scholarly journals Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada

2016 ◽  
Vol 10 (6) ◽  
pp. 2573-2588 ◽  
Author(s):  
Florent Domine ◽  
Mathieu Barrere ◽  
Denis Sarrazin
Keyword(s):  
Thermal Conductivity ◽  
Bimodal Distribution ◽  
Liquid Water Content ◽  
High Arctic ◽  
Frozen Soil ◽  
The Arctic ◽  
Test Model ◽  
Snow Layer ◽  
Bylot Island ◽  
Few Data

Abstract. The values of the snow and soil thermal conductivity, ksnow and ksoil, strongly impact the thermal regime of the ground in the Arctic, but very few data are available to test model predictions for these variables. We have monitored ksnow and ksoil using heated needle probes at Bylot Island in the Canadian High Arctic (73° N, 80° W) between July 2013 and July 2015. Few ksnow data were obtained during the 2013–2014 winter, because little snow was present. During the 2014–2015 winter ksnow monitoring at 2, 12 and 22 cm heights and field observations show that a depth hoar layer with ksnow around 0.02 W m−1 K−1 rapidly formed. At 12 and 22 cm, wind slabs with ksnow around 0.2 to 0.3 W m−1 K−1 formed. The monitoring of ksoil at 10 cm depth shows that in thawed soil ksoil was around 0.7 W m−1 K−1, while in frozen soil it was around 1.9 W m−1 K−1. The transition between both values took place within a few days, with faster thawing than freezing and a hysteresis effect evidenced in the thermal conductivity–liquid water content relationship. The fast transitions suggest that the use of a bimodal distribution of ksoil for modelling may be an interesting option that deserves further testing. Simulations of ksnow using the snow physics model Crocus were performed. Contrary to observations, Crocus predicts high ksnow values at the base of the snowpack (0.12–0.27 W m−1 K−1) and low ones in its upper parts (0.02–0.12 W m−1 K−1). We diagnose that this is because Crocus does not describe the large upward water vapour fluxes caused by the temperature gradient in the snow and soil. These fluxes produce mass transfer between the soil and lower snow layers to the upper snow layers and the atmosphere. Finally, we discuss the importance of the structure and properties of the Arctic snowpack on subnivean life, as species such as lemmings live under the snow most of the year and must travel in the lower snow layer in search of food.

scholarly journals Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada

2016 ◽  
Author(s):  
Florent Domine ◽  
Mathieu Barrere ◽  
Denis Sarrazin
Keyword(s):  
Thermal Conductivity ◽  
Bimodal Distribution ◽  
High Arctic ◽  
Frozen Soil ◽  
The Arctic ◽  
Test Model ◽  
Snow Layer ◽  
Bylot Island ◽  
Physics Model ◽  
Few Data

Abstract. The values of the snow and soil thermal conductivity, ksnow and ksoil, strongly impact the thermal regime of the ground in the Arctic, but very few data are available to test model predictions for these variables. We have monitored ksnow and ksoil using heated needle probes at Bylot Island in the Canadian High Arctic (73° N, 80° W) between July 2013 and July 2015. Few ksnow data were obtained during the 2013–2014 winter, because little snow was present. During the 2014–2015 winter ksnow monitoring at 2, 12 and 22 cm heights and field observations show that a depth hoar layer with ksnow around 0.02 W m−1 K−1 rapidly formed. At 12 and 22 cm, wind slabs with ksnow around 0.2 to 0.3 W m−1 K−1 formed. The monitoring of ksoil at 10 cm depth shows that in thawed soil, ksoil was around 0.7 W m−1 K−1 while in frozen soil it was around 1.9 W m−1 K−1. The transition between both values took place within a few days, so that the use of a bimodal distribution of ksoil for modelling appears adequate, in contrast to conclusions from previous studies. This may be explained by different soil properties or by artefacts caused by using high heating powers for thermal measurements in previous works. Simulations of ksnow using the snow physics model Crocus were performed. Contrary to observations, Crocus predicts high ksnow values at the base of the snowpack (0.12 to 0.27 W m−1 K−1) and low ones in its upper parts (0.02 to 0.12 W m−1 K−1). We diagnose that this is because Crocus does not describe the large upward water vapor fluxes caused by the temperature gradient in the snow and soil. These fluxes produce mass transfer between the soil and lower snow layers to the upper snow layers and the atmosphere. Finally, we discuss the importance of the structure and properties of the Arctic snowpack on subnivean life, as species such as lemmings live under the snow most of the year and must travel in the lower snow layer in search of food.

scholarly journals 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

2021 ◽  
Author(s):  
Florent Domine ◽  
Georg Lackner ◽  
Denis Sarrazin ◽  
Mathilde Poirier ◽  
Maria Belke-Brea
Keyword(s):  
Thermal Conductivity ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
High Arctic ◽  
Specific Humidity ◽  
The Arctic ◽  
Canadian High Arctic ◽  
Validation Data ◽  
Bylot Island

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, short wave and long wave downwelling radiation, atmospheric pressure and precipitation. Validation data include time series of snow depth, shortwave 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.

scholarly journals 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

2021 ◽  
Vol 13 (9) ◽  
pp. 4331-4348
Author(s):  
Florent Domine ◽  
Georg Lackner ◽  
Denis Sarrazin ◽  
Mathilde Poirier ◽  
Maria Belke-Brea
Keyword(s):  
Thermal Conductivity ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
High Arctic ◽  
Specific Humidity ◽  
The Arctic ◽  
Canadian High Arctic ◽  
Validation Data ◽  
Bylot Island

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.

