用深度学习提升20个月的厄尔尼诺预测精度并解释关键信号
Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model
- 融合卷积与注意力机制，整合多源气候数据进行预测
- 预报有效期限达20个月，突破春季可预测性障碍
- 可解释性强，揭示跨洋盆相互作用的关键信号
厄尔尼诺-南方涛动（ENSO）是影响全球气候的重要年际变率模式，其演变受复杂的海气相互作用调控，长期预测面临巨大挑战。本文提出一种多变量深度学习模型CTEFNet，结合卷积神经网络与变压器架构，融合多个海洋与大气预测因子，将有效预报提前期延长至20个月，并显著缓解春季可预测性障碍，优于传统动力模型与现有先进深度学习方法。此外，通过基于梯度的敏感性分析，CTEFNet提供了物理上合理且统计显著的解读，揭示了驱动ENSO演化的关键前兆信号，与经典理论一致，并揭示了太平洋、大西洋与印度洋之间的新交互机制。该模型在预测性能与可解释性上的优势，凸显了深度学习在捕捉复杂气候动力学中的潜力。
原文摘要 · Abstract (English)
El Niño-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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