  Climate models encapsulate our best understanding of the Earth system,
allowing research to be conducted on its future under alternative assumptions
of how human-driven climate forces are going to evolve. An important
application of climate models is to provide metrics of mean and extreme climate
changes, particularly under these alternative future scenarios, as these
quantities drive the impacts of climate on society and natural systems. Because
of the need to explore a wide range of alternative scenarios and other sources
of uncertainties in a computationally efficient manner, climate models can only
take us so far, as they require significant computational resources, especially
when attempting to characterize extreme events, which are rare and thus demand
long and numerous simulations in order to accurately represent their changing
statistics. Here we use deep learning in a proof of concept that lays the
foundation for emulating global climate model output for different scenarios.
We train two "loosely conditioned" Generative Adversarial Networks (GANs) that
emulate daily precipitation output from a fully coupled Earth system model: one
GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are
trained to produce spatiotemporal samples: 32 days of precipitation over a
64x128 regular grid discretizing the globe. We evaluate the generator with a
set of related performance metrics based upon KL divergence, and find the
generated samples to be nearly as well matched to the test data as the
validation data is to test. We also find the generated samples to accurately
estimate the mean number of dry days and mean longest dry spell in the 32 day
samples. Our trained GANs can rapidly generate numerous realizations at a
vastly reduced computational expense, compared to large ensembles of climate
models, which greatly aids in estimating the statistics of extreme events.

Atmospheric and Oceanic Physics;Machine Learning;Neural and Evolutionary Computing;Geophysics