Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5639-2021
© Author(s) 2021. 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-15-5639-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2021
Research article |
| 13 Dec 2021
| 13 Dec 2021
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands
Peter Kuipers Munneke
Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands
Stef Lhermitte
Department of Geoscience & Remote Sensing, Delft University of Technology, Delft, the Netherlands
Maaike Izeboud
Department of Geoscience & Remote Sensing, Delft University of Technology, Delft, the Netherlands
Michiel van den Broeke
Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands
Related authors
No articles found.
Ida Haven, Hans Christian Steen-Larsen, Laura J. Dietrich, Sonja Wahl, Jason E. Box, Michiel R. van den Broeke, Alun Hubbard, Stephan T. Kral, Joachim Reuder, and Maurice van Tiggelen
The Cryosphere, 20, 573–593, https://doi.org/10.5194/tc-20-573-2026, https://doi.org/10.5194/tc-20-573-2026, 2026
Short summary
Short summary
Three independent Eddy-Covariance measurement systems deployed on top of the Greenland Ice Sheet are compared. Using this dataset, we evaluate the reproducibility and quantify the differences between the systems. The fidelity of two regional climate models in capturing the seasonal variability in the latent and sensible heat flux between the snow surface and the atmosphere is assessed. We identify differences between observations and model simulations, especially during the winter period.
Thore Kausch, Stef Lhermitte, Marie G. P. Cavitte, Eric Keenan, and Shashwat Shukla
The Cryosphere, 20, 511–526, https://doi.org/10.5194/tc-20-511-2026, https://doi.org/10.5194/tc-20-511-2026, 2026
Short summary
Short summary
Determining the net balance of snow accumulation on the surface of Antarctica is challenging. Sentinel-1 satellite sensors, which can see through snow, offer a promising method. However, linking their signals to snow amounts is complex due to snow's internal structure and limited on-the-ground data. This study found a connection between satellite signals and snow levels at three locations in Dronning Maud Land. Using models and field data, the method shows potential for wider use in Antarctica.
Horst Machguth, Andrew Tedstone, Peter Kuipers Munneke, Max Brils, Brice Noël, Nicole Clerx, Nicolas Jullien, Xavier Fettweis, and Michiel van den Broeke
The Cryosphere, 20, 427–452, https://doi.org/10.5194/tc-20-427-2026, https://doi.org/10.5194/tc-20-427-2026, 2026
Short summary
Short summary
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.
Valeria Di Biase, Peter Kuipers Munneke, Bert Wouters, Michiel R. van den Broeke, and Maurice van Tiggelen
The Cryosphere, 20, 87–96, https://doi.org/10.5194/tc-20-87-2026, https://doi.org/10.5194/tc-20-87-2026, 2026
Short summary
Short summary
We produce annual maps of Antarctic surface melt volumes from 2012 to 2021 using satellite microwave data. We detect melting days from thresholds on the satellite signal and then use actual melt measurements from weather stations to convert those signals into water‑equivalent volumes. Our maps capture known melt hotspots and show slightly lower totals than climate models. This dataset supports climate and ice‑shelf studies.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin L. Edwards, Xavier Fettweis, Fabien Gillet-Chaulet, Quentin Glaude, Philippe Huybrechts, Sébastien Le clec'h, Ruth Mottram, Brice Noël, Martin Olesen, Charlotte Rahlves, Jeremy Rohmer, Michiel van den Broeke, and Roderik S. W. van de Wal
The Cryosphere, 19, 6887–6906, https://doi.org/10.5194/tc-19-6887-2025, https://doi.org/10.5194/tc-19-6887-2025, 2025
Short summary
Short summary
We present an ensemble of ice sheet model projections for the Greenland ice sheet. The focus is on providing projections that improve our understanding of the range future sea-level rise and the inherent uncertainties over the next 100 to 300 years. Compared to earlier work we more fully account for some of the uncertainties in sea-level projections. We include a wider range of climate model output, more climate change scenarios and we extend projections schematically up to year 2300.
