Articles | Volume 5, issue 1
https://doi.org/10.5194/gi-5-85-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gi-5-85-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Apr 2016
Research article |
| 14 Apr 2016
Arctic Snow Microstructure Experiment for the development of snow emission modelling
William Maslanka
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
Leena Leppänen
Finnish Meteorological Institute, Arctic Research Centre, Sodankylä, Finland
Anna Kontu
Finnish Meteorological Institute, Arctic Research Centre, Sodankylä, Finland
Mel Sandells
CORES Science and Engineering LTD, Burnopfield, UK
Juha Lemmetyinen
Finnish Meteorological Institute, Arctic Research Centre, Sodankylä, Finland
Martin Schneebeli
WSL Institute of Snow and Avalanche Research, SLF, Davos, Switzerland
Martin Proksch
WSL Institute of Snow and Avalanche Research, SLF, Davos, Switzerland
Margret Matzl
WSL Institute of Snow and Avalanche Research, SLF, Davos, Switzerland
Henna-Reetta Hannula
Finnish Meteorological Institute, Arctic Research Centre, Sodankylä, Finland
Robert Gurney
Department of Meteorology, University of Reading, Reading, UK
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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
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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.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
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When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us see what changes on our lands.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
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We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Moein Mellat, Amy R. Macfarlane, Camilla F. Brunello, Martin Werner, Martin Schneebeli, Ruzica Dadic, Stefanie Arndt, Kaisa-Riikka Mustonen, Jeffrey M. Welker, and Hanno Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-719, https://doi.org/10.5194/egusphere-2024-719, 2024
Preprint archived
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Our research, utilizing data from the Arctic MOSAiC expedition, reveals how snow on Arctic sea ice changes due to weather conditions. By analyzing snow samples collected over a year, we found differences in snow layers that tell us about their origins and how they've been affected by the environment. We discovered variations in snow and vapour that reflect the influence of weather patterns and surface processes like wind and sublimation.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
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This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
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We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
Amy R. Macfarlane, Henning Löwe, Lucille Gimenes, David N. Wagner, Ruzica Dadic, Rafael Ottersberg, Stefan Hämmerle, and Martin Schneebeli
The Cryosphere, 17, 5417–5434, https://doi.org/10.5194/tc-17-5417-2023, https://doi.org/10.5194/tc-17-5417-2023, 2023
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Snow acts as an insulating blanket on Arctic sea ice, keeping the underlying ice "warm", relative to the atmosphere. Knowing the snow's thermal conductivity is essential for understanding winter ice growth. During the MOSAiC expedition, we measured the thermal conductivity of snow. We found spatial and vertical variability to overpower any temporal variability or dependency on underlying ice type and the thermal resistance to be directly influenced by snow height.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Jennifer L. Baltzer, Christophe Kinnard, and Alexandre Roy
Biogeosciences, 20, 2941–2970, https://doi.org/10.5194/bg-20-2941-2023, https://doi.org/10.5194/bg-20-2941-2023, 2023
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This review supports the integration of microwave spaceborne information into carbon cycle science for Arctic–boreal regions. The microwave data record spans multiple decades with frequent global observations of soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties, and land cover. This record holds substantial unexploited potential to better understand carbon cycle processes.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Julia Martin and Martin Schneebeli
The Cryosphere, 17, 1723–1734, https://doi.org/10.5194/tc-17-1723-2023, https://doi.org/10.5194/tc-17-1723-2023, 2023
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The grain size of snow determines how light is reflected and other physical properties. The IceCube measures snow grain size at the specific near-infrared wavelength of 1320 nm. In our study, the preparation of snow samples for the IceCube creates a thin layer of small particles. Comparisons of the grain size with computed tomography, particle counting and numerical simulation confirm the aforementioned observation. We conclude that measurements at this wavelength underestimate the grain size.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
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As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