Modeling Surface Temperatures for Snow-Covered Roads: A Case Study for a Heated Pavement in Bærum, Norway

2018 ◽  
Vol 2672 (12) ◽  
pp. 220-231
Author(s):  
Anne D. W. Nuijten ◽  
Inge Hoff ◽  
Knut V. Høyland
Keyword(s):  
Thermal Conductivity ◽  
Water Content ◽  
Liquid Water ◽  
Surface Condition ◽  
Winter Season ◽  
High Water ◽  
Liquid Water Content ◽  
High Water Content ◽  
Snow Layer ◽  
Snow Samples

Heated pavements are used as an alternative to removing snow and ice mechanically and chemically. Usually a heated pavement system is automatically switched on when snowfall starts or when there is a risk of ice formation. Ideally, these systems run based on accurate predictions of surface conditions a couple of hours ahead of time, for which both weather forecasts and reliable surface temperature predictions are needed. The effective thermal conductivity of the snow layer is often described as a function of its density. However the thermal conductivity of a snow layer can vary considerably, not only for snow samples with a different density, but also for snow samples with the same density, but with a variation in the liquid water content. In this paper a physical temperature and surface condition model is described for snow-covered roads. The model is validated for an entire winter season on a heated pavement in Norway. Two different models to describe the thermal conductivity through the snow layer were compared. Results show that the thermal conductivity of the snow layer can be best described as a function of the density for snow with a low liquid water content. For snow with a high water content, the thermal conductivity can be best described as a function of the volume fractions and thermal conductivity of ice, water, and air, in which air and ice are modeled as a series system and water and air/ice in parallel.

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

2018 ◽  
Vol 64 (248) ◽  
pp. 990-1002 ◽  
Author(s):  
FLORENT DOMINE ◽  
MARIA BELKE-BREA ◽  
DENIS SARRAZIN ◽  
LAURENT ARNAUD ◽  
MATHIEU BARRERE ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Wind Speed ◽  
Thermal Conditions ◽  
High Arctic ◽  
Water Vapor Transport ◽  
The Arctic ◽  
Vegetation Growth ◽  
Wind Speeds ◽  
Strong Winds

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.

scholarly journals Using Near Infrared for Studying Lemming Subnival Behavior in the High Arctic

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 11 ◽  
Author(s):  
Davood Kalhor ◽  
Anastasiia Pusenkova ◽  
Mathilde Poirier ◽  
Gilles Gauthier ◽  
Tigran Galstian ◽  
...  
Keyword(s):  
Monitoring System ◽  
Crucial Role ◽  
Near Infrared ◽  
High Arctic ◽  
The Arctic ◽  
Arctic Environment ◽  
Design And Implementation ◽  
Main Challenge ◽  
Bylot Island

Despite the crucial role of lemming in the Arctic ecosystem, many aspects of its ecology are still unknown. The main challenge of studying lemming is that this rodent does not hibernate in winter and remains active under snow. To tackle this challenge, this paper presents a monitoring system based on near infrared. Design and implementation of a system that should work autonomously in the harsh arctic environment is really challenging. After developing the first version of the equipment, we installed three units at Bylot Island, Nunavut, Canada. Retrieved videos were promising and showed the great potential of this system in assisting ecologists to study the subnivean ecology of the Arctic. To the best of our knowledge, these are the first ever videos of lemming that have been recorded under snow in winter in the Arctic.

scholarly journals The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime

2016 ◽  
Vol 13 (23) ◽  
pp. 6471-6486 ◽  
Author(s):  
Florent Domine ◽  
Mathieu Barrere ◽  
Samuel Morin
Keyword(s):  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Thermal Regime ◽  
Land Surface ◽  
Meteorological Data ◽  
Arctic Tundra ◽  
High Arctic ◽  
Snow Density ◽  
Thermal Insulating ◽  
Bylot Island