Thirza N. Feenstra, Willem Jan van de Berg, Gerd-Jan van Zadelhoff, David P. Donovan, Christiaan T. van Dalum, and Michiel R. van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2025-5623, https://doi.org/10.5194/egusphere-2025-5623, 2025
Short summary
Short summary
Cloud representation brings large uncertainties in polar climate modeling. We show the first evaluation of Greenland clouds in the regional climate model RACMO2.4 using new EarthCARE satellite data. Comparing lidar and radar observations and retrieved cloud profiles with co-located RACMO output, we find RACMO captures lower ice clouds but underestimates thin high clouds, mid-altitude liquid clouds, and snowfall. These results highlight EarthCARE’s potential to improve polar climate models.
Julius Sommer, Maaike Izeboud, Sophie de Roda Husman, Bert Wouters, and Stef Lhermitte
The Cryosphere, 19, 5903–5912, https://doi.org/10.5194/tc-19-5903-2025, https://doi.org/10.5194/tc-19-5903-2025, 2025
Short summary
Short summary
Ice shelves, the floating extensions of Antarctica’s ice sheet, play a crucial role in preventing mass ice loss, and understanding their stability is crucial. If surface meltwater lakes drain rapidly through fractures, the ice shelf can destabilize. We analyzed satellite images of four years from the Shackleton Ice Shelf and found that lake drainages occurred in areas where damage is present and developing, and coincided with rising tides, offering insights into the drivers of this process.
Fredrik Boberg, Nicolaj Hansen, Ruth Mottram, Xavier Fettweis, and Michiel R. van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2025-4360, https://doi.org/10.5194/egusphere-2025-4360, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
An ensemble of regional climate model simulations is used to estimate the 21st century change in precipitation on the Greenland ice sheet. For the end of the century, the change is in the range 40 to 170 Gt per year, depending on the emission scenario. Using annual values of 2 m air temperature and precipitation, we estimate an increase in precipitation of 35 Gt per year for every degree of warming.
Ann-Sofie P. Zinck, Bert Wouters, Franka Jesse, and Stef Lhermitte
The Cryosphere, 19, 5509–5529, https://doi.org/10.5194/tc-19-5509-2025, https://doi.org/10.5194/tc-19-5509-2025, 2025
Short summary
Short summary
Ocean-driven basal melting of ice shelves can carve channels into the ice shelf base. These channels represent potential weak areas of the ice shelf. On George VI Ice shelf we discover a new channel which onset coincides with the 2015 El-Nino Southern Oscillation event. Since the channel has developed rapidly and is located within a highly channelized area close to the ice shelf front it poses a potential thread of ice shelf retreat.
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
The Cryosphere, 19, 5157–5173, https://doi.org/10.5194/tc-19-5157-2025, https://doi.org/10.5194/tc-19-5157-2025, 2025
Short summary
Short summary
Perennial firn aquifers (PFAs), year-round bodies of liquid water within firns, can potentially impact ice-shelf and ice-sheet stability. We developed a fast XGBoost firn emulator to predict the 21st-century distribution of PFAs in Antarctica for 12 climatic forcing datasets. Our findings suggest that, in low-emission scenarios, PFAs remain confined to the Antarctic Peninsula. However, in a high-emission scenario, PFAs are projected to expand to a region in West Antarctica and East Antarctica.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
The Cryosphere, 19, 4875–4892, https://doi.org/10.5194/tc-19-4875-2025, https://doi.org/10.5194/tc-19-4875-2025, 2025
Short summary
Short summary
Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), showing that a WIVERN-like radar will provide better estimates than a CloudSat-like radar at smaller spatial and temporal scales.
Christiaan T. van Dalum, Willem Jan van de Berg, Michiel R. van den Broeke, and Maurice van Tiggelen
The Cryosphere, 19, 4061–4090, https://doi.org/10.5194/tc-19-4061-2025, https://doi.org/10.5194/tc-19-4061-2025, 2025
Short summary
Short summary
In this study, we present a new surface mass balance (SMB) and near-surface climate product for Antarctica with the regional climate model RACMO2.4p1. We assess the impact of major model updates on the climate of Antarctica. Locally, the SMB has changed substantially but also agrees well with observations. In addition, we show that the SMB components, surface energy budget, albedo, pressure, temperature, and wind speed compare well with in situ and remote sensing observations.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Peter Kuipers Munneke, and Michiel R. van den Broeke
Earth Syst. Sci. Data, 17, 4933–4955, https://doi.org/10.5194/essd-17-4933-2025, https://doi.org/10.5194/essd-17-4933-2025, 2025
Short summary
Short summary
This paper describes the measurements from the 19 IMAU (Institute for Marine and Atmospheric research Utrecht) automatic weather stations that operated on the Antarctic ice sheet from 1995 through 2022. These stations also measured the net surface radiation and surface height change, allowing for the quantification of the surface energy and mass balance at hourly resolution. These data are invaluable for the evaluation of atmospheric models and for the detection of climatological changes.
