利用可微分气候模型优化地球工程干预
Optimizing Geoengineering Interventions Using Differentiable Climate Models
- University of California, Santa Cruz（加州大学圣克鲁兹分校）
- University of Chicago（芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用可微分大气模型JAX-GCM，通过贪婪优化海表温度冷却模式，有效消除92.3%的陆地增温，并验证了策略在AI模拟器中的鲁棒性，为地球工程干预提供新方法。
AI中文摘要：
实施地球工程计划以冷却地球气候可能迫在眉睫。开发工具以确保此类计划在实现目标的同时最小化干扰至关重要。在此，我们利用最近开发的可微分大气模型，展示了一种新颖的地球工程控制策略。在可微分原始方程大气模型JAX-GCM中，我们施加均匀的+4 K海洋增温，并询问何种海表温度冷却模式——在五个海洋掩蔽的纬向带中，施加规定的海表温度强迫，其振幅自由——能使陆地近地面气温最接近模型自身未增温的气候态。这一理想化设置代表了一种冷却模式，该模式在物理上可通过海洋云增亮或平流层气溶胶注入来实现。混沌动力学中的梯度在Lyapunov视界之外与真实敏感性去相关，因此我们采用后退时域控制，在8至14天的片段上进行贪婪优化。学习到的策略在十成员集合的两年滚动模拟中消除了92.3±0.4%的实现陆地增温，并且三年的运行能够维持这一效果。如果我们使用陆地温度的空间模式作为优化目标，那么陆地降水、蒸发和比湿的分布也会得到恢复，尽管它们并未包含在目标函数中。从JAX-GCM学习到的策略在AI模拟器LUCIE和NeuralGCM中无需重新优化即可成功回放，这表明了其鲁棒性。这些有前景的结果展示了一种设计最优气候干预的策略，可广泛应用于正在考虑的地球工程场景。
英文摘要：
The deployment of a geoengineering program to cool Earth's climate may be imminent. It is crucial that tools be developed to ensure that such a program would achieve its objectives while minimizing disruption. Here we exploit recently developed differentiable atmospheric models to demonstrate a novel geoengineering control strategy. In the differentiable primitive-equation atmospheric model JAX-GCM we impose a uniform $+4$\,K ocean warming and ask what pattern of sea-surface temperature cooling -- in five ocean-masked zonal bands of prescribed SST forcings whose amplitudes are free -- returns land near-surface air temperature closest to the model's own unwarmed climatology. This idealized set-up represents a cooling pattern that could be delivered physically either by marine cloud brightening or stratospheric aerosol injection. Gradients through chaotic dynamics decorrelate from the true sensitivity beyond the Lyapunov horizon, so we optimize greedily over segments of 8 to 14 days, following receding-horizon control. The learned strategy removes $92.3 \pm 0.4\%$ of the realized land warming across a ten-member ensemble of two-year rollouts, and a three-year run sustains it. If we use the spatial pattern of land temperature as the optimization objective, the distributions of precipitation, evaporation, and specific humidity over land are restored as well, even though they are not included in the objective function. The learned strategy from JAX-GCM replayed in the AI emulators LUCIE and NeuralGCM without re-optimization is successful, suggesting robustness. These promising results demonstrate a strategy for designing optimal climate interventions that can be applied broadly for geoengineering scenarios under consideration.
