Deep Snowpack Research Articles

Comparison of bulk snow density measurements using different methods

Snow density is one of the basic properties used to describe snow cover characteristics, and it is critical for remote sensing retrieval, water resources assessment and modeling inputs. There are many instruments available to measure snow density in situ. However, there are measurement errors of snow density for bulk and layers or gravimetric and electronic instruments, which may affect the accuracy of remote sensing retrieval and model simulation. Especially in China, due to the noticeable heterogeneity of snowpacks, it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack conditions. This study evaluated the performance of different snow density instruments: the Federal Sampler, the model VS–43 snow density cylinder (VS–43), the wedge snow density cutter (WC1000 and WC250), and the Snow Fork. The average bulk snow density of all instrument measurements was set as the reference value for evaluation. The results showed that as compared with the reference, the VS–43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE (11 kg m−3), BIAS (3 kg m−3), and MRE (1.6%). For layer observation, bulk snow density was overestimated by 8.1% with WC1000 and underestimated by 11.4% with Snow Fork which was the worst performance compared with the reference value, and there were greater measurement errors of snow density in the depth hoar than other snow layers. Compared with grassland, the uncertainty of snow density measurements was slightly lower in forests. Overall, the Federal Sampler and VS–43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China, and it is recommended to use WC1000, WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

Surface primary producer phenology in Dease Strait, NU, Canada, examined using submersed oceanographic sensors and satellite remote sensing

Thinning sea ice cover and earlier melt in the Arctic impact primary producer (PP) phenology, causing earlier ice algal bloom termination and phytoplankton bloom commencement. However, logistic constraints limit capturing the complete seasonal evolution of PPs and their physical drivers. Here, we combine spectral irradiance data from subsurface oceanographic moorings with synthetic aperture radar backscatter and meteorological variables to study light in Dease Strait, investigating its relation to timing and magnitude of surface PPs for 2017 and 2019. Ice algal blooms in 2017 and 2019 lasted 66 and 84 days, respectively, peaking within 2 days of snow melt onset. In 2019, lower temperatures and a deeper snowpack before snow melt extended the ice algal bloom. Melt pond formation increased light transmission, enabling a short, 6–7-day under-ice phytoplankton bloom in both years that was likely nutrient-limited. The 2019 phytoplankton bloom was less productive, possibly due to the longer ice algal bloom depleting surface nutrients. After ice break-up in 2019, a 31-day late-summer bloom occurred via wind-driven mixing. Our findings suggest that the combined remote sensing technique has novel applicability in other settings, providing insights into the changing state of PP phenology, and the need for long-term Arctic observations to discern regional climate change effects.

An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers.

Camera traps deployed with olfactory attractants are used to survey rare and elusive carnivores. Study areas with deep snowpack and rugged terrain present challenges and risks to field personnel, who traditionally must revisit camera stations regularly to refresh attractants. In such locations, alternative overwinter survey protocols that include a persistent attractant would improve both the safety and efficiency of camera-trap surveys. We present a protocol for installing camera traps and automated scent dispensers on trees at above-average maximum snow depth to eliminate the need for interim service visits and to enable standardized surveys to be conducted throughout the year. Our protocol proved to be effective at attracting and detecting numerous and repeated visits by wolverines, fishers, and other carnivores in two montane regions of the western contiguous United States. The volume, timing, and composition of liquid scent lure released by automated scent dispensers can be varied to target multiple species of interest, and the dispenser can be used insituations where bait rewards may influence the behavior of target species and/or pose human safety concerns.

Arctic sea ice retreat fuels boreal forest advance.

Climate-induced northward advance of boreal forest is expected to lessen albedo, alter carbon stocks, and replace tundra, but where and when this advance will occur remains largely unknown. Using data from 19 sites across 22 degrees of longitude along the tree line of northern Alaska, we show a stronger temporal correlation of tree ring growth with open water uncovered by retreating Arctic sea ice than with air temperature. Spatially, our results suggest that tree growth, recruitment, and range expansion are causally linked to open water through associated warmer temperatures, deeper snowpacks, and improved nutrient availability. We apply a meta-analysis to 82 circumarctic sites, finding that proportionally more tree lines have advanced where proximal to ongoing sea ice loss. Taken together, these findings underpin how and where changing sea ice conditions facilitate high-latitude forest advance.

Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach

Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.

