Articles | Volume 19, issue 4
https://doi.org/10.5194/tc-19-1527-2025
© Author(s) 2025. 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-19-1527-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Apr 2025
Research article |
| 07 Apr 2025
| 07 Apr 2025
Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning
Lou-Anne Chevrollier
CORRESPONDING AUTHOR
Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
Adrien Wehrlé
Institute of Geography, University of Zürich, Zürich, Switzerland
Joseph M. Cook
Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
Norbert Pirk
Department of Geosciences, University of Oslo, Oslo, Norway
Liane G. Benning
GFZ, Helmholtz Centre for Geosciences, Potsdam, Germany, and Department of Earth Sciences, Free University of Berlin, Berlin, Germany
Alexandre M. Anesio
Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
Martyn Tranter
Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
Related authors
Jenine McCutcheon, James B. McQuaid, Helen Freeman, Jeffrey Paulo H. Perez, Jon Hawkings, Stefanie Lutz, Lou-Anne Chevrollier, Paola Formenti, Servanne Chevaillier, Martyn Tranter, and Liane G. Benning
EGUsphere, https://doi.org/10.5194/egusphere-2026-5031, https://doi.org/10.5194/egusphere-2026-5031, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Soot can travel long distances and settle on snow and ice, where it absorbs light and promotes melting. We examined the size and shape of soot particles deposited on the Greenland Ice Sheet. By combining aerosol and geochemical methods, we show that the soot particles accumulate on the ice sheet surface, especially alongside dense glacier ice algal blooms. Soot accounts for ~15% of the combined darkening effect of algae and soot, highlighting the role of soot in ice albedo reduction and melting.
Jenine McCutcheon, James B. McQuaid, Helen Freeman, Jeffrey Paulo H. Perez, Jon Hawkings, Stefanie Lutz, Lou-Anne Chevrollier, Paola Formenti, Servanne Chevaillier, Martyn Tranter, and Liane G. Benning
EGUsphere, https://doi.org/10.5194/egusphere-2026-5031, https://doi.org/10.5194/egusphere-2026-5031, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Soot can travel long distances and settle on snow and ice, where it absorbs light and promotes melting. We examined the size and shape of soot particles deposited on the Greenland Ice Sheet. By combining aerosol and geochemical methods, we show that the soot particles accumulate on the ice sheet surface, especially alongside dense glacier ice algal blooms. Soot accounts for ~15% of the combined darkening effect of algae and soot, highlighting the role of soot in ice albedo reduction and melting.
Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk, Sebastian Westermann, Clarissa Willmes, and Ruitang Yang
Geosci. Model Dev., 19, 8565–8596, https://doi.org/10.5194/gmd-19-8565-2026, https://doi.org/10.5194/gmd-19-8565-2026, 2026
Short summary
Short summary
AdaPBS (Adaptive Particle Batch Smoother) is a new algorithm to combine observations with cryospheric numerical models. AdaPBS is an iterative algorithm that automatically adjusts computing effort to the task, allowing the implementation of early stopping strategies. We tested AdaPBS at multiple sites with different models, matching or outperforming standard methods, when compared against more complex (computationally expensive) algorithms.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Tiffany Windholz, Kyle A. Arndt, Gerard Rocher-Ros, Mathias Göckede, David Olefeldt, Edward A. G. Schuur, David Bastviken, Richard O'Keefe, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Mika Aurela, Sivakiruthika Balathandayuthabani, Katrin Attermeyer, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andreas Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Beth Holmes, Macall Hock, David Holl, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Cheristy P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natascha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Miska Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martikainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Daniel F. Nadeau, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykänen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleksi Räsänen, Helena Rautakoski, Riikka Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Tarmo Virtanen, Elena Veretennikova, Timo Vesala, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data, 18, 6357–6409, https://doi.org/10.5194/essd-18-6357-2026, https://doi.org/10.5194/essd-18-6357-2026, 2026
Short summary
Short summary
This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Lukasz Stachnik, Meri Korhonen, Jonathan R. Hawkings, Marlena Szeligowska, Katarzyna Koziorowska, Emilia Trudnowska, Karol Kuliński, Beata Szymczycha, Oskar Głowacki, Małgorzata Kitowska, Katarzyna Dragańska-Deja, Marcin Syczewski, Liane G. Benning, Pablo Forjanes, and Mateusz Moskalik
EGUsphere, https://doi.org/10.5194/egusphere-2026-4466, https://doi.org/10.5194/egusphere-2026-4466, 2026
Short summary
Short summary
By comparing two glacier-fed bays in an Arctic fjord, we found that larger glaciers ending in the sea deliver far more sediment and essential micronutrients (Fe, Mn) to coastal waters than smaller glaciers ending on land. We showed that many of these nutrients are transported in particles that can travel farther offshore. As glaciers continue to shrink and surrounding landscapes become more stable, the supply of these nutrients is likely to decline, potentially affecting productivity.
