对比深度学习在西班牙温降水降尺度中的表现与外推能力
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? An Intercomparison for Temperature and Precipitation over Spain
- 用统一框架对比多种深度学习降尺度方法
- 模型在历史数据上表现良好，但未来外推能力有限
- 适合关注气候模拟外推风险的研究者
深度学习（DL）在不同方法下（如完美预报法PP和区域气候模型RCM模拟器）展现出降尺度全球气候变化预测的潜力。与模拟器不同，PP方法基于观测数据训练，因此其能否合理外推未见的未来排放情景下的气候条件仍是开放问题。本文聚焦这一操作化的主要障碍，通过统一实验框架开展跨方法比较，评估现有模型在西班牙地区最小/最大气温与降水量上的性能及外推能力，并考察结果对不同训练副本的敏感性。西班牙气候类型多样，受多种区域过程影响，适合作为测试场景。研究最后讨论了现有方法的局限性与未来发展方向。
原文摘要 · Abstract (English)
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and changes in future emissions scenarios. Here we focus on this problem as the main drawback for the operationalization of these methods and present the results of an intercomparison experiment to evaluate the performance and extrapolation capability of existing models using a common experimental framework, taking into account the sensitivity of results to different training replicas. We focus on minimum and maximum temperatures and precipitation over Spain, a region with a range of climatic conditions with different influential regional processes. We conclude with a discussion of the findings, limitations of existing methods, and prospects for future development.
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