Articles | Volume 16, issue 2
https://doi.org/10.5194/tc-16-559-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-559-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling surface temperature and radiation budget of snow-covered complex terrain
Alvaro Robledano
CORRESPONDING AUTHOR
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Université Grenoble Alpes, Université de Toulouse, 38000 Grenoble, France
Ghislain Picard
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Laurent Arnaud
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Fanny Larue
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Inès Ollivier
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Related authors
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
Short summary
Short summary
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.
Adrien Ooms, Mathieu Casado, Ghislain Picard, Laurent Arnaud, Maria Hörhold, Andrea Spolaor, Rita Traversi, Joel Savarino, Patrick Ginot, Pete Akers, Birthe Twarloh, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3259, https://doi.org/10.5194/egusphere-2025-3259, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This work presents a new approach to the estimation of accumulation rates at Concordia Station, East-Antarctica, for the last 20 years, from a new data set of chemical tracers and snow micro-scale properties measured in a snow trench. Multi-annual and meter to decameter scale variability of accumulation rates are compared again in-situ measurements of surface laser scanner and stake farm, with very good agreement. This further constrains SMB estimation for Antarctica at high temporal resolution.
Titouan Tcheng, Elise Fourré, Christophe Leroy-Dos-Santos, Frédéric Parrenin, Emmanuel Le Meur, Frédéric Prié, Olivier Jossoud, Roxanne Jacob, Bénédicte Minster, Olivier Magand, Cécile Agosta, Niels Dutrievoz, Vincent Favier, Léa Baubant, Coralie Lassalle-Bernard, Mathieu Casado, Martin Werner, Alexandre Cauquoin, Laurent Arnaud, Bruno Jourdain, Ghislain Picard, Marie Bouchet, and Amaëlle Landais
EGUsphere, https://doi.org/10.5194/egusphere-2025-2863, https://doi.org/10.5194/egusphere-2025-2863, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Studying Antarctic ice cores is crucial to assess past climate changes, as they hold historical climate data. This study examines multiple ice cores from three sites in coastal Adélie Land to see if combining cores improves data interpretability. It does at two sites, but at a third, wind-driven snow layer mixing limited benefits. We show that using multiple ice cores from one location can better uncover climate history, especially in areas with less wind disturbance.
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.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
EGUsphere, https://doi.org/10.5194/egusphere-2024-3972, https://doi.org/10.5194/egusphere-2024-3972, 2025
Short summary
Short summary
Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the signal reflected back. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. Saturn's moons' radar brightness cannot be reproduced and must be caused by processes unaccounted for in the model and less active in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
EGUsphere, https://doi.org/10.5194/egusphere-2025-732, https://doi.org/10.5194/egusphere-2025-732, 2025
Short summary
Short summary
This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.
Inès Ollivier, Hans Christian Steen-Larsen, Barbara Stenni, Laurent Arnaud, Mathieu Casado, Alexandre Cauquoin, Giuliano Dreossi, Christophe Genthon, Bénédicte Minster, Ghislain Picard, Martin Werner, and Amaëlle Landais
The Cryosphere, 19, 173–200, https://doi.org/10.5194/tc-19-173-2025, https://doi.org/10.5194/tc-19-173-2025, 2025
Short summary
Short summary
The role of post-depositional processes taking place at the ice sheet's surface on the water stable isotope signal measured in polar ice cores is not fully understood. Using field observations and modelling results, we show that the original precipitation isotopic signal at Dome C, East Antarctica, is modified by post-depositional processes and provide the first quantitative estimation of their mean impact on the isotopic signal observed in the snow.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
Short summary
Short summary
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
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
Short summary
Short summary
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.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Emilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey Severinghaus, Barbara Stenni, and Patricia Martinerie
The Cryosphere, 18, 3741–3763, https://doi.org/10.5194/tc-18-3741-2024, https://doi.org/10.5194/tc-18-3741-2024, 2024
Short summary
Short summary
Ice core δO2/N2 records are useful dating tools due to their local insolation pacing. A precise understanding of the physical mechanism driving this relationship, however, remain ambiguous. By compiling data from 15 polar sites, we find a strong dependence of mean δO2/N2 on accumulation rate and temperature in addition to the well-documented insolation dependence. Snowpack modelling is used to investigate which physical properties drive the mechanistic dependence on these local parameters.
