Biogmchcmiarv o/Scuronolly Snow-Covered Cafchtne~s (Proceedings oTa Boulder Symposium. July 1995). WtS Publ. no. 228. 1995.

Spatial distribution of snow chemical load at the tundra-taiga transition

J. W. POMEROY & P. MARSH ~Vational Hydrology Research Intlirure, Environment Canada. I I Innovation Blvd., Saskaroon, Sashchewan S7N 3H5, Canada

H. G. JOrnS 1:VRS-eau. Sre-Foy, Quebec GJ V 4 ~ 7 . Canada

T. D. DAVIES , Climatic Research Unit, Universiry of Enrr Anglia. Now'ch MP4 7iV, UK

Abstract The chemical composition of seasonal snow covers was studied in the taiga-tundra transition zone near Inuvik, Northwest Territories, Canada. Concentrations of the major ions, snow water equivalent, and winter leaf area index were determined for a series of forested, shrub- tundra and open tun&ra sites along a transect that spans the arctic Weline. The substantial variation in snow and ion load with leaf area index, landscape type, and mesoscale site demonstrates that both the local land surface factors and the broad-scale influences which control snow and ion deposition, must be addressed in order to spatially extrapolate measurements of snow quantity and snow chemistry. Ion loads vary by up to 5-fold in different landscape types within a mesoscale site and up to 18-fold between mesoscale sites. Two factors, operating at two scales, most suongly affect the load of snow and major geochemical ions in snow at the arctic treeline. The first, at a small-scale, is the landscape roughness as parameterized by the leaf area index or by topographic slope. The second, mesoscale wind exposure and relocation of snow, can strongly affect the small-scale landscape-snow relationship. This makes determination of snow accumulation and chemistry from point characteristics extremely difficult. A combination of two-dimensional, physically based models of wind transport of snow and snow chemical loads, operating in a distributed fashion over the mesoscale, must be developed to predict snow accumulation and snow chemistry in complex, windswept environments such as the tundra-wigs transition.

The geochemistry of subarctic and arctic catchments is strongly influenced by the chemistry of snow cover. This is due to the long winter over which chemical species accumulate in snow and the rapid release of the chemical load from the pack upon melt. the delivery of major ions to these catchments is not uniform, as several atmospheric and land surface processes enhance or deplete concentrations in snow and the ":amulation of snow (Woo & Marsh, 1978; Delmas & Jones, 1987; Bamie, 1991;

192 J. W. Pomeroy cr al.

Jones er al., 1993). Pomeroy et al. (1993) showed that wind redistribution of snow tien: smooth to rough surfaces and preferential dry deposition to forested terrain were tiic primary processes controlling the distribution of major ions in the late winter snow of the subarctic-arctic transition zone. The results of Benson's (1982) blowinz-snow aansport study and the sublimation model of Tabler (1975) suggest that from 50 to 75 R of annual snowfall is sublimated from open tundra. In addition, Pomeroy & Schmidt (1993) have shown that sublimation of intercepted snow from coniferous boreal forests can remove 32% of annual snowfall. Thus both dry and wet deposition and the redistribution of snow are sensitive to aerodynamic roughnecs. It follows that schemec

Parsons Lake

Fig. 1 Study site in northwestern Canada. l l ~ e transecr extended nonhward frcm Tundra Lakes to Whitefish Pingo. The treeline is crossed by this transect benvetn Havikpak Creek and Trail Valley Creek.

Spatial distribution of rhe snow chemical load at the tundra-taiga transition 193

to stratify ion load and concentration by landscape class (e.g. vegetation, terrain roughness, hydrography) might use techniques developed by Steppuhn & Dyck (1974) for snow accumulation.

