Translator Disclaimer
1 November 2016 Spatiotemporal Characteristics of Seasonal Snow Cover in Northeast Greenland from in Situ Observations
Author Affiliations +
Abstract

In this study, we quantified the spatiotemporal variability and trends in observations of multiple snow characteristics in High Arctic Zackenberg in Northeast Greenland through 18 years. Annual premelt snow-depth observations collected in 2005–2014 along an elevation gradient showed significant differences in snow depth between vegetation types. The seasonal snow cover was characterized by strong interannual variability in the Zackenberg region. Particularly the timing of snow-cover onset and melt, and the annual maximum accumulation, varied up to an order of magnitude between years. Hence, apart from the snow-cover fraction registered annually on 10 June, which exhibits a significant trend of -2.3% per year over the 18-year period, we found little evidence of significant trends in the observed snow-cover characteristics. Moreover, SnowModel results for the Zackenberg region confirmed that the pronounced interannual variability in snow precipitations has persisted in this High Arctic setting since 1979 and may have masked potential temporal trends. In exception, a significant difference in interannual variability of snow-cover onset timing was observed through the period 1997–2014, which in the recent period since 2006 was 7.3 times more variable.

Introduction

Within the last decades, the Arctic terrestrial snow cover has undergone changes, both in thickness and duration. The snow-cover extent (SCE) in the Arctic region has decreased by approximately 20% per decade during 1979–2013 (Blunden and Arndt, 2014). Moreover, the observed SCE reduction rate exceeds the simulated and projected rates of decreasing SCE, which are based on global climate model ensembles (Derksen and Brown, 2012; Blunden and Arndt, 2014). In addition, the timing of snowmelt onset has advanced 2 weeks on average in the pan-Arctic area since the start of the satellite era in 1979 (Tedesco et al., 2009). Snow model results for the terrestrial pan-Arctic region support in general an earlier snow-free date in spring and show that the maximum winter snow-water equivalent (SWE) decreased, the snow-cover onset occurred later in the autumn/early winter, and the length of the snow-covered period was reduced through the period 1979–2009 (Liston and Hiemstra, 2011). However, trends vary from positive to negative in regions across the Arctic area for the period 1979–2009 (Liston and Hiemstra, 2011) and 2001–2014 (Chen et al., 2015). Within North America, Northern Eurasia, and the ice-free area of Greenland, there are regions with opposing trends in, for example, the annual snow precipitation, the timing of onset, and the end of core snow-covered period (Liston and Hiemstra, 2011).

In this study, we present a ground-based long-term time series of snow observations from Zackenberg in the High Arctic region of Greenland. Only a few ground-based studies using long-term snow observations have been conducted in the High Arctic (e.g., Zhang et al., 2000; Bulygina et al., 2011; Dyrrdal et al., 2013; Stuefer et al., 2013). However, these studies are valuable for mapping the diversity in changes and trends in snow variables within the pan-Arctic area. Furthermore, ground-based observations are requested as calibration and validation data sets for model simulations and remote sensing products and the development of such (Derksen et al., 2014; Bokhorst et al., 2016).

Finally, since the terrestrial snow cover is a key variable controlling Arctic ecosystem processes, these time series of snow observations are valuable in explaining changes seen in both biotic and abiotic components of the Arctic ecosystems (e.g., Jones, 1999; Post et al., 2009; Brooks et al., 2011; Callaghan et al., 2011; Cooper, 2014). The spatial distribution of snow depth is primarily driven by a combination of the dominant wind direction during and after snowfall (Liston and Sturm, 1998; Winstral et al., 2002), the topographic relief and slope orientation (Schirmer et al., 2011), and the vegetation cover, which traps wind-transported snow (Sturm et al., 2001). In addition, during the melt season, the snow-depth distribution is controlled by the availability of melt energy, which is governed by terrain elevation, slope, and aspect (Clark et al., 2011). Especially during the snow-covered season, when insulating properties of the snow cover (Goodrich, 1982; Sturm et al., 1997; Liston et al., 2002) provide stable thermal conditions in the below-snow environment, including the vegetation cover and soil (Schimel et al., 2004; Zhang, 2005; Bokhorst et al., 2011; Johansson et al., 2013). The soil thermal conditions moreover drive the microbial activity, the respiration rate, and the amount of soil organic carbon produced during winter (Elberling, 2007), and it controls the active-layer depth (Westermann et al., 2015). Hence, the snow-depth evolution through the autumn and winter governs the amount and timing of plant-available nutrients at the end of the winter and the following spring in tundra ecosystems (Schimel et al., 2004; Buckeridge and Grogan, 2008). The nutrient availability along with the meltwater released from the snowpack in spring (Jones, 1999) regulate, in turn, the vegetation growth far into the growing season (Blankinship et al., 2014) and thus the main food source for herbivores—for example, the muskoxen (Ovibos moschatus) (Kristensen et al., 2011; Schmidt et al., 2015; Mosbacher et al., 2016). Hence, the snow conditions in the preceding winter may have legacy effects on the following growing season(s), which makes snow observations essential in the understanding of ecosystem functions and feedbacks (Hollesen et al., 2015). Ultimately, due to the governing role of the snow, any observed changes, trends, and/or altered variability in snow conditions, may explain observed (inter-)seasonal effects, changes, and variability in the biotic components of the ecosystem. To identify significant trends and variability in ground-based snow observations, we examine an 18-year record comprising continuous observations of multiple, both point and spatially distributed, seasonal snow metrics collected as part of the Greenland Ecosystem Monitoring program ( http://www.g-e-m.dk). We quantify the spatial variability, the temporal trends, and interannual as well as seasonal variation in a suite of ecologically relevant snow variables, which are all observed during the period 1997 through 2014. Additionally, we investigate whether the snow variables showed a change in interannual variability through the study period. Finally, modeled winter snow amounts for the period 1979–2014 allow us to explore whether the observed temporal variability and trends in the past 18 years of snow observations are unique in comparison to previous decades.

Study Area

The Zackenberg study area is located in the ice-free coastal part of Northeast Greenland (74°27′N, 20°34′W) (Fig. 1). The area has been covered by the Greenland Ice Sheet several times through geological history (Bennike et al., 2008). About 10,000 years ago, the lowland surrounding Zackenberg became ice-free, and today the ice sheet margin is located 70 km west of Zackenberg. The landscape in and surrounding Zackenberg is highly heterogeneous in terms of bedrock and sediment type, and the topography is characterized by deep fjords and valleys with elevations varying from sea level to mountain peaks reaching 1000–1400 meters above sea level (m a.s.l.). Zackenberg is located within the continuous permafrost zone, and the landscape development is thus dominated by periglacial processes (Westermann et al., 2015).

The Zackenberg study area is situated in the High Arctic zone (Bliss and Matveyeva, 1992) with a tundra climate (Kottek et al., 2006), which is mainly governed by the proximity to the Greenland Ice Sheet to the west and the Greenland Sea to the east. For the period 1997 through 2014, the monthly mean temperatures were between -19.8 °C (February) and 6.3 °C (July), and the yearly average temperature was -9.0 °C. There is polar night during 89 days and polar day during 106 days (refraction produces this asymmetry). The predominant wind direction is north during winter and southeast during summer, the latter mainly being triggered by the land-sea interaction. The annual average precipitation (snow and rain) for the hydrological years in Zackenberg (defined here to be 1 September to 31 August and denoted, e.g., “2012/2013”) was 367 mm (1996–2014) varying between 222 mm (2003/2004) and 547 mm (2013/2014).

