Increasing Snow Depth Research Articles

Moisture Transport during Large Snowfall Events in the New Zealand Southern Alps: The Role of Atmospheric Rivers

AbstractSynoptic-scale moisture transport during large snowfall events in the New Zealand Southern Alps is largely unknown due to a lack of long-term snow observations. In this study, records from three recently developed automatic weather stations (Mahanga, Mueller Hut, and Mt Larkins) near the Main Divide of the Southern Alps were used to identify large snowfall events between 2010 and 2018. The large snowfall events are defined as those events with daily snow depth increase by greater than the 90th percentile at each site. ERA-Interim reanalysis data were used to characterize the hydrometeorological features of the selected events. Our findings show that large snowfall events in the Southern Alps generally coincide with strong fields of integrated vapor transport (IVT) within a northwesterly airflow and concomitant deepening low pressure systems. Considering the frequency of large snowfall events, approximately 61% of such events at Mahanga were associated with narrow corridors of strong water vapor flux, known as atmospheric rivers (ARs). The contributions of ARs to the large snowfall events at Mueller Hut and Mt Larkins were 70% and 71%, respectively. Analysis of the vertical profiles of moisture transport dynamics during the passage of a landfalling AR during 11–12 October 2016 revealed the key characteristics of a snow-generating AR in the Southern Alps. An enhanced presence of low- and midlevel moisture between 700 and 850 hPa and pronounced increases of wind velocities (more than 30 m s−1) with high values of the meridional component between 750 and 850 hPa were identified over the Southern Alps during the event.

Bias Analysis and Correction of Ground Surface Temperature Observations across China

Based on the ground surface temperature (GST) and snow surface temperature (SST) measurements during the period of adjustment from manual to automatic observation systems in China, the influence of observation methods on GST and its relationship with snow cover is analyzed. GST is corrected by SST, and the correction effect is evaluated. The results show that, during the parallel observation period, the winter GSTs from automatic observations are generally higher than those from manual observations, with the automatically observed national daily GST 1.18°C higher. The adjustment has a greater impact on GSTs at 0200, 0800, and 2000 Beijing Time (BT) than at 1400 BT, and it has the greatest impact in Northeast and Northwest China, with deviations of 5.24 and 2.09°C, respectively. The GST deviation is closely related to the snow depth and annual snow totals. The average daily GST deviation increases at the rate of 0.66°C per 1-cm increase of snow depth when it is < 15 cm, while it tends to be stable at around 10°C for snow depth over 15 cm. The GST deviation at a station is affected by its winter snow totals in Northeast and Northwest China, where the largest deviations are found where snow totals are all above 1000 cm. After the correction with SST, the mean deviation between the automatic and manual observations as well as the false trend can be effectively reduced. Following the correction, the mean deviation of daily GST decreases by 5.8°C, and its trend decreases from 1.87 to 0.65°C decade−1.

Species-dependent responses of root growth of herbaceous plants to snow cover changes in a temperate desert, Northwest China

Changes in snow cover can influence root growth and distribution of herbaceous species in water limiting desert ecosystems. However, how the growth of root systems of herbaceous species responds to snow cover changes remains unclear. Thus, the present study was aimed to examine the influence of snow cover changes on root growth of herbaceous species in a temperate desert of central Asia. Plots with four snow cover depth treatments in winter were investigated in the Gurbantunggut Desert. The four treatments were snow removal (− S), ambient snow, double snow (+ S), and triple snow (+ 2S). We examined the root growth of two typical herbaceous species: one ephemeral species, Erodium oxyrhinchum, and one annual species, Ceratocarpus arenarius. The root length of the annual plant was significantly reduced by snow removal compared with the ambient treatment. The specific root length and specific surface area of the ephemeral plants increased with increasing snow depth, whereas the annual plants showed the opposite trends. Snow removal significantly increased the root–shoot ratio of the annual plants, with no effects found in the ephemeral plants. The individual root biomass and total underground biomass of the two species had similar responses to the snow depth treatments, with the highest values found with the ambient treatment. These results can contribute to explaining to changing winter snow cover depth can alter plant growth, community structure, and ecosystem function in the growing season in temperate desert ecosystems.

