Articles | Volume 19, issue 1
https://doi.org/10.5194/tc-19-393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication
| 
28 Jan 2025
Brief communication |
| 28 Jan 2025
| 28 Jan 2025
Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors
Claire L. Bachand
CORRESPONDING AUTHOR
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK, USA
Chen Wang
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Baptiste Dafflon
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Lauren N. Thomas
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Geography, University of Colorado Boulder, Boulder, CO, USA
Ian Shirley
Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Sarah Maebius
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Colleen M. Iversen
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Katrina E. Bennett
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
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Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
The Cryosphere, 17, 3987–4006, https://doi.org/10.5194/tc-17-3987-2023, https://doi.org/10.5194/tc-17-3987-2023, 2023
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This study combines field observations, non-parametric statistical analyses, and thermodynamic modeling to characterize the environmental causes of the spatial variability in soil pore water solute concentrations across two Arctic catchments with varying extents of permafrost. Vegetation type, soil moisture and redox conditions, weathering and hydrologic transport, and mineral solubility were all found to be the primary drivers of the existing spatial variability of some soil pore water solutes.
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
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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.
Xiang Huang, Charles J. Abolt, and Katrina E. Bennett
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-8, https://doi.org/10.5194/tc-2023-8, 2023
Manuscript not accepted for further review
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Near-surface humidity is a sensitive parameter for predicting snow depth. Greater values of the relative humidity are obtained if the saturation vapor pressure was calculated with over-ice correction compared to without during the winter. During the summer thawing period, the choice of whether or not to employ an over-ice correction corresponds to significant variability in simulated thaw depths.
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.
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
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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.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
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The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
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
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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.
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
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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
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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
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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.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Bob Busey, Sofia T. Avendaño, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, Cathy J. Wilson, and Katrina E. Bennett
The Cryosphere, 15, 4005–4029, https://doi.org/10.5194/tc-15-4005-2021, https://doi.org/10.5194/tc-15-4005-2021, 2021
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Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
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
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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.
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
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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
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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.
Katrina E. Bennett, Jessica E. Cherry, Ben Balk, and Scott Lindsey
Hydrol. Earth Syst. Sci., 23, 2439–2459, https://doi.org/10.5194/hess-23-2439-2019, https://doi.org/10.5194/hess-23-2439-2019, 2019
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Remotely sensed snow observations may improve operational streamflow forecasting in remote regions, such as Alaska. In this study, we insert remotely sensed observations of snow extent into the operational framework employed by the US National Weather Service’s Alaska Pacific River Forecast Center. Our work indicates that the snow observations can improve snow estimates and streamflow forecasting. This work provides direction for forecasters to implement remote sensing in their operations.
Katrina E. Bennett, Theodore J. Bohn, Kurt Solander, Nathan G. McDowell, Chonggang Xu, Enrique Vivoni, and Richard S. Middleton
Hydrol. Earth Syst. Sci., 22, 709–725, https://doi.org/10.5194/hess-22-709-2018, https://doi.org/10.5194/hess-22-709-2018, 2018
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We applied the Variable Infiltration Capacity hydrologic model to examine scenarios of change under climate and landscape disturbances in the San Juan River basin, a major sub-watershed of the Colorado River basin. Climate change coupled with landscape disturbance leads to reduced streamflow in the San Juan River basin. Disturbances are expected to be widespread in this region. Therefore, accounting for these changes within the context of climate change is imperative for water resource planning.
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
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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
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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.
Randal D. Koster, Alan K. Betts, Paul A. Dirmeyer, Marc Bierkens, Katrina E. Bennett, Stephen J. Déry, Jason P. Evans, Rong Fu, Felipe Hernandez, L. Ruby Leung, Xu Liang, Muhammad Masood, Hubert Savenije, Guiling Wang, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 3777–3798, https://doi.org/10.5194/hess-21-3777-2017, https://doi.org/10.5194/hess-21-3777-2017, 2017
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Large-scale hydrological variability can affect society in profound ways; floods and droughts, for example, often cause major damage and hardship. A recent gathering of hydrologists at a symposium to honor the career of Professor Eric Wood motivates the present survey of recent research on this variability. The surveyed literature and the illustrative examples provided in the paper show that research into hydrological variability continues to be strong, vibrant, and multifaceted.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
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The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Erik A. Hobbie, Janet Chen, Paul J. Hanson, Colleen M. Iversen, Karis J. McFarlane, Nathan R. Thorp, and Kirsten S. Hofmockel
Biogeosciences, 14, 2481–2494, https://doi.org/10.5194/bg-14-2481-2017, https://doi.org/10.5194/bg-14-2481-2017, 2017
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We measured carbon and nitrogen isotope ratios (13C : 12C and 15N : 14N) in peat cores in a northern Minnesota bog to understand how climate, vegetation type, and decomposition affected C and N budgets over the last 9000 years. 13C : 12C patterns were primarily influenced by shifts in temperature, peatland vegetation and atmospheric CO2, whereas tree colonization and upland N influxes affected 15N : 14N ratios. Isotopic markers provided new insights into long-term patterns of CO2 and nitrogen losses.
