  Modeling is crucial to understanding the effect of greenhouse gases, warming,
and ice sheet melting on the ocean. At the same time, ocean processes affect
phenomena such as hurricanes and droughts. Parameters in the models that cannot
be physically measured have a significant effect on the model output. For an
idealized ocean model, we generated perturbed parameter ensemble data and
trained surrogate neural network models. The neural surrogates accurately
predicted the one-step forward dynamics, of which we then computed the
parametric sensitivity.
