Articles | Volume 22, issue 18
https://doi.org/10.5194/bg-22-5031-2025
© 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/bg-22-5031-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Sep 2025
Research article |
| 29 Sep 2025
| 29 Sep 2025
Snow thermal conductivity controls future winter carbon emissions in shrub–tundra
Johnny Rutherford
CORRESPONDING AUTHOR
Department of Geography and Environmental Sciences, Northumbria University, Newcastle Upon Tyne, UK
Nick Rutter
Department of Geography and Environmental Sciences, Northumbria University, Newcastle Upon Tyne, UK
Leanne Wake
Department of Geography and Environmental Sciences, Northumbria University, Newcastle Upon Tyne, UK
Alex J. Cannon
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
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Peter W. Thorne, John M. Nicklas, John J. Kennedy, Bruce Calvert, Baylor Fox-Kemper, Mark T. Richardson, Adrian Simmons, Ed Hawkins, Robert Rhode, Kathryn Cowtan, Nerilie J. Abram, Axel Andersson, Simon Noone, Phillipe Marbaix, Nathan Lenssen, Dirk Olonscheck, Tristram Walsh, Stephen Outten, Ingo Bethke, Bjorn H. Samset, Chris Smith, Anna Pirani, Jan Fuglestvedt, Lavanya Rajamani, Richard A. Betts, Elizabeth C. Kent, Blair Trewin, Colin Morice, Tim Osborn, Samantha N. Burgess, Oliver Geden, Andrew Parnell, Piers M. Forster, Chris Hewitt, Zeke Hausfather, Valerie Masson-Delmotte, Jochem Marotzke, Nathan Gillett, Sonia I. Seneviratne, Gavin A. Schmidt, Duo Chan, Stefan Brönnimann, Andy Reisinger, Matthew Menne, Maisa Rojas Corradi, Christopher Kadow, Peter Huybers, David B. Stephenson, Emily Wallis, Joeri Rogelj, Andrew Schurer, Karen McKinnon, Panmao Zhai, Fatima Driouech, Wilfran Moufouma Okia, Saeed Vazifehkhah, Sophie Szopa, Christopher J. Merchant, Shoji Hirahara, Masayoshi Ishii, Francois A. Engelbrecht, Qingxiang Li, June-Yi Lee, Alex J. Cannon, Christophe Cassou, Karina von Schuckmann, Amir H. Delju, and Ellie Murtagh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-825, https://doi.org/10.5194/essd-2025-825, 2026
Preprint under review for ESSD
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We reassess the basis for determining the present level of long-term global warming. Unbiased estimates of both realised warming and anthropogenic warming are possible that approximate a 20-year retrospective mean. Our resulting estimates of 1.40 [1.23–1.58] °C (realised) and 1.34 [1.18–1.50] °C (anthropogenic) as at end of 2024 highlight the urgency of immediate, far-reaching and sustained climate mitigation actions if we are to meet the long term temperature goal of the Paris Agreement.
Vincent Vionnet, Nicolas R. 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
Geosci. Model Dev., 18, 9119–9147, https://doi.org/10.5194/gmd-18-9119-2025, https://doi.org/10.5194/gmd-18-9119-2025, 2025
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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.
Gabriel Hould Gosselin, Nick Rutter, Paul Mann, Philip Marsh, and Oliver Sonnentag
EGUsphere, https://doi.org/10.5194/egusphere-2025-5637, https://doi.org/10.5194/egusphere-2025-5637, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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We studied how Arctic tundra soils exchange carbon dioxide and methane with the atmosphere during winter in the western Canadian Arctic. Using gas concentration profiles through the snow, we quantified greenhouse gas fluxes and their spatial variability across vegetation and terrain types. Carbon dioxide emissions increased with deeper snow and warmer soils, while some areas absorbed methane. These findings provide key data to improve upscaling of winter carbon fluxes across Arctic landscapes.
Rajesh R. Shrestha, Alex J. Cannon, Sydney Hoffman, Marie Whibley, and Aranildo Lima
Hydrol. Earth Syst. Sci., 29, 2881–2900, https://doi.org/10.5194/hess-29-2881-2025, https://doi.org/10.5194/hess-29-2881-2025, 2025
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We evaluate the historical performance and future projections from a large-scale hydrologic model, the Community Water Model, against a watershed hydrologic model, Variable Infiltration Capacity, for the Liard River basin in Canada. Results from the two models are generally consistent at annual and monthly timescales, suggesting that a calibrated global hydrologic model can provide robust projections. We explain the differences in projections in terms of model uncertainties.