Pomeroy et al. (1994) and Marsh et al. (1995) demonstrated the variability of snow accumulation in catchments in a subarctic region. Marsh et al. (1995) found that a small sheltered catchment (Siksik Creek, 0.83 krn2) received a blowing-snow input equivatent to 25 % of winter snowfall, which more than compensated for its 16% snow loss due to blowing-snow sublimation. Pomeroy et al. (1994) showed that an adjacent larger catchment with terrain more representative of the regional average (Trail Valley Creek. 63 m2), showed no net gain from blowing-snow inputs but rather a loss equivalent to 23 % of winter snowfall due to blowing-snow sublimation. If we presume that the chemical concentrations in freshly deposited snow were equal in both basins, the small catchment would have had a 40% greater winter ion load (per unit area) than the larger catchment.

Subarctic snowmelt occurs in late May or early June. The ion load released to '

streams and soils is the result of 8 to 9 months of wet and dry deposition and various concomitant transformation processes. Historical observations suggest that the annual solute delivery to lakes and streams is dominated by snowmelt in the high Arctic (de March, 1975), whiist in the low Arctic it is most important in years with low rainfall (Welch & Legault, 1986). Climate change models predict relatively large temperature increases (24°C) for high latitudes and also anticipate changes in regional s tom tracks and cyclonic activity (Goodess & Palutikof, 1992). Because of this warming, biophysical models predict a northward advance of the boreal forest into the Subarctic and low .

Arctic (Monserud er al., 1993; Smith et al., 1992). However, the productivity of boreal forests is limited by availability of nitrogen. This limitation extends over much of the northern boreal forest (Boring et al., 1988; Hudson et al., 1994) and is amplified by further growth restrictions due to anthropogenic sulfur deposition at certain treelines (Bucher, 1987). A physically-correct understanding of the processes of snow accumu- lation in northern landscapes is necessary to predict the delivery of snow and major ions to this globally-significant ecotone. It is therefore important to document the (1) spatial patterns of ion load at mesoscales, and (2) relationships to vegetation and terrain which may be used to distribute the ion load using available landscape classifications.

This paper examines the small-scale (10-100 m) and mesoscale (100 m-10 Ian) variability of snow water equivalent, snow chemical concentration, and snow chemical load in the tundra-tai,oa transition zone (200 km north-south) in order to identify the processes and landscape properties that need to be modelled in order to predict the spatial distribution of winter ion accumulation in treeline catchments.

Study site

The study encompasses a 200-km north-south transect spanning the treeline, 2pproxirnately 50 km east of, and parallel to, the Mackenzie Delta, Northwest Territories, Canada (Fig. I). The transect reaches southward from the Arctic Ocean, its Xrthern point being an arctic tundra coastal plain near a prominent pingo (ice-cored

1 94 I. W. Pomeroy er al.

Table 1 Mesoscale sites at which landscape-stratified snow and snow chemistry surveys were conducted. near Inuvk. N.W.T.

- Mesoscale site Latitude Longitude Distance from Dominant Terrain

(N) (w) coast (km) vegetation -

Tundra Lakes 67'37'49' 133'45'36" 198 Spruce, taiga and Low relief muskeg forest and lakes

Havikpak Creek 68'19'12' 133'30'59' 115 Taiga and shrub- Rolling hills and tundra valleys

Trail Valley 68'44'32" 133'29'60' 84 Shrub and open Plateau, hills. and Creek tundra incised valleys

ParsonsLake 69'00'13" 133'35'59' 42 Open tundra Undulating plain and lakes

Whitefish Pingo 6g023'30' 133'32'1 1 ' 0.25 Open tundra Coastal plain with isolated hills

hill) on the Tuktoyaktuk Peninsula. The line passes just east of Inuvik, through a transitional forest-tundra and finishes north of Arctic Red River, in a boreal forest. Long-term snowfall records suggest that snowfall declines sharply with Iatitude in this region, as the 30-year mean annual snowfalls for Inuvik and Tuktoyaktuk are 177 and 86 mm, respectively. These values may underestimate true snowfall (Woo et al., 1983) by up to three-fold, especially in the tundra region, and are not likely to represent snow accumulation because of r'edistribution processes such as blowing snow in open areas and interception of snow in coniferous forests. Snow covers along the transect are uniformly cold and dry during the winter, mean daily high temperatures are below -5°C from October through April and Iows below -30°C for 4 mo. Mean winter wind speeds and hence the frequency of blowing snow are much higher at Tuktoyaktuk (6 m s-') than at Inuvik (2.6 m s"). Straddling the transect are two research basins, Trail Valley Creek and Havlkpak Creek. These basins lie on either side of the treeline and are dominated by tundra and black spruce forest, respectively. Along the transect. landscape-stratified snow surveys and snow chemistry measurements were taken at five "mesoscale sites" which include the two basins. The mesoscale sites are described in Table 1.

