Thoughts on prognostically modeling an eddying double-gyre ensemble mean

We address the question of separating the ocean’s deterministic response to time-dependent forcing from its intrinsic chaotic variability. Ideally, one could compute the ensemble mean directly without performing numerous realizations, but this requires knowledge or closure of the second-order statistics — the classical turbulent-closure problem, here recast for a non-equilibrium, geophysical setting. Building on the ideas of nonlinear midlatitude ocean adjustment, we examine this problem using idealized quasi-geostrophic (QG) double-gyre ensembles subjected to episodic temporal variations in wind forcing. Our objective here is not to develop a subgrid parameterization of unresolved eddies, but rather to construct and test prognostic equations for the ensemble mean itself, using the simplest possible closure assumptions. We find that the performance of ensemble mean closures is highly dependent on the spatiotemporal structure of the forcing. Under slowly varying forcing, approximate closures reproduce the mean evolution reasonably well; under rapidly varying, near-zero-mean forcing, the simplest ensemble-mean closures fail, even at the level of basin-averaged total energy and enstrophy. In both regimes, the ensemble-mean response is not simply the accumulated imprint of the applied forcing, but instead appears as a continuing, non-equilibrated dialogue between the mean and eddy fields.

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Publication Details

Journal
Frontiers in Marine Science
Published
2026-03-11
DOI
https://doi.org/10.3389/fmars.2026.1739607
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
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article

Thoughts on prognostically modeling an eddying double-gyre ensemble mean

Luolin Sun, Andrew C. Poje, Bruno Deremble, Marco Trapanese et al.
Frontiers in Marine Science
Oceanographic and Atmospheric Processes
article

Thoughts on prognostically modeling an eddying double-gyre ensemble mean

Luolin Sun, Andrew C. Poje, Bruno Deremble, Marco Trapanese, Nicolas Wienders, Takaya Uchida, Joseph Schoonover, Quentin Jamet, William K. Dewar, Thierry Penduff
article en

Abstract

We address the question of separating the ocean’s deterministic response to time-dependent forcing from its intrinsic chaotic variability. Ideally, one could compute the ensemble mean directly without performing numerous realizations, but this requires knowledge or closure of the second-order statistics — the classical turbulent-closure problem, here recast for a non-equilibrium, geophysical setting. Building on the ideas of nonlinear midlatitude ocean adjustment, we examine this problem using idealized quasi-geostrophic (QG) double-gyre ensembles subjected to episodic temporal variations in wind forcing. Our objective here is not to develop a subgrid parameterization of unresolved eddies, but rather to construct and test prognostic equations for the ensemble mean itself, using the simplest possible closure assumptions. We find that the performance of ensemble mean closures is highly dependent on the spatiotemporal structure of the forcing. Under slowly varying forcing, approximate closures reproduce the mean evolution reasonably well; under rapidly varying, near-zero-mean forcing, the simplest ensemble-mean closures fail, even at the level of basin-averaged total energy and enstrophy. In both regimes, the ensemble-mean response is not simply the accumulated imprint of the applied forcing, but instead appears as a continuing, non-equilibrated dialogue between the mean and eddy fields.

Frontiers in Marine ScienceVol. 13
Florida State University (US), Centre National de la Recherche Scientifique (FR), College of Staten Island (US), City University of New York (US), Odyssey House (US), Service Hydrographique et Océanographique de la Marine (FR), Technology Dynamics (United States) (US), Institut des Géosciences de l'Environnement (FR), Advanced Fluidics (United States) (US), Institut de Recherche pour le Développement (FR), Université Grenoble Alpes (FR)
Life below water
Openalex Percentile: Top 39%
Oceanographic and Atmospheric Processes
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