Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-1013-2025
© 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-19-1013-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal patterns of accumulation and surface roughness in interior Greenland with a GNSS-IR network
Department of Earth Sciences, Dartmouth College, Hanover, New Hampshire, USA
Robert L. Hawley
Department of Earth Sciences, Dartmouth College, Hanover, New Hampshire, USA
Adam LeWinter
Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA
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Ian A. Raphael, Donald K. Perovich, Christopher M. Polashenski, and Robert L. Hawley
EGUsphere, https://doi.org/10.5194/egusphere-2025-187, https://doi.org/10.5194/egusphere-2025-187, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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Snow plays competing roles in the sea ice cycle by reflecting sunlight during summer (reducing melt) and insulating the ice from the cold atmosphere during winter (reducing growth). Observing where, when, and how much snow accumulates on sea ice is thus central to understanding the Arctic. Here, we describe a new snow depth observation system that is substantially cheaper and lighter than existing tools, and present a study demonstrating its potential to improve snow measurements on sea ice.
Alexander C. Ronan, Robert L. Hawley, and Jonathan W. Chipman
The Cryosphere, 18, 5673–5683, https://doi.org/10.5194/tc-18-5673-2024, https://doi.org/10.5194/tc-18-5673-2024, 2024
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We generate a 2010–2021 time series of CryoSat-2 waveform shape metrics on the Greenland Ice Sheet, and we compare it to CryoSat-2 elevation data to investigate the reliability of two algorithms used to derive elevations from the SIRAL radar altimeter. Retracked elevations are found to depend on a waveform's leading-edge width in the dry-snow zone. The study indicates that retracking algorithms must consider significant climate events and snow conditions when assessing elevation change.
Ian E. McDowell, Kaitlin M. Keegan, S. McKenzie Skiles, Christopher P. Donahue, Erich C. Osterberg, Robert L. Hawley, and Hans-Peter Marshall
The Cryosphere, 18, 1925–1946, https://doi.org/10.5194/tc-18-1925-2024, https://doi.org/10.5194/tc-18-1925-2024, 2024
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Accurate knowledge of firn grain size is crucial for many ice sheet research applications. Unfortunately, collecting detailed measurements of firn grain size is difficult. We demonstrate that scanning firn cores with a near-infrared imager can quickly produce high-resolution maps of both grain size and ice layer distributions. We map grain size and ice layer stratigraphy in 14 firn cores from Greenland and document changes to grain size and ice layer content from the extreme melt summer of 2012.
Sarah E. Esenther, Laurence C. Smith, Adam LeWinter, Lincoln H. Pitcher, Brandon T. Overstreet, Aaron Kehl, Cuyler Onclin, Seth Goldstein, and Jonathan C. Ryan
Geosci. Instrum. Method. Data Syst., 12, 215–230, https://doi.org/10.5194/gi-12-215-2023, https://doi.org/10.5194/gi-12-215-2023, 2023
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Meltwater runoff estimates from the Greenland ice sheet contain uncertainty. To better understand ice sheet hydrology, we installed a weather station and river stage sensors along three proglacial rivers in a cold-bedded area of NW Greenland without firn, crevasse, or moulin influence. The first 3 years (2019–2021) of observations have given us a first look at the seasonal and annual weather and hydrological patterns of this understudied region.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
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Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Alexandra Giese, Aaron Boone, Patrick Wagnon, and Robert Hawley
The Cryosphere, 14, 1555–1577, https://doi.org/10.5194/tc-14-1555-2020, https://doi.org/10.5194/tc-14-1555-2020, 2020
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Rocky debris on glacier surfaces is known to affect the melt of mountain glaciers. Debris can be dry or filled to varying extents with liquid water and ice; whether debris is dry, wet, and/or icy affects how efficiently heat is conducted through debris from its surface to the ice interface. Our paper presents a new energy balance model that simulates moisture phase, evolution, and location in debris. ISBA-DEB is applied to West Changri Nup glacier in Nepal to reveal important physical processes.