Andrew Vincent Grgas-Svirac, Mohammad Fereshtehpour, M. Reza Najafi, Alex J. Cannon, and Hamidreza Shirkhani
EGUsphere, https://doi.org/10.5194/egusphere-2025-2481, https://doi.org/10.5194/egusphere-2025-2481, 2025
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This study explores how long, narrow bands of moist air known as atmospheric rivers increase the risk of inland flooding when combined with other factors. Using climate models, we found that these events are already important drivers of flooding in western North America and will likely become even more intense with climate change. Natural climate shifts also affect how often these events occur. The findings help inform future decisions about flood planning and protection.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
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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.
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
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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.
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
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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
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
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
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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.
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.
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.
Sarah A. Woodroffe, Leanne M. Wake, Kristian K. Kjeldsen, Natasha L. M. Barlow, Antony J. Long, and Kurt H. Kjær
Clim. Past, 19, 1585–1606, https://doi.org/10.5194/cp-19-1585-2023, https://doi.org/10.5194/cp-19-1585-2023, 2023
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Salt marsh in SE Greenland records sea level changes over the past 300 years in sediments and microfossils. The pattern is rising sea level until ~ 1880 CE and sea level fall since. This disagrees with modelled sea level, which overpredicts sea level fall by at least 0.5 m. This is the same even when reducing the overall amount of Greenland ice sheet melt and allowing for more time. Fitting the model to the data leaves ~ 3 mm yr−1 of unexplained sea level rise in SE Greenland since ~ 1880 CE.
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.
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.
Cited articles
Aalto, J., Karjalainen, O., Hjort, J., and Luoto, M.: Statistical forecasting of current and future circum-Arctic ground temperatures and active layer thickness, Geophys. Res. Lett., 45, 4889–4898, 2018.
AMAP: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017, Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, xiv + 269 pp, ISBN 978-82-7971-101-8, 2017.
Andren, O. and Paustian, K.: Barley straw decomposition in the field: a comparison of models, Ecology, 68, 1190–1200, 1987.
Arndt, K. A., Lipson, D. A., Hashemi, J., Oechel, W. C., and Zona, D.: Snow melt stimulates ecosystem respiration in Arctic ecosystems, Glob. Change Biol., 26, 5042–5051, 2020.
Bigalke, S. and Walsh, J. E.: Future changes of snow in Alaska and the Arctic under stabilized global warming scenarios, Atmosphere, 13, 541, https://doi.org/10.3390/atmos13040541, 2022.
Bintanja, R. and Andry, O.: Towards a rain-dominated Arctic, Nat. Clim. Change, 7, 263–267, 2017.
Bliss, L. and Matveyeva, N.: Circumpolar arctic vegetation, Arctic Ecosystems in a Changing Climate: An Ecophysiological Perspective, Academic Press, San Diego, 59–89, https://doi.org/10.1016/B978-0-12-168250-7.50010-9, 1992.
Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N., Radionov, V., Bradley, R. S., Blangy, S., Bulygina, O. N., Christensen, T. R., Colman, J. E., Essery, R. L. H., Forbes, B. C., Forchhammer, M. C., Golubev, V. N., Honrath, R. E., Juday, G. P., Meshcherskaya, A. V., Phoenix, G. K., Pomeroy, J., Rautio, A., Robinson, D. A., Schmidt, N. M., Serreze, M. C., Shevchenko, V. P., Shiklomanov, A. I., Shmakin, A. B., Sköld, P., Sturm, M., Woo, M.-K., and Wood, E. F.: Multiple Effects of Changes in Arctic Snow Cover, AMBIO, 40, 32–45, https://doi.org/10.1007/s13280-011-0213-x, 2011.
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, 2018.
Cannon, A. J., Alford, H., Shrestha, R. R., Kirchmeier-Young, M. C., and Najafi, M. R.: Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias-corrected climate model outputs for terrestrial modelling and attribution studies in North America, Geoscience Data Journal, 9, 288–303, https://doi.org/10.1002/gdj3.142, 2022.
Chen, S., Wang, J., Zhang, T., and Hu, Z.: Climatic, soil, and vegetation controls of the temperature sensitivity (Q10) of soil respiration across terrestrial biomes, Global Ecology and Conservation, 22, e00955, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2020.e00955, 2020.
Christensen, J. H., Kanikicharla, K. K., Aldrian, E., An, S. I., Cavalcanti, I. F. A., de Castro, M., Dong, W., Goswami, P., Hall, A., and Kanyanga, J. K.: Climate phenomena and their relevance for future regional climate change, in: Climate change 2013 the physical science basis: Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, 1217–1308, https://doi.org/10.1017/CBO9781107415324.028, 2013.
