Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3583-2025
© 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/gmd-18-3583-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Jun 2025
Model description paper |
| 18 Jun 2025
| 18 Jun 2025
A Flexible Snow Model (FSM 2.1.1) including a forest canopy
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Giulia Mazzotti
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
INRAE, Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Sarah Barr
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK
Tobias Jonas
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Tristan Quaife
National Centre for Earth Observation, University of Reading, Reading, UK
Nick Rutter
Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK
Related authors
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
Preprint archived
Short summary
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
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.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Adrien Damseaux, Heidrun Matthes, Victoria R. Dutch, Leanne Wake, and Nick Rutter
The Cryosphere, 19, 1539–1558, https://doi.org/10.5194/tc-19-1539-2025, https://doi.org/10.5194/tc-19-1539-2025, 2025
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Models often underestimate the role of snow cover in permafrost regions, leading to soil temperatures and permafrost dynamics inaccuracies. Through the use of a snow thermal conductivity scheme better adapted to this region, we mitigated soil temperature biases and permafrost extent overestimation within a land surface model. Our study sheds light on the importance of refining snow-related processes in models to enhance our understanding of permafrost dynamics in the context of climate change.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Marit van Tiel, Matthias Huss, Massimiliano Zappa, Tobias Jonas, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2025-404, https://doi.org/10.5194/egusphere-2025-404, 2025
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The summer of 2022 was extremely warm and dry in Europe, severely impacting water availability. We calculated water balance anomalies for 88 glacierized catchments in Switzerland, showing that glaciers played a crucial role in alleviating the drought situation by melting at record rates, partially compensating for the lack of rain and snowmelt. By comparing 2022 with past extreme years, we show that while glacier meltwater remains essential during droughts, its contribution is declining.
Samantha Petch, Liang Feng, Paul Palmer, Robert P. King, Tristan Quaife, and Keith Haines
EGUsphere, https://doi.org/10.22541/essoar.173343481.12875858/v1, https://doi.org/10.22541/essoar.173343481.12875858/v1, 2025
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The growth rate of atmospheric CO2 varies year to year, mainly due to land ecosystems. Understanding factors controlling the land carbon uptake is crucial. Our study examines the link between terrestrial water storage and the CO2 growth rate from 2002–2023, revealing a strong negative correlation. We highlight the key role of tropical forests, especially in tropical America, and assess how regional contributions shift over time.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
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In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Christoph Marty, Adrien Michel, Tobias Jonas, Cynthia Steijn, Regula Muelchi, and Sven Kotlarski
EGUsphere, https://doi.org/10.5194/egusphere-2025-413, https://doi.org/10.5194/egusphere-2025-413, 2025
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This work presents the first long-term (since 1962), daily, 1 km gridded dataset of snow depth and water storage for Switzerland. Its quality was assessed by comparing yearly, monthly, and weekly values to a higher-quality model and in-situ measurements. Results show good overall performance, though some limitations exist at low elevations and short timescales. Despite this, the dataset effectively captures trends, offering valuable insights for climate monitoring and elevation-based changes.
Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter Julien Rayner, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2025-109, https://doi.org/10.5194/egusphere-2025-109, 2025
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Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data is a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical method named 4DEnVar to calibrate parameters of a land surface model using this data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Erin N. Raif, Sarah L. Barr, Mark D. Tarn, James B. McQuaid, Martin I. Daily, Steven J. Abel, Paul A. Barrett, Keith N. Bower, Paul R. Field, Kenneth S. Carslaw, and Benjamin J. Murray
Atmos. Chem. Phys., 24, 14045–14072, https://doi.org/10.5194/acp-24-14045-2024, https://doi.org/10.5194/acp-24-14045-2024, 2024
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Ice-nucleating particles (INPs) allow ice to form in clouds at temperatures warmer than −35°C. We measured INP concentrations over the Norwegian and Barents seas in weather events where cold air is ejected from the Arctic. These concentrations were among the highest measured in the Arctic. It is likely that the INPs were transported to the Arctic from distant regions. These results show it is important to consider hemispheric-scale INP processes to understand INP concentrations in the Arctic.
Bertrand Cluzet, Jan Magnusson, Louis Quéno, Giulia Mazzotti, Rebecca Mott, and Tobias Jonas
The Cryosphere, 18, 5753–5767, https://doi.org/10.5194/tc-18-5753-2024, https://doi.org/10.5194/tc-18-5753-2024, 2024
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We use novel wet-snow maps from Sentinel-1 to evaluate simulations of a snow-hydrological model over Switzerland. These data are complementary to available in situ snow depth observations as they capture a broad diversity of topographic conditions. Wet-snow maps allow us to detect a delayed melt onset in the model, which we resolve thanks to an improved parametrization. This paves the way to further evaluation, calibration, and data assimilation using wet-snow maps.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
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As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
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Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Johanna Teresa Malle, Giulia Mazzotti, Dirk Nikolaus Karger, and Tobias Jonas
Earth Syst. Dynam., 15, 1073–1115, https://doi.org/10.5194/esd-15-1073-2024, https://doi.org/10.5194/esd-15-1073-2024, 2024
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Land surface processes are crucial for the exchange of carbon, nitrogen, and energy in the Earth system. Using meteorological and land use data, we found that higher resolution improved not only the model representation of snow cover but also plant productivity and that water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the Earth system.