Colin R. Meyer, Kaitlin M. Keegan, Ian Baker, and Robert L. Hawley
The Cryosphere, 14, 1449–1458, https://doi.org/10.5194/tc-14-1449-2020, https://doi.org/10.5194/tc-14-1449-2020, 2020
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We describe snow compaction laboratory data with a new mathematical model. Using a compression device that is similar to a French press with snow instead of coffee grounds, Wang and Baker (2013) compacted numerous snow samples of different densities at a constant velocity to determine the force required for snow compaction. Our mathematical model for compaction includes airflow through snow and predicts the required force, in agreement with the experimental data.
Gabriel Lewis, Erich Osterberg, Robert Hawley, Hans Peter Marshall, Tate Meehan, Karina Graeter, Forrest McCarthy, Thomas Overly, Zayta Thundercloud, and David Ferris
The Cryosphere, 13, 2797–2815, https://doi.org/10.5194/tc-13-2797-2019, https://doi.org/10.5194/tc-13-2797-2019, 2019
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We present accumulation records from sixteen 22–32 m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western Greenland Ice Sheet percolation zone. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation over the 1996–2016 period.
Alexandra Giese, Steven Arcone, Robert Hawley, Gabriel Lewis, and Patrick Wagnon
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-60, https://doi.org/10.5194/tc-2019-60, 2019
Preprint withdrawn
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This manuscript defines a novel method of determining the depth of debris on a debris-covered glacier using 960 MHz Ground-Penetrating Radar, under circumstances which prevent the detection of a coherent reflection at the debris-ice interface. Our method was verified using full-scale debris-analog experiments and uses internal scattering within the debris layer. We use this method to measure debris thickness on Changri Nup Glacier, in the Nepal Himalaya.
Nancy A. N. Bertler, Howard Conway, Dorthe Dahl-Jensen, Daniel B. Emanuelsson, Mai Winstrup, Paul T. Vallelonga, James E. Lee, Ed J. Brook, Jeffrey P. Severinghaus, Taylor J. Fudge, Elizabeth D. Keller, W. Troy Baisden, Richard C. A. Hindmarsh, Peter D. Neff, Thomas Blunier, Ross Edwards, Paul A. Mayewski, Sepp Kipfstuhl, Christo Buizert, Silvia Canessa, Ruzica Dadic, Helle A. Kjær, Andrei Kurbatov, Dongqi Zhang, Edwin D. Waddington, Giovanni Baccolo, Thomas Beers, Hannah J. Brightley, Lionel Carter, David Clemens-Sewall, Viorela G. Ciobanu, Barbara Delmonte, Lukas Eling, Aja Ellis, Shruthi Ganesh, Nicholas R. Golledge, Skylar Haines, Michael Handley, Robert L. Hawley, Chad M. Hogan, Katelyn M. Johnson, Elena Korotkikh, Daniel P. Lowry, Darcy Mandeno, Robert M. McKay, James A. Menking, Timothy R. Naish, Caroline Noerling, Agathe Ollive, Anaïs Orsi, Bernadette C. Proemse, Alexander R. Pyne, Rebecca L. Pyne, James Renwick, Reed P. Scherer, Stefanie Semper, Marius Simonsen, Sharon B. Sneed, Eric J. Steig, Andrea Tuohy, Abhijith Ulayottil Venugopal, Fernando Valero-Delgado, Janani Venkatesh, Feitang Wang, Shimeng Wang, Dominic A. Winski, V. Holly L. Winton, Arran Whiteford, Cunde Xiao, Jiao Yang, and Xin Zhang
Clim. Past, 14, 193–214, https://doi.org/10.5194/cp-14-193-2018, https://doi.org/10.5194/cp-14-193-2018, 2018
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Temperature and snow accumulation records from the annually dated Roosevelt Island Climate Evolution (RICE) ice core show that for the past 2 700 years, the eastern Ross Sea warmed, while the western Ross Sea showed no trend and West Antarctica cooled. From the 17th century onwards, this dipole relationship changed. Now all three regions show concurrent warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea.
Gabriel Lewis, Erich Osterberg, Robert Hawley, Brian Whitmore, Hans Peter Marshall, and Jason Box
The Cryosphere, 11, 773–788, https://doi.org/10.5194/tc-11-773-2017, https://doi.org/10.5194/tc-11-773-2017, 2017
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We analyze 25 flight lines from NASA's Operation IceBridge Accumulation Radar totaling to determine snow accumulation throughout the dry snow and percolation zone of the Greenland Ice Sheet. Our results indicate that regional differences between IceBridge and model accumulation are large enough to significantly alter the Greenland Ice Sheet surface mass balance, with implications for future global sea-level rise.