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Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
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Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
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The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Jan Pisek, Angela Erb, Lauri Korhonen, Tobias Biermann, Arnaud Carrara, Edoardo Cremonese, Matthias Cuntz, Silvano Fares, Giacomo Gerosa, Thomas Grünwald, Niklas Hase, Michal Heliasz, Andreas Ibrom, Alexander Knohl, Johannes Kobler, Bart Kruijt, Holger Lange, Leena Leppänen, Jean-Marc Limousin, Francisco Ramon Lopez Serrano, Denis Loustau, Petr Lukeš, Lars Lundin, Riccardo Marzuoli, Meelis Mölder, Leonardo Montagnani, Johan Neirynck, Matthias Peichl, Corinna Rebmann, Eva Rubio, Margarida Santos-Reis, Crystal Schaaf, Marius Schmidt, Guillaume Simioni, Kamel Soudani, and Caroline Vincke
Biogeosciences, 18, 621–635, https://doi.org/10.5194/bg-18-621-2021, https://doi.org/10.5194/bg-18-621-2021, 2021
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Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Jacinta Edebeli, Jürg C. Trachsel, Sven E. Avak, Markus Ammann, Martin Schneebeli, Anja Eichler, and Thorsten Bartels-Rausch
Atmos. Chem. Phys., 20, 13443–13454, https://doi.org/10.5194/acp-20-13443-2020, https://doi.org/10.5194/acp-20-13443-2020, 2020
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Earth’s snow cover is very dynamic and can change its physical properties within hours, as is well known by skiers. Snow is also a well-known host of chemical reactions – the products of which impact air composition and quality. Here, we present laboratory experiments that show how the dynamics of snow make snow essentially inert with respect to gas-phase ozone with time despite its content of reactive chemicals. Impacts on polar atmospheric chemistry are discussed.
Neige Calonne, Bettina Richter, Henning Löwe, Cecilia Cetti, Judith ter Schure, Alec Van Herwijnen, Charles Fierz, Matthias Jaggi, and Martin Schneebeli
The Cryosphere, 14, 1829–1848, https://doi.org/10.5194/tc-14-1829-2020, https://doi.org/10.5194/tc-14-1829-2020, 2020
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During winter 2015–2016, the standard program to monitor the structure and stability of the snowpack at Weissflujoch, Swiss Alps, was complemented by additional measurements to compare between various traditional and newly developed techniques. Snow micro-penetrometer measurements allowed monitoring of the evolution of the snowpack's internal structure at a daily resolution throughout the winter. We show the potential of such high-resolution data for detailed evaluations of snowpack models.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
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There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Pirmin Philipp Ebner, Aaron Coulin, Joël Borner, Fabian Wolfsperger, Michael Hohl, and Martin Schneebeli
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-56, https://doi.org/10.5194/tc-2020-56, 2020
Revised manuscript not accepted
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These laboratory measurements allow to analyse wet snow and to find the narrow range of the starting point of water percolation in coarse-grained snow. Based on the electrical monitoring a promising perspective for retrieving water content and water distribution in the snowpack is given. The water distribution is analysed using micro-computer tomography to find preferential spots of the accumulated water. These findings are pertinent to the interpretation of the snow melt run-off of spring snow.
Henna-Reetta Hannula, Kirsikka Heinilä, Kristin Böttcher, Olli-Pekka Mattila, Miia Salminen, and Jouni Pulliainen
Earth Syst. Sci. Data, 12, 719–740, https://doi.org/10.5194/essd-12-719-2020, https://doi.org/10.5194/essd-12-719-2020, 2020
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We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape and collected at four scales. The data record describes the characteristics and variability of the satellite scene reflectance contributors in boreal landscape, thus enabling the development of improved optical satellite snow mapping methods for forested areas.
Achim Heilig, Olaf Eisen, Martin Schneebeli, Michael MacFerrin, C. Max Stevens, Baptiste Vandecrux, and Konrad Steffen
The Cryosphere, 14, 385–402, https://doi.org/10.5194/tc-14-385-2020, https://doi.org/10.5194/tc-14-385-2020, 2020
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We investigate the spatial representativeness of point observations of snow accumulation in SW Greenland. Such analyses have rarely been conducted but are necessary to link regional-scale observations from, e.g., remote-sensing data to firn cores and snow pits. The presented data reveal a low regional variability in density but snow depth can vary significantly. It is necessary to combine pits with spatial snow depth data to increase the regional representativeness of accumulation observations.