Recent Changes in Water Discharge in Snow and Glacier Melt-Dominated Rivers in the Tienshan Mountains, Central Asia

Global warming has generally led to changes in river runoffs fed by snow and glacier meltwater in mountain ranges. The runoff of the Aksu River, which originates in the Southern Tienshan Mountains, exhibited a positive trend during 1979–2002, but this trend reversed during 2002–2015. Through a comprehensive analysis, this study aims to estimate potential reasons for changes in the runoff of its two contrasting headwaters: the Toxkan and Kumalak Rivers, based on climatic data, the altitude of the 0 °C isotherm, glacier mass balance (GMB), snow cover area (SCA), snow depth (SD) and the sensitivity model. For the Toxkan River, the decrease in spring runoff mainly resulted from reductions in precipitation, whereas the decrease in summer runoff was mainly caused by early snowmelt in spring and a much-reduced snow meltwater supply in summer. In addition, the obvious glacier area reduction in the catchment (decreased to less than 4%) also contributed to the reduced summer runoff. For the Kumalak River, a sharp decrease rate of 10.21 × 108 m3/decade in runoff was detected due to summertime cooling of both surface and upper air temperatures. Reduced summer temperatures with a positive trend in precipitation not only inhibited glacier melting but also dropped the 0 °C layer altitude, resulting in a significant increase in summertime SCA and SD, a slowing of the glacier negative mass balance, and a lowering of the snow-line altitude.

Rapid neoglaciation on Ellesmere Island promoted by enhanced summer snowfall in a transient climate model simulation of the middle-late-Holocene

Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development.

Weather stochasticity and alternative reproductive tactics in Northern chamois, Rupicapra rupicapra

Abstract In polygynous ungulates, male breeding success is often achieved through alternative reproductive tactics (ARTs). The maintenance of ARTs in this taxon is largely explained by condition-dependent selection, a decision rule in which individuals switch their breeding behaviour on the basis of internal factors such as ageing (status-dependent selection), or external factors such as weather conditions during the rut (environment-dependent selection). In some species, however, ARTs are fixed during the mating season, and other forms of selective pressures rather than environment-dependent selection may contribute to the evolutionary maintenance of male types within populations. Assuming female vicinity as a proxy of mating opportunity, we explored the effect of snow depth variations on the elevational overlap between females, and territorial and non-territorial male chamois, Rupicapra rupicapra, in three consecutive rutting seasons. In snow-free periods, females overlapped with non-territorial males, but not with territorial ones. With increasing snow depth, females and territorial males progressively occupied similar elevations. Our results suggest that snow cover variations may impose changes in the spatial distribution of males and females. In turn, weather stochasticity over multiple ruts, rather than within-rut environment-dependent selection, may impose variations in mating opportunities of ARTs, possibly contributing to the coexistence of different phenotypes within populations.

Demographic responses of eastern bluebirds to climatic variability in northeastern Arkansas

AbstractAs climate change continues to alter temperature and precipitation patterns, numerous species have declined. However, populations of some species that show responses to climate change, such as eastern bluebirds (Sialia sialis), have increased or remained stable nationwide. To understand how species are adapting to climate change, we estimated demographic parameters and their responses to climatic variability, using nesting and banding‐recapture data between 2003 and 2018 in a northeastern Arkansas eastern bluebird population. Increasing variability in precipitation in the nonbreeding season negatively affected hatchability. Hatching success was negatively affected by increasing variability in maximum temperatures and the number of hot days during the breeding season, but positively affected by increasing winter snow depth. Adult survival was positively affected by increasing snow depth and variability in the number of hot days during the breeding season, but negatively affected by increasing variability in nonbreeding season temperatures. Our results demonstrate that for this study population, annual breeding parameters, though canalized against interannual environmental variation, were affected by seasonal climatic variability. Although climate change may benefit bluebird survival due to increasing variability in winter temperatures and the number of hot days, climatic variability negatively affected breeding parameters and is expected to increase. Because breeding parameters are typically the drivers of population growth rate in short‐lived species, these results raise concern for the future of this population of eastern bluebirds.

Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

AbstractWithin a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic‐wide scale is based on passive microwave satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N‐ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower‐frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher‐frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong.

