO at
long lead times.
Speaker
Author
Important Dates
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Jan 30
2026
Session / Event proposals open
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Mar 31
2026
Session / Event proposals close
（Extended to April 7） -
Apr 30
2026
Session acceptance notifications
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Apr 30
2026
Abstract submissions & registration open
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Jun 30
2026
Abstract submissions close
（Extended to July 21） -
Aug 31
2026
Abstract acceptance notifications
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Sep 15
2026
Scientific program released
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Oct 15
2026
Early bird registration closes
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Jan 12–15
2027
XMAS 2027