Abstract. With climate warming, shrubs have been observed to grow on Arctic tundra. Their presence is known to increase snow height and is expected to increase the thermal insulating effect of the snowpack. An important consequence would be the warming of the ground, which will accelerate permafrost thaw, providing an important positive feedback to warming. At Bylot Island (73° N, 80° W) in the Canadian high Arctic where bushes of willows (Salix richardsonii Hook) are growing, we have observed the snow stratigraphy and measured the vertical profiles of snow density, thermal conductivity and specific surface area (SSA) in over 20 sites of high Arctic tundra and in willow bushes 20 to 40 cm high. We find that shrubs increase snow height, but only up to their own height. In shrubs, snow density, thermal conductivity and SSA are all significantly lower than on herb tundra. In shrubs, depth hoar which has a low thermal conductivity was observed to grow up to shrub height, while on herb tundra, depth hoar only developed to 5 to 10 cm high. The thermal resistance of the snowpack was in general higher in shrubs than on herb tundra. More signs of melting were observed in shrubs, presumably because stems absorb radiation and provide hotspots that initiate melting. When melting was extensive, thermal conductivity was increased and thermal resistance was reduced, counteracting the observed effect of shrubs in the absence of melting. Simulations of the effect of shrubs on snow properties and on the ground thermal regime were made with the Crocus snow physics model and the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface scheme, driven by in situ and reanalysis meteorological data. These simulations did not take into account the summer impact of shrubs. They predict that the ground at 5 cm depth at Bylot Island during the 2014–2015 winter would be up to 13 °C warmer in the presence of shrubs. Such warming may however be mitigated by summer effects.

Role of dimethyl sulfide on the formation and growth of aerosols, and its impact on liquid clouds in the Arctic summer

2020 ◽  
Author(s):  
Roya Ghahreman ◽  
Wanmin Gong ◽  
Ann-Lise Norman ◽  
Stephen R. Beagley ◽  
Ayodeji Akingunola ◽  
...  
Keyword(s):  
Dimethyl Sulfide ◽  
Model Simulation ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Liquid Water Content ◽  
High Arctic ◽  
Cloud Microphysics ◽  
The Arctic ◽  
The Impact ◽  
Arctic Summer

<p>Atmospheric dimethyl sulfide, DMS, is the main biogenic source of sulfate particles in the Arctic atmosphere. Sulfate particles have a net cooling effect, which can partially offset Arctic warming from absorbing aerosols, such as black carbon. As efficient cloud condensation nuclei (CCN), sulfate particles are also able to influence the cloud’s microphysical properties. </p><p>DMS production and emission to the atmosphere increase during the Arctic summer, due to a greater ice-free sea surface area and higher biological activity. In the model simulation of a field campaign conducted over the Canadian high Arctic during the summer of 2014 (NETCARE; Abbatt et al. 2019), the inclusion of DMS in the model, GEM-MACH, resulted in a significant increase, up to 100%, in the modelled atmospheric SO<sub>2</sub> in some regions of the Canadian Arctic. Analysis of the modelled size-segregated aerosol sulfate indicated that DMS has the most significant impact on particles in the size range of 50 – 200 nm in this case. Simulations have shown that localized regions of high seawater DMS can have a significant impact on atmospheric concentrations.</p><p>Further investigation of DMS impact on the Arctic summer cloud microphysics was carried out by using a fully coupled version of GEM-MACH. Overall, the model simulations show that the inclusion of DMS in model leads to an increase in cloud droplet number concentrations (CDNC) and a decrease in droplet mean mass diameters (MMD), and has no significant effects on liquid water content (LWC). The impact of DMS on Canadian weather forecasts will be evaluated using operational forecast tools.</p>

scholarly journals The Importance of Representing Mixed-Phase Clouds for Simulating Distinctive Atmospheric States in the Arctic*

2014 ◽  
Vol 27 (1) ◽  
pp. 265-272 ◽  
Author(s):  
Anders Engström ◽  
Johannes Karlsson ◽  
Gunilla Svensson
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Heat Budget ◽  
Longwave Radiation ◽  
Arctic Basin ◽  
Supercooled Liquid ◽  
Bimodal Distribution ◽  
Liquid Water Content ◽  
Reanalysis Data ◽  
The Arctic

Abstract Observations from the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) suggest that the Arctic Basin is characterized by two distinctly different preferred atmospheric states during wintertime. These states appear as two peaks in the frequency distribution of surface downwelling longwave radiation (LWD), representing radiatively clear and opaque conditions. Here, the authors have investigated the occurrence and representation of these states in the widely used ECMWF Interim Re-Analysis (ERA-Interim) dataset. An interannually recurring bimodal distribution of LWD values is not a clearly observable feature in the reanalysis data. However, large differences in the simulated liquid water content of clouds in ERA-Interim compared to observations are identified and these are linked to the lack of a radiatively opaque peak in the reanalysis. Using a single-column model, dynamically controlled by data from ERA-Interim, the authors show that, by tuning the glaciation speed of supercooled liquid clouds, it is possible to reach a very good agreement between the model and observations from the SHEBA campaign in terms of LWD. The results suggest that the presence of two preferred Arctic states, as observed during SHEBA, is a recurring feature of the Arctic climate system during winter [December–March (DJFM)]. The mean increase in LWD during the Arctic winter compared to ERA-Interim is 15 W m−2. This has a substantial bearing on climate model evaluation in the Arctic as it indicates the importance of representing Arctic states in climate models and reanalysis data and that doing so could have a significant impact on winter ice thickness and surface temperatures in the Arctic.

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