Articles | Volume 11, issue 2
https://doi.org/10.5194/tc-11-857-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-11-857-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Apr 2017
Research article |
| 03 Apr 2017
Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
Haruko M. Wainwright
CORRESPONDING AUTHOR
Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA
Anna K. Liljedahl
Water and Environmental Research Center, University of Alaska Fairbanks, 306 Tanana Loop, Fairbanks, AK 99775-5860, USA
Baptiste Dafflon
Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA
Craig Ulrich
Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA
John E. Peterson
Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA
Alessio Gusmeroli
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
Susan S. Hubbard
Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA
Related authors
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
Short summary
Short summary
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Zexuan Xu, Rebecca Serata, Haruko Wainwright, Miles Denham, Sergi Molins, Hansell Gonzalez-Raymat, Konstantin Lipnikov, J. David Moulton, and Carol Eddy-Dilek
Hydrol. Earth Syst. Sci., 26, 755–773, https://doi.org/10.5194/hess-26-755-2022, https://doi.org/10.5194/hess-26-755-2022, 2022
Short summary
Short summary
Climate change could change the groundwater system and threaten water supply. To quantitatively evaluate its impact on water quality, numerical simulations with chemical and reaction processes are required. With the climate projection dataset, we used the newly developed hydrological and chemical model to investigate the movement of contaminants and assist the management of contamination sites.
Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, https://doi.org/10.5194/hess-26-429-2022, 2022
Short summary
Short summary
This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into
watershed zonesthat have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.
Qina Yan, Haruko Wainwright, Baptiste Dafflon, Sebastian Uhlemann, Carl I. Steefel, Nicola Falco, Jeffrey Kwang, and Susan S. Hubbard
Earth Surf. Dynam., 9, 1347–1361, https://doi.org/10.5194/esurf-9-1347-2021, https://doi.org/10.5194/esurf-9-1347-2021, 2021
Short summary
Short summary
We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope and that the hybrid model provides better accuracy than a machine-learning model.
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
Short summary
Short summary
The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Jonathan Bachman, John Lamb, Craig Ulrich, Neslihan Taş, and Baptiste Dafflon
EGUsphere, https://doi.org/10.5194/egusphere-2025-2341, https://doi.org/10.5194/egusphere-2025-2341, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We studied how Arctic landscapes change as the ground thaws by comparing measurements taken ten years apart. We found that some areas sank and new ponds formed, with different patterns depending on the shape of the land. These changes affect how water and carbon flow and cycle through the environment. The results help understand how and where the Arctic is shifting, and highlight the need for repeated observations to track long-term changes.
Xiang Huang, Yu Zhang, Bo Gao, Charles J. Abolt, Ryan L. Crumley, Cansu Demir, Richard P. Fiorella, Bob Busey, Bob Bolton, Scott L. Painter, and Katrina E. Bennett
EGUsphere, https://doi.org/10.5194/egusphere-2025-1753, https://doi.org/10.5194/egusphere-2025-1753, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make climate models more reliable for understanding water flow in permafrost areas under a changing climate.
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.
Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon
EGUsphere, https://doi.org/10.5194/egusphere-2023-968, https://doi.org/10.5194/egusphere-2023-968, 2023
Preprint archived
Short summary
Short summary
Snow depth has a strong impact on soil temperatures and carbon cycling in the arctic. Because of this, we want to understand why snow is deeper in some places than others. Using cameras mounted on a drone, we mapped snow depth, vegetation height, and elevation across a watershed in Alaska. In this paper, we develop novel techniques using image processing and machine learning to characterize the influence of topography and shrubs on snow depth in the watershed.
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
Short summary
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.
A. Hasan, C. Witharana, M. R. Udawalpola, and A. K. Liljedahl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 103–109, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-103-2022, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-103-2022, 2022
M. R. Udawalpola, C. Witharana, A. Hasan, A. Liljedahl, M. Ward Jones, and B. Jones
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 203–208, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-203-2022, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-203-2022, 2022
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
Short summary
Short summary
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Carlotta Brunetti, John Lamb, Stijn Wielandt, Sebastian Uhlemann, Ian Shirley, Patrick McClure, and Baptiste Dafflon
Earth Surf. Dynam., 10, 687–704, https://doi.org/10.5194/esurf-10-687-2022, https://doi.org/10.5194/esurf-10-687-2022, 2022
Short summary
Short summary
This paper proposes a method to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution from soil temperature time series. We validate and apply this method to synthetic and field case studies. The improved quantification of soil thermal properties is a cornerstone for advancing the indirect estimation of the fraction of soil components needed to predict subsurface storage and fluxes of water, carbon, and nutrients.
Baptiste Dafflon, Stijn Wielandt, John Lamb, Patrick McClure, Ian Shirley, Sebastian Uhlemann, Chen Wang, Sylvain Fiolleau, Carlotta Brunetti, Franklin H. Akins, John Fitzpatrick, Samuel Pullman, Robert Busey, Craig Ulrich, John Peterson, and Susan S. Hubbard
The Cryosphere, 16, 719–736, https://doi.org/10.5194/tc-16-719-2022, https://doi.org/10.5194/tc-16-719-2022, 2022
Short summary
Short summary
This study presents the development and validation of a novel acquisition system for measuring finely resolved depth profiles of soil and snow temperature at multiple locations. Results indicate that the system reliably captures the dynamics in snow thickness, as well as soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes.
Zexuan Xu, Rebecca Serata, Haruko Wainwright, Miles Denham, Sergi Molins, Hansell Gonzalez-Raymat, Konstantin Lipnikov, J. David Moulton, and Carol Eddy-Dilek
Hydrol. Earth Syst. Sci., 26, 755–773, https://doi.org/10.5194/hess-26-755-2022, https://doi.org/10.5194/hess-26-755-2022, 2022
Short summary
Short summary
Climate change could change the groundwater system and threaten water supply. To quantitatively evaluate its impact on water quality, numerical simulations with chemical and reaction processes are required. With the climate projection dataset, we used the newly developed hydrological and chemical model to investigate the movement of contaminants and assist the management of contamination sites.
Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, https://doi.org/10.5194/hess-26-429-2022, 2022
Short summary
Short summary
This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into
watershed zonesthat have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.
Jiancong Chen, Baptiste Dafflon, Anh Phuong Tran, Nicola Falco, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 25, 6041–6066, https://doi.org/10.5194/hess-25-6041-2021, https://doi.org/10.5194/hess-25-6041-2021, 2021
Short summary
Short summary
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.
Qina Yan, Haruko Wainwright, Baptiste Dafflon, Sebastian Uhlemann, Carl I. Steefel, Nicola Falco, Jeffrey Kwang, and Susan S. Hubbard
Earth Surf. Dynam., 9, 1347–1361, https://doi.org/10.5194/esurf-9-1347-2021, https://doi.org/10.5194/esurf-9-1347-2021, 2021
Short summary
Short summary
We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope and that the hybrid model provides better accuracy than a machine-learning model.
