Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3269-2022
© Author(s) 2022. 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-16-3269-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2022
Research article |
| 17 Aug 2022
| 17 Aug 2022
Spatial patterns of snow distribution in the sub-Arctic
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Greta Miller
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Robert Busey
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
Min Chen
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Emma R. Lathrop
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Julian B. Dann
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Mara Nutt
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Ryan Crumley
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Shannon L. Dillard
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Geography, University of Wisconsin–Madison, Madison, WI, USA
Baptiste Dafflon
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Jitendra Kumar
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
W. Robert Bolton
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
Cathy J. Wilson
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Colleen M. Iversen
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Stan D. Wullschleger
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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Cited
20 citations as recorded by crossref.
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests E. Alonso-González et al. https://doi.org/10.5194/tc-20-209-2026
- Editorial: Pan-Arctic snow research A. Spolaor et al. https://doi.org/10.3389/feart.2023.1266810
- Evaluating a hierarchy of bias correction methods for ERA5-Land SWE across Canada N. Kanda & C. Fletcher https://doi.org/10.1088/2515-7620/aded5a
- Scaling Arctic landscape and permafrost features improves active layer depth modeling W. Hantson et al. https://doi.org/10.1088/2752-664X/ad9f6c
- Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors C. Bachand et al. https://doi.org/10.5194/tc-19-393-2025
- Applications of Snow-Covered Areas from Unoccupied Aerial Systems (UAS) Visible Imagery: A Demonstration in Southeastern New Hampshire J. Johnston et al. https://doi.org/10.3390/rs17111885
- Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S) R. Aguayo et al. https://doi.org/10.5194/tc-18-5383-2024
- How strong is Snow? Spatial correlations of snowpack load bearing capacity and micromechanics from NASA SnowEx SnowMicroPen Data at Grand Mesa, Colorado M. Tedesche et al. https://doi.org/10.1016/j.coldregions.2024.104369
- High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning E. Thaler et al. https://doi.org/10.1029/2023EA003015
- Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry Z. Liu et al. https://doi.org/10.1016/j.coldregions.2025.104580
- Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra D. Yang et al. https://doi.org/10.3389/fpls.2025.1724838
- Estimating Permafrost Distribution Using Co‐Located Temperature and Electrical Resistivity Measurements S. Uhlemann et al. https://doi.org/10.1029/2023GL103987
- Vegetation heterogeneity reflects soil thermal state and surface soil displacement in a thawing permafrost landscape M. Farley et al. https://doi.org/10.1088/2752-664X/ae5dd5
- Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome K. Orndahl et al. https://doi.org/10.1016/j.rse.2025.114717
- Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska D. Yang et al. https://doi.org/10.1088/2752-664X/ad9eb8
- A Two-Stage Algorithm for Pan-Asian Haze Mapping with the FY-4A/AGRI Geostationary Imager O. Liu et al. https://doi.org/10.3390/rs18050737
- Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes V. Tyystjärvi et al. https://doi.org/10.5194/tc-18-403-2024
- Explainable Artificial Intelligence in Hydrology: A Review M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1007/s11269-025-04435-9
- Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska X. Huang et al. https://doi.org/10.5194/gmd-19-1193-2026
20 citations as recorded by crossref.
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests E. Alonso-González et al. https://doi.org/10.5194/tc-20-209-2026
- Editorial: Pan-Arctic snow research A. Spolaor et al. https://doi.org/10.3389/feart.2023.1266810
- Evaluating a hierarchy of bias correction methods for ERA5-Land SWE across Canada N. Kanda & C. Fletcher https://doi.org/10.1088/2515-7620/aded5a
- Scaling Arctic landscape and permafrost features improves active layer depth modeling W. Hantson et al. https://doi.org/10.1088/2752-664X/ad9f6c
- Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors C. Bachand et al. https://doi.org/10.5194/tc-19-393-2025
- Applications of Snow-Covered Areas from Unoccupied Aerial Systems (UAS) Visible Imagery: A Demonstration in Southeastern New Hampshire J. Johnston et al. https://doi.org/10.3390/rs17111885
- Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S) R. Aguayo et al. https://doi.org/10.5194/tc-18-5383-2024
- How strong is Snow? Spatial correlations of snowpack load bearing capacity and micromechanics from NASA SnowEx SnowMicroPen Data at Grand Mesa, Colorado M. Tedesche et al. https://doi.org/10.1016/j.coldregions.2024.104369
- High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning E. Thaler et al. https://doi.org/10.1029/2023EA003015
- Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry Z. Liu et al. https://doi.org/10.1016/j.coldregions.2025.104580
- Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra D. Yang et al. https://doi.org/10.3389/fpls.2025.1724838
- Estimating Permafrost Distribution Using Co‐Located Temperature and Electrical Resistivity Measurements S. Uhlemann et al. https://doi.org/10.1029/2023GL103987
- Vegetation heterogeneity reflects soil thermal state and surface soil displacement in a thawing permafrost landscape M. Farley et al. https://doi.org/10.1088/2752-664X/ae5dd5
- Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome K. Orndahl et al. https://doi.org/10.1016/j.rse.2025.114717
- Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska D. Yang et al. https://doi.org/10.1088/2752-664X/ad9eb8
- A Two-Stage Algorithm for Pan-Asian Haze Mapping with the FY-4A/AGRI Geostationary Imager O. Liu et al. https://doi.org/10.3390/rs18050737
- Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes V. Tyystjärvi et al. https://doi.org/10.5194/tc-18-403-2024
- Explainable Artificial Intelligence in Hydrology: A Review M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1007/s11269-025-04435-9
- Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska X. Huang et al. https://doi.org/10.5194/gmd-19-1193-2026
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
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.
In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations...
