Variability In Snow Accumulation Research Articles

Timing of present and future snowmelt from high elevations in northwest Montana

The sensitivity of snowmelt‐driven water supply to climate variability and change is difficult to assess in the mountain west, where strong climatic gradients coupled with complex topography are sampled by sparse ground measurements. We developed a model which ingests daily satellite imagery and meteorological data and is suitable for areas >1000 km2, yet captures spatial variability of snow accumulation and melt in steep mountain terrain. We applied the model for the years 2000–2008 to a 2900 km2 snowmelt‐dominated watershed in NW Montana. We found that >25% of the basin's snow water equivalent (SWE) accumulates above the highest measurement station and >70% accumulates above the mean elevation of surrounding SNOTEL stations. Consequently, scaling point measurements of SWE to describe basin conditions could lead to significant misrepresentation of basin snow. Simulations imply that present‐day temperature variability causes measures of snowmelt timing to vary by over 4 weeks from year‐to‐year. Temperature variability causes a larger spread in snowmelt timing in a warmer climate. On average, snowmelt timing occurs 3 weeks earlier in late 21st century projections, with about 25% of future conditions observed today.

Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signature of snow and complex terrain

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and resulting soil moisture patterns, and that the interaction of dynamic (timing of water input) and static influences propagate that relative constant spatial variability through most of the hydrologic year. The results demonstrate that snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments and suggest that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

Record 2007 Greenland Ice Sheet Surface Melt Extent and Runoff

Record surface melting from the Greenland ice sheet (GrIS) occurred in 2007, according to observations [Mote, 2007; Tedesco, 2007]. The surface melting and freshwater runoff contribution are of considerable importance to, for example, the global eustatic sea level rise and the ocean salinity.Remote locations and harsh climatic conditions are commonly cited as reasons for the lack of knowledge about the snow and ice contained within Greenland. Modeling studies incorporating available data sets are valuable resources in illuminating GrIS melting and runoff changes. Snow‐Model [Liston and Elder, 2006a, 2006b], a snow evolution modeling system simulating snow‐related physical processes, was used to model the GrIS surface water balance, including spatial variations in snow accumulation and redistribution, snowmelt and ice melt, and water balance components such as runoff (see equation below for further information), on the GrIS for 1995–2007. If snow temperature is below freezing, any percolating or liquid water refreezes and is stored in the snow (in the “pores”) as internal accumulation. This provides a method to account for heat and mass transfer processes, such as snowpack ripening, during spring melt.

Stratigraphic analysis of an ice core from the Prince of Wales Icefield, Ellesmere Island, Arctic Canada, using digital image analysis: High‐resolution density, past summer warmth reconstruction, and melt effect on ice core solid conductivity

High‐resolution (1 mm) stratigraphic information was derived from digital image analysis of an ice core from the Prince of Wales (POW) Icefield, Central Ellesmere Island, Canada. Following careful image processing, a profile of ice core transmitted light was derived from the greyscale images and used to reconstruct high‐resolution density variations for the unfractured sections of the core. Images were further classified into infiltration and glacier ice using an automatic thresholding procedure, and were converted to a high‐resolution melt percentage index. The mean annual melt percentage over the last 580 years was 9%, and melting occurred in 8 years out of 10. Melting obliterated most of the original depositional sequence, and seasonal density cycles were mostly unrecognizable. The ice core solid conductivity was greater and more variable in melt features than in glacier ice, owing to washout of strong acids by meltwater (elution) and chemical enrichment upon refreezing. This hindered the identification of acid volcanic layers and further compromised dating by annual layer counting. Comparison of the melt record with those from other Arctic ice caps shows that the melt‐temperature relationship on POW Icefield is site‐specific. We speculate that this is due to the peculiar position of the icefield, which rests on the periphery of the Baffin Bay maritime climate zone, and to the proximity of the North Open Water polynya, which controls snow accumulation variability on the icefield and affects the melt percentage index.

