  Numerical models of the ocean and ice sheets are crucial for understanding
and simulating the impact of greenhouse gases on the global climate. Oceanic
processes affect phenomena such as hurricanes, extreme precipitation, and
droughts. Ocean models rely on subgrid-scale parameterizations that require
calibration and often significantly affect model skill. When model
sensitivities to parameters can be computed by using approaches such as
automatic differentiation, they can be used for such calibration toward
reducing the misfit between model output and data. Because the SOMA model code
is challenging to differentiate, we have created neural network-based
surrogates for estimating the sensitivity of the ocean model to model
parameters. We first generated perturbed parameter ensemble data for an
idealized ocean model and trained three surrogate neural network models. The
neural surrogates accurately predicted the one-step forward ocean dynamics, of
which we then computed the parametric sensitivity.