Long‐term deepened snow cover alters litter layer turnover rate in temperate steppes

Abstract The turnover of litter layer is a biogeochemical process fundamental to carbon and nutrient cycling, influencing seed germination, species coexisting, and carbon storage. Winter snow depth is undergoing increasing trend in Northern China, which has been shown to alter litter decomposition rate of individual species. However, it remains unknown how changes in snow depth affect the turnover rate of the whole litter layer, and whether the responses vary between different steppes. Most current litter decomposition studies are site‐based or short‐term treated, limiting the exploration of the long‐term response of litter layer turnover in regional pattern. In this study, we selected six long‐term (11–13 years) snow fence sites in Inner Mongolia, with three in the dry steppe and another three in the wet steppe, and investigated the responses of community‐weighted litter residence time (LRT) to long‐term increased snow treatment. We found that LRT increased by 0.02 year for every 10 cm increase in snow depth in the wet steppe, but was not affected in the dry steppe. The lack of effect of deepened snow on LRT in the dry steppe was attributed to the offset between the positive effect of the increased plant community‐weighted height possibly via inhibiting photodegradation and the enhanced litter recalcitrance by producing higher proportion of stem litter, and the negative effect of the increased soil moisture via accelerating microbial decomposition. The significantly positive effect of the deepened snow on LRT in the wet steppe was mainly because the deepened snow increased grass biomass but had no effect on forb biomass, which reduced the degradability of the litter layer. Overall, our findings indicated that deepened snow changed plant community, which altered environmental conditions and enhanced litter recalcitrance, thereby increasing LRT in temperate steppes. However, this effect was diminished by enhancing microbial decomposition in the dry but not wet steppe, resulting in different overall responses of LRT to deep‐snow in the two steppes. The slow litter turnover rate in the wet steppe might result in greater litter accumulation under future increased snow depth, which could be unfavourable for seed germination and alter plant diversity. A free Plain Language Summary can be found within the Supporting Information of this article.

Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements

The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.

Evaluation of Brightness Temperature Sensitivity to Snowpack Physical Properties Using Coupled Snow Physics and Microwave Radiative Transfer Models

There are multiple existing microwave radiative transfer models (RTMs) to simulate the brightness temperature (Tb) of snowpacks. It is still challenging to have consistent Tb responses from RTMs due to individual physical formulations of the snowpack scattering process. This article examines three of the widely-used multi-layer RTMs: 1) the microwave emission model of layered snowpacks (MEMLS); 2) the dense media radiative transfer based on the quasi-crystalline approximation (QCA) of Mie scattering of densely packed sticky spheres (DMRT-QMS); and 3) the Helsinki University of Technology (HUT) model. Interestingly, these models yield slightly different Tb responses when driven by the same physical snowpack properties. Tb variations, dependent on the choice of RTMs, are then evaluated to improve the understanding of model differences in microwave emission from a snowpack. We first perform a sensitivity study of the Tb predictions from the three RTMs as a function of snow grain sizes, densities, and depths. While Tb from all three RTMs decreases with increasing snow grain sizes, it is found that a scaling factor is required to have the same amount of Tb attenuation for small grain sizes within the Rayleigh scattering regime. For larger grain sizes, however, a scaling coefficient is not enough to match the model outputs due to the different scattering assumptions of the RTMs. For a single snow layer with increasing snow depths and densities, all three RTMs exhibit Tb attenuations arising from the increase in path lengths and optical depths. Further evaluations are conducted by feeding the three RTMs with the output of a snow physics model driven by in situ weather forcing in a coupled simulation. Outputs of this coupled model include snowpack physical properties and Tbs. By using snow stratigraphy observations, another set of Tb simulation is also conducted with RTMs driven by in situ snowpit observations. The snow physics outputs from the coupled case are compared against in situ snow stratigraphy observations. And both Tb simulations are compared against ground-based microwave observations from the European Space Agency (ESA) Nordic Snow Radar Experiment (NoSREx) 2009–2012. For three consecutive years, the in situ driven Tbs have 21.0-K root-mean-squared error (RMSE) while the coupled simulations have 24.7-K RMSE. However, after isolating the dry snow period and excluding diurnal melting snow conditions, 12.2- and 6.3-K RMSEs are achieved from in situ and coupled cases, respectively, in the 2011 water year.

