On dynamically consistent eddy fluxes

Berloff, Pavel

doi:10.1016/j.dynatmoce.2004.11.003

Cited by 52 publications

(62 citation statements)

References 38 publications

(36 reference statements)

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“…The dynamical eddy analysis (Berloff 2005a), generalized to allow for time-dependent external forcing of the ocean, is now applied. The reference ocean solution is projected on a uniform 129 ϫ 141, 40-km-resolution grid by subsampling all three layer streamfunctions in both space and time, the latter at an interval of 5 days.…”

Section: B Diagnostics Of the Eddiesmentioning

confidence: 99%

Ocean Eddy Dynamics in a Coupled Ocean–Atmosphere Model*

Berloff

Dewar

Kravtsov

et al. 2007

Journal of Physical Oceanography

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The role of mesoscale oceanic eddies is analyzed in a quasigeostrophic coupled ocean-atmosphere model operating at a large Reynolds number. The model dynamics are characterized by decadal variability that involves nonlinear adjustment of the ocean to coherent north-south shifts of the atmosphere. The oceanic eddy effects are diagnosed by the dynamical decomposition method adapted for nonstationary external forcing. The main effects of the eddies are an enhancement of the oceanic eastward jet separating the subpolar and subtropical gyres and a weakening of the gyres. The flow-enhancing effect is due to nonlinear rectification driven by fluctuations of the eddy forcing. This is a nonlocal process involving generation of the eddies by the flow instabilities in the western boundary current and the upstream part of the eastward jet. The eddies are advected by the mean current to the east, where they backscatter into the rectified enhancement of the eastward jet. The gyre-weakening effect, which is due to the time-mean buoyancy component of the eddy forcing, is a result of the baroclinic instability of the westward return currents. The diagnosed eddy forcing is parameterized in a non-eddy-resolving ocean model, as a nonstationary random process, in which the corresponding parameters are derived from the control coupled simulation. The key parameter of the random process-its variance-is related to the large-scale flow baroclinicity index. It is shown that the coupled model with the non-eddy-resolving ocean component and the parameterized eddies correctly simulates climatology and low-frequency variability of the control eddy-resolving coupled solution.

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Section: B Diagnostics Of the Eddiesmentioning

confidence: 99%

Ocean Eddy Dynamics in a Coupled Ocean–Atmosphere Model*

Berloff

Dewar

Kravtsov

et al. 2007

Journal of Physical Oceanography

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“…This class of systems finds practical applications in, e.g, modeling the eddy forcing term withr in ocean flow models [1], and was the inspiration for this work.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…First, the heat bath particles span a great variety of time scales without a scale gap (because the natural frequencies range from O(1) to O(J)), similar to the range of time scales in ocean flow models (as mentioned in 1.1). Also, the reduced heat bath (2.2) and reduced ocean flow models [1] belong to the same class of systems (1.1), in the sense that the stochastic term r enters in an additive fashion (i.e.r is added linearly to the ODE forx, there is no multiplication with a function ofx). These reasons, together with its technical simplicity, make the heat bath model a suitable choice for our experiments.…”

Section: Problem Descriptionmentioning

confidence: 99%

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Data-driven stochastic representations of unresolved features in multiscale models

Verheul

Crommelin

2016

Communications in Mathematical Sciences

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Abstract. In this study we investigate how to use sample data, generated by a fully resolved multiscale model, to construct stochastic representations of unresolved scales in reduced models. We explore three methods to model these stochastic representations. They employ empirical distributions, conditional Markov chains and conditioned Ornstein-Uhlenbeck processes, respectively. The Kac-Zwanzig heat bath model is used as a prototype model to illustrate the methods. We demonstrate that all tested strategies reproduce the dynamics of the resolved model variables accurately. Furthermore, we show that the computational cost of the reduced model is several orders of magnitude lower than that of the fully resolved model.

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“…The integration time-step ∆t is chosen to be half an hour for both the barotropic and baroclinic models, these choices are made to nd comparison with similar set ups (e.g. [3,5,41]). …”

Section: A Appendixmentioning

confidence: 99%

Covariate-based stochastic parameterization of baroclinic ocean eddies

Verheul¹,

Viebahn²,

Crommelin³

2017

Mathematics of Climate and Weather Forecasting

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Abstract:In this study we investigate a covariate-based stochastic approach to parameterize unresolved turbulent processes within a standard model of the idealised, wind-driven ocean circulation. We focus on vertical instead of horizontal coarse-graining, such that we avoid the subtle di culties of horizontal coarsegraining. The corresponding eddy forcing is uniquely de ned and has a clear physical interpretation related to baroclinic instability. We propose to emulate the baroclinic eddy forcing by sampling from the conditional probability distribution functions of the eddy forcing obtained from the baroclinic reference model data. These conditional probability distribution functions are approximated here by sampling uniformly from discrete reference values. We analyze in detail the di erent performances of the stochastic parameterization dependent on whether the eddy forcing is conditioned on a suitable ow-dependent covariate or on a timelagged covariate or on both. The results demonstrate that our non-Gaussian, non-linear methodology is able to accurately reproduce the rst four statistical moments and spatial/temporal correlations of the stream function, energetics, and enstrophy of the reference baroclinic model.

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On dynamically consistent eddy fluxes

Cited by 52 publications

References 38 publications

Ocean Eddy Dynamics in a Coupled Ocean–Atmosphere Model*

Ocean Eddy Dynamics in a Coupled Ocean–Atmosphere Model*

Data-driven stochastic representations of unresolved features in multiscale models

Covariate-based stochastic parameterization of baroclinic ocean eddies

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