Johnny Rutherford, Nick Rutter, Leanne Wake, and Alex Cannon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2445, https://doi.org/10.5194/egusphere-2024-2445, 2024
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The Arctic winter is vulnerable to climate warming and ~1700 Gt of carbon stored in high latitude permafrost ecosystems is at risk of degradation in the future due to enhanced microbial activity. Poorly represented cold season processes, such as the simulation of snow thermal conductivity in Land Surface Models (LSMs), causes uncertainty in projected carbon emission simulations. Improved snow conductivity parameterization in CLM5.0 significantly increases predicted winter CO2 emissions to 2100.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
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Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Bethan L. Harris, Tristan Quaife, Christopher M. Taylor, and Phil P. Harris
Earth Syst. Dynam., 15, 1019–1035, https://doi.org/10.5194/esd-15-1019-2024, https://doi.org/10.5194/esd-15-1019-2024, 2024
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The response of vegetation productivity to rainfall is a crucial process linking the water and carbon cycles and influencing the evolution of the climate system. However, there are many uncertainties in its representation in Earth system models. We show that the vegetation productivity responses to short-term rainfall events are very different between models due to their differing sensitivities to water availability. We also evaluate the models against a range of observational products.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
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Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Florian Zellweger, Eric Sulmoni, Johanna T. Malle, Andri Baltensweiler, Tobias Jonas, Niklaus E. Zimmermann, Christian Ginzler, Dirk Nikolaus Karger, Pieter De Frenne, David Frey, and Clare Webster
Biogeosciences, 21, 605–623, https://doi.org/10.5194/bg-21-605-2024, https://doi.org/10.5194/bg-21-605-2024, 2024
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The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 m spatial resolution across Switzerland using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
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The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
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We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
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We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
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The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
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This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
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Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
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.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
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Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
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To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
K. Koutantou, G. Mazzotti, and P. Brunner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 477–484, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, 2021
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
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The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
Hydrol. Earth Syst. Sci., 24, 5423–5438, https://doi.org/10.5194/hess-24-5423-2020, https://doi.org/10.5194/hess-24-5423-2020, 2020
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Low river flows affect societies and ecosystems. Here we study how precipitation and potential evapotranspiration shape low flows across a network of 380 Swiss catchments. Low flows in these rivers typically result from below-average precipitation and above-average potential evapotranspiration. Extreme low flows result from long periods of the combined effects of both drivers.
Cited articles
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Alonso-González, E., Aalstad, K., Baba, M. W., Revuelto, J., López-Moreno, J. I., Fiddes, J., Essery, R., and Gascoin, S.: The Multiple Snow Data Assimilation System (MuSA v1.0), Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, 2022. a, b
Bartlett, P. A. and Verseghy, D. L.: Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme, Hydrol. Process., 29, 3208–3226, https://doi.org/10.1002/hyp.10431, 2015. a, b
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Blyth, E. M., Harding, R. J., and Essery, R.: A coupled dual source GCM SVAT, Hydrol. Earth Syst. Sci., 3, 71–84, https://doi.org/10.5194/hess-3-71-1999, 1999. a, b
Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., and Burakowski, E. A.: Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0), Geosci. Model Dev., 11, 1467–1496, https://doi.org/10.5194/gmd-11-1467-2018, 2018. a, b
Bonan, G. B., Patton, E. G., Finnigan, J. J., Baldocchi, D. D., and Harman, I. N.: Moving beyond the incorrect but useful paradigm: reevaluating big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes – a review, Agr. Forest Meteorol., 306, 108435, https://doi.org/10.1016/j.agrformet.2021.108435, 2021. a, b
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Bouchard, B., Nadeau, D. F., Domine, F., Wever, N., Michel, A., Lehning, M., and Isabelle, P.-E.: Impact of intercepted and sub-canopy snow microstructure on snowpack response to rain-on-snow events under a boreal canopy, The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, 2024. a, b
Broxton, P. D., Harpold, A. A., Biederman, J. A., Troch, P. A., Molotch, N. P., and Brooks, P. D.: Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed-conifer forests, Ecohydrology, 8, 1073–1094, https://doi.org/10.1002/eco.1565, 2015. a
Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette, R., Karbou, F., and Morin, S.: Simulation of Northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses, J. Hydrometeorol., 14, 203–219, https://doi.org/10.1175/JHM-D-12-012.1, 2013. a
Chalita, S. and Le Treut, H.: The albedo of temperate and boreal forest and the Northern Hemisphere climate: a sensitivity experiment using the LMD GCM, Clim. Dynam., 10, 231–240, https://doi.org/10.1007/BF00208990, 1994. a
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Cohen, J., Ye, H., and Jones, J.: Trends and variability in rain-on-snow events, Geophys. Res. Lett., 42, 7115–7122, https://doi.org/10.1002/2015GL065320, 2015. a
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Essery, R.: Large-scale simulations of snow albedo masking by forests, Geophys. Res. Lett., 40, 5521–5525, https://doi.org/10.1002/grl.51008, 2013. a, b
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, c
Essery, R.: FSM version 2.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.2593345, 2019. a
Essery, R.: Publications using FSM, Github [code], https://github.com/RichardEssery/FSM/blob/master/publications.md (last access: 2 April 2025), 2024. a
Essery, R.: FSM version 2.1.1 revision for Geophysical Model Development paper, Zenodo [code], https://doi.org/10.5281/zenodo.14950260, 2025a. a
Essery, R.: Scripts to produce figures for FSM 2.1.1 revision of GMD paper, Zenodo [code], https://doi.org/10.5281/zenodo.14950395, 2025b. a
Essery, R., Huntley, B., and Rutter, N.: Snow-Vegetation-Atmosphere Interactions over Heterogeneous Landscapes Project: Vegetation and meteorological observations at the Abisko site, NCAS British Atmospheric Data Centre [data set], http://catalogue.ceda.ac.uk/uuid/6947880b98d32e249a8638ebe768efd2/ (last access: 2 April 2025), 2013a. a
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Short summary
How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
How forests influence accumulation and melt of snow on the ground is of long-standing interest,...
