Articles | Volume 19, issue 9
https://doi.org/10.5194/tc-19-3831-2025
© 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-19-3831-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2025
Research article |
| 16 Sep 2025
| 16 Sep 2025
Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter
Marco Mazzolini
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, Oslo, Norway
Kristoffer Aalstad
Department of Geosciences, University of Oslo, Oslo, Norway
Esteban Alonso-González
Pyrenean Institute of Ecology, CSIC, Jaca, Spain
Sebastian Westermann
Department of Geosciences, University of Oslo, Oslo, Norway
Désirée Treichler
Department of Geosciences, University of Oslo, Oslo, Norway
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Joana Pedro Baptista, Gonçalo Brito Guapo Teles Vieira, António Manuel de Carvalho Soares Correia, Hyoungseok Lee, and Sebastian Westermann
The Cryosphere, 19, 3459–3476, https://doi.org/10.5194/tc-19-3459-2025, https://doi.org/10.5194/tc-19-3459-2025, 2025
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Permafrost underlies ice-free areas of Antarctica, but its response to long-term warming is unclear due to a limited number of monitoring sites. To address this, we used the CryoGrid model, forced with climate data, to estimate permafrost temperatures and active layer thickness at King Sejong Station since 1950. The results show ground temperatures rising 0.25 °C per decade and the active layer thickening by 2 m. Warming has accelerated since 2015, highlighting the need for continued monitoring.
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
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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.
Jacqueline K. Knutson, François Clayer, Peter Dörsch, Sebastian Westermann, and Heleen A. de Wit
Biogeosciences, 22, 3899–3914, https://doi.org/10.5194/bg-22-3899-2025, https://doi.org/10.5194/bg-22-3899-2025, 2025
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Thawing permafrost at Iškoras in northern Norway is transforming peat plateaus into thermokarst ponds and wetlands. These small ponds show striking oversaturation of dissolved greenhouse gases, such as carbon dioxide (CO2) and methane (CH4), partly owing to organic matter processing. Streams nearby emit CO2, driven by turbulence. As permafrost disappears, carbon dynamics will change, potentially increasing emissions of CH4. This study highlights the need to integrate these changes into climate models.
Anfisa Pismeniuk, Peter Dörsch, Mats Ippach, Clarissa Willmes, Sunniva Sheffield, Norbert Pirk, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3059, https://doi.org/10.5194/egusphere-2025-3059, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Thermokarst ponds in high latitudes are important methane (CH4) sources in summer. Meanwhile, these lakes 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.
Robin B. Zweigel, Dashtseren Avirmed, Khurelbaatar Temuujin, Clare Webster, Hanna Lee, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2366, https://doi.org/10.5194/egusphere-2025-2366, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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Two years of data along a forest disturbance gradient in Mongolia show a larger annual ground surface temperature range in dead and logged forests than intact forest, while the range is dampened in stands of young regrowth. Compared to intact forest, mean annual ground surface temperatures are 0.5 °C colder in dead and logged forest and dense stands of young regrowth. This is linked to differences in vegetation and surface cover due to the disturbance and patterns in livestock activity.
Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2347, https://doi.org/10.5194/egusphere-2025-2347, 2025
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Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
EGUsphere, https://doi.org/10.5194/egusphere-2025-423, https://doi.org/10.5194/egusphere-2025-423, 2025
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Reliable SWE and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we suggest a Long Short-Term Memory neural network for operational data assimilation in snow hydrology. Once trained, it cuts computation by 70 % versus an EnKF, with a slight RMSE increase (+6 mm SWE, +6 cm snow depth). This work advances deep learning in snow hydrology, offering an efficient, scalable, and low-cost modeling framework.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
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Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Thibault Xavier, Laurent Orgogozo, Anatoly S. Prokushkin, Esteban Alonso-González, Simon Gascoin, and Oleg S. Pokrovsky
The Cryosphere, 18, 5865–5885, https://doi.org/10.5194/tc-18-5865-2024, https://doi.org/10.5194/tc-18-5865-2024, 2024
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Permafrost (permanently frozen soil at depth) is thawing as a result of climate change. However, estimating its future degradation is particularly challenging due to the complex multi-physical processes involved. In this work, we designed and ran numerical simulations for months on a supercomputer to quantify the impact of climate change in a forested valley of central Siberia. There, climate change could increase the thickness of the seasonally thawed soil layer in summer by up to 65 % by 2100.