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.
Commane, R., Lindaas, J., Benmergui, J., Luus, K. A., Chang, R. Y.-W., Daube, B. C., Euskirchen, E. S., Henderson, J. M., Karion, A., and Miller, J. B.: Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra, P. Natl. Acad. Sci. USA, 114, 5361–5366, 2017.
Dutch, V. R., Rutter, N., Wake, L., Sandells, M., Derksen, C., Walker, B., Hould Gosselin, G., Sonnentag, O., Essery, R., Kelly, R., Marsh, P., King, J., and Boike, J.: Impact of measured and simulated tundra snowpack properties on heat transfer, The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, 2022.
Dutch, V. R., Rutter, N., Wake, L., Sonnentag, O., Hould Gosselin, G., Sandells, M., Derksen, C., Walker, B., Meyer, G., Essery, R., Kelly, R., Marsh, P., Boike, J., and Detto, M.: Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site, Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, 2024.
Ednie, M. and Smith, S.: Permafrost temperature data 2008–2014 from community based monitoring sites in Nunavut, Geological Survey of Canada, Open File, 7784, https://doi.org/10.4095/296705, 2015.
Elberling, B. and Brandt, K. K.: Uncoupling of microbial CO2 production and release in frozen soil and its implications for field studies of arctic C cycling, Soil Biol. Biochem., 35, 263–272, 2003.
Euskirchen, E., Bret-Harte, M., Shaver, G., Edgar, C., and Romanovsky, V.: Long-term release of carbon dioxide from arctic tundra ecosystems in Alaska, Ecosystems, 20, 960–974, 2017.
Fahnestock, J. T., Jones, M. H., and Welker, J. M.: Wintertime CO2 efflux from arctic soils: implications for annual carbon budgets, Global Biogeochem. Cy., 13, 775–779, 1999.
Fierer, N., Craine, J. M., McLauchlan, K., and Schimel, J. P.: Litter quality and the temperature sensitivity of decomposition, Ecology, 86, 320–326, 2005.
Grünberg, I. , Cable, B. , Antonova, S. , Lange, S. and Boike, J.: Soil temperature and thaw depth differences associated with tundra vegetation types at Trail Valley Creek, NWT, Canada , EGU General Assembly 2019, Vienna, 7 April 2019–12 April 2019, 2019.
Harr, R. D.: Some characteristics and consequences of snowmelt during rainfall in western Oregon, J. Hydrol., 53, 277–304, 1981.
Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, 2014.
Intergovernmental Panel on Climate, C.: Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009325844, 2023.
Jordan, R.: A One-Dimensional Temperature Model for Snow Cover, Technical Documentation for SNTHERM, US Army Corps of Engineers, Special Report 91–16, 89, https://hdl.handle.net/11681/11677, 1991.
King, J., Derksen, C., Toose, P., Langlois, A., Larsen, C., Lemmetyinen, J., Marsh, P., Montpetit, B., Roy, A., and Rutter, N.: The influence of snow microstructure on dual-frequency radar measurements in a tundra environment, Remote Sens. Environ., 215, 242–254, 2018.
Kittler, F., Heimann, M., Kolle, O., Zimov, N., Zimov, S., and Göckede, M.: Long-term drainage reduces CO2 uptake and CH4 emissions in a Siberian permafrost ecosystem, Global Biogeochem. Cy., 31, 1704–1717, 2017.
Koven, C. D., Riley, W. J., and Stern, A.: Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 Earth System Models, J. Climate, 26, 1877–1900, 2013.
Larson, E. J., Schiferl, L. D., Commane, R., Munger, J. W., Trugman, A. T., Ise, T., Euskirchen, E. S., Wofsy, S., and Moorcroft, P. M.: The changing carbon balance of tundra ecosystems: results from a vertically-resolved peatland biosphere model, Environ. Res. Lett., 17, 014019, https://doi.org/10.1088/1748-9326/ac4070, 2021.
Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J., and Slater, A.: Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions, Environ. Res. Lett., 10, 094011, https://doi.org/10.1088/1748-9326/10/9/094011, 2015.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., and Kennedy, D.: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, 2019.
Marsh, P., Onclin, C., and Neumann, N.: Water and energy fluxes in the lower Mackenzie Valley, 1994/95, Atmos. Ocean, 40, 245–256, 2002.