Astrid Vatne, Norbert Pirk, Kolbjørn Engeland, Ane V. Vollsnes, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 30, 5245–5279, https://doi.org/10.5194/hess-30-5245-2026, https://doi.org/10.5194/hess-30-5245-2026, 2026
Short summary
Short summary
Measurements of evaporation are important to understand how evaporation modifies the water balance of northern ecosystems. However, evaporation data in these regions are scarce. We explored a new dataset of evaporation measurements from four wetland sites in Norway and found that up to 30 % of the annual precipitation evaporate back to the atmosphere. Our results indicate that earlier snow melt-out and drier air can increase annual evaporation in the region.
Antoine Zaninetti, Martin P. Lüthi, Adrien Wehrlé, Janneke van Ginkel, and Ana Nap
The Cryosphere, 20, 4037–4048, https://doi.org/10.5194/tc-20-4037-2026, https://doi.org/10.5194/tc-20-4037-2026, 2026
Short summary
Short summary
We investigate the spectral and thermal properties of the strikingly blue ice present on freshly calved icebergs from polar ice streams using satellite multispectral imaging and on-site thermal imaging. Blue ice exhibits unique properties. Several hypotheses have been proposed, but none can fully explain the phenomenon which might be crucial for the understanding of the fast, complex flow dynamics of ice streams.
Anfisa Pismeniuk, Peter Dörsch, Mats Ippach, Clarissa Willmes, Sunniva Sheffield, Norbert Pirk, and Sebastian Westermann
Biogeosciences, 23, 1497–1514, https://doi.org/10.5194/bg-23-1497-2026, https://doi.org/10.5194/bg-23-1497-2026, 2026
Short summary
Short summary
Thermokarst ponds in high latitudes are important methane (CH4) sources in summer. Meanwhile, these ponds are ice-covered for around 60 % of the year and can accumulate CH4 in the ice and within the underlying water column, which potentially results in high emissions during the ice-off. Here, we present data on wintertime CH4 storage of ponds located within two peat plateaus in Northern Norway. Our results show that the wintertime CH4 storage can contribute up to 40 % to the annual CH4 budget.
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk
Biogeosciences, 22, 4163–4186, https://doi.org/10.5194/bg-22-4163-2025, https://doi.org/10.5194/bg-22-4163-2025, 2025
Short summary
Short summary
Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected spectral anomalies in satellite data associated with the herds. Our method can be used for diverse point sources, thereby improving emission inventories.
Eva L. Doting, Ian T. Stevens, Anne M. Kellerman, Pamela E. Rossel, Runa Antony, Amy M. McKenna, Martyn Tranter, Liane G. Benning, Robert G. M. Spencer, Jon R. Hawkings, and Alexandre M. Anesio
Biogeosciences, 22, 41–53, https://doi.org/10.5194/bg-22-41-2025, https://doi.org/10.5194/bg-22-41-2025, 2025
Short summary
Short summary
This study provides the first evidence for biogeochemical cycling of supraglacial dissolved organic matter (DOM) in meltwater flowing through the porous crust of weathering ice that covers glacier ice surfaces during the melt season. Movement of water through the weathering crust is slow, allowing microbes and solar radiation to alter the DOM in glacial meltwaters. This is important as supraglacial meltwaters deliver DOM to downstream aquatic environments.
