用数学方法破解深度学习预测厄尔尼诺的黑箱，发现热带太平洋是关键。
Explore the Ideology of Deep Learning in ENSO Forecasts
- 基于有界变差函数构建可解释框架，激活饱和神经元提升表达能力。
- 揭示厄尔尼诺可预测性主要来自热带太平洋，印度与大西洋也有贡献。
- 发现春季预报障碍根源在于变量选择不当，建议增加海气耦合变量。
厄尔尼诺-南方涛动（ENSO）对全球气候变异具有深远影响，但其预测仍是重大挑战。近年来深度学习显著提升了预测性能，但模型的不透明性阻碍了科学信任与业务应用。本文提出一种基于有界变差函数的数学可解释性框架，通过恢复激活函数饱和区的“死亡”神经元，增强模型表达能力。分析表明，ENSO可预测性主要源于热带太平洋，印度洋和大西洋亦有贡献，与物理认知一致。受控实验验证了方法的稳健性及与经典预测因子的契合性。特别地，针对持续存在的春季可预报性屏障（SPB），发现尽管春季敏感性增强，预测性能反而下降——可能源于变量选择不佳。结果提示，引入更多海气耦合变量或有助于突破SPB限制，推动长期ENSO预测发展。
原文摘要 · Abstract (English)
The El Ni{~n}o-Southern Oscillation (ENSO) exerts profound influence on global climate variability, yet its prediction remains a grand challenge. Recent advances in deep learning have significantly improved forecasting skill, but the opacity of these models hampers scientific trust and operational deployment. Here, we introduce a mathematically grounded interpretability framework based on bounded variation function. By rescuing the "dead" neurons from the saturation zone of the activation function, we enhance the model's expressive capacity. Our analysis reveals that ENSO predictability emerges dominantly from the tropical Pacific, with contributions from the Indian and Atlantic Oceans, consistent with physical understanding. Controlled experiments affirm the robustness of our method and its alignment with established predictors. Notably, we probe the persistent Spring Predictability Barrier (SPB), finding that despite expanded sensitivity during spring, predictive performance declines-likely due to suboptimal variable selection. These results suggest that incorporating additional ocean-atmosphere variables may help transcend SPB limitations and advance long-range ENSO prediction.
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