Articles | Volume 14, issue 10
https://doi.org/10.5194/tc-14-3287-2020
© Author(s) 2020. 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-14-3287-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys
Michael Studinger
CORRESPONDING AUTHOR
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Brooke C. Medley
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Kelly M. Brunt
Earth System Science Interdisciplinary Center (ESSIC), University of
Maryland, College Park, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Kimberly A. Casey
U.S. Geological Survey, Reston, VA, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Nathan T. Kurtz
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Serdar S. Manizade
Amentum Services Inc., Wallops Island, VA, USA
NASA Wallops Flight Facility, Wallops Island, VA, USA
Thomas A. Neumann
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Thomas B. Overly
Earth System Science Interdisciplinary Center (ESSIC), University of
Maryland, College Park, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Related authors
Benjamin E. Smith, Michael Studinger, Tyler Sutterley, Zachary Fair, and Thomas Neumann
The Cryosphere, 19, 975–995, https://doi.org/10.5194/tc-19-975-2025, https://doi.org/10.5194/tc-19-975-2025, 2025
Short summary
Short summary
This study investigates errors (biases) that may result when green lasers are used to measure the elevation of glaciers and ice sheets. These biases are important because if the snow or ice on top of the ice sheet changes, it can make the elevation of the ice appear to change by the wrong amount. We measure these biases over the Greenland Ice Sheet with a laser system on an airplane and explore how the use of satellite data can let us correct for the biases.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Michael Studinger, Serdar S. Manizade, Matthew A. Linkswiler, and James K. Yungel
The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, https://doi.org/10.5194/tc-16-3649-2022, 2022
Short summary
Short summary
The footprint density and high-resolution imagery of airborne surveys reveal details in supraglacial hydrological features that are currently not obtainable from spaceborne data. The accuracy and resolution of airborne measurements complement spaceborne measurements, can support calibration and validation of spaceborne methods, and provide information necessary for process studies of the hydrological system on ice sheets that currently cannot be achieved from spaceborne observations alone.
Joseph A. MacGregor, Winnie Chu, William T. Colgan, Mark A. Fahnestock, Denis Felikson, Nanna B. Karlsson, Sophie M. J. Nowicki, and Michael Studinger
The Cryosphere, 16, 3033–3049, https://doi.org/10.5194/tc-16-3033-2022, https://doi.org/10.5194/tc-16-3033-2022, 2022
Short summary
Short summary
Where the bottom of the Greenland Ice Sheet is frozen and where it is thawed is not well known, yet knowing this state is increasingly important to interpret modern changes in ice flow there. We produced a second synthesis of knowledge of the basal thermal state of the ice sheet using airborne and satellite observations and numerical models. About one-third of the ice sheet’s bed is likely thawed; two-fifths is likely frozen; and the remainder is too uncertain to specify.
Joseph A. MacGregor, Michael Studinger, Emily Arnold, Carlton J. Leuschen, Fernando Rodríguez-Morales, and John D. Paden
The Cryosphere, 15, 2569–2574, https://doi.org/10.5194/tc-15-2569-2021, https://doi.org/10.5194/tc-15-2569-2021, 2021
Short summary
Short summary
We combine multiple recent global glacier datasets and extend one of them (GlaThiDa) to evaluate past performance of radar-sounding surveys of the thickness of Earth's temperate glaciers. An empirical envelope for radar performance as a function of center frequency is determined, its limitations are discussed and its relevance to future radar-sounder survey and system designs is considered.
Benjamin E. Smith, Michael Studinger, Tyler Sutterley, Zachary Fair, and Thomas Neumann
The Cryosphere, 19, 975–995, https://doi.org/10.5194/tc-19-975-2025, https://doi.org/10.5194/tc-19-975-2025, 2025
Short summary
Short summary
This study investigates errors (biases) that may result when green lasers are used to measure the elevation of glaciers and ice sheets. These biases are important because if the snow or ice on top of the ice sheet changes, it can make the elevation of the ice appear to change by the wrong amount. We measure these biases over the Greenland Ice Sheet with a laser system on an airplane and explore how the use of satellite data can let us correct for the biases.
Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
Short summary
Short summary
This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the V and H SMAP brightness temperature time series well. This model provides a potential to study the aquifer liquid water content with radiometry.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
Short summary
Short summary
Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Beata Csatho, Tony Schenk, and Tom Neumann
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 83–88, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-83-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-83-2024, 2024
Marissa E. Dattler, Brooke Medley, and C. Max Stevens
The Cryosphere, 18, 3613–3631, https://doi.org/10.5194/tc-18-3613-2024, https://doi.org/10.5194/tc-18-3613-2024, 2024
Short summary
Short summary
We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
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
Short summary
Short summary
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.