Marte Gé Hofsteenge, Willem Jan van de Berg, Christiaan van Dalum, Kristiina Verro, Maurice van Tiggelen, and Michiel van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2025-4176, https://doi.org/10.5194/egusphere-2025-4176, 2025
Short summary
Short summary
We use a regional climate model to study how surface melt on Antarctic ice shelves responds to air temperature changes. The relationship is strongly non-linear, mainly due to feedbacks in surface reflectivity, with other energy sources also contributing. Currently colder, drier, and more stable ice shelves will experience more melt at the same temperature than wetter ice shelves, highlighting their vulnerability to fracturing, ice shelf instability, and contributions to global sea-level rise.
Anneke L. Vries, Willem Jan van de Berg, Brice Noël, Lorenz Meire, and Michiel R. van den Broeke
The Cryosphere, 19, 3897–3914, https://doi.org/10.5194/tc-19-3897-2025, https://doi.org/10.5194/tc-19-3897-2025, 2025
Short summary
Short summary
Freshwater flows into Greenland's fjords from various sources. Solid ice discharge (e.g. calving icebergs) dominates freshwater input in the southeast and northwest. In contrast, in the southwest, runoff from the ice sheet and tundra are the most significant. Seasonal data revealed that fjord precipitation and tundra runoff contribute up to 11 % and 35 % of the monthly freshwater input, respectively. Our results provide valuable input for ocean models and for researchers studying fjord ecosystems.
Johanna Van Passel, Koenraad Van Meerbeek, Paulo N. Bernardino, Wanda De Keersmaecker, Stef Lhermitte, Bianca F. Rius, and Ben Somers
EGUsphere, https://doi.org/10.5194/egusphere-2025-4148, https://doi.org/10.5194/egusphere-2025-4148, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
The Amazon forest is important for carbon storage, but climate change might push parts of it towards a tipping point into a degraded state. By studying satellite trends and tree diversity across different spatial scales, we found a larger tipping risk at smaller spatial scales than for the whole region. We also found that higher tree diversity makes the forest more stable and thus less likely to tip, although the effect is relatively weak, highlighting the importance of protecting biodiversity.
Weiran Li, Stef Lhermitte, Bert Wouters, Cornelis Slobbe, Max Brils, and Xavier Fettweis
The Cryosphere, 19, 3419–3442, https://doi.org/10.5194/tc-19-3419-2025, https://doi.org/10.5194/tc-19-3419-2025, 2025
Short summary
Short summary
Due to recurrent melt and refreezing events in recent decades, the snow conditions over Greenland have changed. To observe this, we use a parameter (leading edge width; LeW) derived from satellite altimetry and analyse its spatial and temporal variations. By comparing the LeW variations with modelled firn parameters, we concluded that the 2012 melt event and the recent and increasingly frequent melt events have a long-lasting impact on the volume scattering of Greenland firn.
Sofie Van Winckel, Jonas Simons, Stef Lhermitte, and Bart Muys
Biogeosciences, 22, 4291–4307, https://doi.org/10.5194/bg-22-4291-2025, https://doi.org/10.5194/bg-22-4291-2025, 2025
Short summary
Short summary
Insights on management's impact on forest carbon stocks are crucial for sustainable forest management practices. However, accurately monitoring carbon stocks remains a technological challenge. This study estimates above-ground carbon stock in managed and unmanaged forests using passive optical, synthetic aperture radar (SAR), and light detection and ranging (lidar) remote sensing data. Results show promising potential in using multiple remote sensing predictors and publicly available high-resolution data for mapping forest carbon stocks.
