ACE2-SOM：将机器学习大气仿真器与平板海洋耦合，并学习气候对CO$_2$变化的敏感性
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO$_2$
AI总结：
本文将Ai2气候仿真器第2版与平板海洋模型耦合为ACE2-SOM，训练其学习气候对CO₂变化的敏感性，在平衡态推断中准确再现温度、降水及极端事件响应，并揭示非平衡态情景下能量不守恒等问题。
摘要：
尽管基于自回归机器学习的仿真器已被训练用于在现今和近期过去的气候条件下产生稳定且准确的滚动预测，但迄今为止尚无任何仿真器被训练用于模拟气候对CO$_2$或其他温室气体大幅变化的敏感性。作为第一步，我们将Ai2气候仿真器第2版耦合到一个平板海洋模型（以下简称ACE2-SOM），并在具有不同CO$_2$水平的一组平衡态气候物理参考模拟输出上对其进行训练。我们在训练中已见和未见的CO$_2$浓度下，测试其在平衡态和非平衡态气候情景中的表现。ACE2-SOM在平衡态气候推断中表现良好，无论是样本内还是样本外的CO$_2$浓度，都能准确再现CO$_2$加倍、三倍或四倍时地表温度和降水变化的涌现时间平均空间模态。此外，大气增温的垂直廓线以及高达99.9999百分位的极端降水率变化均与参考模型高度一致。非平衡态气候推断更具挑战性。当CO$_2$以每年2%的速率逐渐增加时，ACE2-SOM能够准确模拟地表和中低层大气场的全球年平均趋势，但在平流层场中产生了不物理的跳变。当CO$_2$突然增加四倍时，机器学习控制的场以不切实际的速度过渡到4xCO$_2$状态。在此过程中，它们违反了全球能量守恒，并表现出地表和大气顶辐射通量对CO$_2$瞬时变化的不物理敏感性。未来为解决这些问题所需的仿真器开发应提高其对多样化气候变化情景的泛化能力。
英文摘要：
While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial changes in CO$_2$ or other greenhouse gases. As an initial step we couple the Ai2 Climate Emulator version 2 to a slab ocean model (hereafter ACE2-SOM) and train it on output from a collection of equilibrium-climate physics-based reference simulations with varying levels of CO$_2$. We test it in equilibrium and non-equilibrium climate scenarios with CO$_2$ concentrations seen and unseen in training. ACE2-SOM performs well in equilibrium-climate inference with both in-sample and out-of-sample CO$_2$ concentrations, accurately reproducing the emergent time-mean spatial patterns of surface temperature and precipitation change with CO$_2$ doubling, tripling, or quadrupling. In addition, the vertical profile of atmospheric warming and change in extreme precipitation rates up to the 99.9999th percentile closely agree with the reference model. Non-equilibrium-climate inference is more challenging. With CO$_2$ increasing gradually at a rate of 2% year$^{-1}$, ACE2-SOM can accurately emulate the global annual mean trends of surface and lower-to-middle atmosphere fields but produces unphysical jumps in stratospheric fields. With an abrupt quadrupling of CO$_2$, ML-controlled fields transition unrealistically quickly to the 4xCO$_2$ regime. In doing so they violate global energy conservation and exhibit unphysical sensitivities of and surface and top of atmosphere radiative fluxes to instantaneous changes in CO$_2$. Future emulator development needed to address these issues should improve its generalizability to diverse climate change scenarios.
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