English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks

Authors

Lyu,  Pumeng
External Organizations;

Tang,  Tao
External Organizations;

Ling,  Fenghua
External Organizations;

Luo,  Jing-Jia
External Organizations;

/persons/resource/Niklas.Boers

Boers,  Niklas
Potsdam Institute for Climate Impact Research;

Ouyang,  Wanli
External Organizations;

Bai,  Lei
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

Lyu, P., Tang, T., Ling, F., Luo, J.-J., Boers, N., Ouyang, W., Bai, L. (2024): ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks. - Advances in Atmospheric Sciences, 41, 1289-1298.
https://doi.org/10.1007/s00376-024-3316-6


Cite as: https://publications.pik-potsdam.de/pubman/item/item_29933
Abstract
Recent studies have shown that deep learning (DL) models can skillfully forecast El Niño–Southern Oscillation (ENSO) events more than 1.5 years in advance. However, concerns regarding the reliability of predictions made by DL methods persist, including potential overfitting issues and lack of interpretability. Here, we propose ResoNet, a DL model that combines CNN (convolutional neural network) and transformer architectures. This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans. We show that ResoNet can robustly predict ENSO at lead times of 19 months, thus outperforming existing approaches in terms of the forecast horizon. According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1- to 18-month leads, we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms, such as the recharge oscillator concept, seasonal footprint mechanism, and Indian Ocean capacitor effect. Moreover, we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet. Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.