Spatial variations in the winter heat flux at SHEBA: estimates from snow-ice interface temperatures

Abstract

The temperature of the snow-ice interface was measured every 2.4 h throughout winter 1997/98 at 30 locations near the Surface Heat Budget of the Arctic Ocean (SHEBA) camp in the Beaufort Sea. Measurements were obtained from young ice, ridges, refrozen melt ponds and ice hummocks. Average snow depths at these locations were 567 cm, while mean interface temperatures ranged from −8° to −25°C, with minimums varying from −12° to −39°C. Interface temperatures were linearly related to snow depth, with increasing scatter at greater depths. The conductive heat flux during the winter, Fc, was estimated for each location using air and interface temperatures, snow depths and measured snow thermal conductivities. Fc was integrated to determine total heat loss for the winter at each site. Losses varied by a factor of four, with variations over short distances (10 m) as large as the variations between ice floes. Spot measurements along traverse lines confirm that large variations in interface temperature are common, and imply that small-scale spatial variability in the conductive flux is widespread. A comparison of the dependence of Fc on snow depth and ice thickness based on our observations with the dependence predicted by a one-dimensional theoretical model suggests that spatial heterogeneity may be an important issue to consider when estimating the heat flux over large aggregate areas. We suggest that the small-scale variability in the conductive flux arises because the combined snow and ice geometry can produce significant horizontal conduction of heat.

Reconciling different observational data sets from Surface Heat Budget of the Arctic Ocean (SHEBA) for model validation purposes

“…In situ measurements of snow thermal conductivity at SHEBA are very consistent with a mean and a standard deviation of 0.14 Wm −1 K −1 and 0.03 Wm −1 K −1 respectively [ Sturm et al , 2001]. The bulk thermal conductivity (inferred from snow depth, basal ice growth, ocean heat flux, and air and snow‐ice interface temperatures) at 15 locations of the mass balance sites Baltimore, Seattle and the Ridge however [ Sturm et al , 2002b], ranges from 0.17 Wm −1 K −1 to 0.70 Wm −1 K −1 .…”

“…[38] In situ needle probe measurements of the snow thermal conductivity at SHEBA gave an average value of 0.14 Wm À1 K À1 and a bulk density of 320 kg m À3 considering all SHEBA sites [Sturm et al, 2001[Sturm et al, , 2002a. These values are not in line with most published empirical equations relating conductivity and density [Sturm et al, 1997].…”

“…The snow cover disappeared completely during the melt season. Considering all measurement sites at SHEBA, the average winter snow cover thickness was 34 cm with a mean bulk density of 320 kg m −3 [ Sturm et al , 2001]. Thus the complete melting of this snow layer corresponds to an average surface ablation of 11 cm snow water equivalent.…”

Vertical Heat Transfer in the Lower Atmosphere over the Arctic Ocean During Clear-sky Periods

“…Another source of uncertainty is the value of k s (e.g., Maykut, 1982). Although a value of k s ¼ 0:3 was used in (5), the measured value of 0.14 was approximately a factor of two smaller (Sturm et al, 2001). The larger value, similar to those measured during previous conductivity studies, was used in (5), due to inconsistencies between the measured value and the observed accretion of ice at the ice/ocean interface during the winter season (Sturm et al, 2004).…”

Nutrient cycling in Alaskan tundra in response to experimental manipulation of growing season length and soil temperature : a climate change scenario

“…The short growing season length and generally low temperatures in the Arctic, snow cover, and topographic position largely determine soil temperature, soil moisture, and soil aeration (Sturm et al 2001), which in turn have strong effects on plant community composition. Although vegetation characteristics can affect all of the factors above (Sturm et al 2001, McGuire et al 2002, the primary effect of community characteristics on ecosystem nutrient cycling is through litter quality, turnover rates, and their effect on nutrient availability (Hobbie et al 1999).…”