Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1033-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/tc-18-1033-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming
Department of Earth, Geographic, and Climate Sciences, University of Massachusetts, Amherst, MA 01003, USA
Ambarish V. Karmalkar
Department of Geosciences, University of Rhode Island, Kingston, RI 02881, USA
Department of Earth, Geographic, and Climate Sciences, University of Massachusetts, Amherst, MA 01003, USA
Related authors
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
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We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
P. Dass, M. A. Rawlins, J. S. Kimball, and Y. Kim
Biogeosciences, 13, 45–62, https://doi.org/10.5194/bg-13-45-2016, https://doi.org/10.5194/bg-13-45-2016, 2016
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Productivity of the vegetation of northern Eurasia has been found to be increasing over the last few decades. Using statistical tools we investigate major factors driving the increase in photosynthetic activity. Most of this change is explained by rising temperatures, which drive an increase in productivity. However, the contribution of changing patterns of rainfall and cloudiness is also significant, especially in the southern parts of the region which exhibit higher drought vulnerability.
Y. Yi, J. S. Kimball, M. A. Rawlins, M. Moghaddam, and E. S. Euskirchen
Biogeosciences, 12, 5811–5829, https://doi.org/10.5194/bg-12-5811-2015, https://doi.org/10.5194/bg-12-5811-2015, 2015
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We found that regional warming promotes widespread deepening of soil thaw in the pan-Arctic area; continued warming will most likely promote permafrost degradation in the warm permafrost areas. We also found that deeper snowpack enhances soil respiration from deeper soil carbon pool more than temperature does, particularly in the cold permafrost areas, where a large amount of soil carbon is stored in deep perennial frozen soils but is potentially vulnerable to mobilization from climate change.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
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We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
Short summary
Short summary
We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
P. Dass, M. A. Rawlins, J. S. Kimball, and Y. Kim
Biogeosciences, 13, 45–62, https://doi.org/10.5194/bg-13-45-2016, https://doi.org/10.5194/bg-13-45-2016, 2016
Short summary
Short summary
Productivity of the vegetation of northern Eurasia has been found to be increasing over the last few decades. Using statistical tools we investigate major factors driving the increase in photosynthetic activity. Most of this change is explained by rising temperatures, which drive an increase in productivity. However, the contribution of changing patterns of rainfall and cloudiness is also significant, especially in the southern parts of the region which exhibit higher drought vulnerability.
Y. Yi, J. S. Kimball, M. A. Rawlins, M. Moghaddam, and E. S. Euskirchen
Biogeosciences, 12, 5811–5829, https://doi.org/10.5194/bg-12-5811-2015, https://doi.org/10.5194/bg-12-5811-2015, 2015
Short summary
Short summary
We found that regional warming promotes widespread deepening of soil thaw in the pan-Arctic area; continued warming will most likely promote permafrost degradation in the warm permafrost areas. We also found that deeper snowpack enhances soil respiration from deeper soil carbon pool more than temperature does, particularly in the cold permafrost areas, where a large amount of soil carbon is stored in deep perennial frozen soils but is potentially vulnerable to mobilization from climate change.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
Related subject area
Discipline: Snow | Subject: Numerical Modelling
Microstructure-based modelling of snow mechanics: experimental evaluation of the cone penetration test
Snow redistribution in an intermediate-complexity snow hydrology modelling framework
Analyzing the sensitivity of a blowing snow model (SnowPappus) to precipitation forcing, blowing snow, and spatial resolution
Exploring the decision-making process in model development: focus on the Arctic snowpack
Exploring the potential of forest snow modelling at the tree and snowpack layer scale
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting
Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect
Elements of future snowpack modeling – Part 1: A physical instability arising from the nonlinear coupling of transport and phase changes
Elements of future snowpack modeling – Part 2: A modular and extendable Eulerian–Lagrangian numerical scheme for coupled transport, phase changes and settling processes
Assessment of neutrons from secondary cosmic rays at mountain altitudes – Geant4 simulations of environmental parameters including soil moisture and snow cover
A seasonal algorithm of the snow-covered area fraction for mountainous terrain
Snow cover duration trends observed at sites and predicted by multiple models
Deep ice layer formation in an alpine snowpack: monitoring and modeling
Multi-physics ensemble snow modelling in the western Himalaya
Micromechanical modeling of snow failure
Changing characteristics of runoff and freshwater export from watersheds draining northern Alaska
Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation
A simulation of a large-scale drifting snowstorm in the turbulent boundary layer
Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain
Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere, 18, 3787–3805, https://doi.org/10.5194/tc-18-3787-2024, https://doi.org/10.5194/tc-18-3787-2024, 2024
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This paper presents the evaluation of a numerical discrete element method (DEM) by simulating cone penetration tests in different snow samples. The DEM model demonstrated a good ability to reproduce the measured mechanical behaviour of the snow, namely the force evolution on the cone and the grain displacement field. Systematic sensitivity tests showed that the mechanical response depends not only on the microstructure of the sample but also on the mechanical parameters of grain contacts.