Kelly M. Brunt, Robert L. Hawley, Eric R. Lutz, Michael Studinger, John G. Sonntag, Michelle A. Hofton, Lauren C. Andrews, and Thomas A. Neumann
The Cryosphere, 11, 681–692, https://doi.org/10.5194/tc-11-681-2017, https://doi.org/10.5194/tc-11-681-2017, 2017
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This manuscript presents an analysis of NASA airborne lidar data based on in situ GPS measurements from the interior of the Greenland Ice Sheet. Results show that for two airborne altimeters, surface elevation biases are less than 0.12 m and measurement precisions are 0.09 m or better. The study concludes that two NASA airborne lidars are sufficiently characterized to form part of a satellite data validation strategy, specifically for ICESat-2, scheduled to launch in 2018.
Thomas B. Overly, Robert L. Hawley, Veit Helm, Elizabeth M. Morris, and Rohan N. Chaudhary
The Cryosphere, 10, 1679–1694, https://doi.org/10.5194/tc-10-1679-2016, https://doi.org/10.5194/tc-10-1679-2016, 2016
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We demonstrate that snow accumulation rates across the Greenland Ice Sheet, determined from RADAR layers and modeled snow density profiles, are identical to ground-based measurements of snow accumulation. Three regional climate models underestimate snow accumulation compared to RADAR layer estimates. Using RADAR increases spatial coverage and improves accuracy of snow accumulation estimates. Incorporating our results into climate models may reduce uncertainty of sea-level rise estimates.
M. P. Lüthi, C. Ryser, L. C. Andrews, G. A. Catania, M. Funk, R. L. Hawley, M. J. Hoffman, and T. A. Neumann
The Cryosphere, 9, 245–253, https://doi.org/10.5194/tc-9-245-2015, https://doi.org/10.5194/tc-9-245-2015, 2015
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We analyze the thermal structure of the Greenland Ice Sheet with a heat flow model. New borehole measurements indicate that more heat is stored within the ice than would be expected from heat diffusion alone. We conclude that temperate paleo-firn and cyro-hydrologic warming are essential processes that explain the measurements.
L. Gray, D. Burgess, L. Copland, R. Cullen, N. Galin, R. Hawley, and V. Helm
The Cryosphere, 7, 1857–1867, https://doi.org/10.5194/tc-7-1857-2013, https://doi.org/10.5194/tc-7-1857-2013, 2013
B. F. Morriss, R. L. Hawley, J. W. Chipman, L. C. Andrews, G. A. Catania, M. J. Hoffman, M. P. Lüthi, and T. A. Neumann
The Cryosphere, 7, 1869–1877, https://doi.org/10.5194/tc-7-1869-2013, https://doi.org/10.5194/tc-7-1869-2013, 2013
Related subject area
Discipline: Ice sheets | Subject: Ice Sheets
The influence of firn layer material properties on surface crevasse propagation in glaciers and ice shelves
Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt
Reconstructing dynamics of the Baltic Ice Stream Complex during deglaciation of the Last Scandinavian Ice Sheet
Assessing the potential for ice flow piracy between the Totten and Vanderford glaciers, East Antarctica
Stagnant ice and age modelling in the Dome C region, Antarctica
Polar firn properties in Greenland and Antarctica and related effects on microwave brightness temperatures
A model of the weathering crust and microbial activity on an ice-sheet surface
PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model
Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections
Brief communication: On calculating the sea-level contribution in marine ice-sheet models
A simple stress-based cliff-calving law
Scaling of instability timescales of Antarctic outlet glaciers based on one-dimensional similitude analysis
A statistical fracture model for Antarctic ice shelves and glaciers
Modelled fracture and calving on the Totten Ice Shelf
Theo Clayton, Ravindra Duddu, Tim Hageman, and Emilio Martínez-Pañeda
The Cryosphere, 18, 5573–5593, https://doi.org/10.5194/tc-18-5573-2024, https://doi.org/10.5194/tc-18-5573-2024, 2024
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We develop and validate new analytical solutions that quantitatively consider how the properties of ice vary along the depth of ice shelves and that can be readily used in existing ice sheet models. Depth-varying firn properties are found to have a profound impact on ice sheet fracture and calving events. Our results show that grounded glaciers are less vulnerable than previously anticipated, while floating ice shelves are significantly more vulnerable to fracture and calving.