Steven Fons, Nathan Kurtz, and Marco Bagnardi
The Cryosphere, 17, 2487–2508, https://doi.org/10.5194/tc-17-2487-2023, https://doi.org/10.5194/tc-17-2487-2023, 2023
Short summary
Short summary
Antarctic sea ice thickness is an important quantity in the Earth system. Due to the thick and complex snow cover on Antarctic sea ice, estimating the thickness of the ice pack is difficult using traditional methods in radar altimetry. In this work, we use a waveform model to estimate the freeboard and snow depth of Antarctic sea ice from CryoSat-2 and use these values to calculate sea ice thickness and volume between 2010 and 2021 and showcase how the sea ice pack has changed over this time.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
Short summary
Short summary
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Megan Thompson-Munson, Nander Wever, C. Max Stevens, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, https://doi.org/10.5194/tc-17-2185-2023, 2023
Short summary
Short summary
To better understand the Greenland Ice Sheet’s firn layer and its ability to buffer sea level rise by storing meltwater, we analyze firn density observations and output from two firn models. We find that both models, one physics-based and one semi-empirical, simulate realistic density and firn air content when compared to observations. The models differ in their representation of firn air content, highlighting the uncertainty in physical processes and the paucity of deep-firn measurements.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
Short summary
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Michael Studinger, Serdar S. Manizade, Matthew A. Linkswiler, and James K. Yungel
The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, https://doi.org/10.5194/tc-16-3649-2022, 2022
Short summary
Short summary
The footprint density and high-resolution imagery of airborne surveys reveal details in supraglacial hydrological features that are currently not obtainable from spaceborne data. The accuracy and resolution of airborne measurements complement spaceborne measurements, can support calibration and validation of spaceborne methods, and provide information necessary for process studies of the hydrological system on ice sheets that currently cannot be achieved from spaceborne observations alone.
Joseph A. MacGregor, Winnie Chu, William T. Colgan, Mark A. Fahnestock, Denis Felikson, Nanna B. Karlsson, Sophie M. J. Nowicki, and Michael Studinger
The Cryosphere, 16, 3033–3049, https://doi.org/10.5194/tc-16-3033-2022, https://doi.org/10.5194/tc-16-3033-2022, 2022
Short summary
Short summary
Where the bottom of the Greenland Ice Sheet is frozen and where it is thawed is not well known, yet knowing this state is increasingly important to interpret modern changes in ice flow there. We produced a second synthesis of knowledge of the basal thermal state of the ice sheet using airborne and satellite observations and numerical models. About one-third of the ice sheet’s bed is likely thawed; two-fifths is likely frozen; and the remainder is too uncertain to specify.
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.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Joseph A. MacGregor, Michael Studinger, Emily Arnold, Carlton J. Leuschen, Fernando Rodríguez-Morales, and John D. Paden
The Cryosphere, 15, 2569–2574, https://doi.org/10.5194/tc-15-2569-2021, https://doi.org/10.5194/tc-15-2569-2021, 2021
Short summary
Short summary
We combine multiple recent global glacier datasets and extend one of them (GlaThiDa) to evaluate past performance of radar-sounding surveys of the thickness of Earth's temperate glaciers. An empirical envelope for radar performance as a function of center frequency is determined, its limitations are discussed and its relevance to future radar-sounder survey and system designs is considered.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
Short summary
Short summary
Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Tessa Gorte, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 14, 4719–4733, https://doi.org/10.5194/tc-14-4719-2020, https://doi.org/10.5194/tc-14-4719-2020, 2020
Short summary
Short summary
In this paper, we analyze several spatial and temporal criteria to assess the ability of models in the CMIP5 and CMIP6 frameworks to recreate past Antarctic surface mass balance. We then compared a subset of the top performing models to all remaining models to refine future surface mass balance predictions under different forcing scenarios. We found that the top performing models predict lower surface mass balance by 2100, indicating less buffering than otherwise expected of sea level rise.
Zachary Fair, Mark Flanner, Kelly M. Brunt, Helen Amanda Fricker, and Alex Gardner
The Cryosphere, 14, 4253–4263, https://doi.org/10.5194/tc-14-4253-2020, https://doi.org/10.5194/tc-14-4253-2020, 2020
Short summary
Short summary
Ice on glaciers and ice sheets may melt and pond on ice surfaces in summer months. Detection and observation of these meltwater ponds is important for understanding glaciers and ice sheets, and satellite imagery has been used in previous work. However, image-based methods struggle with deep water, so we used data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Airborne Topographic Mapper (ATM) to demonstrate the potential for lidar depth monitoring.
