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      Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies

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          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 varying amplitude and time scale is used to represent the transient eddies. The climatological mean, variance and 5 day lag-covariance of the velocity from a single high resolution integration is used to provide an optimisation target. No other high resolution statistics are required. Additional programming effort, for example to build a tangent linear or adjoint model, is not required either. The focus of this paper is on the optimisation scheme and the accuracy of the optimised flow. The method can be applied in future investigations into the physical processes that govern barotropic turbulence and it can perhaps be applied to help understand and correct biases in the mean and variance of a more realistic coarse or eddy-permitting ocean model. The method is complementary to current parameterisations and can be applied at the same time without modification.

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          Isopycnal Mixing in Ocean Circulation Models

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            A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers

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              Stochastic climate models Part I. Theory

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                Author and article information

                Journal
                21 October 2014
                Article
                10.1016/j.ocemod.2014.12.014
                1410.5722
                e0583484-77cd-41ff-aecf-b1169fe4addd

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                19 pages, 13 figures, submitted for publication
                physics.ao-ph nlin.CD physics.flu-dyn

                Thermal physics & Statistical mechanics,Atmospheric, Oceanic and Environmental physics,Nonlinear & Complex systems

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