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.

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 Structure, specific surface area and thermal conductivity of the snowpack around Barrow, Alaska

2012 ◽  
Vol 117 (D14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Florent Domine ◽  
Jean-Charles Gallet ◽  
Josué Bock ◽  
Samuel Morin
Keyword(s):  
Thermal Conductivity ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area

scholarly journals Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

2019 ◽  
Vol 11 (19) ◽  
pp. 2280 ◽  
Author(s):  
Alexander Kokhanovsky ◽  
Maxim Lamare ◽  
Olaf Danne ◽  
Carsten Brockmann ◽  
Marie Dumont ◽  
...  
Keyword(s):  
Grain Size ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Environmental Changes ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
The Arctic ◽  
Observation Data ◽  
Snow Properties

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

scholarly journals Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument

2019 ◽  
Author(s):  
Alexander Kokhanovsky ◽  
Maxim Lamare ◽  
Olaf Danne ◽  
Marie Dumont ◽  
Carsten Brockmann ◽  
...  
Keyword(s):  
Grain Size ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Environmental Changes ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
The Arctic ◽  
Observation Data ◽  
Snow Properties

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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 Thermal conductivity of polyvinylpolymethylsiloxane aerogels with high specific surface area

RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 7833-7841 ◽  
Author(s):  
Lukai Wang ◽  
Junzong Feng ◽  
Yonggang Jiang ◽  
Liangjun Li ◽  
Jian Feng
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Energy Storage ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Heat Insulation ◽  
High Specific Surface Area

The traditional SiO2 aerogels are difficult to apply in the fields of energy storage and heat insulation due to their poor mechanical properties.

Porosity, Specific Surface Area and Effective Thermal Conductivity of Anisotropic Open Cell Lattice Structures

2005 ◽  
Author(s):  
Kiran Balantrapu ◽  
Deepti Rao Sarde ◽  
Christopher M. Herald ◽  
Richard A. Wirtz
Keyword(s):  
Thermal Conductivity ◽  
Specific Surface ◽  
Effective Thermal Conductivity ◽  
Surface Area ◽  
Specific Surface Area ◽  
Structural Characteristics ◽  
Analytical Models ◽  
Lattice Structures ◽  
Open Cell ◽  
Thermally Conductive

Open-cell box-lattice structures consisting of mutually orthogonal thermally conductive cylindrical ligaments can be configured to have wide ranging porosity, a large specific surface area and effective thermal conductivity in a particular direction together with specified structural characteristics. Thermal and mechanical properties can be tuned (and anisotropy introduced) by specification of different filament diameter and pitch for the vertical and horizontal filaments. Analytical models for porosity, specific surface area and effective thermal conductivity of lattice structures having different ligament diameters and pitches (anisotropy) are developed. The models show that all three of these quantities are functions of three dimensionless lengths.   This paper was also originally published as part of the Proceedings of the ASME 2005 Heat Transfer Summer Conference.

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