Cited articles
Abdalati, W., Zwally, H. J., Bindschadler, R., Csatho, B., Farrell, S. L.,
Fricker, H. A., Harding, D., Kwok, R., Lefsky, M., Markus, T., Marshak, A.,
Neumann, T., Palm, S., Schutz, B., Smith, B., Spinhirne, J., and Webb, C.:
The ICESat-2 Laser Altimetry Mission, Proc. IEEE, 98, 735–751,
https://doi.org/10.1109/jproc.2009.2034765, 2010.
Arcone, S. A., Jacobel, R., and Hamilton, G.: Unconformable stratigraphy in
East Antarctica: Part I. Large firn cosets, recrystallized growth, and model
evidence for intensified accumulation, J. Glaciol., 58, 240–252,
https://doi.org/10.3189/2012JoJ11J044, 2012.
Armitage, T. W. K., Wingham, D. J., and Ridout, A. L.: Meteorological Origin
of the Static Crossover Pattern Present in Low-Resolution-Mode CryoSat-2
Data Over Central Antarctica, Geosci. Remote Sens. Lett., IEEE,
11, 1295–1299, https://doi.org/10.1109/LGRS.2013.2292821, 2014.
Arthern, R. J., Wingham, D. J., and Ridout, A. L.: Controls on ERS altimeter
measurements over ice sheets: Footprint-scale topography, backscatter
fluctuations, and the dependence of microwave penetration depth on satellite
orientation, J. Geophys. Res.-Atmos., 106, 33471–33484,
https://doi.org/10.1029/2001jd000498, 2001.
Arthern, R. J., Winebrenner, D. P., and Vaughan, D. G.: Antarctic snow
accumulation mapped using polarization of 4.3-cm wavelength microwave
emission, J. Geophys. Res.-Atmos., 111, D06107,
https://doi.org/10.1029/2004jd005667, 2006.
Boisvert, L. N., Lee, J. N., Lenaerts, J. T. M., Noël, B., Broeke, M.
R., and Nolin, A. W.: Using remotely sensed data from AIRS to estimate the
vapor flux on the Greenland ice sheet: Comparisons with observations and a
regional climate model, J. Geophys. Res.-Atmos., 122,
202–229, https://doi.org/10.1002/2016JD025674, 2017.
Bromwich, D. H., Parish, T. R., and Zorman, C. A.: The confluence zone of
the intense katabatic winds at Terra Nova Bay, Antarctica, as derived from
airborne sastrugi surveys and mesoscale numerical modeling, J. Geophys. Res.-Atmos., 95, 5495–5509,
https://doi.org/10.1029/JD095iD05p05495, 1990.
Brunt, K. M., Neumann, T. A., and Larsen, C. F.: Assessment of altimetry using ground-based GPS data from the 88S Traverse, Antarctica, in support of ICESat-2, The Cryosphere, 13, 579–590, https://doi.org/10.5194/tc-13-579-2019, 2019a.
Brunt, K. M., Neumann, T. A., and Smith, B. E.: Assessment of ICESat-2 Ice
Sheet Surface Heights, Based on Comparisons Over the Interior of the
Antarctic Ice Sheet, Geophys. Res. Lett., 46, 13072–13078,
https://doi.org/10.1029/2019gl084886, 2019b.
Casey, K. A., Fudge, T. J., Neumann, T. A., Steig, E. J., Cavitte, M. G. P.,
and Blankenship, D. D.: The 1500 m South Pole ice core: recovering a 40 ka
environmental record, Ann. Glaciol., 55, 137–146,
https://doi.org/10.3189/2014AoG68A016, 2014.
Chambers, J. R., Smith, M. W., Quincey, D. J., Carrivick, J. L., Ross, A.
N., and James, M. R.: Glacial aerodynamic roughness estimates: uncertainty,
sensitivity and precision in field measurements, J. Geophys.
Res.-Earth Surf., e2019JF005167, https://doi.org/10.1029/2019JF005167, 2019.
Corbett, J. and Su, W.: Accounting for the effects of sastrugi in the CERES clear-sky Antarctic shortwave angular distribution models, Atmos. Meas. Tech., 8, 3163–3175, https://doi.org/10.5194/amt-8-3163-2015, 2015.
Das, I., Bell, R. E., Scambos, T. A., Wolovick, M., Creyts, T. T.,
Studinger, M., Frearson, N., Nicolas, J. P., Lenaerts, J. T. M., and van den
Broeke, M. R.: Influence of persistent wind scour on the surface mass
balance of Antarctica, Nat. Geosci., 6, 367, https://doi.org/10.1038/ngeo1766, 2013.
