1. Global Subseasonal‐to‐Seasonal Marine Heatwave Forecasts Boosted by Explainable Deep Learning
关键词Tags: marine heatwaves marine heatwaves
摘要：海洋热浪（MHW）给海洋生态系统和气候敏感活动带来越来越大的风险。次季节到季节 (S2S) MHW 预测对于预警至关重要，但动态预测系统的可靠性有限。在这里，我们提出了 MHWCorrNet，这是一种基于可解释的深度学习的后处理框架，用于 ECMWF IFS S2S MHW 预测。它在 1-6 周的交付时间内将全球平均 MHW 预报技能提高了约 9%，其中北半球和南半球温带地区的增幅更大，分别为约 15% 和约 22%。我们表明，区域背景国家系统地制定预测修正策略，揭示了以前被忽视的方面。在温带地区，更多变化的海气过程会导致更多的错过事件，因此校正主要改善事件检测。 在热带地区，MHW 和非 MHW 条件之间较弱的热对比会在检测阈值附近产生更多的边缘事件；因此，技能的提高主要来自误报的减少。通过利用可解释的诊断来量化特征重要性，MHWCorrNet 自适应地调节其对跨纬度带关键物理变量的依赖，在温带地区对多个预测变量的依赖性更强，以纠正较大的残差误差，而在热带地区则表现出较弱的依赖性，因为热带地区的海面温度变化受到大规模气候模式的限制，可纠正的误差空间较小。这种基于物理的适应增强了预测的可靠性和可解释性，强调了将物理理解纳入数据驱动的预测框架的价值。
Abstract: Marine heatwaves (MHWs) pose increasing risks to marine ecosystems and climate‐sensitive activities. Subseasonal‐to‐seasonal (S2S) MHW forecasts are essential for early warning, yet dynamical prediction systems show limited reliability. Here, we present MHWCorrNet, an interpretable deep learning‐based post‐processing framework for ECMWF IFS S2S MHW forecasts. It improves globally averaged MHW forecast skill by ∼9% at 1–6‐week lead times, with larger gains of ∼15% and ∼22% in the Northern and Southern Hemisphere extratropics. We show that regional background states systematically shape forecast correction strategies, revealing a previously overlooked aspect. In the extratropics, more variable air‐sea processes cause more missed events, so corrections primarily improve event detection. In the tropics, weaker thermal contrasts between MHW and non‐MHW conditions produce more marginal events near the detection threshold; therefore, skill gains mainly result from reduced false alarms. By leveraging interpretable diagnostics to quantify feature importance, MHWCorrNet adaptively modulates its reliance on key physical variables across latitude bands, placing stronger dependence on multiple predictors in extratropical regions to correct large residual errors, while exhibiting weaker dependence in the tropics, where sea surface temperatures variability is constrained by large‐scale climate modes and the correctable error space is smaller. This physically grounded adaptation enhances both forecast reliability and interpretability, underscoring the value of incorporating physical understanding into data‐driven prediction frameworks.