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
Preprint archived
Short summary
Short summary
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.
Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, Ghislain Picard, Julia A. Shates, Sebastian Marinsek, Liliana Margonari, Martin Truffer, and Erin C. Pettit
The Cryosphere, 18, 1709–1731, https://doi.org/10.5194/tc-18-1709-2024, https://doi.org/10.5194/tc-18-1709-2024, 2024
Short summary
Short summary
On the Antarctic Peninsula, there is a small bay that had sea ice fastened to the shoreline (
fast ice) for over a decade. The fast ice stabilized the glaciers that fed into the ocean. In January 2022, the fast ice broke away. Using satellite data we found that this was because of low sea ice concentrations and a high long-period ocean wave swell. We find that the glaciers have responded to this event by thinning, speeding up, and retreating by breaking off lots of icebergs at remarkable rates.
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
Short summary
Short summary
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.
Claudio Stefanini, Giovanni Macelloni, Marion Leduc-Leballeur, Vincent Favier, Benjamin Pohl, and Ghislain Picard
The Cryosphere, 18, 593–608, https://doi.org/10.5194/tc-18-593-2024, https://doi.org/10.5194/tc-18-593-2024, 2024
Short summary
Short summary
Local and large-scale meteorological conditions have been considered in order to explain some peculiar changes of snow grains on the East Antarctic Plateau from 2000 to 2022, by using remote sensing observations and reanalysis. We identified some extreme grain size events on the highest ice divide, resulting from a combination of conditions of low wind speed and low temperature. Moreover, the beginning of seasonal grain growth has been linked to the occurrence of atmospheric rivers.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Thomas Dethinne, Quentin Glaude, Ghislain Picard, Christoph Kittel, Patrick Alexander, Anne Orban, and Xavier Fettweis
The Cryosphere, 17, 4267–4288, https://doi.org/10.5194/tc-17-4267-2023, https://doi.org/10.5194/tc-17-4267-2023, 2023
Short summary
Short summary
We investigate the sensitivity of the regional climate model
Modèle Atmosphérique Régional(MAR) to the assimilation of wet-snow occurrence estimated by remote sensing datasets. The assimilation is performed by nudging the MAR snowpack temperature. The data assimilation is performed over the Antarctic Peninsula for the 2019–2021 period. The results show an increase in the melt production (+66.7 %) and a decrease in surface mass balance (−4.5 %) of the model for the 2019–2020 melt season.
Yaowen Zheng, Nicholas R. Golledge, Alexandra Gossart, Ghislain Picard, and Marion Leduc-Leballeur
The Cryosphere, 17, 3667–3694, https://doi.org/10.5194/tc-17-3667-2023, https://doi.org/10.5194/tc-17-3667-2023, 2023
Short summary
Short summary
Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application in Antarctica. We have constructed a novel grid-cell-level spatially distributed PDD (dist-PDD) model and assessed its accuracy. We suggest that an appropriately parameterized dist-PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
Short summary
Short summary
Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
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
Short summary
Short summary
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.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
Short summary
Short summary
To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Ghislain Picard, Marion Leduc-Leballeur, Alison F. Banwell, Ludovic Brucker, and Giovanni Macelloni
The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, https://doi.org/10.5194/tc-16-5061-2022, 2022
Short summary
Short summary
Using a snowpack radiative transfer model, we investigate in which conditions meltwater can be detected from passive microwave satellite observations from 1.4 to 37 GHz. In particular, we determine the minimum detectable liquid water content, the maximum depth of detection of a buried wet snow layer and the risk of false alarm due to supraglacial lakes. These results provide information for the developers of new, more advanced satellite melt products and for the users of the existing products.
