深度学习方法是否适用于全球气候预估的降尺度？针对西班牙气温与降水的一项比较研究
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? An Intercomparison for Temperature and Precipitation over Spain
AI总结：
本文通过统一框架下的比较实验，评估深度学习完美预报降尺度模型在西班牙气温和降水上的性能及对未来情景的外推能力，并指出其业务化的主要障碍与改进方向。
摘要：
深度学习（DL）在不同方法下已展现出对全球气候变化预估进行降尺度的潜力，这些方法包括完美预报（PP）和区域气候模式（RCM）仿真。与仿真器不同，PP降尺度模型是在观测数据上训练的，因此它们能否合理地外推未来排放情景中未见的条件和变化，仍然是一个悬而未决的问题。本文聚焦于这一问题，将其视为这些方法业务化的主要障碍，并展示了一项比较实验的结果，该实验采用统一的实验框架评估现有模型的性能和外推能力，同时考虑了结果对不同训练重复样本的敏感性。我们重点关注西班牙的最低气温、最高气温和降水，该地区具有多种气候条件，并受不同区域过程的影响。最后，我们讨论了研究发现、现有方法的局限性以及未来发展的前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
