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Multiscale Stuart-Landau emulators: application to wind-driven ocean gyres

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Title: Multiscale Stuart-Landau emulators: application to wind-driven ocean gyres
Authors: Kondrashov, D
Chekroun, MD
Berloff, P
Item Type: Journal Article
Abstract: The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inverse stochastic modeling techniques introduced in (Chekroun and Kondrashov, (2017), Chaos, 27), allow for reproducing with high fidelity the main statistical properties of multiscale variability in a coarse-grained eddy-resolving ocean flow. This fully-data-driven approach relies on extraction of frequency-ranked time-dependent coefficients describing the evolution of spatio-temporal DAH modes (DAHMs) in the oceanic flow data. In turn, the time series of these coefficients are efficiently modeled by a family of low-order stochastic differential equations (SDEs) stacked per frequency, involving a fixed set of predictor functions and a small number of model coefficients. These SDEs take the form of stochastic oscillators, identified as multilayer Stuart–Landau models (MSLMs), and their use is justified by relying on the theory of Ruelle–Pollicott resonances. The good modeling skills shown by the resulting DAH-MSLM emulators demonstrates the feasibility of using a network of stochastic oscillators for the modeling of geophysical turbulence. In a certain sense, the original quasiperiodic Landau view of turbulence, with the amendment of the inclusion of stochasticity, may be well suited to describe turbulence.
Issue Date: 1-Mar-2018
Date of Acceptance: 1-Mar-2018
URI: http://hdl.handle.net/10044/1/61666
DOI: https://dx.doi.org/10.3390/fluids3010021
ISSN: 2311-5521
Publisher: MDPI
Journal / Book Title: Fluids
Volume: 3
Issue: 1
Copyright Statement: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Sponsor/Funder: Natural Environment Research Council (NERC)
Funder's Grant Number: NE/R011567/1
Keywords: Science & Technology
Physical Sciences
Physics, Fluids & Plasmas
Physics
cross-correlations
eddy-resolving
Hankel matrices
inverse modeling
low-frequency variability
Ruelle-Pollicott resonances
stochastic modeling
stochastic oscillators
LOW-FREQUENCY VARIABILITY
DYNAMIC-MODE DECOMPOSITION
PART II
CLIMATE VARIABILITY
NONLINEAR DYNAMICS
COMPONENT ANALYSIS
OPTIMAL PREDICTION
MESOSCALE EDDIES
STOCHASTIC-MODEL
CLOSURE MODELS
Publication Status: Published
Article Number: ARTN 21
Appears in Collections:Mathematics
Centre for Environmental Policy
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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