Guillaume Lamarche-Gagnon, Marek Stibal, Alexandre M. Anesio, Jemma L. Wadham, Jon Hawkings, Lukáš Falteisek, Kristýna Vrbická, Petra Klímová, Jakub D. Žárský, Tyler J. Kohler, Elizabeth A. Bagshaw, Jade E. Hatton, Alex D. Beaton, and Jon Telling
EGUsphere, https://doi.org/10.5194/egusphere-2024-817, https://doi.org/10.5194/egusphere-2024-817, 2024
Preprint archived
Short summary
Short summary
To better understand the microbial ecosystems that underlay Earth’s glaciers, studies often rely on indirect sampling of the subglacial environment via proglacial meltwater runoff. Our research in Greenland reveals that fluctuations in glacier melt can affect microbial composition in runoff, highlighting important biases often overlooked in studies of glacial runoff that might skew interpretations as to the subglacial origin of microbial communities exported within meltwaters.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
Short summary
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Beatriz Gill-Olivas, Jon Telling, Mark Skidmore, and Martyn Tranter
Biogeosciences, 20, 929–943, https://doi.org/10.5194/bg-20-929-2023, https://doi.org/10.5194/bg-20-929-2023, 2023
Short summary
Short summary
Microbial ecosystems have been found in all subglacial environments sampled to date. Yet, little is known of the sources of energy and nutrients that sustain these microbial populations. This study shows that crushing of sedimentary rocks, which contain organic carbon, carbonate and sulfide minerals, along with previously weathered silicate minerals, produces a range of compounds and nutrients which can be utilised by the diverse suite of microbes that inhabit glacier beds.
Adrien Wehrlé, Martin P. Lüthi, and Andreas Vieli
The Cryosphere, 17, 309–326, https://doi.org/10.5194/tc-17-309-2023, https://doi.org/10.5194/tc-17-309-2023, 2023
Short summary
Short summary
We characterized short-lived episodes of ice mélange weakening (IMW) at the front of three major Greenland outlet glaciers. Through a continuous detection at the front of Kangerdlugssuaq Glacier during the June-to-September period from 2018 to 2021, we found that 87 % of the IMW episodes occurred prior to a large-scale calving event. Using a simple model for ice mélange motion, we further characterized the IMW process as self-sustained through the existence of an IMW–calving feedback.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
Short summary
Short summary
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
Short summary
Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Outi Meinander, Pavla Dagsson-Waldhauserova, Pavel Amosov, Elena Aseyeva, Cliff Atkins, Alexander Baklanov, Clarissa Baldo, Sarah L. Barr, Barbara Barzycka, Liane G. Benning, Bojan Cvetkovic, Polina Enchilik, Denis Frolov, Santiago Gassó, Konrad Kandler, Nikolay Kasimov, Jan Kavan, James King, Tatyana Koroleva, Viktoria Krupskaya, Markku Kulmala, Monika Kusiak, Hanna K. Lappalainen, Michał Laska, Jerome Lasne, Marek Lewandowski, Bartłomiej Luks, James B. McQuaid, Beatrice Moroni, Benjamin Murray, Ottmar Möhler, Adam Nawrot, Slobodan Nickovic, Norman T. O’Neill, Goran Pejanovic, Olga Popovicheva, Keyvan Ranjbar, Manolis Romanias, Olga Samonova, Alberto Sanchez-Marroquin, Kerstin Schepanski, Ivan Semenkov, Anna Sharapova, Elena Shevnina, Zongbo Shi, Mikhail Sofiev, Frédéric Thevenet, Throstur Thorsteinsson, Mikhail Timofeev, Nsikanabasi Silas Umo, Andreas Uppstu, Darya Urupina, György Varga, Tomasz Werner, Olafur Arnalds, and Ana Vukovic Vimic
Atmos. Chem. Phys., 22, 11889–11930, https://doi.org/10.5194/acp-22-11889-2022, https://doi.org/10.5194/acp-22-11889-2022, 2022
Short summary
Short summary
High-latitude dust (HLD) is a short-lived climate forcer, air pollutant, and nutrient source. Our results suggest a northern HLD belt at 50–58° N in Eurasia and 50–55° N in Canada and at >60° N in Eurasia and >58° N in Canada. Our addition to the previously identified global dust belt (GDB) provides crucially needed information on the extent of active HLD sources with both direct and indirect impacts on climate and environment in remote regions, which are often poorly understood and predicted.