Lotte Wendt, Line Rouyet, Hanne H. Christiansen, Tom Rune Lauknes, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2972, https://doi.org/10.5194/egusphere-2024-2972, 2024
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In permafrost environments, the ground surface moves due to the formation and melt of ice in the ground. This study compares ground surface displacements measured from satellite images against field data of ground ice contents. We find good agreement between the detected seasonal subsidence and observed ground ice melt. Our results show the potential of satellite remote sensing for mapping ground ice variability, but also indicate that ice in excess of the pore space must be considered.
Robin Benjamin Zweigel, Avirmed Dashtseren, Khurelbaatar Temuujin, Anarmaa Sharkhuu, Clare Webster, Hanna Lee, and Sebastian Westermann
Biogeosciences, 21, 5059–5077, https://doi.org/10.5194/bg-21-5059-2024, https://doi.org/10.5194/bg-21-5059-2024, 2024
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Intense grazing at grassland sites removes vegetation, reduces the snow cover, and inhibits litter layers from forming. Grazed sites generally have a larger annual ground surface temperature amplitude than ungrazed sites, but the net effect depends on effects in the transitional seasons. Our results also suggest that seasonal use of pastures can reduce ground temperatures, which can be a strategy to protect currently degrading grassland permafrost.
Sigrid Trier Kjær, Sebastian Westermann, Nora Nedkvitne, and Peter Dörsch
Biogeosciences, 21, 4723–4737, https://doi.org/10.5194/bg-21-4723-2024, https://doi.org/10.5194/bg-21-4723-2024, 2024
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Permafrost peatlands are thawing due to climate change, releasing large quantities of carbon that degrades upon thawing and is released as CO2, CH4 or dissolved organic carbon (DOC). We incubated thawed Norwegian permafrost peat plateaus and thermokarst pond sediment found next to permafrost for up to 350 d to measure carbon loss. CO2 production was initially the highest, whereas CH4 production increased over time. The largest carbon loss was measured at the top of the peat plateau core as DOC.
Juditha Aga, Livia Piermattei, Luc Girod, Kristoffer Aalstad, Trond Eiken, Andreas Kääb, and Sebastian Westermann
Earth Surf. Dynam., 12, 1049–1070, https://doi.org/10.5194/esurf-12-1049-2024, https://doi.org/10.5194/esurf-12-1049-2024, 2024
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Coastal rock cliffs on Svalbard are considered to be fairly stable; however, long-term trends in coastal-retreat rates remain unknown. This study examines changes in the coastline position along Brøggerhalvøya, Svalbard, using aerial images from 1970, 1990, 2010, and 2021. Our analysis shows that coastal-retreat rates accelerate during the period 2010–2021, which coincides with increasing storminess and retreating sea ice.
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
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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.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
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Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Josep Bonsoms, Juan I. López-Moreno, Esteban Alonso-González, César Deschamps-Berger, and Marc Oliva
Nat. Hazards Earth Syst. Sci., 24, 245–264, https://doi.org/10.5194/nhess-24-245-2024, https://doi.org/10.5194/nhess-24-245-2024, 2024
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Climate warming is changing mountain snowpack patterns, leading in some cases to rain-on-snow (ROS) events. Here we analyzed near-present ROS and its sensitivity to climate warming across the Pyrenees. ROS increases during the coldest months of the year but decreases in the warmest months and areas under severe warming due to snow cover depletion. Faster snow ablation is anticipated in the coldest and northern slopes of the range. Relevant implications in mountain ecosystem are anticipated.
Moritz Langer, Jan Nitzbon, Brian Groenke, Lisa-Marie Assmann, Thomas Schneider von Deimling, Simone Maria Stuenzi, and Sebastian Westermann
The Cryosphere, 18, 363–385, https://doi.org/10.5194/tc-18-363-2024, https://doi.org/10.5194/tc-18-363-2024, 2024
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Using a model that can simulate the evolution of Arctic permafrost over centuries to millennia, we find that post-industrialization permafrost warming has three "hotspots" in NE Canada, N Alaska, and W Siberia. The extent of near-surface permafrost has decreased substantially since 1850, with the largest area losses occurring in the last 50 years. The simulations also show that volcanic eruptions have in some cases counteracted the loss of near-surface permafrost for a few decades.
Bernd Etzelmüller, Ketil Isaksen, Justyna Czekirda, Sebastian Westermann, Christin Hilbich, and Christian Hauck
The Cryosphere, 17, 5477–5497, https://doi.org/10.5194/tc-17-5477-2023, https://doi.org/10.5194/tc-17-5477-2023, 2023
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Permafrost (permanently frozen ground) is widespread in the mountains of Norway and Iceland. Several boreholes were drilled after 1999 for long-term permafrost monitoring. We document a strong warming of permafrost, including the development of unfrozen bodies in the permafrost. Warming and degradation of mountain permafrost may lead to more natural hazards.
