Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies
Fenwick C. Cooper, Laure Zanna

TL;DR
This paper develops an optimization scheme to accurately represent sub-grid scale forcing in an idealized chaotic ocean model, improving the simulation of mean, variance, and lag-covariance without complex programming.
Contribution
It introduces a novel optimization method that adjusts stochastic and deterministic forcing terms to match high-resolution statistics in a low-resolution ocean model.
Findings
Optimized forcing reproduces high-resolution climatological mean and variance.
The scheme accurately captures 5-day lag-covariance of velocity.
No need for tangent linear or adjoint models in the optimization process.
Abstract
An optimisation scheme is developed to accurately represent the sub-grid scale forcing of a high dimensional chaotic ocean system. Using a simple parameterisation scheme, the velocity components of a 30km resolution shallow water ocean model are optimised to have the same climatological mean and variance as that of a less viscous 7.5km resolution model. The 5 day lag-covariance is also optimised, leading to a more accurate estimate of the high resolution response to forcing using the low resolution model. The system considered is an idealised barotropic double gyre that is chaotic at both resolutions. Using the optimisation scheme, we find and apply the constant in time, but spatially varying, forcing term that is equal to the time integrated forcing of the sub-mesoscale eddies. A linear stochastic term, independent of the large-scale flow, with no spatial correlation but a spatially…
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Taxonomy
TopicsOceanographic and Atmospheric Processes · Climate variability and models · Meteorological Phenomena and Simulations