Sanket Jantre, Matthew J. Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, and John D. Jakeman
The Cryosphere, 18, 5207–5238, https://doi.org/10.5194/tc-18-5207-2024, https://doi.org/10.5194/tc-18-5207-2024, 2024
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We investigate potential sea-level rise from Antarctica's Lambert Glacier, once considered stable but now at risk due to projected ocean warming by 2100. Using statistical methods and limited supercomputer simulations, we calibrated our ice-sheet model using three observables. We find that, under high greenhouse gas emissions, glacier retreat could raise sea levels by 46–133 mm by 2300. This study highlights the need for better observations to reduce uncertainty in ice-sheet model projections.
Izabela Szuman, Jakub Z. Kalita, Christiaan R. Diemont, Stephen J. Livingstone, Chris D. Clark, and Martin Margold
The Cryosphere, 18, 2407–2428, https://doi.org/10.5194/tc-18-2407-2024, https://doi.org/10.5194/tc-18-2407-2024, 2024
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A Baltic-wide glacial landform-based map is presented, filling in a geographical gap in the record that has been speculated about by palaeoglaciologists for over a century. Here we used newly available bathymetric data and provide landform evidence of corridors of fast ice flow that we interpret as ice streams. Where previous ice-sheet-scale investigations inferred a single ice source, our mapping identifies flow and ice margin geometries from both Swedish and Bothnian sources.
Felicity S. McCormack, Jason L. Roberts, Bernd Kulessa, Alan Aitken, Christine F. Dow, Lawrence Bird, Benjamin K. Galton-Fenzi, Katharina Hochmuth, Richard S. Jones, Andrew N. Mackintosh, and Koi McArthur
The Cryosphere, 17, 4549–4569, https://doi.org/10.5194/tc-17-4549-2023, https://doi.org/10.5194/tc-17-4549-2023, 2023
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Changes in Antarctic surface elevation can cause changes in ice and basal water flow, impacting how much ice enters the ocean. We find that ice and basal water flow could divert from the Totten to the Vanderford Glacier, East Antarctica, under only small changes in the surface elevation, with implications for estimates of ice loss from this region. Further studies are needed to determine when this could occur and if similar diversions could occur elsewhere in Antarctica due to climate change.
Ailsa Chung, Frédéric Parrenin, Daniel Steinhage, Robert Mulvaney, Carlos Martín, Marie G. P. Cavitte, David A. Lilien, Veit Helm, Drew Taylor, Prasad Gogineni, Catherine Ritz, Massimo Frezzotti, Charles O'Neill, Heinrich Miller, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 17, 3461–3483, https://doi.org/10.5194/tc-17-3461-2023, https://doi.org/10.5194/tc-17-3461-2023, 2023
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We combined a numerical model with radar measurements in order to determine the age of ice in the Dome C region of Antarctica. Our results show that at the current ice core drilling sites on Little Dome C, the maximum age of the ice is almost 1.5 Ma. We also highlight a new potential drill site called North Patch with ice up to 2 Ma. Finally, we explore the nature of a stagnant ice layer at the base of the ice sheet which has been independently observed and modelled but is not well understood.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
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The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
Tilly Woods and Ian J. Hewitt
The Cryosphere, 17, 1967–1987, https://doi.org/10.5194/tc-17-1967-2023, https://doi.org/10.5194/tc-17-1967-2023, 2023
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Solar radiation causes melting at and just below the surface of the Greenland ice sheet, forming a porous surface layer known as the weathering crust. The weathering crust is home to many microbes, and the growth of these microbes is linked to the melting of the weathering crust and vice versa. We use a mathematical model to investigate what controls the size and structure of the weathering crust, the number of microbes within it, and its sensitivity to climate change.