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
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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.
Anatoly O. Sinitsyn, Sara Bazin, Rasmus Benestad, Bernd Etzelmüller, Ketil Isaksen, Hanne Kvitsand, Julia Lutz, Andrea L. Popp, Lena Rubensdotter, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2950, https://doi.org/10.5194/egusphere-2023-2950, 2023
Preprint archived
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This study looked at under the ground on Svalbard, an archipelago close to the North Pole. We found something very surprising – there is water under the all year around frozen soil. This was not known before. This water could be used for drinking if we manage it carefully. This is important because getting clean drinking water is very difficult in Svalbard, and other Arctic places. Also, because the climate is getting warmer, there might be even more water underground in the future.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Juditha Aga, Julia Boike, Moritz Langer, Thomas Ingeman-Nielsen, and Sebastian Westermann
The Cryosphere, 17, 4179–4206, https://doi.org/10.5194/tc-17-4179-2023, https://doi.org/10.5194/tc-17-4179-2023, 2023
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This study presents a new model scheme for simulating ice segregation and thaw consolidation in permafrost environments, depending on ground properties and climatic forcing. It is embedded in the CryoGrid community model, a land surface model for the terrestrial cryosphere. We describe the model physics and functionalities, followed by a model validation and a sensitivity study of controlling factors.
Matan Ben-Asher, Florence Magnin, Sebastian Westermann, Josué Bock, Emmanuel Malet, Johan Berthet, Ludovic Ravanel, and Philip Deline
Earth Surf. Dynam., 11, 899–915, https://doi.org/10.5194/esurf-11-899-2023, https://doi.org/10.5194/esurf-11-899-2023, 2023
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Quantitative knowledge of water availability on high mountain rock slopes is very limited. We use a numerical model and field measurements to estimate the water balance at a steep rock wall site. We show that snowmelt is the main source of water at elevations >3600 m and that snowpack hydrology and sublimation are key factors. The new information presented here can be used to improve the understanding of thermal, hydrogeological, and mechanical processes on steep mountain rock slopes.
Brian Groenke, Moritz Langer, Jan Nitzbon, Sebastian Westermann, Guillermo Gallego, and Julia Boike
The Cryosphere, 17, 3505–3533, https://doi.org/10.5194/tc-17-3505-2023, https://doi.org/10.5194/tc-17-3505-2023, 2023
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It is now well known from long-term temperature measurements that Arctic permafrost, i.e., ground that remains continuously frozen for at least 2 years, is warming in response to climate change. Temperature, however, only tells half of the story. In this study, we use computer modeling to better understand how the thawing and freezing of water in the ground affects the way permafrost responds to climate change and what temperature trends can and cannot tell us about how permafrost is changing.
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
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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.
Ixeia Vidaller, Eñaut Izagirre, Luis Mariano del Rio, Esteban Alonso-González, Francisco Rojas-Heredia, Enrique Serrano, Ana Moreno, Juan Ignacio López-Moreno, and Jesús Revuelto
The Cryosphere, 17, 3177–3192, https://doi.org/10.5194/tc-17-3177-2023, https://doi.org/10.5194/tc-17-3177-2023, 2023
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The Aneto glacier, the largest glacier in the Pyrenees, has shown continuous surface and ice thickness losses in the last decades. In this study, we examine changes in its surface and ice thickness for 1981–2022 and the remaining ice thickness in 2020. During these 41 years, the glacier has shrunk by 64.7 %, and the ice thickness has decreased by 30.5 m on average. The mean ice thickness in 2022 was 11.9 m, compared to 32.9 m in 1981. The results highlight the critical situation of the glacier.
Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Erin Emily Thomas, and Sebastian Westermann
The Cryosphere, 17, 2941–2963, https://doi.org/10.5194/tc-17-2941-2023, https://doi.org/10.5194/tc-17-2941-2023, 2023
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Here, we present high-resolution simulations of glacier mass balance (the gain and loss of ice over a year) and runoff on Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model. We find a small overall loss of mass over the simulation period of −0.08 m yr−1 but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with a significant increasing trend of 6.3 Gt per decade.
Justyna Czekirda, Bernd Etzelmüller, Sebastian Westermann, Ketil Isaksen, and Florence Magnin
The Cryosphere, 17, 2725–2754, https://doi.org/10.5194/tc-17-2725-2023, https://doi.org/10.5194/tc-17-2725-2023, 2023
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We assess spatio-temporal permafrost variations in selected rock walls in Norway over the last 120 years. Ground temperature is modelled using the two-dimensional ground heat flux model CryoGrid 2D along nine profiles. Permafrost probably occurs at most sites. All simulations show increasing ground temperature from the 1980s. Our simulations show that rock wall permafrost with a temperature of −1 °C at 20 m depth could thaw at this depth within 50 years.
