Arctic Snowpack Research Articles

Exploring the decision-making process in model development: focus on the Arctic snowpack

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

Convection of snow: when and why does it happen?

Convection of water vapor in snowpacks is supposed to have a major impact on snow density and microstructure profiles with strong implications for the thermal regime and snow stability. However, the process has never been directly measured and only recently been simulated for idealized conditions. The analysis suggests that natural convection is not likely to happen in typical horizontally homogeneous polar or Alpine snow covers. This paper studies the potential impact of heterogeneity induced, e.g., by shrubs on convection of water vapor. We find that natural convection triggered by buoyancy occurs even with sub-critical Rayleigh number as low as 5 due to heterogeneity in snow density. This leads to complementing contributions of diffusive and convective flux divergence on snow density changes. The combined effect of diffusion and convection helps to generate the often-observed low density foot and high-density top of, e.g., Arctic snowpacks. The strongest effect of convection is not for very thin or thick snow covers but for snow covers with thickness in the order of 0.5 m. This scale facilitates the development of convection cells. Further work should address the additional effects of sub-snow lateral temperature variations and assess the effect of convective vapor fluxes on snow microstructure.

Snow and meteorological conditions at Villum Research Station, Northeast Greenland: on the adequacy of using atmospheric reanalysis for detailed snow simulations

Reliable and detailed measurements of atmospheric and snow conditions in the Arctic are limited. While modern atmospheric reanalyses could potentially replace the former, the latter can be principally simulated by dedicated snow modelling. However, because the uncertainties of reanalyses and modelling are still exceptionally large at high latitudes, a thorough analysis of the performance of atmospheric reanalyses and the snow model simulations are required. Specifically, we aim to answer the following questions for Villum Research Station (VRS), northeast Greenland: (1) What are the predominant snow and meteorological conditions? (2) What are systematic differences between the modern atmospheric reanalysis ERA5 and in situ measurements? (3) Can the snow model Crocus simulate reliably snow depth and stratigraphy? We systematically compare atmospheric in situ measurements and ERA5 reanalysis (November 2015–August 2018) and evaluate simulated and measured snow depth (October 2014–September 2018). Moreover, modelled and measured vertical profiles of snow density and snow specific surface area (SSA) are analysed for two days where a survey had taken place. We found good agreement between in situ and ERA5 atmospheric variables with correlation coefficients >0.84 except for precipitation, wind speed, and wind direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Crocus can simulate satisfactorily the evolution of snow depth, but simulations of SSA and density profiles, whether driven by ERA5 or in situ measurements are biased compared to measurements. Unexpectedly, measured snow depth agrees better with ERA5 driven simulation than with simulation forced with in situ measurements (explained variance: 0.73 versus 0.23). This is due to differences in snowfall, humidity and air temperature between both forcing datasets. In conclusion, ERA5 has great potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.

Long-range transport and deposition on the Arctic snowpack of nuclear contaminated particulate matter

The primary environmental concern related to nuclear power is the production of radioactive waste hazardous to humans and the environment. The main scientific and technological problems to address this are related to the storage and disposal of the nuclear waste and monitoring the dispersion of radioactive species into the environment. In this work, we determined an anomalously high 14C activity, well above the modern natural background, on surface and seasonal snow sampled in early May 2019 on glaciers in the Hornsund fjord area (Svalbard). Due to the lack of local sources, the high snow concentrations of 14C suggest long-range atmospheric transport of nuclear waste particles from lower latitudes, where nuclear power plants and treatment stations are located. The analysis of the synoptic and local meteorological data allowed us to associate the long-range transport of this anomalous 14C concentration to an intrusion event of a warm and humid air mass that likely brought pollutants from Central Europe to the Arctic in late April 2019. Elemental and organic carbon, trace element concentration data, and scanning electron microscopy morphological analysis were performed on the same snow samples to better constrain the transport process that might have led to the high 14C radionuclide concentrations in Svalbard. In particular, the highest 14C values found in the snowpack (> 200 percent of Modern Carbon, pMC) were associated with the lowest OC/EC ratios (< 4), an indication of an anthropogenic industrial source, and with the presence of spherical particles rich in iron, zirconium, and titanium which, altogether, suggest an origin related to nuclear waste reprocessing plants. This study highlights the role of long-range transport in exposing Arctic environments to human pollution. Given that the frequency and intensity of these atmospheric warming events are predicted to increase due to ongoing climate change, improving our knowledge of their possible impact to Arctic pollution is becoming urgent.

