Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-51-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-51-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jan 2023
Research article |
| 10 Jan 2023
| 10 Jan 2023
Observed and predicted trends in Icelandic snow conditions for the period 1930–2100
Faculty of Civil and Environmental Engineering, University of
Iceland, Reykjavik, Iceland
Sigurdur M. Gardarsson
Faculty of Civil and Environmental Engineering, University of
Iceland, Reykjavik, Iceland
Andri Gunnarsson
Research and Development Division, Landsvirkjun, Reykjavik, Iceland
Oli Gretar Blondal Sveinsson
Research and Development Division, Landsvirkjun, Reykjavik, Iceland
Related authors
Darri Eythorsson, Sigurdur M. Gardarsson, Shahryar K. Ahmad, and Oli Gretar Blondal Sveinsson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-564, https://doi.org/10.5194/hess-2019-564, 2019
Preprint withdrawn
Short summary
Short summary
We studied recent trends in the Icelandic climate and snow regimes. Climate was classified based on climate models and snow cover trends were assessed using satellite imagery. Our results showed a significant increase in the frequency of snow cover, especially in the eastern highlands. At the same our results show warmer climate classes spreading both northward and to higher elevations. Based on projected climate, we expect a significant warming of local climates in Iceland during this century.
Hordur Bragi Helgason, Andri Gunnarsson, Óli Grétar Blöndal Sveinsson, and Bart Nijssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4186, https://doi.org/10.5194/egusphere-2024-4186, 2025
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This study analyses streamflow variability and trends in Iceland. The results show a large inter-annual variability in streamflow. Positive trends are found for precipitation, which has led to increased streamflow in most gauges for both the last 30 and 50 years of annual and seasonal flows. This marks the first study to report such consistent results for streamflow trends in Iceland. Glaciated rivers show positive melt season trends over the last 50 years, but negative over the last 30 years.
Andri Gunnarsson, Sigurdur M. Gardarsson, and Finnur Pálsson
The Cryosphere, 17, 3955–3986, https://doi.org/10.5194/tc-17-3955-2023, https://doi.org/10.5194/tc-17-3955-2023, 2023
Short summary
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A model was developed with the possibility of utilizing satellite-derived daily surface albedo driven by high-resolution climate data to estimate the surface energy balance (SEB) for all Icelandic glaciers for the period 2000–2021.
Andri Gunnarsson, Sigurdur M. Gardarsson, Finnur Pálsson, Tómas Jóhannesson, and Óli G. B. Sveinsson
The Cryosphere, 15, 547–570, https://doi.org/10.5194/tc-15-547-2021, https://doi.org/10.5194/tc-15-547-2021, 2021
Short summary
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Surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. During the melt season in the Northern Hemisphere solar energy absorbed by snow- and ice-covered surfaces is mainly controlled by surface albedo. For Icelandic glaciers, air temperature and surface albedo are the dominating factors governing annual variability of glacier surface melt. Satellite data from the MODIS sensor are used to create a data set spanning the glacier melt season.
Darri Eythorsson, Sigurdur M. Gardarsson, Shahryar K. Ahmad, and Oli Gretar Blondal Sveinsson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-564, https://doi.org/10.5194/hess-2019-564, 2019
Preprint withdrawn
Short summary
Short summary
We studied recent trends in the Icelandic climate and snow regimes. Climate was classified based on climate models and snow cover trends were assessed using satellite imagery. Our results showed a significant increase in the frequency of snow cover, especially in the eastern highlands. At the same our results show warmer climate classes spreading both northward and to higher elevations. Based on projected climate, we expect a significant warming of local climates in Iceland during this century.
Andri Gunnarsson, Sigurður M. Garðarsson, and Óli G. B. Sveinsson
Hydrol. Earth Syst. Sci., 23, 3021–3036, https://doi.org/10.5194/hess-23-3021-2019, https://doi.org/10.5194/hess-23-3021-2019, 2019
Short summary
Short summary
In this study a gap-filled snow cover product for Iceland is developed using MODIS satellite data and validated with both in situ observations and alternative remote sensing data sources with good agreement. Information about snow cover extent, duration and changes over time is presented, indicating that snow cover extent has been increasing slightly for the past few years.