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
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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.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Josep Bonsoms, Juan Ignacio López-Moreno, and Esteban Alonso-González
The Cryosphere, 17, 1307–1326, https://doi.org/10.5194/tc-17-1307-2023, https://doi.org/10.5194/tc-17-1307-2023, 2023
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This work analyzes the snow response to temperature and precipitation in the Pyrenees. During warm and wet seasons, seasonal snow depth is expected to be reduced by −37 %, −34 %, and −27 % per degree Celsius at low-, mid-, and high-elevation areas, respectively. The largest snow reductions are anticipated at low elevations of the eastern Pyrenees. Results anticipate important impacts on the nearby ecological and socioeconomic systems.
Cas Renette, Kristoffer Aalstad, Juditha Aga, Robin Benjamin Zweigel, Bernd Etzelmüller, Karianne Staalesen Lilleøren, Ketil Isaksen, and Sebastian Westermann
Earth Surf. Dynam., 11, 33–50, https://doi.org/10.5194/esurf-11-33-2023, https://doi.org/10.5194/esurf-11-33-2023, 2023
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One of the reasons for lower ground temperatures in coarse, blocky terrain is a low or varying soil moisture content, which most permafrost modelling studies did not take into account. We used the CryoGrid community model to successfully simulate this effect and found markedly lower temperatures in well-drained, blocky deposits compared to other set-ups. The inclusion of this drainage effect is another step towards a better model representation of blocky mountain terrain in permafrost regions.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
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
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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.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
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The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Frank Paul, Livia Piermattei, Désirée Treichler, Lin Gilbert, Luc Girod, Andreas Kääb, Ludivine Libert, Thomas Nagler, Tazio Strozzi, and Jan Wuite
The Cryosphere, 16, 2505–2526, https://doi.org/10.5194/tc-16-2505-2022, https://doi.org/10.5194/tc-16-2505-2022, 2022
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Glacier surges are widespread in the Karakoram and have been intensely studied using satellite data and DEMs. We use time series of such datasets to study three glacier surges in the same region of the Karakoram. We found strongly contrasting advance rates and flow velocities, maximum velocities of 30 m d−1, and a change in the surge mechanism during a surge. A sensor comparison revealed good agreement, but steep terrain and the two smaller glaciers caused limitations for some of them.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Joel Fiddes, Kristoffer Aalstad, and Michael Lehning
Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, https://doi.org/10.5194/gmd-15-1753-2022, 2022
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This study describes and evaluates a new downscaling scheme that addresses the need for hillslope-scale atmospheric forcing time series for modelling the local impact of regional climate change on the land surface in mountain areas. The method has a global scope and is able to generate all model forcing variables required for hydrological and land surface modelling. This is important, as impact models require high-resolution forcings such as those generated here to produce meaningful results.
Sarah E. Chadburn, Eleanor J. Burke, Angela V. Gallego-Sala, Noah D. Smith, M. Syndonia Bret-Harte, Dan J. Charman, Julia Drewer, Colin W. Edgar, Eugenie S. Euskirchen, Krzysztof Fortuniak, Yao Gao, Mahdi Nakhavali, Włodzimierz Pawlak, Edward A. G. Schuur, and Sebastian Westermann
Geosci. Model Dev., 15, 1633–1657, https://doi.org/10.5194/gmd-15-1633-2022, https://doi.org/10.5194/gmd-15-1633-2022, 2022
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We present a new method to include peatlands in an Earth system model (ESM). Peatlands store huge amounts of carbon that accumulates very slowly but that can be rapidly destabilised, emitting greenhouse gases. Our model captures the dynamic nature of peat by simulating the change in surface height and physical properties of the soil as carbon is added or decomposed. Thus, we model, for the first time in an ESM, peat dynamics and its threshold behaviours that can lead to destabilisation.
