Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model

• URL: http://arxiv.org/abs/2503.19502v1
• Date: Tue, 25 Mar 2025 09:50:19 GMT
• Title: Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model
• Authors: Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang,
• Abstract summary: El Nino-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts.<n>We introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting.
• Score: 8.609672620715674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: El Ni\~no-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.

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