Sebastian Hinck, Evan J. Gowan, Xu Zhang, and Gerrit Lohmann
The Cryosphere, 16, 941–965, https://doi.org/10.5194/tc-16-941-2022, https://doi.org/10.5194/tc-16-941-2022, 2022
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Proglacial lakes were pervasive along the retreating continental ice margins after the Last Glacial Maximum. Similarly to the marine ice boundary, interactions at the ice-lake interface impact ice sheet dynamics and mass balance. Previous numerical ice sheet modeling studies did not include a dynamical lake boundary. We describe the implementation of an adaptive lake boundary condition in PISM and apply the model to the glacial retreat of the Laurentide Ice Sheet.
Heiko Goelzer, Brice P. Y. Noël, Tamsin L. Edwards, Xavier Fettweis, Jonathan M. Gregory, William H. Lipscomb, Roderik S. W. van de Wal, and Michiel R. van den Broeke
The Cryosphere, 14, 1747–1762, https://doi.org/10.5194/tc-14-1747-2020, https://doi.org/10.5194/tc-14-1747-2020, 2020
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Future sea-level change projections with process-based ice sheet models are typically driven with surface mass balance forcing derived from climate models. In this work we address the problems arising from a mismatch of the modelled ice sheet geometry with the one used by the climate model. The proposed remapping method reproduces the original forcing data closely when applied to the original geometry and produces a physically meaningful forcing when applied to different modelled geometries.
Heiko Goelzer, Violaine Coulon, Frank Pattyn, Bas de Boer, and Roderik van de Wal
The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, https://doi.org/10.5194/tc-14-833-2020, 2020
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In our ice-sheet modelling experience and from exchange with colleagues in different groups, we found that it is not always clear how to calculate the sea-level contribution from a marine ice-sheet model. This goes hand in hand with a lack of documentation and transparency in the published literature on how the sea-level contribution is estimated in different models. With this brief communication, we hope to stimulate awareness and discussion in the community to improve on this situation.
Tanja Schlemm and Anders Levermann
The Cryosphere, 13, 2475–2488, https://doi.org/10.5194/tc-13-2475-2019, https://doi.org/10.5194/tc-13-2475-2019, 2019
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We provide a simple stress-based parameterization for cliff calving of ice sheets. According to the resulting increasing dependence of the calving rate on ice thickness, the parameterization might lead to a runaway ice loss in large parts of Greenland and Antarctica.
Anders Levermann and Johannes Feldmann
The Cryosphere, 13, 1621–1633, https://doi.org/10.5194/tc-13-1621-2019, https://doi.org/10.5194/tc-13-1621-2019, 2019
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Using scaling analysis we propose that the currently observed marine ice-sheet instability in the Amundsen Sea sector might be faster than all other potential instabilities in Antarctica.
Veronika Emetc, Paul Tregoning, Mathieu Morlighem, Chris Borstad, and Malcolm Sambridge
The Cryosphere, 12, 3187–3213, https://doi.org/10.5194/tc-12-3187-2018, https://doi.org/10.5194/tc-12-3187-2018, 2018
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The paper includes a model that can be used to predict zones of fracture formation in both floating and grounded ice in Antarctica. We used observations and a statistics-based model to predict fractures in most ice shelves in Antarctica as an alternative to the damage-based approach. We can predict the location of observed fractures with an average success rate of 84% for grounded ice and 61% for floating ice and mean overestimation error of 26% and 20%, respectively.
Sue Cook, Jan Åström, Thomas Zwinger, Benjamin Keith Galton-Fenzi, Jamin Stevens Greenbaum, and Richard Coleman
The Cryosphere, 12, 2401–2411, https://doi.org/10.5194/tc-12-2401-2018, https://doi.org/10.5194/tc-12-2401-2018, 2018
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The growth of fractures on Antarctic ice shelves is important because it controls the amount of ice lost as icebergs. We use a model constructed of multiple interconnected blocks to predict the locations where fractures will form on the Totten Ice Shelf in East Antarctica. The results show that iceberg calving is controlled not only by fractures forming near the front of the ice shelf but also by fractures which formed many kilometres upstream.
