Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-4355-2024
© Author(s) 2024. 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-18-4355-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How well can satellite altimetry and firn models resolve Antarctic firn thickness variations?
Maria T. Kappelsberger
CORRESPONDING AUTHOR
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Martin Horwath
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Eric Buchta
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Matthias O. Willen
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Ludwig Schröder
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
now at: Bundesamt für Kartographie und Geodäsie, Leipzig, Germany
Sanne B. M. Veldhuijsen
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Peter Kuipers Munneke
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Michiel R. van den Broeke
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
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Weiran Li, Sanne B. M. Veldhuijsen, and Stef Lhermitte
The Cryosphere, 19, 37–61, https://doi.org/10.5194/tc-19-37-2025, https://doi.org/10.5194/tc-19-37-2025, 2025
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This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.
Abelardo Romero, Andreas Richter, Amilcar Juarez, Federico Suad Corbetta, Eric Marderwald, Pedro Granovsky, Thorben Döhne, and Martin Horwath
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Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Peter Kuipers Munneke, Nicolaj Hansen, Fredrik Boberg, Christoph Kittel, Charles Amory, and Michiel R. van den Broeke
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Horst Machguth, Andrew Tedstone, Peter Kuipers Munneke, Max Brils, Brice Noël, Nicole Clerx, Nicolas Jullien, Xavier Fettweis, and Michiel van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2024-2750, https://doi.org/10.5194/egusphere-2024-2750, 2024
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Due to increasing air temperatures, surface melt expands to higher elevations on the Greenland ice sheet. This is visible on satellite imagery in the form of rivers of meltwater running across the surface of the ice sheet. We compare model results of meltwater at high elevations on the ice sheet to satellite observations. We find that each of the models shows strengths and weaknesses. A detailed look into the model results reveals potential reasons for the differences between models.
Christiaan T. van Dalum, Willem Jan van de Berg, Srinidhi N. Gadde, Maurice van Tiggelen, Tijmen van der Drift, Erik van Meijgaard, Lambertus H. van Ulft, and Michiel R. van den Broeke
The Cryosphere, 18, 4065–4088, https://doi.org/10.5194/tc-18-4065-2024, https://doi.org/10.5194/tc-18-4065-2024, 2024
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Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
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We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere, 18, 3315–3332, https://doi.org/10.5194/tc-18-3315-2024, https://doi.org/10.5194/tc-18-3315-2024, 2024
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Comprehensive datasets of calving-front changes are essential for studying and modeling outlet glaciers. Current records are limited in temporal resolution due to manual delineation. We use deep learning to automatically delineate calving fronts for 23 glaciers in Greenland. Resulting time series resolve long-term, seasonal, and subseasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.
Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 18, 1983–1999, https://doi.org/10.5194/tc-18-1983-2024, https://doi.org/10.5194/tc-18-1983-2024, 2024
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We use the IMAU firn densification model to simulate the 21st-century evolution of Antarctic firn air content. Ice shelves on the Antarctic Peninsula and the Roi Baudouin Ice Shelf in Dronning Maud Land are particularly vulnerable to total firn air content (FAC) depletion. Our results also underline the potentially large vulnerability of low-accumulation ice shelves to firn air depletion through ice slab formation.
Matthias O. Willen, Martin Horwath, Eric Buchta, Mirko Scheinert, Veit Helm, Bernd Uebbing, and Jürgen Kusche
The Cryosphere, 18, 775–790, https://doi.org/10.5194/tc-18-775-2024, https://doi.org/10.5194/tc-18-775-2024, 2024
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Shrinkage of the Antarctic ice sheet (AIS) leads to sea level rise. Satellite gravimetry measures AIS mass changes. We apply a new method that overcomes two limitations: low spatial resolution and large uncertainties due to the Earth's interior mass changes. To do so, we additionally include data from satellite altimetry and climate and firn modelling, which are evaluated in a globally consistent way with thoroughly characterized errors. The results are in better agreement with independent data.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
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How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Lena G. Buth, Valeria Di Biase, Peter Kuipers Munneke, Stef Lhermitte, Sanne B. M. Veldhuijsen, Sophie de Roda Husman, Michiel R. van den Broeke, and Bert Wouters
EGUsphere, https://doi.org/10.5194/egusphere-2023-2000, https://doi.org/10.5194/egusphere-2023-2000, 2023
Preprint archived
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
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By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Max Brils, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 17, 1675–1696, https://doi.org/10.5194/tc-17-1675-2023, https://doi.org/10.5194/tc-17-1675-2023, 2023
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Firn is the transition of snow to glacier ice and covers 99 % of the Antarctic ice sheet. Knowledge about the firn layer and its variability is important, as it impacts satellite-based estimates of ice sheet mass change. Also, firn contains pores in which nearly all of the surface melt is retained. Here, we improve a semi-empirical firn model and simulate the firn characteristics for the period 1979–2020. We evaluate the performance with field and satellite measures and test its sensitivity.