Bernd Etzelmüller, Justyna Czekirda, Florence Magnin, Pierre-Allain Duvillard, Ludovic Ravanel, Emanuelle Malet, Andreas Aspaas, Lene Kristensen, Ingrid Skrede, Gudrun D. Majala, Benjamin Jacobs, Johannes Leinauer, Christian Hauck, Christin Hilbich, Martina Böhme, Reginald Hermanns, Harald Ø. Eriksen, Tom Rune Lauknes, Michael Krautblatter, and Sebastian Westermann
Earth Surf. Dynam., 10, 97–129, https://doi.org/10.5194/esurf-10-97-2022, https://doi.org/10.5194/esurf-10-97-2022, 2022
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This paper is a multi-authored study documenting the possible existence of permafrost in permanently monitored rockslides in Norway for the first time by combining a multitude of field data, including geophysical surveys in rock walls. The paper discusses the possible role of thermal regime and rockslide movement, and it evaluates the possible impact of atmospheric warming on rockslide dynamics in Norwegian mountains.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
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Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Léo C. P. Martin, Jan Nitzbon, Johanna Scheer, Kjetil S. Aas, Trond Eiken, Moritz Langer, Simon Filhol, Bernd Etzelmüller, and Sebastian Westermann
The Cryosphere, 15, 3423–3442, https://doi.org/10.5194/tc-15-3423-2021, https://doi.org/10.5194/tc-15-3423-2021, 2021
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It is important to understand how permafrost landscapes respond to climate changes because their thaw can contribute to global warming. We investigate how a common permafrost morphology degrades using both field observations of the surface elevation and numerical modeling. We show that numerical models accounting for topographic changes related to permafrost degradation can reproduce the observed changes in nature and help us understand how parameters such as snow influence this phenomenon.
Juditha Undine Schmidt, Bernd Etzelmüller, Thomas Vikhamar Schuler, Florence Magnin, Julia Boike, Moritz Langer, and Sebastian Westermann
The Cryosphere, 15, 2491–2509, https://doi.org/10.5194/tc-15-2491-2021, https://doi.org/10.5194/tc-15-2491-2021, 2021
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This study presents rock surface temperatures (RSTs) of steep high-Arctic rock walls on Svalbard from 2016 to 2020. The field data show that coastal cliffs are characterized by warmer RSTs than inland locations during winter seasons. By running model simulations, we analyze factors leading to that effect, calculate the surface energy balance and simulate different future scenarios. Both field data and model results can contribute to a further understanding of RST in high-Arctic rock walls.
Thomas Schneider von Deimling, Hanna Lee, Thomas Ingeman-Nielsen, Sebastian Westermann, Vladimir Romanovsky, Scott Lamoureux, Donald A. Walker, Sarah Chadburn, Erin Trochim, Lei Cai, Jan Nitzbon, Stephan Jacobi, and Moritz Langer
The Cryosphere, 15, 2451–2471, https://doi.org/10.5194/tc-15-2451-2021, https://doi.org/10.5194/tc-15-2451-2021, 2021
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Climate warming puts infrastructure built on permafrost at risk of failure. There is a growing need for appropriate model-based risk assessments. Here we present a modelling study and show an exemplary case of how a gravel road in a cold permafrost environment in Alaska might suffer from degrading permafrost under a scenario of intense climate warming. We use this case study to discuss the broader-scale applicability of our model for simulating future Arctic infrastructure failure.
Esteban Alonso-González and Víctor Fernández-García
Earth Syst. Sci. Data, 13, 1925–1938, https://doi.org/10.5194/essd-13-1925-2021, https://doi.org/10.5194/essd-13-1925-2021, 2021
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We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area products. The database inludes monthly scenes with the dNBR, RdNBR and post-burn NBR spectral indices at 500 m spatial resolution from November 2000 onwards. Moreover, in this work we show that there is a close relationship between the burn severity metrics included in MOSEV and the same ones obtained from Landsat-8.
Jan Nitzbon, Moritz Langer, Léo C. P. Martin, Sebastian Westermann, Thomas Schneider von Deimling, and Julia Boike
The Cryosphere, 15, 1399–1422, https://doi.org/10.5194/tc-15-1399-2021, https://doi.org/10.5194/tc-15-1399-2021, 2021
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We used a numerical model to investigate how small-scale landscape heterogeneities affect permafrost thaw under climate-warming scenarios. Our results show that representing small-scale heterogeneities in the model can decide whether a landscape is water-logged or well-drained in the future. This in turn affects how fast permafrost thaws under warming. Our research emphasizes the importance of considering small-scale processes in model assessments of permafrost thaw under climate change.
