Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model

Andrew Ross, Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda, Laure Zanna

Research output: Contribution to journalArticlepeer-review


Recently, a growing number of studies have used machine learning (ML) models to parameterize computationally intensive subgrid-scale processes in ocean models. Such studies typically train ML models with filtered and coarse-grained high-resolution data and evaluate their predictive performance offline, before implementing them in a coarse resolution model and assessing their online performance. In this work, we systematically benchmark the online performance of such models, their generalization to domains not encountered during training, and their sensitivity to data set design choices. We apply this proposed framework to compare a large number of physical and neural network (NN)-based parameterizations. We find that the choice of filtering and coarse-graining operator is particularly critical and this choice should be guided by the application. We also show that all of our physics-constrained NNs are stable and perform well when implemented online, but generalize poorly to new regimes. To improve generalization and also interpretability, we propose a novel equation-discovery approach combining linear regression and genetic programming with spatial derivatives. We find this approach performs on par with neural networks on the training domain but generalizes better beyond it. We release code and data to reproduce our results and provide the research community with easy-to-use resources to develop and evaluate additional parameterizations.

Original languageEnglish (US)
Article numbere2022MS003258
JournalJournal of Advances in Modeling Earth Systems
Issue number1
StatePublished - Jan 2023

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences


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