Anders Lindroth, Norbert Pirk, Ingibjörg S. Jónsdóttir, Christian Stiegler, Leif Klemedtsson, and Mats B. Nilsson
Biogeosciences, 19, 3921–3934, https://doi.org/10.5194/bg-19-3921-2022, https://doi.org/10.5194/bg-19-3921-2022, 2022
Short summary
Short summary
We measured the fluxes of carbon dioxide and methane between a moist moss tundra and the atmosphere on Svalbard in order to better understand how such ecosystems are affecting the climate and vice versa. We found that the system was a small sink of carbon dioxide and a small source of methane. These fluxes are small in comparison with other tundra ecosystems in the high Arctic. Analysis of temperature sensitivity showed that respiration was more sensitive than photosynthesis above about 6 ℃.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
Short summary
Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Adrien Wehrlé, Martin P. Lüthi, Andrea Walter, Guillaume Jouvet, and Andreas Vieli
The Cryosphere, 15, 5659–5674, https://doi.org/10.5194/tc-15-5659-2021, https://doi.org/10.5194/tc-15-5659-2021, 2021
Short summary
Short summary
We developed a novel automated method for the detection and the quantification of ocean waves generated by glacier calving. This method was applied to data recorded with a terrestrial radar interferometer at Eqip Sermia, Greenland. Results show a high calving activity at the glacier front sector ending in deep water linked with more frequent meltwater plumes. This suggests that rising subglacial meltwater plumes strongly affect glacier calving in deep water, but weakly in shallow water.
Cited articles
Bohn, N., Painter, T. H., Thompson, D. R., Carmon, N., Susiluoto, J., Turmon, M. J., Helmlinger, M. C., Green, R. O., Cook, J. M., and Guanter, L.: Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements, Remote Sens. Environ., 264, 112613, https://doi.org/10.1016/j.rse.2021.112613, 2021. a
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., and Kinne, S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, 2013. a
Cook, J. M., Hodson, A. J., Gardner, A. S., Flanner, M., Tedstone, A. J., Williamson, C., Irvine-Fynn, T. D. L., Nilsson, J., Bryant, R., and Tranter, M.: Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, 2017. a, b
Cook, J. M., Tedstone, A. J., Williamson, C., McCutcheon, J., Hodson, A. J., Dayal, A., Skiles, M., Hofer, S., Bryant, R., McAree, O., McGonigle, A., Ryan, J., Anesio, A. M., Irvine-Fynn, T. D. L., Hubbard, A., Hanna, E., Flanner, M., Mayanna, S., Benning, L. G., van As, D., Yallop, M., McQuaid, J. B., Gribbin, T., and Tranter, M.: Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet, The Cryosphere, 14, 309–330, https://doi.org/10.5194/tc-14-309-2020, 2020.
Cook, J. M., Chevrollier, L.-A., Gölles T., and Bohn N: jmcook1186/biosnicar-py: 2.2 (2.2), Zenodo [code], https://doi.org/10.5281/zenodo.15118252, 2025. a, b
Dauchet, J., Blanco, S., Cornet, J.-F., and Fournier, R.: Calculation of the radiative properties of photosynthetic microorganisms, J. Quant. Spectrosc. Ra., 161, 60–84, 2015. a
Developers, T.: TensorFlow, Zenodo [software], https://doi.org/10.5281/zenodo.12726004, 2024. a
Di Mauro, B., Fava, F., Ferrero, L., Garzonio, R., Baccolo, G., Delmonte, B., and Colombo, R.: Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations, J. Geophys. Res.-Atmos., 120, 6080–6097, https://doi.org/10.1002/2015jd023287, 2015. a
Donahue, C., Skiles, S. M., and Hammonds, K.: Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements, The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, 2022. a, b, c
Donahue, C. P., Menounos, B., Viner, N., Skiles, S. M., Beffort, S., Denouden, T., Arriola, S. G., White, R., and Heathfield, D.: Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals, Remote Sens. Environ., 299, 113849, https://doi.org/10.1016/j.rse.2023.113849, 2023. a
Dumont, M. and Tuzet, F.: Light-absorbing Particles in Snow and Climate, in: Chemistry in the Cryosphere (In 2 Parts), 795–830, World Scientific, https://doi.org/10.1142/9789811230134_0016, 2022. a
