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：云云,特别是低云云,对于调节地球的能源平衡和调节气候系统对温室气体浓度变化的反应至关重要。尽管它们对气候很重要,但它们仍然相对缺乏理解,在气候模型中代表的不准确。一个主要原因是,用大型模拟模拟(LES)模拟云的计算成本高昂,抑制了广泛和系统的数值实验和产生大型数据集,用于培训气候模型的模拟方案。在这里,我们展示了天线处理股(TPUs)的低云层的激光,这是最初为机器学习应用程序开发的应用专用集成集成电路。我们显示,与定制软件实施相配合的TPUP可被用来模拟在海洋蒸气累积(DYCOMS)动态和化学(DYCOMS)实地研究期间观察到的具有挑战性的云层云层云。基于TPU的LES代码在DYCOMS期间成功地复制了云层,并打开了用于云层模拟的低层处理单位(TPUs)现有的大量公开计算资源。该代码使得SLES的空前弱和强缩缩缩缩缩缩,使得在实时的轨道上可以进行实时的递增速度。