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.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
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Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-2926, https://doi.org/10.5194/egusphere-2023-2926, 2024
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Computer models, like those used in climate change studies, are written by modelers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modeling as an example, we examine the process behind the decisions to understand what motivates or limits modelers in their decision-making. We found that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency into our research practice.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2023-2781, https://doi.org/10.5194/egusphere-2023-2781, 2023
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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.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
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We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
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We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
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This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
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This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
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Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
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.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
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Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
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This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
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We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
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This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
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Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
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A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
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In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
Cited articles
Ahmed, R., Prowse, T., Dibike, Y., Bonsal, B., and O’Neil, H.: Recent Trends in Freshwater Influx to the Arctic Ocean from Four Major Arctic-Draining Rivers, Water, 12, 1189, https://doi.org/10.3390/w12041189, 2020. a
Alexeev, V., Nicolsky, D., Romanovsky, V., and Lawrence, D.: An evaluation of deep soil configurations in the CLM3 for improved representation of permafrost, Geophys. Res. Lett., 34, L08501, https://doi.org/10.1029/2007GL029536, 2007. a
Amon, R. M. W., Rinehart, A. J., Duan, S., Louchouarn, P., Prokushkin, A., Guggenberger, G., Bauch, D., Stedmon, C., Raymond, P. A., Holmes, R. M., McClelland, J. W., Peterson, B. J., Walker, S. J., and Zhulidov, A. V.: Dissolved organic matter sources in large Arctic rivers, Geochim. Cosmochim. Ac., 94, 217–237, https://doi.org/10.1016/j.gca.2012.07.015, 2012. a, b
Andresen, C. G. and Lougheed, V. L.: Disappearing Arctic tundra ponds: Fine-scale analysis of surface hydrology in drained thaw lake basins over a 65 year period (1948–2013), J. Geophys. Res.-Biogeo, 120, 466–479, https://doi.org/10.1002/2014JG002778, 2015. a
Anisimov, O. and Reneva, S.: Permafrost and Changing Climate: The Russian Perspective, AMBIO, 35, 169–175, https://doi.org/10.1579/0044-7447(2006)35[169:PACCTR]2.0.CO;2, 2006. a
Arp, C., Whitman, M., Kemnitz, R., and Stuefer, S.: Evidence of hydrological intensification and regime change from northern Alaskan watershed runoff, Geophys. Res. Lett., 47, e2020GL089186, https://doi.org/10.1029/2020GL089186, 2020. a, b, c
Arp, C. D. and Whitman, M. S.: Lake basins drive variation in catchment-scale runoff response over a decade of increasing rainfall in Arctic Alaska, Hydrol. Process., 36, e14583, https://doi.org/10.1002/hyp.14583, 2022. a
Barnhart, K. R., Miller, C. R., Overeem, I., and Kay, J. E.: Mapping the future expansion of Arctic open water, Nat. Clim. Change., 6, 280–285, https://doi.org/10.1038/nclimate2848, 2016. a