Ana Moreno, Miguel Bartolomé, Juan Ignacio López-Moreno, Jorge Pey, Juan Pablo Corella, Jordi García-Orellana, Carlos Sancho, María Leunda, Graciela Gil-Romera, Penélope González-Sampériz, Carlos Pérez-Mejías, Francisco Navarro, Jaime Otero-García, Javier Lapazaran, Esteban Alonso-González, Cristina Cid, Jerónimo López-Martínez, Belén Oliva-Urcia, Sérgio Henrique Faria, María José Sierra, Rocío Millán, Xavier Querol, Andrés Alastuey, and José M. García-Ruíz
The Cryosphere, 15, 1157–1172, https://doi.org/10.5194/tc-15-1157-2021, https://doi.org/10.5194/tc-15-1157-2021, 2021
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Our study of the chronological sequence of Monte Perdido Glacier in the Central Pyrenees (Spain) reveals that, although the intense warming associated with the Roman period or Medieval Climate Anomaly produced important ice mass losses, it was insufficient to make this glacier disappear. By contrast, recent global warming has melted away almost 600 years of ice accumulated since the Little Ice Age, jeopardising the survival of this and other southern European glaciers over the next few decades.
Simone Maria Stuenzi, Julia Boike, William Cable, Ulrike Herzschuh, Stefan Kruse, Luidmila A. Pestryakova, Thomas Schneider von Deimling, Sebastian Westermann, Evgenii S. Zakharov, and Moritz Langer
Biogeosciences, 18, 343–365, https://doi.org/10.5194/bg-18-343-2021, https://doi.org/10.5194/bg-18-343-2021, 2021
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Boreal forests in eastern Siberia are an essential component of global climate patterns. We use a physically based model and field measurements to study the interactions between forests, permanently frozen ground and the atmosphere. We find that forests exert a strong control on the thermal state of permafrost through changing snow cover dynamics and altering the surface energy balance, through absorbing most of the incoming solar radiation and suppressing below-canopy turbulent fluxes.
Lei Cai, Hanna Lee, Kjetil Schanke Aas, and Sebastian Westermann
The Cryosphere, 14, 4611–4626, https://doi.org/10.5194/tc-14-4611-2020, https://doi.org/10.5194/tc-14-4611-2020, 2020
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A sub-grid representation of excess ground ice in the Community Land Model (CLM) is developed as novel progress in modeling permafrost thaw and its impacts under the warming climate. The modeled permafrost degradation with sub-grid excess ice follows the pathway that continuous permafrost transforms into discontinuous permafrost before it disappears, including surface subsidence and talik formation, which are highly permafrost-relevant landscape changes excluded from most land models.
Cited articles
Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a, b, c
Aitchison, J. and Shen, S. M.: Logistic-Normal Distributions: Some Properties and Uses, Biometrika, 67, 261–272, https://doi.org/10.2307/2335470, 1980. a
Alonso-González, E.: Inputs (forcing and observations) ready for use by “MuSA: The Multiscale Snow Data Assimilation System (v1.0)”, Zenodo [data set], https://doi.org/10.5281/zenodo.7248635, 2022. a, b, c
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., Bouchet, M., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area, Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, 2021. a
Alonso-González, E., Aalstad, K., Pirk, N., Mazzolini, M., Treichler, D., Leclercq, P., Westermann, S., López-Moreno, J. I., and Gascoin, S.: Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation, Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l
Alonso-González, E., Mazzolini, M., and Aalstad, K.: ealonsogzl/MuSA: v2.1 TC submission (v2.1), Zenodo [code], https://doi.org/10.5281/zenodo.11147258, 2024. a, b
Anonymous Referee: RC2: “Comment on egusphere-2023-954”, https://doi.org/10.5194/egusphere-2023-954-RC2, 2023. a
Cho, E., Kwon, Y., Kumar, S. V., and Vuyovich, C. M.: Assimilation of airborne gamma observations provides utility for snow estimation in forested environments, Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, 2023. a
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, https://doi.org/10.1029/2011WR010745, 2011. a
Cluzet, B., Lafaysse, M., Deschamps-Berger, C., Vernay, M., and Dumont, M.: Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network, The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, 2022. a
Cressie, N. A. C.: Statistics for spatio-temporal data, Wiley series in probability and statistics, Hoboken, New Jersey, ISBN 1-119-24304-1, 2011. a
De Lannoy, G., Reichle, R., Arsenault, K., Houser, P., Kumar, S., Verhoest, N., and Pauwels, V.: Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado, Water Resour. Res., 48, https://doi.org/10.1029/2011WR010588, 2012. a
De Lannoy, G. J. M., Bechtold, M., Albergel, C., Brocca, L., Calvet, J.-C., Carrassi, A., Crow, W. T., de Rosnay, P., Durand, M., Forman, B., Geppert, G., Girotto, M., Hendricks Franssen, H.-J., Jonas, T., Kumar, S., Lievens, H., Lu, Y., Massari, C., Pauwels, V. R. N., Reichle, R. H., and Steele-Dunne, S.: Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication, Frontiers in Water, 4, https://doi.org/10.3389/frwa.2022.981745, 2022. a
