Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction
In one sentence. Transformer ENSO-PhyNet embeds heat-budget processes in self-attention, reaching Niño3.4 skill up to 22-month lead with attention maps showing physically consistent drivers.
Abstract
While deep learning models have shown promising capabilities in ENSO prediction, their inherent black-box nature often leads to a lack of physical consistency and interpretability. Here, we introduce ENSO-PhyNet, a Transformer-based model for ENSO prediction, which incorporates heat budget dynamical processes through self-attention computations. The model predicts sea surface temperature (SST) in the equatorial Pacific and achieves skillful predictions of the Niño 3.4 index with a lead time of up to 22 months. The self-attention maps reveal how the model makes predictions by focusing on specific processes in certain regions. Case analyses of recent El Niño and La Niña events underscore the impact of thermocline feedback and zonal advection feedback on the warming of the 2015 event, as well as the crucial role of anomalous easterlies in the emergence of the second-year La Niña in 2021. These findings demonstrate the model’s interpretability and its ability to identify signals that are physically consistent with the development of ENSO events.
Labels
- Topic
- Sub-seasonal to seasonal, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- Transformer / ViT, Physics-informed / physics-constrained
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1038/s41612-024-00741-y OpenAlex:W4402289624
- Added
- 2026-10-22
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