Sea-ice thickness and roughness in the Ross Sea, Antarctica

Tin, Tina; Jeffries, Martin O.

doi:10.3189/172756401781818770

Cited by 25 publications

(31 citation statements)

References 21 publications

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“…One of the factors which contributes to the overestimated sea ice in summer in the offline model is that atmospheric boundary conditions are not daily but monthly data, which tend to yield smaller seasonal cycle [Wu et al, 1997]. The simulated ice thickness ranges from several tens of centimeters to more than 1 m in both models, which is in approximate agreement with the limited observation (Figure 4) [Tin and Jeffries, 2001;Adolphs, 1998;Worby et al, 1996;Andreas et al, 1993;Rind et al, 1995]. Annual mean sea ice velocity simulated by the two models are also displayed in Figure 5.…”

Section: Standard Simulationsupporting

confidence: 71%

Effects of sea ice dynamics on the Antarctic sea ice distribution in a coupled ocean atmosphere model

2004

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[1] Impact of sea ice dynamics on the Southern Ocean sea ice distribution is investigated using a coupled ocean-sea ice-atmosphere general circulation model (OAGCM) and a separate offline sea ice model driven by monthly mean climatological boundary conditions. Sea ice dynamics considerably affects sea ice distribution of the OAGCM as well as of the offline model. When sea ice dynamics (advection) is turned on, summer ice extent and winter ice thickness decrease in both models. Results of the OAGCM indicate that variation of clouds plays an important role when sea surface energy budget responds to the sea ice dynamics in summer. Surface wind field in summer responds to the sea ice dynamics so that northerly wind anomaly is directed to where sea ice retreats and air temperature is kept higher, leading to a positive feedback. In addition, meridional ocean circulation and upward ocean heat flux to the sea surface appear to be sensitive to the sea ice dynamics, which contributes to the response of winter sea ice thickness. Results obtained in the present study underline the importance of atmosphere-sea ice-ocean interaction when discussing the impact of sea ice dynamics on sea ice distribution.

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Section: Standard Simulationsupporting

confidence: 71%

Effects of sea ice dynamics on the Antarctic sea ice distribution in a coupled ocean atmosphere model

2004

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“…Based on analyses of in situ data from the Arctic and Antarctic [e.g., Sturm et al , 2006; Tin and Jeffries , 2001; Massom et al , 1997], we expect to be able to infer a subgrid cell snow depth distribution by utilizing relationships between snow depth and sea ice thickness, freeboard, roughness, and surface reflectivity. For example, we expect points with low reflectivity at wavelengths used by laser altimeters to have little to no snow cover so that we can infer sea ice thickness directly from the freeboard estimate.…”

Section: Scaling Of Large‐scale Snow Depth Data To the Altimeter Resomentioning

confidence: 99%

Estimation of sea ice thickness distributions through the combination of snow depth and satellite laser altimetry data

et al. 2009

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[1] Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High-resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high-resolution satellite altimeter measurements with low-resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the nonlinear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the $70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25-km mean ice thickness values.

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“…These form in the lee of these obstacles and can extend for up to tens of meters from the obstacle [Radionov et al, 1997;Sturm et al, 1998Sturm et al, , 2002Massom et al, 2001]. Due to changing wind directions relative to the floe orientation, drift deposits typically form aprons on both sides of the ridge so that deeper snow is often associated with ridges [Tin and Jeffries, 2001;Sturm et al, 2002], and more heavily deformed floes may tend to support deeper snow cover. This relative wind rotation also leads to cross bedding of snow dunes on level ice [Sturm and Massom, 2009].…”

Section: Introductionmentioning

confidence: 99%

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

et al. 2016

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Snow distribution over sea ice is an important control on sea ice physical and biological processes. We combine measurements of the atmospheric boundary layer and blowing snow on an Antarctic sea ice floe with terrestrial laser scanning to characterize a typical storm and its influence on the spatial patterns of snow distribution at resolutions of 1–10 cm over an area of 100 m × 100 m. The pre‐storm surface exhibits multidirectional elongated snow dunes formed behind aerodynamic obstacles. Newly deposited dunes are elongated parallel to the predominant wind direction during the storm. Snow erosion and deposition occur over 62% and 38% of the area, respectively. Snow deposition volume is more than twice that of erosion (351 m3 versus 158 m3), resulting in a modest increase of 2 ± 1 cm in mean snow depth, indicating a small net mass gain despite large mass relocation. Despite significant local snow depth changes due to deposition and erosion, the statistical distributions of elevation and the two‐dimensional correlation functions remain similar to those of the pre‐storm surface. Pre‐storm and post‐storm surfaces also exhibit spectral power law relationships with little change in spectral exponents. These observations suggest that for sea ice floes with mature snow cover features under conditions similar to those observed in this study, spatial statistics and scaling properties of snow surface morphology may be relatively invariant. Such an observation, if confirmed for other ice types and conditions, may be a useful tool for model parameterizations of the subgrid variability of sea ice surfaces.

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Sea-ice thickness and roughness in the Ross Sea, Antarctica

Cited by 25 publications

References 21 publications

Effects of sea ice dynamics on the Antarctic sea ice distribution in a coupled ocean atmosphere model

Effects of sea ice dynamics on the Antarctic sea ice distribution in a coupled ocean atmosphere model

Estimation of sea ice thickness distributions through the combination of snow depth and satellite laser altimetry data

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

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