Silvan Leinss, Henning Löwe, Martin Proksch, and Anna Kontu
The Cryosphere, 14, 51–75, https://doi.org/10.5194/tc-14-51-2020, https://doi.org/10.5194/tc-14-51-2020, 2020
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The anisotropy of the snow microstructure, given by horizontally aligned ice crystals and vertically interlinked crystal chains, is a key quantity to understand mechanical, dielectric, and thermodynamical properties of snow. We present a model which describes the temporal evolution of the anisotropy. The model is driven by snow temperature, temperature gradient, and the strain rate. The model is calibrated by polarimetric radar data (CPD) and validated by computer tomographic 3-D snow images.
Kévin Fourteau, Patricia Martinerie, Xavier Faïn, Christoph F. Schaller, Rebecca J. Tuckwell, Henning Löwe, Laurent Arnaud, Olivier Magand, Elizabeth R. Thomas, Johannes Freitag, Robert Mulvaney, Martin Schneebeli, and Vladimir Ya. Lipenkov
The Cryosphere, 13, 3383–3403, https://doi.org/10.5194/tc-13-3383-2019, https://doi.org/10.5194/tc-13-3383-2019, 2019
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Understanding gas trapping in polar ice is essential to study the relationship between greenhouse gases and past climates. New data of bubble closure, used in a simple gas-trapping model, show inconsistency with the final air content in ice. This suggests gas trapping is not fully understood. We also use a combination of high-resolution measurements to investigate the effect of polar snow stratification on gas trapping and find that all strata have similar pores, but that some close in advance.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
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This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Isabelle Gouttevin, Moritz Langer, Henning Löwe, Julia Boike, Martin Proksch, and Martin Schneebeli
The Cryosphere, 12, 3693–3717, https://doi.org/10.5194/tc-12-3693-2018, https://doi.org/10.5194/tc-12-3693-2018, 2018
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Snow insulates the ground from the cold air in the Arctic winter, majorly affecting permafrost. This insulation depends on snow characteristics and is poorly quantified. Here, we characterize it at a carbon-rich permafrost site, using a recent technique that retrieves the 3-D structure of snow and its thermal properties. We adapt a snowpack model enabling the simulation of this insulation over a whole winter. We estimate that local snow variations induce up to a 6 °C spread in soil temperatures.
Ghislain Picard, Melody Sandells, and Henning Löwe
Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, https://doi.org/10.5194/gmd-11-2763-2018, 2018
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The Snow Microwave Radiative Transfer (SMRT) is a novel model developed to calculate how microwaves are scattered and emitted by snow. The model is built from separate, interconnecting modules to make it easy to compare different aspects of the theory. SMRT is the first model to allow a choice of how to represent the microstructure of the snow, which is extremely important, and has been used to unite multiple previous studies. This model will ultimately be used to observe snow from space.
Jake J. Gristey, J. Christine Chiu, Robert J. Gurney, Cyril J. Morcrette, Peter G. Hill, Jacqueline E. Russell, and Helen E. Brindley
Atmos. Chem. Phys., 18, 5129–5145, https://doi.org/10.5194/acp-18-5129-2018, https://doi.org/10.5194/acp-18-5129-2018, 2018
Jonas Svensson, Johan Ström, Niku Kivekäs, Nathaniel B. Dkhar, Shresth Tayal, Ved P. Sharma, Arttu Jutila, John Backman, Aki Virkkula, Meri Ruppel, Antti Hyvärinen, Anna Kontu, Henna-Reetta Hannula, Matti Leppäranta, Rakesh K. Hooda, Atte Korhola, Eija Asmi, and Heikki Lihavainen
Atmos. Meas. Tech., 11, 1403–1416, https://doi.org/10.5194/amt-11-1403-2018, https://doi.org/10.5194/amt-11-1403-2018, 2018
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Receding glaciers in the Himalayas are of concern. Here we present measurements of light-absorbing impurities, known to contribute to the ongoing glacier decrease, in snow from Indian Himalayas and compare them to snow samples from the Finnish Arctic. The soot particles in the snow are shown to have lower light absorbing efficiency, possibly affecting their radiative forcing potential in the snow. Further, dust influences the snow in the Himalayas to a much greater extent than in Finland.