FIGURE 1.

Zackenberg study area (QuickBird image from 31 May 2012) showing locations of climate stations C1, M2, and M3 (orange squares); ZERO-line transect (black line); the two regions Valley floor and Hill slope (blue line); the region for annual snow-cover estimations on 10 June (red line); and Zackenberg Research Station, ZAC (yellow triangle).

f01_653.jpg

Methods

Snow Quantities and Snow-Cover Timing

The snow variables observed in Zackenberg include snow cover (i.e., presence or absence), snow depth and snow density, hence snow-water equivalent (SWE), and snowfall events. The metadata and sampling methods for these variables are described in Table 1, and the data are given in Tables 2 and 3. All observations are made by the climate and geophysical monitoring programs, ClimateBasis and GeoBasis, respectively. The spatial analysis of snow cover and topographic features was performed for two separate regions, Valley floor and Hill slope, where the snow-cover monitoring was conducted by GeoBasis (Fig. 1). The sampling methods were kept constant throughout the time series to allow for interannual comparison (Sigsgaard et al., 2014). The statistical methods, which are used for the quantification of the temporal trends and interannual variability, are described in the section Statistical Analysis.

TABLE 1

Snow observations collected in Zackenberg by GeoBasis and ClimateBasis. All station locations are indicated in Figure 1. Data collection procedures are described by Sigsgaard et al. (2014). All data are available from  http://www.zackenberg.dk, and data descriptions are given in ZERO Annual Reports 1–18 ( http://www.zackenberg.dk/publications/annual-reports/).

t01_653.gif

To quantify the changes, trends, and temporal variability in the observed snow variables, we have calculated several snow metrics. Snowfall events (large snowfall events) were defined as times with a snow-depth increase (at climate station C1, Fig. 1) of more than 0.05 m (0.20 m) from one day to the next. In this way, snowfall events also include the large snowfall events. The thresholds (0.05 m and 0.20 m) were set according to the local snow conditions, where the snow depth varied up to ±0.05 m from day to day because blowing snow was deposited and eroded from the snow surface and not because of snowfall. The snow-depth threshold for determining whether the ground was continuously snow-covered or snow-free was defined to be 0.10 m. This height equaled the maximum vegetation height and microtopographic relief in the Valley floor region surrounding the snow-depth sensor at C1 (Fig. 1). Further, the snow-cover fraction for the Valley floor region (described in the section Spatial Distribution of Snow) of 90%–100% coincided in time with snow depths above 0.10 m being measured at the automated snow-depth sensor at C1. Snow-cover onset is, herein, defined as the first day of a period, where the snow depth at C1 is continuously above 0.10 m. Snow-cover end is the last day in this period—that is, the day when the snow depth decreases below 0.10 m. The core snow-covered period corresponds to the period between snow-cover onset and end and is the longest continuous period in a winter when the snow depth is larger than 0.10 m. All metrics are calculated for the longest snow-depth time series from C1 (Table 1) in the observational data set. These point measurements are considered representative for the Valley floor region (Fig. 1), because it is a flat and homogeneous area, and also in terms of vegetation type and cover.

TABLE 2

Snow variables observed in Zackenberg from 1997 through 2014.

t02_653.gif

Spatial Distribution of Snow

To describe the spatial distribution of snow in the landscape in Zackenberg, we investigated the snow-depth temporal and spatial variability along an elevation gradient on a mountain slope (ZERO-line) and the spatial snow-cover depletion in the two confined regions, Valley floor and Hill slope. The monitoring transect, ZERO-line (Fredskild and Mogensen, 1997; Meltofte et al., 2008a), was established in 1992 on a southwest-facing slope covering an elevation gradient from sea level to 420 m a.s.l. (Fig. 1). In order to get an estimate of the snow accumulation during the preceding winter, snow-depth observations have been annually collected since 2005 along ZERO-line at the end of winter or early spring before substantial melting had occurred in the snowpack. One premelt snow-depth transect per year was included in the analysis for the period 2005–2014. To verify that no or only limited melting had occurred previous to the snow-depth measurements, we defined two requirements: (1) the positive degree-day (PDD) sum during spring in Zackenberg should be less than 5 PDDs, and (2) the cumulated snow-depth decrease should be less than 0.07 m from the day of maximum snow depth to the day when the ZERO-line observations were made. The 0.07 m threshold corresponded to our observed maximum premelt snowpack settling. For these estimates, we used air temperature and snow-depth observations recorded at station C1 (Table 1, Fig. 1). All years in the period 2005–2014 met these requirements.

TABLE 3

Timing and duration of the snowmelt periods in the Valley floor region and Hill slope region.

t03_653.gif

To investigate variations in the snow distribution amongst vegetation types and along the altitudinal gradient, we used the ZERO-line end-of-winter snow depths (Table 1). For each snow-depth measurement point along the ZERO-line in the period 2005–2014, we extracted the corresponding vegetation type from a vegetation map by Elberling et al. (2008), including Dryas heath, fell-field, fen, Cassiope heath, grassland, and Salix snowbed. Likewise, we extracted the elevation from a digital elevation model (DEM) with a 10-m horizontal resolution (originally rescaled from a DEM with 0.10-m horizontal and vertical resolution based on digital photos taken from drone flights over Zackenberg Valley in summer 2012). This 10-m resolution adequately described the Zackenberg area landscape features.

The temporal evolution of the snow-cover fractions for the Valley floor and Hill slope regions was estimated during the snowmelt season, to derive the timing of the spatially distributed spring snowmelt and to investigate its change and variability through the study period. For this, we used digital photos, which were daily acquired from April through August by an automated camera. These photos were geo-referenced and orthorectified by applying the method of Buus-Hinkler et al. (2006) and also used by Mernild et al. (2007), and subsequently we performed a snow-classification in the image-processing program ENVI ( http://www.exelisvis.com). Similarly, the snow-cover fraction was estimated on 10 June within a region covering 47 km2 of the Zackenberg Valley and the eastern mountain slopes from sea level to 880 m a.s.l. (see region marked in red in Fig. 1). The annual 10 June snow-cover fraction was referred to as “spring snow cover.” Previous studies have demonstrated that this date can be linked to a range of ecological processes in the tundra ecosystem in Zackenberg (e.g., Forchhammer et al., 2005; Schmidt et al., 2006; Meltofte et al., 2008b; Pellissier et al., 2013). The timing of 80%, 50%, and 20% snow cover marked the onset, middle, and end of the core snowmelt season, respectively, for the regions Valley floor and Hill slope (Fig. 1). Hence, the duration of the core snowmelt period was defined as the difference in days between 80% and 20% snow cover.

In order to obtain a measure of the topographic variability that may influence the spatial snow distribution within each region, the mean terrain slope was calculated using the ArcMap GIS 10.1 “Spatial Analyst Surface Slope” tool and is defined as the average of slopes for all grid cells within a region. The terrain ruggedness (TR) was estimated using a method by Riley et al. (1999) and the GIS terrain analysis tool “Spatial Analyst Neighborhood Focal Statistics.” TR is the summed change in elevation between a grid cell and the eight-cell neighborhood (Riley et al., 1999). The mean TR is the average TR of all grid cells within a defined region. Both the terrain slope and TR analysis was conducted on the 10-m horizontal resolution DEM, and the terrain slope was calculated over areas of 100 m by 100 m.

