Observations of Surface Wave Dispersion in the Marginal Ice Zone

Collins, Clarence O.; Doble, M; Lund, Björn; Smith, Madison

doi:10.1029/2018jc013788

Cited by 29 publications

(37 citation statements)

References 38 publications

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“…As the open water dispersion relation is likely to hold for waves in the MIZ with periods in the range of 3-10 s (Collins et al, 2018), a linear paradigm is used to model the wave propagation speed. As the open water dispersion relation is likely to hold for waves in the MIZ with periods in the range of 3-10 s (Collins et al, 2018), a linear paradigm is used to model the wave propagation speed.…”

Section: 1029/2019gl082945mentioning

confidence: 99%

Wave Attenuation by Sea Ice Turbulence

Voermans

Babanin

Thomson

et al. 2019

Geophysical Research Letters

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The dissipation of wave energy in the marginal ice zone is often attributed to wave scattering and the dissipative mechanisms associated with the ice layer. In this study we present observations indicating that turbulence generated by the differential velocity between the sea ice cover and the orbital wave motion may be an important dissipative mechanism of wave energy. Through field measurements of under‐ice turbulence dissipation rates in pancake and frazil ice, it is shown that turbulence‐induced wave attenuation coefficients are in agreement with observed wave attenuation in the marginal ice zone. The results suggest that the turbulence‐induced attenuation rates can be parameterized by the characteristic wave properties and a coefficient. The coefficient is determined by the ice layer properties.

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Section: 1029/2019gl082945mentioning

confidence: 99%

Wave Attenuation by Sea Ice Turbulence

Voermans

Babanin

Thomson

et al. 2019

Geophysical Research Letters

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“…However, the former uses the Doble buoys, while the latter uses SWIFT buoys operated by the University of Washington (Thomson, ), which are equipped with cameras, required for the ice‐type observation reported here (W. E. Rogers, private communication, 2019). In Figure c, we show the normalized wavenumber data of Collins et al () using h =4,18,30 cm, with the first two values being the lower and upper bounds of h that we assume for the Arctic attenuation rate data in Figure b. Despite the uncertainty in h , the field wavenumber data set collapses onto the laboratory data sets in the normalized plane; see Figure c.…”

Section: Application To Datamentioning

confidence: 92%

“…Collins et al () analyzed the data and concluded that the effect of ice on wavelength is insignificant for frequencies f <0.3 Hz. Since Collins et al's () data set (see Figure 14 in that paper) presents measurements of wavenumber rather than attenuation rate, we include it in Figures a and c, comparing with the laboratory data sets of wavenumber. Collins et al () reported that the ice layer thickness h <30 cm with loose pancakes and frazil ice.…”

Section: Application To Datamentioning

confidence: 99%

“…Since Collins et al's () data set (see Figure 14 in that paper) presents measurements of wavenumber rather than attenuation rate, we include it in Figures a and c, comparing with the laboratory data sets of wavenumber. Collins et al () reported that the ice layer thickness h <30 cm with loose pancakes and frazil ice. Collins et al's wavenumber data and Rogers et al's attenuation rate data are from the same wave array (WA3) during the “Sea State” experiments and are roughly colocated and therefore in similar ice conditions.…”

Section: Application To Datamentioning

confidence: 99%

“…Collins et al () reported that the ice layer thickness h <30 cm with loose pancakes and frazil ice. Collins et al's wavenumber data and Rogers et al's attenuation rate data are from the same wave array (WA3) during the “Sea State” experiments and are roughly colocated and therefore in similar ice conditions. However, the former uses the Doble buoys, while the latter uses SWIFT buoys operated by the University of Washington (Thomson, ), which are equipped with cameras, required for the ice‐type observation reported here (W. E. Rogers, private communication, 2019).…”

Section: Application To Datamentioning

confidence: 99%

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A Scaling for Wave Dispersion Relationships in Ice‐Covered Waters

Rogers

Wang

2019

JGR Oceans

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We consider the scaling of dispersion relationships for wave propagation on ice-covered waters, aiming to identify a set of parameters that are physically meaningful and can be used in various continuum-based theories. These parameters characterize the relative importance of the effects of ice inertia, effective viscosity, and elasticity, hence can be used to guide the dynamic similarity between different scales. Application to laboratory and field measurements shows scale collapse of data sets toward a general trend. From the dimensionless parameters of the theoretical prediction, the effective ice properties can be estimated more consistently.Plain Language Summary Sea ice covering the ocean surface, in the form of large sheets, floes, or slushy ice-water mixture, can modify the wavelength and dissipate the energy of ocean waves. Despite the difficulty of modeling the highly heterogeneous ice condition, various mathematical theories exist, describing the dependence of wavelength and dissipation on wave frequency under the effects of ice. These are called the wave dispersion relationships in ice-covered waters. In this paper, we consider a more efficient way to present various mathematical theories and compare them in a physically meaningful way. We find that the problem can be redefined in terms of nondimensional variables, which, to some extent, reconcile the apparent discrepancies between dissimilar laboratory and field data in the literature. The proposed nondimensionalization (scaling) also allows us to estimate the apparent viscosity and elasticity of the ice cover with better consistence. Nondimensional parameters are identified that characterize the relative importance of the apparent ice viscosity and elasticity, as well as ice inertia, by comparison. Therefore, they can guide the dynamical similarity of wave propagation under ice in different scales.

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Overview of the Arctic Sea State and Boundary Layer Physics Program

Ackley²,

et al. 2018

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A large collaborative program has studied the coupled air‐ice‐ocean‐wave processes occurring in the Arctic during the autumn ice advance. The program included a field campaign in the western Arctic during the autumn of 2015, with in situ data collection and both aerial and satellite remote sensing. Many of the analyses have focused on using and improving forecast models. Summarizing and synthesizing the results from a series of separate papers, the overall view is of an Arctic shifting to a more seasonal system. The dramatic increase in open water extent and duration in the autumn means that large surface waves and significant surface heat fluxes are now common. When refreezing finally does occur, it is a highly variable process in space and time. Wind and wave events drive episodic advances and retreats of the ice edge, with associated variations in sea ice formation types (e.g., pancakes, nilas). This variability becomes imprinted on the winter ice cover, which in turn affects the melt season the following year.

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Observations of Surface Wave Dispersion in the Marginal Ice Zone

Cited by 29 publications

References 38 publications

Wave Attenuation by Sea Ice Turbulence

Wave Attenuation by Sea Ice Turbulence

A Scaling for Wave Dispersion Relationships in Ice‐Covered Waters

Overview of the Arctic Sea State and Boundary Layer Physics Program

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