Dattler, M. E., Lenaerts, J. T. M., and Medley, B.: Significant Spatial
Variability in Radar-Derived West Antarctic Accumulation Linked to Surface
Winds and Topography, Geophys. Res. Lett., 46, 13126–13134,
https://doi.org/10.1029/2019gl085363, 2019.
Dominguez, R. T.: IceBridge DMS L1B Geolocated and Orthorectified Images,
Version 1. [2014, 2016]: NASA National Snow and Ice Data Center Distributed
Active Archive Center, https://doi.org/10.5067/OZ6VNOPMPRJ0,
2010, updated 2018.
Fahnestock, M. A., Scambos, T. A., Shuman, C. A., Athern, R. J.,
Winebrenner, D. P., and Kwok, R.: Snow megadune fields on the East Antarctic
Plateau: extreme atmosphere-ice interaction, Geophys. Res. Lett.,
27, 3719–3722, 2000.
Favier, V., Agosta, C., Parouty, S., Durand, G., Delaygue, G., Gallée, H., Drouet, A.-S., Trouvilliez, A., and Krinner, G.: An updated and quality controlled surface mass balance dataset for Antarctica, The Cryosphere, 7, 583–597, https://doi.org/10.5194/tc-7-583-2013, 2013.
Frezzotti, M., Urbini, S., Proposito, M., Scarchilli, C., and Gandolfi, S.:
Spatial and temporal variability of surface mass balance near Talos Dome,
East Antarctica, J. Geophys. Res.-Earth Surf., 112, F02032,
https://doi.org/10.1029/2006jf000638, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
Silva, A. M. D., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J.
Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017.
Gomez-Garcia, D., Rodriguez-Morales, F., Leuschen, C., and Gogineni, S.:
KU-Band radar altimeter for surface elevation measurements in polar regions
using a wideband chirp generator with improved linearity, 2012 IEEE
International Geoscience and Remote Sensing Symposium, 4617–4620, 2012.
Gorodetskaya, I. V., Tsukernik, M., Claes, K., Ralph, M. F., Neff, W. D.,
and Van Lipzig, N. P. M.: The role of atmospheric rivers in anomalous snow
accumulation in East Antarctica, Geophys. Res. Lett., 41,
6199–6206, https://doi.org/10.1002/2014gl060881, 2014.
Gow, A. J.: On the Accumulation and Seasonal Stratification Of Snow at the
South Pole, J. Glaciol., 5, 467–477, https://doi.org/10.3189/S002214300001844X,
1965.
Grima, C., Blankenship, D. D., Young, D. A., and Schroeder, D. M.: Surface
slope control on firn density at Thwaites Glacier, West Antarctica: Results
from airborne radar sounding, Geophys. Res. Lett., 41, 6787–6794,
https://doi.org/10.1002/2014GL061635, 2014.
Hamilton, G. S.: Topographic control of regional accumulation rate
variability at South Pole and implications for ice-core interpretation,
Ann. Glaciol., 39, 214–218, https://doi.org/10.3189/172756404781814050, 2004.
Haran, T., Bohlander, J., Scambos, T. A., Painter, T. H., and Fahnestock, M.
A.: MODIS Mosaic of Antarctica 2008–2009 (MOA2009) Image Map 2009: NASA
National Snow and Ice Data Center Distributed Active Archive Center,
https://doi.org/10.7265/N5KP8037, 2014.
Haran, T., Klinger, M., Bohlander, J., Fahnestock, M., Painter, T., and Scambos, T.: MEaSUREs MODIS Mosaic of Antarctica 2013–2014 (MOA2014) Image Map, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/RNF17BP824UM, 2018, updated 2019.
Harpold, R., Yungel, J., Linkswiler, M., and Studinger, M.: Intra-scan
intersection method for the determination of pointing biases of an airborne
altimeter, Int. J. Remote Sens., 37, 648–668,
https://doi.org/10.1080/01431161.2015.1137989, 2016.
Harris, J. M.: An analysis of 5-day midtropospheric flow patterns for the
South Pole: 1985–1989, Tellus B, 44, 409–421,
https://doi.org/10.1034/j.1600-0889.1992.00016.x, 1992.
Helm, V., Humbert, A., and Miller, H.: Elevation and elevation change of Greenland and Antarctica derived from CryoSat-2, The Cryosphere, 8, 1539–1559, https://doi.org/10.5194/tc-8-1539-2014, 2014a.