Field measurements

Measurements were conducted in late April and early May 1993, severaI weeks before meIt. All measurements were made on landscape-stratified snow courses, established in six prominent landscape classes: (1) open tundra - short grass or lichen tundra with vegetation Iess than 30 cm tall; (2) shrub tundra - alder or willow bush tundra with vegetation from 30 cm to 3 m tall; (3) taiga - open canopy spruce forest of stunted trees, 3-8 m tall; (4) forest - closed canopy spruce forest of taIl trees, 5-15 m tall; (5) valleys - sheltered valley bottoms of open or shrub vegetation; (6) drifts - hillsides with slope gradients greater than 9% (8") and an open upwind

fetch.

Spatial distribution of the snow chemical loud ar the tundra-taiga transition 195

Care was taken to center snow courses in each landscape class in order to minimize,edge effects and to include the characteristic variation of terrain and vegetation in each landscape over the course length. Snow courses were located at each mesoscale site where an example of a particular landscape class could be found.

Leaf Area Index (LAT) measurements were made during snow surveys and in June 1994, before deciduous leaves formed and after all snow had melted from the sites. At least 10 LA1 measurements were made along each snow course, with some repeat series of measurements to eliminate any effects of variable sky conditions. LA1 was measured with a LICOR 2000 Plant Canopy Analyzer which measures scattered light intensity in 5 cones through the vegetation canopy and compares these intensities to a background unobscured-sky reference intensity (Welles & Norman, 1991). From the resulting vesetation transmissivity values, the extinction cefficient of vegetation is calculated and then presuming a random leaf distribution, the LAI. For this late winter case, LAI is defined as the cumulative horizontal area of stems, needles, and branches per unit area of ground and is dimensionless.

Snow water equivalent was sampled using snow depth rods and an ESC-30 density sampler along snow courses established within each landscape class. Snow depth for each course was measured at 25 points, spaced 5 m apart and density at five points selected from the 25 depth points.

Snow for chemical analysis was colIected from three pits in each course seIected amongst points for density measurements. All snow was cold and dry and had not undergone melt. Snow was removed uniformly from each layer using a Teflon scoop and placed in 1-1 HDPE bags, one bag corresponding to one pit.

Laboratory measurements

Snow was kept frozen until required, rapidIy melted, and filtered through 0.2-pm nucleopore filters. The filtrate was kept at a temperature of 4°C and shipped to Saskatoon for ion chromatograph and AAS analysis. Anion analyses were performed using a Dionex 2010i ion chromatograph with a 100-pl injection loop, 0.75 m M NaHC03/1 .5 mM Na2C0, eluent and suppressed conductivity detection. Samples were introduced by Technicon sampler into the loop after a 9 to 1 sample to eluent dilution. A Dionex 4270 Integrator measured peak area and a Linear 100 recorder measured peak height of Dionex concentration traces. Numerous blanks on scoops, bottles, filters, and analytical equipment indicated no measurable contamination of samples. The level of arecision and limits of detection for the ion chromatograph are normally at least an order of magnitude less than the levels measured.

