Twenty-seven studies on the thermal conductivity of snow (Keff) have been published since 1886. Combined, they comprise 354 values of Keff, and have been used to derive over 13 regression equation and predicting Keff vs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation,where ρ is in g cm−3, and Keff is in W m−1K−1, can be fit to the new data (R2 = 0.79). A logarithmic expression,can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.

Abstract. Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner ice in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow-ice formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large-scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions. INTRODUCTIONAt maximum extent each year (September-October), sea ice covers a vast area of the Southern Ocean (---19 million km2), attaining latitudes as far north as ---55øS [Gloersen et al., 1992]. In so doing, it profoundly alters the exchange of energy and mass between ocean and atmosphere and forms an integral part of the global climate system. These effects are significantly amplified by the presence of an insulative snow cover which is itself highly variable in thickness and properties. Persistently strong winds redistribute the snow, and its properties [Gordon and Huber, 1990] on snow distribution and properties have only been conducted in the past 5-10 years. These studies are beginning to establish the full significance of snow on Antarctic sea ice as a key component of the global climate system. In this paper we review the major findings. Section 2 is a summary of snow data from five Antarctic sectors (designated by Gloersen et al. [1992]), namely, the Weddell Sea (20øE-60øW), the Indian Ocean (20øE-90øE), the western Pacific Ocean (90øE-160øE), the Ross Sea (160øE-140øW), and the Bellingshausen and Amundsen Seas (140øW-60øW), as shown in Figure 1. The Indian and western Pacific Ocean sectors are collectively referred to as the East Antarctic sector. Section 3 assesses the significance of snow in the air-sea-ice interaction system. New findings have significant implications for modeling (both physical and biological) and remotesensing studies of Antarctic sea ice. Gaps in our current knowledge are identified. Finally, the possible enhanced role of snow under global warming conditions is examined. Throughout, snow is described using the combined morphological and process-oriented classification of snow types of Colbeck et al. [1990] As a result, thickness may not be directly related to either the frequency or duration of snowfall.Mean snow thi...

Twenty-seven studies on the thermal conductivity of snow (Keff) have been published since 1886. Combined, they comprise 354 values ofKeff, and have been used to derive over 13 regression equation and predictingKeffvs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation,whereρis in g cm−3, andKeffis in W m−1K−1, can be fit to the new data (R2= 0.79). A logarithmic expression,can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.

Abstract:A one-dimensional thermodynamic model for simulating lake-ice phenology is presented and evaluated. The model can be driven with observed daily or hourly atmospheric forcing of air temperature, relative humidity, wind speed, cloud amount and snowfall. In addition to computing the energy balance components, key model output includes the temperature profile at an arbitrary number of levels within the ice/snow (or the water temperature if there is no ice) and ice thickness (clear ice and snow-ice) on a daily basis, as well as freeze-up and break-up dates. The lake-ice model is used to simulate ice-growth processes on shallow lakes in arctic, sub-arctic, and high-boreal forest environments. Model output is compared with field and remote sensing observations gathered over several ice seasons. Simulated ice thickness, including snow-ice formation, compares favourably with field measurements. Ice-on and ice-off dates are also well simulated when compared with field and satellite observations, with a mean absolute difference of 2 days. Model simulations and observations illustrate the key role that snow cover plays on the seasonal evolution of ice thickness and the timing of spring break-up. It is also shown that lake morphometry, depth in particular, is a determinant of ice-off dates for shallow lakes at high latitudes.

Changes in ERS 1 C band synthetic aperture radar (SAR) backscatter intensity (o •) from ice growing on shallow tundra lakes at three locations in NW Alaska are described. Ice core analysis shows that at all lakes on the coast at Barrow the ice, whether floating or frozen to the bottom, includes an inclusion-free layer overlying a layer of ice with tubular bubbles oriented parallel to the direction of growth. The clear ice may also be overlain by a discontinuous layer of bubbly snow ice. Backscatter is low (-16 to-22 dB) at the time of initial ice formation, probably due to the specular nature of the upper and lower ice surfaces causing the radar pulse to be reflected away from the radar. As the ice thickens during the autumn, backscatter rises steadily. Once the ice freezes to the lake bottom, regardless of the presence of forward scattering tubular bubbles, low backscatter values of-17 to-18 dB are caused by absorption of the radar signal in the lake bed. For ice that remains afloat all winter the ice-water interface and the tubular bubbles combine, presumably via an incoherent double-bounce mechanism, to cause maximum backscatter values of the order of -6 to -7 dB. The go saturates at -6 to -7 dB before maximum ice thickness and tubular bubble content are attained. A simple ice growth model suggests that the layer of ice with tubular bubbles need be only a few centimeters thick midway through the growth season to cause maximum backscatter from floating ice. During the spring thaw a previously unreported backscatter reversal is observed on the floating and grounded portions of the coastal lakes but not on the lakes farther inland. This reversal may be related to the ice surface topography and wetness plus the effects of a longer, cooler melt period by the coast. Time series of backscatter variations from shallow tundra lakes are a record of (1) the development of tubular bubbles in the ice and, by association, changes in the gas content of the underlying water and (2) the freezing of ice to the bottoms of the lakes and therefore lake bathymetry and water availability. SAR is also able to detect the onset of lake ice growth in autumn and the initiation of the spring thaw and thus has potential for monitoring high-latitude lake ice growth and decay processes in relation to climate variability. IntroductionThe North Slope of Alaska is a relatively fiat and featureless region of tundra with thousands of lakes, located between the Brooks Range and the Arctic Ocean. Most of the lakes are shallow, with few exceeding 2-4 m deep, and apart from a short open water season from June to August they are ice covered. During the summer the lakes provide important habitats for plants and animals. In winter those lakes that do not freeze completely to the bottom can continue to support aquatic life and serve as sources of fresh water, a material that is in short supply in an otherwise frozen landscape.The radar signatures of a variety of such ice-covered North Slope lakes have been investigated previously using airborne real aper...