Spatial and temporal variability in snow accumulation at the West Antarctic Ice Sheet Divide over recent centuries

Ice cores collected in 2000 (ITASE 00‐1) and 2005 (WDC05A, WDC05Q) from the West Antarctic Ice Sheet Divide (WAIS Divide) project site were used to investigate the spatial and temporal variability in accumulation. The ice cores were dated based on annual layer counting of multiple glaciochemical measurements resulting in bottom depth ages for WDC05A, WDC05Q, and ITASE 00‐1 of 1775, 1521, and 1653 A.D., with mean annual accumulation rates of 0.200, 0.204, and 0.221 mweq a−1, respectively. Small‐scale spatial variability (SSV) was determined using an analysis of variance of accumulation in the ice core array, thereby quantifying the uncertainty in individual accumulation records. Results indicate that the spatial variability was 0.030 mweq a−1, or approximately 15% of the average annual accumulation. An accumulation record representative of the WAIS Divide local area over recent centuries was developed using a principal component analysis to identify the coherent accumulation signal. The WAIS Divide local record exhibited 14% interannual variability (1 standard deviation of the mean) with the SSV reduced to 0.017 mweq a−1. Correlations of the WAIS Divide local accumulation record with atmospheric indices (e.g., Antarctic Oscillation) exhibited periods when the records oscillate in and out of phase. Thus, reconstructing local and global atmospheric indices from WAIS Divide accumulation records over recent centuries may prove problematic.

Ground‐based measurements of spatial and temporal variability of snow accumulation in East Antarctica

The East Antarctic Ice Sheet is the largest, highest, coldest, driest, and windiest ice sheet on Earth. Understanding of the surface mass balance (SMB) of Antarctica is necessary to determine the present state of the ice sheet, to make predictions of its potential contribution to sea level rise, and to determine its past history for paleoclimatic reconstructions. However, SMB values are poorly known because of logistic constraints in extreme polar environments, and they represent one of the biggest challenges of Antarctic science. Snow accumulation is the most important parameter for the SMB of ice sheets. SMB varies on a number of scales, from small‐scale features (sastrugi) to ice‐sheet‐scale SMB patterns determined mainly by temperature, elevation, distance from the coast, and wind‐driven processes. In situ measurements of SMB are performed at single points by stakes, ultrasonic sounders, snow pits, and firn and ice cores and laterally by continuous measurements using ground‐penetrating radar. SMB for large regions can only be achieved practically by using remote sensing and/or numerical climate modeling. However, these techniques rely on ground truthing to improve the resolution and accuracy. The separation of spatial and temporal variations of SMB in transient regimes is necessary for accurate interpretation of ice core records. In this review we provide an overview of the various measurement techniques, related difficulties, and limitations of data interpretation; describe spatial characteristics of East Antarctic SMB and issues related to the spatial and temporal representativity of measurements; and provide recommendations on how to perform in situ measurements.

The influence of forest and topography on snow accumulation and melt at the watershed-scale

Summary Snow accumulation and melt are the two most important processes contributing to the spring freshet, the main hydrological event in snow-dominated watersheds. The magnitude and timing of the spring freshet is determined by the spatial variability of melt rates and peak snow water equivalent (SWE). Although it is known that the spatial variability of SWE is controlled by topography and vegetation, their combined influence on snow accumulation and melt at the watershed-scale has only been addressed in a few studies. The high small-scale variability of a snowpack combined with the classical sampling technique along transects makes it difficult to estimate how much of the observed spatial variability in SWE can be attributed to topographic and vegetative controls such as elevation, aspect, wind, and forest type, and how much of it is local variability. A nested stratified sampling design was developed for the snow courses in this study, where small-scale variability at the plot-scale was integrated by 12 snow density and 60 snow depth measurements, while the variability at the watershed-scale was captured by 19 strata, defined by elevation, aspect, and forest cover (either clearcut or forest). Snow course sampling was carried out during the spring of 2005, a year with 25% below average snow accumulation, and in spring of 2006, a year with 25% above average snow accumulation for the region. Snow accumulation in both years and snowmelt for a period in the spring of 2006 are analysed with multiple linear regressions and spatial autocorrelation of SWE with variogram analysis. The combination of elevation, aspect, and forest cover explained about 80–90% of the large-scale variability in snow accumulation for both years. At the plot-scale the coefficient of variation was higher under the forest canopy than in clearcuts, but did not relate to topographic controls. Elevation had the greatest influence on SWE in both years, with a steeper gradient in the snow rich year, whereas aspect had a similar effect in both years. In the mild winter of 2005, forests accumulated 39% less snow than clearcuts, while in 2006, forests had 27% less snow than clearcuts. Relative to the mean, aspect had a greater influence on snow accumulation than forest cover in the mild winter of 2005. Elevation played the most important role in snowmelt, while both aspect and forest cover had a comparable effect with slightly less influence than elevation. Almost all variograms show a clear sill, confirming that in the majority of cases, a 60 × 60 m plot size was adequate in capturing the variability of the plot-scale. In general, the degree of spatial autocorrelations is persistent in the snowmelt season for both years. This study has shown that it is possible to separate large-scale variability created by topography and forest cover from plot-scale variability through specifically designed snow surveys. The methods and data set of this study are useful in testing spatially-distributed snowmelt models and in developing new algorithms that reflect the relationships between the factors controlling the spatial variability of SWE.