Data analysis

LA1 measurements were averaged for each snow course and then averaged for each landscape class (except drift, which was defined by topography) across the uansect. Snow water equivalent was calculated by assigning densities derived from the ESC-30 .

neasurement to the nearest (or most similar in the case of a vegetation gradient) snow depth and multiplying. Averages and coefficients of variation were calculated frcm the 25 derived snow water equivalent values in each course. Ion Ioad was calcuIatcd by

196 J. W. Pomeroy et al.

mulriplyin~ bulk ion concentration by the snow water equivalent. The range of ion ~53. centrations was quite low in each course, therefore ion loads for each course could L??

confidently determined by multiplying mean concentrations from the three pits by the mean snow water equivalent as determined by the five densities assigned to the adjacent 25 deprhs for each course. Mean concentrations for samples from the mesoscale sites were not weighted by the areal extent of the landscape type where samples were collect- ed. However the same number of samples were collected from each landscape type at each mesoscale site. Thus, although the site sample means are not true "regional means" of concentrations for each mesoscale site, they do provide local ranges of values which are comparable to each other because of the consistent manner of collection. The stronn similarity of values within each mesoscale location suggests that ion concentrations chr: be lumped in this manner to calculate means without prejudicing the analysis.

Concentrations along the latitudinal transect

Mean values and coefficients of variation (CV) for ion concentrations (Cl', SO^^-. NO3? collected from all landscape types are shown for each mesoscale site along the transect in Table 2. The highest concentrations and CVs were found in the more northerly and windswept locations. The increase in concentration with latitude is extreme in the case of a sea-salt derived ion such as C1-, which increases 8-fold in concentration from Tundra Lakes to Whitefish Pingo but still notabIe for so4*- and NO; which increase 2.4- and 1.7-fold, respectively. Two significant environmental transitions occur as one travels northward along this latitudinal gradient; i.e. increasing proximity to sources of sea spray in the Arctic Ocean and increasing frequency of blowing snow events as the treeline is crossed. The effect of these transitions on snow chemistry is not uniform for all chemical species. The input of sea spray to precipitation and to dry deposition primarily enhances C1' concentrations whilst blowing-snow phenomena enhance all concentrations to some degree. Pomeroy et al. (1991) reported similar evidence of preferential scavenging of sea-salt ions by blowing-snow particles and a general ion concentration increase as the blowing-snow particles sublimated. Their study showed that preferential scavenging during transport approximately doubled the concentration of sea-salts in wind-blown snow (in addition to subIimation-derived concentration changes) after one afternoon's blizzard. The concentrations of so4'' increased 20%, in

Table 2 Concentrations in peq 1'' and coefficients of variation (in parentheses) of chrec major ions in snow. Inuvik regioa. spring 1993.

Mesoscale site Cl- NO,' SO:-

Tundra Lakes 6.2 (0.11) 2.6 (0.05) . 4.5 (0.09) Havikpak Cnek 10.5 (0.24) 2.6 (0.10) 5.1 (0.15) Trail Valley Creek 17.2 (0.12) 3.4 (0.06) 6.6 (0.12) Parsons Lake 16.6 (0.26) 2.7 (0.14) 6.7 (0.14) Whitefish Pingo 47.1 (0.21) 4.5 (0.19) 10.8 (0.19)

Spatial dirrribution of the snow chemical load at the tundra-taiga transition 197

proportion to the sublimation loss from blowing snow but the NO,' increased somewhat less than proportionately (Pomeroy er al.. 1991).

The blowing snow model of Porneroy & Gray (1994) was applied using meteorological data collected at the Tuktoyaktuk weather station (wind speed, air temperature, humidity, snow depth, snow fall) to estimate the annual blowing snow sublimation loss over the winter of 1992-1993. The annual snowfall and annual sublimation may be used to calculate enhancement of ion concentrations in wind-blown snow if the ions are presumed to be "conservative" with respect to sublimation, i.e. they do not volatize. This enhancement effect, Ef, is defined as

where L, is the annual load of ion (x) in snow, L, is the annual snowfall (mm snow water equivalent) and Lb is the annual blowing snow sublimation loss (rnm snow water equivalent). Pomeroy & Gray's (1994) model predicts that for tundra with a fetch of 3 km near Tuktoyaktuk in the winter of 1992-1993, 65% of annual snowfall sublimates as blowing snow. Using this result and presuming that (I) parent snowfall concentrations and dry deposition of NO,' and SO,'' are similar across the transect (likely because of level terrain and the remoteness of the region from any sources); (2) blowing snow sublimation is negligible in the forest at the southern edge of the transect; and (3) both species are conservative during sublimation, then blowing snow sublimationshould lead to an Ef value of 2.2 in concentrations from forest to tundra. Such an increase is quite consistent with the 1.7- and-2.4 fold increases in NO3- and SO:', respectively, in the transect from Tundra Lakes to Whitefish Pingo. The latitudinal increase in wind speed and decrease in terrain roughness suggest that the latitudinal increase in SO:' and NO3- concentrations are almost entirely due to. blowing snow sublimation and that the latitudinal increase in C1- concentrations are due to a combination of blowing snow sublimation and enhanced wet and dry deposition to snow because of proximity to the ocean and sea spray and the high incidence of blowing snow.