Disappearing green: Shrubs decline and bryophytes increase with nine years of increased snow accumulation in the High Arctic

AbstractQuestionHow does increased snow depth affect plant community composition of High Arctic tundra, and can the Normalized Differential Vegetation Index (NDVI) detect induced changes?LocationAdventdalen, Spitsbergen, Svalbard (78°10′ N, 16°04′ E).MethodsWe manipulated snow depth on the tundra using fences, resulting in Deep, Medium, and Ambient snow regimes. Increased snow led to warmer winter soil temperatures, a delayed onset of growing season and wetter conditions during the early growing season. Plant community composition of living and dead plant material was recorded after nine years. NDVI was measured at the plot level using a handheld sensor.ResultsCommunity composition and the abundance of typically dominant shrub species were substantially different in the Deep compared to the Ambient regime. Deep had lower cover of live shrubs (Cassiope tetragona, Dryas octopetala and Salix polaris) and Luzula confusa, and higher cover of dead shrubs (Cassiope and Dryas) compared to the other snow regimes. Bryophyte cover was highest in Medium. NDVI was positively correlated to the cover of living vascular plants and negatively correlated to cover of dead vascular plants. Accordingly, Deep snow regime had reduced NDVI, reflecting the contribution of dead Cassiope and Dryas.ConclusionSnow regime strongly influenced community composition in High Arctic plant communities. Enhanced snow regimes had more dead shrubs, reduced Luzula and increased bryophyte cover than ambient conditions. These differences were detectable by handheld NDVI sensors.

Role of autumn Arctic Sea ice in the subsequent summer precipitation variability over East Asia

AbstractThis study explored the interannual relationship between autumn Arctic sea ice concentration (SIC) and the subsequent summer precipitation over East Asia (EASP). Since the late‐1990s, the declining SIC in the Kara–Laptev Seas has been significantly correlated with EASP as well as extremely positive anomalies in northern China and intensely negative anomalies in central‐eastern East Asia. However, there was a weak correlation between autumn SIC and EASP before the late‐1990s. Furthermore, the anomalous precipitation pattern in summer and its connection with autumn SIC variability can be explained by the seasonal persistence of continental processes (snow depth and soil moisture) into the spring. In particular, a decreasing SIC was connected with simultaneously positive and negative precipitation anomalies over northeastern China and the Siberian region, respectively, since the late‐1990s and tends to produce corresponding soil moisture anomalies over the Eurasian continent. Declining SIC also favours increased snow depth anomalies in winter over northeastern East Asia. These anomalous signals of surface processes can persist from winter into the subsequent spring, making the connection between the autumn SIC and EASP possible. The Community Earth System Model Large Ensemble simulations further verified these physical processes. More detailed mechanism for this relationship needs to be stressed in further work by numerical simulations. The results have important implications for extending the seasonal prediction validity of EASP. Moreover, before the late‐1990s, SIC‐related circulation anomalies shifted westward and northward as negative precipitation anomalies developed over west Siberia in autumn. As a result, anomalous dry soil conditions in Siberia persisted into the subsequent spring and then led to wetter‐than‐normal conditions through locally negative soil moisture–precipitation feedback before the late‐1990s.

Sub-kilometer Precipitation Datasets for Snowpack and Glacier Modeling in Alpine Terrain

apturing the spatial and temporal variability of precipitation at fine scale is necessary for high-resolution modeling of snowpack and glacier mass balance in alpine terrain. In this study, we assess the impact of three sub-kilometer precipitation datasets on distributed simulations of snowpack and glacier mass balance with the detailed snowpack model Crocus for winter 2011-2012. The different precipitation datasets at 500-m grid spacing over the northern and central French Alps are coming from (i) the SAFRAN reanalysis specially developed for alpine terrain interpolated at 500-m grid spacing, (ii) the numerical weather prediction (NWP) system AROME at 2.5-km resolution downscaled with a precipitation-elevation adjustment factor and (iii) a version of AROME at 500-m grid spacing. The spatial patterns of seasonal snowfall are first analyzed for the different precipitation datasets. Large differences between SAFRAN and the two versions of AROME are found at high-altitude and in regions of strong orographic precipitation enhancement. Results of Crocus snowpack simulations are then evaluated against (i) point measurements of snow depth, (ii) maps of snow covered areas retrieved from optical satellite data (MODIS) and (iii) field measurements of winter accumulation of six glaciers. The two versions of AROME lead to an overestimation of snow depth and snow-covered area, which are substantially improved by SAFRAN. However, all the precipitation datasets lead to an underestimation of snow depth increase at the daily scale and cumulated over the season, with AROME 500 m providing the best performances at the seasonal scale. The low correlation found between the biases in snow depth and in cumulated snow depth increase illustrates that total snow depth has a limited significance for the evaluation of precipitation datasets. Measurements of glacier winter mass balance showed a systematic underestimation of high-elevation snow accumulation with SAFRAN. The two versions of AROME overestimate the winter mass balance at four glaciers and produce nearly unbiased estimation for two of them. Our study illustrates the need for improvements in the precipitation field from high-resolution NWP systems for snow and glacier modeling in alpine terrain.