Dominic Saunderson, Andrew Mackintosh, Felicity McCormack, Richard Selwyn Jones, and Ghislain Picard
The Cryosphere, 16, 4553–4569, https://doi.org/10.5194/tc-16-4553-2022, https://doi.org/10.5194/tc-16-4553-2022, 2022
Short summary
Short summary
We investigate the variability in surface melt on the Shackleton Ice Shelf in East Antarctica over the last 2 decades (2003–2021). Using daily satellite observations and the machine learning approach of a self-organising map, we identify nine distinct spatial patterns of melt. These patterns allow comparisons of melt within and across melt seasons and highlight the importance of both air temperatures and local controls such as topography, katabatic winds, and albedo in driving surface melt.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
Short summary
Short summary
Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
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.
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.
Christian Vincent, Diego Cusicanqui, Bruno Jourdain, Olivier Laarman, Delphine Six, Adrien Gilbert, Andrea Walpersdorf, Antoine Rabatel, Luc Piard, Florent Gimbert, Olivier Gagliardini, Vincent Peyaud, Laurent Arnaud, Emmanuel Thibert, Fanny Brun, and Ugo Nanni
The Cryosphere, 15, 1259–1276, https://doi.org/10.5194/tc-15-1259-2021, https://doi.org/10.5194/tc-15-1259-2021, 2021
Short summary
Short summary
In situ glacier point mass balance data are crucial to assess climate change in different regions of the world. Unfortunately, these data are rare because huge efforts are required to conduct in situ measurements on glaciers. Here, we propose a new approach from remote sensing observations. The method has been tested on the Argentière and Mer de Glace glaciers (France). It should be possible to apply this method to high-spatial-resolution satellite images and on numerous glaciers in the world.
Alison F. Banwell, Rajashree Tri Datta, Rebecca L. Dell, Mahsa Moussavi, Ludovic Brucker, Ghislain Picard, Christopher A. Shuman, and Laura A. Stevens
The Cryosphere, 15, 909–925, https://doi.org/10.5194/tc-15-909-2021, https://doi.org/10.5194/tc-15-909-2021, 2021
Short summary
Short summary
Ice shelves are thick floating layers of glacier ice extending from the glaciers on land that buttress much of the Antarctic Ice Sheet and help to protect it from losing ice to the ocean. However, the stability of ice shelves is vulnerable to meltwater lakes that form on their surfaces during the summer. This study focuses on the northern George VI Ice Shelf on the western side of the AP, which had an exceptionally long and extensive melt season in 2019/2020 compared to the previous 31 seasons.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
Short summary
Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
Short summary
Short summary
Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Cited articles
Adams, E., McKittrick, L., Slaughter, A., Staron, P., Shertzer, R., Miller, D.,
Leonard, T., Mccabe, D., Henninger, I., Catharine, D., Cooperstein, M., and
Laveck, K.: Modeling variation of surface hoar and radiation
recrystallization across a slope, ISSW 09 – International Snow Science
Workshop, Proceedings, 27 September–2 October 2009, Davos, Switzerland, 97–101, 2009. a
Adams, E., Slaughter, A., McKittrick, L., and Miller, D.: Local
terrain-topography and thermal-properties influence on energy and mass
balance of a snow cover, Ann. Glaciol., 52, 169–175,
https://doi.org/10.3189/172756411797252257, 2011. a
Arnaud, L., Picard, G., Champollion, N., Domine, F., Gallet, J., Lefebvre, E.,
Fily, M., and Barnola, J.: Measurement of vertical profiles of snow specific
surface area with a 1 cm resolution using infrared reflectance: instrument
description and validation, J. Glaciol., 57, 17–29,
https://doi.org/10.3189/002214311795306664, 2011. a
Arnold, N. S., Rees, W. G., Hodson, A. J., and Kohler, J.: Topographic controls
on the surface energy balance of a high Arctic valley glacier, J.