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, Alicia Bråtner, and Javed Hassan
Earth Syst. Sci. Data, 17, 3047–3071, https://doi.org/10.5194/essd-17-3047-2025, https://doi.org/10.5194/essd-17-3047-2025, 2025
Short summary
Short summary
The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and be useful for GIS ice sheet modeling.
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
Short summary
Short summary
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.
Maria T. Kappelsberger, Martin Horwath, Eric Buchta, Matthias O. Willen, Ludwig Schröder, Sanne B. M. Veldhuijsen, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 18, 4355–4378, https://doi.org/10.5194/tc-18-4355-2024, https://doi.org/10.5194/tc-18-4355-2024, 2024
Short summary
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.
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
Short summary
Short summary
We present a new version of the polar Regional Atmospheric Climate Model (RACMO), version 2.4p1, and show first results for Greenland, Antarctica and the Arctic. We provide an overview of all changes and investigate the impact that they have on the climate of polar regions. By comparing the results with observations and the output from the previous model version, we show that the model performs well regarding the surface mass balance of the ice sheets and near-surface climate.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ann-Sofie Priergaard Zinck, Bert Wouters, Erwin Lambert, and Stef Lhermitte
The Cryosphere, 17, 3785–3801, https://doi.org/10.5194/tc-17-3785-2023, https://doi.org/10.5194/tc-17-3785-2023, 2023
Short summary
Short summary
The ice shelves in Antarctica are melting from below, which puts their stability at risk. Therefore, it is important to observe how much and where they are melting. In this study we use high-resolution satellite imagery to derive 50 m resolution basal melt rates of the Dotson Ice Shelf. With the high resolution of our product we are able to uncover small-scale features which may in the future help us to understand the state and fate of the Antarctic ice shelves and their (in)stability.
Diana Francis, Ricardo Fonseca, Kyle S. Mattingly, Stef Lhermitte, and Catherine Walker
The Cryosphere, 17, 3041–3062, https://doi.org/10.5194/tc-17-3041-2023, https://doi.org/10.5194/tc-17-3041-2023, 2023
Short summary
Short summary
Role of Foehn Winds in ice and snow conditions at the Pine Island Glacier, West Antarctica.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Weiran Li, Cornelis Slobbe, and Stef Lhermitte
The Cryosphere, 16, 2225–2243, https://doi.org/10.5194/tc-16-2225-2022, https://doi.org/10.5194/tc-16-2225-2022, 2022
Short summary
Short summary
This study proposes a new method for correcting the slope-induced errors in satellite radar altimetry. The slope-induced errors can significantly affect the height estimations of ice sheets if left uncorrected. This study applies the method to radar altimetry data (CryoSat-2) and compares the performance with two existing methods. The performance is assessed by comparison with independent height measurements from ICESat-2. The assessment shows that the method performs promisingly.
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
Short summary
Short summary
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.
Weiran Li, Stef Lhermitte, and Paco López-Dekker
The Cryosphere, 15, 5309–5322, https://doi.org/10.5194/tc-15-5309-2021, https://doi.org/10.5194/tc-15-5309-2021, 2021
Short summary
Short summary
Surface meltwater lakes have been observed on several Antarctic ice shelves in field studies and optical images. Meltwater lakes can drain and refreeze, increasing the fragility of the ice shelves. The combination of synthetic aperture radar (SAR) backscatter and interferometric information (InSAR) can provide the cryosphere community with the possibility to continuously assess the dynamics of the meltwater lakes, potentially helping to facilitate the study of ice shelves in a changing climate.
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
Short summary
Short summary
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.
Annelies Voordendag, Marion Réveillet, Shelley MacDonell, and Stef Lhermitte
The Cryosphere, 15, 4241–4259, https://doi.org/10.5194/tc-15-4241-2021, https://doi.org/10.5194/tc-15-4241-2021, 2021
Short summary
Short summary
The sensitivity of two snow models (SNOWPACK and SnowModel) to various parameterizations and atmospheric forcing biases is assessed in the semi-arid Andes of Chile in winter 2017. Models show that sublimation is a main driver of ablation and that its relative contribution to total ablation is highly sensitive to the selected albedo parameterization and snow roughness length. The forcing and parameterizations are more important than the model choice, despite differences in physical complexity.
