August 21, 2025Open Access

Ocean Emulation With Fourier Neural Operators: Double Gyre

SBSuyash BirePlanetary Science Institute BLBjörn LütjensPlanetary Science Institute KAKamyar AzizzadenesheliNvidia (United States)

Key Points

Prediction skill is maximized at an intermediate time scale, enhancing model efficiency and performance.
Using Fourier neural operators, the study trains the neural network at one resolution and generates models at another.
The methodology addresses computational resource challenges typically faced in traditional numerical simulations.
Deep learning complements classical methods to capture the underlying dynamics of the ocean system.

Abstract

Abstract A data‐driven emulator for the baroclinic double gyre ocean simulation is presented in this study. Traditional numerical simulations using partial differential equations (PDEs) often require substantial computational resources, hindering real‐time applications and inhibiting model scalability. This study presents a novel approach employing Fourier neural operators to address these challenges in an idealized double‐gyre ocean simulation. We propose a deep learning approach capable of learning the underlying dynamics of the ocean system, complementing the classical methods. Additionally, we show how Fourier neural operators allow us to train the network at one resolution and generate ensembles at a different resolution. We find that there is an intermediate time scale where the prediction skill is maximized.

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