A. Hasan, M. R. Udawalpola, C. Witharana, and A. K. Liljedahl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 67–72, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-67-2021, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-67-2021, 2021
M. Udawalpola, A. Hasan, A. K. Liljedahl, A. Soliman, and C. Witharana
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 175–180, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-175-2021, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-175-2021, 2021
C. Witharana, M. A. E. Bhuiyan, and A. K. Liljedahl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-2-2020, 111–116, https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020, https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020, 2020
Nathan A. Wales, Jesus D. Gomez-Velez, Brent D. Newman, Cathy J. Wilson, Baptiste Dafflon, Timothy J. Kneafsey, Florian Soom, and Stan D. Wullschleger
Hydrol. Earth Syst. Sci., 24, 1109–1129, https://doi.org/10.5194/hess-24-1109-2020, https://doi.org/10.5194/hess-24-1109-2020, 2020
Short summary
Short summary
Rapid warming in the Arctic is causing increased permafrost temperatures and ground ice degradation. To study the effects of ice degradation on water distribution, tracer was applied to two end members of ice-wedge polygons – a ubiquitous landform in the Arctic. End member type was found to significantly affect water distribution as lower flux was observed with ice-wedge degradation. Results suggest ice degradation can influence partitioning of sequestered carbon as carbon dioxide or methane.
Benjamin Mary, Luca Peruzzo, Jacopo Boaga, Nicola Cenni, Myriam Schmutz, Yuxin Wu, Susan S. Hubbard, and Giorgio Cassiani
SOIL, 6, 95–114, https://doi.org/10.5194/soil-6-95-2020, https://doi.org/10.5194/soil-6-95-2020, 2020
Short summary
Short summary
The use of non-invasive geophysical imaging of root system processes is of increasing interest to study soil–plant interactions. The experiment focused on the behaviour of grapevine plants during a controlled infiltration experiment. The combination of the mise-à-la-masse (MALM) method, a variation of the classical electrical tomography map (ERT), for which the current is transmitted directly into the stem, holds the promise of being able to image root distribution.
Andrew Bliss, Regine Hock, Gabriel Wolken, Erin Whorton, Caroline Aubry-Wake, Juliana Braun, Alessio Gusmeroli, Will Harrison, Andrew Hoffman, Anna Liljedahl, and Jing Zhang
Earth Syst. Sci. Data, 12, 403–427, https://doi.org/10.5194/essd-12-403-2020, https://doi.org/10.5194/essd-12-403-2020, 2020
Short summary
Short summary
Extensive field observations were conducted in the Upper Susitna basin in central Alaska in 2012–2014. This paper describes the weather, glacier mass balance, snow cover, and soils of the basin. We found that temperatures over the glacier are cooler than over land at the same elevation. The glaciers have been losing mass faster in recent years than in the 1980s. Measurements of glacier mass change with traditional methods closely matched radar measurements.
Elchin E. Jafarov, Dylan R. Harp, Ethan T. Coon, Baptiste Dafflon, Anh Phuong Tran, Adam L. Atchley, Youzuo Lin, and Cathy J. Wilson
The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, https://doi.org/10.5194/tc-14-77-2020, 2020
Short summary
Short summary
Improved subsurface parameterization and benchmarking data are needed to reduce current uncertainty in predicting permafrost response to a warming climate. We developed a subsurface parameter estimation framework that can be used to estimate soil properties where subsurface data are available. We utilize diverse geophysical datasets such as electrical resistance data, soil moisture data, and soil temperature data to recover soil porosity and soil thermal conductivity.
Emmanuel Léger, Baptiste Dafflon, Yves Robert, Craig Ulrich, John E. Peterson, Sébastien C. Biraud, Vladimir E. Romanovsky, and Susan S. Hubbard
The Cryosphere, 13, 2853–2867, https://doi.org/10.5194/tc-13-2853-2019, https://doi.org/10.5194/tc-13-2853-2019, 2019
Short summary
Short summary
We propose a new strategy called distributed temperature profiling (DTP) for improving the estimation of soil thermal properties through the use of an unprecedented number of laterally and vertically distributed temperature measurements. We tested a DTP system prototype by moving it sequentially across a discontinuous permafrost environment. The DTP enabled high-resolution identification of near-surface permafrost location and covariability with topography, vegetation, and soil properties.
Benjamin Mary, Luca Peruzzo, Jacopo Boaga, Myriam Schmutz, Yuxin Wu, Susan S. Hubbard, and Giorgio Cassiani
Hydrol. Earth Syst. Sci., 22, 5427–5444, https://doi.org/10.5194/hess-22-5427-2018, https://doi.org/10.5194/hess-22-5427-2018, 2018
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
Short summary
Short summary
The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Anh Phuong Tran, Baptiste Dafflon, and Susan S. Hubbard
The Cryosphere, 11, 2089–2109, https://doi.org/10.5194/tc-11-2089-2017, https://doi.org/10.5194/tc-11-2089-2017, 2017
Short summary
Short summary
Soil organics carbon (SOC) and its influence on terrestrial ecosystem feedbacks to global warming in permafrost regions are particularly important for the prediction of future climate variation. Our study proposes a new surface–subsurface, joint deterministic–stochastic hydrological–thermal–geophysical inversion approach and documents the benefit of including multiple types of data to estimate the vertical profile of SOC content and its influence on hydrological–thermal dynamics.
Anh Phuong Tran, Baptiste Dafflon, Susan S. Hubbard, Michael B. Kowalsky, Philip Long, Tetsu K. Tokunaga, and Kenneth H. Williams
Hydrol. Earth Syst. Sci., 20, 3477–3491, https://doi.org/10.5194/hess-20-3477-2016, https://doi.org/10.5194/hess-20-3477-2016, 2016
Short summary
Short summary
Quantifying water and heat fluxes in the shallow subsurface is particularly important due to their strong control on recharge, evaporation and biogeochemical processes. This study developed and tested a new inversion scheme to estimate subsurface hydro-thermal parameters by joint using different hydrological, thermal and geophysical data. It is especially useful for the increasing number of studies that are taking advantage of autonomously collected measurements to explore ecosystem dynamics.
Lei Cai, Vladimir A. Alexeev, Christopher D. Arp, Benjamin M. Jones, Anna Liljedahl, and Anne Gädeke
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2016-31, https://doi.org/10.5194/essd-2016-31, 2016
Preprint withdrawn
Short summary
Short summary
This study produced a high-resolution dynamical downscaling data set for the Alaskan North Slope and surrounding areas. It helps to resolve the problem of the sparse observation over this region, where routinely and accurately measuring climatic variables is extremely difficult. This data set boosts up multiple research projects that explore the various climatic impacts over the Alaskan North Slope of the past and the future.
Lei Cai, Vladimir A. Alexeev, Christopher D. Arp, Benjamin M. Jones, Anna Liljedahl, and Anne Gädeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-87, https://doi.org/10.5194/tc-2016-87, 2016
Preprint withdrawn
Short summary
Short summary
This paper introduces the development process of a data set that specifically made for climatic impacts research over the Alaskan North Slope. This data set can offset to some extent the sparseness of observation on spatial and temporal scales, retrieving high-resolution climatic backgrounds that enable various studies in the fields of climatology, hydrology, ecology, etc.