Assessment of Arctic snow stratigraphy and water equivalent using a portable Frequency Modulated Continuous Wave RADAR

There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement.

Bacterial Colonisation: From Airborne Dispersal to Integration Within the Soil Community.

The deposition of airborne microorganisms into new ecosystems is the first stage of colonisation. However, how and under what circumstances deposited microorganisms might successfully colonise a new environment is still unclear. Using the Arctic snowpack as a model system, we investigated the colonisation potential of snow-derived bacteria deposited onto Arctic soils during and after snowmelt using laboratory-based microcosm experiments to mimic realistic environmental conditions. We tested different melting rate scenarios to evaluate the influence of increased precipitation as well as the influence of soil pH on the composition of bacterial communities and on the colonisation potential. We observed several candidate colonisations in all experiments; with a higher number of potentially successful colonisations in acidoneutral soils, at the average snowmelt rate measured in the Arctic. While the higher melt rate increased the total number of potentially invading bacteria, it did not promote colonisation (snow ASVs identified in the soil across multiple sampling days and still present on the last day). Instead, most potential colonists were not identified by the end of the experiments. On the other hand, soil pH appeared as a determinant factor impacting invasion and subsequent colonisation. In acidic and alkaline soils, bacterial persistence with time was lower than in acidoneutral soils, as was the number of potentially successful colonisations. This study demonstrated the occurrence of potentially successful colonisations of soil by invading bacteria. It suggests that local soil properties might have a greater influence on the colonisation outcome than increased precipitation or ecosystem disturbance.

Chemical Fractionation of Trace Elements in Arctic PM10 Samples

In this study, the information potential of a two-step sequential extraction procedure was evaluated. For this purpose, first of all the elemental composition of Arctic PM10 samples collected in Ny-Ålesund (Svalbard Islands) from 28 February 2015 to 21 October 2015 was investigated. Enrichment Factors, Principal Component Analysis and Hierarchical Cluster Analysis were performed to identify PM10 sources and to understand the effects of short- and long-range transport processes. The investigation of the potential source areas was also aided by taking into account back-trajectories. Then, the sequential extraction procedure was applied to some of the samples in order to obtain more information on these sources. This approach allowed us to establish that most of the elements prevalently having an anthropogenic origin not only were present in higher concentrations, but they were also more easily extractable in late winter and early spring. This confirms the common statement that the anthropogenic portion of the elements present in a sample is generally loosely bound to the particulate matter structure, and so it is more easily extractable and releasable on the Arctic snowpack. Moreover, in the samples collected in late winter and early spring, even the elements prevalently having a crustal origin were more easily extractable, probably due to the particle size selection occurred during the long-range transport.

Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models

Abstract. Seasonal snow covers Arctic lands 6 to 10 months of the year and is therefore an essential element of the Arctic geosphere and biosphere. Yet, even the most sophisticated snow physics models are not able to simulate fundamental physical properties of Arctic snowpacks such as density, thermal conductivity and specific surface area. The development of improved snow models is in progress, but testing requires detailed driving and validation data for high Arctic herb tundra sites, which are presently not available. We present 6 years of such data for an ice-wedge polygonal site in the Canadian high Arctic, in Qarlikturvik valley on Bylot Island at 73.15∘ N. The site is on herb tundra with no erect vegetation and thick permafrost. Detailed soil properties are provided. Driving data are comprised of air temperature, air relative and specific humidity, wind speed, shortwave and longwave downwelling radiation, atmospheric pressure, and precipitation. Validation data include time series of snow depth, shortwave and longwave upwelling radiation, surface temperature, snow temperature profiles, soil temperature and water content profiles at five depths, snow thermal conductivity at three heights, and soil thermal conductivity at 10 cm depth. Field campaigns in mid-May for 5 of the 6 years of interest provided spatially averaged snow depths and vertical profiles of snow density and specific surface area in the polygon of interest and at other spots in the valley. Data are available at https://doi.org/10.5885/45693CE-02685A5200DD4C38 (Domine et al., 2021). Data files will be updated as more years of data become available.

Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures

The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes—including the effect of Arctic low vegetation and wind on snowpack—in the Crocus detailed snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of −0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979–2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5°, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of −0.33 cm dec−1. With the ERAi air temperature trend of +0.84 K dec−1 featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec−1 in winter. We show that the implemented snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec−1) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology.

Measurement of Snow Physical Properties and Stable Isotope Variations in the Canadian Sub-Arctic and Arctic Snowpack

ABSTRACT In northern Canada, the annual peak in river discharge is dominated by the seasonal input of snowmelt. As such, climatic changes that alter snowmelt properties and timing will have cascading impacts on the hydrological system as the Arctic warms. Geochemical tracers provide a tool to characterize the various processes governing the seasonal evolution of the snowpack; however, a lack of snow observations from a variety of Arctic landscapes limits the broad applicability of such tracers and further impedes our understanding of the various processes governing snowpack evolution and its ultimate contribution to the spring discharge peak. This study aims to gain a better understanding of the spatial distribution and the temporal evolution of the natural stable isotope signatures of snow from two distinct ecoregions: open tundra and taiga. More specifically, we describe the geophysical and stable isotope properties of the snow cover at Wekweètì (Northwest Territories), a high sub-Arctic taiga site, and within the Greiner Lake Watershed, near Cambridge Bay (Nunavut), an open Arctic tundra site. Results illustrate a link between snowpack formation and stable isotope distributions at both study sites. Stable oxygen isotope ratios of snow (δ 18O-H2O) show a wide range from −41‰ to −17‰ across all snow depth classes; however, heavy isotope enrichment is clearly visible in the bottom snow layers at both sites. Vapour flux from the ground under a strong temperature gradient is considered to be the main driver for this enrichment due to kinetic metamorphism, which is more prominent at the open tundra site. The stable isotope signatures of the bottom hoar layers during winter were found to be similar to river water values sampled during spring and summer, highlighting the need for more in-depth hydrological cycle assessment.

Spatiotemporally variable snow properties drive habitat use of an Arctic mesopredator.

Climate change is rapidly altering the composition and availability of snow, with implications for snow-affected ecological processes, including reproduction, predation, habitat selection, and migration. How snowpack changes influence these ecological processes is mediated by physical snowpack properties, such as depth, density, hardness, and strength, each of which is in turn affected by climate change. Despite this, it remains difficult to obtain meaningful snow information relevant to the ecological processes of interest, precluding a mechanistic understanding of these effects. This problem is acute for species that rely on particular attributes of the subnivean space, for example depth, thermal resistance, and structural stability, for key life-history processes like reproduction, thermoregulation, and predation avoidance. We used a spatially explicit snow evolution model to investigate how habitat selection of a species that uses the subnivean space, the wolverine, is related to snow depth, snow density, and snow melt on Arctic tundra. We modeled these snow properties at a 10m spatial and a daily temporal resolution for 3 years, and used integrated step selection analyses of GPS collar data from 21 wolverines to determine how these snow properties influenced habitat selection and movement. We found that wolverines selected deeper, denser snow, but only when it was not undergoing melt, bolstering the evidence that these snow properties are important to species that use the Arctic snowpack for subnivean resting sites and dens. We discuss the implications of these findings in the context of climate change impacts on subnivean species.

Arctic Snow Isotope Hydrology: A Comparative Snow-Water Vapor Study

The Arctic’s winter water cycle is rapidly changing, with implications for snow moisture sources and transport processes. Stable isotope values (δ18O, δ2H, d-excess) of the Arctic snowpack have potential to provide proxy records of these processes, yet it is unclear how well the isotope values of individual snowfall events are preserved within snow profiles. Here, we present water isotope data from multiple taiga and tundra snow profiles sampled in Arctic Alaska and Finland, respectively, during winter 2018–2019. We compare the snowpack isotope stratigraphy with meteoric water isotopes (vapor and precipitation) during snowfall days, and combine our measurements with satellite observations and reanalysis data. Our analyses indicate that synoptic-scale atmospheric circulation and regional sea ice coverage are key drivers of the source, amount, and isotopic composition of Arctic snowpacks. We find that the western Arctic tundra snowpack profiles in Alaska preserved the isotope values for the most recent storm; however, post depositional processes modified the remaining isotope profiles. The overall seasonal evolution in the vapor isotope values were better preserved in taiga snow isotope profiles in the eastern Arctic, where there is significantly less wind-driven redistribution than in the open Alaskan tundra. We demonstrate the potential of the seasonal snowpack to provide a useful proxy for Arctic winter-time moisture sources and propose future analyses.

