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On transport tensor of dynamically unresolved oceanic mesoscale eddies

Published online by Cambridge University Press:  23 March 2022

E.A. Ryzhov
Affiliation:
Department of Mathematics, Imperial College London, Huxley Building, London SW7 2AZ, UK Pacific Oceanological Institute, Baltiyskaya 43, 690041, Vladivostok, Russia
P. Berloff*
Affiliation:
Department of Mathematics, Imperial College London, Huxley Building, London SW7 2AZ, UK Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina 8, 119333, Moscow, Russia
*
Email address for correspondence: [email protected]

Abstract

Parameterizing mesoscale eddies in ocean circulation models remains an open problem due to the ambiguity with separating the eddies from large-scale flow, so that their interplay is consistent with the resolving skill of the employed non-eddy-resolving model. One way to address the issue is by using recently formulated dynamically filtered eddies. These eddies are obtained as the field errors of fitting some given reference ocean circulation into the employed coarse-grid ocean model. The main strengths are (i) no explicit spatio-temporal filter is needed for separating the large-scale and eddy flow components, (ii) the eddies are dynamically translated into the error-correcting forcing that perfectly augments the coarse-grid model towards reproducing the reference circulation. We uncovered physical properties of the eddies by interpreting involved nonlinear eddy/large-scale interactions via the classical flux-gradient relation. We described the eddies in terms of their full, space–time dependent transport tensor, which was made unique by constraining it to be the same for the potential vorticity, momentum and buoyancy fluxes. Both diffusive and advective parts of the transport tensor were found to be significant. The diffusive tensor component is characterised by polar eigenvalues and is further decomposed into isotropic and filamentation components. The latter component completely dominates, therefore, it should be taken into account by eddy parameterizations, which is not yet the case. We also showed that spatial inhomogeneities of the transport tensor components are important. Comparing these properties with those obtained for more common, locally filtered eddies revealed that they are distinctly different.