SnowModel 1979–2014

In order to investigate whether the observed temporal variability and trends in the past 18 years of snow observations have changed with respect to the previous decades, we used modeled SWE to extend the observational time series. The spatially distributed snow modeling tools, MicroMet (Liston and Elder, 2006a) and SnowModel (Liston and Elder, 2006b), were applied in the Zackenberg Valley using reanalysis data from ERA-Interim (Dee et al., 2011). This model setup, including a precipitation correction by assimilating observed SWE using SnowAssim (Liston and Hiemstra, 2008) for a terrestrial Greenland domain, is described in detail by Pedersen et al. (2015). For the period 2004–2014, modeled SWE was extracted from the grid cell corresponding to the location of the ground observations of SWE from snow pits dug in the end of winter (within the Valley floor region in all years) and at the same date as the ground observations were made. We compared the variability of the observed and modeled end-of-winter SWE to validate the ability of SnowModel to reproduce the interannual variability. To quantify if the variability in annual snow amounts had changed through the period 1979–2014, we compared the temporal variability in modeled end-of-winter SWE in both 5-year and 11-year periods within the 35-year modeled time series.

Statistical Analysis

For the trend analysis we used linear regression (ordinary least squares) between a given variable and time in order to quantify the direction of the trend (positive or negative) as well as the statistical significance of the trend (different from 0.0, i.e., p < 0.05). Furthermore, linear regression was also used for finding relationships between snow characteristics. Linear relationships between snowmelt duration in the two regions were quantified with correlation coefficients (r) as well as the covariance between annual maximum snow depths observed at the three Zackenberg meteorological stations (Table 1). F-tests were used to evaluate whether the variances of a variable (e.g., timing of onset or end of snow cover) in two periods were significantly different. We used an ANOVA to examine if snow depth varied among vegetation types and with elevation. Snow-depth data were logarithmic transformed—that is, log(snow depth + 0.01m)—to stabilize the variances to allow the application of the ANOVA. To account for the interannual variation in snow depth, we included the “year” as an explanatory variable in the model. As post hoc test we used Tukey's Honest Significant Differences for multiple comparisons of means (p < 0.01) to identify for which vegetation-types the mean snow depth were significantly different at a 95% confidence level. We used the software R ( http://www.r-project.org/) for the statistical analysis.

Results

Snow Quantities and Temporal Variation

Snow-Covered Period

The 17 winters of snow depth from C1 in Zackenberg showed variable duration of the core snow-covered period (Fig. 2). The maximum snow-cover duration of 265 days was observed in winter 2009/2010 and the minimum duration was observed in winter 2012/2013, where the snow depth was above 0.10 m for only 74 consecutive days. In 2008/2009, the second shortest snow-cover duration of 85 days was registered (Figs. 2 and 3). Snow-poor winters (e.g., 2008/2009 and 2012/2013) had several shorter periods (2–17 days) with snow-covered ground before or after the core snow-covered period. The onset of the snow-covered period was also highly variable, ranging between 24 September and 5 March, through the 17-year period and thus contributed to the high interannual variability in the snow-cover duration (Fig. 3, Table 2). However, the end of the snow-covered period (snow-cover end) varied less—that is, between 16 May (2009) and 3 July (1999). The variance in timing of snow-cover onset in the early period (1997/1998–2005/2006) was significantly different (F = 0.124, p < 0.001) from the variance in the most recent period (2006/2007–2013/2014), and the variance was 7.3 times larger in the latter. This difference was not found for snow-cover end (F = 0.274, p = 0.090) between the same two periods. Likewise, the variances in the timing of 50% snow cover during spring in the Valley floor and Hill slope areas were not significantly different in the two periods 1997/1998–2005/2006 and 2006/2007–2013/2014 (F = 0.730, p = 0.664 and F = 1.914, p = 0.408, respectively). Additionally, the variance in snow-cover onset was approximately an order of magnitude (ratio = 8.7) higher than the variance in snow-cover end during the 17 winters. A trend analysis of the snow-cover timing through the period 1998–2014 showed no significant trends in snow-cover onset, snow-cover end, snow-cover duration, or timing of 50% snow cover (Table 4). However, the timing of 50% snow cover in Valley floor was tightly linked with snow-cover duration (R2 = 0.84, F = 60.87, p < 0.001) (Fig. 3, Tables 2 and 3).

Snow Depth

Across years, the 10% highest snow depth (above 90th percentile for each winter) occurred between February and late May. No temporal trends were observed in either the mean snow depth or the annual maximum snow depth or the timing of the latter through the study period (Table 4). The annual maximum snow depth had a pronounced interannual variation ranging from 0.13 m in winter 2012/2013 to 1.33 m in 2001/2002 (Fig. 2). Measured maximum snow depth showed an interannual covariance in the period from 2003 through 2014 between C1 and M3 (covariance (C1, M3) = 0.12, t = 2.533, p = 0.035, r = 0.667), but no correlation with M2 (data not shown). The highest annual maximum snow depth (2.53 m in 2013/2014) was found at M2, which is installed in a snowdrift, and the lowest (0.09 m in 2012/2013) at M3, installed on a southwest-facing slope at 410 m a.s.l. (Fig. 1, Table 1).

Snow Density and Snow-Water Equivalent

The snow densities observed during the period 2004–2014 ranged between 173 kg m-3 (September 2009) and 685 kg m-3 (June 2005) and the mean density was 398 kg m-3 ± 104 kg m-3, which compared with the bulk density of 380 kg m-3 for the tundra snow-cover class defined by Sturm et al. (1995). The mean monthly snow density showed an increase through the snow-covered season from September through June. The densities in June were in some years collected from melting snowpacks that may have contained liquid and/or refrozen meltwater causing the density increase in June (Fig. 4). SWE, estimated from all available bulk density observations and corresponding snow depths, had a median of 0.22 m of water equivalent (w.e.) in the period 2004–2014, and the maximum SWE observed in the period was 2.13 m w.e. in a 5-m-deep snowdrift (11 April 2008).

Snowfall Events

Snowfall events occurred from September through May and totaled 150 snowfall events in 1997–2014 with the highest frequency (36) in January (Fig. 5, total given without parentheses). The highest number of annual snowfall events (16) occurred during the winter 2005/2006, whereas the winters 2001/2002 and 2007/2008 both included 15 snowfall events. Included in the 150 snowfall events were 17 large snowfall events (Fig. 5, totals given in parentheses). The highest frequency of large snowfall events (4) was found in winter 1998/1999, and over the years the month of February had the highest frequency of large snowfall events. There were no significant temporal trends in the number of all snowfall events or large snowfall events through the study period (Table 4).

FIGURE 2.

Daily snow depth (m) measured at C1 in the hydrological year (1 September–31 August) for the period 1 September 1997 through 31 August 2014. The gray shaded area marks the core snow-covered period from snow-cover onset (snow depth at C1 is above 0.10 m) to snow-cover end/snowmelt (snow depth at C1 is below 0.10 m), that is, the timing of the longest continuous period with snow depth above 0.10 m for each hydrological year.

f02_653.jpg

FIGURE 3.

Snow-cover fraction estimated from georeferenced, orthorectified, and snow-classified photos taken at 10 June in years 1997–2014 (black points). Linear fit (dashed black regression line) statistics: slope = -2.3, R2 = 0.27, F = 5.803, p = 0.028. Timing of 50% snow cover in Valley floor region, estimated from georeferenced, orthorectified, and snow-classified photos in years 1997–2014 (gray points). Snow-cover duration (days) for each hydrological year defined as the length of the period between snow-cover onset and end, where snow depth was consistently above 0.10 m at C1 (red points).

f03_653.jpg

FIGURE 4.

