对比两种新型气候模型对大气变化的模拟能力，发现其在关键周期上仍有不足。
Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM
- 用数据驱动的AI模型和混合模型评估大气波动谱与急流相互作用
- 两者能模拟热带波和中纬度涡旋-平均流互动，但无法准确复现QBO（约28月）和南半球环流（约150天）
- 为未来气候模型改进提供动态基准，尤其适合关注极端气候外推的研究者
基于物理过程的大气-陆面模型在设定海表温度条件下虽取得显著成果，但在模拟大气变率方面仍存在偏差。近年来，人工智能模拟器和混合模型展现出克服这些偏差的潜力，但仍需通过基于基本大气动力学的指标进行系统评估。本文评估了完全数据驱动的AI模拟器ACE2-ERA5和混合模型NeuralGCM在四种大气变率基准指标上的表现。结果显示，两者均能捕捉大尺度热带波的谱特征及中纬度涡旋-平均流相互作用，包括临界层效应；然而，在准两年振荡（QBO，约28个月）和南半球环状模传播（约150天）的时间尺度上表现不佳。这些动态指标可作为初期基准工具，指导人工智能模型开发并揭示其局限性，对拓展至未见气候场景（如外推至新气候状态）的应用具有重要意义。
原文摘要 · Abstract (English)
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).
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