Type
JFM Papers
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

REFERENCES

Agarwal, N., Ryzhov, E., Kondrashov, D. & Berloff, P. 2021 Correlation-based flow decomposition and statistical analysis of the eddy forcing. J. Fluid Mech. 924, A5.10.1017/jfm.2021.604CrossRefGoogle Scholar
Bachman, S. & Fox-Kemper, B. 2013 Eddy parameterization challenge suite I: Eady spindown. Ocean Model. 64, 1228.10.1016/j.ocemod.2012.12.003CrossRefGoogle Scholar
Bachman, S., Fox-Kemper, B. & Bryan, F. 2015 A tracer-based inversion method for diagnosing eddy-induced diffusivity and advection. Ocean Model. 86, 114.10.1016/j.ocemod.2014.11.006CrossRefGoogle Scholar
Bachman, S., Fox-Kemper, B. & Bryan, F. 2020 A diagnosis of anisotropic eddy diffusion from a high-resolution global ocean model. J. Adv. Model. Earth Syst. 12, e2019MS001904.10.1029/2019MS001904CrossRefGoogle Scholar
Berloff, P.S. & McWilliams, J.C. 1999 Quasigeostrophic dynamics of the western boundary current. J. Phys. Oceanogr. 29 (10), 26072634.10.1175/1520-0485(1999)029<2607:QDOTWB>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Berloff, P., Ryzhov, E. & Shevchenko, I. 2021 On dynamically unresolved oceanic mesoscale motions. J. Fluid Mech. 920, A41.10.1017/jfm.2021.477CrossRefGoogle Scholar
Eden, C. & Greatbatch, R. 2009 A diagnosis of isopycnal mixing by mesoscale eddies. Ocean Model. 27, 98106.10.1016/j.ocemod.2008.12.002CrossRefGoogle Scholar
Gent, P. & McWilliams, J. 1990 Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr. 20 (1), 150155.10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Griffies, S. 1998 The Gent–McWilliams skew flux. J. Phys. Oceanogr. 26, 831841.10.1175/1520-0485(1998)028<0831:TGMSF>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Grooms, I. 2016 A Gaussian-product stochastic Gent–McWilliams parameterization. Ocean Model. 106, 2743.10.1016/j.ocemod.2016.09.005CrossRefGoogle Scholar
Haigh, M. & Berloff, P. 2021 On co-existing diffusive and anti-diffusive tracer transport by oceanic mesoscale eddies. Ocean Model. 168, 101909.10.1016/j.ocemod.2021.101909CrossRefGoogle Scholar
Haigh, M. & Berloff, P. 2022 On the stability of tracer simulations with opposite-signed diffusivities. J. Fluid Mech. (submitted).10.1017/jfm.2022.126CrossRefGoogle Scholar
Haigh, M., Sun, L., McWilliams, J. & Berloff, P. 2021 a On eddy transport in the ocean. Part I: the diffusion tensor. Ocean Model. 164, 101831.10.1016/j.ocemod.2021.101831CrossRefGoogle Scholar
Haigh, M., Sun, L., McWilliams, J. & Berloff, P. 2021 b On eddy transport in the ocean. Part II: the advection tensor. Ocean Model. 165, 101845.10.1016/j.ocemod.2021.101845CrossRefGoogle Scholar
Haigh, M., Sun, L., Shevchenko, I. & Berloff, P. 2020 Tracer-based estimates of eddy-induced diffusivities. Deep-Sea Res. 160, 103264.10.1016/j.dsr.2020.103264CrossRefGoogle Scholar
Jansen, M., Adcroft, A., Hallberg, R. & Held, I. 2015 Parameterization of eddy fluxes based on a mesoscale energy budget. Ocean Model. 92, 2841.10.1016/j.ocemod.2015.05.007CrossRefGoogle Scholar
Jansen, M., Adcroft, A., Khani, S. & Kong, H. 2019 Toward an energetically consistent, resolution aware parameterization of ocean mesoscale eddies. J. Adv. Model. Earth Syst. 11, 28442860.10.1029/2019MS001750CrossRefGoogle Scholar
Jansen, M. & Held, I. 2014 Parameterizing subgrid-scale eddy effects using energetically consistent backscatter. Ocean Model. 80, 3648.10.1016/j.ocemod.2014.06.002CrossRefGoogle Scholar
Juricke, S., Danilov, S., Kutsenko, A. & Oliver, M. 2019 Ocean kinetic energy backscatter parametrizations on unstructured grids: impact on mesoscale turbulence in a channel. Ocean Model. 138, 5167.10.1016/j.ocemod.2019.03.009CrossRefGoogle Scholar
Kamenkovich, I., Berloff, P., Haigh, M., Sun, L. & Lu, Y. 2021 Complexity of mesoscale eddy diffusivity in the ocean. Geophys. Res. Lett. 48, e2020GL091719.10.1029/2020GL091719CrossRefGoogle Scholar
Kamenkovich, I., Rypina, I. & Berloff, P. 2015 Properties and origins of the anisotropic eddy-induced transport in the North Atlantic. J. Phys. Oceanogr. 45, 778791.10.1175/JPO-D-14-0164.1CrossRefGoogle Scholar
Klower, M., Jansen, M., Claus, M., Greatbatch, R. & Thomsen, S. 2018 Energy budget-based backscatter in a shallow water model of a double gyre basin. Ocean Model. 132, 111.10.1016/j.ocemod.2018.09.006CrossRefGoogle Scholar
Lau, N. & Wallace, J. 1979 On the distribution of horizontal transports by transient eddies in the northern hemisphere wintertime circulation. J. Atmos. Sci. 36, 18441861.10.1175/1520-0469(1979)036<1844:OTDOHT>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Maddison, J., Marshall, D. & Shipton, J. 2015 On the dynamical influence of ocean eddy potential vorticity fluxes. Ocean Model. 92, 169182.10.1016/j.ocemod.2015.06.003CrossRefGoogle Scholar
Marshall, J. & Shutts, G. 1981 A note on rotational and divergent eddy fluxes. J. Phys. Oceanogr. 11, 16771680.10.1175/1520-0485(1981)011<1677:ANORAD>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
McWilliams, J. 2008 The nature and consequences of oceanic eddies. In Eddy-Resolving Ocean Modeling (ed. M. Hecht & H. Hasumi). AGU Monograph, 5–15.Google Scholar
Rypina, I., Kamenkovich, I., Berloff, P. & Pratt, L. 2012 Eddy-induced particle dispersion in the near-surface North Atlantic. J. Phys. Oceanogr. 42, 22062228.10.1175/JPO-D-11-0191.1CrossRefGoogle Scholar
Ryzhov, E., Kondrashov, D., Agarwal, N. & Berloff, P. 2019 On data-driven augmentation of low-resolution ocean model dynamics. Ocean Model. 142, 101464.10.1016/j.ocemod.2019.101464CrossRefGoogle Scholar
Ryzhov, E., Kondrashov, D., Agarwal, N., McWilliams, J. & Berloff, P. 2020 On data-driven induction of the low-frequency variability in a coarse-resolution ocean model. Ocean Model. 153, 101664.CrossRefGoogle Scholar
Shevchenko, I. & Berloff, P. 2015 Multi-layer quasi-geostrophic ocean dynamics in eddy-resolving regimes. Ocean Model. 94, 114.10.1016/j.ocemod.2015.07.018CrossRefGoogle Scholar
Shevchenko, I. & Berloff, P. 2016 Eddy backscatter and counter-rotating gyre anomalies of midlatitude ocean dynamics. Fluids 1, 26.10.3390/fluids1030028CrossRefGoogle Scholar
Shevchenko, I. & Berloff, P. 2017 On the roles of baroclinic modes in eddy-resolving midlatitude ocean dynamics. Ocean Model. 111, 5565.10.1016/j.ocemod.2017.02.003CrossRefGoogle Scholar
Shevchenko, I. & Berloff, P. 2021 a A method for preserving large-scale flow patterns in low-resolution ocean simulations. Ocean Model. 161, 101795.10.1016/j.ocemod.2021.101795CrossRefGoogle Scholar
Shevchenko, I. & Berloff, P. 2021 b On a minimum set of equations for parameterisations in comprehensive ocean circulation models. Ocean Model. 168, 101913.CrossRefGoogle Scholar
Sun, L., Haigh, M., Shevchenko, I., Berloff, P. & Kamenkovich, I. 2021 On non-uniqueness of the mesoscale eddy diffusivity. J. Fluid Mech. 920, A32.10.1017/jfm.2021.472CrossRefGoogle Scholar
Swarztrauber, P. 1974 A direct method for the discrete solution of separable elliptic equations. SIAM J. Numer. Anal. 11, 11361150.10.1137/0711086CrossRefGoogle Scholar
Zhurbas, V., Lyzhkov, D. & Kuzmina, N. 2014 Drifter-derived estimates of lateral eddy diffusivity in the world ocean with emphasis on the indian ocean and problems of parameterisation. Deep-Sea Res. 83, 111.10.1016/j.dsr.2013.09.001CrossRefGoogle Scholar