Cited articles
Albert, M. R. and Hawley, R. L.: Seasonal changes in snow surface roughness characteristics at Summit, Greenland: Implications for snow and firn ventilation, Ann. Glaciol., 35, 510–514, https://doi.org/10.3189/172756402781816591, 2002. a, b, c
Bolzan, J. F. and Strobel, M.: Accumulation-rate variations around Summit, Greenland, J. Glaciol., 40, 56–66, https://doi.org/10.3189/S0022143000003798, 1994. a
Bourlier, C., Pinel, N., and Fabbro, V.: Illuminated height PDF of a random rough surface and its impact on the forward propagation above oceans at grazing angles, in: 2006 First European Conference on Antennas and Propagation, Nice, France, 6–10 November 2006, 1–6, https://doi.org/10.1109/EUCAP.2006.4584894, 2006. a
Dahl-Jensen, T. S., Citterio, M., Jakobsen, J., Ahlstrøm, A. P., Larson, K. M., and Khan, S. A.: Snow depth measurements by GNSS-IR at an automatic weather station, NUK-K, Remote Sens., 14, 2563, https://doi.org/10.3390/rs14112563, 2022. a, b
Dibb, J. E. and Fahnestock, M.: Snow accumulation, surface height change, and firn densification at Summit, Greenland: Insights from 2 years of in situ observation, J. Geophys. Res.-Atmos., 109, D24113, https://doi.org/10.1029/2003JD004300, 2004. a, b, c
Filhol, S. and Sturm, M.: Snow bedforms: A review, new data, and a formation model, J. Geophys. Res.-Earth Surf., 120, 1645–1669, https://doi.org/10.1002/2015JF003529, 2015. a, b
Fisher, D. A., Reeh, N., and Clausen, H. B.: Stratigraphic noise in time series derived from ice cores, Ann. Glaciol., 7, 76–83, https://doi.org/10.3189/S0260305500005942, 1985. a
Georgiadou, P. Y. and Kleusberg, A.: On carrier signal multipath effects in relative GPS positioning, Manuscripta geodaetica, 13, 172–179, https://research.utwente.nl/en/publications/on-carrier-signal-multipath-effects-in-relative-gps-positioning (last access: 20 August 2024), 1988. a
Global Modeling And Assimilation Office and Pawson, S.: MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4, https://doi.org/10.5067/7MCPBJ41Y0K6, 2015. a
Gossart, A., Helsen, S., Lenaerts, J. T. M., Broucke, S. V., Lipzig, N. P. M. v., and Souverijns, N.: An evaluation of surface climatology in state-of-the-art reanalyses over the Antarctic Ice Sheet, J. Geophys. Res.-Atmos., 32, 6899–6915, https://doi.org/10.1175/JCLI-D-19-0030.1, 2019. a
Gutmann, E. D., Larson, K. M., Williams, M. W., Nievinski, F. G., and Zavorotny, V.: Snow measurement by GPS interferometric reflectometry: An evaluation at Niwot Ridge, Colorado, Hydrol. Process., 26, 2951–2961, https://doi.org/10.1002/hyp.8329, 2012. a, b
Hawley, R. L., Pickell, D. J., McConnell, J. R., Neumann, T. A., and Dorsi, S. W.: Index of /mirror/summit/ftp/science/bamboo_forest/ [data set], https://conus.summitcamp.org/mirror/summit/ftp/science/bamboo_forest/, last access: 20 August 2024. a
Howat, I. M.: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet, J. Glaciol., 68, 1076–1084, https://doi.org/10.1017/jog.2022.21, 2022. a, b
Howat, I. M., Negrete, A., and Smith, B. E.: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere, 8, 1509–1518, https://doi.org/10.5194/tc-8-1509-2014, 2014. a
Howat, I. M., de la Peña, S., Desilets, D., and Womack, G.: Autonomous ice sheet surface mass balance measurements from cosmic rays, The Cryosphere, 12, 2099–2108, https://doi.org/10.5194/tc-12-2099-2018, 2018. a
Hristov, H. D.: Fresnal Zones in Wireless Links, Zone Plate Lenses and Antennas, Artech House, Inc., USA, 1st edn., ISBN 978-0-89006-849-6, 2000. a
Kuhns, H., Davidson, C., Dibb, J., Stearns, C., Bergin, M., and Jaffrezo, J.-L.: Temporal and spatial variability of snow accumulation in central Greenland, J. Geophys. Res.-Atmos., 102, 30059–30068, https://doi.org/10.1029/97JD02760, 1997. a