Behnke, M. I., McClelland, J. W., Tank, S. E., Kellerman, A. M., Holmes, R. M., Haghipour, N., Eglinton, T. I., Raymond, P. A., Suslova, A., Zhulidov, A. V., Gurtovaya, T., Zimov, N., Zimov, S., Mutter, E. A., Amos, E., and Spencer, R. G. M.: Pan-Arctic Riverine Dissolved Organic Matter: Synchronous Molecular Stability, Shifting Sources and Subsidies, Global Biogeochem. Cy., 35, e2020GB006871, https://doi.org/10.1029/2020GB006871, 2021. a
Bintanja, R.: The impact of Arctic warming on increased rainfall, Sci. Rep., 8, 1–6, https://doi.org/10.1038/s41598-018-34450-3, 2018. a
Bintanja, R. and Selten, F. M.: Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat, Nature, 509, 479–482, https://doi.org/10.1038/nature13259, 2014. a, b
Bintanja, R., van der Wiel, K., Van der Linden, E., Reusen, J., Bogerd, L., Krikken, F., and Selten, F.: Strong future increases in Arctic precipitation variability linked to poleward moisture transport, Sci. Adv., 6, eaax6869, https://doi.org/10.1126/sciadv.aax6869, 2020. a
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 1–11, https://doi.org/10.1038/s41467-018-08240-4, 2019. a
Blaskey, D., Koch, J. C., Gooseff, M. N., Newman, A. J., Cheng, Y., O’Donnell, J. A., and Musselman, K. N.: Increasing Alaskan river discharge during the cold season is driven by recent warming, Environ. Res. Lett., 18, 024042, https://doi.org/10.1088/1748-9326/acb661, 2023. a
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M., Parmentier, F.-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S., Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W., Meier, W. N., Wouters, B., Mernild, S., Mård, J., Pawlak, J., and Olsen, M. S.: Key indicators of Arctic climate change: 1971–2017, Environ. Res. Lett., 14, 045010, https://doi.org/10.1088/1748-9326/aafc1b, 2019. a, b
Bring, A., Asokan, S. M., Jaramillo, F., Jarsjö, J., Levi, L., Pietroń, J., Prieto, C., Rogberg, P., and Destouni, G.: Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins, Earth's Future, 3, 206–217, https://doi.org/10.1002/2014EF000296, 2015. a
Brodzik, M. J. and Knowles, K.: EASE-Grid: A Versatile Set of Equal-Area Projections and Grids, in: Discrete Global Grids, edited by: Goodchild, M., Santa Barbara, CA, National Center for Geographic Information and Analysis, USA, https://nsidc.org/data/user-resources/help-center/guide-ease-grids (last access: 17 April 2023), 2002. a
Brown Jr., J. O. J. F., Heginbottom, J. A., and Melnikov, E. S.: Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Tech. rep., National Snow and Ice Data Center/World Data Center for Glaciology, digital Media, revised 2001, 2001. a
Brown, J., Ferrians, O., Heginbottom, J. A., and Melnikov, E.: Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Version 2, Boulder, Colorado USA, National Snow and Ice Data Center [data set], https://doi.org/10.7265/skbg-kf16, 2002. a
Burke, E. J., Zhang, Y., and Krinner, G.: Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change, The Cryosphere, 14, 3155–3174, https://doi.org/10.5194/tc-14-3155-2020, 2020. a
Christensen, T. R., Johansson, T., Åkerman, H. J., Mastepanov, M., Malmer, N., Friborg, T., Crill, P., and Svensson, B. H.: Thawing sub-arctic permafrost: Effects on vegetation and methane emissions, Geophys. Res. Lett., 31, L04501, https://doi.org/10.1029/2003GL018680, 2004. a
Clilverd, H. M., White, D. M., Tidwell, A. C., and Rawlins, M. A.: The Sensitivity of Northern Groundwater Recharge to Climate Change: A Case Study in Northwest Alaska, J. Am. Water Resour. Assoc., 47, 1–13, https://doi.org/10.1111/j.1752-1688.2011.00569.x, 2011. a
Connolly, C. T., Cardenas, M. B., Burkart, G. A., Spencer, R. G., and McClelland, J. W.: Groundwater as a major source of dissolved organic matter to Arctic coastal waters, Nat. Commun., 11, 1–8, https://doi.org/10.1038/s41467-020-15250-8, 2020. a
Cooper, M. G., Zhou, T., Bennett, K. E., Bolton, W., Coon, E., Fleming, S. W., Rowland, J. C., and Schwenk, J.: Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession, Water Resour. Res., 59, e2022WR033154, https://doi.org/10.1029/2022WR033154, 2023. a
Croghan, D., Ala-Aho, P., Lohila, A., Welker, J., Vuorenmaa, J., Kløve, B., Mustonen, K.-R., Aurela, M., and Marttila, H.: Coupling of Water-Carbon Interactions During Snowmelt in an Arctic Finland Catchment, Water Resour. Res., 59, e2022WR032892, https://doi.org/10.1029/2022WR032892, 2023. a
Cubasch, U., Meehl, G., Boer, G., Stouffer, R., Dix, M., Noda, A., Senior, C., Raper, S., and Yap, K.: Projections of future climate change, in: Climate Change 2001: The Scientific Basis: Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel, edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van der Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A., pp. 526–582, 2001. a