Snow bulk density (kg m-3) collected in 2004–2014 within 5–10 m distance of the three meteorological stations: C1 on the Valley floor, M2 in a snowdrift, and M3 on a southwest-facing mountain slope. The total number of observations was 260, and most densities (91) were collected in May. No density observations were collected in November–February because the Zackenberg Research Station is unmanned during these months. The whiskers mark the data ranging between 1st quartile - 1.5 × IQR (the interquartile range) and 3rd quartile + 1.5 × IQR.

f04_653.jpg

Spring Snow Cover

The 10 June snow cover showed a pronounced interannual variation through the 18-year period ranging between 3% (7 June 2013) and 92% (10 June 1999) (Fig. 3). Furthermore, the 10 June snow-cover fraction showed a statistically significant decreasing trend and decreased on average 50% from 1997 to 2014 (R2 = 0.27, F = 5.803, p = 0.028, Table 4, Fig. 3). There was no significant difference in the variance between the periods prior to and after 2006. However, the 10 June snow-cover time series varied up to 24 percentage points in standard deviation throughout the period.

TABLE 4

Statistical details for temporal trends (significant trend at the 95% confidence level in bold) for snow variables observed in Zackenberg, Northeast Greenland.

t04_653.gif

Spatial Distribution of Snow

Snow-Depth Variability with Elevation and Vegetation Type

The mean snow depth in the 250-m distance intervals along the ZERO-line ranged between 0.3 m and 0.9 m for the years 2005–2014 (black points in Fig. 6, part a), and the maximum snow-depth point measurement was 3.5 m in 2012 at the lowest elevations (0–50 m a.s.l.) in a snowdrift near the M2 climate station (Fig. 1). The maximum snow depths of the ZERO-line time series were observed in the end of the snow-rich winter 2011/2012—that is, in spring 2012 (red points in Fig. 6, part a)—while the minimum snow depths were observed in spring 2009 along the transect (blue points in Fig. 6, part a). The snow-depth observations along ZERO-line (Fig. 1) showed the highest snow depth in the lowland (0–150 m a.s.l., 0–4 km in distance from the transect starting point) with varying slopes of smaller hills and depressions, where snow accumulated (Fig. 6, parts a and c). The lower snow depths were found at higher elevation (150–300 m a.s.l. and 4–5 km distance), where steeper slopes (~15%) and outcrops are blown free of snow. At elevation 300–350 m a.s.l., we found locally higher snow depths potentially due to snowdrifts building up on the lee-side of the edge of a relatively flat, wind-blown plateau at approximately 350 m a.s.l., where the slope decreased. There was no significant temporal trend in the annual variance in snow depth observations through the period 2005–2014 (p > 0.05).

FIGURE 5.

The monthly and annual sums of identified snowfall events (>0.05 m snow-depth increase per day, black points) and their distribution across the period September 1997 through August 2014. Large snowfall events (>0.20 m snow-depth increase per day) are marked with red points (large red point = 2 and small red point = 1 large snowfall event). In the margin, annual and monthly sums of snowfall events are given with the number of large snowfall events in parentheses.

f05_653.jpg

Across the years, snow depth exhibited a consistent pattern of statistically significant different snow depths among vegetation types (p < 0.01) (Fig. 6, part b) and altitude (p < 0.01). The effect of “year” was also significant (p < 0.01), reflecting the interannual variation in the amounts of snow precipitation. We identified the vegetation types that had significantly different mean snow depth from other vegetation types (p < 0.01) (Fig. 6, part d).

Snowmelt and Snow-cover Depletion

The Valley floor and Hill slope, located in elevation levels at 0–50 m and 150–300 m a.s.l., respectively, were characterized by different topographic relief with a mean terrain ruggedness of 8 ± 5 m and 42 ± 8 m, and a mean terrain slope of 3.3% ± 3.7% and 14.3% ± 1.6%, respectively. Snow-cover depletion curves derived from the automatic cameras showed a difference in snowmelt timing and depletion rate between the two regions (Figs. 7 and 8). The timing of 80% and 20% snow cover was derived for each year-specific depletion curve for each region (Fig. 8, Table 3). The duration of the snowmelt period (80%–20% snow cover) lasted 3–13 days in the Valley floor and 11–47 days in the Hill slope. In all years, the Valley floor had the shortest snowmelt period of the two regions (Fig. 8). The snowmelt start (80% snow cover) occurred between 19 May and 30 June in the two regions (Figs. 7 and 8). The timing of snowmelt end (20% snow cover) occurred between 30 May and 6 July in the Valley floor and 30 May and 16 August in the Hill slope—that is, the snowmelt end occurred later in the Hill slope than in the Valley floor. Hence, the timing of snowmelt end varied less between years in the Valley floor than in the Hill slope region. We found no significant trends in the duration of snowmelt period for the two regions from 1998 through 2014 (Table 4). Episodic snowmelt events, when rapid snowmelt occurs during foehn wind events, are identified in other regions of ice-free Greenland (Pedersen et al., 2015). However, no such events have been identified in Zackenberg using either local meteorological station data or reanalysis data (Pedersen et al., 2015).

Modeled SWE 1979–2014

The modeled and observed end-of-winter SWE during the period 2004–2014 showed strong correspondence (Fig. 9, R2 = 0.82, F = 37.51, p < 0.001, intercept = -0.02, slope = 1.03), and the interannual variances for the two data sets were not significantly different in either 5-year or 11-year intervals (F = 0.85–1.80, p > 0.05). Hence, the SnowModel reproduced the temporal variability in the snow conditions in Zackenberg and was a valid tool to model snow variables back in time. The variances in modeled end-of-winter SWE in 5-year periods (1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, and 2010–2014) and 11-year periods (1980–1990, 1991–2001, and 2002–2014) did not differ significantly (F = 0.10–5.69, p > 0.05). The modeled end-of-winter SWE showed no linear trend through the 35-year time series (R2 < 0.01, p > 0.05, slope = 0.001), but the interannual variability was as variable as in the recent 11 years (2004–2014).

FIGURE 6.

(a) Mean snow depth (m) (error bars are error on the mean) aggregated per 250 m along the ZERO-line transect in distance (km) from the starting point for all observations in the years 2005–2014 (black points), a snow-rich year 2011/2012 (red points), and a snow-poor year 2008/2009 (blue points). Gray shading: all snow-depth observations for all years excluding the 10% lowest and 10% highest values. (b) Mean snow depth (m) (error bars are error on the mean) for the period 2005–2014 for the six vegetation types observed along ZERO-line. (c) ZERO-line elevation (m above sea level) and slope (%) with distance (m) from the starting point of the transect (Fig. 1). (d) Matrix presenting results from Tukey's Honest Significant Differences for multiple comparisons of mean snow depths in six vegetation types. Red circles mark significantly different (p < 0.01) vegetation types in the pairwise comparison of mean snow depths.

