Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1523-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1523-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Meteorological, snow and soil data, CO2, water and energy fluxes from a low-Arctic valley of Northern Quebec
Takuvik Joint International Laboratory, Université Laval (Canada) and CNRS-INSU (France), Québec, Canada
Centre d'Études Nordiques, Université Laval, Québec, Canada
Department of Chemistry, Université Laval, Québec, Canada
Denis Sarrazin
Centre d'Études Nordiques, Université Laval, Québec, Canada
Daniel F. Nadeau
Department of Civil and Water Engineering, Université Laval, Québec, Canada
CentrEau, Université Laval, Québec, Canada
Georg Lackner
Takuvik Joint International Laboratory, Université Laval (Canada) and CNRS-INSU (France), Québec, Canada
Centre d'Études Nordiques, Université Laval, Québec, Canada
Department of Civil and Water Engineering, Université Laval, Québec, Canada
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Maria Belke-Brea
Takuvik Joint International Laboratory, Université Laval (Canada) and CNRS-INSU (France), Québec, Canada
Centre d'Études Nordiques, Université Laval, Québec, Canada
currently at: Department of Biology, Wilfrid Laurier University, Waterloo, Canada
Related authors
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Florent Domine, Mireille Quémener, Ludovick Bégin, Benjamin Bouchard, Valérie Dionne, Sébastien Jerczynski, Raphaël Larouche, Félix Lévesque-Desrosiers, Simon-Olivier Philibert, Marc-André Vigneault, Ghislain Picard, and Daniel C. Côté
The Cryosphere, 19, 1757–1774, https://doi.org/10.5194/tc-19-1757-2025, https://doi.org/10.5194/tc-19-1757-2025, 2025
Short summary
Short summary
Shrubs buried in snow absorb solar radiation and reduce irradiance in the snowpack. This decreases photochemical reaction rates and emissions to the atmosphere. By monitoring irradiance in snowpacks with and without shrubs, we conclude that shrubs absorb solar radiation as much as 140 ppb of soot and reduce irradiance by a factor of 2. Shrub expansion in the Arctic may therefore affect tropospheric composition during the snow season with climatic effects.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
Short summary
Short summary
Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
Short summary
Short summary
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
Short summary
Short summary
We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
Short summary
Short summary
Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Florent Domine, Georg Lackner, Denis Sarrazin, Mathilde Poirier, and Maria Belke-Brea
Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, https://doi.org/10.5194/essd-13-4331-2021, 2021
Short summary
Short summary
Current sophisticated snow physics models were mostly designed for alpine conditions and cannot adequately simulate the physical properties of Arctic snowpacks. New snow models will require Arctic data sets for forcing and validation. We provide an extensive driving and testing data set from a high Arctic herb tundra site in Canada. Unique validating data include continuous time series of snow and soil thermal conductivity and temperature profiles. Field observations in spring are provided.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 2739–2755, https://doi.org/10.5194/tc-15-2739-2021, https://doi.org/10.5194/tc-15-2739-2021, 2021
Short summary
Short summary
The thermal conductivity of snow is an important physical property governing the thermal regime of a snowpack and its substrate. We show that it strongly depends on the kinetics of water vapor sublimation and that previous experimental data suggest a rather fast kinetics. In such a case, neglecting water vapor leads to an underestimation of thermal conductivity by up to 50 % for light snow. Moreover, the diffusivity of water vapor in snow is then directly related to the thermal conductivity.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 389–406, https://doi.org/10.5194/tc-15-389-2021, https://doi.org/10.5194/tc-15-389-2021, 2021
Short summary
Short summary
There has been a long controversy to determine whether the effective diffusion coefficient of water vapor in snow is superior to that in free air. Using theory and numerical modeling, we show that while water vapor diffuses more than inert gases thanks to its interaction with the ice, the effective diffusion coefficient of water vapor in snow remains inferior to that in free air. This suggests that other transport mechanisms are responsible for the large vapor fluxes observed in some snowpacks.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Florent Domine, Mireille Quémener, Ludovick Bégin, Benjamin Bouchard, Valérie Dionne, Sébastien Jerczynski, Raphaël Larouche, Félix Lévesque-Desrosiers, Simon-Olivier Philibert, Marc-André Vigneault, Ghislain Picard, and Daniel C. Côté
The Cryosphere, 19, 1757–1774, https://doi.org/10.5194/tc-19-1757-2025, https://doi.org/10.5194/tc-19-1757-2025, 2025
Short summary
Short summary
Shrubs buried in snow absorb solar radiation and reduce irradiance in the snowpack. This decreases photochemical reaction rates and emissions to the atmosphere. By monitoring irradiance in snowpacks with and without shrubs, we conclude that shrubs absorb solar radiation as much as 140 ppb of soot and reduce irradiance by a factor of 2. Shrub expansion in the Arctic may therefore affect tropospheric composition during the snow season with climatic effects.
