Machine Learning Surrogate Models for Oceanic Simulations' Parameter Tuning

The role of mesoscale eddies is crucial for ocean circulation and its energy budget. At scales of 10 to 300 km, the mesoscale eddies transfer hydrographic properties and energy at different spatial and temporal scales, hence contributing to equilibrating large scale ocean dynamics and thermodynamics, which is paramount for long-term climate modeling. Representing correctly their effect in ocean models is of greatest importance. However, ocean-eddying models remain prohibitively expensive to run, thus the development of low-resolution models with a skill comparable to their high-resolution counterparts is of high interest to the community.

In this work we use machine learning based surrogates that emulates the effect of changing parameters of closure models, then use the results for parameter selection by throwing out the "bad" sets of parameters according to some given metrics. We show the relevance of our method firstly on toy models (e.g. Lorenz96), then explicit its challenges and promises. An application on a NEMO low resolution model (1°) is considered in this work and preliminary results are presented.