Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model

• Published in: Journal of advances in modeling earth systems Vol. 15; no. 10
• Main Authors: Zhang, Cheng, Perezhogin, Pavel, Gultekin, Cem, Adcroft, Alistair, Fernandez‐Granda, Carlos, Zanna, Laure
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01.10.2023; American Geophysical Union (AGU)
• Subjects: Backscatter; Climate models; Deep learning; Eddies; Finite volume method; General circulation models; Libraries; Machine learning; Mathematical models; Mesoscale eddies; Modelling; MOM6; Momentum; Numerical models; Ocean circulation; Ocean circulation models; Oceans; Parameterization; Programming languages; Simulation; stochastic deep learning; Stochastic models; subgrid ocean processes; Turbulence models; Water circulation
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2023MS003697

Abstract We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021, https://doi.org/10.1002/essoar.10506419.1 ) 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 GCM 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: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. 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. Plain Language Summary This paper discusses how machine learning can be used to make climate models more accurate. Specifically, we import an existing machine learning model that predicts how small eddies (in the order of 10–100 km) in the ocean affect larger currents. We test this machine learning model in a different ocean circulation model than the one it was originally designed for, and found that it worked well. However, we also found some limitations: the model works differently at different depths in the ocean, and it does not work as well near the coasts of the ocean. We also found that the model takes a long time to run on normal computers. Overall, we concluded that the model is promising, but more work is needed to make it work well in realistic situations. Key Points A stochastic‐deep learning model is implemented in an ocean circulation model, MOM6 We evaluate the online performance of the stochastic‐deep learning model as a subgrid parameterization We identify certain limitations of the machine learned parameterization which otherwise has the potential to improve specific metrics