Marte G. Hofsteenge, Nicolas J. Cullen, Carleen H. Reijmer, Michiel van den Broeke, Marwan Katurji, and John F. Orwin
The Cryosphere, 16, 5041–5059, https://doi.org/10.5194/tc-16-5041-2022, https://doi.org/10.5194/tc-16-5041-2022, 2022
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In the McMurdo Dry Valleys (MDV), foehn winds can impact glacial meltwater production and the fragile ecosystem that depends on it. We study these dry and warm winds at Joyce Glacier and show they are caused by a different mechanism than that found for nearby valleys, demonstrating the complex interaction of large-scale winds with the mountains in the MDV. We find that foehn winds increase sublimation of ice, increase heating from the atmosphere, and increase the occurrence and rates of melt.
Lena G. Buth, Bert Wouters, Sanne B. M. Veldhuijsen, Stef Lhermitte, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-127, https://doi.org/10.5194/tc-2022-127, 2022
Manuscript not accepted for further review
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Vasaw Tripathi, Andreas Groh, Martin Horwath, and Raaj Ramsankaran
Hydrol. Earth Syst. Sci., 26, 4515–4535, https://doi.org/10.5194/hess-26-4515-2022, https://doi.org/10.5194/hess-26-4515-2022, 2022
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GRACE/GRACE-FO provided global observations of water storage change since 2002. Scaling is a common approach to compensate for the spatial filtering inherent to the results. However, for complex hydrological basins, the compatibility of scaling with the characteristics of regional hydrology has been rarely assessed. We assess traditional scaling approaches and a new scaling approach for the Indus Basin. Our results will help users with regional focus understand implications of scaling choices.
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 16, 1071–1089, https://doi.org/10.5194/tc-16-1071-2022, https://doi.org/10.5194/tc-16-1071-2022, 2022
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In this study, we improve the regional climate model RACMO2 and investigate the climate of Antarctica. We have implemented a new radiative transfer and snow albedo scheme and do several sensitivity experiments. When fully tuned, the results compare well with observations and snow temperature profiles improve. Moreover, small changes in the albedo and the investigated processes can lead to a strong overestimation of melt, locally leading to runoff and a reduced surface mass balance.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
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Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Maaike Izeboud, and Michiel van den Broeke
The Cryosphere, 15, 5639–5658, https://doi.org/10.5194/tc-15-5639-2021, https://doi.org/10.5194/tc-15-5639-2021, 2021
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Antarctica is shrinking, and part of the mass loss is caused by higher temperatures leading to more snowmelt. We use computer models to estimate the amount of melt, but this can be inaccurate – specifically in the areas with the most melt. This is because the model cannot account for small, darker areas like rocks or darker ice. Thus, we trained a computer using artificial intelligence and satellite images that showed these darker areas. The model computed an improved estimate of melt.
Kenneth D. Mankoff, Xavier Fettweis, Peter L. Langen, Martin Stendel, Kristian K. Kjeldsen, Nanna B. Karlsson, Brice Noël, Michiel R. van den Broeke, Anne Solgaard, William Colgan, Jason E. Box, Sebastian B. Simonsen, Michalea D. King, Andreas P. Ahlstrøm, Signe Bech Andersen, and Robert S. Fausto
Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, https://doi.org/10.5194/essd-13-5001-2021, 2021
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We estimate the daily mass balance and its components (surface, marine, and basal mass balance) for the Greenland ice sheet. Our time series begins in 1840 and has annual resolution through 1985 and then daily from 1986 through next week. Results are operational (updated daily) and provided for the entire ice sheet or by commonly used regions or sectors. This is the first input–output mass balance estimate to include the basal mass balance.
Lukas Müller, Martin Horwath, Mirko Scheinert, Christoph Mayer, Benjamin Ebermann, Dana Floricioiu, Lukas Krieger, Ralf Rosenau, and Saurabh Vijay
The Cryosphere, 15, 3355–3375, https://doi.org/10.5194/tc-15-3355-2021, https://doi.org/10.5194/tc-15-3355-2021, 2021
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Harald Moltke Bræ, a marine-terminating glacier in north-western Greenland, undergoes remarkable surges of episodic character. Our data show that a recent surge from 2013 to 2019 was initiated at the glacier front and exhibits a pronounced seasonality with flow velocities varying by 1 order of magnitude, which has not been observed at Harald Moltke Bræ in this way before. These findings are crucial for understanding surge mechanisms at Harald Moltke Bræ and other marine-terminating glaciers.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
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We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Mirko Scheinert, Christoph Mayer, Martin Horwath, Matthias Braun, Anja Wendt, and Daniel Steinhage
Polarforschung, 89, 57–64, https://doi.org/10.5194/polf-89-57-2021, https://doi.org/10.5194/polf-89-57-2021, 2021
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Ice sheets, glaciers and further ice-covered areas with their changes as well as interactions with the solid Earth and the ocean are subject of intensive research, especially against the backdrop of global climate change. The resulting questions are of concern to scientists from various disciplines such as geodesy, glaciology, physical geography and geophysics. Thus, the working group "Polar Geodesy and Glaciology", founded in 2013, offers a forum for discussion and stimulating exchange.
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 15, 1823–1844, https://doi.org/10.5194/tc-15-1823-2021, https://doi.org/10.5194/tc-15-1823-2021, 2021
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Absorption of solar radiation is often limited to the surface in regional climate models. Therefore, we have implemented a new radiative transfer scheme in the model RACMO2, which allows for internal heating and improves the surface reflectivity. Here, we evaluate its impact on the surface mass and energy budget and (sub)surface temperature, by using observations and the previous model version for the Greenland ice sheet. New results match better with observations and introduce subsurface melt.
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
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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.
J. Melchior van Wessem, Christian R. Steger, Nander Wever, and Michiel R. van den Broeke
The Cryosphere, 15, 695–714, https://doi.org/10.5194/tc-15-695-2021, https://doi.org/10.5194/tc-15-695-2021, 2021
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This study presents the first modelled estimates of perennial firn aquifers (PFAs) in Antarctica. PFAs are subsurface meltwater bodies that do not refreeze in winter due to the isolating effects of the snow they are buried underneath. They were first identified in Greenland, but conditions for their existence are also present in the Antarctic Peninsula. These PFAs can have important effects on meltwater retention, ice shelf stability, and, consequently, sea level rise.
Baojuan Huai, Michiel R. van den Broeke, and Carleen H. Reijmer
The Cryosphere, 14, 4181–4199, https://doi.org/10.5194/tc-14-4181-2020, https://doi.org/10.5194/tc-14-4181-2020, 2020
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This study presents the surface energy balance (SEB) of the Greenland Ice Sheet (GrIS) using a SEB model forced with observations from automatic weather stations (AWSs). We correlate ERA5 with AWSs to show a significant positive correlation of GrIS summer surface temperature and melt with the Greenland Blocking Index and weaker and opposite correlations with the North Atlantic Oscillation. This analysis may help explain melting patterns in the GrIS with respect to circulation anomalies.
Jenny V. Turton, Amélie Kirchgaessner, Andrew N. Ross, John C. King, and Peter Kuipers Munneke
The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, https://doi.org/10.5194/tc-14-4165-2020, 2020
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Föhn winds are warm and dry downslope winds in the lee of a mountain range, such as the Antarctic Peninsula. Föhn winds heat the ice of the Larsen C Ice Shelf at the base of the mountains and promote more melting than during non-föhn periods in spring, summer and autumn in both model output and observations. Especially in spring, when they are most frequent, föhn winds can extend the melt season by over a month and cause a similar magnitude of melting to that observed in summer.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
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We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Baptiste Vandecrux, Ruth Mottram, Peter L. Langen, Robert S. Fausto, Martin Olesen, C. Max Stevens, Vincent Verjans, Amber Leeson, Stefan Ligtenberg, Peter Kuipers Munneke, Sergey Marchenko, Ward van Pelt, Colin R. Meyer, Sebastian B. Simonsen, Achim Heilig, Samira Samimi, Shawn Marshall, Horst Machguth, Michael MacFerrin, Masashi Niwano, Olivia Miller, Clifford I. Voss, and Jason E. Box
The Cryosphere, 14, 3785–3810, https://doi.org/10.5194/tc-14-3785-2020, https://doi.org/10.5194/tc-14-3785-2020, 2020
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In the vast interior of the Greenland ice sheet, snow accumulates into a thick and porous layer called firn. Each summer, the firn retains part of the meltwater generated at the surface and buffers sea-level rise. In this study, we compare nine firn models traditionally used to quantify this retention at four sites and evaluate their performance against a set of in situ observations. We highlight limitations of certain model designs and give perspectives for future model development.
Christiaan T. van Dalum, Willem Jan van de Berg, Stef Lhermitte, and Michiel R. van den Broeke
The Cryosphere, 14, 3645–3662, https://doi.org/10.5194/tc-14-3645-2020, https://doi.org/10.5194/tc-14-3645-2020, 2020
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The reflectivity of sunlight, which is also known as albedo, is often inadequately modeled in regional climate models. Therefore, we have implemented a new snow and ice albedo scheme in the regional climate model RACMO2. In this study, we evaluate a new RACMO2 version for the Greenland ice sheet by using observations and the previous model version. RACMO2 output compares well with observations, and by including new processes we improve the ability of RACMO2 to make future climate projections.
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
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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.
Vincent Verjans, Amber A. Leeson, Christopher Nemeth, C. Max Stevens, Peter Kuipers Munneke, Brice Noël, and Jan Melchior van Wessem
The Cryosphere, 14, 3017–3032, https://doi.org/10.5194/tc-14-3017-2020, https://doi.org/10.5194/tc-14-3017-2020, 2020
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Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
Cited articles
Abshire, J., Sun, X., Riris, H., Sirota, J., MCGarry, J., Palm, S., Yi, D., and Liiva, P.: Geoscience Laser Altimeter System (GLAS) on the ICESat Mission: On–orbit measurement performance, Geophys. Res. Lett., 32, L21S02, https://doi.org/10.1029/2005GL024028, 2005. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a, b, c
Amory, C., Buizert, C., Buzzard, S., Case, E., Clerx, N., Culberg, R., Datta, R., Dey, R., Drews, R., Dunmire, D., Eayrs, C., Hansen, N., Humbert, A., Kaitheri, A., Keegan, K., Kuipers Munneke, P., Lenaerts, J., Lhermitte, S., Mair, D., McDowell, I., Mejia, J., Meyer, C., Morris, E., Moser, D., Oraschewski, F., Pearce, E., de Roda Husman, S., Schlegel, N.-J., Schultz, T., Simonsen, S., Stevens, C., Thomas, E., Thompson-Munson, M., Wever, N., and Wouters, B.: Firn on ice sheets, Nat. Rev. Earth Environ., 5, 79–99, https://doi.org/10.1038/s43017-023-00507-9, 2024. a
Arthern, R., Vaughan, D., Rankin, A., Mulvaney, R., and Thomas, E.: In situ measurements of Antarctic snow compaction compared with predictions of models, J. Geophys. Res., 115, F03011, https://doi.org/10.1029/2009JF001306, 2010. a
Bodart, J. and Bingham, R.: The Impact of the Extreme 2015-16 El Niño on the Mass Balance of the Antarctic Ice Sheet, Geophys. Res. Lett., 46, 13862–13871, https://doi.org/10.1029/2019GL084466, 2019. a
Boening, C., Lebsock, M., Landerer, F., and Stephens, G.: Snowfall-driven mass change on the East Antarctic ice sheet, Geophys. Res. Lett., 39, L21501, https://doi.org/10.1029/2012GL053316, 2012. a
Boergens, E., Rangelova, E., Sideris, M., and Kusche, J.: Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data, J. Geophys. Res.-Sol. Ea., 119, 4429–4447, https://doi.org/10.1002/2013JB010452, 2014. a
Bos, M., Fernandes, R., Williams, S., and Bastos, L.: Fast error analysis of continuous GNSS observations with missing data, J. Geod., 87, 351–360, https://doi.org/10.1007/s00190-012-0605-0, 2012. a, b, c
Brils, M., Kuipers Munneke, P., Van de Berg, W., and Van den Broeke, M.: IMAU-FDM v1.2G GDM release (v1.2G), Zenodo [code], https://doi.org/10.5281/zenodo.5172513, 2021. a
Brockley, D., Baker, S., Femenias, P., Martinez, B., Massmann, F.-H., Otten, M., Paul, F., Picard, B., Prandi, P., Roca, M., Rudenko, S., Scharroo, R., and Visser, P.: REAPER: Reprocessing 12 Years of ERS-1 and ERS-2 Altimeters and Microwave Radiometer Data, IEEE T. Geosci. Remote, 55, 5506–5514, https://doi.org/10.1109/TGRS.2017.2709343, 2017. a
Dadic, R., Mott, R., Horgan, H., and Lehning, M.: Observations, theory, and modeling of the differential accumulation of Antarctic megadunes, J. Geophys. Res.-Earth, 118, 2343–2353, https://doi.org/10.1002/2013JF002844, 2013. a
Davis, C. and Ferguson, A.: Elevation change of the Antarctic ice sheet, 1995–2000, from ERS-2 satellite radar altimetry, IEEE T. Geosci. Remote, 42, 2437–2445, https://doi.org/10.1109/TGRS.2004.836789, 2004. a, b
Davison, B., Hogg, A., Rigby, R., Veldhuijsen, S., van Wessem, J., van den Broeke, M., Holland, P., Selley, H., and Dutrieux, P.: Sea level rise from West Antarctic mass loss significantly modified by large snowfall anomalies, Nat. Commun., 14, 1479, https://doi.org/10.1038/s41467-023-36990-3, 2023. a
Eisen, O., Frezzotti, M., Genthon, C., Isaksson, E., Magand, O., van den Broeke, M., Dixon, D., Ekaykin, A., Holmlund, P., Kameda, T., Karlöf, L., Kaspari, S., Lipenkov, V., Oerter, H., Takahashi, S., and Vaughan, D.: Ground-based measurements of spatial and temporal variability of snow accumulation in East Antarctica, Rev. Geophys., 46, RG2001, https://doi.org/10.1029/2006RG000218, 2008. a
Fahnestock, M., Scambos, T., Shuman, C., Arthern, R., Winebrenner, D., and Kwok, R.: Snow megadune fields on the East Antarctic Plateau: Extreme atmosphere-ice interaction, Geophys. Res. Lett., 27, 3719–3722, https://doi.org/10.1029/1999GL011248, 2000. a, b
Ferguson, A., Davis, C., and Cavanaugh, J.: An Autoregressive Model for Analysis of Ice Sheet Elevation Change Time Series, IEEE T. Geosci. Remote, 42, 2426–2436, https://doi.org/10.1109/TGRS.2004.836788, 2004. a
Forootan, E. and Kusche, J.: Separation of global time-variable gravity signals into maximally independent components, J. Geod., 86, 477–497, https://doi.org/10.1007/s00190-011-0532-5, 2012. a
Fox-Kemper, B., Hewitt, H., Xiao, C., Drijfhout, S., Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I., Ruiz, L., Sallée, J.-B., Slangen, A., and Yu, Y.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chap. Ocean, Cryosphere and Sea Level Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013. a
Frezzotti, M., Gandolfi, S., and Urbini, S.: Snow megadunes in Antarctica: Sedimentary structure and genesis, J. Geophys. Res., 107, 4344, https://doi.org/10.1029/2001JD000673, 2002. a
Gelaro, R., McCarty, W., Suárez, M., Todling, R., Molod, A., Takacs, L., Randles, C., Darmenov, A., Bosilovich, M., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S., 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. a
Groh, A., Horwath, M., Horvath, A., Meister, R., Sørensen, L., Barletta, V., Forsberg, R., Wouters, B., Ditmar, P., Ran, J., Klees, R., Su, X., Shang, K., Guo, J., Shum, C., Schrama, E., and Shepherd, A.: Evaluating GRACE Mass Change Time Series for the Antarctic and Greenland Ice Sheet—Methods and Results, Geosciences, 9, 415, https://doi.org/10.3390/geosciences9100415, 2019. a
Gutiérrez, J., Jones, R., Narisma, G., Alves, L., Amjad, M., Gorodetskaya, I., Grose, M., Klutse, N., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L., Mernild, S., Ngo-Duc, T., van den Hurk, B., and Yoon, J.-H.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chap. Atlas, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1927–2058, https://doi.org/10.1017/9781009157896.021, 2021. a, b, c
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, 2014. a
Helm, V., Dehghanpour, A., Hänsch, R., Loebel, E., Horwath, M., and Humbert, A.: AWI-ICENet1: A convolutional neural network retracker for ice altimetry, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-80, in review, 2023. a
Helsen, M., van den Broeke, M., van de Wal, R., van de Berg, W., van Meijgaard, E., Davis, C., Li, Y., and Goodwin, I.: Elevation Changes in Antarctica Mainly Determined by Accumulation Variability, Science, 320, 1626–1629, https://doi.org/10.1126/science.1153894, 2008. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Horwath, M., Legrésy, B., Rémy, F., Blarel, F., and Lemoine, J.-M.: Consistent patterns of Antarctic ice sheet interannual variations from ENVISAT radar altimetry and GRACE satellite gravimetry, Geophys. J. Int., 189, 863–876, https://doi.org/10.1111/j.1365-246X.2012.05401.x, 2012. a
Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, 2022. a, b
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chap. Summary for Policymakers, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 3–32, https://doi.org/10.1017/9781009157896.001, 2021. a
Jolliffe, I.: Principal Component Analysis, Springer Series in Statistics, Springer New York, NY, 2nd Edn., ISBN 978-0-387-22440-4, https://doi.org/10.1007/b98835, 2002. a
Kaitheri, A., Mémin, A., and Rémy, F.: Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model, Remote Sens., 13, 2199, https://doi.org/10.3390/rs13112199, 2021. a, b
Khvorostovsky, K.: Merging and Analysis of Elevation Time Series Over Greenland Ice Sheet From Satellite Radar Altimetry, IEEE T. Geosci. Remote, 50, 23–36, https://doi.org/10.1109/TGRS.2011.2160071, 2012. a
Kim, B.-H., Seo, K.-W., Eom, J., Chen, J., and Wilson, C.: Antarctic ice mass variations from 1979 to 2017 driven by anomalous precipitation accumulation, Sci. Rep., 10, 2045–2322, https://doi.org/10.1038/s41598-020-77403-5, 2020. a
King, M. and Watson, C.: Antarctic Surface Mass Balance: Natural Variability, Noise, and Detecting New Trends, Geophys. Res. Lett., 47, e2020GL087493, https://doi.org/10.1029/2020GL087493, 2020. a, b, c
Kuipers Munneke, P., Ligtenberg, S. R. M., Noël, B. P. Y., Howat, I. M., Box, J. E., Mosley-Thompson, E., McConnell, J. R., Steffen, K., Harper, J. T., Das, S. B., and van den Broeke, M. R.: Elevation change of the Greenland Ice Sheet due to surface mass balance and firn processes, 1960–2014, The Cryosphere, 9, 2009–2025, https://doi.org/10.5194/tc-9-2009-2015, 2015. a
Lenaerts, J., van den Broeke, M., Déry, S., van Meijgaard, E., van de Berg, W., Palm, S., and Sanz Rodrigo, J.: Modeling drifting snow in Antarctica with a regional climate model: 1. Methods and model evaluation, J. Geophys. Res., 117, D05108, https://doi.org/10.1029/2011JD016145, 2012. a, b
Lenaerts, J., van Meijgaard, E., van den Broeke, M., Ligtenberg, S., Horwath, M., and Isaksson, E.: Recent snowfall anomalies in Dronning Maud Land, East Antarctica, in a historical and future climate perspective, Geophys. Res. Lett., 40, 2684–2688, https://doi.org/10.1002/grl.50559, 2013. a
Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere, 5, 809–819, https://doi.org/10.5194/tc-5-809-2011, 2011. a, b
Ligtenberg, S., Horwath, M., van den Broeke, M., and Legrésy, B.: Quantifying the seasonal “breathing” of the Antarctic ice sheet, Geophys. Res. Lett., 39, L23501, https://doi.org/10.1029/2012GL053628, 2012. a
Lundin, J., Stevens, C., Arthern, R., Buizert, C., Orsi, A., Ligtenberg, S., Simonsen, S., Cummings, E., Essery, R., Leahy, W., Harris, P., Helsen, M., and Waddington, E.: Firn Model Intercomparison Experiment (FirnMICE), J. Glaciol., 63, 401–422, https://doi.org/10.1017/jog.2016.114, 2017. a
Marsaglia, G., Tsang, W., and Wang, J.: Evaluating Kolmogorov's Distribution, J. Stati. Softw., 8, 1–4, https://doi.org/10.18637/jss.v008.i18, 2003. a
Massey, F.: The Kolmogorov-Smirnov Test for Goodness of Fit, J. Ame. Stat. Assoc., 46, 68–78, 1951. a
Medley, B., Neumann, T., Zwally, H., Smith, B., and Stevens, C.: NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: 1980–2021, Zenodo [data set], https://doi.org/10.5281/zenodo.7054574, 2022b. a
Mémin, A., Flament, T., Alizier, B., Watson, C., and Rémy, F.: Interannual variation of the Antarctic Ice Sheet from a combined analysis of satellite gravimetry and altimetry data, Earth Planet. Sc. Lett., 422, 150–156, https://doi.org/10.1016/j.epsl.2015.03.045, 2015. a
Miller, L.: Table of Percentage Points of Kolmogorov Statistics, J. Am. Stat. Assoc., 51, 111–121, 1956. a
Mottram, R., Hansen, N., Kittel, C., van Wessem, J. M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates, The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, 2021. a, b, c, d, e, f
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. a
Nilsson, J., Gardner, A., and Paolo, F.: MEaSUREs ITS_LIVE Antarctic Grounded Ice Sheet Elevation Change, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/L3LSVDZS15ZV, 2021. a, b
Noble, T., Rohling, E., Aitken, A., Bostock, H., Chase, Z., Gomez, N., Jong, L., King, M., Mackintosh, A., McCormack, F., McKay, R., Menviel, L., Phipps, S., Weber, M., Fogwill, C., Gayen, B., Golledge, N., Gwyther, D., Hogg, A., Martos, Y., Pena-Molino, B., Roberts, J., van de Flierdt, T., and Williams, T.: The sensitivity of the Antarctic Ice Sheet to a changing climate: Past, present and future, Rev. Geophys., 58, 2019RG000663, https://doi.org/10.1029/2019RG000663, 2020. a
Noël, B., van de Berg, W. J., Machguth, H., Lhermitte, S., Howat, I., Fettweis, X., and van den Broeke, M. R.: A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10, 2361–2377, https://doi.org/10.5194/tc-10-2361-2016, 2016. a
Noël, B., van Wessem, J., Wouters, B., Trusel, L., Lhermitte, S., and van den Broeke, M.: Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution, Nat. Commun., 14, 7949, https://doi.org/10.1038/s41467-023-43584-6, 2023. a
North, G., Bell, T., Cahalan, R., and Moeng, F.: Sampling errors in the estimation of empirical orthogonal functions, Mon. Weather Rev., 110, 699–706, 1982. a
Otosaka, I., Horwath, M., Mottram, R., and Nowicki, S.: Mass Balances of the Antarctic and Greenland Ice Sheets Monitored from Space, Surv. Geophys., 44, 1615–1652, https://doi.org/10.1007/s10712-023-09795-8, 2023a. a, b, c
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023b. a, b
Preisendorfer, R.: Principal Component Analysis in Meteorology and Oceanography, Elsevier Science Publishers B.V., Amsterdam, ISBN 978-0444430144, 1988. a
Rémy, F., Flament, T., Blarel, F., and Benveniste, J.: Radar altimetry measurements over antarctic ice sheet: A focus on antenna polarization and change in backscatter problems, Adv. Space Res., 50, 998–1006, https://doi.org/10.1016/j.asr.2012.04.003, 2012. a, b
Richter, A., Ekaykin, A., Willen, M., Lipenkov, V., Groh, A., Popov, S., Scheinert, M., Horwath, M., and Dietrich, R.: Surface Mass Balance Models Vs. Stake Observations: A Comparison in the Lake Vostok Region, Central East Antarctica, Front. Earth Sci., 9, 388, https://doi.org/10.3389/feart.2021.669977, 2021. a
Rignot, E., Mouginot, J., and Scheuchl, B.: Ice Flow of the Antarctic Ice Sheet, Science, 333, 1427–1430, https://doi.org/10.1126/science.1208336, 2011a. a, b
Rignot, E., Mouginot, J., and Scheuchl, B.: Antarctic grounding line mapping from differential satellite radar interferometry, Geophys. Res. Lett., 38, L10504, https://doi.org/10.1029/2011GL047109, 2011b. a, b
Rignot, E., Mouginot, J., Scheuchl, B., van den Broeke, M., Van Wessem, J., and Morlighem, M.: Four decades of Antarctic Ice Sheet mass balance from 1979–2017, P. Natl. Acad. Sci. USA, 116, 1095–1103, https://doi.org/10.1073/pnas.1812883116, 2019. a
Sasgen, I., Dobslaw, H., Martinec, Z., and Thomas, M.: Satellite gravimetry observation of Antarctic snow accumulation related to ENSO, Earth Planet. Sc. Lett., 299, 352–358, https://doi.org/10.1016/j.epsl.2010.09.015, 2010. a
Scambos, T., Frezzotti, M., Haran, T., Bohlander, J., Lenaerts, J., Van Den Broeke, M., 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. a, b
Schlegel, N.-J., Seroussi, H., Schodlok, M. P., Larour, E. Y., Boening, C., Limonadi, D., Watkins, M. M., Morlighem, M., and van den Broeke, M. R.: Exploration of Antarctic Ice Sheet 100-year contribution to sea level rise and associated model uncertainties using the ISSM framework, The Cryosphere, 12, 3511–3534, https://doi.org/10.5194/tc-12-3511-2018, 2018. a
Schröder, L., Horwath, M., Dietrich, R., Helm, V., van den Broeke, M. R., and Ligtenberg, S. R. M.: Four decades of Antarctic surface elevation changes from multi-mission satellite altimetry, The Cryosphere, 13, 427–449, https://doi.org/10.5194/tc-13-427-2019, 2019a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Schröder, L., Horwath, M., Dietrich, R., Helm, V., van den Broeke, M., and Ligtenberg, S.: Gridded surface elevation changes from multi-mission satellite altimetry 1978–2017, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.897390, 2019b. a
Shepherd, A., Gilbert, L., Muir, A., Konrad, H., McMillan, M., Slater, T., Briggs, K., Sundal, A., Hogg, A., and Engdahl, M.: Trends in Antarctic Ice Sheet Elevation and Mass, Geophys. Res. Lett., 46, 8174–8183, https://doi.org/10.1029/2019GL082182, 2019. a, b
Shi, T., Fukuda, Y., Doi, K., and Okuno, J.: Extraction of GRACE/GRACE-FO observed mass change patterns across Antarctica via independent component analysis (ICA), Geophys. J. Int., 229, 1914–1926, https://doi.org/10.1093/gji/ggac033, 2022. a
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. a
Stevens, C. M., Verjans, V., Lundin, J. M. D., Kahle, E. C., Horlings, A. N., Horlings, B. I., and Waddington, E. D.: The Community Firn Model (CFM) v1.0, Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, 2020. a
Stevens, M., Vo, H., Emmakahle, and Jboat: UWGlaciology/CommunityFirnModel: Version 1.1.6 (v1.1.6), Zenodo [code], https://doi.org/10.5281/zenodo.5719748, 2021. a
Su, X., Shum, C., Guo, J., Howat, I., Kuo, C., Jezek, K., Duan, J., and Yi, Y.: High-Resolution Interannual Mass Anomalies of the Antarctic Ice Sheet by Combining GRACE Gravimetry and ENVISAT Altimetry, IEEE T. Geosci. Remote, 56, 539–546, https://doi.org/10.1109/TGRS.2017.2751070, 2018. a