A. L. Atchley, S. L. Painter, D. R. Harp, E. T. Coon, C. J. Wilson, A. K. Liljedahl, and V. E. Romanovsky
Geosci. Model Dev., 8, 2701–2722, https://doi.org/10.5194/gmd-8-2701-2015, https://doi.org/10.5194/gmd-8-2701-2015, 2015
Short summary
Short summary
Development and calibration of a process-rich model representation of thaw-depth dynamics in Arctic tundra is presented. Improved understanding of polygonal tundra thermal hydrology processes, of thermal conduction, surface and subsurface saturation and snowpack dynamics is gained by using measured field data to calibrate and refine model structure. The refined model is then used identify future data needs and observational studies.
D. Zona, D. A. Lipson, J. H. Richards, G. K. Phoenix, A. K. Liljedahl, M. Ueyama, C. S. Sturtevant, and W. C. Oechel
Biogeosciences, 11, 5877–5888, https://doi.org/10.5194/bg-11-5877-2014, https://doi.org/10.5194/bg-11-5877-2014, 2014
Related subject area
Arctic (e.g. Greenland)
Predicting avalanche danger in northern Norway using statistical models
Impact of snow thermal conductivity schemes on pan-Arctic permafrost dynamics in the Community Land Model version 5.0
Long-term development of a perennial firn aquifer on the Lomonosovfonna ice cap, Svalbard
Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach
Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors
Characterizing southeast Greenland fjord surface ice and freshwater flux to support biological applications
Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
Evidence suggesting frazil ice crystal formation at the front of Hisinger Glacier in Dickson Fjord, Northeast Greenland
Sea-ice conditions from 1880 to 2017 on the Northeast Greenland continental shelf: a biomarker and observational record comparison
The radiative and geometric properties of melting first-year landfast sea ice in the Arctic
Calibrating calving parameterizations using graph neural network emulators: Application to Helheim Glacier, East Greenland
Sensitivity to forecast surface mass balance outweighs sensitivity to basal sliding descriptions for 21st century mass loss from three major Greenland outlet glaciers
Improving short-term sea ice concentration forecasts using deep learning
Validation of pan-Arctic soil temperatures in modern reanalysis and data assimilation systems
Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)
Sea-ice variations and trends during the Common Era in the Atlantic sector of the Arctic Ocean
Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model
Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023
Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning
Modeled variations in the inherent optical properties of summer Arctic ice and their effects on the radiation budget: a case based on ice cores from 2008 to 2016
Characterization of atmospheric methane release in the outer Mackenzie River delta from biogenic and thermogenic sources
Spatially heterogeneous effect of climate warming on the Arctic land ice
Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice
Summer sea ice floe perimeter density in the Arctic: high-resolution optical satellite imagery and model evaluation
Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents
A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020
Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021
Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals
Hydraulic suppression of basal glacier melt in sill fjords
Direct measurement of warm Atlantic Intermediate Water close to the grounding line of Nioghalvfjerdsfjorden (79° N) Glacier, northeast Greenland
Rapid sea ice changes in the future Barents Sea
Assessment of Arctic seasonal snow cover rates of change
Causes and evolution of winter polynyas north of Greenland
Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection
Observed and predicted trends in Icelandic snow conditions for the period 1930–2100
Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment
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
Improving model-satellite comparisons of sea ice melt onset with a satellite simulator
Accelerated mobilization of organic carbon from retrogressive thaw slumps on the northern Taymyr Peninsula
Snowfall and snow accumulation during the MOSAiC winter and spring seasons
Modelling the mass budget and future evolution of Tunabreen, central Spitsbergen
Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar
Brief communication: Preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness change
Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020
The importance of freeze–thaw cycles for lateral tracer transport in ice-wedge polygons
Uncertainties in projected surface mass balance over the polar ice sheets from dynamically downscaled EC-Earth models
Perspectives on future sea ice and navigability in the Arctic
Lasting impact of winds on Arctic sea ice through the ocean's memory
Holocene sea-ice dynamics in Petermann Fjord in relation to ice tongue stability and Nares Strait ice arch formation
Kai-Uwe Eiselt and Rune Grand Graversen
The Cryosphere, 19, 1849–1871, https://doi.org/10.5194/tc-19-1849-2025, https://doi.org/10.5194/tc-19-1849-2025, 2025
Short summary
Short summary
In this study we optimise and train a random forest model to predict avalanche danger in northern Norway based on meteorological reanalysis data. The model performance is at the low end compared to recent similar studies. A hindcast of the frequency of avalanche days (based on the avalanche-danger level) is performed from 1970 to 2024, 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
The Cryosphere, 19, 1539–1558, https://doi.org/10.5194/tc-19-1539-2025, https://doi.org/10.5194/tc-19-1539-2025, 2025
Short summary
Short summary
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
The Cryosphere, 19, 1513–1525, https://doi.org/10.5194/tc-19-1513-2025, https://doi.org/10.5194/tc-19-1513-2025, 2025
Short summary
Short summary
Liquid water can persist within old snow on glaciers and ice caps if it can percolate into the snow before it refreezes. Snow is a good insulator, and it 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 the present day.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 731–752, https://doi.org/10.5194/tc-19-731-2025, https://doi.org/10.5194/tc-19-731-2025, 2025
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
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.
Twila A. Moon, Benjamin Cohen, Taryn E. Black, Kristin L. Laidre, Harry L. Stern, and Ian Joughin
The Cryosphere, 18, 4845–4872, https://doi.org/10.5194/tc-18-4845-2024, https://doi.org/10.5194/tc-18-4845-2024, 2024
Short summary
Short summary
The complex geomorphology of southeast Greenland (SEG) creates dynamic fjord habitats for top marine predators, featuring glacier-derived floating ice, pack and landfast sea ice, and freshwater flux. We study the physical environment of SEG fjords, focusing on surface ice conditions, to provide a regional characterization that supports biological research. As Arctic warming persists, SEG may serve as a long-term refugium for ice-dependent wildlife due to the persistence of regional ice sheets.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
The Cryosphere, 18, 4687–4702, https://doi.org/10.5194/tc-18-4687-2024, https://doi.org/10.5194/tc-18-4687-2024, 2024
Short summary
Short summary
Arctic sea ice is thinning and retreating because of global warming. Thus, the region is transitioning to a new state featuring an expansion of the marginal ice zone, a region where mobile ice interacts with waves from the open ocean. By analyzing 30 years of sea ice reconstructions that combine numerical models and observations, this paper proves that an ensemble of global ocean and sea ice reanalyses is an adequate tool for investigating the changing Arctic sea ice cover.