Larson, K.: gnssrefl: an open source python software package for environmental GNSS interferometric reflectometry applications, Zenodo [software], https://doi.org/10.5281/zenodo.10796409, 2024. a, b
Larson, K. M.: GPS interferometric reflectometry: Applications to surface soil moisture, snow depth, and vegetation water content in the western United States, WIREs Water, 3, 775–787, https://doi.org/10.1002/wat2.1167, 2016. a
Larson, K. M. and Nievinski, F. G.: GPS snow sensing: results from the EarthScope Plate Boundary Observatory, GPS Solutions, 17, 41–52, https://doi.org/10.1007/s10291-012-0259-7, 2013. a, b
Larson, K. M. and Small, E. E.: Estimation of snow depth using L1 GPS signal-to-noise ratio data, IEEE J. Sel. Top. Appl. Earth Obs., 9, 4802–4808, https://doi.org/10.1109/JSTARS.2015.2508673, 2016. a, b
Larson, K. M., Gutmann, E. D., Zavorotny, V. U., Braun, J. J., Williams, M. W., and Nievinski, F. G.: Can we measure snow depth with GPS receivers?, Geophys. Res. Lett., 36, L17502, https://doi.org/10.1029/2009GL039430, 2009. a
Larson, K. M., MacFerrin, M., and Nylen, T.: Brief Communication: Update on the GPS reflection technique for measuring snow accumulation in Greenland, The Cryosphere, 14, 1985–1988, https://doi.org/10.5194/tc-14-1985-2020, 2020. a, b
Lenaerts, J. T. M., van den Broeke, M. R., van Angelen, J. H., van Meijgaard, E., and Déry, S. J.: Drifting snow climate of the Greenland ice sheet: a study with a regional climate model, The Cryosphere, 6, 891–899, https://doi.org/10.5194/tc-6-891-2012, 2012. a
McConnell, J. R., Bales, R. C., and Davis, D. R.: Recent intra-annual snow accumulation at South Pole: Implications for ice core interpretation, J. Geophys. Res.-Atmos., 102, 21947–21954, https://doi.org/10.1029/97JD00848, 1997. a
McConnell, J. R., Arthern, R. J., Mosley-Thompson, E., Davis, C. H., Bales, R. C., Thomas, R., Burkhart, J. F., and Kyne, J. D.: Changes in Greenland ice sheet elevation attributed primarily to snow accumulation variability, Nature, 406, 877–879, https://doi.org/10.1038/35022555, 2000. a
McCreight, J. L., Small, E. E., and Larson, K. M.: Snow depth, density, and SWE estimates derived from GPS reflection data: Validation in the western U. S, Water Resour. Res., 50, 6892–6909, https://doi.org/10.1002/2014WR015561, 2014. a
Montgomery, L., Koenig, L., and Alexander, P.: The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland, Earth Syst. Sci. Data, 10, 1959–1985, https://doi.org/10.5194/essd-10-1959-2018, 2018. a
Nievinski, F. G. and Larson, K. M.: Inverse modeling of GPS multipath for snow depth estimation – part I: Formulation and simulations, IEEE T. Geosci. Remote, 52, 6555–6563, https://doi.org/10.1109/TGRS.2013.2297681, 2014. a
Pettersen, C., Bennartz, R., Merrelli, A. J., Shupe, M. D., Turner, D. D., and Walden, V. P.: Precipitation regimes over central Greenland inferred from 5 years of ICECAPS observations, Atmos. Chem. Phys., 18, 4715–4735, https://doi.org/10.5194/acp-18-4715-2018, 2018. a
Picard, G., Arnaud, L., Caneill, R., Lefebvre, E., and Lamare, M.: Observation of the process of snow accumulation on the Antarctic Plateau by time lapse laser scanning, The Cryosphere, 13, 1983–1999, https://doi.org/10.5194/tc-13-1983-2019, 2019. a, b
Pickell, D. J.: glaciology/OGRE: v3.0, Zenodo [software], https://doi.org/10.5281/zenodo.14893707, 2025. a
Pickell, D. J. and Hawley, R. L.: An east-west network of twelve global navigation satellite system (GNSS) stations in the Summit region of Greenland: RINEX data, 2022–2024, Arctic Data Center [data set], https://doi.org/10.18739/A2736M41C, 2024a. a
Pickell, D. J. and Hawley, R. L.: Performance characterization of a new, low-cost multi-GNSS instrument for the cryosphere, J. Glaciol., 70, e41, https://doi.org/10.1017/jog.2023.97, 2024b. a