Dumont, M., Brissaud, O., Picard, G., Schmitt, B., Gallet, J.-C., and Arnaud, Y.: High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths – comparison with modelling results, Atmos. Chem. Phys., 10, 2507–2520, https://doi.org/10.5194/acp-10-2507-2010, 2010. a, b, c, d
Dumont, M., Arnaud, L., Picard, G., Libois, Q., Lejeune, Y., Nabat, P., Voisin, D., and Morin, S.: In situ continuous visible and near-infrared spectroscopy of an alpine snowpack, The Cryosphere, 11, 1091–1110, https://doi.org/10.5194/tc-11-1091-2017, 2017. a, b
Engstrom, C. B., Williamson, S. N., Gamon, J. A., and Quarmby, L. M.: Seasonal development and radiative forcing of red snow algal blooms on two glaciers in British Columbia, Canada, summer 2020, Remote Sens. Environ., 280, 113164, https://doi.org/10.1016/j.rse.2022.113164, 2022. a, b
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M.: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155, 2011. a
Flanner, M. G., Liu, X., Zhou, C., Penner, J. E., and Jiao, C.: Enhanced solar energy absorption by internally-mixed black carbon in snow grains, Atmos. Chem. Phys., 12, 4699–4721, https://doi.org/10.5194/acp-12-4699-2012, 2012. a
Flanner, M. G., Arnheim, J. B., Cook, J. M., Dang, C., He, C., Huang, X., Singh, D., Skiles, S. M., Whicker, C. A., and Zender, C. S.: SNICAR-ADv3: a community tool for modeling spectral snow albedo, Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, 2021. a, b, c, d, e, f, g, h, i
Ganey, G. Q., Loso, M. G., Burgess, A. B., and Dial, R. J.: The role of microbes in snowmelt and radiative forcing on an Alaskan icefield, Nat. Geosci., 10, 754–759, https://doi.org/10.1038/ngeo3027, 2017. a
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, J. Geophys. Res.-Earth, 115, F01009, https://doi.org/10.1029/2009JF001444, 2010. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Harrold, Z. R., Hausrath, E. M., Garcia, A., Murray, A. E., Tschauner, O., Raymond, J. A., and Huang, S.: Bioavailability of mineral-bound iron to a snow algal-bacterial coculture and implications for albedo-altering snow algal blooms, Appl. Environ. Microb., 84, e02322–17, https://doi.org/10.1128/AEM.02322-17, 2018. a
He, C.: Modelling light-absorbing particle–snow–radiation interactions and impacts on snow albedo: fundamentals, recent advances and future directions, Environ. Chem., 19, 296–311, 2022. a
Healy, S. M. and Khan, A. L.: Albedo change from snow algae blooms can contribute substantially to snow melt in the North Cascades, USA, Communications Earth & Environment, 4, 142, https://doi.org/10.1038/s43247-023-00768-8, 2023. a, b, c
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Kandilian, R., Soulies, A., Pruvost, J., Rousseau, B., Legrand, J., and Pilon, L.: Simple method for measuring the spectral absorption cross-section of microalgae, Chem. Eng. Sci., 146, 357–368, 2016. a
Kaspari, S., Painter, T. H., Gysel, M., Skiles, S. M., and Schwikowski, M.: Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings, Atmos. Chem. Phys., 14, 8089–8103, https://doi.org/10.5194/acp-14-8089-2014, 2014. a, b
Khan, A. L., Dierssen, H. M., Scambos, T. A., Höfer, J., and Cordero, R. R.: Spectral characterization, radiative forcing and pigment content of coastal Antarctic snow algae: approaches to spectrally discriminate red and green communities and their impact on snowmelt, The Cryosphere, 15, 133–148, https://doi.org/10.5194/tc-15-133-2021, 2021. a
Kokhanovsky, A., Di Mauro, B., Garzonio, R., and Colombo, R.: Retrieval of dust properties from spectral snow reflectance measurements, Frontiers in Environmental Science, 9, 644551, https://doi.org/10.3389/fenvs.2021.644551, 2021. a
Lutz, S., Anesio, A. M., Raiswell, R., Edwards, A., Newton, R. J., Gill, F., and Benning, L. G.: The biogeography of red snow microbiomes and their role in melting arctic glaciers, Nat. Commun., 7, 11968, https://doi.org/10.1038/ncomms11968, 2016. a