Amélie Pouliot, Isabelle Laurion, Antoine Thiboult, and Daniel F. Nadeau
EGUsphere, https://doi.org/10.5194/egusphere-2025-1497, https://doi.org/10.5194/egusphere-2025-1497, 2025
Short summary
Short summary
Small thermokarst lakes release greenhouse gases (GHGs) as permafrost thaws, but most studies focus on diurnal measurements, potentially overlooking significant variations. We measured GHG fluxes from 2 lakes in Nunavik over twosummers—one colder, one warmer—alongside two years of continuous water column monitoring. Fluxes were higher in the warmer summer, with strong day-night differences. Our findings show that accurate GHG estimates require full diel measurements and seasonal considerations.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
Short summary
Short summary
Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
Short summary
Short summary
Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
Short summary
Short summary
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
Short summary
Short summary
We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
Short summary
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
Short summary
Short summary
Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Florent Domine, Georg Lackner, Denis Sarrazin, Mathilde Poirier, and Maria Belke-Brea
Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, https://doi.org/10.5194/essd-13-4331-2021, 2021
Short summary
Short summary
Current sophisticated snow physics models were mostly designed for alpine conditions and cannot adequately simulate the physical properties of Arctic snowpacks. New snow models will require Arctic data sets for forcing and validation. We provide an extensive driving and testing data set from a high Arctic herb tundra site in Canada. Unique validating data include continuous time series of snow and soil thermal conductivity and temperature profiles. Field observations in spring are provided.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 2739–2755, https://doi.org/10.5194/tc-15-2739-2021, https://doi.org/10.5194/tc-15-2739-2021, 2021
Short summary
Short summary
The thermal conductivity of snow is an important physical property governing the thermal regime of a snowpack and its substrate. We show that it strongly depends on the kinetics of water vapor sublimation and that previous experimental data suggest a rather fast kinetics. In such a case, neglecting water vapor leads to an underestimation of thermal conductivity by up to 50 % for light snow. Moreover, the diffusivity of water vapor in snow is then directly related to the thermal conductivity.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 389–406, https://doi.org/10.5194/tc-15-389-2021, https://doi.org/10.5194/tc-15-389-2021, 2021
Short summary
Short summary
There has been a long controversy to determine whether the effective diffusion coefficient of water vapor in snow is superior to that in free air. Using theory and numerical modeling, we show that while water vapor diffuses more than inert gases thanks to its interaction with the ice, the effective diffusion coefficient of water vapor in snow remains inferior to that in free air. This suggests that other transport mechanisms are responsible for the large vapor fluxes observed in some snowpacks.
Cited articles
Abramowitz, G.: Towards a benchmark for land surface models, Geophys. Res. Lett., 32, L22702, https://doi.org/10.1029/2005GL024419, 2005.
Barrere, M., Domine, F., Decharme, B., Morin, S., Vionnet, V., and Lafaysse, M.: Evaluating the performance of coupled snow–soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site, Geosci. Model Dev., 10, 3461–3479, https://doi.org/10.5194/gmd-10-3461-2017, 2017.
Bhiry, N., Delwaide, A., Allard, M., Bégin, Y., Filion, L., Lavoie, M., Nozais, C., Payette, S., Pienitz, R., Saulnier-Talbot, E., and Vincent, W. F.: Environmental change in the Great Whale River region, Hudson Bay: Five decades of multidisciplinary research by Centre d'études nordiques (CEN), Écoscience, 18, 182–203, https://doi.org/10.2980/18-3-3469, 2011.
Boisvert, L. N., Wu, D. L., Vihma, T., and Susskind, J.: Verification of air/surface humidity differences from AIRS and ERA- Interim in support of turbulent flux estimation in the Arctic, J. Geophys. Res.-Atmos., 120, 945–963, https://doi.org/10.1002/2014JD021666, 2015.
Callaghan, T. V., Crawford, R. M. M., Eronen, M., Hofgaard, A., Payette, S., Rees, W. G., Skre, O., Sveinbjörnsson, B., Vlassova, T. K., and Werkman, B. R.: The dynamics of the Tundra-Taiga Boundary: An overview and suggested coordinated and integrated approach to research, Ambio, 12, 3–5, http://www.jstor.org/stable/25094569 (last access: 13 March 2024), 2002.
