对AI模拟器ACE2和混合模型NeuralGCM中的大气环流变异性进行基准测试
Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM
AI总结：
本文评估了ACE2-ERA5和NeuralGCM在大气变异性基准指标上的表现，发现两者在捕捉季风波和极地涡旋-均流相互作用的频谱方面有效，但难以捕捉准双年振荡和南半球环流模式传播的时间尺度。
摘要：
基于海表温度预设的物理大气-陆地模型在表示大气变异性方面有显著成功但也存在偏差，与观测相比。最近，AI模拟器和混合模型出现，有潜力克服这些偏差，但仍需系统评估基于基本大气动力学的指标。本文评估了四个大气变异性基准指标在完全数据驱动的AI模拟器（ACE2-ERA5）和混合模型（NeuralGCM）中的表现。混合模型和模拟器能够捕捉大尺度热带波和极地涡旋-均流相互作用的频谱，包括关键水平。然而，两者在捕捉准双年振荡（QBO，约28个月）和南半球环流模式传播（约150天）的时间尺度方面存在困难。这些动力学指标作为初始基准工具，用于指导AI模型开发并理解其局限性，这可能对非分布应用（如外推到未见气候）至关重要。
英文摘要：
Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations. Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded in fundamental atmospheric dynamics. Here, we evaluate the representation of four atmospheric variability benchmarking metrics in a fully data-driven AI emulator (ACE2-ERA5) and hybrid model (NeuralGCM). The hybrid model and emulator can capture the spectra of large-scale tropical waves and extratropical eddy-mean flow interactions, including critical levels. However, both struggle to capture the timescales associated with quasi-biennial oscillation (QBO, $\sim 28$ months) and Southern annular mode propagation ($\sim 150$ days). These dynamical metrics serve as an initial benchmarking tool to inform AI model development and understand their limitations, which may be essential for out-of-distribution applications (e.g., extrapolating to unseen climates).
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。