The Impact of Diffusive Water Vapor Transport on Snow Profiles in Deep and Shallow Snow Covers and on Sea Ice

Water vapor transport has been highlighted as a critical process in Arctic snowpacks, shaping the snow cover structure in terms of density, thermal conductivity, and temperature profile among others. Here, we present an attempt to describe the thermally-induced vertical diffusion of water vapor in the snow cover and its effects of the snowpack structure using the SNOWPACK model. Convection, that may also constitute a significant part of vapor transport, is not addressed. Assuming saturated conditions at the upper boundary of the snowpack and as initial condition, the vapor flux between snow layers is expressed by a 1-dimensional transient diffusion equation, which is solved with a finite difference routine. The implications on the snowpack of this vertical diffusive flux, are analyzed using metrics such as the cumulative density change due to diffusive vapor transport, the degree of over- or undersaturation, the instantaneous snow density change rate, and the percentage of snow density change. We present results for four different regions sampling the space of natural snow cover variability: Alpine, Subarctic, Arctic, and Antarctic sea ice. The largest impact of diffusive water vapor transport is observed in snow on sea ice in the Weddell Sea and the shallow Arctic snowpack. The simulations show significant density reductions upon inclusion of diffusive water vapor transport: cumulative density changes from diffusive vapor transport can reach �62 and �66 kg m�3 for the bottom layer in the shallow Arctic snowpack and snow on sea ice, respectively. For comparison, in deeper snow covers, they rarely exceed �40 kg m�3. This leads to changes in density for shallow snowpacks at the soil-snow interface in the range of �5 to �21%. Mirroring the density decease at depth is a thicker deposition layer above it with increase in density around 7.5%. Similarly, for the sea ice, the density decreased at the sea ice-snow interface by �20%. We acknowledge that vapor transport by diffusion may in some snow covers�such as in thin tundra snow�be small compared to convective transport, which will have to be addressed in future work.

Black Carbon and Inorganic Aerosols in Arctic Snowpack

AbstractBlack carbon (BC) deposited on snow lowers its albedo, potentially contributing to warming in the Arctic. Atmospheric distributions of BC and inorganic aerosols, which contribute directly and indirectly to radiative forcing, are also greatly influenced by depositions. To quantify these effects, accurate measurement of the spatial distributions of BC and ionic species representative of inorganic aerosols (ionic species hereafter) in snowpack in various regions of the Arctic is needed, but few such measurements are available. We measured mass concentrations of size‐resolved BC (CMBC) and ionic species in snowpack by using a single‐particle soot photometer and ion chromatography, respectively, over Finland, Alaska, Siberia, Greenland, and Spitsbergen during early spring in 2012–2016. Total BC mass deposited per unit area (DEPMBC) during snow accumulation periods was derived from CMBC and snow water equivalent (SWE). Our analyses showed that the spatial distributions of anthropogenic BC emission flux, total precipitable water, and topography strongly influenced latitudinal variations of CMBC, BC size distributions, SWE, and DEPMBC. The average size distributions of BC in Arctic snowpack shifted to smaller sizes with decreasing CMBC due to an increase in the removal efficiency of larger BC particles during transport from major sources. Our measurements of CMBC were lower by a factor of ~13 than previous measurements made with an Integrating Sphere/Integrating Sandwich spectrophotometer due mainly to interference from coexisting non‐BC particles such as mineral dust. The SP2 data presented here will be useful for constraining climate models that estimate the effects of BC on the Arctic climate.

What guides lemmings movements through the snowpack?