Variation in snow cover drives differences in soil properties and microbial biomass of BSCs in the Gurbantunggut Desert—3 years of snow manipulations

AbstractVariations in snow cover (as specific precipitation) can have an important effect on development of biological soil crusts (BSCs) in arid and semiarid regions, where water is the principal limiting factor for microorganisms. However, there is still limited knowledge available regarding the effects of snowfall on soil properties and microbial biomass in BSCs. To examine these effects of snow cover, three types of BSCs (cyanobacteria‐dominated, lichen‐dominated, and moss‐dominated crusts) were collected and exposed to five snow depths for 3 years. The results indicated that most of the soil properties and soil microbial biomass were significantly (p &lt; .05) affected by 3 years of snow manipulation in three types of BSCs. In BSCs, the values of most soil properties and microbial biomass were higher in the increased snow depth treatments relative the snow removal treatments. Moreover, there were variations in soil properties and microbial biomass among BSC types. In short, increases/decreases in snow cover had different effects on three types of BSCs after 3 years of manipulation. Moreover, increases in snow cover had a positive influence on soil properties and microbial biomass, but negative effects were observed in all BSCs following snow removal and snow reduction. Thus, variations in snow cover can drive differences in soil properties and microbial biomass of BSCs, which may further affect development and succession of BSCs in arid and semiarid regions.

REDUCTION IN AMBIENT GAMMA DOSE RATE FROM RADIOCESIUM DUE TO SNOW COVER.

In the present study, variations in ambient gamma dose rate associated with snow cover were examined in a radioactive-contaminated site in Fukushima Prefecture, Japan. The ambient gamma dose rates decreased with increasing snow depth. The reduction trends were different between fresh snow (0.1-0.2 g/cm3) and granular snow (0.3-0.4 g/cm3) depending on snow density. Snow cover water content (snow water equivalent) calculated from snow depth and density was a key parameter governing the reduction in the ambient gamma dose rate. The ambient gamma dose rates reduced to 0.6 and 0.5 at 4 g/cm2 and 8 g/cm2 of snow water equivalent, respectively. Based on gamma-ray flux density distributions, the ambient gamma dose rates from the primary gamma rays decreased more compared to those from scattered gamma rays due to snow cover.

Tree spatial patterns modulate peak snow accumulation and snow disappearance

Forests and snow covered regions frequently co-occur across the northern hemisphere. In these environments, forests are structurally and spatially complex mosaics of tree neighborhoods that are intrinsically linked to ecosystem functions. Tree and canopy structures influence snow accumulation and disappearance processes through interception and radiation attenuation. However, it is unclear if spatial heterogeneity within the forest canopy induces heterogeneity in snow accumulation and persistence. We quantitatively identified different tree neighborhoods and tested the differential effects of these within-stand neighborhoods on snow processes. Neighborhood types included individual ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii) and western larch (Larix occidentalis) trees, dense overstory tree clumps, openings, and regeneration patches. Intensive measurements of snow accumulation (density and depth) and persistence (disappearance date) were made within replicate neighborhoods for three years. Overall, neighborhood type and year had a significant effect on accumulation and snow disappearance. Openings were significantly different from clumps and individuals, always accumulating more snow. Openings retained snow significantly later than clumps but were not significantly different from individuals. Within the individual tree neighborhood type, a nested species effect indicated no differences in accumulation but significant differences in disappearance between deciduous and evergreen conifers, with snow persisting longer beneath deciduous western larch. Our results suggest that canopy interception is the primary mechanism driving the accumulation phase, while snow disappearance patterns are largely a consequence of spatial variation of longwave radiation. Reducing canopy interception and longwave radiation by increasing the abundance of widely spaced single trees and small openings with silvicultural treatments should increase snow depth and duration, and thus snow water storage. Maintaining a heterogeneous canopy structure that includes tree clumps can be used to meet multiple objectives such as provision of ungulate winter range habitat, and heterogenous understory plant phenology.

