  We address the question of how to use a machine learned parameterization in a
general circulation model, and assess its performance both computationally and
physically. We take one particular machine learned parameterization
\cite{Guillaumin1&Zanna-JAMES21} and evaluate the online performance in a
different model from which it was previously tested. This parameterization is a
deep convolutional network that predicts parameters for a stochastic model of
subgrid momentum forcing by mesoscale eddies. We treat the parameterization as
we would a conventional parameterization once implemented in the numerical
model. This includes trying the parameterization in a different flow regime
from that in which it was trained, at different spatial resolutions, and with
other differences, all to test generalization. We assess whether tuning is
possible, which is a common practice in general circulation model development.
We find the parameterization, without modification or special treatment, to be
stable and that the action of the parameterization to be diminishing as spatial
resolution is refined. We also find some limitations of the machine learning
model in implementation: 1) tuning of the outputs from the parameterization at
various depths is necessary; 2) the forcing near boundaries is not predicted as
well as in the open ocean; 3) the cost of the parameterization is prohibitively
high on CPUs. We discuss these limitations, present some solutions to problems,
and conclude that this particular ML parameterization does inject energy, and
improve backscatter, as intended but it might need further refinement before we
can use it in production mode in contemporary climate models.

Atmospheric and Oceanic Physics