Helm, V., Humbert, A., and Miller, H/: Elevation Model of Antarctica derived from CryoSat-2 in the period 2011 to 2013, links to DEM and uncertainty map as GeoTIFF, PANGAEA, https://doi.org/10.1594/PANGAEA.831392, 2014b.
Herron, M. M. and Langway, C. C.: Firn Densification: An Empirical Model,
J. Glaciol., 25, 373–385, https://doi.org/10.3189/S0022143000015239, 1980.
Hirasawa, N., Nakamura, H., and Yamanouchi, T.: Abrupt changes in
meteorological conditions observed at an inland Antarctic Station in
association with wintertime blocking, Geophys. Res. Lett., 27,
1911–1914, https://doi.org/10.1029/1999gl011039, 2000.
Johnson, A. J., Larsen, C. F., Murphy, N., Arendt, A. A., and Zirnheld, S.
L.: Mass balance in the Glacier Bay area of Alaska, USA, and British
Columbia, Canada, 1995–2011, using airborne laser altimetry, J.
Glaciol., 59, 632–648, https://doi.org/10.3189/2013JoG12J101, 2013.
King, J. C., Anderson, P. S., Vaughan, D. G., Mann, G. W., Mobbs, S. D., and
Vosper, S. B.: Wind-borne redistribution of snow across an Antarctic ice
rise, J. Geophys. Res.-Atmos., 109, D11104,
https://doi.org/10.1029/2003JD004361, 2004.
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow, Appl.
Optics, 43, 1589–1602, https://doi.org/10.1364/AO.43.001589, 2004.
Kovacs, A., Gow, A. J., and Morey, R. M.: The in-situ dielectric constant of
polar firn revisited, Cold Reg. Sci. Technol., 23, 245–256,
https://doi.org/10.1016/0165-232X(94)00016-Q, 1995.
Krabill, W., Abdalati, W., Frederick, E., Manizade, S., Martin, C., Sonntag,
J., Swift, R., Thomas, R., Wright, W., and Yungel, J.: Greenland ice sheet:
High-elevation balance and peripheral thinning, Science, 289, 428–430,
https://doi.org/10.1126/science.289.5478.428, 2000.
Krabill, W. B., Abdalati, W., Frederick, E. B., Manizade, S. S., Martin, C.
F., Sonntag, J. G., Swift, R. N., Thomas, R. H., and Yungel, J. G.: Aircraft
laser altimetry measurement of elevation changes of the greenland ice sheet:
technique and accuracy assessment, J. Geodynam., 34, 357–376,
https://doi.org/10.1016/s0264-3707(02)00040-6, 2002.
Kurtz, N. T., Galin, N., and Studinger, M.: An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting, The Cryosphere, 8, 1217–1237, https://doi.org/10.5194/tc-8-1217-2014, 2014.
Larsen, C. F.: IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation
Triplets, Version 1. [2017], NASA National Snow and Ice Data Center
Distributed Active Archive Center, https://doi.org/10.5067/AATE4JJ91EHC, 2010, updated 2018.
Larue, F., Picard, G., Arnaud, L., Ollivier, I., Delcourt, C., Lamare, M., Tuzet, F., Revuelto, J., and Dumont, M.: Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model, The Cryosphere, 14, 1651–1672, https://doi.org/10.5194/tc-14-1651-2020, 2020.
Leroux, C. and Fily, M.: Modeling the effect of sastrugi on snow
reflectance, J. Geophys. Res.-Planets, 103, 25779–25788,
https://doi.org/10.1029/98JE00558, 1998.
Leuschen, C.: IceBridge Snow Radar L1B Geolocated Radar Echo Strength
Profiles, Version 2. [2014, 2016], NASA National Snow and Ice Data Center
Distributed Active Archive Center, https://doi.org/10.5067/FAZTWP500V70, 2014, updated 2018.
Leuschen, C., Gogineni, P., Rodriguez-Morales, F., Paden, J., and Allen, C.:
IceBridge MCoRDS L2 Ice Thickness, Version 1: NASA National Snow and Ice
Data Center Distributed Active Archive Center, https://doi.org/10.5067/GDQ0CUCVTE2Q, 2010, updated 2018.
Lister, H. and Pratt, G.: Geophysical Investigations of the Commonwealth
Trans-Antarctic Expedition, The Geographical Journal published by The Royal
Geographical Society (with the Institute of British Geographers), 125,
343–354, 1959.
Liu, H., Jezek, K., Li, B., and Zhao, Z.: Radarsat Antarctic Mapping Project
Digital Elevation Model, Version 2, NASA National Snow and Ice Data Center
Distributed Active Archive Center, https://doi.org/10.5067/8JKNEW6BFRVD, 2015.