Fleur Juliëtte Rooijakkers, Ebbe Poulsen, Eugenio Ruiz-Castillo, and Søren Rysgaard
EGUsphere, https://doi.org/10.5194/egusphere-2024-2168, https://doi.org/10.5194/egusphere-2024-2168, 2024
Short summary
Short summary
Glacier melting contributes to global sea level rise, largely because of interactions between the ocean and glacier fronts. Understanding these interactions is crucial. This study explores these processes in a fjord in the understudied Northeast Greenland, using a drone to collect data near the glacier. We discovered that meltwater refreezes when it meets the cold polar water and forms ice crystals, which then float to the surface and melt when exposed to warmer ocean temperatures.
Joanna Davies, Kirsten Fahl, Matthias Moros, Alice Carter-Champion, Henrieka Detlef, Ruediger Stein, Christof Pearce, and Marit-Solveig Seidenkrantz
The Cryosphere, 18, 3415–3431, https://doi.org/10.5194/tc-18-3415-2024, https://doi.org/10.5194/tc-18-3415-2024, 2024
Short summary
Short summary
Here, we evaluate the use of biomarkers for reconstructing sea ice between 1880 and 2017 from three sediment cores located in a transect across the Northeast Greenland continental shelf. We find that key changes, specifically the decline in sea-ice cover identified in observational records between 1971 and 1984, align with our biomarker reconstructions. This outcome supports the use of biomarkers for longer reconstructions of sea-ice cover in this region.
Nathan J. M. Laxague, Christopher J. Zappa, Andrew R. Mahoney, John Goodwin, Cyrus Harris, Robert E. Schaeffer, Roswell Schaeffer Sr., Sarah Betcher, Donna D. W. Hauser, Carson R. Witte, Jessica M. Lindsay, Ajit Subramaniam, Kate E. Turner, and Alex Whiting
The Cryosphere, 18, 3297–3313, https://doi.org/10.5194/tc-18-3297-2024, https://doi.org/10.5194/tc-18-3297-2024, 2024
Short summary
Short summary
The state of sea ice strongly affects its absorption of solar energy. In May 2019, we flew uncrewed aerial vehicles (UAVs) equipped with sensors designed to quantify the sunlight that is reflected by sea ice at each wavelength over the sea ice of Kotzebue Sound, Alaska. We found that snow patches get darker (up to ~ 20 %) as they get smaller, while bare patches get darker (up to ~ 20 %) as they get larger. We believe that this difference is due to melting around the edges of small features.
Younghyun Koo, Gong Cheng, Mathieu Morlighem, and Maryam Rahnemoonfar
EGUsphere, https://doi.org/10.5194/egusphere-2024-1620, https://doi.org/10.5194/egusphere-2024-1620, 2024
Short summary
Short summary
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
J. Rachel Carr, Emily A. Hill, and G. Hilmar Gudmundsson
The Cryosphere, 18, 2719–2737, https://doi.org/10.5194/tc-18-2719-2024, https://doi.org/10.5194/tc-18-2719-2024, 2024
Short summary
Short summary
The Greenland Ice Sheet is one of the world's largest glaciers and is melting quickly in response to climate change. It contains fast-flowing channels of ice that move ice from Greenland's centre to its coasts and allow Greenland to react quickly to climate warming. As a result, we want to predict how these glaciers will behave in the future, but there are lots of uncertainties. Here we assess the impacts of two main sources of uncertainties in glacier models.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Short summary
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Tyler C. Herrington, Christopher G. Fletcher, and Heather Kropp
The Cryosphere, 18, 1835–1861, https://doi.org/10.5194/tc-18-1835-2024, https://doi.org/10.5194/tc-18-1835-2024, 2024
Short summary
Short summary
Here we validate soil temperatures from eight reanalysis products across the pan-Arctic and compare their performance to a newly calculated ensemble mean soil temperature product. We find that most product soil temperatures have a relatively large RMSE of 2–9 K. It is found that the ensemble mean product outperforms individual reanalysis products. Therefore, we recommend the ensemble mean soil temperature product for the validation of climate models and for input to hydrological models.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
Short summary
Short summary
We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
Ana Lúcia Lindroth Dauner, Frederik Schenk, Katherine Elizabeth Power, and Maija Heikkilä
The Cryosphere, 18, 1399–1418, https://doi.org/10.5194/tc-18-1399-2024, https://doi.org/10.5194/tc-18-1399-2024, 2024
Short summary
Short summary
In this study, we analysed 14 sea-ice proxy records and compared them with the results from two different climate simulations from the Atlantic sector of the Arctic Ocean over the Common Era (last 2000 years). Both proxy and model approaches demonstrated a long-term sea-ice increase. The good correspondence suggests that the state-of-the-art sea-ice proxies are able to capture large-scale climate drivers. Short-term variability, however, was less coherent due to local-to-regional scale forcings.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Zuzanna M. Swirad, A. Malin Johansson, and Eirik Malnes
The Cryosphere, 18, 895–910, https://doi.org/10.5194/tc-18-895-2024, https://doi.org/10.5194/tc-18-895-2024, 2024
Short summary
Short summary
We used satellite images to create sea ice maps of Hornsund fjord, Svalbard, for nine seasons and calculated the percentage of the fjord that was covered by ice. On average, sea ice was present in Hornsund for 158 d per year, but it varied from year to year. April was the "iciest'" month and 2019/2020, 2021/22 and 2014/15 were the "iciest'" seasons. Our data can be used to understand sea ice conditions compared with other fjords of Svalbard and in studies of wave modelling and coastal erosion.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
Short summary
Short summary
How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
Short summary
Short summary
Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Daniel Wesley, Scott Dallimore, Roger MacLeod, Torsten Sachs, and David Risk
The Cryosphere, 17, 5283–5297, https://doi.org/10.5194/tc-17-5283-2023, https://doi.org/10.5194/tc-17-5283-2023, 2023
Short summary
Short summary
The Mackenzie River delta (MRD) is an ecosystem with high rates of methane production from biologic and geologic sources, but little research has been done to determine how often geologic or biogenic methane is emitted to the atmosphere. Stable carbon isotope analysis was used to identify the source of CH4 at several sites. Stable carbon isotope (δ13C-CH4) signatures ranged from −42 to −88 ‰ δ13C-CH4, indicating that CH4 emission in the MRD is caused by biologic and geologic sources.
Damien Maure, Christoph Kittel, Clara Lambin, Alison Delhasse, and Xavier Fettweis
The Cryosphere, 17, 4645–4659, https://doi.org/10.5194/tc-17-4645-2023, https://doi.org/10.5194/tc-17-4645-2023, 2023
Short summary
Short summary
The Arctic is warming faster than the rest of the Earth. Studies have already shown that Greenland and the Canadian Arctic are experiencing a record increase in melting rates, while Svalbard has been relatively less impacted. Looking at those regions but also extending the study to Iceland and the Russian Arctic archipelagoes, we see a heterogeneity in the melting-rate response to the Arctic warming, with the Russian archipelagoes experiencing lower melting rates than other regions.