Haruko M. Wainwright, Anna K. Liljedahl, Baptiste Dafflon, Craig Ulrich, John E. Peterson, Alessio Gusmeroli, and Susan S. Hubbard
The Cryosphere, 11, 857–875, https://doi.org/10.5194/tc-11-857-2017, https://doi.org/10.5194/tc-11-857-2017, 2017
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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.
Jitendra Kumar, Nathan Collier, Gautam Bisht, Richard T. Mills, Peter E. Thornton, Colleen M. Iversen, and Vladimir Romanovsky
The Cryosphere, 10, 2241–2274, https://doi.org/10.5194/tc-10-2241-2016, https://doi.org/10.5194/tc-10-2241-2016, 2016
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Microtopography of the low-gradient polygonal tundra plays a critical role in these ecosystem; however, patterns and drivers are poorly understood. A modeling-based approach was developed in this study to characterize and represent the permafrost soils in the model and simulate the thermal dynamics using a mechanistic high-resolution model. Results shows the ability of the model to simulate the patterns and variability of thermal regimes and improve our understanding of polygonal tundra.
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
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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.
J. Mao, D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen, and R. J. Norby
Biogeosciences, 13, 641–657, https://doi.org/10.5194/bg-13-641-2016, https://doi.org/10.5194/bg-13-641-2016, 2016
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The aim of this study is to implement, calibrate and evaluate the CLM4 against carbon and hydrology observations from a shading and labeling experiment in a stand of young loblolly pines. We found a combination of parameters measured on-site and calibration targeting biomass, transpiration, and 13C discrimination gave good agreement with pretreatment measurements. We also used observations from the experiment to develop a conceptual model of short-term photosynthate storage and transport.
Related subject area
Discipline: Snow | Subject: Arctic (e.g. Greenland)
Long-term development of a perennial firn aquifer on the Lomonosovfonna ice cap, Svalbard
Assessment of Arctic seasonal snow cover rates of change
Observed and predicted trends in Icelandic snow conditions for the period 1930–2100
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
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.
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
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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.
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.
Cited articles
Bachand, C.: cbachand-LANL/iButton-SnowDepth-ML: Code for “Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow-ground interface temperature sensors” (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.14657741, 2025.
Bachand, C., Wang, C., Dafflon, B., Thomas, L., Shirley, I., Maebius, S., Iversen, C., and Bennett, K.: Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico, Next-Generation Ecosystem Experiments (NGEE) Arctic, ESS-DIVE repository [data set], https://doi.org/10.15485/2371854, 2024.
Bennett, K. E., Miller, G., Busey, R., Chen, M., Lathrop, E. R., Dann, J. B., Nutt, M., Crumley, R., Dillard, S. L., Dafflon, B., Kumar, J., Bolton, W. R., Wilson, C. J., Iversen, C. M., and Wullschleger, S. D.: Spatial patterns of snow distribution in the sub-Arctic, The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, 2022.
Bennett, K., Bachand, C., Thomas, L., Gasarch, E., Thaler, E., and Crumley, R.: iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022–2023, Next-Generation Ecosystem Experiments (NGEE) Arctic, ESS-DIVE repository [data set], https://doi.org/10.15485/2319246, 2024.
Besso, H., Shean, D., and Lundquist, J. D.: Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry, Remote Sens. Environ., 300, 113843, https://doi.org/10.1016/j.rse.2023.113843, 2024.
Bigalke, S. and Walsh, J. E.: Future Changes of Snow in Alaska and the Arctic under Stabilized Global Warming Scenarios, Atmosphere, 13, 541, https://doi.org/10.3390/atmos13040541, 2022.
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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.
Temporally continuous snow depth estimates are important for understanding changing snow...
