Work

Calibration of a Neural Network Ocean Closure for Improved Mean State and Variability

Year: 2026

Type: article

Abstract: Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemble Kalman Inversion (EKI). We optimize parameters of a neural network parameterization of mesoscale eddies in two idealized ocean models at coarse resolution. The calibrated parameterization reduces errors in the time‐averaged fluid interfaces and their variability by factors of 1.7–3.3 compared to the unparameterized model, depending on the metric and configuration. The EKI method is robust to noise in time‐averaged statistics arising from chaotic ocean dynamics. Furthermore, we propose an efficient calibration protocol that bypasses integration to statistical equilibrium by carefully choosing an initial condition. These results demonstrate that systematic calibration can substantially improve coarse‐resolution ocean simulations and provide a practical pathway for reducing biases in global ocean models.

Source: Geophysical Research Letters

DOI: 10.1029/2026gl123513

Authors: Pavel Perezhogin, Alistair Adcroft, Laure Zanna

Institutions: New York University, Princeton University

Language: English

Topic: Oceanographic and Atmospheric Processes

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Life below water, Climate action

Open Access status: gold

Estimated APC paid: $3,190