Magand, O., Genthon, C., Fily, M., Krinner, G., Picard, G., Frezzotti, M.,
and Ekaykin, A. A.: An up-to-date quality-controlled surface mass balance
data set for the 90∘–180∘ E Antarctica sector and
1950–2005 period, J. Geophys. Res.-Atmos., 112, D12106,
https://doi.org/10.1029/2006jd007691, 2007.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B.,
Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R.,
Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R.,
Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E.,
Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation
Satellite-2 (ICESat-2): Science requirements, concept, and implementation,
Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Martin, C. F., Krabill, W. B., Manizade, S. S., Russell, R. L., Sonntag, J.
G., Swift, R. N., and Yungel, J. K.: Airborne Topographic Mapper Calibration
Procedures and Accuracy Assessment, NASA Goddard Space Flight Center, Greenbelt, MD, report number NASA/TM-2012-215891, GSFC.TM.5893.2012, available at: https://ntrs.nasa.gov/citations/20120008479 (last access: September 2020), 2012.
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.
Medley, B., Joughin, I., Das, S. B., Steig, E. J., Conway, H., Gogineni, S.,
Criscitiello, A. S., McConnell, J. R., Smith, B. E., van den Broeke, M. R.,
Lenaerts, J. T. M., Bromwich, D. H., and Nicolas, J. P.: Airborne-radar and
ice-core observations of annual snow accumulation over Thwaites Glacier,
West Antarctica confirm the spatiotemporal variability of global and
regional atmospheric models, Geophys. Res. Lett., 40, 3649–3654,
https://doi.org/10.1002/grl.50706, 2013.
Medley, B., Joughin, I., Smith, B. E., Das, S. B., Steig, E. J., Conway, H., Gogineni, S., Lewis, C., Criscitiello, A. S., McConnell, J. R., van den Broeke, M. R., Lenaerts, J. T. M., Bromwich, D. H., Nicolas, J. P., and Leuschen, C.: Constraining the recent mass balance of Pine Island and Thwaites glaciers, West Antarctica, with airborne observations of snow accumulation, The Cryosphere, 8, 1375–1392, https://doi.org/10.5194/tc-8-1375-2014, 2014.
Medley, B., Ligtenberg, S. R. M., Joughin, I., Van den Broeke, M. R.,
Gogineni, S., and Nowicki, S.: Antarctic firn compaction rates from
repeat-track airborne radar data: I. Methods, Ann. Glaciol., 56,
155–166, https://doi.org/10.3189/2015AoG70A203, 2015.
Morlighem, M., Rignot, E., Binder, T., Blankenship, D., Drews, R., Eagles,
G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V.,
Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat,
I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W. S., Matsuoka, K.,
Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J., Rosier, S.,
Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., Broeke, M.
R. V. D., Ommen, T. D. V., Wessem, M. V., and Young, D. A.: Deep glacial
troughs and stabilizing ridges unveiled beneath the margins of the Antarctic
ice sheet, Nat. Geosci., 13, 132–137, https://doi.org/10.1038/s41561-019-0510-8, 2020.
Mosley-Thompson, E., Paskievitch, J. F., Gow, A. J., and Thompson, L. G.:
Late 20th Century increase in South Pole snow accumulation, J. Geophys. Res.-Atmos., 104, 3877–3886, https://doi.org/10.1029/1998jd200092,
1999.
Mouginot, J., Rignot, E., and Scheuchl, B.: Continent-Wide, Interferometric
SAR Phase, Mapping of Antarctic Ice Velocity, Geophys. Res. Lett.,
46, 9710–9718, https://doi.org/10.1029/2019gl083826, 2019.
Nicolas, J. P. and Bromwich, D. H.: Climate of West Antarctica and
Influence of Marine Air Intrusions, J. Climate, 24, 49–67,
https://doi.org/10.1175/2010jcli3522.1, 2011.
Nilsson, J., Gardner, A., Sandberg Sørensen, L., and Forsberg, R.: Improved retrieval of land ice topography from CryoSat-2 data and its impact for volume-change estimation of the Greenland Ice Sheet, The Cryosphere, 10, 2953–2969, https://doi.org/10.5194/tc-10-2953-2016, 2016.
Nolin, A. W. and Payne, M. C.: Classification of glacier zones in western
Greenland using albedo and surface roughness from the Multi-angle Imaging
SpectroRadiometer (MISR), Remote Sens. Environ., 107, 264–275,
https://doi.org/10.1016/j.rse.2006.11.004, 2007.
Nolin, A. W., Fetterer, F. M., and Scambos, T. A.: Surface roughness
characterizations of sea ice and ice sheets: case studies with MISR data,
IEEE T. Geosci. Remote, 40, 1605–1615,
https://doi.org/10.1109/TGRS.2002.801581, 2002.