Pirmin Philipp Ebner, Hans Christian Steen-Larsen, Barbara Stenni, Martin Schneebeli, and Aldo Steinfeld
The Cryosphere, 11, 1733–1743, https://doi.org/10.5194/tc-11-1733-2017, https://doi.org/10.5194/tc-11-1733-2017, 2017
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Stable water isotopes (δ18O) obtained from snow and ice samples from polar regions are used to reconstruct past climate variability. We present an experimental study on the effect on the snow isotopic composition by airflow through a snowpack in controlled laboratory conditions. The disequilibrium between snow and vapor isotopes changed the isotopic content of the snow. These measurements suggest that metamorphism and its history affect the snow isotopic composition.
Sascha Bellaire, Martin Proksch, Martin Schneebeli, Masashi Niwano, and Konrad Steffen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-55, https://doi.org/10.5194/tc-2017-55, 2017
Preprint withdrawn
Melody Sandells, Richard Essery, Nick Rutter, Leanne Wake, Leena Leppänen, and Juha Lemmetyinen
The Cryosphere, 11, 229–246, https://doi.org/10.5194/tc-11-229-2017, https://doi.org/10.5194/tc-11-229-2017, 2017
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This study looks at a wide range of options for simulating sensor signals for satellite monitoring of water stored as snow, though an ensemble of 1323 coupled snow evolution and microwave scattering models. The greatest improvements will be made with better computer simulations of how the snow microstructure changes, followed by how the microstructure scatters radiation at microwave frequencies. Snow compaction should also be considered in systems to monitor snow mass from space.
Craig D. Smith, Anna Kontu, Richard Laffin, and John W. Pomeroy
The Cryosphere, 11, 101–116, https://doi.org/10.5194/tc-11-101-2017, https://doi.org/10.5194/tc-11-101-2017, 2017
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One of the objectives of the WMO Solid Precipitation Intercomparison Experiment (SPICE) was to assess the performance of automated instruments that measure snow water equivalent and make recommendations on the best measurement practices and data interpretation. This study assesses the Campbell Scientific CS725 and the Sommer SSG100 for measuring SWE. Different measurement principals of the instruments as well as site characteristics influence the way that the SWE data should be interpreted.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
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Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Silvan Leinss, Henning Löwe, Martin Proksch, Juha Lemmetyinen, Andreas Wiesmann, and Irena Hajnsek
The Cryosphere, 10, 1771–1797, https://doi.org/10.5194/tc-10-1771-2016, https://doi.org/10.5194/tc-10-1771-2016, 2016
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Four years of anisotropy measurements of seasonal snow are presented in the paper. The anisotropy was measured every 4 h with a ground-based polarimetric radar. An electromagnetic model has been developed to measured the anisotropy with radar instruments from ground and from space. The anisotropic permittivity was derived with Maxwell–Garnett-type mixing formulas which are shown to be equivalent to series expansions of the permittivity tensor based on spatial correlation function of snow.
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
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The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Kaisa Lakkala, Hanne Suokanerva, Juha Matti Karhu, Antti Aarva, Antti Poikonen, Tomi Karppinen, Markku Ahponen, Henna-Reetta Hannula, Anna Kontu, and Esko Kyrö
Geosci. Instrum. Method. Data Syst., 5, 315–320, https://doi.org/10.5194/gi-5-315-2016, https://doi.org/10.5194/gi-5-315-2016, 2016
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This paper describes the laboratory facilities at the Finnish Meteorological Institute – Arctic Research Centre (FMI-ARC). They comprise an optical laboratory, a facility for biological studies, and an office. The facilities are ideal for responding to the needs of international multidisciplinary research, giving the possibility to calibrate and characterize the research instruments as well as handle and store samples.
Richard Essery, Anna Kontu, Juha Lemmetyinen, Marie Dumont, and Cécile B. Ménard
Geosci. Instrum. Method. Data Syst., 5, 219–227, https://doi.org/10.5194/gi-5-219-2016, https://doi.org/10.5194/gi-5-219-2016, 2016
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Physically based models that predict the properties of snow on the ground are used in many applications, but meteorological input data required by these models are hard to obtain in cold regions. Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. The data are used to show that a sophisticated snow model developed for warmer and wetter sites can perform well in very different conditions.
