A review of sea ice density

Timco, G.W.; Frederking, R.

doi:10.1016/0165-232x(95)00007-x

Cited by 202 publications

(195 citation statements)

References 8 publications

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“…A denser network of in situ measurements is necessary to verify the hydrostatic equilibrium assumption in areas of deformed ice. Timco and Frederking (1996) reported that FY ice density is typically between 840 and 910 kg m −3 , while MY ice density is between 720 and 910 kg m −3 . Densities of MY and FY ice samples taken below the waterline are not significantly different, and both ice types have typical values between 900 and 940 kg m −3 .…”

Section: Ice Densitymentioning

confidence: 99%

“…The density values can vary significantly, and the methods used to estimate them have impact on the results. The following four methods can be used to estimate ice density: (i) measurement of mass and volume of a given ice body, (ii) displacement (submersion) technique, (iii) specific gravity technique, and (iv) freeboardthickness technique (Timco and Frederking, 1996). Each method has advantages and limitations.…”

Section: Ice Densitymentioning

confidence: 99%

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The relation between sea ice thickness and freeboard in the Arctic

Alexandrov

Sandven²,

Wå̊hlin³

et al. 2010

The Cryosphere

138

164

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Abstract. Retrieval of Arctic sea ice thickness fromCryoSat-2 radar altimeter freeboard data requires observational data to verify the relation between these two variables. In this study in-situ ice and snow data from 689 observation sites, obtained during the Sever expeditions in the 1980s, have been used to establish an empirical relation between thickness and freeboard of FY ice in late winter. Estimates of mean and variability of snow depth, snow density and ice density were produced on the basis of many field observations. These estimates have been used in the hydrostatic equilibrium equation to retrieve ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈ 1.0 m (2.0 m) thickness has an uncertainty of 46% (37%), and for MY ice, retrieval of 2.4 m (3.0 m) thickness has an uncertainty of 20% (18%), assuming that the freeboard error is ± 0.03 m for both ice types. For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than that for FY ice. If the freeboard error can be reduced to 0.01 m by averaging measurements from CryoSat-2, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.4 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat-2 data.Correspondence to: V. Alexandrov

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Section: Ice Densitymentioning

confidence: 99%

Section: Ice Densitymentioning

confidence: 99%

The relation between sea ice thickness and freeboard in the Arctic

Alexandrov

Sandven²,

Wå̊hlin³

et al. 2010

The Cryosphere

138

164

View full text Add to dashboard Cite

show abstract

“…The derived scattering cross section values are typical of sea ice (e.g. Grenfell and Maykut, 1977;Timco and Frederking, 1996;Perovich, 1996), while the derived absorption cross sections show the presence of other absorbing impurities in the undoped ice, which match the absorption spectra of algae.…”

Section: Discussionmentioning

confidence: 89%

“…Typical sea ice densities are reviewed by Timco and Frederking (1996) reporting first-year sea ice densities in the range 0.84 to 0.94 g cm −3 . The density of sea ice created in the simulator ranged from 0.85 to 0.95 g cm −3 , thus being in the range of natural ice.…”

Section: Realism Of Artificial Sea Icementioning

confidence: 99%

Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison

2017

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Abstract. Radiative-transfer calculations of the light reflectivity and extinction coefficient in laboratory-generated sea ice doped with and without black carbon demonstrate that the radiative-transfer model TUV-snow can be used to predict the light reflectance and extinction coefficient as a function of wavelength. The sea ice is representative of first-year sea ice containing typical amounts of black carbon and other light-absorbing impurities. The experiments give confidence in the application of the model to predict albedo of other sea ice fabrics.Sea ices, ∼ 30 cm thick, were generated in the Royal Holloway Sea Ice Simulator (∼ 2000 L tanks) with scattering cross sections measured between 0.012 and 0.032 m 2 kg −1 for four ices. Sea ices were generated with and without ∼ 5 cm upper layers containing particulate black carbon. Nadir reflectances between 0.60 and 0.78 were measured along with extinction coefficients of 0.1 to 0.03 cm −1 (efolding depths of 10-30 cm) at a wavelength of 500 nm. Values were measured between light wavelengths of 350 and 650 nm. The sea ices generated in the Royal Holloway Sea Ice Simulator were found to be representative of natural sea ices.Particulate black carbon at mass ratios of ∼ 75, ∼ 150 and ∼ 300 ng g −1 in a 5 cm ice layer lowers the albedo to 97, 90 and 79 % of the reflectivity of an undoped "clean" sea ice (at a wavelength of 500 nm).

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“…Densities of multi-year ice and first-year ice samples taken below the waterline are not significantly different. Timco and Frederking (1996) report that bulk density of first-year ice is typically between 840 and 910 kg m −3 , while that of multi-year ice is between 720 and 910 kg m −3 .…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

Remote sensing of sea ice: advances during the DAMOCLES project

et al. 2012

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Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMO-CLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer AS-CAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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A review of sea ice density

Cited by 202 publications

References 8 publications

The relation between sea ice thickness and freeboard in the Arctic

The relation between sea ice thickness and freeboard in the Arctic

Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison

Remote sensing of sea ice: advances during the DAMOCLES project

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