On the Thermal Properties of Ice and Snow in a Low Temperature Region

Sakatume, Shinji; Seki, Nobuhiro

doi:10.1299/kikai1938.44.2059

Cited by 20 publications

(20 citation statements)

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“…where k i 5 1.16 (1.91 2 8.66 3 10 23 h 1 2.97 3 10 25 h 2 ) W (m K) 21 is the conductivity of pure ice [Schwerdtfeger, 1963;Sakazume and Seki, 1978], k a 5 0.03 W (m K) 21 is the conductivity of air [Weeks and Ackley, 1986], and we have assumed a constant V a 5 0.025 as the fractional volume of air in sea ice [Timco and Frederking, 1996].…”

Section: Heat and Salt Balances In The Sea Ice And Ice Lidmentioning

confidence: 99%

The refreezing of melt ponds on Arctic sea ice

et al. 2015

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The presence of melt ponds on the surface of Arctic sea ice significantly reduces its albedo, inducing a positive feedback leading to sea ice thinning. While the role of melt ponds in enhancing the summer melt of sea ice is well known, their impact on suppressing winter freezing of sea ice has, hitherto, received less attention. Melt ponds freeze by forming an ice lid at the upper surface, which insulates them from the atmosphere and traps pond water between the underlying sea ice and the ice lid. The pond water is a store of latent heat, which is released during refreezing. Until a pond freezes completely, there can be minimal ice growth at the base of the underlying sea ice. In this work, we present a model of the refreezing of a melt pond that includes the heat and salt balances in the ice lid, trapped pond, and underlying sea ice. The model uses a two-stream radiation model to account for radiative scattering at phase boundaries. Simulations and related sensitivity studies suggest that trapped pond water may survive for over a month. We focus on the role that pond salinity has on delaying the refreezing process and retarding basal sea ice growth. We estimate that for a typical sea ice pond coverage in autumn, excluding the impact of trapped ponds in models overestimates ice growth by up to 265 million km 3 , an overestimate of 26%.

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Section: Heat and Salt Balances In The Sea Ice And Ice Lidmentioning

confidence: 99%

The refreezing of melt ponds on Arctic sea ice

et al. 2015

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“…After Schwerdtfeger [1963], we take k bi and k b to be and where k i = 1.16(1.91 − 8.66 × 10 −3 K −1 θ + 2.97 × 10 −5 K −2 θ 2 ) W (m K) −1 is the conductivity of pure ice [ Sakazume and Seki , 1978], k a = 0.03 W (m K) −1 is the conductivity of air [ Weeks and Ackley , 1986], and V a = 0.025 is the fractional volume of air in sea ice [ Timco and Frederking , 1996].…”

Section: Model Descriptionmentioning

confidence: 99%

A model for the consolidation of rafted sea ice

Bailey¹,

Feltham²,

Sammonds³

2010

J. Geophys. Res.

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[1] Rafting is one of the important deformation mechanisms of sea ice. This process is widespread in the north Caspian Sea, where multiple rafting produces thick sea ice features, which are a hazard to offshore operations. Here we present a one-dimensional, thermal consolidation model for rafted sea ice. We consider the consolidation between the layers of both a two-layer and a three-layer section of rafted sea ice. The rafted ice is assumed to be composed of layers of sea ice of equal thickness, separated by thin layers of ocean water. Results show that the thickness of the liquid layer reduced asymptotically with time, such that there always remained a thin saline liquid layer. We propose that when the liquid layer is equal to the surface roughness the adjacent layers can be considered consolidated. Using parameters representative of the north Caspian, the Arctic, and the Antarctic, our results show that for a choice of standard parameters it took under 15 h for two layers of rafted sea ice to consolidate. Sensitivity studies showed that the consolidation model is highly sensitive to the initial thickness of the liquid layer, the fraction of salt release during freezing, and the height of the surface asperities. We believe that further investigation of these parameters is needed before any concrete conclusions can be drawn about rate of consolidation of rafted sea ice features.

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“…Other parameters are either physical constants or taken from the external forcing. For calculation of the ice thermal conductivity and heat capacity we used the following formulations, which represent their dependency on the ice temperature and salinity (Schwerdtfecer, 1963;Feltham et al, 2006;Sakatume and Seki, 1978):…”

Section: Description Of the Sea Ice Parameterization Schemementioning

confidence: 99%

“…sea ice microwave emissivity as discussed by Karbou et al, 2014) are rarely used for assimilation in short-range NWP systems, and very few of them are used for validation. In ocean modelling systems, sea ice concentration and sea ice drift data from different sources, including remote sensing from passive microwave and visible channels, are used (see, for example, Posey et al, 2015;Sakov et al, 2012). Acquisition of the sea ice depth data from active remote sensing is being developed (Tilling et al, 2016), but the latency of these data is not yet acceptable for operational use in short-range forecasting.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

2018

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Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.

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On the Thermal Properties of Ice and Snow in a Low Temperature Region

Cited by 20 publications

References 0 publications

The refreezing of melt ponds on Arctic sea ice

The refreezing of melt ponds on Arctic sea ice

A model for the consolidation of rafted sea ice

Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

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