Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large-eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large datasets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on Tensor Processing Units (TPUs), application-specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU-based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with $10\times$ speedup over real-time evolution in domains with a $34.7~\mathrm{km} \times 53.8~\mathrm{km}$ horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds.

翻译：云，尤其是低云，对于调节地球能量平衡以及介导气候系统对温室气体浓度变化的响应至关重要。尽管其对气候具有重要意义，但人们对它们的理解仍相对有限，且在气候模型中的表征存在不准确性。主要原因在于，使用涡旋解析数值模拟（LES）模拟云的高昂计算成本，阻碍了广泛而系统的数值实验，以及为气候模型训练参数化方案所需的大型数据集的生成。在此，我们展示了在张量处理单元（TPU）上对低云进行LES的成果——TPU是一种最初为机器学习应用开发的专用集成电路。我们证明，TPU结合定制化的软件实现，可用于模拟在海洋层积云动力学与化学（DYCOMS）实地研究中所观测到的计算上具有挑战性的层积云条件。基于TPU的LES代码成功再现了DYCOMS期间的云，并为云模拟开启了TPU上可用的巨大计算资源。该代码实现了LES前所未有的弱扩展和强扩展能力，使得例如在水平截面为34.7公里×53.8公里的区域内，以超过实时演变10倍的速度模拟层积云成为可能。这些结果为计算实验以及大幅扩充可用于训练低云参数化方案的LES样本开辟了新途径。