Historical behaviour of Dome C and Talos Dome (East Antarctica) as investigated by snow accumulation and ice velocity measurements

Ice divide–dome behaviour is used for ice sheet mass balance studies and interpretation of ice core records. In order to characterize the historical behaviour (last 400 yr) of Dome C and Talos Dome (East Antarctica), ice velocities have been measured since 1996 using a GPS system, and the palaeo-spatial variability of snow accumulation has been surveyed using snow radar and firn cores. The snow accumulation distribution of both domes indicates distributions of accumulation that are non-symmetrical in relation to dome morphology. Changes in spatial distributions have been observed over the last few centuries, with a decrease in snow accumulation gradient along the wind direction at Talos Dome and a counter-clockwise rotation of accumulation distribution in the northern part of Dome C. Observations at Dome C reveal a significant increase in accumulation since the 1950s, which could correlate to altered snow accumulation patterns due to changes in snowfall trajectory. Snow accumulation mechanisms are different at the two domes: a wind-driven snow accumulation process operates at Talos Dome, whereas snowfall trajectory direction is the main factor at Dome C. Repeated GPS measurements made at Talos Dome have highlighted changes in ice velocity, with a deceleration in the NE portion, acceleration in the SW portion and migration of dome summit, which are apparently correlated with changes in accumulation distribution. The observed behaviour in accumulation and velocity indicates that even the most remote areas of East Antarctica have changed from a decadal to secular scale.

Influence of canopy density on snow distribution in a temperate mountain range

AbstractWe analyse spatial variability and different evolution patterns of snowpack in a mixed beech–fir stand in the central Pyrenees. Snow depth and density were surveyed weekly along six transects of contrasting forest cover during a complete accumulation and melting season; we also surveyed a sector unaffected by canopy cover. Forest density was measured using the sky view factor (SVF) obtained from digital hemispherical photographs. During periods of snow accumulation and melting, noticeable differences in snow depth and density were found between the open site and those areas covered by forest canopy. Principal component analysis provided valuable information in explaining these observations. The results indicate a high variability in snow accumulation within forest areas related to differences in canopy density. Maximum snow water equivalent (SWE) was reduced by more than 50% beneath dense canopies compared with clearings, and this difference increased during the melting period. We also found significant temporal variations: when melting began in sectors with low SVF, most of the snow had already thawed in areas with high SVF. However, specific conditions occasionally produced a different response of SWE to forest cover, with lower melting rates observed beneath dense canopies. The high values of correlation coefficients for SWE and SVF (r > 0·9) indicate the reliability of predicting the spatial distribution of SWE in forests when only a moderate number of observations are available. Digital hemispherical photographs provide an appropriate tool for this type of analysis, especially for zenith angles in the range 35–55 . Copyright © 2007 John Wiley & Sons, Ltd.

Snowpack Variations in the Central Andes of Argentina and Chile, 1951–2005: Large-Scale Atmospheric Influences and Implications for Water Resources in the Region

Abstract The snowpack in the central Andes (30°–37°S) is the primary source for streamflow in central Chile and central-western Argentina, but few published studies are available on snowpack variability in the region. This paper develops the first regional snowpack series (1951–2005) from Chilean and Argentinean snow course records. This series shows a strong regional signal, marked interannual variability, and a positive, though nonsignificant, linear trend. Correlations with local precipitation and temperature records reveal a marked association with conditions in central Chile. High snow accumulation is generally concurrent with El Niño events in the tropical Pacific, but only 5 of the 10 driest years coincided with La Niña events. Evaluation of 500-hPa geopotential height anomaly maps during extreme snow years highlights the crucial significance of tropospheric conditions in the subtropical and southeast Pacific in modulating snowfall. Correlations with gridded SST and SLP data and multiple regressions with large-scale climatic indices corroborate a Pacific ENSO-related influence largely concentrated during the austral winter months. This hampers the predictability of snowpack before the onset of the cold season. Annual and warm-season river discharges on both sides of the cordillera are significantly correlated with the regional snowpack record and show positive linear trends over the 1951–2004 common period, probably related to a greater frequency of above-average snowpacks during recent decades. Future demand and competition for water resources in these highly populated regions will require detailed information about temporal and spatial variations in snow accumulation over the Andes. The results indicate that the relationships between snowpack and atmospheric circulation patterns prior to the winter season are complex, and more detailed analyses are necessary to improve prediction of winter snowfall totals.