Pinat, E., Defraigne, P., Bergeot, N., Chevalier, J.-M., and Bertrand, B.: Long-term snow height variations in Antarctica from GNSS interferometric reflectometry, Remote Sens., 13, 1164, https://doi.org/10.3390/rs13061164, 2021. a
Roesler, C. and Larson, K. M.: Software tools for GNSS interferometric reflectometry (GNSS-IR), GPS Solutions, 22, 80, https://doi.org/10.1007/s10291-018-0744-8, 2018. a, b
Scanlan, K. M., Rutishauser, A., and Simonsen, S. B.: Observing the Near-Surface Properties of the Greenland Ice Sheet, Geophys. Res. Lett., 50, e2022GL101702, https://doi.org/10.1029/2022GL101702, 2023. a
Shean, D. E., Christianson, K., Larson, K. M., Ligtenberg, S. R. M., Joughin, I. R., Smith, B. E., Stevens, C. M., Bushuk, M., and Holland, D. M.: GPS-derived estimates of surface mass balance and ocean-induced basal melt for Pine Island Glacier ice shelf, Antarctica, The Cryosphere, 11, 2655–2674, https://doi.org/10.5194/tc-11-2655-2017, 2017. a
Smith, B., Fricker, H. A., Gardner, A. S., Medley, B., Nilsson, J., Paolo, F. S., Holschuh, N., Adusumilli, S., Brunt, K., Csatho, B., Harbeck, K., Markus, T., Neumann, T., Siegfried, M. R., and Zwally, H. J.: Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes, Science, 368, 1239–1242, https://doi.org/10.1126/science.aaz5845, 2020. a
Takahashi, S. and Kameda, T.: Snow density for measuring surface mass balance using the stake method, J. Glaciol., 53, 677–680, https://doi.org/10.3189/002214307784409360, 2007. a, b
van den Broeke, M. R., Enderlin, E. M., Howat, I. M., Kuipers Munneke, P., Noël, B. P. Y., van de Berg, W. J., van Meijgaard, E., and Wouters, B.: On the recent contribution of the Greenland ice sheet to sea level change, The Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, 2016. a
van der Veen, C. J. and Bolzan, J. F.: Interannual variability in net accumulation on the Greenland Ice Sheet: Observations and implications for mass balance measurements, J. Geophys. Res.-Atmos., 104, 2009–2014, https://doi.org/10.1029/1998JD200082, 1999. a
van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Wouters, B., Steiner, J. F., Nieuwstraten, E. J., Immerzeel, W. W., and van den Broeke, M. R.: Mapping the aerodynamic roughness of the Greenland Ice Sheet surface using ICESat-2: evaluation over the K-transect, The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, 2021. a, b
Wang, W., Zender, C. S., van As, D., and Miller, N. B.: Spatial distribution of melt season cloud radiative effects over Greenland: Evaluating satellite observations, reanalyses, and model simulations against in situ measurements, J. Geophys. Res.-Atmos., 124, 57–71, https://doi.org/10.1029/2018JD028919, 2019. a
Wells, A., Rounce, D., Sass, L., Florentine, C., Garbo, A., Baker, E., and McNeil, C.: GNSS reflectometry from low-cost sensors for continuous in situ contemporaneous glacier mass balance and flux divergence, J. Glaciol., 70, e5, https://doi.org/10.1017/jog.2024.54, 2024. a
Zuhr, A. M., Münch, T., Steen-Larsen, H. C., Hörhold, M., and Laepple, T.: Local-scale deposition of surface snow on the Greenland ice sheet, The Cryosphere, 15, 4873–4900, https://doi.org/10.5194/tc-15-4873-2021, 2021. a, b, c, d
Short summary
We use a low-cost, low-power GNSS network to measure surface accumulation in Greenland's interior using the interferometric reflectometry technique. Additionally, we extend this method to also estimate centimeter- to meter-scale surface roughness. Our results closely align with a validation record and highlight a period of unusually high accumulation from late 2022 to 2023, along with seasonal variations in surface roughness.
We use a low-cost, low-power GNSS network to measure surface accumulation in Greenland's...