f06_653.jpg

Discussion

In the present study, we found few temporal trends but high interannual variability in a suite of snow variables in the High Arctic ecosystem in Zackenberg. The spring snow cover decreased significantly through the period 1997–2014, indicating an increasingly patchy snow cover in the beginning of June. In addition, the spring snow cover on 10 June showed large interannual variation from 3% to 92% in the Valley floor region. The amount of snow—that is, the annual maximum snow depth—showed no significant temporal trend but a pronounced interannual variation up to an order of magnitude between years in Zackenberg. This interannual variation was particularly related to the variable number of snowfall events per winter, ranging between 0 and 16 events through the period 1997–2014, and occurring throughout the winter season from September through May. Moreover, the highly variable number of snowfall events and variable timing of snow-cover onset, which ranged over a period of approximately 162 days (~5 months) caused the durations of the snow-covered period to range from 74 to 265 days. Such variable duration of the snow-covered period effectively led to a relatively prolonged and shortened snow-free period from 1997 through 2014. In Svalbard, the onset of snowmelt shows large interannual variation (Rotschky et al., 2011), which can cause similar fluctuations in the length of snow-free period. In Zackenberg, where the lemming, a key species in the Arctic food web, has been monitored since 1996 (Meltofte and Berg, 2006), the predation pressure (by Arctic fox, stoat, snowy owl, and long-tailed skua) is high (Schmidt et al., 2008; Barraquand et al., 2014). Since the snow cover acts as protection and limits the foraging on lemmings for the local predators during winter, the lemming population size is sensitive to the length of the snow-free period and the variation between prolonged and shortened snow-free periods. Furthermore, the increased variability in the snow-cover onset in the recent years, may have caused the disappearance of the lemming population cycles in Zackenberg (Gilg et al., 2009; Schmidt et al., 2012) and hence affected the food web as a whole in this Arctic ecosystem.

FIGURE 7.

Snow-cover fraction per day during the snowmelt season (gray circles) for the two regions, Valley floor (top) and Hill slope (bottom) in the years 1998–2014. Snow-cover fractions in the snow-poor spring 2013 (red circles) and the snow-rich spring 1999 (blue circles). Arrows indicate the range in timing of 50% snow cover.

f07_653.jpg

However, despite years with contrasting snow conditions within the 11-year ZERO-line time series, with 2011/2012 being a snow-rich winter and 2008/2009 being a snow-poor winter, the relative spatial distribution of snow depth was similar from year to year (Fig. 6, part a), and we found no temporal trend in the variability of the transect snow depths through the observational period. The spatial pattern of relatively deeper snow and shallow snow packs along ZERO-line persisted from year to year given that the snow (re-)distribution during accumulation and erosion by wind is mainly controlled by the topography (Schirmer et al., 2011) and the spatial variability in slope. More specifically, there is a clear correspondence between areas with slope increase or decrease (i.e., a change in elevation; Fig. 6, part c) and mean snow depth (Fig. 6, part a). Hence in these areas, where wind-transported snow will accumulate on the lee-side slope of hills, the snow depth is found to be controlled by topography. The predominant wind direction during winter was from north in Zackenberg (observed at M3 and C1) and showed no interannual variation during the observation period. A similar persistent pattern in snow-depth distributions is found in annually repeated snow-depth transects in, for example, Alaska, where it is primarily driven by the topographic relief but also the vegetation height (0–100 cm) and stand structure (Sturm and Wagner, 2010). However, for Zackenberg, located in the High Arctic climate zone where the vegetation cover is sparse and the vegetation height does not exceed 20 cm (Bay, 1998), it is most likely that the persistent pattern of the snow-depth distribution along the ZERO-line mainly controls the vegetation growth conditions and thus the spatial distribution of the vegetation types and not vice versa. This linkage was supported by the fact that the observed snow depths in the six vegetation types along the ZERO-line were significantly different and that vegetation types characteristic of wet and dry (i.e., snow-rich and snow-poor) environments could be distinguished from the snow depth data. The deepest snowpack was found in snowbeds dominated by Salix arctica (Fig. 6, part b), and the mean snow depth herein was consistently different from the mean snow depth in all other vegetation types across the 10 years (Fig. 6, part d). This may be expected since snowbed vegetation develop in moist environments, preferably down-slope from perennial snowdrifts (Björk and Molau, 2007). However, the mean snow depths in Cassiope heath, Fen, and Grassland did not differ significantly from each other, whereas both Dryas heath and Fell-field vegetation, which occur in more snow-poor areas (Fig. 6, part b), had a significantly different snow depth than observed for the remaining four vegetation types (Fig. 6, part d).

FIGURE 8.

Timing and duration of the snowmelt period between 80% (left end of lines) and 20% (right end of lines) snow cover for two regions, Valley floor (black) and Hill slope (gray) in Zackenberg in 1998–2014. No data was available for Valley floor in 1998.

f08_653.jpg

FIGURE 9.

Modeled end-of-winter snow-water equivalent (SWE, m water equivalent [m w.e.]) from SnowModel in the period 1979 through 2014 compared with observed end-of-winter SWE (m w.e.) from snow pits dug in the period 26 March–6 June in 2004–2014 in Valley floor region, Zackenberg. Subplot: linear regression between observed and modeled end-of-winter SWE in 2004–2014 (no available SWE observation in 2009). Linear fit statistics: R2 = 0.82, F = 37.51, p < 0.001, intercept = -0.02, slope = 1.03, n = 10.

f09_653.jpg

Despite the larger snow depth in the lowland than at higher altitude on the slope, seen on the ZERO-line (Fig. 6, part a), the duration of the snowmelt period was more than 1.5 times longer on the Hill slope than in the Valley floor. The snowmelt started on average at the same time in the two regions (Figs. 7 and 8), which is controlled by the time when ambient temperature rises above freezing (Liston, 1995; Liston and Hall, 1995; Clow, 2009). However, the Hill slope snowmelt almost consistently ended later than in the Valley floor (Figs. 7 and 8). This extended snowmelt season on the Hill slope may be explained by the snowdrifts that built up in the more rough terrain on the slope, which eventually created a larger spatial variability in snow depth, resulting in a less steep snow-cover depletion curve than for the Valley floor (Clark et al., 2011). These snowdrifts take a longer time to melt away, hence postponing the snowmelt end until July/August. In Zackenberg snowdrifts persisted throughout the summer in some snow-rich years—for example, 1999 and 2014 (unpublished observations from automatic digital camera photos in summer 1999 and 2014). This may explain the higher interannual standard deviation in the length of the snowmelt period at the Hill slope than at the Valley floor (Table 4), since it is dependent on the presence and absence of snowdrifts and their extent and thickness from year to year; that is, the timing of snowmelt (20% snow cover) is primarily dependent on the maximum snow depth (R2 = 0.76, F = 42.62, p < 0.001). Furthermore, we identified that the 80% snow cover occurred on average on 10 June in both regions. This may highlight that currently 10 June is a relevant indicator for spring snow cover since it marks the beginning of the snowmelt season. However, in a warming Arctic, when snow is predicted to melt earlier in the spring than currently observed, we could expect a change in this date (e.g., Barnett et al., 2005).

Modeled end-of-winter SWE lacked significant temporal trends, and the pronounced interannual variation in end-of-winter SWE in the recent years (2004–2014) did not differ from the interannual variation in end-of-winter SWE in the periods 1980–1990 and 1991–2001. SWE was directly linked to winter precipitation amounts, and therefore a main driver for the interannual variation in the snow variables presented in this study (snow depth, spring snow cover, and indirectly timing of snowmelt). Hence, the magnitude of interannual variation in these snow variables has likely remained unchanged at least since 1979. Additionally, the interannual variation seen the last decade, including the particularly dry winter 2012/2013, did not appear unusual in the 35-year time series. The finding of no change in temporal variability through the 35-year period was supported by the fact that we found no significant difference between snow variables in recent years (2006/2007–2013/2014) and the early years (1997/1998–2005/2006), except for the timing of snow-cover onset, which in the recent period was 7.3 times more variable. This increased temporal variability may point to a change in the autumn weather conditions, when the snow cover is established.

