The representation of nonlinear sub-grid processes, especially clouds, has
been a major source of uncertainty in climate models for decades.
Cloud-resolving models better represent many of these processes and can now be
run globally but only for short-term simulations of at most a few years because
of computational limitations. Here we demonstrate that deep learning can be
used to capture many advantages of cloud-resolving modeling at a fraction of
the computational cost. We train a deep neural network to represent all
atmospheric sub-grid processes in a climate model by learning from a
multi-scale model in which convection is treated explicitly. The trained neural
network then replaces the traditional sub-grid parameterizations in a global
general circulation model in which it freely interacts with the resolved
dynamics and the surface-flux scheme. The prognostic multi-year simulations are
stable and closely reproduce not only the mean climate of the cloud-resolving
simulation but also key aspects of variability, including precipitation
extremes and the equatorial wave spectrum. Furthermore, the neural network
approximately conserves energy despite not being explicitly instructed to.
Finally, we show that the neural network parameterization generalizes to new
surface forcing patterns but struggles to cope with temperatures far outside
its training manifold. Our results show the feasibility of using deep learning
for climate model parameterization. In a broader context, we anticipate that
data-driven Earth System Model development could play a key role in reducing
climate prediction uncertainty in the coming decade.

利用深度学习可以以极小的计算代价代替传统的云细度建模的层状参数化方法，实现全球多年的气候预测。