Geophys. Res., 111, F02011, https://doi.org/10.1029/2005jf000426, 2006. a, b, c
Arya, S. P.: Chapter 2 Energy Budget near the Surface, in: Introduction to
Micrometeorology, edited by: Arya, S. P., vol. 42 of International
Geophysics, Academic Press, 9–20,
https://doi.org/10.1016/S0074-6142(08)60417-9, 1988. a
Brock, B. W., Willis, I. C., and Sharp, M. J.: Measurement and parameterization
of aerodynamic roughness length variations at Haut Glacier d’Arolla,
Switzerland, J. Glaciol., 52, 281–297,
https://doi.org/10.3189/172756506781828746, 2006. a, b, c
Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C.: An energy and
mass model of snow cover suitable for operational avalanche forecasting, J.
Glaciol., 35, 333–342, 1989. a
Chen, X., Su, Z., Ma, Y., Yang, K., and Wang, B.: Estimation of surface energy fluxes under complex terrain of Mt. Qomolangma over the Tibetan Plateau, Hydrol. Earth Syst. Sci., 17, 1607–1618, https://doi.org/10.5194/hess-17-1607-2013, 2013. a
Domine, F., Salvatori, R., Legagneux, L., Salzano, R., Fily, M., and Casacchia,
R.: Correlation between the specific surface area and the short wave infrared
(SWIR) reflectance of snow, Cold Reg. Sci. Technol., 46, 60–68,
https://doi.org/10.1016/j.coldregions.2006.06.002, 2006. a
Domine, F., Taillandier, A. S., and Simpson, W. R.: A parameterization of the
specific surface area of snow in models of snowpack evolution, based on 345
measurements, J. Geophys. Res., 112, F02031, https://doi.org/10.1029/2006JF000512, 2007. a
Domine, F., Albert, M., Huthwelker, T., Jacobi, H.-W., Kokhanovsky, A. A., Lehning, M., Picard, G., and Simpson, W. R.: Snow physics as relevant to snow photochemistry, Atmos. Chem. Phys., 8, 171–208, https://doi.org/10.5194/acp-8-171-2008, 2008. a
Dozier, J., Bruno, J., and Downey, P.: A faster solution to the horizon
problem, Comput. Geosci., 7, 145–151,
https://doi.org/10.1016/0098-3004(81)90026-1, 1981. a
Duguay, C. R.: Radiation Modeling in Mountainous Terrain Review and Status,
Mount. Res. Develop., 13, 339, https://doi.org/10.2307/3673761, 1993. a
Essery, R. and Etchevers, P.: Parameter sensitivity in simulations of snowmelt,
J. Geophys. Res., 109, 20111, https://doi.org/10.1029/2004JD005036, 2004. a, b
Fierz, C., Riber, P., Adams, E. E., Curran, A. R., Föhn, P. M., Lehning,
M., and Plüss, C.: Evaluation of snow-surface energy balance models in
alpine terrain, J. Hydrol., 282, 76–94,
https://doi.org/10.1016/S0022-1694(03)00255-5, 2003. a
Filhol, S. and Sturm, M.: Snow bedforms: A review, new data, and a formation
model, J. Geophys. Res.-Earth Surf., 120, 1645–1669,
https://doi.org/10.1002/2015jf003529, 2015. a
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. a
Greuell, W., Knap, W. H., and Smeets, P. C.: Elevational changes in
meteorological variables along a midlatitude glacier during summer, J. Geophys. Res.-Atmos., 102, 25941–25954,
https://doi.org/10.1029/97JD02083, 1997. a
Helbig, N., Mott, R., Van Herwijnen, A., Winstral, A., and Jonas, T.:
Parameterizing surface wind speed over complex topography, J.
