Alluvial fans are common features of mountainous landscapes in circumpolar regions and are characterized by a suit of hillslope processes that drive sediment distribution. At present there is little known about the biogeochemistry of these systems. Thus, this study aimed to understand alluvial fan soil carbon (C) dynamics. Surface and permafrost soil was retrieved in the apex, mid-section, and foot of a fan on Bylot Island in the Canadian Arctic. Soil characteristics such as grain size distribution, ice content and major ions, electric conductivity, as well as total C and nitrogen (N) contents were determined. Moreover, soil organic carbon (SOC) pools were assessed using density fractionation in combination with acid hydrolysis. Despite the strong influence of hillslope processes on physical sediment characteristics, hillslope location had no effect on SOC and N stocks. However, fractionation analysis showed that hillslope processes facilitate the degradation of soil C prior to its burial and integration into permafrost soil, where over 90% of the SOC pool associated with the mineral-fraction is resistant to degradation. Hence, SOC pools at the foot of alluvial fans may be considered relatively stable.
Abstract Expanding shrubs in the Arctic trap blowing snow, increasing snow height and accelerating permafrost warming. Topography also affects snow height as snow accumulates in hollows. The respective roles of topography and erect vegetation in snow accumulation were investigated using a UAV-borne lidar at two nearby contrasted sites in northern Quebec, Canada. The North site featured tall vegetation up to 2.5 m high, moderate snow height, and smooth topography. The South site featured lower vegetation, greater snow height, and rougher topography. There was little correlation between topography and vegetation height at both sites. Vegetation lower than snow height had very little effect on snow height. When vegetation protruded above the snow, snow height was well correlated with vegetation height. The topographic position index (TPI) was well correlated with snow height when it was not masked by the effect of protruding vegetation. The North site with taller vegetation therefore showed a good correlation between vegetation height and snow height, R 2 = 0.37, versus R 2 = 0.04 at the South site. Regarding topography, the reverse was observed between TPI and snow height, with R 2 = 0.29 at the North site and R 2 = 0.67 at the South site. The combination of vegetation height and TPI improved the prediction of snow height at the North site ( R 2 = 0.59) but not at the South site because vegetation height has little influence there. Vegetation was therefore the main factor determining snow height when it protruded above the snow. When it did not protrude, snow height was mostly determined by topography. Significance Statement Wind-induced snow drifting is a major snow redistribution process in the Arctic. Shrubs trap drifting snow, and drifting snow accumulates in hollows. Determining the respective roles of both these processes in snow accumulation is required to predict permafrost temperature and its emission of greenhouse gases, because thicker snow limits permafrost winter cooling. Using a UAV-borne lidar, we have determined snow height distribution over two contrasted sites in the Canadian low Arctic, with varied vegetation height and topography. When snow height exceeds vegetation height, topography is a good predictor of snow height, with negligible effect of buried vegetation. When vegetation protrudes above the snow, combining both topography and vegetation height is required for a good prediction of snow height.
Abstract. The specific surface area (SSA) of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA) of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the heat conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leads to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.
Abstract Accurately 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.
Abstract. Snow phenology, recurrent seasonal patterns in snow cover and snowfall, has been significantly affected by global warming. Through the interaction with the climate, the dynamic variability of snow phenology affects the regional climate environment, vegetation ecosystem, soil properties, agricultural water resources, snow disasters and animal migration. First, this study compares the advantages, disadvantages and applicability of different sources of observation data and the principal research methods involved in studying snow phenology. Then, this work discusses the spatiotemporal variability and changing trends of snow phenology in the Northern Hemisphere, and summarizes the relationship between climate, vegetation and snow phenology. Finally, this review highlights the key areas related to snow phenology that require further study. Overall, during the past 50 years in the Northern Hemisphere, the snow cover end date (SCED) has shown a significantly advanced trend, the snow cover onset date (SCOD) has also been occurring slowly earlier, and the snow cover days (SCD) has shortened, but these two trends are not significant. The snow phenology variation is closely related to climate factors, atmospheric circulation, vegetation status and some spatial factors. Snow cover impacts climate change through interactions with atmospheric circulation systems. The rise in temperature will delay the SCOD, and the SCED is closely related to the temperature of the snowmelt season. The interaction between seasonal snow cover and climate will either stimulate or impede vegetation growth. With the change in snow cover, especially the decrease in snow cover in the melting stage can impact the climate change, the rise in temperature will change the growth conditions and extend the vegetation growth season. The relationship between snow cover and vegetation is inconsistent in different elevations and latitudes. Snow phenology variation is very complex and is the result of the combined action of many factors. Additionally, snow phenology will also have a great impact on the cryosphere. Therefore, we must understand snow phenology variation and prepare for future changes.
Abstract. Arctic landscapes are covered in snow for at least six months of the year. The energy balance of the snow cover plays a key role in these environments, influencing the surface albedo, the thermal regime of the permafrost, and other factors. Our goal is to quantify all major heat fluxes above, within, and below a low Arctic snowpack at a shrub tundra site on the east coast of Hudson Bay in eastern Canada. The study is based on observations from a flux tower that uses the eddy covariance approach and from profiles of temperature and thermal conductivity in the snow and soil. Additionally, we compared the observations with simulations produced using the Crocus snow model. We found that radiative losses due to negative longwave radiation are mostly counterbalanced by the sensible heat flux, whereas the latent heat flux is minimal. At the snow surface, the heat flux into the snow is similar in magnitude to the sensible heat flux. Because the snow cover stores very little heat, the majority of the heat flux into the snow is used to cool the soil. Overall, the model was able to reproduce the observed energy balance, but due to the effects of atmospheric stratification, showed some deficiencies when simulating turbulent heat fluxes at an hourly time scale.
Abstract. In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure was up to now characterized by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parametrizations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg−1. Incorporating the optical diameter as a fully-fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.
Abstract. The forest–tundra ecotone is a large circumpolar transition zone between the Arctic tundra and the boreal forest, where snow properties are spatially variable due to changing vegetation. The extent of this biome through all circumpolar regions influences the climate. In the forest–tundra ecotone near Umiujaq in northeastern Canada (56∘33′31′′ N, 76∘28′56′′ W), we contrast the snow properties between two sites, TUNDRA (located in a low-shrub tundra) and FOREST (located in a boreal forest), situated less than 1 km apart. Furthermore, we evaluate the capability of the snow model Crocus, initially developed for alpine snow, to simulate the snow in this subarctic setting. Snow height and density differed considerably between the two sites. At FOREST, snow was about twice as deep as at TUNDRA. The density of snow at FOREST decreased slightly from the ground to the snow surface in a pattern that is somewhat similar to alpine snow. The opposite was observed at TUNDRA, where the pattern of snow density was typical of the Arctic. We demonstrate that upward water vapor transport is the dominant mechanism that shapes the density profile at TUNDRA, while a contribution of compaction due to overburden becomes visible at FOREST. Crocus was not able to reproduce the density profiles at either site using its standard configuration. We therefore implemented some modifications for the density of fresh snow, the effect of vegetation on compaction, and the lateral transport of snow by wind. These adjustments partly compensate for the lack of water vapor transport in the model but may not be applicable at other sites. Furthermore, the challenges using Crocus suggest that the general lack of water vapor transport in the snow routines used in climate models leads to an inadequate representation of the density profiles of even deep and moderately cold snowpacks, with possible major impacts on meteorological forecasts and climate projections.