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
Short summary
Short summary
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.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
Short summary
Short summary
The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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.
Cited articles
Arthur, J. F., Stokes, C., Jamieson, S. S., Carr, J. R., and Leeson, A. A.:
Recent understanding of Antarctic supraglacial lakes using satellite remote
sensing, Prog. Phys. Geogr., 44, 837–869, 2020. a
Bindschadler, R., Choi, H., Wichlacz, A., Bingham, R., Bohlander, J., Brunt, K., Corr, H., Drews, R., Fricker, H., Hall, M., Hindmarsh, R., Kohler, J., Padman, L., Rack, W., Rotschky, G., Urbini, S., Vornberger, P., and Young, N.: Getting around Antarctica: new high-resolution mappings of the grounded and freely-floating boundaries of the Antarctic ice sheet created for the International Polar Year, The Cryosphere, 5, 569–588, https://doi.org/10.5194/tc-5-569-2011, 2011. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Cape, M., Vernet, M., Skvarca, P., Marinsek, S., Scambos, T., and Domack, E.:
Foehn winds link climate-driven warming to ice shelf evolution in Antarctica,
J. Geophys. Res.-Atmos., 120, 11–037, 2015. a
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in:
Proceedings of the 22nd acm sigkdd international conference on knowledge
discovery and data mining, 13–17 August 2016, San Francisco, California, USA, 785–794, 2016. a
Elvidge, A. D. and Renfrew, I. A.: The causes of foehn warming in the lee of
mountains, B. Am. Meteorol. Soc., 97, 455–466, 2016. a
ESA: Sentinel data, available at: https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 10 December 2021. a
Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S.,
Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, S. N., Nurhati, I., Ruiz, L., Sallée, J.-B., Slangen, A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change Supplementary Material, in: Climate
Change 2021: The Physical Science Basis. Contribution of Working Group I to
the Sixth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews,J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, available at: https://www.ipcc.ch/, last access: 3 December 2021. a, b
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Tavard, L., Rankl, M., Braun, M., and Gagliardini, O.: The safety band of Antarctic ice shelves, Nat. Clim. Change, 6, 479–482, 2016. a
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the
development of a new physically based broadband albedo parameterization, J.
Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010. a
Gilbert, E. and Kittel, C.: Surface Melt and Runoff on Antarctic Ice Shelves at 1.5 ∘C, 2 ∘C, and 4 ∘C of Future Warming, Geophy. Res. Lett., 48, e2020GL091733, https://doi.org/10.1029/2020GL091733, 2021. a
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep
feedforward neural networks, in: Proceedings of the thirteenth international
conference on artificial intelligence and statistics, 13–15 May 2010,
Sardinia, Italy, 249–256, 2010. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: Proceedings of the IEEE conference on computer vision and
pattern recognition, 27–30 June 2016, Las Vegas, NV, USA, 770–778, 2016. a
Henderson, D., L'Ecuyer, T. S., Vane, D., Stephens, G. L., and Reinke, D.:
Level 2B Fluxes and Heating Rates and 2B Fluxes and Heating Rates w/Lidar
Process Description and Interface Control Document, available at: https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-flxhr-lidar/2B-FLXHR-LIDAR_PDICD.P2_R04.20111220.pdf
(last access: 3 December 2021), 2011. a
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, 1997. a
Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf, Zenodo [data set], https://doi.org/10.5281/zenodo.5769661, 2021. a
IMAU: Ice and Climate: Regional modelling, IMAU [data set], https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 10 December 2021. a