Distributed and integrated response of a geographic information system‐based hydrologic model in the eastern Palouse region, Idaho

AbstractVerification of distributed hydrologic models is rare owing to the lack of spatially detailed field measurements and a common mismatch between the scale at which soil hydraulic properties are measured and the scale of a single modelling unit. In this study, two of the most commonly calibrated parameters, i.e. soil depth and the vertical distribution of lateral saturated hydraulic conductivity Ks, were eliminated by a spatially detailed soil characterization and results of a hillslope‐scale field experiment. The soil moisture routing (SMR) model, a geographic information system‐based hydrologic model, was modified to represent the dominant hydrologic processes for the Palouse region of northern Idaho. Modifications included Ks as a double exponential function of depth in a single soil layer, a snow accumulation and melt algorithm, and a simple relationship between storage and perched water depth (PWD) using the drainable porosity. The model was applied to a 2 ha catchment without calibration to measured data. Distributed responses were compared with observed PWD over a 3‐year period on a 10 m × 15 m grid. Integrated responses were compared with observed surface runoff at the catchment outlet. The modified SMR model simulated the PWD fluctuations remarkably well, especially considering the shallow soils in this catchment: a 0·20 m error in PWD is equivalent to only a 1·6% error in predicted soil moisture content. Simulations also captured PWD fluctuations during a year with high spatial variability of snow accumulation and snowmelt rates at upslope, mid‐slope, and toe slope positions with errors as low as 0·09 m, 0·12 m, and 0·12 m respectively. Errors in distributed and integrated model simulations were attributed mostly to misrepresentation of rain events and snowmelt timing problems. In one location in the catchment, simulated PWD was consistently greater than observed PWD, indicating a localized recharge zone, which was not identified by the soil morphological survey. Copyright © 2006 John Wiley & Sons, Ltd.

Strong spatial variability of snow accumulation observed with helicopter‐borne GPR on two adjacent Alpine glaciers

This study compares high‐resolution helicopter‐borne radar measurements to extensive ground‐based profiling of the snow cover on Findel‐ and Adler Glacier, Switzerland. The results demonstrate that derived accumulation values of either method are well in accordance. The spatial distribution of radar based snow depth allows a clear distinction of three zones of different accumulation characteristics: (1) The lower part of Findel Glacier shows a clear altitudinal trend while (2) the upper part has no trend in altitude but high spatial fluctuations in snow depth. (3) Adler Glacier's accumulation characteristics are similar to zone (2). However, despite their close vicinity, accumulation on (3) is reduced by 40% compared to (2). The observed strong spatial variability emphasises the need for spatially continuous measurements for studies involving accumulation on glaciers. Finally, reasons for observed variations (e.g., preferential snow deposition and snow redistribution) are discussed.

Reconstructing climate variability from Greenland ice sheet accumulation: An ERA40 study

Re‐analysis data covering the period 1958–2001 are used to investigate the relationship between regional, inter‐annual snow accumulation variability over the Greenland Ice Sheet (GrIS) and large scale circulation patterns, cyclone frequency, and strength. Four regions of the GrIS have been identified that are highly independent with respect to accumulation variability. Accumulation indices of three of these regions are associated with distinct large‐scale circulation patterns: Central‐western GrIS reveals an inverse relationship with a NAO‐like pattern, the south‐west a positive correlation with a high pressure bridge from central North Atlantic to Scandinavia, and the south‐eastern GrIS a positive correlation with a high‐pressure anomaly over the Greenland Sea. These large‐scale patterns also impact European climate in different ways. Accumulation variability in north‐eastern GrIS, however, is dominated by cyclones originating from the Greenland Sea. Thus, Greenland ice core accumulation records offer the potential to reconstruct various large‐scale circulation patterns and regional storm activity.