Geoffrey J. Dawson and Jack C. Landy
The Cryosphere, 17, 4165–4178, https://doi.org/10.5194/tc-17-4165-2023, https://doi.org/10.5194/tc-17-4165-2023, 2023
Short summary
Short summary
In this study, we compared measurements from CryoSat-2 and ICESat-2 over Arctic summer sea ice to understand any possible biases between the two satellites. We found that there is a difference when we measure elevation over summer sea ice using CryoSat-2 and ICESat-2, and this is likely due to surface melt ponds. The differences we found were in good agreement with theoretical predictions, and this work will be valuable for summer sea ice thickness measurements from both altimeters.
Yanan Wang, Byongjun Hwang, Adam William Bateson, Yevgeny Aksenov, and Christopher Horvat
The Cryosphere, 17, 3575–3591, https://doi.org/10.5194/tc-17-3575-2023, https://doi.org/10.5194/tc-17-3575-2023, 2023
Short summary
Short summary
Sea ice is composed of small, discrete pieces of ice called floes, whose size distribution plays a critical role in the interactions between the sea ice, ocean and atmosphere. This study provides an assessment of sea ice models using new high-resolution floe size distribution observations, revealing considerable differences between them. These findings point not only to the limitations in models but also to the need for more high-resolution observations to validate and calibrate models.
Sascha Willmes, Günther Heinemann, and Frank Schnaase
The Cryosphere, 17, 3291–3308, https://doi.org/10.5194/tc-17-3291-2023, https://doi.org/10.5194/tc-17-3291-2023, 2023
Short summary
Short summary
Sea ice is an important constituent of the global climate system. We here use satellite data to identify regions in the Arctic where the sea ice breaks up in so-called leads (i.e., linear cracks) regularly during winter. This information is important because leads determine, e.g., how much heat is exchanged between the ocean and the atmosphere. We here provide first insights into the reasons for the observed patterns in sea-ice leads and their relation to ocean currents and winds.
Isolde A. Glissenaar, Jack C. Landy, David G. Babb, Geoffrey J. Dawson, and Stephen E. L. Howell
The Cryosphere, 17, 3269–3289, https://doi.org/10.5194/tc-17-3269-2023, https://doi.org/10.5194/tc-17-3269-2023, 2023
Short summary
Short summary
Observations of large-scale ice thickness have unfortunately only been available since 2003, a short record for researching trends and variability. We generated a proxy for sea ice thickness in the Canadian Arctic for 1996–2020. This is the longest available record for large-scale sea ice thickness available to date and the first record reliably covering the channels between the islands in northern Canada. The product shows that sea ice has thinned by 21 cm over the 25-year record in April.
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, https://doi.org/10.5194/tc-17-3013-2023, 2023
Short summary
Short summary
Sea ice has a large interannual variability, and studying its evolution requires long time series of observations. In this paper, we propose the first method to extend Arctic sea ice thickness time series to the ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each year during the winter period between 1995 and 2021.
Nina Raoult, Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, and Vladislav Bastrikov
The Cryosphere, 17, 2705–2724, https://doi.org/10.5194/tc-17-2705-2023, https://doi.org/10.5194/tc-17-2705-2023, 2023
Short summary
Short summary
Greenland ice sheet melting due to global warming could significantly impact global sea-level rise. The ice sheet's albedo, i.e. how reflective the surface is, affects the melting speed. The ORCHIDEE computer model is used to simulate albedo and snowmelt to make predictions. However, the albedo in ORCHIDEE is lower than that observed using satellites. To correct this, we change model parameters (e.g. the rate of snow decay) to reduce the difference between simulated and observed values.
Johan Nilsson, Eef van Dongen, Martin Jakobsson, Matt O'Regan, and Christian Stranne
The Cryosphere, 17, 2455–2476, https://doi.org/10.5194/tc-17-2455-2023, https://doi.org/10.5194/tc-17-2455-2023, 2023
Short summary
Short summary
We investigate how topographical sills suppress basal glacier melt in Greenlandic fjords. The basal melt drives an exchange flow over the sill, but there is an upper flow limit set by the Atlantic Water features outside the fjord. If this limit is reached, the flow enters a new regime where the melt is suppressed and its sensitivity to the Atlantic Water temperature is reduced.
Michael J. Bentley, James A. Smith, Stewart S. R. Jamieson, Margaret R. Lindeman, Brice R. Rea, Angelika Humbert, Timothy P. Lane, Christopher M. Darvill, Jeremy M. Lloyd, Fiamma Straneo, Veit Helm, and David H. Roberts
The Cryosphere, 17, 1821–1837, https://doi.org/10.5194/tc-17-1821-2023, https://doi.org/10.5194/tc-17-1821-2023, 2023
Short summary
Short summary
The Northeast Greenland Ice Stream is a major outlet of the Greenland Ice Sheet. Some of its outlet glaciers and ice shelves have been breaking up and retreating, with inflows of warm ocean water identified as the likely reason. Here we report direct measurements of warm ocean water in an unusual lake that is connected to the ocean beneath the ice shelf in front of the 79° N Glacier. This glacier has not yet shown much retreat, but the presence of warm water makes future retreat more likely.
Ole Rieke, Marius Årthun, and Jakob Simon Dörr
The Cryosphere, 17, 1445–1456, https://doi.org/10.5194/tc-17-1445-2023, https://doi.org/10.5194/tc-17-1445-2023, 2023
Short summary
Short summary
The Barents Sea is the region of most intense winter sea ice loss, and future projections show a continued decline towards ice-free conditions by the end of this century but with large fluctuations. Here we use climate model simulations to look at the occurrence and drivers of rapid ice change events in the Barents Sea that are much stronger than the average ice loss. A better understanding of these events will contribute to improved sea ice predictions in the Barents Sea.
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
Short summary
Short summary
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.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Short summary
During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
Short summary
Short summary
We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
Darri Eythorsson, Sigurdur M. Gardarsson, Andri Gunnarsson, and Oli Gretar Blondal Sveinsson
The Cryosphere, 17, 51–62, https://doi.org/10.5194/tc-17-51-2023, https://doi.org/10.5194/tc-17-51-2023, 2023
Short summary
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.
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022, https://doi.org/10.5194/tc-16-4617-2022, 2022
Short summary
Short summary
Indicators for the start and end of annual breakup and freeze-up of sea ice at various coastal locations around the Arctic are developed. Relative to broader offshore areas, some of the coastal indicators show an earlier freeze-up and later breakup, especially at locations where landfast ice is prominent. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in synthesized metrics of the coastal breakup/freeze-up indicators.
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
Short summary
Short summary
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
Short summary
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.
Abigail Smith, Alexandra Jahn, Clara Burgard, and Dirk Notz
The Cryosphere, 16, 3235–3248, https://doi.org/10.5194/tc-16-3235-2022, https://doi.org/10.5194/tc-16-3235-2022, 2022
Short summary
Short summary
The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in climate models compares to satellite observations. Here, we utilize a new tool for creating more direct comparisons between climate model projections and satellite observations of Arctic sea ice, such that the melt onset dates are defined the same way. This tool allows us to identify climate model biases more clearly and gain more information about what the satellites are observing.