Leena Leppänen, Anna Kontu, Henna-Reetta Hannula, Heidi Sjöblom, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, https://doi.org/10.5194/gi-5-163-2016, 2016
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The manual snow survey program of Finnish Meteorological Institute consists of numerous observations of natural seasonal snowpack in Sodankylä, in northern Finland. Systematic snow measurements began in 1911 with snow depth and snow water equivalent. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from snow pits. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack.
Pascal Hagenmuller, Margret Matzl, Guillaume Chambon, and Martin Schneebeli
The Cryosphere, 10, 1039–1054, https://doi.org/10.5194/tc-10-1039-2016, https://doi.org/10.5194/tc-10-1039-2016, 2016
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The paper focuses on the characterization of snow microstructure with X-ray microtomography, a technique that is progressively becoming the standard for snow characterization. In particular, it rigorously investigates how the image processing algorithms affect the subsequent microstructure characterization in terms of density and specific surface area. From this analysis, practical recommendations concerning the processing X-ray tomographic images of snow are provided.
Jaakko Ikonen, Juho Vehviläinen, Kimmo Rautiainen, Tuomo Smolander, Juha Lemmetyinen, Simone Bircher, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, https://doi.org/10.5194/gi-5-95-2016, 2016
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A comprehensive, distributed network of in situ measurement stations gathering information on soil moisture has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network is used as a tool to evaluate the validity of satellite retrievals of soil properties. We present the soil moisture observation network and the results of comparisons of top layer soil moisture between 2012 and 2014 against ESA CCI product soil moisture retrievals.
Pirmin Philipp Ebner, Martin Schneebeli, and Aldo Steinfeld
The Cryosphere, 10, 791–797, https://doi.org/10.5194/tc-10-791-2016, https://doi.org/10.5194/tc-10-791-2016, 2016
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Changes of the porous ice structure were observed in a snow sample. Sublimation occurred due to the slight undersaturation of the incoming air into the warmer ice matrix. Diffusion of water vapor opposite to the direction of the temperature gradient counteracted the mass transport of advection. Therefore, the total net ice change was negligible, leading to a constant porosity profile. However, the strong recrystallization of water molecules in snow may impact its isotopic or chemical content.
Martin Proksch, Nick Rutter, Charles Fierz, and Martin Schneebeli
The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, https://doi.org/10.5194/tc-10-371-2016, 2016
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Density is a fundamental property of porous media such as snow. During the MicroSnow Davos 2014 workshop, different approaches (box-, wedge- and cylinder-type density cutters, micro-computed tomography) to measure snow density were applied in a controlled laboratory environment and in the field. In general, results suggest that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably.
O. Meinander, A. Aarva, A. Poikonen, A. Kontu, H. Suokanerva, E. Asmi, K. Neitola, E. Rodriguez, R. Sanchez, M. Mei, G. de Leeuw, and E. Kyrö
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2015-31, https://doi.org/10.5194/gi-2015-31, 2016
Revised manuscript not accepted
J. I. Peltoniemi, M. Gritsevich, T. Hakala, P. Dagsson-Waldhauserová, Ó. Arnalds, K. Anttila, H.-R. Hannula, N. Kivekäs, H. Lihavainen, O. Meinander, J. Svensson, A. Virkkula, and G. de Leeuw
The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, https://doi.org/10.5194/tc-9-2323-2015, 2015
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Light-absorbing impurities change the reflectance of snow in different ways. Some particles are heated by the Sun and they sink out of sight. During the process, snow may look darker than pure snow when observed by nadir, but at larger view zenith angles the snow may look as white as clean snow. Thus an observer on the ground may overestimate the albedo, while a satellite underestimates the albedo. Climate studies need to examine how the contaminants behave in snow, not only their total amounts.
M. Proksch, C. Mätzler, A. Wiesmann, J. Lemmetyinen, M. Schwank, H. Löwe, and M. Schneebeli
Geosci. Model Dev., 8, 2611–2626, https://doi.org/10.5194/gmd-8-2611-2015, https://doi.org/10.5194/gmd-8-2611-2015, 2015
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The measurement of snow properties on global scale relies on microwave remote sensing data. The interpretation of the data is however challenging. Here we introduce MEMLS3&a, an extension of the snow emission model MEMLS, to include a backscatter model for active microwave remote sensing. In MEMLS3&a, snow input parameters can be derived by objective measurement methods, which avoids fitting the scattering efficiency of snow. The model is validated with combined active and passive measurements.
