No abstract

In the Arctic, where wind transport of snow is common, the depth and insulative properties of the snow cover can be determined as much by the wind as by spatial variations in precipitation. Where shrubs are more abundant and larger, greater amounts of drifting snow are trapped and suffer less loss due to sublimation. The snow in shrub patches is both thicker and a better thermal insulator per unit thickness than the snow outside of shrub patches. As a consequence, winter soil surface temperatures are substantially higher, a condition that can promote greater winter decomposition and nutrient release, thereby providing a positive feedback that could enhance shrub growth. If the abundance, size, and coverage of arctic shrubs increases in response to climate warming, as is expected, snow-shrub interactions could cause a widespread increase (estimated 10%-25%) in the winter snow depth. This would increase spring runoff, winter soil temperatures, and probably winter CO 2 emissions. The balance between these winter effects and changes in the summer energy balance associated with the increase in shrubs probably depends on shrub density, with the threshold for winter snow trapping occurring at lower densities than the threshold for summer effects such as shading. It is suggested that snow-shrub interactions warrant further investigation as a possible factor contributing to the transition of the arctic land surface from moist graminoid tundra to shrub tundra in response to climatic warming.

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.

[1] The evolution and spatial distribution of the snow cover on the sea ice of the Arctic Ocean was observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) project. The snow cover built up in October and November, reached near maximum depth by midDecember, then remained relatively unchanged until snowmelt. Ten layers were deposited, the result of a similar number of weather events. Two basic types of snow were present: depth hoar and wind slab. The depth hoar, 37% of the pack, was produced by the extreme temperature gradients imposed on the snow. The wind slabs, 42% of the snowpack, were the result of two storms in which there was simultaneous snow and high winds (>10 m s À1 ). The slabs impacted virtually all bulk snow properties emphasizing the importance of episodic events in snowpack development. The mean snow depth (n = 21,169) was 33.7 cm with a bulk density of 0.34 g cm À3 (n = 357, r 2 of 0.987), giving an average snow water equivalent of 11.6 cm, 25% higher than the amount record by precipitation gauge. Both depth and stratigraphy varied significantly with ice type, the greatest depth, and the greatest variability in depth occurring on deformed ice (ridges and rubble fields). Across all ice types a persistent structural length in depth variations of $20 m was found. This appears to be the result of drift features at the snow surface interacting with small-scale ice surface structures. A number of simple ways of representing the complex temporal and spatial variations of the snow cover in ice-ocean-atmosphere models are suggested.

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.

[1] Eighty-nine point measurements of the thermal conductivity (k s ) of the snow on the sea ice of the Beaufort Sea were made using a heated needle probe. Average values ranged from 0.078 W m À1 K À1 for new snow to 0.290 W m À1 K À1 for an ubiquitous wind slab. k s increased with increasing density, consistent with published equations, but could also be reliably estimated from the metamorphic state of the snow. Using measured values of k s and snow stratigraphy, the average bulk value for the full snowpack was 0.14 W m À1 K À1 . In contrast, k s inferred from ice growth and temperature gradients in the snow was 0.33 W m À1 K À1 . The mismatch arises in part because the second estimate is based on measurements from an aggregate scale that includes enhanced heat flow due to two-and three-dimensional snow and ice geometry. A finite element model suggests that the complex geometry produces areas of concentrated heat loss at the snow surface. These ''hot spots,'' however, increase the apparent conductivity only by a factor of 1.4, not enough to fully explain the mismatch. Nonconductive heat transfer mechanisms, like natural and forced air convection, may also be operating in the snowpack, though the ubiquitous presence of low permeability wind slabs potentially limits their effectiveness. The relative contributions of effects due to snow and ice geometric and nonconductive processes within the snowpack remain uncertain.INDEX TERMS: 1863 Hydrology: Snow and ice (1827); 4207 Oceanography: General: Arctic and Antarctic oceanography; 4540 Oceanography: Physical: Ice mechanics and air/sea/ice exchange processes; KEYWORDS: snow, snow cover, Arctic ocean, thermal conductivity, heat flow, sea ice Citation: Sturm, M., D. K. Perovich, and J. Holmgren, Thermal conductivity and heat transfer through the snow on the ice of the

An automatic snow depth probe (magnaprobe) patented in 1999 (United States Patent 5,864,059, 1999) and produced commercially by Snow‐Hydro LLC has now been used to obtain more than a million simultaneous snow depths (up to 140 cm) and GPS measurements during a wide range of field validation campaigns. The magnaprobe consists of a ski pole‐like rod housing a magnetostrictive device along which a basket and magnet assembly slides. The rod is inserted to the base of the snow, the basket floats on the snow, and when a button is pushed, the distance between rod tip and basket is measured while a position is acquired. The nature of the substrate beneath the snow controls the snow depth accuracy with errors ranging from near zero for hard bases to +5 cm in soft vegetation. The Wide Area Augmentation System‐enabled GPS provides a position accurate to ±2.5 m. The probe increases the speed with which a depth and position measurement can be obtained by a factor of 10 compared to measuring with a traditional ruler probe and writing down the results. The highest boost in snow depth measurement efficiency occurs when the distance between measuring locations is kept relatively small (<10 m). Magnaprobes have materially improved our ability to evaluate airborne and satellite‐based snow depth products and are likely to see continued heavy use over the next decade as efforts to develop satellite systems for monitoring snow remotely are tested in various field settings.

During April 2007, a coordinated series of snow measurements was made across the Northwest Territories and Nunavut, Canada, during a snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. The purpose of the measurements was to document the general nature of the snowpack across this region for the evaluation of satellite- and model-derived estimates of snow water equivalent (SWE). Although detailed, local snow measurements have been made as part of ongoing studies at tundra field sites (e.g., Daring Lake and Trail Valley Creek in the Northwest Territories; Toolik Lake and the Kuparak River basin in Alaska), systematic measurements at the regional scale have not been previously collected across this region of northern Canada. The snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (&lt;40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, and more metamorphosed than where it was deposited on tundra. Although highly variable locally, no longitudinal gradients in snow distribution, magnitude, or structure were detected. This regional homogeneity allowed us to identify that the observed spatial variability in passive microwave brightness temperatures was related to subgrid fractional lake cover. Correlation analysis between lake fraction and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature showed frequency dependent, seasonally evolving relationships consistent with lake ice drivers. Simulations of lake ice thickness and snow depth on lake ice produced from the Canadian Lake Ice Model (CLIMo) indicated that at low frequencies (6.9, 10.7 GHz), correlations with lake fraction were consistent through the winter season, whereas at higher frequencies (18.7, 36.5 GHz), the strength and direction of the correlations evolved consistently with the penetration depth as the influence of the subice water was replaced by emissions from the ice and snowpack. A regional rain-on-snow event created a surface ice lens that was detectable using the AMSR-E 36.5-GHz polarization gradient due to a strong response at the horizontal polarization. The appropriate polarization for remote sensing of the tundra snowpack depends on the application: horizontal measurements are suitable for ice lens detection; vertically polarized measurements are appropriate for deriving SWE estimates.