Conclusions

This study presented and discussed interannual variability and trends in a suite of snow variables in the High Arctic environment of Zackenberg in Northeast Greenland between 1979 and 2014. In the observation period (1997–2014), apart from 10 June snow-cover fraction, no presented snow variables showed significant temporal trends across the 18-year time series. The observed pronounced interannual variability mainly masked any potential trends. The distribution of end-of-winter snow depth observed in the ZERO-line transect was significantly affected by elevation and showed a significant difference in snow depth between the present vegetation types. SnowModel reproduced the temporal variability in the snow conditions in Zackenberg. Extending the time series of end-of-winter SWE to 1979 using SnowModel showed that the pronounced interannual variability in snow amounts observed in the recent decade, also was present over the last several decades back to 1979.

We have left the detailed explanation of the interactions between the abiotic and biotic components of this ecosystem to future studies. Likewise, we acknowledge that the temporal variability in the time series of snow observations presented herein possibly originate from changes in large-scale patterns and trends in, for example, Arctic synoptic-scale circulation patterns, the Greenland Ice Sheet—related weather patterns, and/or the near-shore sea-ice extent and concentration in the Greenland Sea. However, it is beyond the scope of this paper to explain the effect of these large-scale patterns and trends on our results. Hence, further research into large-scale circulation patterns' effect on local-scale snowfall events and timing of snowmelt is required in order to explain the interannual variation observed in Zackenberg.

Acknowledgments

We owe enormous gratitude to all GeoBasis field assistants, who have collected the snow observations in the Zackenberg Valley during the period 1997–2014. We wish to thank the logistics team at Zackenberg Research Station, Aarhus University, for their support and flexibility in the “shoulder-seasons.” Thank you to two anonymous reviewers and R. De Troch for valuable comments. Data from the Greenland Ecosystem Monitoring Program ClimateBasis were provided by Asiaq—Greenland Survey, Nuuk, Greenland. Data from the Greenland Ecosystem Monitoring Program GeoBasis were provided by the Department of Bioscience, Aarhus University, Denmark, in collaboration with Department of Geosciences and Natural Resource Management, Copenhagen University, Denmark. Data collection was financed through the DANCEA funds from the Danish Ministry of Environment and Danish Ministry of Climate and Energy.