Social organization of boreal woodland caribou (Rangifer tarandus caribou) in response to decreasing annual snow depth

Global climate warming is causing reductions in the depth and duration of snow cover across much of the distributional range of threatened boreal woodland caribou (Rangifer tarandus caribou). Plasticity in group size and composition among gregarious ungulates likely reflects a response to a variety of proximate factors, including environmental conditions such as snow depth. Yet, longitudinal studies of social organization by ungulates in response to environmental conditions are limited. We used 11 years of aerial survey data to examine winter group size in a northern population of boreal woodland caribou in response to late-winter snow depth. We documented caribou social organization (size and types of groups) and annual variability in group size. We tested the hypothesis that group size increases with increasing snow depth. Most groups (61%; n = 456) were ≤ 5 animals; however, they only constituted 37% of the animals. Caribou were distributed among four types of groups, including solitary females, males only, females only, and mixed groups. Group sizes differed among group types. Mixed groups were both the most common and largest groups encountered in late winter. We found a positive relationship between snow depth and group size; however, there was no relationship between snow depth and the percentage of mixed groups. These caribou may form larger groups in winters with deep snow as a behavioral strategy to reduce energetic costs, predation risk, or both. Climate-induced decreases in late-winter snow depth will likely reduce group sizes in boreal woodland caribou. However, further work is required to understand if this change in social organization will affect the persistence of local boreal woodland caribou populations.

Dead or Alive; or Does It Really Matter? Level of Congruency Between Trophic Modes in Total and Active Fungal Communities in High Arctic Soil.

Describing dynamics of belowground organisms, such as fungi, can be challenging. Results of studies based on environmental DNA (eDNA) may be biased as the template does not discriminate between metabolically active cells and dead biomass. We analyzed ribosomal DNA (rDNA) and ribosomal RNA (rRNA) coextracted from 48 soil samples collected from a manipulated snow depth experiment in two distinct vegetation types in Svalbard, in the High Arctic. Our main goal was to compare if the rDNA and rRNA metabarcoding templates produced congruent results that would lead to consistent ecological interpretation. Data derived from both rDNA and rRNA clustered according to vegetation types. Different sets of environmental variables explained the community composition based on the metabarcoding template. rDNA and rRNA-derived community composition of symbiotrophs and saprotrophs, unlike pathotrophs, clustered together in a similar way as when the community composition was analyzed using all OTUs in the study. Mean OTU richness was higher for rRNA, especially in symbiotrophs. The metabarcoding template was more important than vegetation type in explaining differences in richness. The proportion of symbiotrophic, saprotrophic and functionally unassigned reads differed between rDNA and rRNA, but showed similar trends. There was no evidence for increased snow depth influence on fungal community composition or richness. Our findings suggest that template choice may be especially important for estimating biodiversity, such as richness and relative abundances, especially in Helotiales and Agaricales, but not for inferring community composition. Differences in study results originating from rDNA or rRNA may directly impact the ecological conclusions of one’s study, which could potentially lead to false conclusions on the dynamics of microbial communities in a rapidly changing Arctic.

Snow depth reconstruction over last century: Trend and distribution in the Tianshan Mountains, China

Abstract Clarifying the long-term variations in snow depth is important for hydrological, meteorological and ecological implications. However, the short period of snow depth records in the mountains has made the assessment of regional long-term variation of snow depth difficult. Based on reanalysis datasets from 1901 to 2014 as well as observational data from 1961 to 2014, this study applied the artificial neural networks (ANN) method to reconstruct the historical snow depths in the Tianshan Mountains of China from 1901 to 1960. The variations in monthly snow depth were analyzed during the periods of 1901–1960, 1961–2014, and 1901–2014 as historical, observed, and overall periods, respectively. The results indicated that the reconstructed snow depths captured the long-term variation and spatial distribution in the study area. For temporal scale, increases in snow depth were detected in the southern and eastern Tianshan Mountains during all three periods. The trends in snow depth indicated an increase in the western, northern and overall Tianshan Mountains during the periods of 1901–1960 and 1961–2014, but showed a decrease during the overall period of 1901–2014. The difference in variation of snow depth trends in different temporal scales indicates that the time scale of snow depth increase is decadal rather than centennial. For spatial scale, higher values of snow depth occurred in the western and northern Tianshan Mountains, while lower ones appeared in the southern and eastern Tianshan Mountains. In addition, the increasing trend in the reconstructed snow depth was more profound with increased elevation in the Tianshan Mountains during 1901–1960, with the smallest increasing rate at the elevation of 1000 m–1500 m. Although some uncertainties exist within the reconstruction, this work proposed a method for developing historical snow depth for observation-limited areas, which provided additional data for hydrological and ecological simulation. The results also allow us to achieve a better understanding of regional climate change.