Relative dispersion in the Nordic Seas

Koszalka, Inga Monika; LaCasce, J. H.; Orvik, Kjell Arild

doi:10.1357/002224009790741102

Cited by 91 publications

(113 citation statements)

References 29 publications

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“…Exponential dispersion has also been observed at the ocean surface, at scales below the Rossby radius of deformation [15][16][17]. The balloon-and drifter-derived results agree with independent Eulerian estimates at these scales, in the atmosphere [18] and ocean [4].…”

Section: Introductionsupporting

confidence: 72%

Calculating Energy Spectra from Drifters

LaCasce

2016

Preprint

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Abstract:The relations between the kinetic energy spectrum and the second order longitudinal structure function in two dimensions are derived, and several examples are considered. The forward conversion (from spectrum to structure function) is illustrated first with idealized power law spectra, representing turbulent inertial ranges. The forward conversion is also applied to the kinetic energy spectrum of Nastrom and Gage (1985), derived from aircraft data in the upper troposphere, and the result agrees well with the longitudinal structure function of Lindborg (1999), using the same data. The inverse conversion (from structure function to spectrum) is tested with data from 2D turbulence simulations. When applied to the theoretical structure function (derived from the forward conversion of the spectrum), the result closely resembles the original spectrum, except at the largest wavenumbers. However the inverse conversion is much less successful when applied to the structure function obtained from pairs of particles in the flow. This is because the inverse conversion favors large pair separations, which are typically noisy with particle data. Fitting the structure function to a polynomial improves the result, but not sufficiently to distinguish the correct inertial range dependencies. Furthermore the inversion of non-local spectra is largely unsuccessful. Thus it appears that focusing on structure functions with Lagrangian data is preferable to estimating spectra.

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Section: Introductionsupporting

confidence: 72%

Calculating Energy Spectra from Drifters

LaCasce

2016

Preprint

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“…[Note that the apparent scale dependence of K s would be consistent with a broad range of scales of submesoscale flow structures, with the diffusivity at a given scale being determined by the flow structures with scale smaller than the given scale (Richardson 1926;Richardson and Stommel 1948;Okubo 1971). See, for example, Lumpkin and Elipot (2010) or Koszalka et al (2009) for oceanic measurements showing this scale dependence.] The clear conclusion is that the K s required to account for the filament width in stage 2 must be significantly larger, by two orders of magnitude, than K i .…”

mentioning

confidence: 69%

Estimating a Submesoscale Diffusivity Using a Roughness Measure Applied to a Tracer Release Experiment in the Southern Ocean

Boland

Shuckburgh

Haynes

et al. 2015

Journal of Physical Oceanography

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The use of a measure to diagnose submesoscale isopycnal diffusivity by determining the best match between observations of a tracer and simulations with varying small-scale diffusivities is tested. Specifically, the robustness of a ''roughness'' measure to discriminate between tracer fields experiencing different submesoscale isopycnal diffusivities and advected by scaled altimetric velocity fields is investigated. This measure is used to compare numerical simulations of the tracer released at a depth of about 1.5 km in the Pacific sector of the Southern Ocean during the Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) field campaign with observations of the tracer taken on DIMES cruises. The authors find that simulations with an isopycnal diffusivity of ;20 m 2 s 21 best match observations in the Pacific sector of the Antarctic Circumpolar Current (ACC), rising to ;20-50 m 2 s 21 through Drake Passage, representing submesoscale processes and any mesoscale processes unresolved by the advecting altimetry fields. The roughness measure is demonstrated to be a statistically robust way to estimate a small-scale diffusivity when measurements are relatively sparse in space and time, although it does not work if there are too few measurements overall. The planning of tracer measurements during a cruise in order to maximize the robustness of the roughness measure is also considered. It is found that the robustness is increased if the spatial resolution of tracer measurements is increased with the time since tracer release.

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“…Koszalka et al, 2009). Beron-Vera and LaCasce (2016) conducted such an analysis for data from the Grand Lagrangian Deployment experiment (GLAD), in which more than 300 drifters were deployed in the Gulf of Mexico.…”

Section: Introductionmentioning

confidence: 99%

Surface drifters in the German Bight: model validation considering windage and Stokes drift

et al. 2017

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Abstract. Six surface drifters (drogued at about 1 m depth) deployed in the inner German Bight (North Sea) were tracked for between 9 and 54 days. Corresponding simulations were conducted offline based on surface currents from two independent models (BSHcmod and TRIM). Inclusion of a direct wind drag (0.6 % of 10 m wind) was needed for successful simulations based on BSHcmod currents archived for a 5 m depth surface layer. Adding 50 % of surface Stokes drift simulated with a third-generation wave model (WAM) was tested as an alternative approach. Results resembled each other during most of the time. Successful simulations based on TRIM surface currents (1 m depth) suggest that both approaches were mainly needed to compensate insufficient vertical resolution of hydrodynamic currents.The study suggests that the main sources of simulation errors were inaccurate Eulerian currents and lacking representation of sub-grid-scale processes. Substantial model errors often occurred under low wind conditions. A lower limit of predictability (about 3-5 km day −1 ) was estimated from two drifters that were initially spaced 20 km apart but converged quickly and diverged again after having stayed at a distance of 2 km or less for about 10 days. In most cases, errors in simulated 25 h drifter displacements were of similar order of magnitude.

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Relative dispersion in the Nordic Seas

Cited by 91 publications

References 29 publications

Calculating Energy Spectra from Drifters

Calculating Energy Spectra from Drifters

Estimating a Submesoscale Diffusivity Using a Roughness Measure Applied to a Tracer Release Experiment in the Southern Ocean

Surface drifters in the German Bight: model validation considering windage and Stokes drift

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