On the choice of turbulence eddy fluxes to learn from in data-driven methods

Recent works have demonstrated the viability of employing data-driven / machine learning 
methods for the purposes of learning more about ocean turbulence, with applications to turbulence parameterisations in ocean general circulation models. Focusing on mesoscale geostrophic turbulence in the ocean context, works thus far have mostly focused on the choice of algorithms and testing of trained up models. Here we focus instead on the choice of eddy flux data to learn from. We argue that, for mesoscale geostrophic turbulence, it might be beneficial from a theoretical as well as practical point of view to learn from eddy fluxes with dynamically inert rotational fluxes removed (ideally in a gauge invariant fashion), instead of the divergence of the eddy fluxes as has been considered thus far. Outlooks for physically constrained and interpretable machine learning will be given in light of the results.