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. a, b, c
Dankers, R. and Middelkoop, H.: River discharge and freshwater runoff to the Barents Sea under present and future climate conditions, Clim. Change, 87, 131–153, 2008. a
Dean, J., van der Velde, Y., Garnett, M. H., Dinsmore, K. J., Baxter, R., Lessels, J. S., Smith, P., Street, L. E., Subke, J.-A., Tetzlaff, D., and Washbourne, I.: Abundant pre-industrial carbon detected in Canadian Arctic headwaters: implications for the permafrost carbon feedback, Environ. Res. Lett., 13, 034024, https://doi.org/10.1088/1748-9326/aaa1fe, 2018. a, b
Debolskiy, M. V., Alexeev, V. A., Hock, R., Lammers, R. B., Shiklomanov, A., Schulla, J., Nicolsky, D., Romanovsky, V. E., and Prusevich, A.: Water balance response of permafrost-affected watersheds to changes in air temperatures, Environ. Res. Lett., 16, 084054, https://doi.org/10.1088/1748-9326/ac12f3, 2021. a
Del Vecchio, J. D., Palucis, M. C., and Meyer, C. R.: Permafrost extent sets drainage density in the Arctic, P. Natl. Acad. Sci. USA, 121, e2307072120, https://doi.org/10.1073/pnas.2307072120, 2024. a
Déry, S. J., Hernández-Henríquez, M. A., Burford, J. E., and Wood, E. F.: Observational evidence of an intensifying hydrological cycle in northern Canada, Geophys. Res. Lett., 36, L13402, https://doi.org/10.1029/2009GL038852, 2009. a
Du, J., Kimball, J. S., and Jones, L. A.: Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E, IEEE T. Geosci. Remote, 54, 597–608, https://doi.org/10.1109/TGRS.2015.2462758, 2016. a
ECMWF: ECMWF Reanalysis v5 (ERA5), ECMWF [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, last access: 19 March 2023. a
Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., and Ishitsuka, Y.: Recent changes to Arctic river discharge, Nat. Commun., 12, 6917, https://doi.org/10.1038/s41467-021-27228-1, 2021. a, b, c, d
Ford, V. L. and Frauenfeld, O. W.: Arctic precipitation recycling and hydrologic budget changes in response to sea ice loss, Global Planet. Change, 209, 103752, https://doi.org/10.1016/j.gloplacha.2022.103752, 2022. a
Frey, K. E. and McClelland, J. W.: Impacts of permafrost degradation on arctic river biogeochemistry, Hydrol. Process., 23, 169–182, https://doi.org/10.1002/hyp.7196, 2009. a, b, c
Frey, K. E. and Smith, L. C.: Amplified carbon release from vast West Siberian peatlands by 2100, Geophys. Res. Lett., 32, L09401, https://doi.org/10.1029/2004GL022025, 2005. a, b
GLEAM: Global Land Evaporation Amsterdam Model, GLEAM [data set], https://www.gleam.eu/, last access: 17 April 2023. a
Guo, D., Wang, A., Li, D., and Hua, W.: Simulation of Changes in the Near-Surface Soil Freeze/Thaw Cycle Using CLM4.5 With Four Atmospheric Forcing Data Sets, J. Geophys. Res.-Atmos., 123, 2509–2523, https://doi.org/10.1002/2017JD028097, 2018. a
Hinzman, L. D., Deal, C. J., McGuire, A. D., Mernild, S. H., Polyakov, I. V., and Walsh, J. E.: Trajectory of the Arctic as an integrated system, Ecol. Appl., 23, 1837–1868, https://doi.org/10.1890/11-1498.1, 2013. a
Hodson, T. O.: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15, 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, 2022. a
Hu, Y., Ma, R., Sun, Z., Zheng, Y., Pan, Z., and Zhao, L.: Groundwater Plays an Important Role in Controlling Riverine Dissolved Organic Matter in a Cold Alpine Catchment, the Qinghai–Tibet Plateau, Water Resour. Res., 59, e2022WR032426, https://doi.org/10.1029/2022WR032426, 2023. a
Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson, D. K.: The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, 2013. a
Huntington, T. G.: Evidence for intensification of the global water cycle: Review and synthesis, J. Hydrol., 319, 83–95, https://doi.org/10.1016/j.jhydrol.2005.07.003, 2006. a
Huntington, T. G.: Climate Warming-Induced Intensification of the Hydrologic Cycle: An Assessment of the Published Record and Potential Impacts on Agriculture, Adv. Agron., 109, 1–53, https://doi.org/10.1016/B978-0-12-385040-9.00001-3, 2010. a
Jin, H., Huang, Y., Bense, V. F., Ma, Q., Marchenko, S. S., Shepelev, V. V., Hu, Y., Liang, S., Spektor, V. V., Jin, X., Li, X., and Li, X.: Permafrost Degradation and Its Hydrogeological Impacts, Water, 14, 372, https://doi.org/10.3390/w14030372, 2022. a