Characteristics of Rain-Snow Transitions Over the Canadian Rockies and their Changes in Warmer Climate Conditions

ABSTRACT The southern Canadian Rockies are prone to extreme precipitation that often leads to high streamflow, deep snowpacks, and avalanche risks. Many of these precipitation events are associated with rain–snow transitions, which are highly variable in time and space due to the complex topography. A warming climate will certainly affect these extremes and the associated rain–snow transitions. The goal of this study is to investigate the characteristics and variability of rain–snow transitions aloft and how they will change in the future. Weather Research and Forecasting (WRF) simulations were conducted from 2000 to 2013 and these were repeated in a warmer pseudo-global warming (PGW) future. Rain–snow transitions occurred aloft throughout the year over the southern Canadian Rockies, but their elevations and depths were highly variable, especially across the continental divide. In PGW conditions, with future air temperatures up to 4–5°C higher on average over the Canadian Rockies, rain–snow transitions are projected to occur more often throughout the year, except during summer. The near-0°C conditions associated with rain–snow transitions are expected to increase in elevation by more than 500 m, resulting in more rain reaching the surface. Overall, this study illustrates the variability of rain–snow transitions, which often impact the location of the snowline. This study also demonstrates the non-uniform changes under PGW conditions, due in part to differences in the types of weather patterns that generate rain–snow transitions across the region.

Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons

Abstract. Dissolved organic matter (DOM) mobilized from the organic (O) horizons of forest soils is a temporally dynamic flux of carbon (C) and nutrients, and the fate of this DOM in downstream pools is dependent on the rate and pathways of water flow as well as its chemical composition. Here, we present observations of the composition of DOM mobilized weekly to monthly from O horizons in mature forest and adjacent harvested treatment plots. The study site was experimentally harvested, without replanting, 10-years prior to this study. Thus, the treatments differ significantly in terms of forest stand and soil properties, and they interact differently with the regional hydrometeorological conditions. This presented an opportunity to investigate the role of forest structure relative to environmental variation on soil DOM mobilization. On an annual basis, fluxes of total dissolved nitrogen (TDN) and dissolved organic nitrogen (DON) were largest from the warmer and thinner O horizons of the harvested (H) treatment compared to the forest (F) treatment; however, neither phosphate nor ammonium fluxes differed by treatment type. On a short-term basis in both H and F treatments, all fluxes were positively correlated to water input, and all concentrations were positively correlated to soil temperature and negatively correlated to water input. Soil moisture was negatively correlated to the C : N of DOM. These results suggest common seasonal controls on DOM mobilization regardless of harvesting treatment. Optical characterization of seasonally representative samples additionally supported a stronger control of season over harvesting. The chemical character of DOM mobilized during winter and snowmelt: lower C : N, higher specific ultraviolet absorbance and lower molecular weight of chromophoric DOM (CDOM; higher spectral slope ratio) were representative of relatively more decomposed DOM compared to that mobilized in summer and autumn. This shows that the decomposition of soil organic matter underneath a consistently deep snowpack is a key determinant of the composition of DOM mobilized from O horizons during winter and the hydrologically significant snowmelt period regardless of harvesting impact. Despite the higher proportion of aromatic DOM in the snowmelt samples, its lower molecular weight and rapid delivery from O to mineral horizons suggests that the snowmelt period is not likely to be a significant period of DOM sequestration by mineral soil. Rather, the higher-molecular-weight, high-C : N DOM mobilized during slow and relatively infrequent delivery during summer and rapid, frequent delivery during autumn are more likely to support periods of mineral soil sequestration and increased export of fresher terrestrial DOM, respectively. These observed seasonal dynamics in O horizon DOM suggest the predicted decreases in winter and spring snowfall and increasing autumn and winter rainfall with climate warming in this region will enhance mobilization of DOM that is more reactive to mineral interactions in deeper soil, but also more biological and photoreactive in the aquatic environment. Understanding the downstream consequences of this mobilized DOM in response to these shifts in precipitation timing and form can improve our ability to predict and manage forest C balance but requires understanding the response of landscape hydrology to these changing precipitation regimes.

Lemming winter habitat: the quest for warm and soft snow.

