  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.

Atmospheric and Oceanic Physics;Numerical Analysis;Numerical Analysis;Computational Physics;Fluid Dynamics;Geophysics