Jones, B. M., Grosse, G., Farquharson, L. M., Roy-Léveillée, P., Veremeeva, A., Kanevskiy, M. Z., Gaglioti, B. V., Breen, A. L., Parsekian, A. D., Ulrich, M., and Hinkel, K. M.: Lake and drained lake basin systems in lowland permafrost regions, Nat. Rev. Earth Environ, 3, 85–98, https://doi.org/10.1038/s43017-021-00238-9, 2022. a
Koch, J. C., Bogard, M. J., Butman, D. E., Finlay, K., Ebel, B., James, J., Johnston, S. E., Jorgenson, M. T., Pastick, N. J., Spencer, R. G., Striegl, R., Walvoord, M., and Wickland, K. P.: Heterogeneous Patterns of Aged Organic Carbon Export Driven by Hydrologic Flow Paths, Soil Texture, Fire, and Thaw in Discontinuous Permafrost Headwaters, Global Biogeochem. Cy., 36, e2021GB007242, https://doi.org/10.1029/2021GB007242, 2022. a
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, https://doi.org/10.1175/JCLI-D-12-00228.1, 2013. a, b
Lafrenière, M. J. and Lamoureux, S. F.: Effects of changing permafrost conditions on hydrological processes and fluvial fluxes, Earth-Sci. Rev., 191, 212–223, https://doi.org/10.1016/j.earscirev.2019.02.018, 2019. a
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019a. a, b
Lange, S.: WFDE5 over land merged with ERA5 over the ocean (W5E5). V. 1.0. GFZ Data Services [data set], https://doi.org/10.5880/pik.2019.023, 2019b. a
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Schmied, H. M., Hersbach, H., Buontempo, C., and Cagnazzo, C.: WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0), ISIMIP [data set], https://doi.org/10.48364/ISIMIP.342217, 2021. a, b
Lawrence, D. M. and Slater, A. G.: Incorporating organic soil into a global climate model, Clim. Dynam., 30, 145–160, https://doi.org/10.1007/s00382-007-0278-1, 2008. a
Liljedahl, A. K., Boike, J., Daanen, R. P., Fedorov, A. N., Frost, G. V., Grosse, G., Hinzman, L. D., Iijma, Y., Jorgenson, J. C., Matveyeva, N., Necsoiu, M., Raynolds, M. K., Romanovsky, V. E., Schulla, J., Tape, K. D., Walker, D. A., Wilson, C. J., Yabuki H., and Zona, D.: Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology, Nat. Geosci., 9, 312–318, https://doi.org/10.1038/ngeo2674, 2016. a
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S., and Tabler, R. D.: Simulating complex snow distributions in windy environments using SnowTran-3D, J. Glaciol., 53, 241–256, https://doi.org/10.3189/172756507782202865, 2007. a
Liu, S., Wang, P., Yu, J., Wang, T., Cai, H., Huang, Q., Pozdniakov, S. P., Zhang, Y., and Kazak, E. S.: Mechanisms behind the uneven increases in early, mid-and late winter streamflow across four Arctic river basins, J. Hydrol., 606, 127425, https://doi.org/10.1016/j.jhydrol.2021.127425, 2022. a
Mann, P. J., Strauss, J., Palmtag, J., Dowdy, K., Ogneva, O., Fuchs, M., Bedington, M., Torres, R., Polimene, L., Overduin, P., Mollenhauer, G., Grosse, G., Rachold, V., Sobczak, W., Spencer, R., and Juhls, B.: Degrading permafrost river catchments and their impact on Arctic Ocean nearshore processes, Ambio, 51, 439–455, https://doi.org/10.1007/s13280-021-01666-z, 2022. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
McClelland, J. W., Holmes, R. M., Peterson, B. J., and Stieglitz, M.: Increasing river discharge in the Eurasian Arctic: Consideration of dams, permafrost thaw, and fires as potential agents of change, J. Geophys. Res.-Atmos., 109, D18102, https://doi.org/10.1029/2004JD004583, 2004. a, b
McClelland, J. W., Déry, S. J., Peterson, B. J., Holmes, R. M., and Wood, E. F.: A pan-arctic evaluation of changes in river discharge during the latter half of the 20th century, Geophys. Res. Lett., 33, L06715, https://doi.org/10.1029/2006GL025753, 2006. a, b
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. a, b
McKenzie, J. M., Kurylyk, B. L., Walvoord, M. A., Bense, V. F., Fortier, D., Spence, C., and Grenier, C.: Invited perspective: What lies beneath a changing Arctic?, The Cryosphere, 15, 479–484, https://doi.org/10.5194/tc-15-479-2021, 2021. a
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a
Mohammed, A. A., Guimond, J. A., Bense, V. F., Jamieson, R. C., McKenzie, J. M., and Kurylyk, B. L.: Mobilization of subsurface carbon pools driven by permafrost thaw and reactivation of groundwater flow: a virtual experiment, Environ. Res. Lett., 17, 124036, https://doi.org/10.1088/1748-9326/aca701, 2022. a, b
NASA: Modern-Era Retrospective analysis for Research and Applications (MERRA), NASA [data set], https://gmao.gsfc.nasa.gov/reanalysis/MERRA/, last access: 23 January 2023. a
Nash, D., Waliser, D., Guan, B., Ye, H., and Ralph, F. M.: The Role of Atmospheric Rivers in Extratropical and Polar Hydroclimate, J. Geophys. Res.-Atmos., 123, 6804–6821, https://doi.org/10.1029/2017JD028130, 2018. a