Mastepanov, M., Sigsgaard, C., Tagesson, T., Ström, L., Tamstorf, M. P., Lund, M., and Christensen, T. R.: Revisiting factors controlling methane emissions from high-Arctic tundra, Biogeosciences, 10, 5139–5158, https://doi.org/10.5194/bg-10-5139-2013, 2013.
Mazurkiewicz, A. B., Callery, D. G., and McDonnell, J. J.: Assessing the controls of the snow energy balance and water available for runoff in a rain-on-snow environment, J. Hydrol., 354, 1–14, 2008.
McCrystall, M. R., Stroeve, J., Serreze, M., Forbes, B. C., and Screen, J. A.: New climate models reveal faster and larger increases in Arctic precipitation than previously projected, Nat. Commun., 12, 6765, https://doi.org/10.1038/s41467-021-27031-y, 2021.
McGinnis, S. and Mearns, L.: Building a climate service for North America based on the NA-CORDEX data archive, Climate Services, 22, 100233, https://doi.org/10.1016/j.cliser.2021.100233, 2021.
Miner, K. R., Turetsky, M. R., Malina, E., Bartsch, A., Tamminen, J., McGuire, A. D., Fix, A., Sweeney, C., Elder, C. D., and Miller, C. E.: Permafrost carbon emissions in a changing Arctic, Nature Reviews Earth & Environment, 3, 55–67, 2022.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020.
Mudryk, L., Elias Chereque, A., Derksen, C., Luojus, K., and Decharme, B: Arctic Report Card 2021: Terrestrial Snow Cover, https://doi.org/10.25923/16xy-9h55, 2021.
Müller, J., Paudel, R., Shoemaker, C. A., Woodbury, J., Wang, Y., and Mahowald, N.: CH4 parameter estimation in CLM4.5bgc using surrogate global optimization, Geosci. Model Dev., 8, 3285–3310, https://doi.org/10.5194/gmd-8-3285-2015, 2015.
Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M., Selbmann, A.-K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., and Birch, L.: Large loss of CO2 in winter observed across the northern permafrost region, Nat. Clim. Change, 9, 852–857, 2019.
Pedron, S., Jespersen, R., Xu, X., Khazindar, Y., Welker, J., and Czimczik, C.: More snow accelerates legacy carbon emissions from Arctic permafrost, AGU Advances, 4, e2023AV000942, https://doi.org/10.1029/2023AV000942, 2023.
Pongracz, A., Wårlind, D., Miller, P. A., and Parmentier, F.-J. W.: Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS, Biogeosciences, 18, 5767–5787, https://doi.org/10.5194/bg-18-5767-2021, 2021.
Quinton, W., Gray, D., and Marsh, P.: Subsurface drainage from hummock-covered hillslopes in the Arctic tundra, J. Hydrol., 237, 113–125, 2000.
Raz-Yaseef, N., Torn, M. S., Wu, Y., Billesbach, D. P., Liljedahl, A. K., Kneafsey, T. J., Romanovsky, V. E., Cook, D. R., and Wullschleger, S. D.: Large CO2 and CH4 emissions from polygonal tundra during spring thaw in northern Alaska, Geophys. Res. Lett., 44, 504–513, 2017.
Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., Mahowald, N. M., and Hess, P.: Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925–1953, https://doi.org/10.5194/bg-8-1925-2011, 2011.
Royer, A., Picard, G., Vargel, C., Langlois, A., Gouttevin, I., and Dumont, M.: Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures, Front. Earth Sci., 9, 515, https://doi.org/10.3389/feart.2021.685140, 2021.
Schaefer, K. and Jafarov, E.: A parameterization of respiration in frozen soils based on substrate availability, Biogeosciences, 13, 1991–2001, https://doi.org/10.5194/bg-13-1991-2016, 2016.
Schimel, J. P., Bilbrough, C., and Welker, J. M.: Increased snow depth affects microbial activity and nitrogen mineralization in two Arctic tundra communities, Soil Biol. Biochem., 36, 217–227, 2004.
Schimel, J. P., Fahnestock, J., Michaelson, G., Mikan, C., Ping, C.-L., Romanovsky, V. E., and Welker, J.: Cold-season production of CO2 in arctic soils: can laboratory and field estimates be reconciled through a simple modeling approach?, Arct. Antarct. Alp. Res., 38, 249–256, 2006.
Schuur, E. A., McGuire, A. D., Schädel, C., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., and Lawrence, D. M.: Climate change and the permafrost carbon feedback, Nature, 520, 171–179, 2015.