P. P. Ebner, M. Schneebeli, and A. Steinfeld
The Cryosphere, 9, 1363–1371, https://doi.org/10.5194/tc-9-1363-2015, https://doi.org/10.5194/tc-9-1363-2015, 2015
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Time-lapse X-ray microtomography was used to investigate the structural dynamics of isothermal snow metamorphism exposed to an advective airflow and possible effects on natural snowpacks were discussed. The results showed that isothermal advection with saturated air have no influence on the coarsening rate that is typical for isothermal snow metamorphism. It is driven by sublimation-deposition caused by Kelvin effect and is the limiting factor independently of the transport regime in the pores.
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
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Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
J. Schwaab, M. Bavay, E. Davin, F. Hagedorn, F. Hüsler, M. Lehning, M. Schneebeli, E. Thürig, and P. Bebi
Biogeosciences, 12, 467–487, https://doi.org/10.5194/bg-12-467-2015, https://doi.org/10.5194/bg-12-467-2015, 2015
S. Schleef, H. Löwe, and M. Schneebeli
The Cryosphere, 8, 1825–1838, https://doi.org/10.5194/tc-8-1825-2014, https://doi.org/10.5194/tc-8-1825-2014, 2014
P. P. Ebner, S. A. Grimm, M. Schneebeli, and A. Steinfeld
Geosci. Instrum. Method. Data Syst., 3, 179–185, https://doi.org/10.5194/gi-3-179-2014, https://doi.org/10.5194/gi-3-179-2014, 2014
O. Meinander, A. Kontu, A. Virkkula, A. Arola, L. Backman, P. Dagsson-Waldhauserová, O. Järvinen, T. Manninen, J. Svensson, G. de Leeuw, and M. Leppäranta
The Cryosphere, 8, 991–995, https://doi.org/10.5194/tc-8-991-2014, https://doi.org/10.5194/tc-8-991-2014, 2014
H. Löwe, F. Riche, and M. Schneebeli
The Cryosphere, 7, 1473–1480, https://doi.org/10.5194/tc-7-1473-2013, https://doi.org/10.5194/tc-7-1473-2013, 2013
T. Bartels-Rausch, S. N. Wren, S. Schreiber, F. Riche, M. Schneebeli, and M. Ammann
Atmos. Chem. Phys., 13, 6727–6739, https://doi.org/10.5194/acp-13-6727-2013, https://doi.org/10.5194/acp-13-6727-2013, 2013
Related subject area
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Observing desert dust devils with a pressure logger
Sarah E. Esenther, Laurence C. Smith, Adam LeWinter, Lincoln H. Pitcher, Brandon T. Overstreet, Aaron Kehl, Cuyler Onclin, Seth Goldstein, and Jonathan C. Ryan
Geosci. Instrum. Method. Data Syst., 12, 215–230, https://doi.org/10.5194/gi-12-215-2023, https://doi.org/10.5194/gi-12-215-2023, 2023
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Meltwater runoff estimates from the Greenland ice sheet contain uncertainty. To better understand ice sheet hydrology, we installed a weather station and river stage sensors along three proglacial rivers in a cold-bedded area of NW Greenland without firn, crevasse, or moulin influence. The first 3 years (2019–2021) of observations have given us a first look at the seasonal and annual weather and hydrological patterns of this understudied region.
Monika Bociarska, Julia Rewers, Dariusz Wójcik, Weronika Materkowska, Piotr Środa, and the AniMaLS Working Group
Geosci. Instrum. Method. Data Syst., 10, 183–202, https://doi.org/10.5194/gi-10-183-2021, https://doi.org/10.5194/gi-10-183-2021, 2021
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This paper describes a seismic dataset acquired by network of broadband sensors in Poland and technical issues related to data acquisition. We describe a new azimuth-transfer device for precise sensor orientation and apply methods for data-based orientation checking. We analyse the seismic noise level and discuss effect of geology at sites on character of seismic data and noise. We show data examples and describe methods of seismic data interpretation for studies of lithospheric structure.