Sea Ice Amplification

GEOLOGY Numerous, millennial-scale warming episodes, called Dansgaard-Oeschger (D-O) events, punctuated the last glacial period. These events, first discovered in deep ice cores from Greenland, are visible in climate records extending from pole to pole, and in Pacific as well as Atlantic marine sediments. One popular hypothesis about the cause of these abrupt climate warmings invokes changes in the strength of the ocean's thermohaline circulation, which affect ocean heat transport. Such a model, however, cannot explain the size of the temperature swings in Greenland, which were as large as 5° to10°C. Li et al. use an atmospheric general circulation model to show that warming and cooling of the magnitude observed in Greenland can be caused by only small changes in the amount of sea ice around it. Furthermore, the sea ice changes that they suggest would also account for variations in snow accumulation and oxygen isotope composition similar to those measured in ice cores from Greenland. Finally, the amount of sea ice retreat proposed is consistent with forcing either by ocean thermohaline circulation variations, or by changes in surface wind stress in the North Atlantic. Thus, sea ice can provide a positive feedback strong enough to cause warming like that which occurred during D-O events. — HJS Geophys. Res. Lett. 32 , L19702 (2005).

Spatial and temporal variability of snow accumulation in East Antarctica from traverse data

AbstractRecent snow accumulation rate is a key quantity for ice-core and mass-balance studies. Several accumulation measurement methods (stake farm, fin core, snow-radar profiling, surface morphology, remote sensing) were used, compared and integrated at eight sites along a transect from Terra Nova Bay to Dome C, East Antarctica, to provide information about the spatial and temporal variability of snow accumulation. Thirty-nine cores were dated by identifying tritium/b marker levels (1965_66) and non-sea-salt (nss) SO42_ spikes of the Tambora (Indonesia) volcanic event (1816) in order to provide information on temporal variability. Cores were linked by snow radar and global positioning system surveys to provide detailed information on spatial variability in snow accumulation. Stake-farm and ice-core accumulation rates are observed to differ significantly, but isochrones (snow radar) correlate well with ice-core derived accumulation. The accumulation/ablation pattern from stake measurements suggests that the annual local noise (metre scale) in snow accumulation can approach 2 years of ablation and more than four times the average annual accumulation, with no accumulation or ablation for a 5 year period in up to 40% of cases. The spatial variability of snow accumulation at the kilometre scale is one order of magnitude higher than temporal variability at the multi-decadal/secular scale. Stake measurements and firn cores at Dome C confirm an approximate 30% increase in accumulation over the last two centuries, with respect to the average over the last 5000 years

Snow accumulation variability and random walk: how to interpret changes of surface elevation in Antarctica

Because snow accumulation above an ice sheet is a cumulative process, a random fluctuation of snow accumulation can lead to significant variability in ice sheet mass or elevation, without being linked to a long-term climatic change. Moreover, ice sheet mass balance cannot be measured directly. We can determine the ice sheet surface elevation through altimetric data but, due to the densification process, the snow accumulation variability affects the surface elevation variations more than the mass variations. We investigated the effect of the recent snow accumulation variability on both ice sheet mass and volume at the time scale of the satellite era. There is more than 10% chance of measuring an artificial trend greater than 15% of the mean accumulation rate, from a 10-year elevation series. In our simplified model, the impact of snow accumulation variability only depends on the ratio between the snow densification time and the variability time period: It decreases with the densification rate and increases with the variability frequency, so that the induced error is maximum for high-frequency noise in the central part of Antarctica. Finally, our results showed that knowledge of snow accumulation variability for the past 5–10 years is needed to eliminate the delayed induced error.

Simulation of snow shielding corrections for cosmogenic nuclide surface exposure studies

Snow cover reduces cosmogenic nuclide production rates in bedrock. Corrections for snow cover can be more than 10% in mountainous, mid-latitude regions where many glacial chronologies have been constructed using cosmogenic nuclide surface dating of landforms. Most published snow corrections use historic climate data of limited duration that are not likely to reflect adequately the full range of snow conditions over the time of exposure. We present a model for describing the impact of snow burial on long-term exposure histories of landforms. The model applies an energy balance approach to snowpack evolution and incorporates both historic and long-term climate proxy data. Attenuation of cosmogenic fast neutrons is modeled alternatively as a simple exponential decrease with increased shielding or as a thin surface layer with constant production followed by an exponential decrease with increasing depth. The choice of attenuation model has little effect on the modeled results for the Cairngorms but will have a more significant effect in regions characterized by thinner, less dense snowpacks. Spatial variability in snow cover is modeled as a function of elevation only, ignoring local variability in snow accumulation as a result of slope aspect, wind redistribution and local topography. Thus, model results reveal general spatial and temporal trends in snow shielding effects, rather than site-specific corrections. Applications to data from the Cairngorm Mountains of Scotland show that the constant-plus-exponential (CPE) production rate-depth profile reduces but does not eliminate snow-shielding effects. Under present-day conditions, snow at 900 m in the Cairngorm Mountains reduces average production rates by 6% using the CPE profile and 9% with the exponential profile (EP). Long-term climate simulations from 15.5 ka through today produce larger snow shielding effects, mainly because they predict an increased proportion of precipitation as snowfall during the Younger Dryas. At 900 m, this long-term simulation reduces average cosmogenic isotope production rates by 12% (CPE) and 14% (EP). These results indicate that snow-shielding corrections based on historic climate records may be a potential source of systematic error in midlatitude mountainous regions.

