  The atmosphere affects humans in a multitude of ways, from loss of life due
to adverse weather effects to long-term social and economic impacts on
societies. Computer simulations of atmospheric dynamics are, therefore, of
great importance for the well-being of our and future generations. Here, we
propose AtmoRep, a novel, task-independent stochastic computer model of
atmospheric dynamics that can provide skillful results for a wide range of
applications. AtmoRep uses large-scale representation learning from artificial
intelligence to determine a general description of the highly complex,
stochastic dynamics of the atmosphere from the best available estimate of the
system's historical trajectory as constrained by observations. This is enabled
by a novel self-supervised learning objective and a unique ensemble that
samples from the stochastic model with a variability informed by the one in the
historical record. The task-independent nature of AtmoRep enables skillful
results for a diverse set of applications without specifically training for
them and we demonstrate this for nowcasting, temporal interpolation, model
correction, and counterfactuals. We also show that AtmoRep can be improved with
additional data, for example radar observations, and that it can be extended to
tasks such as downscaling. Our work establishes that large-scale neural
networks can provide skillful, task-independent models of atmospheric dynamics.
With this, they provide a novel means to make the large record of atmospheric
observations accessible for applications and for scientific inquiry,
complementing existing simulations based on first principles.
