Articles | Volume 16, issue 4
https://doi.org/10.5194/tc-16-1341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness change
Christian J. Taubenberger
CORRESPONDING AUTHOR
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, United States of America
Department of Environmental Health and Engineering, Johns Hopkins University,
Baltimore, MD 21218, United States of America
Denis Felikson
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, United States of America
Goddard Earth Sciences Technology and Research Studies and
Investigations II, Morgan State University, Baltimore, MD 21251, United
States of America
Thomas Neumann
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, United States of America
Related authors
No articles found.
Kevin Shionalyn, Ginny Catania, Daniel Trugman, Michael Shahin, Leigh Stearns, and Denis Felikson
EGUsphere, https://doi.org/10.5194/egusphere-2025-3483, https://doi.org/10.5194/egusphere-2025-3483, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The ocean-facing front of a glacier changes with the seasons. We know this cycle is controlled by the shape and speed of the glacier as well as by the climate, but we do not have a full understanding of these processes. Our study uses 20 years of data and a machine learning model to predict this pattern and identifies which factors matter most. We find that while several factors influence the seasonal cycle, the shape of the glacier plays a key role in how much a glacier changes annually.
Derek J. Pickell, Robert L. Hawley, Denis Felikson, and Jamie C. Good
EGUsphere, https://doi.org/10.5194/egusphere-2025-2683, https://doi.org/10.5194/egusphere-2025-2683, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We compared satellite measurements of ice surface height in Greenland with ground-based observations, revealing sub-centimeter accuracy of the satellite instrument. We also demonstrated a new autonomous method using reflected radio signals to measure the surface without human traverses. This method produces comparable results, and we find no long-term changes in satellite performance to date.
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.
Youngmin Choi, Alek Petty, Denis Felikson, and Jonathan Poterjoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-301, https://doi.org/10.5194/egusphere-2025-301, 2025
Short summary
Short summary
In this study, we combined numerical models with satellite data using the ensemble Kalman filter to improve predictions of glacier states and their basal conditions. Simulations showed that adding more data enhances prediction accuracy. We also tested the effect of various data types and found that the high-resolution data improve model performance. This method could inform the design of better observation systems and refine future projections of ice sheet behavior.
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
Denis Felikson, Sophie Nowicki, Isabel Nias, Beata Csatho, Anton Schenk, Michael J. Croteau, and Bryant Loomis
The Cryosphere, 17, 4661–4673, https://doi.org/10.5194/tc-17-4661-2023, https://doi.org/10.5194/tc-17-4661-2023, 2023
Short summary
Short summary
We narrow the spread in model simulations of the Greenland Ice Sheet using velocity change, dynamic thickness change, and mass change observations. We find that the type of observation chosen can lead to significantly different calibrated probability distributions. Further work is required to understand how to best calibrate ensembles of ice sheet simulations because this will improve probability distributions of projected sea-level rise, which is crucial for coastal planning and adaptation.
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.
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.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
Short summary
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.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, https://doi.org/10.5194/tc-14-3071-2020, 2020
Short summary
Short summary
In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Cited articles
Carroll, D., Sutherland, D. A., Shroyer, E. L., Nash, J. D., Catania, G. A.,
and Stearns, L. A.: Subglacial discharge-driven renewal of tidewater
glacier fjords, J. Geophys. Res.-Oceans, 125, 2293,
https://doi.org/10.1002/grl.50825, 2017.
Catania, G. A., Stearns, L. A., Sutherland, D. A., Fried, M. J.,
Bartholomaus, T. C., Morlighem, M., Shroyer, E., and Nash, J.: Geometric
Controls on Tidewater Glacier Retreat in Central Western Greenland, J. Geophys. Res.-Earth Surf., 29, 1–15,
https://doi.org/10.1029/2017JF004499, 2018.
Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019, The Cryosphere, 15, 1663–1675, https://doi.org/10.5194/tc-15-1663-2021, 2021.
Choi, Y., Morlighem, M., Rignot, E., and Wood, M.: Ice dynamics will remain a
primary driver of Greenland ice sheet mass loss over the next century,
Commun. Earth Environ., 2, 26,
https://doi.org/10.1038/s43247-021-00092-z, 2021.