Jakobs, C. L., Reijmer, C. H., Smeets, C. J. P. P., Trusel, L. D., van de Berg, W. J., den Broeke, M. R. V., and van Wessem, J. M.: A benchmark dataset of in situ Antarctic surface melt rates and energy balance, J. Glaciol., 66, 291–302, https://doi.org/10.1017/jog.2020.6, 2020a. a, b
Jakobs, C., Reijmer, C., van den Broeke, M. R., Smeets, P., and König-Langlo, G.: High-resolution meteorological observations, Surface Energy Balance components and miscellaneous data from 10 AWS and one staffed station in Antarctica, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.910473, 2020b. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
preprint: arXiv:1412.6980, 2014. a
Kingslake, J., Ely, J. C., Das, I., and Bell, R.: Widespread movement of
meltwater onto and across Antarctic ice shelves, Nature, 544, 349–352,
https://doi.org/10.1038/nature22049, 2017. a
Kuipers Munneke, P., Reijmer, C. H., van den Broeke, M. R., Stammes, P.,
König-Langlo, G., and Knap, W. H.: Analysis of clear-sky Antarctic snow
albedo using observations and radiative transfer modeling, J. Geophys. Res., 113, D17118, https://doi.org/10.1029/2007JD009653, 2008. a
Kuipers Munneke, P., van den Broeke, M. R., Lenaerts, J. T. M., Flanner, M. G., Gardner, A. S., and van de Berg, W. J.: A new albedo parameterization for use in climate models over the Antarctic ice sheet, J. Geophys. Res., 116, D05114, https://doi.org/10.1029/2010JD015113, 2011. a, b
Kuipers Munneke, P., Picard, G., van den Broeke, M. R., Lenaerts, J. T. M.,
and van Meijgaard, E.: Insignificant change in Antarctic snowmelt volume
since 1979, Geophys. Res. Lett., 39, L01501, https://doi.org/10.1029/2011GL050207, 2012a. a
Kuipers Munneke, P., van den Broeke, M. R., King, J. C., Gray, T., and
Reijmer, C. H.: Near-surface climate and surface energy budget of Larsen C ice shelf, Antarctic Peninsula, The Cryosphere, 6, 35300363,
https://doi.org/10.5194/tc-6-353-2012, 2012b. a
Kuipers Munneke, P., Ligtenberg, S. R., Van Den Broeke, M. R., and Vaughan,
D. G.: Firn air depletion as a precursor of Antarctic ice-shelf collapse, J. Glaciol., 60, 205–214, 2014. a
Kuipers Munneke, P., Luckman, A., Bevan, S., Smeets, C., Gilbert, E., Van den
Broeke, M., Wang, W., Zender, C., Hubbard, B., Ashmore, D., Orr, A., King, J. C., and Kulessa, B.: Intense winter surface melt on an Antarctic ice shelf, Geophys. Res. Lett., 45, 7615–7623, https://doi.org/10.1029/2018GL077899, 2018a. a
Kuipers Munneke, P., Smeets, C. J. P. P., Reijmer, C. H., Oerlemans, J.,
van de Wal, R. S. W., and van den Broeke, M. R.: The K-transect on the western Greenland Ice Sheet: surface energy balance (2003–2016), Arct.
Antarct. Alp. Res., 50, e1420952, https://doi.org/10.1080/15230430.2017.1420952, 2018b. a
Lenaerts, J., Lhermitte, S., Drews, R., Ligtenberg, S., Berger, S., Helm, V.,
Smeets, C., Van Den Broeke, M., Van De Berg, W. J., Van Meijgaard, E.,
Eijkelboom, M., Eisen, O., and Pattyn, F.: Meltwater produced by wind–albedo interaction stored in an East Antarctic ice shelf, Nat. Clim. Change, 7, 58–62, https://doi.org/10.1038/nclimate3180, 2017. a, b, c, d, e
Lhermitte, S., Sun, S., Shuman, C., Wouters, B., Pattyn, F., Wuite, J.,
Berthier, E., and Nagler, T.: Damage accelerates ice shelf instability and
mass loss in Amundsen Sea Embayment, P. Natl. Acad. Sci. USA, 117, 24735–24741, 2020. a
Marshall, G. J., Orr, A., Van Lipzig, N. P., and King, J. C.: The impact of a
changing Southern Hemisphere Annular Mode on Antarctic Peninsula summer
temperatures, J. Climate, 19, 5388–5404, 2006. a
Moussavi, M., Pope, A., Halberstadt, A. R. W., Trusel, L. D., Cioffi, L., and
Abdalati, W.: Antarctic supraglacial lake detection using Landsat 8 and
Sentinel-2 imagery: Towards continental generation of lake volumes, Remote
Sens., 12, 134, https://doi.org/10.3390/rs12010134, 2020. a
Ng, A. Y.: Feature selection, L 1 vs. L 2 regularization, and rotational
invariance, in: Proceedings of the twenty-first international conference on
Machine learning, 4–8 July 2004, Banff, Alberta, Canada, p. 78,
https://doi.org/10.1145/1015330.1015435, 2004. a
Orr, A., Marshall, G. J., Hunt, J. C., Sommeria, J., Wang, C.-G., Van Lipzig,
N. P., Cresswell, D., and King, J. C.: Characteristics of summer airflow over
the Antarctic Peninsula in response to recent strengthening of westerly
circumpolar winds, J. Atmos. Sci., 65, 1396–1413, 2008. a
Philpot, W. D.: Bathymetric mapping with passive multispectral imagery, Appl. Optics, 28, 1569–1578, 1989. a
Pirazzini, R.: Surface albedo measurements over Antarctic sites in summer, J. Geophys. Res.-Atmos., 109, D20118, https://doi.org/10.1029/2004JD004617, 2004. a
Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Nicolai, M., Okem, A., Petzold, J., Rama B., and Weyer, N. M.: IPCC special report on the ocean and cryosphere in a changing climate, IPCC – Intergovernmental Panel on Climate Change, available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/03_SROCC_SPM_FINAL.pdf
(last access: 30 December 2020), 2019. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, 2019. a
Schaaf, C. and Wang, Z.: MCD43A3 MODIS/Terra+Aqua
BRDF/Albedo Daily L3 Global – 500 m V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A3.006, 2015. a, b, c
Smeets, C. J. P. P., Kuipers Munneke, P., van den Broeke, M. R., Boot, W.,
Oerlemans, J., Snellen, H., Reijmer, C. H., and van de Wal, R. S. W.: The
K-transect in west Greenland: Automatic weather station data (1993–2016), Arct. Antarct. Alp. Res., 50, e1420954, https://doi.org/10.1080/15230430.2017.1420954, 2018. a
Steffen, K., Abdalati, W., and Stroeve, J.: Climate sensitivity studies of the Greenland ice sheet using satellite AVHRR, SMMR, SSM/I and in situ data,
Meteorol. Atmos. Phys., 51, 239–258, 1993. a
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes, J. Atmos. Sci., 35, 2123–2132,
https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978. a
The IMBIE team: Mass balance of the Antarctic Ice Sheet from 1992 to 2017, Nature, 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y, 2018. a, b
Trusel, L., Frey, K. E., and Das, S. B.: Antarctic surface melting dynamics:
Enhanced perspectives from radar scatterometer data, J. Geophys. Res.-Earth, 117, F02023, https://doi.org/10.1029/2011JF002126, 2012. a, b
Trusel, L. D., Frey, K. E., Das, S. B., Karnauskas, K. B., Kuipers Munneke, P., Van Meijgaard, E., and Van Den Broeke, M. R.: Divergent trajectories of
Antarctic surface melt under two twenty-first-century climate scenarios, Nat. Geosci., 8, 927–932, 2015. a
Turton, J. V., Kirchgaessner, A., Ross, A. N., King, J. C., and Kuipers Munneke, P.: The influence of föhn winds on annual and seasonal surface melt on the Larsen C Ice Shelf, Antarctica, The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, 2020. 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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, in: Advances in
neural information processing systems, 5998–6008, available at: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
(last access: 3 December 2021), 2017. a
Vermote, E. and Wolfe, R.: MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD09GA.006, 2015. a, b
Zheng, L., Zhou, C., and Liang, Q.: Variations in Antarctic Peninsula snow
liquid water during 1999–2017 revealed by merging radiometer, scatterometer
and model estimations, Remote Sens. Environ., 232, 111219, https://doi.org/10.1016/j.rse.2019.111219, 2019. a
Zheng, L., Zhou, C., Zhang, T., Liang, Q., and Wang, K.: Recent changes in pan-Antarctic region surface snowmelt detected by AMSR-E and AMSR2, The Cryosphere, 14, 3811–3827, https://doi.org/10.5194/tc-14-3811-2020, 2020. a
Short summary
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.
Antarctica is shrinking, and part of the mass loss is caused by higher temperatures leading to...