Deschamps-Berger, C., Gascoin, S., Shean, D., Besso, H., Guiot, A., and López-Moreno, J. I.: Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data, The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, 2023. a, b, c
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a
Emerick, A. A.: Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data, Comput. Geosci., 22, 1175–1186, https://doi.org/10.1007/s10596-018-9745-5, 2018. a
Enderlin, E. M., Elkin, C. M., Gendreau, M., Marshall, H. P., O'Neel, S., McNeil, C., Florentine, C., and Sass, L.: Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions, Remote Sens. Environ., 283, 113307, https://doi.org/10.1016/j.rse.2022.113307, 2022. a, b, c
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015. a, b
Essery, R.: FSM2 quickstart guide, GitHub [code], https://github.com/RichardEssery/FSM2 (last access: 1 December 2023), 2023. a
Essery, R., Mazzotti, G., Barr, S., Jonas, T., Quaife, T., and Rutter, N.: A Flexible Snow Model (FSM 2.1.1) including a forest canopy, Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, 2025. a
Evensen, G., Vossepoel, F. C., and van Leeuwen, P. J.: Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem, Springer Textbooks in Earth Sciences, Geography and Environment, Springer International Publishing, Cham, ISBN 978-3-030-96708-6, https://doi.org/10.1007/978-3-030-96709-3, 2022. a, b, c
Fiddes, J., Aalstad, K., and Westermann, S.: Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering, Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, 2019. a, b
Fiddes, J., Aalstad, K., and Lehning, M.: TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1, Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, 2022. a
Filhol, S., Fiddes, J., and Aalstad, K.: TopoPyScale: A Python Package for Hillslope Climate Downscaling, Journal of Open Source Software, 8, 5059, https://doi.org/10.21105/joss.05059, 2023. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019. a
Gascoin, S., Luojus, K., Nagler, T., Lievens, H., Masiokas, M., Jonas, T., Zheng, Z., and De Rosnay, P.: Remote sensing of mountain snow from space: status and recommendations, Front. Earth Sci., 12, https://doi.org/10.3389/feart.2024.1381323, 2024. a, b
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Girotto, M., Margulis, S. A., and Durand, M.: Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques, Hydrol. Process., 28, 3875–3895, https://doi.org/10.1002/hyp.9887, 2014. a, b
Girotto, M., Musselman, K. N., and Essery, R. L. H.: Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow, Current Climate Change Reports, 6, 81–94, https://doi.org/10.1007/s40641-020-00159-7, 2020. a, b, c
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, [data set], https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Guidicelli, M., Aalstad, K., Treichler, D., and Salzmann, N.: A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks, J. Hydrol., 25, 100190, https://doi.org/10.1016/j.hydroa.2024.100190, 2024. a, b, c
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, Journal of Open Research Software, 5(1), p. 10, [data set], https://doi.org/10.5334/jors.148, 2017.
Hultstrand, D. M., Fassnacht, S. R., Stednick, J. D., and Hiemstra, C. A.: Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs, Atmosphere, 13, 3, https://doi.org/10.3390/atmos13010003, 2022. a
Katzfuss, M., Stroud, J. R., and Wikle, C. K.: Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models, J. Am. Stat. Assoc., 115, 866–885, https://doi.org/10.1080/01621459.2019.1592753, 2020. a
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00325, 2020. a
Liu, Z., Filhol, S., and Treichler, D.: Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry, Cold Regions Science and Technology, 239, 104580, ISSN 0165-232X, https://doi.org/10.1016/j.coldregions.2025.104580, 2025. a
Ma, X., Li, D., Fang, Y., Margulis, S. A., and Lettenmaier, D. P.: Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data, Hydrol. Earth Syst. Sci., 27, 21–38, https://doi.org/10.5194/hess-27-21-2023, 2023. a
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods, Water Resour. Res., 50, 7816–7835, https://doi.org/10.1002/2014WR015302, 2014. a
Magruder, L., Brunt, K., Neumann, T., Klotz, B., and Alonzo, M.: Passive Ground-Based Optical Techniques for Monitoring the On-Orbit ICESat-2 Altimeter Geolocation and Footprint Diameter, Earth Space Science, 8, e2020EA001414, https://doi.org/10.1029/2020EA001414, 2021. a
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A Particle Batch Smoother Approach to Snow Water Equivalent Estimation, J. Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015. a
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.: A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015), J. Hydrometeorol., 17, 1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a
Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., and Andreadis, K.: The Utility of Infrequent Snow Depth Images for Deriving Continuous Space-Time Estimates of Seasonal Snow Water Equivalent, Geophys. Res. Lett., 46, 5331–5340, https://doi.org/10.1029/2019GL082507, 2019. a, b
