标题： 深度学习大气模型可靠地模拟了样本外陆地热浪和冷波频率 显示英文标题

标题： Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies

摘要： 基于深度学习（DL）的通用环流模型（GCMs）正在成为快速模拟器，但它们在训练范围之外复制极端事件的能力仍然未知。 在这里，我们评估了两种这样的模型——混合神经通用环流模型（NGCM）和纯数据驱动的深度学习地球系统模型（DL\textit{ESy}M）——与传统的高分辨率陆地-大气模型（HiRAM）在模拟陆地热浪和冷浪方面的表现。 所有模型都使用1900-2020年的观测海面温度和海冰进行强迫，重点关注样本外的20世纪早期时期（1900-1960）。 这两种DL模型成功推广到未见过的气候条件，在1900-1960年间广泛再现了热浪和冷浪事件的频率和空间模式，其技能与HiRAM相当。 例外的是在北亚和北美的部分区域，所有模型在1940-1960年期间表现较差。 由于温度自相关过强，DL\textit{ESy}M倾向于高估热浪和冷浪的频率，而物理-深度学习混合模型NGCM表现出更类似于HiRAM的持续性。 显示英文摘要

摘要： Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models -- the hybrid Neural General Circulation Model (NGCM) and purely data-driven Deep Learning Earth System Model (DL\textit{ESy}M) -- against a conventional high-resolution land-atmosphere model (HiRAM) in simulating land heatwaves and coldwaves. All models are forced with observed sea surface temperatures and sea ice over 1900-2020, focusing on the out-of-sample early-20th-century period (1900-1960). Both DL models generalize successfully to unseen climate conditions, broadly reproducing the frequency and spatial patterns of heatwave and cold wave events during 1900-1960 with skill comparable to HiRAM. An exception is over portions of North Asia and North America, where all models perform poorly during 1940-1960. Due to excessive temperature autocorrelation, DL\textit{ESy}M tends to overestimate heatwave and cold wave frequencies, whereas the physics-DL hybrid NGCM exhibits persistence more similar to HiRAM.