Wind‐borne redistribution of snow across an Antarctic ice rise

Redistribution of snow by the wind can drive spatial and temporal variations in snow accumulation that may affect the reconstruction of paleoclimate records from ice cores. In this paper we investigate how spatial variations in snow accumulation along a 13 km transect across Lyddan Ice Rise, Antarctica, are related to wind‐borne snow redistribution. Lyddan Ice Rise is an approximately two‐dimensional ridge which rises about 130 m above the surrounding ice shelves. Local slopes on its flanks never exceed 0.04. Despite this very smooth profile, there is a pronounced gradient in snow accumulation across the feature. Accumulation is highest on the ice shelf to the east (climatologically upwind) of the ice rise and decreases moving westward, with the lowest accumulation seen to the west (climatologically downwind) of the ice rise crest. Superimposed on this broad‐scale gradient are large (20–30%), localized variations in accumulation on a scale of around 1 km that appear to be associated with local variations in surface slope of less than 0.01. The broad‐scale accumulation gradient is consistent with estimates of wind‐borne redistribution of snow made using wind speed observations from three automatic weather stations. The small‐scale variability in accumulation is reproduced quite well using a snow transport model driven by surface winds obtained from an airflow model, providing that both the wind shear and static stability of the upwind flow are taken into account. We conclude that great care needs to be exercised in selecting ice core sites in order to avoid the possibility of blowing snow transport confounding climate reconstructions.

Spatial characteristics of snow accumulation in Dronning Maud Land, Antarctica

An accurate knowledge of spatial variations in snow accumulation on ice sheets is vital for numerical reconstructions and future projections of ice sheet dynamics, with implications for changes in global climate and sea level. This study considers net snow accumulation in Dronning Maud Land, East Antarctica. Ground-penetrating radar (GPR) soundings were carried out along two ground traverses, 800 and 1000 km long, extending from the ice shelves to the high plateau in order to map snow accumulation rates continuously along the route. Comparisons between the net accumulation rates obtained by GPR and those obtained by stake-line measurements exhibited a close correlation. Large dissimilarities in the accumulation distribution were observed between the two traverses. The ice sheet surface topography and the regional distribution of air pressure systems and cyclone tracks can explain these differences to a large extent. Spatial trends in snow accumulation rate were determined for characteristic traverse sections. The spatial variability was calculated as the standard deviation in snow accumulation rate over 5 km, expressed as percentages of the average accumulation within the 5-km section. On ice shelves, the spatial variability was less than 10%. In grounded coastal areas, it ranged between 20% and 40%, and on the high plateau, it ranged up to 20%. In areas of intense wind erosion, the spatial variability often exceeded 50%. The generally high spatial variability demonstrates that the spatial representativeness of individual point measurements is of vital importance in Antarctic snow accumulation studies. This is a problem that is presently not properly attributed.

The application of depletion curves for parameterization of subgrid variability of snow

AbstractParameterization of subgrid‐scale variability in snow accumulation and melt is important for improvements in distributed snowmelt modelling. We have taken the approach of using depletion curves that relate fractional snow‐covered area to element‐average snow water equivalent to parameterize the effect of snowpack heterogeneity within a physically based mass and energy balance snowpack model. Comparisons of parameterization outputs with distributed model outputs and observations show performance comparable to the distributed model and reasonable performance relative to observations for time series modelling of snow water equivalent and snow‐covered area. Examination of the relationship between the shapes of the depletion curves and parametric distributions shows that the shapes of dimensionless depletion curves depend primarily on the coefficient of variation and to a lesser extent on the shape of the snow distribution function. The methods presented here are a generalization of several previously used methods to estimate depletion curve shapes. Comparison of several years of observed depletion curves from the study basin show that the shapes of the depletion curves change little from year to year. Copyright © 2004 John Wiley & Sons, Ltd.