Abstract The presence of a snowpack, which may last up to 9 months in the Arctic, can provide insulation from the cold winter temperature for small mammals living beneath it, such as lemmings. Since lemmings have to move through the snowpack during that period, it is important to better understand how the physical properties of snow affect the way they dig tunnels. Here, we tested 1) whether lemmings systematically dig in the snowpack at the ground level where they can find their food plants, and 2) whether they choose the softest snow layer in which to dig, which is usually the depth hoar layer in the arctic snowpack. We found 33 lemming tunnels in 2017 and 2018 by digging through the snow at the sites of arctic fox attacks on lemmings. Contrary to our expectation, almost all the tunnels (32/33) were found to be higher than ground level, probably because of the presence of obstacles (i.e., melt-freeze crusts or hummocks) at the base of the snowpack. As predicted, all tunnels were dug in the soft depth hoar layer, which had a lower density than snow layers below and above it. Lemmings also showed a preference to dig their tunnels at the top of the depth hoar, just below a hard snow layer. Systematically digging their tunnels in the lowest-density snow layer, regardless of its height in the snow pack, could be a strategy for lemmings to minimize energy expenditure, which could improve their survival and chances of reproducing in winter.

Deposition of ionic species and black carbon to the Arctic snowpack: combining snow pit observations with modeling

Abstract. Although aerosols in the Arctic have multiple and complex impacts on the regional climate, their removal due to deposition is still not well quantified. We combined meteorological, aerosol, precipitation, and snowpack observations with simulations to derive information about the deposition of sea salt components and black carbon (BC) from November 2011 to April 2012 to the Arctic snowpack at two locations close to Ny-Ålesund, Svalbard. The dominating role of sea salt and the contribution of dust for the composition of atmospheric aerosols were reflected in the seasonal composition of the snowpack. The strong alignment of the concentrations of the major sea salt components in the aerosols, the precipitation, and the snowpack is linked to the importance of wet deposition for transfer from the atmosphere to the snowpack. This agreement was less strong for monthly snow budgets and deposition, indicating important relocation of the impurities inside the snowpack after deposition. Wet deposition was less important for the transfer of nitrate, non-sea-salt sulfate, and BC to the snow during the winter period. The average BC concentration in the snowpack remains small, with a limited impact on snow albedo and melting. Nevertheless, the observations also indicate an important redistribution of BC in the snowpack, leading to layers with enhanced concentrations. The complex behavior of bromide due to modifications during sea salt aerosol formation and remobilization in the atmosphere and in the snow were not resolved because of the lack of bromide measurements in aerosols and precipitation.

Diversity of metals and metal-interactive bacterial populations in different types of Arctic snow and frost flowers: Implications on snow freeze-melt processes in a changing climate

Arctic snow has been shown to be a reactive interface for key physical, chemical, and microbiological processes, affecting the Arctic's oxidation, biodiversity, radiation, and climate. To explore the potential links between snow-borne metal contaminants and metal-interactive bacteria, to freezing/melting processes, we performed concurrent chemical characterization, genomic, and morphological analysis of five different Arctic snowpack (accumulated, blowing, fresh falling, surface hoar, and wind pack snow) and frost flower in Utqiaġvik (Barrow), Alaska, using Montreal urban snow as reference. Several complementary analytical techniques, including triple quad ICP-MS/MS along with various chromatography techniques, thermal ionization mass spectrometer (TIMS), high-resolution transition electron microscopy with electron dispersive X-ray spectroscopy (HR-TEM/EDS), and next generation sequencing (NGS), were deployed. Distinct metal composition and bacterial distribution among samples were observed. The concentration of 27 different transition, post-transition, rare, and radioactive metals were determined in molten snow and frost flower, as well as filtered samples. The range of three highest detected metal concentrations among samples were: Hg (3.294–134.485 μg/L), Fe (0.719–34.469 μg/L), and Sr (1.676–19,297.000 μg/L). NGS analysis led to the identification of metal interacting bacteria in all types of snow and frost flowers in the Arctic (blowing snow (1239), surface hoar snow (2243), windpack (2431), frost flowers (1440)), and Montreal urban snow (5498)) with specific bacterial genera such as: Acinetobacter, Arcenicella, Azospirillum (surface hoar snow), Arthrobacter, Paenibacillus (blowing snow), and Cycloclasticus, OM182 clade (frost flower). Several types of bacteria with confirmed or associated ice nucleation activity were observed in different types of snow, and frost flower including Pseudomonas genera (e.g., Pseudomonas fluorescens), Flavobacterium, Corynebacterium, and Pseudoxanthomonas. The implications of the above findings to snow-air interactions including nanoparticles, namely during melting and freezing cycles, and to probe the impact of various natural and anthropogenic activities are herein discussed.