Philipp Bernhard, Simon Zwieback, and Irena Hajnsek
The Cryosphere, 16, 2819–2835, https://doi.org/10.5194/tc-16-2819-2022, https://doi.org/10.5194/tc-16-2819-2022, 2022
Short summary
Short summary
With climate change, Arctic hillslopes above ice-rich permafrost are vulnerable to enhanced carbon mobilization. In this work elevation change estimates generated from satellite observations reveal a substantial acceleration of carbon mobilization on the Taymyr Peninsula in Siberia between 2010 and 2021. The strong increase occurring in 2020 coincided with a severe Siberian heatwave and highlights that carbon mobilization can respond sharply and non-linearly to increasing temperatures.
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
Short summary
Short summary
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.
Johannes Oerlemans, Jack Kohler, and Adrian Luckman
The Cryosphere, 16, 2115–2126, https://doi.org/10.5194/tc-16-2115-2022, https://doi.org/10.5194/tc-16-2115-2022, 2022
Short summary
Short summary
Tunabreen is a 26 km long tidewater glacier. It is the most frequently surging glacier in Svalbard, with four documented surges in the past 100 years. We have modelled this glacier to find out how it reacts to future climate change. Careful calibration was done against the observed length record for the past 100 years. For a 50 m increase in the equilibrium line altitude (ELA) the length of the glacier will be shortened by 10 km after about 100 years.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
Short summary
Short summary
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Christian J. Taubenberger, Denis Felikson, and Thomas Neumann
The Cryosphere, 16, 1341–1348, https://doi.org/10.5194/tc-16-1341-2022, https://doi.org/10.5194/tc-16-1341-2022, 2022
Short summary
Short summary
Outlet glaciers are projected to account for half of the total ice loss from the Greenland Ice Sheet over the 21st century. We classify patterns of seasonal dynamic thickness changes of outlet glaciers using new observations from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Our results reveal seven distinct patterns that differ across glaciers even within the same region. Future work can use our results to improve our understanding of processes that drive seasonal ice sheet changes.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
Short summary
Short summary
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Elchin E. Jafarov, Daniil Svyatsky, Brent Newman, Dylan Harp, David Moulton, and Cathy Wilson
The Cryosphere, 16, 851–862, https://doi.org/10.5194/tc-16-851-2022, https://doi.org/10.5194/tc-16-851-2022, 2022
Short summary
Short summary
Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
Fredrik Boberg, Ruth Mottram, Nicolaj Hansen, Shuting Yang, and Peter L. Langen
The Cryosphere, 16, 17–33, https://doi.org/10.5194/tc-16-17-2022, https://doi.org/10.5194/tc-16-17-2022, 2022
Short summary
Short summary
Using the regional climate model HIRHAM5, we compare two versions (v2 and v3) of the global climate model EC-Earth for the Greenland and Antarctica ice sheets. We are interested in the surface mass balance of the ice sheets due to its importance when making estimates of future sea level rise. We find that the end-of-century change in the surface mass balance for Antarctica is 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3), and for Greenland it is −290 Gt yr−1 (v2) and −1640 Gt yr−1 (v3).
Jinlei Chen, Shichang Kang, Wentao Du, Junming Guo, Min Xu, Yulan Zhang, Xinyue Zhong, Wei Zhang, and Jizu Chen
The Cryosphere, 15, 5473–5482, https://doi.org/10.5194/tc-15-5473-2021, https://doi.org/10.5194/tc-15-5473-2021, 2021
Short summary
Short summary
Sea ice is retreating with rapid warming in the Arctic. It will continue and approach the worst predicted pathway released by the IPCC. The irreversible tipping point might show around 2060 when the oldest ice will have completely disappeared. It has a huge impact on human production. Ordinary merchant ships will be able to pass the Northeast Passage and Northwest Passage by the midcentury, and the opening time will advance to the next 10 years for icebreakers with moderate ice strengthening.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
Short summary
Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Henrieka Detlef, Brendan Reilly, Anne Jennings, Mads Mørk Jensen, Matt O'Regan, Marianne Glasius, Jesper Olsen, Martin Jakobsson, and Christof Pearce
The Cryosphere, 15, 4357–4380, https://doi.org/10.5194/tc-15-4357-2021, https://doi.org/10.5194/tc-15-4357-2021, 2021
Short summary
Short summary
Here we examine the Nares Strait sea ice dynamics over the last 7000 years and their implications for the late Holocene readvance of the floating part of Petermann Glacier. We propose that the historically observed sea ice dynamics are a relatively recent feature, while most of the mid-Holocene was marked by variable sea ice conditions in Nares Strait. Nonetheless, major advances of the Petermann ice tongue were preceded by a shift towards harsher sea ice conditions in Nares Strait.
Cited articles
Anderson, B. T., McNamara, J. P., Marshall, H. P., and Flores, A. N.: Insights into the physical processes controlling correlations between snow distribution and terrain properties, Water Resour. Res., 50, 4545–4563, 2014.
Anderson, K. and Gaston, K. J.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Front. Ecol. Environ., 11, 138–146, 2013.
Annan, A. P.: Ground penetrating radar, in near surface geophysics, in: Investigations in Geophysics, edited by: Butler, D. K., Society of Exploration Geophysicists, Tulsa, OK, USA, 13, 357–438, 2005.
Benson, C. S. and Sturm, M.: Structure and wind transport of seasonal snow on the Arctic slope of Alaska, Ann. Glaciol., 18, 261–267, 1993.
Berezovskaya, S. and Kane, D. L.: Measuring snow water equivalent for hydrological applications: part 1, accuracy of observations, in: 16th International Northern Research Basins Symposium and Workshop Petrozavodsk, Russia, 27 August–2 September 2007, available at: http://resources.krc.karelia.ru/krc/doc/publ2007/SYMPOSIUM_029-35.pdf (last access: 12 March 2017), 2007.
Bowling, L. C., Kane, D. L., Gieck, R. E., Hinzman, L. D., and Lettenmaier, D. P.: The role of surface storage in a low-gradient Arctic watershed, Water Resour. Res., 39, 1087, https://doi.org/10.1029/2002WR001466, 2003.
Bjørke, J. T. and Nilsen, S.: Wavelets applied to simplification of digital terrain models, Int. J. Geogr. Inf. Sci., 17, 601–621, 2003.
Brown, J.: Tundra soils formed over ice wedges, northern Alaska, Soil Sci. Soc. Am. J., 31, 686–691, 1967.
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, Supplement 1, 32–45, https://doi.org/10.1007/s13280-011-0213-x, 2011.
Clein, J. S. and Schimel, J. P.: Microbial activity of tundra and taiga soils at sub-zero temperatures, Soil Biol. Biochem., 27, 1231–1234, 1995.
Dafflon, B., Hubbard, S., Ulrich, C., and Peterson, J. E.: Low-altitude remote sensing dataset of DEM and RGB mosaic for AB corridor on July 13 2013 and L2 corridor on July 21 2013, https://doi.org/10.5440/1177858, 2014.