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E., Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017. a, b
Mazzolini, M., Kristoffer, A., Alonso-González, E., and Désirée, T.: Inputs (forcing, observations and config file) for the experiments included in “Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter” version “Second submission experiments”, Zenodo [data set], https://doi.org/10.5281/zenodo.13860511, 2024. a
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6, https://doi.org/10.3389/feart.2018.00197, 2018. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Front. Earth Sci., 11, https://doi.org/10.3389/feart.2023.1228158, 2023. a
Murphy, K.: Probabilistic Machine Learning: Advanced Topics, MIT Press, http://probml.github.io/book2 (last access: 8 September 2025), 2023. a
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner, A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W., Harbeck, K., Lee, J., Kurtz, N. T., Luers, P. J., Luthcke, S. B., Magruder, L., Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and Thomas, T. C.: The Ice, Cloud, and Land Elevation Satellite – 2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System, Remote Sens. Environ., 233, 111325, https://doi.org/10.1016/j.rse.2019.111325, 2019. a, b, c
Neumann, T., Brenner, A., Hancock, D., Robins, J., Saba, J., Harbeck, K., Gibbons, A., Lee, J., Luthcke, S., and Rebold, T.: Ice, Cloud, and Land Elevation Satellite (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons ATL03, version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/GA5KCLJT7LOT, 2023a. a
Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Gibbons, A., Lee, J., Harbeck, K., Saba, J., Luthcke, S. B., and Rebold, T.: ATLAS/ICESat-2 L2A Global Geolocated Photon Data, ATL03, version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL03.006, 2023b. a
Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011. a
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C., and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically based modeling for mapping snow water equivalent and snow albedo, Remote Sens. Environ., 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016. 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
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a, b
Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., and Vicente-Serrano, S. M.: Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence, The Cryosphere, 8, 1989–2006, https://doi.org/10.5194/tc-8-1989-2014, 2014. a, b
Revuelto, J., Azorin-Molina, C., Alonso-González, E., Sanmiguel-Vallelado, A., Navarro-Serrano, F., Rico, I., and López-Moreno, J. I.: Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017, Earth Syst. Sci. Data, 9, 993–1005, https://doi.org/10.5194/essd-9-993-2017, 2017. a
Revuelto, J., Alonso-Gonzalez, E., Vidaller-Gayan, I., Lacroix, E., Izagirre, E., Rodríguez-López, G., and López-Moreno, J. I.: Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain, Cold Reg. Sci. Technol., 190, 103344, https://doi.org/10.1016/j.coldregions.2021.103344, 2021. a
Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Comput. Geosci., 15, 225–237, https://doi.org/10.1007/s10596-010-9202-6, 2011. a, b
Shean, D., Swinski, J. P., Smith, B., Sutterley, T., Henderson, S., Ugarte, C., Lidwa, E., and Neumann, T.: SlideRule: Enabling rapid, scalable, open science for the NASA ICESat-2 mission and beyond, Journal of Open Source Software, 8, 4982, [data set], https://doi.org/10.21105/joss.04982, 2023. a, b
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution modeling: An Arctic example, Water Resour. Res., 46, https://doi.org/10.1029/2010WR009434, 2010. a
Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A trillion dollar science question, Water Resour. Res., 53, 3534–3544, https://doi.org/10.1002/2017WR020840, 2017. a
Sutterley, T. and Gibbons, A.: pyYAPC: Python interpretation of the NASA Goddard Space Flight Center YAPC (“Yet Another Photon Classifier”) algorithm, Zenodo [code], https://doi.org/10.5281/zenodo.6717591, 2021. a
Weiss, A.: Topographic position and landforms analysis, in: Poster presentation, ESRI user conference, San Diego, CA, vol. 200, https://env761.github.io/assets/files/tpi-poster-tnc_18x22.pdf, 2001. a
Winstral, A., Elder, K., and Davis, R. E.: Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters, J. Hydrometeorol., 3, 524–538, https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2, 2002. a
xDEM contributors: xdem, version v0.0.2, Zenodo [code], https://doi.org/10.5281/zenodo.4809698, 2021. a, b
Short summary
In this work, we showcase the use the satellite laser altimeter ICESat-2, which is able to retrieve snow depth in areas where snow amounts are still poorly estimated despite the importance of these water resources. We can update snow models with these observations through algorithms that spatially propagate the information beyond the satellite profiles. The positive results show the potential of the approach to improve snow simulations, in terms of average snow depth and spatial distribution.
In this work, we showcase the use the satellite laser altimeter ICESat-2, which is able to...