During the cold arctic winter, small mammals like lemmings seek refuge inside the snowpack to keep warm and they dig tunnels in the basal snow layer, usually formed of a soft depth hoar, to find vegetation on which they feed. The snowpack, however, is a heterogenous medium and lemmings should use habitats where snow properties favor their survival and winter reproduction. We determined the impact of snow physical properties on lemming habitat use and reproduction in winter by sampling their winter nests for 13years and snow properties for 6years across 4 different habitats (mesic, riparian, shrubland, and wetland) on Bylot Island in the Canadian High Arctic. We found that lemmings use riparian habitat most intensively because snow accumulates more rapidly, the snowpack is the deepest and temperature of the basal snow layer is the highest in this habitat. However, in the deepest snowpacks, the basal depth hoar layer was denser and less developed than in habitats with shallower snowpacks, and those conditions were negatively related to lemming reproduction in winter. Shrubland appeared a habitat of moderate quality for lemmings as it favored a soft basal snow layer and a deep snowpack compared with mesic and wetland, but snow conditions in this habitat critically depend on weather conditions at the beginning of the winter. With climate change, a hardening of the basal layer of the snowpack and a delay in snow accumulation are expected, which could negatively affect the winter habitat of lemmings and be detrimental to their populations.

A Hydrograph-Based Approach to Improve Satellite-Derived Snow Water Equivalent at the Watershed Scale

For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.

Modeling Snow Dynamics and Stable Water Isotopes Across Mountain Landscapes

AbstractA coupled hydrologic and snowpack stable water isotope model assesses controls on isotopic inputs across a mountainous basin. Annually, the most depleted isotope conditions occur in the upper subalpine where snow accumulation is high, and rainfall is low. Snowmelt isotopic evolution over time indicates fractionation processes account for <25% snowmelt enrichment. Meltwater isotopic inputs are largely determined by controls on the amount, phase and isotopic mass of precipitation coincident with the ablation period. Effect of vapor loss from the snowpack on d‐excess in snowmelt is a balance between energy and snow‐availability. It is highest above treeline, and in the grass and aspen‐dominated portions of the upper montane where vegetation shading is low. Deep snowpack in conifer forests limit the influence of vapor loss in the subalpine. Wet years reduce the effects of vapor loss on snowmelt across the basin, except in the lower montane where added snowfall bolsters snow‐limited conditions.

Process and perturbation in a high elevation alluvial system, Yosemite National Park, USA

High-elevation alluvial river environments are little studied by geomorphologists despite their sensitivity to disturbance and their critical role as the landform supporting montane meadow ecosystems. Here, we establish the fundamental fluvial functioning and legacy factors inherent to the upper Tuolumne River in Tuolumne Meadows (elevation 2620 m asl), Yosemite National Park, USA. Evidence was drawn from structured field reconnaissance, morphological surveys, and sediment analysis; integrated with LiDAR monitoring and assessments of flood frequency, flood inundation, and sediment transport potential. Results indicate a sinuous single-thread channel in dynamic equilibrium with fluvial processes but with reduced rates of meander activity over the past century. Geomorphic processes are dominated by the influence of snowmelt discharge, partly because atmospheric river events rarely influence this high elevation. Process intensity is thus directly related to snowpack depth with monitored bank erosion rates substantially higher following the deep snowpack of 2016–17. Diurnal cycling of flow means that bedload-transport potential scales very well with total annual discharge volume. The legacy of human activity is unique here, with perturbations caused primarily by early-to-mid twentieth century infrastructure development rather than typical patterns of watershed development. Pre-eminent was 1930s instream aggregate extraction for road improvements. In conjunction with naturally very low rates of sediment supply, the resulting pit is still only one-quarter full and could disconnect downstream sediment supplies for several centuries more. The gravel pit appears to explain incision of the upper channel reach and reduced rates of lateral activity farther downstream. Future impacts will be dominated by climate change, including the potential increased influence of atmospheric river events, reduced snowpack depth, and tradeoffs in sediment production and connectivity as headwater glaciers recede. The upper Tuolumne River and its meadow typify the unusual, but not singular, geomorphology of high-elevation alluvial rivers, whose high ecological and social values justify greater attention.

Spatial Trend and Impact of Snowmelt Rate in Spring across China’s Three Main Stable Snow Cover Regions over the Past 40 Years Based on Remote Sensing

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China’s three main stable snow cover regions during the past 40 years (1981–2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China’s snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6~0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.

Sufficient conditions for rapid range expansion of a boreal conifer

Unprecedented modern rates of warming are expected to advance boreal forest into Arctic tundra1, thereby reducing albedo2–4, altering carbon cycling4 and further changing climate1–4, yet the patterns and processes of this biome shift remain unclear5. Climate warming, required for previous boreal advances6–17, is not sufficient by itself for modern range expansion of conifers forming forest–tundra ecotones5,12–15,17–20. No high-latitude population of conifers, the dominant North American Arctic treeline taxon, has previously been documented5 advancing at rates following the last glacial maximum (LGM)6–8. Here we describe a population of white spruce (Picea glauca) advancing at post-LGM rates7 across an Arctic basin distant from established treelines and provide evidence of mechanisms sustaining the advance. The population doubles each decade, with exponential radial growth in the main stems of individual trees correlating positively with July air temperature. Lateral branches in adults and terminal leaders in large juveniles grow almost twice as fast as those at established treelines. We conclude that surpassing temperature thresholds1,6–17, together with winter winds facilitating long-distance dispersal, deeper snowpack and increased soil nutrient availability promoting recruitment and growth, provides sufficient conditions for boreal forest advance. These observations enable forecast modelling with important insights into the environmental conditions converting tundra into forest.

Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale

There has been a statement that the satellite and reanalyses significantly overestimate snow depths (SDs) as compared with in-situ observations over the Tibetan Plateau (TP). The inconsistency may be partly due to representations related to different spatial resolutions. To further clarify matters of scale, this work estimates and compares the fractional snow cover (FSC) from MODIS (500 m), SDs from Sentinel-1 C-band SAR (1 km), ERA5-Land (9 km) and ERA5 (31 km), together with ground observations from GHCN-D and at 6 newly-established stations in the Namco watershed. SD and FSC from MERRA-2 (50 km) are also discussed in comparison with ERA5s. Results indicate that SD from fine resolution reanalysis has better consistency to in-situ observations over the TP. SD of ERA5-Land matches in-situ measurements better than ERA5 and MERRA-2. Overestimates of SDs in reanalyses are likely for shallow snowpack. However, underestimates are found for deep snow particularly late in the snow season. Improvements are displayed in ERA5-Land, while increased SD is attributed by altitude in fine resolution. Overall SDs of ERA5 and ERA5-Land have similar spatial distributions and annual cycle patterns over the TP, consistent with satellite-based datasets. A notable defect of ERA5s is related to delayed ablation of deep snowpack during spring and early summer. It causes cold temperature biases at surface that may impact on the land-atmosphere interaction. Snow analysis that combines the information of FSC into SD has been demonstrated in MERRA-2 data. Snow analysis has the potential to improve SDs of ERA5s over the TP.

Snow Nitrate Isotopes in Central Antarctica Record the Prolonged Period of Stratospheric Ozone Depletion From ∼1960 to 2000

AbstractInterpretation of NO3− variability recorded in ice cores remains challenging as it can be lost from snow. Here, we present 60‐year records of NO3− and its isotopic composition (δ15N, δ18O, and Δ17O) in snow in central Antarctica, Dome A. In the upper ∼90 cm snowpack, variations in concentration and isotopic composition of NO3− are dominated by photolytic loss, and δ18O and Δ17O of NO3− are associated with the recycling of NOx to NO3− in the condensed phase driven by photolysis. In the deeper snowpack (∼1960–2000), we observe prolonged trends in concentration and isotopic composition of NO3−, which are best explained as enhanced snow NO3− photolysis due to long‐term decreasing total column ozone (TCO). That is, the prolonged period of trends in NO3− and its isotopes in extremely low snow accumulation sites such as Dome A relay information on variations in TCO and consequently surface solar ultraviolet radiation over time.

Impacts of ecological succession and climate warming on permafrost aggradation in drained lake basins of the Tuktoyaktuk Coastlands, Northwest Territories, Canada

AbstractRapidly increasing air temperatures will alter permafrost conditions across the Arctic, but variation in soils, vegetation, snow conditions, and their effects on ground thermal regime complicate prediction across spatial and temporal scales. Processes that result in the emergence of new surfaces (lake drainage, channel migration, isostatic uplift, etc.) provide an opportunity to assess the factors influencing permafrost aggradation and terrain evolution under a warming climate. In this study we describe ground temperatures, vegetation, and snow and soil conditions at six drained lake basins (DLBs) that have exposed new terrain in the Tuktoyaktuk Coastlands in the last 20–100 years. We also use one‐dimensional thermal modeling to assess the impact of ecological succession and future climate scenarios on permafrost conditions in historical and future DLBs. Our field observations show that deep snow pack and shallow organic layers at shrub‐dominated DLBs promote increased thaw depth and ground temperatures compared to a sedge‐dominated DLB and two ancient DLB reference sites. Modeling of past and future drainages shows that climate warming projected under RCP 8.5 will reduce rates of permafrost aggradation and thickness, and drive top‐down thaw that could degrade permafrost in shrub‐dominated DLBs by the end of the century. Permafrost at sedge‐dominated sites was more resilient to warming under RCP 8.5, with the onset of top‐down thaw delayed until about 2080. Together, this indicates that the effects of ecological succession on organic soil development and snow drifting will strongly influence the aggradation and resilience of permafrost in DLBs. Our analysis suggests that DLBs and other emergent landscapes will be the first permafrost‐free environments to develop under a warming climate in the continuous permafrost zone.

