  Numerical models based on physics represent the state-of-the-art in earth
system modeling and comprise our best tools for generating insights and
predictions. Despite rapid growth in computational power, the perceived need
for higher model resolutions overwhelms the latest-generation computers,
reducing the ability of modelers to generate simulations for understanding
parameter sensitivities and characterizing variability and uncertainty. Thus,
surrogate models are often developed to capture the essential attributes of the
full-blown numerical models. Recent successes of machine learning methods,
especially deep learning, across many disciplines offer the possibility that
complex nonlinear connectionist representations may be able to capture the
underlying complex structures and nonlinear processes in earth systems. A
difficult test for deep learning-based emulation, which refers to function
approximation of numerical models, is to understand whether they can be
comparable to traditional forms of surrogate models in terms of computational
efficiency while simultaneously reproducing model results in a credible manner.
A deep learning emulation that passes this test may be expected to perform even
better than simple models with respect to capturing complex processes and
spatiotemporal dependencies. Here we examine, with a case study in
satellite-based remote sensing, the hypothesis that deep learning approaches
can credibly represent the simulations from a surrogate model with comparable
computational efficiency. Our results are encouraging in that the deep learning
emulation reproduces the results with acceptable accuracy and often even faster
performance. We discuss the broader implications of our results in light of the
pace of improvements in high-performance implementations of deep learning as
well as the growing desire for higher-resolution simulations in the earth
sciences.