Snow ion load and vegetation density

Relationships between snow water equivalent, snow ion load, and LA1 as a vegetation density index were developed for the three mesoIocations where all landscape classes were represented. In latitudinal order along the transect these are Tundra Lakes. Havikpak Creek, and Trail Valley Creek (Fig. 2). The LAI values represent measurements from all landscape classes except drift areas. Drift areas are excluded from the vegetation density analysis because the preferential wind deposition is caused more by topography than by vegetation. Three environmental gradients are of note in this comparison: wind speed, snow accumulation, and ion concenuation increm Ia:imdinally for the same landscape type. In all mesoscale locations the regions with low L.11 accumulated the least snow and the least ions. Interestin_ely. though higher LM developed greater ion and snow loads. a linear increase in ion or snow loads with LM

- 1 - "E E -0.6 -- be.--- + ..-- ------- rn . '. C ____-_d----- = 0.4 -- 2 0.2 c -0 0 .

0 0.2 0.4 0.6 0.8 1 Leaf Area lndex

0 0.2 0.4 0.6 0.8 1 Leaf Area lndex

0 0.2 0.4 0.6 0.8 1 Leaf Area lndex

Fig. 2 Ion load and snow water equivdent as functions of leaf area index for Tundra Lakes, Havikpak Creek, and Trail Valley Creek. Note increasing scales for ion load and SWE from top to bottom.

was not consistent and the highest LAI did not necessarily correspond to the highest water equivalent or ion load. The nonlinear relationship between snow accumulation and LA1 may explain why Timoney et at. (1992) found no significant correlation to a proposed linear relationship between snow depth and tree density in the subarctic near Great Slave Lake, N.W.T. A peak in snow and ion load occurs at an LAI between 0.25 and 0.35, corresponding to transitional vegetation which tends to be deciduous and at the leading edge of high roughness areas where wind-blown snow is deposited. This peak is strongest at Trail Valley Creek where the wind speeds are highest. Vegetation along the leading edge receives wind-blown snow and its chemical constituents from adjacent low LA1 areas. The snow in the low LAI areas undergoes scouring, relocation, and sublimation, resulting in low snow retention. The highest LA1 areas are coniferous forests which do not usually receive blowing snow inputs because they are fringed by more open taiga and shrub-tundra. The coniferous forests may lose some snow over the winter due to sublimation of intercepted snow. however snow surveys suggest that this loss is low for the subarctic.

Spatial distribution of the snow chemical bad at the tundra-taiga transition 199

Open Tundra

80

0 W loo 150 200 Distance from Coast (km)

A Shrub-Tundra + 240

- 0 60 loo 150 200

Distance from Coast (bn)

0 50 100 150 Distance from Coast (km)

0 50 100 150 200 Dlstance from Coast (km)

r 3.5 B 3

2.5 2 en c 1.5

u - 0.5 9 0

Fig. 3 Ion load and snow water equivalent as functions of distance from the coasr for open tundra. shrub-tundra, taiga. forest, and drift landscape classes. Note differing scales for ion load and SWE for each landscape class.

- Fomt 160 8

\\\ 120 - \ '-.- .-. -

0 50 loo 150 200 Distance from Coast (bn) .