Nolin, A. W. and Mar, E.: Arctic Sea Ice Surface Roughness Estimated from
Multi-Angular Reflectance Satellite Imagery, Remote Sensing, 11, 50, 2018.
NSIDC: Operation IceBridge Data Portal, available at: https://nsidc.org/icebridge/portal, last access: 2019.
Paden, J., Li, J., Leuschen, C., Rodriguez-Morales, F., and Hale, R.:
IceBridge Ku-Band Radar L1B Geolocated Radar Echo Strength Profiles, Version
2., NASA National Snow and Ice Data Center Distributed Active Archive
Center, https://doi.org/10.5067/D7DX7J7J5JN9, 2014, updated
2018.
Palm, S. P., Kayetha, V., Yang, Y., and Pauly, R.: Blowing snow sublimation and transport over Antarctica from 11 years of CALIPSO observations, The Cryosphere, 11, 2555–2569, https://doi.org/10.5194/tc-11-2555-2017, 2017.
Panzer, B., Gomez-Garcia, D., Leuschen, C., Paden, J., Rodriguez-Morales,
F., Patel, A., Markus, T., Holt, B., and Gogineni, P.: An ultra-wideband,
microwave radar for measuring snow thickness on sea ice and mapping
near-surface internal layers in polar firn, J. Glaciol., 59,
244–254, https://doi.org/10.3189/2013JoG12J128, 2013.
Picciotto, E., Crozaz, G., and De Breuck, W.: Accumulation on the South Pole
– Queen Maud Land Traverse, 1964–1968, in: Antarctic Snow and Ice Studies
II, edited by: Crary, A. P., Antarctic Research Series, 16, American
Geophysical Union, Washinton, D.C., 257–315, 1971.
Rodriguez-Morales, F., Gogineni, S., Leuschen, C. J., Paden, J. D., Jilu,
L., Lewis, C. C., Panzer, B., Gomez-Garcia Alvestegui, D., Patel, A., Byers,
K., Crowe, R., Player, K., Hale, R. D., Arnold, E. J., Smith, L., Gifford,
C. M., Braaten, D., and Panton, C.: Advanced Multifrequency Radar
Instrumentation for Polar Research, Geoscience and Remote Sensing, IEEE
Transactions, 52, 2824–2842, https://doi.org/10.1109/TGRS.2013.2266415, 2014.
Scagliola, M.: CryoSat footprints, Technical Note, ESA Document
XCRY-GSEG-EOPG-TN-13-0013, 2013.
Scambos, T. A. and Fahnestock, M. A.: Improving digital elevation models
over ice sheets using AVHRR-based photoclinometry, J. Glaciol.,
44, 97–103, https://doi.org/10.3189/S0022143000002392, 1998.
Scambos, T. A., Frezzotti, M., Haran, T., Bohlander, J., Lenaerts, J. T. M.,
Van Den Broeke, M. R., Jezek, K., Long, D., Urbini, S., Farness, K.,
Neumann, T., Albert, M., and Winther, J. G.: Extent of low-accumulation
“wind glaze” areas on the East Antarctic plateau: implications for
continental ice mass balance, J. Glaciol., 58, 633–647,
https://doi.org/10.3189/2012JoG11J232, 2012.
Slater, T., Shepherd, A., McMillan, M., Muir, A., Gilbert, L., Hogg, A. E., Konrad, H., and Parrinello, T.: A new digital elevation model of Antarctica derived from CryoSat-2 altimetry, The Cryosphere, 12, 1551–1562, https://doi.org/10.5194/tc-12-1551-2018, 2018.
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S.,
Brunt, K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J.,
and Siegfried, M. R.: Land ice height-retrieval algorithm for NASA's
ICESat-2 photon-counting laser altimeter, Remote Sens. Environ.,
233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019.
Smith, B. E., Raymond, C. F., and Scambos, T.: Anisotropic texture of ice
sheet surfaces, J. Geophys. Res.-Earth Surf., 111, F01019,
https://doi.org/10.1029/2005JF000393, 2006.
Smith, M. W.: Roughness in the Earth Sciences, Earth-Sci. Rev., 136,
202–225, https://doi.org/10.1016/j.earscirev.2014.05.016, 2014.
Studinger, M.: IceBridge ATM L1B Elevation and Return Strength, Version 2.
[2014, 2016], NASA National Snow and Ice Data Center Distributed Active
Archive Center, https://doi.org/10.5067/19SIM5TXKPGT, 2013,
updated 2018.