Carvill, J.: 3 – Thermodynamics and heat transfer, in: Mechanical Engineer's Data Handbook, edited by: Carvill, J., Butterworth-Heinemann, Oxford, 102–145, https://doi.org/10.1016/B978-0-08-051135-1.50008-X, 1993.
CEN: Climate station data from the Umiujaq region in Nunavik, Quebec, Canada, Nordicana D9 [data set], https://doi.org/10.5885/45120SL-067305A53E914AF0, 1997–2020.
Conger, S. and McClung, D.: Instruments and methods comparison of density cutters for snow profile observations, J. Glaciol., 55, 163–169, https://doi.org/10.3189/002214309788609038, 2009.
Domine, F., Barrere, M., Sarrazin, D., Morin, S., and Arnaud, L.: Automatic monitoring of the effective thermal conductivity of snow in a low-Arctic shrub tundra, The Cryosphere, 9, 1265–1276, https://doi.org/10.5194/tc-9-1265-2015, 2015.
Domine, F., Barrere, M., and Sarrazin, D.: Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada, The Cryosphere, 10, 2573–2588, https://doi.org/10.5194/tc-10-2573-2016, 2016.
Domine, F., Picard, G., Morin, S., Barrere, M., Madore, J.-B., and Langlois, A.: Major Issues in Simulating Some Arctic Snowpack Properties Using Current Detailed Snow Physics Models: Consequences for the Thermal Regime and Water Budget of Permafrost, J. Adv. Model. Earth Sy., 11, 34–44, https://doi.org/10.1029/2018MS001445, 2019.
Domine, F., Lackner, G., Sarrazin, D., Poirier, M., and Belke-Brea, M.: 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, Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, 2021.
Domine, F., Sarrazin, D., Nadeau, D., Lackner, G. and Belke-Brea, M.: Hydrometeorological, snow and soil data from a low-Arctic valley in the forest-tundra ecotone in Northern Quebec, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.964743, 2024.
Finkelstein, P. L. and Sims, P. F.: Sampling error in eddy correlation flux measurements, J. Geophys. Res.-Atmos., 106, 3503–3509, https://doi.org/10.1029/2000JD900731, 2001.
Gagnon, M., Domine, F., and Boudreau, S.: The carbon sink due to shrub growth on Arctic tundra: a case study in a carbon-poor soil in eastern Canada, Environmental Research Communications, 1, 091001, https://doi.org/10.1088/2515-7620/ab3cdd, 2019.
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009.
Gouttevin, I., Langer, M., Löwe, H., Boike, J., Proksch, M., and Schneebeli, M.: Observation and modelling of snow at a polygonal tundra permafrost site: spatial variability and thermal implications, The Cryosphere, 12, 3693–3717, https://doi.org/10.5194/tc-12-3693-2018, 2018.
Guo, W., Rees, G., and Hofgaard, A.: Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery, Int. J. Remote Sens., 41, 6384–6408, https://doi.org/10.1080/01431161.2020.1734254, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hsu, K.-L., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B.: Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38, 38-1–38-17, https://doi.org/10.1029/2001WR000795, 2002.
Isaac, P., Cleverly, J., McHugh, I., van Gorsel, E., Ewenz, C., and Beringer, J.: OzFlux data: network integration from collection to curation, Biogeosciences, 14, 2903–2928, https://doi.org/10.5194/bg-14-2903-2017, 2017.
Jafari, M., Gouttevin, I., Couttet, M., Wever, N., Michel, A., Sharma, V., Rossmann, L., Maass, N., Nicolaus, M., and Lehning, M.: The impact of diffusive water vapor transport on snow profiles in deep and shallow snow covers and on sea ice, Front. Earth Sci., 8, 249, https://doi.org/10.3389/feart.2020.00249, 2020.
Jiménez, C., Michel, D., Hirschi, M., Ermida, S., and Prigent, C.: Applying multiple land surface temperature prod- ucts to derive heat fluxes over a grassland site, Remote Sensing Applications: Society and Environment, 6, 15–24, https://doi.org/10.1016/j.rsase.2017.01.002, 2017.
Jones, B.: Recent Arctic tundra fire initiates widespread thermokarst development, Scientific Reports, 5, 15865, https://doi.org/10.1038/srep15865, 2015.
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Meyers, T., Buisan, S., Isaksen, K., Brækkan, R., Landolt, S., and Jachcik, A.: Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE, Hydrol. Earth Syst. Sci., 22, 1437–1452, https://doi.org/10.5194/hess-22-1437-2018, 2018.
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018.
Lackner, G., Domine, F., Nadeau, D. F., Lafaysse, M., and Dumont, M.: Snow properties at the forest–tundra ecotone: predominance of water vapor fluxes even in deep, moderately cold snowpacks, The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, 2022.