Rolf Hut, Thanda Thatoe Nwe Win, and Thom Bogaard
Geosci. Instrum. Method. Data Syst., 9, 435–442, https://doi.org/10.5194/gi-9-435-2020, https://doi.org/10.5194/gi-9-435-2020, 2020
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GPS drifters that float down rivers are important tools in studying rivers, but they can be expensive. Recently, both GPS receivers and cellular modems have become available at lower prices to tinkering scientists due to the rise of open hardware and the Arduino. We provide detailed instructions on how to build a low-power GPS drifter with local storage and a cellular model that we tested in a fieldwork in Myanmar. These instructions allow fellow geoscientists to recreate the device.
Hasan Hakan Yavaşoğlu, Mehmet Nurullah Alkan, Serdar Bilgi, and Öykü Alkan
Geosci. Instrum. Method. Data Syst., 9, 25–40, https://doi.org/10.5194/gi-9-25-2020, https://doi.org/10.5194/gi-9-25-2020, 2020
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This study has been carried out within the scope of a project supported by the Istanbul Technical, Afyon Kocatepe and Hitit universities. The data were obtained from annual GPS campaigns. With this study, the actual velocity field of the region was revealed and the deformations of the region were determined. In particular, the creep, which is a rare phenomenon in tectonics, was studied in order to understand its mechanism and whether it is present in the region or not.
Romane Berthelin, Michael Rinderer, Bartolomé Andreo, Andy Baker, Daniela Kilian, Gabriele Leonhardt, Annette Lotz, Kurt Lichtenwoehrer, Matías Mudarra, Ingrid Y. Padilla, Fernando Pantoja Agreda, Rafael Rosolem, Abel Vale, and Andreas Hartmann
Geosci. Instrum. Method. Data Syst., 9, 11–23, https://doi.org/10.5194/gi-9-11-2020, https://doi.org/10.5194/gi-9-11-2020, 2020
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We present the setup of a soil moisture monitoring network, which is implemented at five karstic sites with different climates across the globe. More than 400 soil moisture probes operating at a high spatio-temporal resolution will improve the understanding of groundwater recharge and evapotranspiration processes in karstic areas.
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
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The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Leena Leppänen, Anna Kontu, Henna-Reetta Hannula, Heidi Sjöblom, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, https://doi.org/10.5194/gi-5-163-2016, 2016
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The manual snow survey program of Finnish Meteorological Institute consists of numerous observations of natural seasonal snowpack in Sodankylä, in northern Finland. Systematic snow measurements began in 1911 with snow depth and snow water equivalent. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from snow pits. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack.
T. Read, V. F. Bense, R. Hochreutener, O. Bour, T. Le Borgne, N. Lavenant, and J. S. Selker
Geosci. Instrum. Method. Data Syst., 4, 197–202, https://doi.org/10.5194/gi-4-197-2015, https://doi.org/10.5194/gi-4-197-2015, 2015
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The monitoring and measurement of water flow in groundwater wells allows us to understand how aquifers transmit water. In this paper we develop a simple method, which we call T-POT, that allows flows to be estimated by tracking the movement of a small parcel of warmed water. The parcel is tracked using fibre optic temperature sensing - a technology that allows detailed measurements of temperature, and therefore flow using the T-POT method, to be made in the well.
A. Portal, P. Labazuy, J.-F. Lénat, S. Béné, P. Boivin, E. Busato, C. Cârloganu, C. Combaret, P. Dupieux, F. Fehr, P. Gay, I. Laktineh, D. Miallier, L. Mirabito, V. Niess, and B. Vulpescu
Geosci. Instrum. Method. Data Syst., 2, 47–54, https://doi.org/10.5194/gi-2-47-2013, https://doi.org/10.5194/gi-2-47-2013, 2013
R. D. Lorenz
Geosci. Instrum. Method. Data Syst., 1, 209–220, https://doi.org/10.5194/gi-1-209-2012, https://doi.org/10.5194/gi-1-209-2012, 2012
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Short summary
The paper presents the initial findings of the Arctic Snow Microstructure Experiment in Sodankylä, Finland. The experiment observed the microwave emission of extracted snow slabs on absorbing and reflecting bases. Snow parameters were recorded to simulate the emission upon those bases using two different emission models. The smallest simulation errors were associated with the absorbing base at vertical polarization. The observations will be used for the development of snow emission modelling.
The paper presents the initial findings of the Arctic Snow Microstructure Experiment in...