Malinka, A.: Stereological approach to radiative transfer in porous materials. Application to the optics of snow, J. Quant. Secptrosc. Ra., 295, 108410, https://doi.org/10.1016/j.jqsrt.2022.108410, 2023. a
McCutcheon, J., Lutz, S., Williamson, C., Cook, J. M., Tedstone, A. J., Vanderstraeten, A., Wilson, S., Stockdale, A., Bonneville, S., Anesio, A. M., and Yallop, M. L.: Mineral phosphorus drives glacier algal blooms on the Greenland Ice Sheet, Nat. Commun., 12, 570, https://doi.org/10.1038/s41467-020-20627-w, 2021. a
pandas development team, T.: pandas-dev/pandas: Pandas, Zenodo [software], https://doi.org/10.5281/zenodo.10957263, 2024. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Picard, G. and Libois, Q.: Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model, Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, 2024. 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
Pirk, N., Aalstad, K., Yilmaz, Y. A., Vatne, A., Popp, A. L., Horvath, P., Bryn, A., Vollsnes, A. V., Westermann, S., Berntsen, T. K., Stordal, F., and Tallaksen, L. M.: Snow–vegetation–atmosphere interactions in alpine tundra, Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, 2023. a
Prahl, S.: miepython: Pure python implementation of Mie scattering, Zenodo [software], https://doi.org/10.5281/zenodo.8023972, 2023. a
Robledano, A., Picard, G., Dumont, M., Flin, F., Arnaud, L., and Libois, Q.: Unraveling the optical shape of snow, Nat. Commun., 14, 3955, https://doi.org/10.1038/s41467-023-39671-3, 2023. a
Rowe, P., Fergoda, M., and Neshyba, S.: Temperature-dependent optical properties of liquid water from 240 to 298 K, J. Geophys. Res.-Atmos., 125, e2020JD032624, https://doi.org/10.1029/2020JD032624, 2020. a
Segelstein, D. J.: The complex refractive index of water, Ph.D. thesis, University of Missouri–Kansas City, http://hdl.handle.net/10355/11599, 1981. a
Stafford, B., Louis, R., Wilson, R., Rejeté, F., Steinmetz, F., Borsetti, M., Bachmann, A., Aronovitch, A., Blennerhassett, E., Hardy, W., steelman, Kiselev, A., Firing, E., Semeniuc, E., Kundert, P., Arron-Walker, T., McNaughton, W., solarjoe, Wallace, A., bfrobin446, Luiten, C., Rackwitz, C., Sharma, D., Maliarik, D., Hasan, Deane, J., van Haarst, J., Heikkilä, J., Maron, J., and Contreras, J. L. L.: pingswept/pysolar: 0.11, Zenodo [software], https://doi.org/10.5281/zenodo.8184359, 2023. a
Stramski, D., Reynolds, R. A., Kaczmarek, S., Uitz, J., and Zheng, G.: Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region, Appl. Opt., 54, 6763–6782, 2015. 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
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, 1982. a
Warren, S. G.: Can black carbon in snow be detected by remote sensing?, J. Geophys. Res.-Atmos, 118, 779–786, 2013. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008. a
Wehrlé, A., Chevrollier, L.-A., and Antsalacia: openosmia/snowlaps: v0.1.0 (v0.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.14639602, 2025. a
Wes McKinney: Data Structures for Statistical Computing in Python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., 56–61, https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Whicker, C. A., Flanner, M. G., Dang, C., Zender, C. S., Cook, J. M., and Gardner, A. S.: SNICAR-ADv4: a physically based radiative transfer model to represent the spectral albedo of glacier ice, The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, 2022. a, b
Short summary
Light-absorbing particles (LAPs) are often present as a mixture on snow surfaces and are important to disentangle because their darkening effects vary but also because the processes governing their presence and accumulation on snow surfaces are different. This study presents a novel method to retrieve the concentration and albedo-reducing effect of different LAPs present at the snow surface from surface spectral albedo. The method is then successfully applied to ground observations on seasonal snow.
Light-absorbing particles (LAPs) are often present as a mixture on snow surfaces and are...
