发表机构
Massachusetts Institute of Technology; City University of Hong Kong; Google Research（麻省理工学院; 香港城市大学; 谷歌研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对气候降尺度中动态一致性缺失的挑战，提出DySCo框架，通过数据驱动的张弛法重构训练动态配对轨迹，训练两阶段算子，在保持统计性能的同时提升动态一致性，优于现有方法。
AI 中文摘要
区域气候风险评估对基础设施设计、灾害预报和保险资源分配等应用至关重要。然而，利用全球气候模式（GCM）估算区域（即高空间分辨率）风险的计算成本过高，这推动了针对粗分辨率GCM输出的降尺度方法的发展。降尺度对稀有事件至关重要，因为量化其极端属性需要高空间分辨率和极长时间的GCM模拟。这些方法以非侵入式方式提高GCM分辨率，同时校正未解析的精细尺度过程带来的统计偏差，从而提高长回报期极端事件统计的准确性。一个关键挑战是保持动态一致性，因为自由演化的GCM轨迹不应与用于训练校正算子的观测数据集跟踪一致，这对基于故事情节的风险评估（即极端事件目录）的因果极端事件分析至关重要，而后者是有效规划的必要条件。我们通过引入动态和统计一致的降尺度方法（DySCo）来应对这一挑战，这是一个非侵入式框架，可产生与粗分辨率GCM动态一致的高分辨率气候预测。DySCo依赖于数据驱动的 nudging（张弛法）重构，以创建动态配对的训练轨迹，且无需侵入式修改GCM。利用这些配对轨迹，我们训练了一个动态和统计一致的两阶段算子。我们通过对Community Earth System Model v2 Large Ensemble（LENS2）进行时间和空间降尺度到历史再分析来评估该方法。结果显示，DySCo实现了与粗分辨率GCM轨迹更优的动态一致性，实质上对GCM进行了最小的因果校正，同时保留了与最先进无监督模型相当的顶级统计性能。
英文摘要
Regional climate risk assessment is critical for applications such as infrastructure design, disaster forecasting, and insurance resource allocation. However, estimating regional (i.e., high-spatial-resolution) risk with global climate models (GCMs) remains computationally prohibitive, which has driven the development of downscaling methods for coarse GCM outputs. Downscaling is vital for rare events, since quantifying their extreme properties requires high spatial resolution and very long GCM simulations. These methods non-intrusively increase GCM resolution while correcting statistical biases from unresolved fine-scale processes, thereby improving the accuracy of extreme event statistics with long return periods. A key challenge is preserving dynamical consistency, as freely evolving GCM trajectories are not expected to track the observational dataset used for training the correction operator. This is critical for causal extreme event analyses, where storyline-based risk assessment, i.e., extreme event catalogs, is necessary for effective planning. We address this challenge by introducing Dynamically and Statistically Consistent downscaling (DySCo), a non-intrusive framework yielding high-resolution climate projections consistent with coarse GCM dynamics. DySCo relies on a data-driven reformulation of nudging to create dynamically paired training trajectories without intrusive GCM modifications. Using these paired trajectories, we train a dynamically and statistically consistent, two-stage operator. We evaluate the method by downscaling the Community Earth System Model v2 Large Ensemble (LENS2) in time and space towards historical reanalysis. Results show DySCo achieves superior dynamical consistency with the coarse GCM trajectories, essentially applying a minimal, causal correction to the GCM, preserving top statistical performance comparable to state-of-the-art unsupervised models.