Ni, J., Wu, T., Zhu, X., Hu, G., Zou, D., Wu, X., Li, R., Xie, C., Qiao, Y., Pang, Q., Hao, J., and Yang, C.: Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models, J. Geophys. Res.-Atmos., 126, e2020JD033402, https://doi.org/10.1029/2020JD033402, 2021. a
Nicolsky, D., Romanovsky, V., Alexeev, V., and Lawrence, D.: Improved modeling of permafrost dynamics in a GCM land-surface scheme, Geophys. Res. Lett, 34, L08501, https://doi.org/10.1029/2007GL029525, 2007. a, b
Numerical Terradynamic Simulation Group: Pan-Arctic Evapotraspiration Data, Numerical Terradynamic Simulation Group, University of Montana [data set], http://files.ntsg.umt.edu/data/PA_Monthly_ET/, last access: 16 April 2023. a
Overland, J., Dunlea, E., Box, J. E., Corell, R., Forsius, M., Kattsov, V., Olsen, M. S., Pawlak, J., Reiersen, L.-O., and Wang, M.: The urgency of Arctic change, Polar Sci., 21, 6–13, https://doi.org/10.1016/j.polar.2018.11.008, 2019. a
Painter, S. L., Coon, E. T., Khattak, A. J., and Jastrow, J. D.: Drying of tundra landscapes will limit subsidence-induced acceleration of permafrost thaw, P. Natl. Acad. Sci. USA, 120, e2212171120, https://doi.org/10.1073/pnas.2212171120, 2023. a, b, c
Peng, X., Zhang, T., Frauenfeld, O. W., Wang, K., Luo, D., Cao, B., Su, H., Jin, H., and Wu, Q.: Spatiotemporal Changes in Active Layer Thickness under Contemporary and Projected Climate in the Northern Hemisphere, J. Climate, 31, 251–266, https://doi.org/10.1175/JCLI-D-16-0721.1, 2018. a
Peterson, B. J., Holmes, R. M., McClelland, J. W., Vörösmarty, C. J., Lammers, R. B., Shiklomanov, A. I., Shiklomanov, I. A., and Rahmstorf, S.: Increasing River Discharge to the Arctic Ocean, Science, 298, 2171–2173, https://doi.org/10.1126/science.1077445, 2002. a, b, c, d
Ran, Y., Li, X., Cheng, G., Che, J., Aalto, J., Karjalainen, O., Hjort, J., Luoto, M., Jin, H., Obu, J., Hori, M., Yu, Q., and Chang, X.: New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere, Earth Syst. Sci. Data, 14, 865–884, https://doi.org/10.5194/essd-14-865-2022, 2022 (data available at: https://data.tpdc.ac.cn/en/data/5093d9ff-a5fc-4f10-a53f-c01e7b781368/, last access: 3 February 2023). a, b, c, d
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Rawlins, M. and Karmalkar, A.: Modeled estimates of permafrost hydrology and related fields for pan-Arctic region over the period 1980–2100. Quantifying Variability and Controls of Riverine Dissolved Organic Carbon Exported to Arctic Coastal Margins of North America, ESS-DIVE repository [code and data set], https://doi.org/10.15485/2290364, 2024. a
Rawlins, M. A.: Increasing freshwater and dissolved organic carbon flows to Northwest Alaska’s Elson lagoon, Environ. Res. Lett., 16, 105014, https://doi.org/10.1088/1748-9326/ac2288, 2021. a, b, c
Rawlins, M. A., Lammers, R. B., Frolking, S., Fekete, B. M., and Vörösmarty, C. J.: Simulating Pan-Arctic Runoff with a Macro-Scale Terrestrial Water Balance Model, Hydrol. Process., 17, 2521–2539, https://doi.org/10.1002/hyp.1271, 2003. a, b
Rawlins, M. A., Steele, M., Holland, M. M., Adam, J. C., Cherry, J. E., Francis, J. A., Groisman, P. Y., Hinzman, L. D., Huntington, T. G., Kane, D. L., Kmball, J. S., Kwok, R., Lammers, R. B., Lee, C. M. Lettenmaier, D. P., McDonald, K. C., Podest, E., Pundsack, J. W., Rudels, B., Serreze, M. C., Shiklomanov, A., Skagseth, Ø., Troy, T. J., Vörösmarty, C. J., Wensnahan, M., Wood, E. F., Woodgate, R., Yang, D., Zhang, K., and Zhang, T.: Analysis of the Arctic System for Freshwater Cycle Intensification: Observations and Expectations, J. Climate, 23, 5715–5737, https://doi.org/10.1175/2010JCLI3421.1, 2010. a, b, c, d
Rawlins, M. A., Nicolsky, D. J., McDonald, K. C., and Romanovsky, V. E.: Simulating soil freeze/thaw dynamics with an improved pan-Arctic water balance model, J. Adv. Model. Earth Sy., 5, 659–675, https://doi.org/10.1002/jame.20045, 2013. a, b
Rawlins, M. A., Cai, L., Stuefer, S. L., and Nicolsky, D.: Changing characteristics of runoff and freshwater export from watersheds draining northern Alaska, The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, 2019. a, b, c
Rawlins, M. A., Connolly, C. T., and McClelland, J. W.: Modeling Terrestrial Dissolved Organic Carbon Loading to Western Arctic Rivers, J. Geophys. Res.-Biogeo, 126, e2021JG006420, https://doi.org/10.1029/2021JG006420, 2021. a, b