Studinger, M.: IceBridge ATM L2 ICESSN Elevation, Slope, and Roughness,
Version 2. [2014, 2016], NASA National Snow and Ice Data Center Distributed
Active Archive Center, https://doi.org/10.5067/CPRXXK3F39RV, 2014, updated 2018.
Studinger, M., Bell, R. E., Fitzgerald, P. G., and Buck, W. R.: Crustal
architecture of the Transantarctic Mountains between the Scott and Reedy
Glacier region and South Pole from aerogeophysical data, Earth Planet.
Sc. Lett., 250, 182–199, https://doi.org/10.1016/j.epsl.2006.07.035, 2006.
Taylor, L. D.: Glaciological studies on the South Pole Traverse 1962–1963,
in: Antarctic Snow and Ice Studies II, edited by: Crary, A. P., Antarctic
Research Series, 16, American Geophysical Union, Washington, D.C., 209–224,
1971.
Thomas, R. H. and Investigators, P.: Program for arctic regional climate
assessment (PARCA): Goals, key findings, and future directions, J.
Geophys. Res.-Atmos., 106, 33691–33705, https://doi.org/10.1029/2001jd900042,
2001.
van der Veen, C. J., Krabill, W. B., Csatho, B. M., and Bolzan, J. F.:
Surface roughness on the Greenland ice sheet from airborne laser altimetry,
Geophys. Res. Lett., 25, 3887–3890, https://doi.org/10.1029/1998gl900041, 1998.
van der Veen, C. J., Ahn, Y., Csatho, B. M., Mosley-Thompson, E., and
Krabill, W. B.: Surface roughness over the northern half of the Greenland
Ice Sheet from airborne laser altimetry, J. Geophys.
Res.-Earth Surf., 114, F01001, https://doi.org/10.1029/2008jf001067, 2009.
Vaughan, D. G., Comiso, J. C., Allison, I., Carrasco, J., Kaser, G., Kwok,
R., Mote, P., Murray, T., Paul, F., Ren, J., Rignot, E., Solomina, O.,
Steffen, K., and Zhang, T.: Observations: Cryosphere, in: Climate Change
2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.
K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA
2013.
Warren, S. G., Brandt, R. E., and O'Rawe Hinton, P.: Effect of surface
roughness on bidirectional reflectance of Antarctic snow, J.
Geophys. Res.-Planets, 103, 25789–25807, https://doi.org/10.1029/98JE01898,
1998.
Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D.,
Cullen, R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos,
C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D.
W.: CryoSat: A mission to determine the fluctuations in Earth's land and
marine ice fields, in: Natural Hazards and Oceanographic Processes from
Satellite Data, edited by: Singh, R. P. and Shea, M. A., Adv. Space
Res.-Series, 4, 841–871, 2006.
Winski, D. A., Fudge, T. J., Ferris, D. G., Osterberg, E. C., Fegyveresi, J. M., Cole-Dai, J., Thundercloud, Z., Cox, T. S., Kreutz, K. J., Ortman, N., Buizert, C., Epifanio, J., Brook, E. J., Beaudette, R., Severinghaus, J., Sowers, T., Steig, E. J., Kahle, E. C., Jones, T. R., Morris, V., Aydin, M., Nicewonger, M. R., Casey, K. A., Alley, R. B., Waddington, E. D., Iverson, N. A., Dunbar, N. W., Bay, R. C., Souney, J. M., Sigl, M., and McConnell, J. R.: The SP19 chronology for the South Pole Ice Core – Part 1: volcanic matching and annual layer counting, Clim. Past, 15, 1793–1808, https://doi.org/10.5194/cp-15-1793-2019, 2019.
Yi, D., Harbeck, J. P., Manizade, S. S., Kurtz, N. T., Studinger, M., and
Hofton, M.: Arctic Sea Ice Freeboard Retrieval With Waveform Characteristics
for NASA's Airborne Topographic Mapper (ATM) and Land, Vegetation, and Ice
Sensor (LVIS), Ieee T. Geosci. Remote, 53,
1403–1410, https://doi.org/10.1109/tgrs.2014.2339737, 2015.
Zwally, H. J., Schutz, B., Abdalati, W., Abshire, J., Bentley, C., Brenner,
A., Bufton, J., Dezio, J., Hancock, D., Harding, D., Herring, T., Minster,
B., Quinn, K., Palm, S., Spinhirne, J., and Thomas, R.: ICESat's laser
measurements of polar ice, atmosphere, ocean, and land, J.
Geodynam., 34, 405–445, https://doi.org/10.1016/s0264-3707(02)00042-x, 2002.
Short summary
We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar...