用可微气候模型生成极端热浪最坏情景，效率远超传统方法。
Constructing Extreme Heatwave Storylines with Differentiable Climate Models
- 通过可微混合模型优化初始条件，生成物理一致的热浪路径。
- 2021年太平洋西北热浪模拟中，强度比75人集合最高成员高3.7℃。
- 适合气候风险评估与极端事件预警研究者使用。
理解极端天气事件可能的上限对气候变化下的风险评估至关重要。现有基于物理模型的大规模集合方法往往计算成本高或难以精确模拟罕见高影响极端事件。本文提出一种新框架，利用可微混合气候模型NeuralGCM，优化初始条件并生成物理一致的最坏情况热浪轨迹。以2021年太平洋西北热浪为例，该方法生成的热浪强度比75人集合中最极端成员高出3.7 °C。这些轨迹表现出增强的大气阻塞和放大的罗斯贝波特征，是严重热浪的典型标志。结果表明，可微气候模型能高效探索事件概率分布的尾部，为气候变化下构建针对性极端天气情景提供了强大新方法。
原文摘要 · Abstract (English)
Understanding the plausible upper bounds of extreme weather events is essential for risk assessment in a warming climate. Existing methods, based on large ensembles of physics-based models, are often computationally expensive or lack the fidelity needed to simulate rare, high-impact extremes. Here, we present a novel framework that leverages a differentiable hybrid climate model, NeuralGCM, to optimize initial conditions and generate physically consistent worst-case heatwave trajectories. Applied to the 2021 Pacific Northwest heatwave, our method produces heatwave intensity up to 3.7 $^\circ$C above the most extreme member of a 75-member ensemble. These trajectories feature intensified atmospheric blocking and amplified Rossby wave patterns-hallmarks of severe heat events. Our results demonstrate that differentiable climate models can efficiently explore the upper tails of event likelihoods, providing a powerful new approach for constructing targeted storylines of extreme weather under climate change.
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