Geophys. Res., 122, 651–667, https://doi.org/10.1002/2016JD025593, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018. a, b
IGN: Geoservices IGN (Open Data), https://geoservices.ign.fr/rgealti
(last access: 12 January 2022), 2021. a
ISO 2533:1975: Standard Atmosphere, Tech. rep., International Organization for
Standardization, 1975. a
Iziomon, M. G., Mayer, H., and Matzarakis, A.: Downward atmospheric longwave
irradiance under clear and cloudy skies: Measurement and parameterization,
J. Atmos. Solar-Terr. Phy., 65, 1107–1116,
https://doi.org/10.1016/j.jastp.2003.07.007, 2003. a
Jiménez, P. A. and Dudhia, J.: Improving the representation of resolved
and unresolved topographic effects on surface wind in the wrf model, J. Appl. Meteorol. Climatol., 51, 300–316,
https://doi.org/10.1175/JAMC-D-11-084.1, 2012. a
Jiménez-Muñoz, J. C. and Sobrino, J. A.: A generalized
single-channel method for retrieving land surface temperature from remote
sensing data, J. Geophys. Res.-Atmos., 108,
https://doi.org/10.1029/2003JD003480, 2003. a, b, c, d
Jin, M., Li, J., Wang, C., and Shang, R.: A Practical Split-Window Algorithm
for Retrieving Land Surface Temperature from Landsat-8 Data and a Case Study
of an Urban Area in China, Remote Sens., 7, 4371–4390,
https://doi.org/10.3390/rs70404371, 2015. a
Kokhanovsky, A., Lamare, M., Danne, O., Brockmann, C., Dumont, M., Picard, G.,
Arnaud, L., Favier, V., Jourdain, B., Meur, E. L., Mauro, B. D., Aoki, T.,
Niwano, M., Rozanov, V., Korkin, S., Kipfstuhl, S., Freitag, J., Hoerhold,
M., Zuhr, A., Vladimirova, D., Faber, A.-K., Steen-Larsen, H., Wahl, S.,
Andersen, J., Vandecrux, B., van As, D., Mankoff, K., Kern, M., Zege, E., and
Box, J.: Retrieval of Snow Properties from the Sentinel-3 Ocean and Land
Colour Instrument, Remote Sens., 11, 2280, https://doi.org/10.3390/rs11192280, 2019. a
Kuipers Munneke, P., van den Broeke, M. R., Reijmer, C. H., Helsen, M. M., Boot, W., Schneebeli, M., and Steffen, K.: The role of radiation penetration in the energy budget of the snowpack at Summit, Greenland, The Cryosphere, 3, 155–165, https://doi.org/10.5194/tc-3-155-2009, 2009. a
Lagouarde, J.-P., Bhattacharya, B. K., Crébassol, P., Gamet, P., Adlakha, D.,
Murthy, C. S., Singh, S. K., Mishra, M., Nigam, R., Raju, P. V., Babu, S. S.,
Shukla, M. V., Pandya, M. R., Boulet, G., Briottet, X., Dadou, I., Dedieu,
G., Gouhier, M., Hagolle, O., Irvine, M., Jacob, F., Kumar, K. K., Laignel,
B., Maisongrande, P., Mallick, K., Olioso, A., Ottlé, C., Roujean, J.-L.,
Sobrino, J., Ramakrishnan, R., Sekhar, M., and Sarkar, S. S.: Indo-French
high-resolution thermal infrared space mission for Earth natural resources
assessment and monitoring – Concept and definition of TRISHNA, Int. Arch.
Photogramm., XLII-3/W6, 403–407,
https://doi.org/10.5194/isprs-archives-XLII-3-W6-403-2019, 2019. a
Lamare, M., Dumont, M., Picard, G., Larue, F., Tuzet, F., Delcourt, C., and Arnaud, L.: Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain, The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, 2020. a
Lee, W. L., Liou, K. N., and Wang, C.: Impact of 3-D topography on surface
radiation budget over the Tibetan Plateau, Theor. Appl.
Climatol., 113, 95–103, https://doi.org/10.1007/s00704-012-0767-y, 2013. a
Lenot, X., Achard, V., and Poutier, L.: SIERRA: A new approach to atmospheric
and topographic corrections for hyperspectral imagery, Remote Sens.