Related subject area
Discipline: Snow | Subject: Arctic (e.g. Greenland)
Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors
Predicting Avalanche Danger in Northern Norway Using Statistical Models
Impact of Snow Thermal Conductivity Schemes on pan-Arctic Permafrost Dynamics in CLM5.0
Long-term development of a perennial firn aquifer on the Lomonosovfonna ice cap, Svalbard
Assessment of Arctic seasonal snow cover rates of change
Snow properties at the forest–tundra ecotone: predominance of water vapor fluxes even in deep, moderately cold snowpacks
Spatial patterns of snow distribution in the sub-Arctic
Snowfall and snow accumulation during the MOSAiC winter and spring seasons
Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval
Claire L. Bachand, Chen Wang, Baptiste Dafflon, Lauren N. Thomas, Ian Shirley, Sarah Maebius, Colleen M. Iversen, and Katrina E. Bennett
The Cryosphere, 19, 393–400, https://doi.org/10.5194/tc-19-393-2025, https://doi.org/10.5194/tc-19-393-2025, 2025
Short summary
Short summary
Temporally continuous snow depth estimates are important for understanding changing snow patterns and impacts on frozen ground in the Arctic. In this work, we developed an approach to predict snow depth from variability in snow–ground interface temperature using small temperature sensors that are cheap and easy to deploy. This new technique enables spatially distributed and temporally continuous snowpack monitoring that has not previously been possible.
Kai-Uwe Eiselt and Rune Grand Graversen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2865, https://doi.org/10.5194/egusphere-2024-2865, 2024
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In this study we optimise and train a random forest model to predict avalanche danger in northern Norway based on meteorological reanalysis data. A 4-level and a binary case are considered. The model performance in the 4-level case is at the low end compared to recent similar studies. A hindcast of a measure for avalanche activity is performed from 1970-2023 and a correlation is found with the Arctic Oscillation. This has potential implications for longer-term avalanche predictability.
Adrien Damseaux, Heidrun Matthes, Victoria R. Dutch, Leanne Wake, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-1412, https://doi.org/10.5194/egusphere-2024-1412, 2024
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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.
Tim van den Akker, Ward van Pelt, Rickard Petterson, and Veijo A. Pohjola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1345, https://doi.org/10.5194/egusphere-2024-1345, 2024
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Liquid water can persist within old snow on glaciers and ice caps, if it can percolate into it before it refreezes. Snow is a good insulator, and snow is porous where the percolated water can be stored. If this happens, the water piles up and forms a groundwater-like system. Here, we show observations of such a groundwater-like system found in Svalbard. We demonstrate that it behaves like a groundwater system, and use that to model the development of the water table from 1957 until present day.
Chris Derksen and Lawrence Mudryk
The Cryosphere, 17, 1431–1443, https://doi.org/10.5194/tc-17-1431-2023, https://doi.org/10.5194/tc-17-1431-2023, 2023
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We examine Arctic snow cover trends through the lens of climate assessments. We determine the sensitivity of change in snow cover extent to year-over-year increases in time series length, reference period, the use of a statistical methodology to improve inter-dataset agreement, version changes in snow products, and snow product ensemble size. By identifying the sensitivity to the range of choices available to investigators, we increase confidence in reported Arctic snow extent changes.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
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We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, https://doi.org/10.5194/tc-16-3269-2022, 2022
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In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
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Short summary
In this study we researched past and predicted snow conditions in Iceland based on manual snow observations recorded in Iceland and compared these with satellite observations. Future snow conditions were predicted through numerical computer modeling based on climate models. The results showed that average snow depth and snow cover frequency have increased over the historical period but are projected to significantly decrease when projected into the future.
In this study we researched past and predicted snow conditions in Iceland based on manual snow...
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