References Cited

  1. Barnett, T. P., Adam, J. C., and Lettenmaier, D. P., 2005: Potential impacts of a warming climate on water availability in snow-dominated regions. Nature , 438(7066): 303–309. Google Scholar
  2. Barraquand, F., Høye, T. T., Henden, J.-A., Yoccoz, N. G., Gilg, O., Schmidt, N. M., Sittler, B., and Ims, R. A., 2014: Demographic responses of a site-faithful and territorial predator to its fluctuating prey: long-tailed skuas and Arctic lemmings. Journal of Animal Ecology , 83(2): 375–387. Google Scholar
  3. Bay, C., 1998: Vegetation mapping of Zackenberg Valley, Northeast Greenland. Danish Polar Center & Botanical Museum, University of Copenhagen, Denmark. Google Scholar
  4. Bennike, O., Sørensen, M., Fredskild, B., Jacobsen, B. H., Böcher, J., Amsinck, S. L., Jeppesen, E., Andreasen, C., Christiansen, H. H., and Humlum, O., 2008: Late Quaternary environmental and cultural changes in the Wollaston Forland Region, Northeast Greenland. In High-Arctic Ecosystem Dynamics in a Changing Climate. Advances in Ecological Research , 40: 45–79. Google Scholar
  5. Björk, R. G., and Molau, U., 2007: Ecology of alpine snowbeds and the impact of global change. Arctic, Antarctic, and Alpine Research , 39(1): 34–43. Google Scholar
  6. Blankinship, J. C., Meadows, M. W., Lucas, R. G., and Hart, S. C., 2014: Snowmelt timing alters shallow but not deep soil moisture in the Sierra Nevada. Water Resources Research , 50(2): 1448–1456. Google Scholar
  7. Bliss, L., and Matveyeva, N., 1992: Circumpolar Arctic vegetation. In Chapin, F. S., III , Jefferies, R. L., Reynolds, J. F., Shaver, G. R., Svoboda, J., and Chu, E. W. (eds.), Arctic Ecosystems in a Changing Climate: An Ecophysiological Perspective. San Diego, California: Academic Press, 59–89. Google Scholar
  8. Blunden, J., and Arndt, D. S., 2014: State of the climate in 2013. Bulletin of the American Meteorological Society , 95(7): S1–S279. Google Scholar
  9. Bokhorst, S., Bjerke, J. W., Street, L. E., Callaghan, T. V., and Phoenix, G. K., 2011: Impacts of multiple extreme winter warming events on sub-Arctic heathland: phenology, reproduction, growth, and CO2 flux responses. Global Change Biology , 17(9): 2817–2830. Google Scholar
  10. Bokhorst, S., Pedersen, S. H., Brucker, L., Anisimov, O., Bjerke, J. W., Brown, R. D., Ehrich, D., Essery, R. L. H., Heilig, A., Ingvander, S., Johansson, C., Johansson, M., Jónsdóttir, I. S., Inga, N., Luojus, K., Macelloni, G., Mariash, H., McLennan, D., Rosqvist, G. N., Sato, A., Savela, H., Schneebeli, M., Sokolov, A., Sokratov, S. A., Terzago, S., Vikhamar-Schuler, D., Williamson, S., Qiu, Y., and Callaghan, T. V., 2016: Changing Arctic snow cover: a review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio , 45(5): 516–537. Google Scholar
  11. Brooks, P. D., Grogan, P., Templer, P. H., Groffman, P., Öquist, M. G., and Schimel, J., 2011: Carbon and nitrogen cycling in snow-covered environments. Geography Compass , 5(9): 682–699. Google Scholar
  12. Buckeridge, K. M., and Grogan, P., 2008: Deepened snow alters soil microbial nutrient limitations in arctic birch hummock tundra. Applied Soil Ecology , 39(2): 210–222. Google Scholar
  13. Bulygina, O. N., Groisman, P. Y., Razuvaev, V. N., and Korshunova, N. N., 2011: Changes in snow cover characteristics over Northern Eurasia since 1966. Environmental Research Letters , 6(4): doi  http://dx.doi.org/10.1088/1748-9326/6/4/045204Google Scholar
  14. Buus-Hinkler, J., Hansen, B. U., Tamstorf, M. P., and Pedersen, S. B., 2006: Snow-vegetation relations in a High Arctic ecosystem: inter-annual variability inferred from new monitoring and modeling concepts. Remote Sensing of Environment , 105(3): 237–247. Google Scholar
  15. Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N., Radionov, V., Bradley, R. S., Blangy, S., Bulygina, O. N., Christensen, T. R., Colman, J. E., Essery, R. L. H., Forbes, B. C., Forchhammer, M. C., Golubev, V. N., Honrath, R. E., Juday, G. P., Meshcherskaya, A. V., Phoenix, G. K., Pomeroy, J., Rautio, A., Robinson, D. A., Schmidt, N. M., Serreze, M. C., Shevchenko, V. P., Shiklomanov, A. I., Shmakin, A. B., Skold, P., Sturm, M., Woo, M. K., and Wood, E. F., 2011: Multiple effects of changes in Arctic snow cover. Ambio, 40(Supplement 1): 32–45. Google Scholar
  16. Chen, X., Liang, S., Cao, Y., He, T., and Wang, D., 2015: Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001–2014. Scientific Reports , 5: doi  http://dx.doi.org/10.1038/srep16820Google Scholar
  17. Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A., 2011: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: a review. Water Resources Research , 47(7). Google Scholar
  18. Clow, D. W., 2009: Changes in the timing of snowmelt and streamflow in Colorado: a response to recent warming. Journal of Climate , 23(9): 2293–2306. Google Scholar
  19. Clyde, G. D., 1932: Circular no. 99—Utah snow sampler and scales for measuring water content of snow. UAES Circulars, Paper 90:  http://digitalcommons.usu.edu/uaes_circulars/90Google Scholar
  20. Cooper, E. J., 2014: Warmer shorter winters disrupt Arctic terrestrial ecosystems. Annual Review of Ecology, Evolution, and Systematics , 45: 271–295. Google Scholar
  21. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society , 137(656): 553–597. Google Scholar
  22. Derksen, C., and Brown, R., 2012: Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections. Geophysical Research Letters , 39(19). Google Scholar
  23. Derksen, C., Lemmetyinen, J., Toose, P., Silis, A., Pulliainen, J., and Sturm, M., 2014: Physical properties of Arctic versus subarctic snow: implications for high latitude passive microwave snow water equivalent retrievals. Journal of Geophysical Research: Atmospheres , 119(12): 7254–7270. Google Scholar
  24. Dyrrdal, A. V., Saloranta, T., Skaugen, T., and Stranden, H. B., 2013: Changes in snow depth in Norway during the period 1961–2010. Hydrology Research , 44(1): 169–179. Google Scholar
  25. Elberling, B., 2007: Annual soil CO2 effluxes in the High Arctic: the role of snow thickness and vegetation type. Soil Biology and Biochemistry , 39(2): 646–654. Google Scholar
  26. Elberling, B., Tamstorf, M. P., Michelsen, A., Arndal, M. F., Sigsgaard, C., Illeris, L., Bay, C., Hansen, B. U., Christensen, T. R., and Hansen, E. S., 2008: Soil and plant community-characteristics and dynamics at Zackenberg. In Meltofte, H., Christensen, T. R., Elberling, B., Forchhammer, M. C., and Rasch, M. (eds.), High-Arctic Ecosystem Dynamics in a Changing Climate. Amsterdam: Elsevier, Advances in Ecological Research , 40: 223–248. Google Scholar
  27. Forchhammer, M. C., Post, E., Berg, T. B., Høye, T. T., and Schmidt, N. M., 2005: Local-scale and short-term herbivore-plant spatial dynamics reflect influences of large-scale climate. Ecology , 86(10): 2644–2651. Google Scholar
  28. Fredskild, B., and Mogensen, G. S., 1997: Zero Line: Final Report 1997: a Description of the Plant Communities Along the ZERO Line from Young Sund to the Top of Aucellabjerg and the Common Plant Communities in the Zackenberg Valley, Northeast Greenland, Greenland. Copenhagen, Denmark: Botanical Survey & Botanical Museum,University of Copenhagen. Google Scholar
  29. Gilg, O., Sittler, B., and Hanski, I., 2009: Climate change and cyclic predator-prey population dynamics in the High Arctic. Global Change Biology , 15(11): 2634–2652. Google Scholar
  30. Goodrich, L., 1982: The influence of snow cover on the ground thermal regime. Canadian Geotechnical Journal , 19(4): 421–432. Google Scholar
  31. Hollesen, J., Buchwal, A., Rachlewicz, G., Hansen, B. U., Hansen, M. O., Stecher, O., and Elberling, B., 2015: Winter warming as an important co-driver for Betula nana growth in western Greenland during the past century. Global Change Biology , 21(6): 2410–2423. Google Scholar
  32. Johansson, M., Callaghan, T. V., Bosiö, J., Åkerman, H. J., Jackowicz-Korczynski, M., and Christensen, T. R., 2013: Rapid responses of permafrost and vegetation to experimentally increased snow cover in sub-Arctic Sweden. Environmental Research Letters , 8(3): doi  http://dx.doi.org/10.1088/1748-9326/8/3/035025Google Scholar
  33. Jones, H. G., 1999: The ecology of snow-covered systems: a brief overview of nutrient cycling and life in the cold. Hydrological Processes , 13(14–15): 2135–2147. Google Scholar
  34. Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F., 2006: World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift , 15(3): 259–263. Google Scholar
  35. Kristensen, D. K., Kristensen, E., Forchhammer, M. C., Michelsen, A., and Schmidt, N. M., 2011: Arctic herbivore diet can be inferred from stable carbon and nitrogen isotopes in C3 plants, faeces, and wool. Canadian Journal of Zoology , 89(10): 892–899. Google Scholar