Properties and stratigraphy of polar ice patches in the Canadian High Arctic reveal their current resilience to warm summers

Ice patches are ubiquitous in polar regions and are a key element for landscape evolution. We present new insights into polar desert ice patch formation based on snow and ice properties at Ward Hunt Island (Canadian High Arctic, 83°N). Our results demonstrate that ice patches are composed of two distinct units. The upper unit is characterized by very fine granular and bubbly ice with a clear oblique layering. By contrast, the lower unit is strikingly different with coarse crystals, lower porosity, and a high frequency of fractures. For both units, superimposed ice formation at the base of the deep snowpack stands out as the primary ice aggradation process. The distinct properties of the lower unit likely result from a long period of kinetic ice crystal growth indicating a minimum age of several hundred years. A radiocarbon date of 3 487 ± 20 cal BP suggests that ice patches could potentially date back to the late Holocene. This old ice was recently truncated during warmer summers between 2008 and 2012, but the ice patch quickly recovered its volume during cooler summers. The old age of the ice patches and their rapid regeneration after melt events suggest their resilience to current warmer summers.

Future of Winter in Northeastern North America: Climate Indicators Portray Warming and Snow Loss That Will Impact Ecosystems and Communities

Winters in northeastern North America have warmed faster than summers, with impacts on ecosystems and society. Global climate models (GCMs) indicate that winters will continue to warm and lose snow in the future, but uncertainty remains regarding the magnitude of warming. Here, we project future trends in winter indicators under lower and higher climate-warming scenarios based on emission levels across northeastern North America at a fine spatial scale (1/16°) relevant to climate-related decision making. Under both climate scenarios, winters continue to warm with coincident increases in days above freezing, decreases in days with snow cover, and fewer nights below freezing. Deep snowpacks become increasingly short-lived, decreasing from a historical baseline of 2 months of subnivium habitat to <1 month under the warmer, higher-emissions climate scenario. Warmer winter temperatures allow invasive pests such as Adelges tsugae (Hemlock Woolly Adelgid) and Dendroctonus frontalis (Southern Pine Beetle) to expand their range northward due to reduced overwinter mortality. The higher elevations remain more resilient to winter warming compared to more southerly and coastal regions. Decreases in natural snowpack and warmer temperatures point toward a need for adaptation and mitigation in the multi-million-dollar winter-recreation and forest-management economies.

Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)

Abstract. Continuous and spatially distributed data of snow mass (water equivalent of snow cover, SWE) from automatic ground-based measurements are increasingly required for climate change studies and for hydrological applications (snow hydrological-model improvement and data assimilation). We present and compare four new-generation sensors, now commercialized, that are non-invasive and based on different radiations that interact with snow for SWE monitoring: cosmic-ray neutron probe (CRNP), gamma ray monitoring (GMON) scintillator, frequency-modulated continuous-wave radar (FMCW radar) at 24 GHz and global navigation satellite system (GNSS) receivers (GNSSr). All four techniques have relatively low power requirements, provide continuous and autonomous SWE measurements, and can be easily installed in remote areas. A performance assessment of their advantages, drawbacks and uncertainties is discussed from experimental comparisons and a literature review. Relative uncertainties are estimated to range between 9 % and 15 % when compared to manual in situ snow surveys that are also discussed. Results show the following. (1) CRNP can be operated in two modes of functioning: beneath the snow, it is the only system able to measure very deep snowpacks (&gt; 2000 mm w.e.) with reasonable uncertainty across a wide range of measurements; CRNP placed above the snow allows for SWE measurements over a large footprint (∼ 20 ha) above a shallow snowpack. In both cases, CRNP needs ancillary atmospheric measurements for SWE retrieval. (2) GMON is the most mature instrument for snowpacks that are typically up to 800 mm w.e. Both CRNP (above snow) and GMON are sensitive to surface soil moisture. (3) FMCW radar needs auxiliary snow-depth measurements for SWE retrieval and is not recommended for automatic SWE monitoring (limited to dry snow). FMCW radar is very sensitive to wet snow, making it a very useful sensor for melt detection (e.g., wet avalanche forecasts). (4) GNSSr allows three key snowpack parameters to be estimated simultaneously: SWE (range: 0–1000 mm w.e.), snow depth and liquid water content, according to the retrieval algorithm that is used. Its low cost, compactness and low mass suggest a strong potential for GNSSr application in remote areas.