Church, J. A., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S.,
Levermann, A., Merrifield, M. A., Milne, G. A., Nerem, R. S., Nunn, P. D.,
Payne, A. J., Pfeffer, W. T., Stammer, D., and Unnikrishnan, A. S.: Sea Level
Change, 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., Boshung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, https://doi.org/10.1017/CBO9781107415324.026, 2013.
Felikson, D., Catania, G. A., Bartholomaus, T. C., Morlighem, M., and
Noël, B. P. Y.: Steep Glacier Bed Knickpoints Mitigate Inland Thinning
in Greenland, Geophys. Res. Lett., 48, e2020GL090112,
https://doi.org/10.1029/2020GL090112, 2021.
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.,
da Silva, A. M., Gu, W., Kim, G., 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,
2017.
Goliber, S., Black, T., Catania, G., Lea, J. M., Olsen, H., Cheng, D., Bevan, S., Bjørk, A., Bunce, C., Brough, S., Carr, J. R., Cowton, T., Gardner, A., Fahrner, D., Hill, E., Joughin, I., Korsgaard, N., Luckman, A., Moon, T., Murray, T., Sole, A., Wood, M., and Zhang, E.: TermPicks: A century of Greenland glacier terminus data for use in machine learning applications, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-311, in review, 2021.
Gray, L., Burgess, D., Copland, L., Langley, K., Gogineni, P., Paden, J.,
Leuschen, C., van As, D., Fausto, R., Joughin, I., and Smith, B.: Measuring
height change around the periphery of the greenland ice sheet with radar
altimetry, Front. Earth Sci., 7, 146,
https://doi.org/10.3389/feart.2019.00146, 2019.
Johannessen, O. M., Khvorostovsky, K., Miles, M. W., and Bobylev, L. P.:
Recent Ice-Sheet Growth in the Interior of Greenland, Science, vol. 310, no.
5750, American Association for the Advancement of Science, 1013–1016,
https://doi.org/10.1126/science.1115356, 2005.
Joughin, I., Moon, T., Joughin, J., and Black, T.: MEaSUREs Annual Greenland
Outlet Glacier Terminus Positions from SAR Mosaics, Version 1. Boulder,
Colorado USA. NASA National Snow and Ice Data Center Distributed Active
Archive Center [data set], https://doi.org/10.5067/DC0MLBOCL3EL, 2015, 2017.
Joughin, I., Shean, D. E., Smith, B. E., and Floricioiu, D.: A decade of variability on Jakobshavn Isbræ: ocean temperatures pace speed through influence on mélange rigidity, The Cryosphere, 14, 211–227, https://doi.org/10.5194/tc-14-211-2020, 2020.
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., 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.
McMillan, M., Leeson, A., Shepherd, A., Briggs, K., Armitage, T. W. K.,
Hogg, A., Munneke, P. K., van den Broeke, M., Noël, B., van de Berg W.
J., Ligtenberg, S., Horwath, M., Groh, A., Muir, A., and Gilbert, L.: A
high-resolution record of Greenland mass balance, Geophys. Res. Lett., 43, 7002–7010,
https://doi.org/10.1002/2016GL069666, 2016.
Medley, B., Neumann, T. A., Zwally, H. J., and Smith, B. E.: Forty-year Simulations of Firn Processes over the Greenland and Antarctic Ice Sheets, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-266, in review, 2020.
Moon, T., Joughin, I., Smith, B., van den Broeke, M. R., van de Berg, W. J.,
Noël, B., and Usher, M.: Distinct patterns of seasonal Greenland glacier
velocity, Geophys. Res. Lett., 41, 7209–7216,
https://doi.org/10.1002/2014GL061836, 2014.
Mouginot, J., Rignot, E., Bjørk, A., van den Broeke, M., Millan, R.,
Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-Six Years of
Greenland Ice Sheet Mass Balance from 1972 to 2018, P.
Natl. Acad. Sci. USA, 116, 9239,
https://doi.org/10.1073/pnas.1904242116, 2019.