Tian, B., Lee, H., Waliser, D. E., Ferraro, R., Kim, J., Case, J., Iguchi, T., Kemp, E., Wu, D., Putman, W., and Wang, W.: Development of a Model Performance Metric and Its Application to Assess Summer Precipitation over the U.S. Great Plains in Downscaled Climate Simulations, J. Hydrometeorol., 18, 2781–2799, https://doi.org/10.1175/JHM-D-17-0045.1, 2017. a
van Dalum, C. T., van de Berg, W. J., Gadde, S. N., van Tiggelen, M., van der Drift, T., van Meijgaard, E., van Ulft, L. H., and van den Broeke, M. R.: First results of the polar regional climate model RACMO2.4, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-895, 2024. a
van den Broeke, M.: Depth and density of the Antarctic firn layer, Arct. Antarct. Alp. Res., 40, 432–438, 2008. a
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 Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley, B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., De Van Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, J. Glaciol., 60, 761–770, https://doi.org/10.3189/2014JoG14J051, 2014. a
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018. a, b, c, d
Veldhuijsen, S. B. M., van de Berg, W. J., Brils, M., Kuipers Munneke, P., and van den Broeke, M. R.: Characteristics of the 1979–2020 Antarctic firn layer simulated with IMAU-FDM v1.2A, The Cryosphere, 17, 1675–1696, https://doi.org/10.5194/tc-17-1675-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Verjans, V., Leeson, A. M., Stevens, C., van Wessem, J. M., van de Berg, W., van den Broeke, M., Kittel, C., Amory, C., Fettweis, X., Hansen, N., Boberg, F., and Mottram, R.: Uncertainty in East Antarctic Firn Thickness Constrained Using a Model Ensemble Approach, Geophys. Res. Lett., 48, e2020GL092060, https://doi.org/10.1029/2020GL092060, 2021. a, b, c, d
Willen, M., Broerse, T., Groh, A., Wouters, B., Kuipers Munneke, P., Horwath, M., van den Broeke, M., and Schröder, L.: Separating Long-Term and Short-Term Mass Changes of Antarctic Ice Drainage Basins: A Coupled State Space Analysis of Satellite Observations and Model Products, J. Geophys. Res.-Earth, 126, e2020JF005966, https://doi.org/10.1029/2020JF005966, 2021. a, b, c
Willen, M., Horwath, M., Groh, A., Helm, V., Uebbing, B., and Kusche, J.: Feasibility of a global inversion for spatially resolved glacial isostatic adjustment and ice sheet mass changes proven in simulation experiments, J. Geod., 96, 1–21, https://doi.org/10.1007/s00190-022-01651-8, 2022. a
Williams, S., Moore, P., King, M., and Whitehouse, P.: Revisiting GRACE Antarctic ice mass trends and accelerations considering autocorrelation, Earth Planet. Sc. Lett., 385, 12–21, https://doi.org/10.1016/j.epsl.2013.10.016, 2014. a
Wingham, D., Ridout, A., Scharroo, R., Arthern, R., and Shum, C.: Antarctic Elevation Change from 1992 to 1996, Science, 282, 456–458, https://doi.org/10.1126/science.282.5388.456, 1998. a
Wouters, B., Bamber, J., van den Broeke, M., Lenaerts, J., and Sasgen, I.: Limits in detecting acceleration of ice sheet mass loss due to climate variability, Nat. Geosci., 6, 613–616, https://doi.org/10.1038/ngeo1874, 2013. a
Zhang, B., Yao, Y., Liu, L., and Yang, Y.: Interannual ice mass variations over the Antarctic ice sheet from 2003 to 2017 were linked to El Niño-Southern Oscillation, Earth Planet. Sc. Lett., 560, 116796, https://doi.org/10.1016/j.epsl.2021.116796, 2021. a
Zwally, H., Giovinetto, M., Li, J., Cornejo, H., Beckley, M., Brenner, A., Saba, J., and Yi, D.: Mass changes of the Greenland and Antarctic ice sheets and shelves and contributions to sea-level rise: 1992–2002, J. Glaciol., 51, 509–527, https://doi.org/10.3189/172756505781829007, 2005. a
Short summary
The interannual variations in the height of the Antarctic Ice Sheet (AIS) are mainly due to natural variations in snowfall. Precise knowledge of these variations is important for the detection of any long-term climatic trends in AIS surface elevation. We present a new product that spatially resolves these height variations over the period 1992–2017. The product combines the strengths of atmospheric modeling results and satellite altimetry measurements.
The interannual variations in the height of the Antarctic Ice Sheet (AIS) are mainly due to...