  Stochastic subgrid-scale parametrizations aim to incorporate effects of
unresolved processes in an effective model by sampling from a distribution
usually described in terms of resolved modes. This is an active research area
in climate, weather and ocean science where processes evolved in a wide range
of spatial and temporal scales. In this study, we evaluate the performance of
conditional generative adversarial network (GAN) in parametrizing subgrid-scale
effects in a finite-difference discretization of stochastically forced Burgers
equation. We define resolved modes as local spatial averages and deviations
from these averages are the unresolved degrees of freedom. We train a
Wasserstein GAN (WGAN) conditioned on the resolved variables to learn the
distribution of subgrid flux tendencies for resolved modes and, thus, represent
the effect of unresolved scales. The resulting WGAN is then used in an
effective model to reproduce the statistical features of resolved modes. We
demonstrate that various stationary statistical quantities such as spectrum,
moments, autocorrelation, etc. are well approximated by this effective model.