Sazonova, T. S. and Romanovsky, V. E.: A model for regional-scale estimation of temporal and spatial variability of active layer thickness and mean annual ground temperatures, Permafrost Periglac., 14, 125–139, https://doi.org/10.1002/ppp.449, 2003. a
Schroeder, R., McDonald, K. C., Zimmerman, R., Podest, E., and Rawlins, M.: North Eurasian Inundation Mapping with Passive and Active Microwave Remote Sensing, Environ. Res. Lett., 5, 015003, https://doi.org/10.1088/1748-9326/5/1/015003, 2010. a
Schwab, M. S., Hilton, R. G., Raymond, P. A., Haghipour, N., Amos, E., Tank, S. E., Holmes, R. M., Tipper, E. T., and Eglinton, T. I.: An Abrupt Aging of Dissolved Organic Carbon in Large Arctic Rivers, Geophys. Res. Lett., 47, e2020GL088823, https://doi.org/10.1029/2020GL088823, 2020. a
Serreze, M. C. and Meier, W. N.: The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic, Ann. N. Y. Acad. Sci., 1436, 36–53, https://doi.org/10.1111/nyas.13856, 2019. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611, https://doi.org/10.2307/2333709, 1965. a
Shiklomanov, A. I., Lammers, R. B., Lettenmaier, D. P., Polischuk, Y. M., Savichev, O. G., Smith, L. C., and Chernokulsky, A. V.: Hydrological Changes: Historical Analysis, Contemporary Status, and Future Projections, Regional Environmental Changes in Siberia and Their Global Consequences, Springer, 111–154, https://doi.org/10.1007/978-94-007-4569-8_4, 2013. a
Shiklomanov, I. A. and Shiklomanov, A. I.: Climatic Change and Dynamics of River Discharge into the Arctic Ocean, Water Resour., 30, 593–601, 2003. a
Shiklomanov, I. A., Shiklomanov, A. I., Lammers, R. B., Peterson, B. J., and Vörösmarty, C. J.: The dynamics of river water inflow to the Arctic Ocean, in: The Freshwater Budget of the Arctic Ocean, edited by: Lewis, E. L., Jones, E. P., Lemke, P., Prowse, T. D., and Wadhams, P., 281–296, Kluwer Academic Press, Dordrecht, 2000. a
Sjöberg, Y., Jan, A., Painter, S. L., Coon, E. T., Carey, M. P., O'Donnell, J. A., and Koch, J. C.: Permafrost Promotes Shallow Groundwater Flow and Warmer Headwater Streams, Water Resour. Res., 57, e2020WR027463, https://doi.org/10.1029/2020WR027463, 2021. a
Slater, A. G. and Lawrence, D. M.: Diagnosing Present and Future Permafrost from Climate Models, J. Climate, 26, 5608–5623, https://doi.org/10.1175/JCLI-D-12-00341.1, 2013. a
Smith, L. C., Sheng, Y., MacDonald, G. M., and Hinzman, L. D.: Disappearing Arctic Lakes, Science, 308, 1429, https://doi.org/10.1029/2004JD005518, 2005. a
Spencer, R. G., Mann, P. J., Dittmar, T., Eglinton, T. I., McIntyre, C., Holmes, R. M., Zimov, N., and Stubbins, A.: Detecting the signature of permafrost thaw in Arctic rivers, Geophys. Res. Lett., 42, 2830–2835, https://doi.org/10.1002/2015GL063498, 2015. a
St. Jacques, J. M. and Sauchyn, D. J.: Increasing winter baseflow and mean annual streamflow from possible permafrost thawing in the Northwest Territories, Canada, Geophys. Res. Lett., 36, L01401, https://doi.org/10.1029/2008GL035822, 2009. a, b
Streletskiy, D. A., Tananaev, N. I., Opel, T., Shiklomanov, N. I., Nyland, K. E., Streletskaya, I. D., Tokarev, I., and Shiklomanov, A. I.: Permafrost hydrology in changing climatic conditions: seasonal variability of stable isotope composition in rivers in discontinuous permafrost, Environ. Res. Lett., 10, 095003, https://doi.org/10.1088/1748-9326/10/9/095003, 2015. a
Striegl, R. G., Aiken, G. R., Dornblaser, M. M., Raymond, P. A., and Wickland, K. P.: A decrease in discharge-normalized DOC export by the Yukon River during summer through autumn, Geophys. Res. Lett., 32, L21413, https://doi.org/10.1029/2005GL024413, 2005. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a
Sturm, M. J., Holmgren, J., and Liston, G. E.: A Seasonal Snow Cover Classification System for Local to Global Applications, J. Climate, 8, 1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995. a
Tananaev, N., Makarieva, O., and Lebedeva, L.: Trends in annual and extreme flows in the Lena River basin, Northern Eurasia, Geophys. Res. Lett., 43, 10764–10772, https://doi.org/10.1002/2016GL070796, 2016. a
Tananaev, N., Teisserenc, R., and Debolskiy, M.: Permafrost Hydrology Research Domain: Process-Based Adjustment, Hydrology, 7, 6, https://doi.org/10.3390/hydrology7010006, 2020. a
Tank, S. E., Striegl, R. G., McClelland, J. W., and Kokelj, S. V.: Multi-decadal increases in dissolved organic carbon and alkalinity flux from the Mackenzie drainage basin to the Arctic Ocean, Environ. Res. Lett., 11, 054015, https://doi.org/10.1088/1748-9326/11/5/054015, 2016. a
