Jon Holmgren

A Seasonal Snow Cover Classification System for Local to Global Applications

Abstract A new classification system for seasonal snow covers is proposed. It has six classes (tundra, taiga, alpine, maritime, prairie, and ephemeral, each class defined by a unique ensemble of textural and stratigraphic characteristics including the sequence of snow layers, their thickness, density, and the crystal morphology and grain characteristics within each layer. The classes can also be derived using a binary system of three climate variables: wind, precipitation, and air temperature. Using this classification system, the Northern Hemisphere distribution of the snow cover classes is mapped on a 0.5° lat × 0.5° long grid. These maps are compared to maps prepared from snow cover data collected in the former Soviet Union and Alaska. For these areas where both climatologically based and texturally based snow cover maps are available, there is 62% and 90% agreement, respectively. Five of the six snow classes are found in Alaska. From 1989 through 1992, hourly measurements, consisting of 40 thermal and...

Snow–Shrub Interactions in Arctic Tundra: A Hypothesis with Climatic Implications

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.

The thermal conductivity of seasonal snow

Twenty-seven studies on the thermal conductivity of snow ( K eff ) have been published since 1886. Combined, they comprise 354 values of K eff , and have been used to derive over 13 regression equation and predicting K eff 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 K eff is in W m −1 K −1 , can be fit to the new data ( R 2 = 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.

Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing

Abstract 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 (<40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, ...

Snow Depth and Ice Thickness Measurements From the Beaufort and Chukchi Seas Collected During the AMSR-Ice03 Campaign

In March 2003, a field validation campaign was conducted on the sea ice near Barrow, AK. The goal of this campaign was to produce an extensive dataset of sea ice thickness and snow properties (depth and stratigraphy) against which remote sensing products collected by aircraft and satellite could be compared. Chief among these were products from the Polarimetric Scanning Radiometer (PSR) flown aboard a NASA P-3B aircraft and the Aqua Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The data were collected in four field areas: three on the coastal sea ice near Barrow, AK, and the fourth out on the open ice pack 175 km northeast of Barrow. The snow depth ranged from 9.4-20.8 cm in coastal areas (n=9881 for three areas) with the thinnest snow on ice that had formed late in the winter. Out in the main pack ice, the snow was 20.6 cm deep (n=1906). The ice in all four areas ranged from 138-219 cm thick (n=1952), with the lower value again where the ice had formed late in the winter. Snow layer and grain characteristics observed in 118 snow pits indicated that 44% of observed snow layers were depth hoar; 46% were wind slab. Snow and ice measurements were keyed to photomosaics produced from low-altitude vertical aerial photographs. Using these, and a distinctive three-way relationship between ice roughness, snow surface characteristics, and snow depth, strip maps of snow depth, each about 2 km wide, were produced bracketing the traverse lines. These maps contain an unprecedented level of snow depth detail against which to compare remote sensing products. The maps are used in other papers in this special issue to examine the retrieval of snow properties from the PSR and AMSR-E sensors

Extensive measurements of snow depth using FM-CW radar

A sled-mounted X-band FM-CW radar and field data reduction system was developed and field tested. An integral part of the measurement program was the use of a computer algorithm to pick peak radar amplitudes, which were needed to convert radar data into depths in the field. A set of field protocols, designed to collocate radar and hand-probe depth measurements, were used with the algorithm to locally calibrate the radar because, without local calibration, depths were unreliable. Mean snow depths determined using the calibrated radar agreed with mean depths determined by hand to within 3% but had a consistently larger variance because of radar measurement errors. An analysis of the errors indicates that they are random and can be removed by filtering using an Optimal (Wiener) filter, thereby producing both the same mean and variance in snow depth from the radar as that obtained by hand-probing.

Characteristics and Growth of a Snowdrift in Arctic Alaska, U.S.A

In arctic Alaska, 15% of the total winter snowpack is contained in large drifts. Stratigraphic sections reveal that these can form during as few as five weather events during winter, while comparison of stratigraphy and weather records show that significant deposition (up to 43% of the total drift volume) can occur during a single event of short duration (<72 h). Based on three years of wind, snowfall, and snow transport records, a set of rules was developed for predicting when periods of drift growth would occur. The rules were: 10-m wind speed >5.3 m s−1 for at least 3 h, wind direction within 30° of the normal to drift trap axis, and recent snowfall available for transport. When used, these rules successfully identified all drift-growth events, plus a few “extra” events that did not contribute substantially to drift growth. The extra events were invariably periods when there was sufficient wind to move snow, but insufficient snow for transport. In arctic Alaska drift size currently appears to be limited by precipitation rather than wind, leading us to speculate that an increase in precipitation could increase drift size and intensify the ecological, hydrological, and climatic impact of drifts on this arctic system.

Spatial Variability of Barrow-Area Shore-Fast Sea Ice and Its Relationships to Passive Microwave Emissivity

Aircraft-acquired passive microwave data, laser radar height observations, RADARSAT synthetic aperture radar imagery, and in situ measurements obtained during the AMSR-Ice03 experiment are used to investigate relationships between microwave emission and ice characteristics over several space scales. The data fusion allows delineation of the shore-fast ice and pack ice in the Barrow area, AK, into several ice classes. Results show good agreement between observed and Polarimetric Scanning Radiometer (PSR)-derived snow depths over relatively smooth ice, with larger differences over ridged and rubbled ice. The PSR results are consistent with the effects on snow depth of the spatial distribution and nature of ice roughness, ridging, and other factors such as ice age. Apparent relationships exist between ice roughness and the degree of depolarization of emission at 10, 19, and 37 GHz. This depolarization would yield overestimates of total ice concentration using polarization-based algorithms, with indications of this seen when the NT-2 algorithm is applied to the PSR data. Other characteristics of the microwave data, such as effects of grounding of sea ice and large contrast between sea ice and adjacent land, are also apparent in the PSR data. Overall, the results further demonstrate the importance of macroscale ice roughness conditions such as ridging and rubbling on snow depth and microwave emissivity

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.

The AMSRIce03 validation project: activities and results

A multidisciplinary, multi-institution team of scientists has been working for over three years to evaluate the performance of sea ice parameter algorithms applied to data from the AMSR-E (Advanced Microwave Scanning Radiometer - EOS) carried aboard NASAs Aqua platform. The AMSR-E data and derived sea ice geophysical products have been compared against a variety of measurements, including ground truth data from an ice field camp, imagery from aerosondes and an aircraft-borne microwave radiometer, and imagery from RADARSAT, MODIS, and AVHRR. Arctic ice environments examined include first-year and multiyear pack ice in the Beaufort and Chukchi Seas, polynyas and flaw leads in the Bering Sea, and the ice edge. This paper will outline the AMSRIce03 project, cover the validation methodology in detail, and discuss the results and their implications for use of sea ice products derived from the AMSR-E.