Serreze, M. C., Gustafson, J., Barrett, A. P., Druckenmiller, M. L., Fox, S., Voveris, J., Stroeve, J., Sheffield, B., Forbes, B. C., and Rasmus, S.: Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts, Environ. Res. Lett., 16, 105009, https://doi.org/10.1088/1748-9326/ac269b, 2021.
Shogren, A. J., Zarnetske, J. P., Abbott, B. W., Iannucci, F., and Bowden, W. B.: We cannot shrug off the shoulder seasons: Addressing knowledge and data gaps in an Arctic headwater, Environ. Res. Lett., 15, 104027, https://doi.org/10.1088/1748-9326/ab9d3c, 2020.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity of seasonal snow, J. Glaciol., 43, 26–41, 1997.
Sturtevant, C. S., Oechel, W. C., Zona, D., Kim, Y., and Emerson, C. E.: Soil moisture control over autumn season methane flux, Arctic Coastal Plain of Alaska, Biogeosciences, 9, 1423–1440, https://doi.org/10.5194/bg-9-1423-2012, 2012.
Tao, J., Zhu, Q., Riley, W. J., and Neumann, R. B.: Improved ELMv1-ECA simulations of zero-curtain periods and cold-season CH4 and CO2 emissions at Alaskan Arctic tundra sites, The Cryosphere, 15, 5281–5307, https://doi.org/10.5194/tc-15-5281-2021, 2021.
Thompson, A., Kelly, R., and Marsh, P.: Spatial variability of snow at Trail Valley Creek, NWT. In 73rd Eastern Snow Conference, Columbus, Ohio, USA, 101–108, 2016.
Treat, C. C., Virkkala, A. M., Burke, E., Bruhwiler, L., Chatterjee, A., Fisher, J. B., Hashemi, J., Parmentier, F. J. W., Rogers, B. M., and Westermann, S.: Permafrost carbon: Progress on understanding stocks and fluxes across northern terrestrial ecosystems, J. Geophys. Res.-Biogeo., 129, e2023JG007638, https://doi.org/10.1029/2023JG007638, 2024.
Trenberth, K. E.: Atmospheric moisture residence times and cycling: Implications for rainfall rates and climate change, Climatic Change, 39, 667–694, 1998.
Tutton, R., Darkin, B., Essery, R., Griffith, J., Hould Gosselin, G., Marsh, P., Sonnentag, O., Thorne, R., and Walker, B.: A 885 hydro-meterological dataset from the taiga-tundra ecotone in the western Canadian Arctic: Trail Valley Creek, Northwest Territories [data set], Borealis, https://doi.org/10.5683/SP3/BXV4DE, 2024.
Voigt, C., Virkkala, A.-M., Hould Gosselin, G., Bennett, K. A., Black, T. A., Detto, M., Chevrier-Dion, C., Guggenberger, G., Hashmi, W., and Kohl, L.: Arctic soil methane sink increases with drier conditions and higher ecosystem respiration, Nat. Clim. Change, 13, 1095–1104, 2023.
Webb, E. E., Schuur, E. A., Natali, S. M., Oken, K. L., Bracho, R., Krapek, J. P., Risk, D., and Nickerson, N. R.: Increased wintertime CO2 loss as a result of sustained tundra warming, J. Geophys. Res.-Biogeo., 121, 249–265, 2016.
Welker, J., Fahnestock, J., and Jones, M.: Annual CO2 flux in dry and moist arctic tundra: field responses to increases in summer temperatures and winter snow depth, Climatic Change, 44, 139–150, 2000.
Wilcox, E. J., Keim, D., de Jong, T., Walker, B., Sonnentag, O., Sniderhan, A. E., Mann, P., and Marsh, P.: Tundra shrub expansion may amplify permafrost thaw by advancing snowmelt timing, Arctic Science, 5, 202–217, 2019.
Yan, D., Li, J., Pei, J., Cui, J., Nie, M., and Fang, C.: The temperature sensitivity of soil organic carbon decomposition is greater in subsoil than in topsoil during laboratory incubation, Sci. Rep., 7, 5181, 2017.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., Dinardo, S. J., Dengel, S., Sweeney, C., and Karion, A.: Cold season emissions dominate the Arctic tundra methane budget, P. Natl. Acad. Sci. USA, 113, 40–45, 2016.
Short summary
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), cause uncertainty in projected carbon emission simulations. Improved snow conductivity parameterization in CLM5.0 significantly increases predicted winter CO2 emissions to 2100.
The Arctic winter is vulnerable to climate warming, and ~1700 Gt of carbon stored in...
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