Tank, S. E., McClelland, J. W., Spencer, R. G., Shiklomanov, A. I., Suslova, A., Moatar, F., Amon, R. M., Cooper, L. W., Elias, G., Gordeev, V. V., Guay, C., Gurtovaya, T. Y., Kosmenko, L. S., Mutter, E. A., Peterson, B. J., Peucker-Ehrenbrink, B., Raymond, P. A., Schuster, P. F., Scott, L., Staples, R., Striegl, R. G., Tretiakov, M., Zhulidov, A. V., Zimov, N., Zimov, S., and Holmes, R. M.: Recent trends in the chemistry of major northern rivers signal widespread Arctic change, Nat. Geosci., 16, 1–8, https://doi.org/10.1038/s41561-023-01247-7, 2023. a
Wagner, A., Lohmann, G., and Prange, M.: Arctic river discharge trends since 7 ka BP, Global Planet. Change, 79, 48–60, https://doi.org/10.1016/j.gloplacha.2011.07.006, 2011. a
Walsh, J. E., Chapman, W. L., Romanovsky, V., Christensen, J. H., and Stendel, M.: Global climate model performance over Alaska and Greenland, J. Climate, 21, 6156–6174, https://doi.org/10.1175/2008JCLI2163.1, 2008. a
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost–A review, Vadose Zone J., 15, vzj2016.01.0010, https://doi.org/10.2136/vzj2016.01.0010, 2016. a
Walvoord, M. A. and Striegl, R. G.: Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: Potential impacts on lateral export of carbon and nitrogen, Geophys. Res. Lett., 34, L12402, https://doi.org/10.1029/2007GL030216, 2007. a
Wang, P., Huang, Q., Pozdniakov, S. P., Liu, S., Ma, N., Wang, T., Zhang, Y., Yu, J., Xie, J., Fu, G., Frolova, N. L., and Liu, C.: Potential role of permafrost thaw on increasing Siberian river discharge, Environ. Res. Lett., 16, 034046, https://doi.org/10.1088/1748-9326/abe326, 2021. a
Wang, Y.-R., Hessen, D. O., Samset, B. H., and Stordal, F.: Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data, Remote Sens. Environ., 280, 113181, https://doi.org/10.1016/j.rse.2022.113181, 2022. a
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The inter-sectoral impact model intercomparison project (ISI–MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014. a
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82, 2005. a
Willmott, C. J. and Matsuura, K.: Terrestrial Precipitation: 1900–2008 Gridded Monthly Time Series, Version 2.01, https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html (last access: 3 February 2023), 2009. a
Woo, M.-K., Kane, D. L., Carey, S. K., and Yang, D.: Progress in permafrost hydrology in the new millennium, Permafrost Periglac., 19, 237–254, https://doi.org/10.1002/ppp.613, 2008. a, b
Yi, Y., Chen, R. H., Kimball, J. S., Moghaddam, M., Xu, X., Euskirchen, E. S., Das, N., and Miller, C. E.: Potential Satellite Monitoring of Surface Organic Soil Properties in Arctic Tundra From SMAP, Water Resour. Res., 58, e2021WR030957, https://doi.org/10.1029/2021WR030957, 2022. a
Zhang, K., Kimball, J. S., Mu, Q., Jones, L. A., Goetz, S. J., and Running, S. W.: Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005, J. Hydrol., 379, 92–110, https://doi.org/10.1016/j.jhydrol.2009.09.047, 2009. a
Zhang, P., Chen, G., Ting, M., Ruby Leung, L., Guan, B., and Li, L.: More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice, Nat. Clim. Change., 13, 266–273, https://doi.org/10.1038/s41558-023-01599-3, 2023. a
Zhang, S.-M., Mu, C.-C., Li, Z.-L., Dong, W.-W., Wang, X.-Y., Streletskaya, I., Grebenets, V., Sokratov, S., Kizyakov, A., and Wu, X.-D.: Export of nutrients and suspended solids from major Arctic rivers and their response to permafrost degradation, Adv. Clim. Chang., 12, 466–474, https://doi.org/10.1016/j.accre.2021.06.002, 2021. a
Zhang, X., He, J., Zhang, J., Polyakov, I., Gerdes, R., Inoue, J., and Wu, P.: Enhanced poleward moisture transport and amplified northern high-latitude wetting trend, Nat. Clim. Change., 3, 47–51, https://doi.org/10.1038/nclimate1631, 2013. a, b
Co-editor-in-chief
This study provides new estimates of historical and projected changes in pan-Arctic runoff, with emphasis on the impact of permafrost changes and sub-surface flows on large scale hydrology. The impact of permafrost change on hydrological processes is a key uncertainty facing the cold regions hydrology community, and requires comprehensive model-based analysis as presented in this study. The analysis also addresses changes to the terrestrial runoff contribution to the freshwater budget of the Arctic, and so is of interest to a wide range of disciplines.
This study provides new estimates of historical and projected changes in pan-Arctic runoff, with...
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We...