Environ., 113, 1664–1677, https://doi.org/10.1016/j.rse.2009.03.016, 2009. a
Leroux, C. and Fily, M.: Modeling the effect of sastrugi on snow reflectance,
J. Geophys. Res., 103, 25779, https://doi.org/10.1029/98je00558, 1998. a
Lhermitte, S., Abermann, J., and Kinnard, C.: Albedo over rough snow and ice surfaces, The Cryosphere, 8, 1069–1086, https://doi.org/10.5194/tc-8-1069-2014, 2014. a
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., and
Sobrino, J. A.: Satellite-derived land surface temperature: Current status
and perspectives, Remote Sens. Environ., 131, 14–37,
https://doi.org/10.1016/j.rse.2012.12.008, 2013. a
Libois, Q., Picard, G., Dumont, M., Arnaud, L., Sergent, C., Pougatch, E.,
Sudul, M., and Vial, D.: Experimental determination of the absorption
enhancement parameter of snow, J. Glaciol., 60, 714–724,
https://doi.org/10.3189/2014jog14j015, 2014. a
Lliboutry, L.: The Origin of Penitents, J. Glaciol., 2, 331–338,
https://doi.org/10.3189/S0022143000025181, 1954. a, b
Marks, D. and Dozier, J.: A clear-sky longwave radiation model for remote
alpine areas, Archiv für Meteorologie, Geophysik und Bioklimatologie
Serie B, 27, 159–187, https://doi.org/10.1007/BF02243741, 1979. a
Martin, E. and Lejeune, Y.: Turbulent fluxes above the snow surface, Ann. Glaciol., 26, 179–183, https://doi.org/10.3189/1998aog26-1-179-183, 1998. a, b
Mattar, C., Durán-Alarcón, C., Jiménez-Muñoz, J. C.,
Santamaría-Artigas, A., Olivera-Guerra, L., and Sobrino, J. A.: Global
Atmospheric Profiles from Reanalysis Information (GAPRI): a new database for
earth surface temperature retrieval, Int. J. Remote Sens.,
36, 5045–5060, https://doi.org/10.1080/01431161.2015.1054965, 2015. a
Montanaro, M., Gerace, A., Lunsford, A., and Reuter, D.: Stray Light Artifacts
in Imagery from the Landsat 8 Thermal Infrared Sensor, Remote Sens., 6,
10435–10456, https://doi.org/10.3390/rs61110435, 2014. a
Neumark, S.: Chapter 3 – Quartic equation, in: Solution of Cubic and Quartic
Equations, edited by: Neumark, S., Pergamon, 12–24,
https://doi.org/10.1016/B978-0-08-011220-6.50006-8, 1965. a, b
Olson, M., Rupper, S., and Shean, D. E.: Terrain Induced Biases in Clear-Sky
Shortwave Radiation Due to Digital Elevation Model Resolution for Glaciers in
Complex Terrain, Front. Earth Sci., 7, 216,
https://doi.org/10.3389/feart.2019.00216, 2019. a, b
Picard, G.: ghislainp/snowoptics: TC paper (tc_paper), Zenodo [code], https://doi.org/10.5281/zenodo.3742138, 2020. a
Picard, G.: ghislainp/atmosrt: version_used_in_robledano_TC2022 (roughseb_paper_robledano_TC2022), Zenodo [code], https://doi.org/10.5281/zenodo.6046832, 2022. a
Picard, G., Libois, Q., and Arnaud, L.: Refinement of the ice absorption spectrum in the visible using radiance profile measurements in Antarctic snow, The Cryosphere, 10, 2655–2672, https://doi.org/10.5194/tc-10-2655-2016, 2016. a
Picard, G., Dumont, M., Lamare, M., Tuzet, F., Larue, F., Pirazzini, R., and Arnaud, L.: Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections, The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, 2020. a
Plüss, C. and Ohmura, A.: Longwave radiation on snow-covered mountainous
surfaces, J. Appl. Meteorol., 36, 818–824,
https://doi.org/10.1175/1520-0450-36.6.818, 1997. a
Pomeroy, J. W., Essery, R. L., and Helgason, W. D.: Aerodynamic and radiative
controls on the snow surface temperature, J. Hydrometeorol., 17,
2175–2189, https://doi.org/10.1175/JHM-D-15-0226.1, 2016. a
Revuelto, J., Lecourt, G., Lafaysse, M., Zin, I., Charrois, L., Vionnet, V.,
Dumont, M., Rabatel, A., Six, D., Condom, T., Morin, S., Viani, A., and
Sirguey, P.: Multi-Criteria Evaluation of Snowpack Simulations in Complex
Alpine Terrain Using Satellite and In Situ Observations, Remote Sens., 10, 1171,
https://doi.org/10.3390/rs10081171, 2018. a
Ricchiazzi, P., Yang, S., Gautier, C., and Sowle, D.: SBDART: A Research and
Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's
Atmosphere, B. Am. Meteorol. Soc., 79, 2101–2114,
https://doi.org/10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2, 1998.
