Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 10 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 80% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources.

翻译：现有的基于机器学习的大气模型不适用于需要长期稳定性和物理一致性的气候预测。我们提出了ACE（AI²气候模拟器），这是一个拥有2亿参数的自回归机器学习模拟器，可模拟现有的100公里分辨率综合全球大气模型。ACE的公式化设计使其能够评估质量守恒和水汽守恒等物理定律。该模拟器可稳定运行10年，在无需显式约束的情况下几乎能保持柱状水汽守恒，并忠实地再现了参考模型的气候特征，在超过80%的追踪变量上优于一个具有挑战性的基线模型。与使用通常可用资源的参考模型相比，ACE所需挂钟时间减少了近100倍，能源效率提高了100倍。