Can diffusion model generate ocean states compatible with OGCM ?

Ocean General Circulation Models (hereafter OGCM) are critical to the study of past and present climate, and the production of future projections. Unfortunately, they require large amounts of computations at simulation time. On the other hand, deep learning emulators trained on reanalyses are starting to deliver accurate short-term predictions, using comparatively small computational resources, yet they struggle to deliver long term predictions, are not interpretable and do no't factor in the uncertainty in physical parameters. As a result, they cannot be used by climate scientists to understand mechanisms of climate variability, such as tipping points, nor adjustment processes to greenhouse gas emissions.

Aiming to take the best from each world and establish a close interaction between emulators and OGCM, we investigate whether an emulator can be used to provide state variables to an ocean model, which would then handle the temporal integration using physics equations. Namely, we trained a diffusion model on a large dataset of ocean variables produced by NEMO, analysed the newly generated states, propose metrics to assess their physical consistency, and use them as initial conditions of simulations to assess their compatibility (physical and numerical) with NEMO.

Overall, we want to determine whether unconstrained generative models are able to produce realistic solutions, and to assess the tolerance of OGCM to externally generated ocean states, what we consider as a first step towards building an hybrid OGCM.