Latifovic, R., Pouliot, D., and Olthof, I.: Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development, Remote Sensing, 9, 1098, https://doi.org/10.3390/rs9111098, 2017.
Lemieux, J.-M., Fortier, R., Murray, R., Dagenais, S., Cochand, M., Delottier, H., Therrien, R., Molson, J., Pryet, A., and Parhizkar, M.: Groundwater dynamics within a watershed in the discontinuous permafrost zone near Umiujaq (Nunavik, Canada), Hydrogeol. J., 28, 833–851, https://doi.org/10.1007/s10040-020-02110-4, 2020.
Mack, M., Bret-Harte, M., Hollingsworth, T., Jandt, R., Schuur, E., Shaver, G., and Verbyla, D.: Carbon loss from an unprecedented Arctic tundra wildfire, Nature, 475, 489–92, https://doi.org/10.1038/nature10283, 2011.
Martin, M. A., Ghent, D., Pires, A. C., Göttsche, F.-M., Cermak, J., and Remedios, J. J.: Comprehensive in situ validation of five satellite land surface temperature data sets over multiple stations and years, Remote Sensing, 11, 479, https://doi.org/10.3390/rs11050479, 2019.
Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H. P., Schmidt, M., and Steinbrecher, R.: A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements, Agr. Forest Meteorol., 169, 122–135, https://doi.org/10.1016/j.agrformet.2012.09.006, 2013.
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M., Ottersen, G., Pritchard, H., and Schuur, E.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegria, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., https://www.ipcc.ch/srocc/chapter/chapter-3-2/ (last access: 22 November 2022), 2019.
Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A. D., Barr, A. G., Beckstein, C., Braswell, B. H., Churkina, G., Desai, A. R., Falge, E., Gove, J. H., Heimann, M., Hui, D., Jarvis, A. J., Kattge, J., Noormets, A., and Stauch, V. J.: Comprehen- sive comparison of gap-filling techniques for eddy covariance net carbon fluxes, Agr. Forest Meteorol., 147, 209–232, https://doi.org/10.1016/j.agrformet.2007.08.011, 2007.
Moncrieff, J., Massheder, J., de Bruin, H., Elbers, J., Friborg, T., Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., and Verhoef, A.: A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide, J. Hydrometeorol., 188-189, 589–611, https://doi.org/10.1016/S0022-1694(96)03194-0, 1997.
Pearson, R., Phillips, S., Loranty, M., Beck, P., Damoulas, T., Knight, S., and Goetz, S.: Shifts in Arctic vegetation and associated feedbacks under climate change, Nat. Clim. Change, 3, 673–677, https://doi.org/10.1038/nclimate1858, 2013.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Qu, M., Pang, X., Zhao, X., Zhang, J., Ji, Q., and Fan, P.: Estimation of turbulent heat flux over leads using satellite thermal images, The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, 2019.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Riihelä, A., Key, J. R., Meirink, J. F., Kuipers Munneke, P., Palo, T., and Karlsson, K.-G.: An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic, J. Geophys. Res.-Atmos., 122, 4829–4848, https://doi.org/10.1002/2016JD026443, 2017.
Robichaud, B. and Mullock, J.: The Weather of Atlantic Canada and Eastern Quebec, NAV Canada, 207 pp., https://www.navcanada.ca/en/lawm-atlantic-en.pdf (last access: 5 March 2024), 2001.
Royer, A., Domine, F., Roy, A., Langlois, A., Marchand, N., and Davesne, G.: New northern snowpack classification linked to vegetation cover on a latitudinal mega-transect across northeastern Canada, Écoscience, 28, 225–242, https://doi.org/10.1080/11956860.2021.1898775, 2021.
Simson, A., Löwe, H., and Kowalski, J.: Elements of future snowpack modeling – Part 2: A modular and extendable Eulerian–Lagrangian numerical scheme for coupled transport, phase changes and settling processes, The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, 2021.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water vapour transfer, Q. J. Roy. Meteor. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980.
Wilczak, J., Oncley, S., and Stage, S.: Sonic anemometer tilt correction algorithms, Bound.-Lay. Meteorol., 99, 127–150, https://doi.org/10.1023/A:1018966204465, 2001.
Short summary
The forest–tundra ecotone is the transition region between the boreal forest and Arctic tundra. It spans over 13 000 km across the Arctic and is evolving rapidly because of climate change. We provide extensive data sets of two sites 850 m apart, one in tundra and one in forest in this ecotone for use in various models. Data include meteorological and flux data and unique snow and soil physics data.
The forest–tundra ecotone is the transition region between the boreal forest and Arctic tundra....
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