Dafflon, B., Hubbard, S., Ulrich, C., Peterson, J., Wu, Y., Wainwright, H., and Kneafsey, T. J.: Geophysical estimation of shallow permafrost distribution and properties in an ice-wedge polygon-dominated Arctic tundra region, Geophysics, 81, WA247–WA263, 2016.
Davison, A. C.: Statistical models, in: Cambridge Series in Statistical and Probabilistic Mathematics (No. 11), Cambridge University Press, Cambridge, UK, 2003.
Day-Lewis, F. D. and Lane Jr., J. W.: Assessing the resolution-dependent utility of tomograms for geostatistics, Geophys. Res. Lett., 31, L07503, https://doi.org/10.1029/2004GL019617, 2004.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, J. Glaciol., 59, 467–479, 2013.
Derksen, C., Silis, A., Sturm, M., Holmgren, J., Liston, G., Huntington, H., and Solie, D.: Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing, J. Hydrometeorol., 10, 448–463, https://doi.org/10.1175/2008JHM1074.1, 2009.
Diggle, P. and Ribeiro Jr., P. J.: Model-based geostatistics, Springer Science & Business Media, New York, USA, 2007.
Dvornikov, Y., Khomutov, A., Mullanurov, D., Ermokhina, K., Gubarkov, A., and Leibman, M.: GIS and field data based modelling of snow water equivalent in shrub tundra, Fennia, 193, 53–65, https://doi.org/10.15356/2076-6734-2015-2-69-80, 2015.
Engstrom, R., Hope, A., Kwon, H., Stow, D., and Zamolodchikov, D.: Spatial distribution of near surface soil moisture and its relationship to microtopography in the Alaskan Arctic coastal plain, Hydrol. Res., 36, 219–234, 2005.
Gamerman, D. and Lopes, H. F.: Markov Chain Monte Carlo – Stochastic simulation for Bayesian inference, 2nd Edn., Chapman & Hall/CRC, Boca Raton, USA, 2006.
Gamon, J. A., Kershaw, G. P., Williamson, S., and Hik, D. S.: Microtopographic patterns in an arctic baydjarakh field: do fine-grain patterns enforce landscape stability?, Environ. Res. Lett., 7, 015502, https://doi.org/10.1088/1748-9326/7/1/015502, 2012.
Geweke, J.: Evaluating the accuracy of sampling-based approaches to calculating posterior moments, in: Bayesian Statistics 4, edited by: Bernado, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M., Clarendon Press, Oxford, UK, 1992.
Gillin, C., Bailey, S., McGuire, K., and Gannon, J.: Mapping of Hydropedologic Spatial Patterns in a Steep Headwater Catchment, Soil Sci. Soc. Am. J., 79, 440, https://doi.org/10.2136/sssaj2014.05.0189, 2015.
Gusmeroli, A. and Grosse, G.: Ground penetrating radar detection of subsnow slush on ice-covered lakes in interior Alaska, The Cryosphere, 6, 1435–1443, https://doi.org/10.5194/tc-6-1435-2012, 2012.
Gusmeroli, A., Wolken, G., and Arendt, A.: Helicopter-borne radar imaging of snow cover on and around glaciers in Alaska, Ann. Glaciol., 55, 78–88, https://doi.org/10.3189/2014aog67a029, 2014.
Harper, J. T. and Bradford, J. H.: Snow stratigraphy over a uniform depositional surface: spatial variability and measurement tools, Cold Reg. Sci. Technol., 37, 289–298, 2003.
Harpold, A. A., Guo, Q., Molotch, N., Brooks, P. D., Bales, R., Fernandez-Diaz, J. C., Musselman, K. N., Swetnam, T. L., Kirchner, P., Meadows, M. W., Flanagan, J., and Lucas, R.: LiDAR-derived snowpack data sets from mixed conifer forests across the Western United States, Water Resour. Res., 50, 2749–2755, https://doi.org/10.1002/2013WR013935, 2014.
Hinkel, K. M., Eisner, W. R., Bockheim, J. G., Nelson, F. E., Peterson, K. M., and Dai, X.: Spatial extent, age, and carbon stocks in drained thaw lake basins on the Barrow Peninsula, Alaska, Arct. Antarct. Alp. Res., 35, 291–300, 2003.
Hirashima, H., Kodama, Y., Sato, N., Ohata, T., Yabuki, H., and Georgiadi, A.: Nonuniform Distribution of Tundra Snow Cover in Eastern Siberia, J. Hydrometeorol., 5, 373–389, https://doi.org/10.1175/1525-7541(2004)005<0373:NDOTSC>2.0.CO;2, 2004.
Hubbard, S. and Rubin, Y.: Hydrogeophysics, Chapter 1, in: Hydrogeophysics, editd by: Rubin, Y. and Hubbard, S., Elsevier, Amsterdam, Netherlands, 2005.
Hubbard, S. S., Gangodagamage, C., Dafflon, B., Wainwright, H., Peterson, J. E., Gusmeroli, A., Ulrich, C., Wu, Y., Wilson, C., Rowland, J., Tweedie, C., and Wullschleger, S. D.: Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets, Hydrogeol. J., 21, 149–169, 2013.
Jansson, J. K. and Taş, N.: The microbial ecology of permafrost, Nat. Rev. Microbiol., 12, 414–425, https://doi.org/10.1038/nrmicro3262, 2014.
Jol, H. M.: Ground Penetrating Radar Theory and Applications, Elsevier Science, Amsterdam, Netherlands, 2009.
Kane, D. L., Hinzman, L. D., Benson, C. S., and Liston, G. E.: Snow hydrology of a headwater Arctic basin: 1. Physical measurements and process studies, Water Resour. Res., 27, 1099–1109, https://doi.org/10.1029/91WR00262, 1991.
Kalbermatten, M.: Multiscale analysis of high resolution digital elevation models using the wavelet transform, thesis, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, 2010.
Kalbermatten, M., Van De Ville, D., Turberg, P., Tuia, D., and Joost, S.: Multiscale analysis of geomorphological and geological features in high resolution digital elevation models using the wavelet transform, Geomorphology, 138, 352–363, 2012.
Lavigne, A., Eckert, N., Bel, L., Deschâtres, M., and Parent, E.: Modelling the spatio-temporal repartition of right-truncated data: an application to avalanche runout altitudes in Hautes-Savoie, Stoch. Env. Res. Risk A., 31, 629–644, https://doi.org/10.1007/s00477-016-1301-z, 2017.
Leffingwell, E. de K.: Ground-ice wedges: The dominant form of ground-ice on the north coast of Alaska, J. Geol., 23, 635–654, 2015.
Liljedahl, A. K., Ulrich,U., Wullshelger, S., and Hinzman, L.: Snow Depth and Density at End-of-Winter for NGEE Areas A, B, C and D, Barrow, Alaska, 2012–2014, https://doi.org/10.5440/1236472, 2014.
Liljedahl, A. K., Hinzman, L. D., Harazono, Y., Zona, D., Tweedie, C. E., Hollister, R. D., Engstrom, R., and Oechel, W. C.: Nonlinear controls on evapotranspiration in arctic coastal wetlands, Biogeosciences, 8, 3375–3389, https://doi.org/10.5194/bg-8-3375-2011, 2011.