  36. Liston, G. E., 1995: Local advection of momentum, heat, and moisture during the melt of patchy snow covers. Journal of Applied Meteorology , 34(7): 1705–1715. Google Scholar
  37. Liston, G. E., and Elder, K., 2006a: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). Journal of Hydrometeorology , 7(2): 217–234. Google Scholar
  38. Liston, G. E., and Elder, K., 2006b: A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology , 7(6): 1259–1276. Google Scholar
  39. Liston, G. E., and Hall, D. K., 1995: An energy-balance model of lake-ice evolution. Journal of Glaciology , 41(138): 373–382. Google Scholar
  40. Liston, G. E., and Hiemstra, C. A., 2008: A simple data assimilation system for complex snow distributions (snowassim). Journal of Hydrometeorology , 9(5): 989–1004. Google Scholar
  41. Liston, G. E., and Hiemstra, C. A., 2011: The changing cryosphere: Pan-Arctic snow trends (1979–2009). Journal of Climate , 24(21): 5691–5712. Google Scholar
  42. Liston, G. E., and Sturm, M., 1998: A snow-transport model for complex terrain. Journal of Glaciology , 44(148): 498–516. Google Scholar
  43. Liston, G. E., McFadden, J. P., Sturm, M., and Pielke, R. A., 2002: Modelled changes in Arctic tundra snow, energy and moisture fluxes due to increased shrubs. Global Change Biology , 8(1): 17–32. Google Scholar
  44. Meltofte, H., and Berg, T. B., 2006: BioBasis—Conceptual Design and Sampling Procedures of the Biological Programme of Zackenberg Basic. Roskilde, Denmark: National Environmental Research Institute. Google Scholar
  45. Meltofte, H., Christensen, T. R., Elberling, B., Forchhammer, M. C., and Rasch, M., 2008a: High-Arctic Ecosystem Dynamics in a Changing Climate. London: Elsevier Academic Press. Google Scholar
  46. Meltofte, H., Høye, T. T., and Schmidt, N. M., 2008b: Effects of food availability, snow and predation on breeding performance of waders at Zackenberg. Advances in Ecological Research , 40: 325–343. Google Scholar
  47. Mernild, S. H., Liston, G. E., and Hasholt, B., 2007: Snow-distribution and melt modelling for glaciers in Zackenberg river drainage basin, north-eastern Greenland. Hydrological Processes , 21(24): 3249–3263. Google Scholar
  48. Mosbacher, J. B., Kristensen, D. K., Michelsen, A., Stelvig, M., and Schmidt, N. M., 2016: Quantifying muskox plant biomass removal and spatial relocation of nitrogen in a High Arctic Tundra ecosystem. Arctic, Antarctic, and Alpine Research , 48(2): 229–240. Google Scholar
  49. Pedersen, S. H., Liston, G. E., Tamstorf, M. P., Westergaard-Nielsen, A., and Schmidt, N. M., 2015: Quantifying episodic snowmelt events in Arctic ecosystems. Ecosystems , 18(5): 839–856. Google Scholar
  50. Pellissier, L., Meltofte, H., Hansen, J., Schmidt, N. M., Tamstorf, M. P., Maiorano, L., Aastrup, P., Olsen, J., Guisan, A., and Wisz, M. S., 2013: Suitability, success and sinks: How do predictions of nesting distributions relate to fitness parameters in High Arctic waders? Diversity and Distributions , 19(12): 1496–1505. Google Scholar
  51. Post, E., Forchhammer, M. C., Bret-Harte, M. S., Callaghan, T. V., Christensen, T. R., Elberling, B., Fox, A. D., Gilg, O., Hik, D. S., and Høye, T. T., 2009: Ecological dynamics across the Arctic associated with recent climate change. Science , 325(5946): 1355–1358. Google Scholar
  52. Proksch, M., Rutter, N., Fierz, C., and Schneebeli, M., 2016: Intercomparison of snow density measurements: bias, precision, and vertical resolution. The Cryosphere , 10(1): 371–384. Google Scholar
  53. Riley, S. J., DeGloria, S. D., and Elliot, R., 1999: A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences , 5(1–4): 23–27. Google Scholar
  54. Rotschky, G., Schuler, T. V., Haarpaintner, J., Kohler, J., and Isaksson, E., 2011: Spatio-temporal variability of snowmelt across Svalbard during the period 2000–08 derived from QuikSCAT/SeaWinds scatterometry. Polar Research , 30: doi  http://dx.doi.org/10.3402/polar.v30i0.5963Google Scholar
  55. Schimel, J. P., Bilbrough, C., and Welker, J. M., 2004: Increased snow depth affects microbial activity and nitrogen mineralization in two Arctic tundra communities. Soil Biology and Biochemistry , 36(2): 217–227. Google Scholar
  56. Schirmer, M., Wirz, V., Clifton, A., and Lehning, M., 2011: Persistence in intra-annual snow depth distribution: 1. Measurements and topographic control. Water Resources Research , 47(9): doi  http://dx.doi.org/10.1029/2010WR009426Google Scholar
  57. Schmidt, N. M., Baittinger, C., and Forchhammer, M. C., 2006: Reconstructing century-long snow regimes using estimates of High Arctic Salix arctica radial growth. Arctic, Antarctic, and Alpine Research , 38(2): 257–262. Google Scholar
  58. Schmidt, N. M., Berg, T. B., Forchhammer, M. C., Hendrichsen, D. K., Kyhn, L. A., Meltofte, H., and Høye, T. T., 2008: Vertebrate predator—prey interactions in a seasonal environment. In Meltofte, H., Christensen, T. R., Elberling, B., Forchhammer, M. C., and Morten, R. (eds.), Advances in Ecological Research. London and New York: Academic Press, 345–370. Google Scholar
  59. Schmidt, N. M., Ims, R. A., Høye, T. T., Gilg, O., Hansen, L. H., Hansen, J., Lund, M., Fuglei, E., Forchhammer, M. C., and Sittler, B., 2012: Response of an Arctic predator guild to collapsing lemming cycles. Proceedings of the Royal Society of London B: Biological Sciences , 279(1746): 4417–4422. Google Scholar
  60. Schmidt, N. M., Pedersen, S. H., Mosbacher, J. B., and Hansen, L. H., 2015: Long-term patterns of muskox (Ovibos moschatus) demographics in High Arctic Greenland. Polar Biology , 38: 1667–1675. Google Scholar
  61. Sigsgaard, C., Mylius, M. R., and Skov, K., 2014: Zackenberg Ecological Research Operations. GeoBasis—Guidelines and Sampling Procedures for the Geographical Monitoring Programme of Zackenberg Basic. Department of Bioscience, Aarhus University and Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark. Google Scholar
  62. Stuefer, S., Kane, D. L., and Liston, G. E., 2013: In situ snow water equivalent observations in the US Arctic. Hydrology Research , 44(1): 21–34. Google Scholar
  63. Sturm, M., and Wagner, A. M., 2010: Using repeated patterns in snow distribution modeling: an Arctic example. Water Resources Research , 46: doi  http://dx.doi.org/10.1029/2010WR009434Google Scholar
  64. Sturm, M., Holmgren, J., and Liston, G. E., 1995: A seasonal snow cover classification-system for local to global applications. Journal of Climate , 8(5): 1261–1283. Google Scholar
  65. Sturm, M., Holmgren, J., Konig, M., and Morris, K., 1997: The thermal conductivity of seasonal snow. Journal of Glaciology , 43(143): 26–41. Google Scholar
  66. Sturm, M., McFadden, J. P., Liston, G. E., Chapin, F. S., Racine, C. H., and Holmgren, J., 2001: Snow-shrub interactions in Arctic tundra: a hypothesis with climatic implications. Journal of Climate , 14(3): 336–344. Google Scholar
  67. Tedesco, M., Brodzik, M., Armstrong, R., Savoie, M., and Ramage, J., 2009: Pan Arctic terrestrial snowmelt trends (1979–2008) from spaceborne passive microwave data and correlation with the Arctic Oscillation. Geophysical Research Letters , 36(21): L21402, doi  http://dx.doi.org/10.1029/2009GL039672Google Scholar
  68. Westermann, S., Elberling, B., Højlund Pedersen, S., Stendel, M., Hansen, B., and Liston, G., 2015: Future permafrost conditions along environmental gradients in Zackenberg, Greenland. The Cryosphere , 9(2): 719–735. Google Scholar
  69. Winstral, A., Elder, K., and Davis, R. E., 2002: Spatial snow modeling of wind-redistributed snow using terrain-based parameters. Journal of Hydrometeorology , 3(5): 524–538. Google Scholar
  70. Zhang, T., 2005: Influence of the seasonal snow cover on the ground thermal regime: an overview. Reviews of Geophysics , 43(4): doi  http://dx.doi.org/10.1029/2004RG000157Google Scholar
  71. Zhang, X., Vincent, L. A., Hogg, W. D., and Niitsoo, A., 2000: Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean , 38(3): 395–429. Google Scholar
© 2016 Regents of the University of Colorado
Stine Højlund Pedersen, Mikkel P. Tamstorf, Jakob Abermann, Andreas Westergaard-Nielsen, Magnus Lund, Kirstine Skov, Charlotte Sigsgaard, Maria Rask Mylius, Birger Ulf Hansen, Glen E. Liston and Niels Martin Schmidt "Spatiotemporal Characteristics of Seasonal Snow Cover in Northeast Greenland from in Situ Observations," Arctic, Antarctic, and Alpine Research 48(4), (1 November 2016). https://doi.org/10.1657/AAAR0016-028
Received: 29 March 2016; Accepted: 1 August 2016; Published: 1 November 2016
JOURNAL ARTICLE
19 PAGES


SHARE
ARTICLE IMPACT
Back to Top