HONO, Particulate Nitrite, and Snow Nitrite at a Midlatitude Urban Site during Wintertime

Nitrous acid (HONO) plays an important role in the oxidative capacity of the atmosphere during wintertime via photolysis to produce hydroxyl radicals (OH). While it is known that HONO is emitted from the Arctic snowpack, sparse observations of HONO in the midlatitude urban wintertime environment have hindered our understanding of cold-season atmospheric chemistry. In this study, measurements of ambient HONO, particulate nitrite (pN(III); N(III) = NO2(aq)– + HONO(aq) + H2ONO(aq)+), and snow nitrite (sN(III)) were conducted in Kalamazoo, Michigan during January–February 2018. Elevated levels of HONO and particulate nitrite were observed over snow-covered ground, likely due to emissions of HONO from the snowpack, as well as weak turbulent mixing in the atmospheric boundary layer. The noontime peak in HONO of 87 ± 60 (1σ) parts per trillion (ppt) over snow-covered ground suggests photochemical snowpack HONO production, likely in part through snowpack nitrate photolysis, with only a minor contribution from par...

Major Issues in Simulating Some Arctic Snowpack Properties Using Current Detailed Snow Physics Models: Consequences for the Thermal Regime and Water Budget of Permafrost

AbstractAccurately simulating the physical properties of Arctic snowpacks is essential for modeling the surface energy budget and the permafrost thermal regime. We show that the detailed snow physics models Crocus and SNOWPACK cannot simulate critical snow physical variables. Both models simulate basal layers with high density and high thermal conductivity, and top layers with low values for both variables, while field measurements yield opposite results. We explore the impact of an inverted snow stratigraphy on the permafrost thermal regime at a high Arctic site using a simplified heat transfer model and idealized snowpacks with three layers. One snowpack has a typical Arctic stratification with a low‐density insulating basal layer, while the other (called Alpine‐type snowpack) has a dense conducting basal layer. Snowpack stratification impacts simulated ground temperatures at 5 cm depth by less than 0.3 °C. Heat conduction through layered snowpacks is therefore determined by thermal insulance rather than by stratification. Ground dehydration caused by upward water vapor diffusion is 4 times greater under Arctic stratification, leading to a larger latent heat loss, but also to a lower soil thermal conductivity caused by ice loss, so that the overall effect of dehydration on ground temperature is uncertain. Snowpack stratification is found to affect snow surface temperature by up to 4 °C. Lastly, different snow metamorphism rates lead to a lower Alpine snowpack albedo, contributing to a warmer ground. Quantifying all these effects is needed for adequately simulating permafrost temperature. This requires the development of a snow and soil model that describes water vapor fluxes.

Soil moisture, wind speed and depth hoar formation in the Arctic snowpack

ABSTRACTBasal depth hoar that forms in Arctic snowpacks often has a low thermal conductivity, strongly contributing to the snowpack thermal insulance and impacting the permafrost thermal regime. At Ward Hunt Island (Canadian high Arctic, 83°05′N, 74°07′W) almost no depth hoar was observed in spring 2016 despite favorable thermal conditions. We hypothesize that depth hoar formation was impeded by the combination of two factors (1) strong winds in fall that formed hard dense wind slabs where water vapor transport was slow and (2) low soil moisture that led to rapid ground cooling with no zero-curtain period, which reduced soil temperature and the temperature gradient in the snowpack. Comparisons with detailed data from the subsequent winter at Ward Hunt and from Bylot Island (73°09′N, 80°00′W) and with data from Barrow and Alert indicate that both high wind speeds after snow onset and low soil moisture are necessary to prevent Arctic depth hoar formation. The role of convection to form depth hoar is discussed. A simple preliminary strategy to parameterize depth hoar thermal conductivity in snow schemes is proposed based on wind speed and soil moisture. Finally, warming-induced vegetation growth and soil moisture increase should reduce depth hoar thermal conductivity, potentially affecting permafrost temperature.