Liljedahl, A. K., Boike, J., Daanen, R. P., Fedorov, A. N., Frost, G. V., Grosse, G., Hinzman, L. D., Y., Iijma, 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.
Liston, G. E. and Sturm, M.: A snow–transport model for complex terrain, J. Glaciol., 44, 498–516, 1998.
Liston, G. E. and Sturm, M.: Winter precipitation patterns in arctic Alaska determined from a blowing-snow model and snow-depth observations, J. Hydrometeorol., 3, 646–659, 2002.
MacKay, J. R.: Thermally induced movements in ice-wedge polygons, western Arctic coast: a long-term study, Geogr. Phys. Quatern., 54, 41–68, 2000.
Machguth, H., Eisen, O., Paul, F., and Hoelzle, M.: Strong spatial variability of snow accumulation observed with helicopter borne GPR on two adjacent Alpine glaciers, Geophys. Res. Lett., 33, L13503, https://doi.org/10.1029/2006GL026576, 2006.
Murakami, H., Chen, X., Hahn, M. S., Liu, Y., Rockhold, M. L., Vermeul, V. R., Zachara, J. M., and Rubin, Y.: Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area, Hydrol. Earth Syst. Sci., 14, 1989–2001, https://doi.org/10.5194/hess-14-1989-2010, 2010.
Nobrega, S. and Grogan, P.: Deeper snow enhances winter respiration from both plant-associated and bulk soil carbon pools in birch hummock tundra, Ecosystems, 10, 419–431, https://doi.org/10.1007/s10021-007-9033-z, 2007.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015.
Osterkamp, T. E.: Causes of warming and thawing permafrost in Alaska, Eos T. Am. Geophys. Un., 88, 522–523, https://doi.org/10.1029/2007EO480002, 2007.
Peterson, J. E., Dafflon, B., Ulrich, C., and Hubbard, S.: Active Layer and Moisture Measurements for Intensive Site 0 and 1, Barrow, Alaska https://doi.org/10.5440/1177857, 2015a.
Peterson, J. E., Dafflon, B., Ulrich, C., and Hubbard, S.: Ground Penetrating Radar, Intensive Site1 AB Oct 2012–2014, https://doi.org/10.5440/1171723, 2015b.
Pomeroy, J. W., Gray, D. M., and Landine, P. G.: The Prairie Blowing Snow Model: characteristics, validation, operation, J. Hydrol., 144, 165–192, 1993.
Pomeroy, J. W., Marsh, P., and Gray, D. M.: Application of a distributed blowing snow model to the Arctic, Hydrol. Process., 11, 1451–1464, https://doi.org/10.1002/(SICI)1099-1085(199709)11:11<1451::AID-HYP449>3.0.CO;2-Q, 1997.
Proksch, M., Löwe, H., and Schneebeli, M.: Density, specific surface area, and correlation length of snow measured by high-resolution penetrometry, J. Geophys. Res.-Earth, 120, 346–362, https://doi.org/10.1002/2014JF003266, 2015.
Rees, A., English, M., Derksen, C., Toose, P., and Silis, A.: Observations of late winter Canadian tundra snow cover properties, Hydrol. Process., 28, 3962–3977, https://doi.org/10.1002/hyp.9931, 2014.
Ribeiro Jr., P. J. and Diggle, P. J.: geoR: A Package for Geostatistical Analysis, R News, 1/2, 15–18, 2001.
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.
Sjögersten, S., van der Wal, R., and Woodin, S. J.: Small-scale hydrological variation determines landscape CO2 fluxes in the high Arctic, Biogeochemistry, 80, 205–216, https://doi.org/10.1007/s10533-006-9018-6, 2006.
Smith, M. J., Chandler, J., and Rose, J.: High spatial resolution data acquisition for the geosciences: kite aerial photography, Earth Surf. Proc. Land., 34, 155–161, 2009.
Stieglitz, M., Deéry, S. J., Romanovsky, V. E., and Osterkamp, T. E.: The role of snow cover in the warming of arctic permafrost, Geophys. Res. Lett., 30, 1721, https://doi.org/10.1029/2003GL017337, 2003.
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution modeling: An Arctic example, Water Resour. Res., 46, W12549, https://doi.org/10.1029/2010WR009434, 2010.
Sturm, M., Schimel, J., Michaelson, G., Welker, J. M., Oberbauer, S. F., Liston, G. E., Fahnestock, J., and Romanovsky, V. E.: Winter biological processes could help convert arctic tundra to shrubland, BioScience, 55, 17–26, 2005.
Tiuri, M. E., Sihvola, A. H., Nyfors, E. G., and Hallikaiken, M. T.: The complex dielectric constant of snow at microwave frequencies, IEEE J. Oceanic Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Wainwright, H. M., Chen, J., Sassen, D. S., and Hubbard, S. S.: Bayesian hierarchical approach and geophysical data sets for estimation of reactive facies over plume scales, Water Resour. Res., 50, 4564–4584, https://doi.org/10.1002/2013WR013842, 2014.
Wainwright, H. M., Dafflon, B., Smith, L. J., Hahn, M. S., Curtis, J. B., Wu, Y., Ulrich, C., Peterson, J. E., Torn, M. S., and Hubbard, S. S.: Identifying multi scale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem, J. Geophys. Res.-Biogeo., 120, 788–808, https://doi.org/10.1002/2014JG002799, 2015.
Wainwright, H. M., Flores Orozco, A., Bücker, M., Dafflon, B., Chen, J., Hubbard, S. S., and Williams, K. H.: Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging, Water Resour. Res., 52, 533–551, https://doi.org/10.1002/2015WR017763, 2016.
Wikle, C. K., Milliff, R. F., Nychka, D., and Berliner, L. M.: Spatiotemporal hierarchical Bayesian modeling: Tropical ocean surface winds, J. Am. Stat. Assoc., 96, 382–397, 2001.
Zhang, T. J.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Rev. Geophys., 43, RG4002, https://doi.org/10.1029/2004RG000157, 2005.
Zona, D., Lipson, D. A., Zulueta, R. C., Oberbauer, S. F., and Oechel, W. C.: Microtopographic controls on ecosystem functioning in the Arctic Coastal Plain, J. Geophys. Res., 116, G00I08, https://doi.org/10.1029/2009JG001241, 2011.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., and Chang, R. Y. W.: Cold season emissions dominate the Arctic tundra methane budget, P. Natl. Acad. Sci. USA, 113, 40–45, 2016.
Short summary
Snow has a profound impact on permafrost and ecosystem functioning in the Arctic tundra. This paper aims to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. In addition, we develop a Bayesian geostatistical method to integrate multiscale observational platforms (a snow probe, ground penetrating radar, unmanned aerial system and airborne lidar) for estimating snow depth in high resolution over a large area.
Snow has a profound impact on permafrost and ecosystem functioning in the Arctic tundra. This...