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M.R., Brenner, A.
C., Brunt, K. M., Cavanaugh, J., Fernandes, S.T., Hancock, D. W., Harbeck,
K., Lee, J., Kurtz, N. T., Luers,P. J., Luthcke, S. B., Magruder, L.,
Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and Thomas,
T. C.: The Ice,Cloud, and Land Elevation Satellite – 2 mission: A global
geolo-cated photon product derived from the Advanced Topographic Laser
Altimeter System, Remote Sens. Environ., 233,
111325, https://doi.org/10.1016/j.rse.2019.111325, 2019.
Noh, M. J. and Howat, I. M.: Automated stereo-photogrammetric DEM
generation at high latitudes: Surface Extraction with TIN-based Search-space
Minimization (SETSM) validation and demonstration over glaciated regions,
GIScience Remote Sens,, 52, 198–217,
https://doi.org/10.1080/15481603.2015.1008621, 2015.
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K.,
Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T.,
Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea
Level Rise and Implications for Low-Lying Islands, Coasts and Communities,
in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M.,
Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A.,
Petzold, J., Rama, B., and Weyer, N. M., IPCC Special Report on the Ocean and
Cryosphere in a Changing Climate, 126, in press,
https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/08_SROCC_Ch04_FINAL.pdf (last access: 30 March 2022), 2019.
Porter, C., Morin, P., Howat, I., Noh, M. J., Bates, B., Peterman, K.,
Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C.,
Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M.
Jr., Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W., Becker,
P., Doshi, A., D'Souza, C., Cummens, P., Laurier, F., and Bojesen, M.:
“ArcticDEM”, Harvard Dataverse [data set], V1, https://doi.org/10.7910/DVN/OHHUKH,
2018.
Rignot, E. and Mouginot, J.: Ice flow in Greenland for the International Polar
Year 2008–2009, Geophys. Res. Lett., 39, L11501,
https://doi.org/10.1029/2012GL051634, 2012.
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., Fricker, H. A., Gardner, A., Siegfried, M. R., Adusumilli, S.,
Csathó, B. M., Holschuh, N., Nilsson, J., Paolo, F. S., and the ICESat-2
Science Team.: ATLAS/ICESat-2 L3A Land Ice Height, Version 3, Boulder,
Colorado USA, NASA National Snow and Ice Data Center Distributed Active
Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL06.003, 2020.
Smith, B. E., Harbeck, K., Roberts, L., Neumann, T., Brunt, K., Fricker,
H. A., Gardner, A., Seigfried, M. R., Adusumilli, S., Csatho, B. M., Holschuh,
N., Nilsson, J., and Paolo, F. S.: ICESat-2 Algorithm Theoretical Basis Document
(ATBD) for Land Ice Along- Track Height (ATL06), Applied Physics Laboratory,
University of Washington, Seattle, WA,
https://icesat-2.gsfc.nasa.gov/science/data-products (last access: 30 March 2022), 2021.
Sutterley, T. C., Velicogna, I., Fettweis, X., Rignot, E., Noel, B., and
van den Broeke, M. R: Evaluation of reconstructions of snow/ice melt in
Greenland by regional atmospheric climate models using laser altimetry data,
Geophys. Res. Lett., 45, 8324–8333,
https://doi.org/10.1029/2018GL078645, 2018.
Vijay, S., Khan, S. A., Kusk, A., Solgaard, A. M., Moon, T., and Bjørk,
A. A.: Resolving Seasonal Ice Velocity of 45 Greenlandic Glaciers With Very
High Temporal Details, Geophys. Res. Lett., 46, 1485–1495,
https://doi.org/10.1029/2018GL081503, 2019.
Vijay, S., King, M., Howat, I., Solgaard, A., Khan, S., and Noël, B.:
Greenland ice-sheet wide glacier classification based on two distinct
seasonal ice velocity behaviors, J. Glaciol., 67, 1241–1248,
https://doi.org/10.1017/jog.2021.89, 2021.
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.
Outlet glaciers are projected to account for half of the total ice loss from the Greenland Ice...