1Mesoscale rariation in snow ion load

A revizw of Fig. 2 shows that the range of ion loads differs substantially amongst the Tundra Lakes, Havik~ak Creek, and Trail Valley Creek mesoscale locations. The load of ions and snow water equivalent within a landscape type are shown for five landscape classes as a function of distance from the Arctic Ocean coast in Fig. 3. Greater distance from the coast leads to lower sea salt inputs, lower wind speed. warmer temperature, less frequent blowing snow, and increasing domination of land cover by dense vegetation landscapes (shrub-tundra, taiga, and forest). Snowfall is at

a minimum near the coast, reaches a peak at Trail Valley Creek (80 krn south of the coast), and declines somewhat south of that location. The nature of snow and ion redistribution in the landscape result in varying relationships between load and latitude in different landscape classes.

Chloride shows enhanced concentrations near the coast and in more open regions, trends which distort the relationship between CV and snow load. Because sea-salts are readily incorporated in wind-blown snow, ion load declines with increasing distance from the coast. The differential in ion load amongst landscape classes varies dramatically with distance from the coast. At Whitefish Pingo the C1 load in drifts is 3 times greater than that on open tundra, but 45 km inland at Parsons Lake it is 15 times greater and at Tundra Lakes 200 km inland it has returned to being only 4 times greater. Open areas exhibit a steady decline in snow water equivalent (SWE) and more dramatically (except for NO<), ion load with distance from the coast. Snow load halves and C1- load drops &fold in the first 50 km, with a slighter decline as distance increases. The change in SWE is due not primarily to a change in snow depth but in density; lower wind speeds at lower latitudes result in less frequent blowing snow and Iess densification during snow relocation. Sublimation of blowing snow causes a concentration enhancement which is most noticeable near the coast. This concentration enhancement leads to a load enhancement because in windswept, open areas the maximum possible snow depth is largely controlled by the height of vegetation. With a fixed vegetation heizht, regions that experience more blowing snow will deveIop denser snowpacks and higher concentrations of most ions in these snowpacks, leading to higher loads of ion and snow. The exception is NO,' which does not appreciably change in load with mesoscale site, only changing with landscape type. It would appear that a loss of NO;, proponional to the loss of snow due to sublimation, reduces the enhancement effect and hence the mesoscale variation in load.

Shrub tundra receives much of the blowing snow from adjacent open areas. Its peak snow load (and ion load except for C1-) 80 km south of the coast reflects the excellent snow-trapping abilities of relatively tall shrub vegetation in windy environments. Although shrub height increases to the south, snowfall declines, the frequency of blowing snow decreases, and overall snow loads are smaller. Drift areas show extremely high snow and ion loads. Similar to shrub tundra. the highest snow loads in drifts are not found near the coast because the flat terrain there provides limited areas where drifts may form. The size of drifts thus reaches a maximum south of the coast and then declines with increasing distance. The limited transects for taiga and forest landscapes (no examples were found less than 80 krn from the coast) suggest a decline in snowfall south of Trail Valley. Both forest and taiga have relatively small ion loads.

Spatial disrriburion of the snow chemical load ar the tundra-taiga trunsition 20 1

DISCUSSION

The variations in snow and ion load with LAI, landscape type, and mesoscale site demonstrate that both the local land surface factors and the broad-scale influences must be addressed in order to spatially extrapolate measurements of snow quantity and snow chemistry. This can be conceptualized in a simple mathematical model where the load of an element (e.g. snow, ion in snow) to a point in a complex landscape is a result of a two-stage process. In the first stage there are atmospheric fluxes in three dimensions, not necessarily in steady-states, to a parceI of air directly over the landscape surface. The vertical turbulent and energy fluxes in this parcel of air are strongly influenced by the aerodynamic and energy flux characteristics of the underlying surface. The rate of change in the concentration of the element in this parcel of air, aXJat, is a function of nonsteady fluxes in three dimensions, where

and Fdenotes an instantaneous flux (comprised of mean and fluctuating components) in the vertical (w) or two horizontal (x, y) directions. The second stage is a one- dimensional, vertical flux, FL, fkom the parcel of air to the underlying surface under conditions which tend towards a steady-state,

where K is a turbulent diffusivity for Xp. Because equations (2) and (3) are only in steady-state over long time periods it ~s difficult to solve the systems of equations necessary for solution. However, this conceptual model identifies important characteristics of the system with respect to scale. For an air parceI within 1 or 2 m of the surface the horizontal scale necessary for steady state flow is usually the order of tens of meters. Where the horizontal fluxes, F, and Fy are small (low wind speeds, no blowing snow) the system resolves into a classical one-dimensional flux between the atmosphere and the surface. Most snow and chemical deposition models are based on such a flux and require no further information on upwind landscape conditions (outside of the immediate area) nor fluxes. However, when the horizontal fluxes are lase as in windswept regions, then the local landscape flux FL is dependent upon the array of upwind conditions and fluxes. For blowing snow, Tabler (1975) suggests an average snow-particle transport distance of 3 km before complete sublimation, confiied by ?enson (1982) for Arctic Alaska. This finding suggests that the scale for equauon (2) fuxes in wind-blown, open areas is several kilometers. Summing the fluxes over a winter season calculates the annual deposition of snow and ions. Hence, the local spatial configurations of landscapes and fluxes are necessary to calculate the seasonal flux at a point in windswept regions.

The load of snow and ions in a landscape type is therefore not due entirely to the characteristics of that landscape type but to the spatial configtu-ation of the landscape type within the mesoscale landscape. It is therefore errreme& difficult to calculate the snow water equivalent and ion load to a landscape type in a rvindnvept region using on& soint knowledge of the armosphetic deposition rate and the specific aero@numic

202 J. W. Porneroy ef al.

characrensrics of rite landscape type. This multiple-scale effect becomes most pronounced in the higher wind speed environments that undergo wind relocation of snow and more pronounced for ions such as C1- and SO," that are conservative during sublimation or scavenged by blowing snow processes. Therefore, to model snow and ion accumulation in windswept environments, a combination of twodimensional, physically based models of wind transport of snow and snow chemical accumulation, operating in a distributed fashion over the mesoscale will be required. These models can be operated and their results accounted for in existing geographical information syster;: environments. Methods that do not explicitly take into account the regional configuratior, of landscape types must do so implicitly, in an empirical manner. Unfortunately, such empirical models cannot be confidently applied outside of their region of origin.

CONCLUSIONS

Ion loads vary by up to 5-fold in different landscape types within a mesoscale site and up to 18-fold between mesoscale sites, along a transect that spans the arctic treeline. The loads of ions and snow are sensitive to small and mesoscale variation in blowing-snow regime in the following order

C1- > r S W E > > NO3-

Two factors, operating at two scaIes, most strongly affect the load of snow and major geochemical ions in snow at the arctic treeline. The first is the landscape roughness at a small-scale (tens of meters), as parameterized by the leaf area index or by topographic slope. There is a trend for rougher landscapes to receive greater inputs of snow and ions. However, this trend becomes masked and eventually nonlinear where high wind speeds and well-exposed regional landscapes promote relocation of snow. The second factor operates at the mesoscale and is due to frequent blowing snow, causing the maximum snow and ion loads to develop in moderately rough landscapes. In the subarctic these landscapes are "snow sinks, " fringing the low-roughness landscapes from which blowing snow is removed. The mesoscale (roughly 3 km) wind exposure and relocation of snow can therefore strongly affect the small-scale landscape-snow relationship, making determination of snow accumulation and chemistry from point measurements impossible. A combination of twodimensional,' physically based modeIs of wind transport of snow and snow chemical accumulation, operating in a distributed fashion over the mesoscale will be required to predict snow accumulation and snow accumulation chemistry in complex, windswept environments such as the tundra-taiga transition.

Acknowledgments This work was supported by the authors' respective institutions, the NATO Collaborative Grants Programme, the Canadian GEWEX Programme, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Polar Continental Shelf Project, and the Science Institute of the Northwest Territories. The efforts of Cuyler Onclin, William Quinton, Robert Reid, Ken Dion, Natasha Neumam, and Art Dalton in the field and Joni Onclin and Ken Supeene in data reduction and chemical analysis are greatly appreciated.

Sparial distribution of the snow chemical load at the tundra-taiga tramition 203

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