a
Robledano, A., Picard, G., and Arnaud, L.: Snow surface temperature in mountainous areas, PerSCiDO [data set], https://doi.org/10.18709/perscido.2022.02.ds365, 2022. a
Rotach, M. W. and Zardi, D.: On the boundary-layer structure over highly
complex terrain: Key findings from MAP, Q. J. Roy.
Meteor. Soc., 133, 937–948, https://doi.org/10.1002/qj.71,
2007. a
Rotach, M. W., Gohm, A., Lang, M. N., Leukauf, D., Stiperski, I., and Wagner,
J. S.: On the vertical exchange of heat, mass, and momentum over complex,
mountainous terrain, Front. Earth Sci, 3, 76, https://doi.org/10.3389/feart.2015.00076, 2015. a
Sturm, M., Holmgren, J., Koenig, M., and Morris, K.: The thermal conductivity
of seasonal snow, J. Glaciol., 43, 26–41, 1997. a
Tardy, B., Rivalland, V., Huc, M., Hagolle, O., Marcq, S., and Boulet, G.: A
Software Tool for Atmospheric Correction and Surface Temperature Estimation
of Landsat Infrared Thermal Data, Remote Sens., 8, 696,
https://doi.org/10.3390/rs8090696, 2016. a
Tuzet, F., Dumont, M., Picard, G., Lamare, M., Voisin, D., Nabat, P., Lafaysse, M., Larue, F., Revuelto, J., and Arnaud, L.: Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps), The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, 2020. a, b, c
USGS: How do I rescale Landsat Level-1 digital numbers to reflectance,
radiance, and brightness temperature?,
https://www.usgs.gov/faqs/how-do-i-rescale-landsat-level-1-digital-numbers-reflectance-radiance-and-brightness?qt-news_science_products=0#
(last access: 12 January 2022), 2021. a
Varade, D. and Dikshit, O.: Assessment of winter season land surface
temperature in the Himalayan regions around the Kullu area in India using
landsat-8 data, Geocarto International, 35, 641–662,
https://doi.org/10.1080/10106049.2018.1520928, 2020. a
Warren, S. G., Brandt, R. E., and O’Rawe Hinton, P.: Effect of surface
roughness on bidirectional reflectance of Antarctic snow, J.
Geophys. Res., 103, 25789, https://doi.org/10.1029/98je01898, 1998. a, b, c
Wood, N., Brown, A. R., and Hewer, F. E.: Parametrizing the effects of
orography on the boundary layer: An alternative to effective roughness
lengths, Q. J. Roy. Meteor. Soc., 127,
759–777, https://doi.org/10.1002/qj.49712757303, 2001. a
Yan, G., Wang, T., Jiao, Z., Mu, X., Zhao, J., and Chen, L.: Topographic
radiation modeling and spatial scaling of clear-sky land surface longwave
radiation over rugged terrain, Remote Sens. Environ., 172, 15–27,
https://doi.org/10.1016/j.rse.2015.10.026, 2016. a, b, c
Short summary
Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Topography controls the surface temperature of snow-covered, mountainous areas. We developed a...