标题： 使用概率机器学习的气候模型区域气候风险评估 显示英文标题

标题： Regional climate risk assessment from climate models using probabilistic machine learning

摘要： 在公里尺度上准确且可操作的气候信息对于稳健的自然灾害风险评估和基础设施规划至关重要。 在这些分辨率下模拟气候仍然难以实现，迫使依赖于降尺度处理：无论是基于物理的方法还是统计方法，将粗粒度的气候模拟转换为与影响相关的分辨率。 降尺度的一个主要挑战是如何全面捕捉感兴趣的气候过程之间的相互依赖性，这是表示气候危害的前提条件。 然而，当前的方法要么缺乏所需的可扩展性，要么专门针对特定类型的危害。 我们介绍了 GenFocal，这是一种计算效率高、通用性强、端到端生成式框架，能够对在精细时空尺度上交互的复杂气候过程进行全面的概率表征。 与领先的方法相比，包括用于美国第五次国家气候评估的方法，GenFocal 更能准确地评估当前气候中的极端风险。 它生成热带气旋的合理路径，并提供它们生成和演变的准确统计数据，即使在相应的气候模拟中没有出现热带气旋时也是如此。 GenFocal 在预测气候对十年时间尺度的影响方面也显示出与文献一致的令人信服的结果。 GenFocal 革命性地改变了如何高效地将气候模拟与观测数据相结合，并利用这些数据来支持地方和区域社区相关时空尺度上的未来气候影响评估。 我们认为这项工作确立了生成式人工智能（genAI）作为建模复杂、高维多变量统计相关性的有效范式，这些相关性阻碍了对野火、极端高温、热带气旋和洪水等灾害所关联的气候风险的精确量化；从而使得评估适应策略成为可能。 显示英文摘要

摘要： Accurate, actionable climate information at km scales is crucial for robust natural hazard risk assessment and infrastructure planning. Simulating climate at these resolutions remains intractable, forcing reliance on downscaling: either physics-based or statistical methods that transform climate simulations from coarse to impact-relevant resolutions. One major challenge for downscaling is to comprehensively capture the interdependency among climate processes of interest, a prerequisite for representing climate hazards. However, current approaches either lack the desired scalability or are bespoke to specific types of hazards. We introduce GenFocal, a computationally efficient, general-purpose, end-to-end generative framework that gives rise to full probabilistic characterizations of complex climate processes interacting at fine spatiotemporal scales. GenFocal more accurately assesses extreme risk in the current climate than leading approaches, including one used in the US 5th National Climate Assessment. It produces plausible tracks of tropical cyclones, providing accurate statistics of their genesis and evolution, even when they are absent from the corresponding climate simulations. GenFocal also shows compelling results that are consistent with the literature on projecting climate impact on decadal timescales. GenFocal revolutionizes how climate simulations can be efficiently augmented with observations and harnessed to enable future climate impact assessments at the spatiotemporal scales relevant to local and regional communities. We believe this work establishes genAI as an effective paradigm for modeling complex, high-dimensional multivariate statistical correlations that have deterred precise quantification of climate risks associated with hazards such as wildfires, extreme heat, tropical cyclones, and flooding; thereby enabling the evaluation of adaptation strategies.