Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4933-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-4933-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Sep 2025
Data description paper |
| 26 Sep 2025
| 26 Sep 2025
Institute for Marine and Atmospheric Research Utrecht (IMAU) Antarctic automatic weather station data, including surface radiation balance (1995–2022)
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
Paul C. J. P. Smeets
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
Carleen H. Reijmer
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
Peter Kuipers Munneke
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
Michiel R. van den Broeke
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
Related authors
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.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
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.
Valeria Di Biase, Peter Kuipers Munneke, Bert Wouters, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2900, https://doi.org/10.5194/egusphere-2025-2900, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-711, https://doi.org/10.5194/egusphere-2025-711, 2025
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.
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.
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.
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.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
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.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3098, https://doi.org/10.5194/egusphere-2025-3098, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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.
Valeria Di Biase, Peter Kuipers Munneke, Bert Wouters, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2900, https://doi.org/10.5194/egusphere-2025-2900, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-711, https://doi.org/10.5194/egusphere-2025-711, 2025
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2855, https://doi.org/10.5194/egusphere-2024-2855, 2024
Short summary
Short summary
Perennial firn aquifers (PFAs), year-round bodies of liquid water within firn, can potentially impact ice-shelf and ice-sheet stability. We developed a fast XGBoost firn emulator to predict 21st-century distribution of PFAs in Antarctica for 12 climatic forcings datasets. Our findings suggest that under low emission scenarios, PFAs remain confined to the Antarctic Peninsula. However, under a high-emission scenario, PFAs are projected to expand to a region in West Antarctica and East Antarctica.
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.
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
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.
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.
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.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
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.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
Short summary
Short summary
We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
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.
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
Short summary
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.
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.
Yetang Wang, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Shugui Hou, and Cunde Xiao
Earth Syst. Sci. Data, 13, 3057–3074, https://doi.org/10.5194/essd-13-3057-2021, https://doi.org/10.5194/essd-13-3057-2021, 2021
Short summary
Short summary
Accurate observation of surface mass balance (SMB) under climate change is essential for the reliable present and future assessment of Antarctic contribution to global sea level. This study presents a new quality-controlled dataset of Antarctic SMB observations at different temporal resolutions and is the first ice-sheet-scale compilation of multiple types of measurements. The dataset can be widely applied to climate model validation, remote sensing retrievals, and data assimilation.
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Allison, I.: Surface climate of the interior of the Lambert Glacier basin, Antarctica, from automatic weather station data, Ann. Glaciol., 27, 515–520, https://doi.org/10.3189/1998aog27-1-515-520, 1998. a
Amory, C.: Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East Antarctica, The Cryosphere, 14, 1713–1725, https://doi.org/10.5194/tc-14-1713-2020, 2020. a
Anderson: A method for rescaling humidity sensors at temperatures well below freezing, J. Atmos. Ocean. Tech., 11, 1388, https://doi.org/10.1175/1520-0426(1994)011<1388:AMFRHS>2.0.CO;2, 1994. a, b
Andreas, E.: A theory for the scalar roughness and the scalar transfer coefficients over snow and sea ice, Bound.-Lay. Meteorol., 38, 159–184, 1986. a
Behrens, K.: Radiation Sensors, Springer International Publishing, 297–357, ISBN 978-3-030-52171-4, https://doi.org/10.1007/978-3-030-52171-4_11, 2021. a
Bintanja, R. and Reijmer, C. H.: Meteorological conditions over Antarctic blue-ice areas and their influence on the local surface mass balance, J. Glaciol., 47, 37–50, https://doi.org/10.3189/172756501781832557, 2001. a
Bumbaco, K. A., Hakim, G. J., Mauger, G. S., Hryniw, N., and Steig, E. J.: Evaluating the Antarctic observational network with the Antarctic Mesoscale Prediction System (AMPS), Mon. Weather Rev., 142, 3847–3859, https://doi.org/10.1175/MWR-D-13-00401.1, 2014. a, b
Buzzard, S., Feltham, D., and Flocco, D.: Modelling the fate of surface melt on the Larsen C Ice Shelf, The Cryosphere, 12, 3565–3575, https://doi.org/10.5194/tc-12-3565-2018, 2018. a
Ding, M., Zou, X., Sun, Q., Yang, D., Zhang, W., Bian, L., Lu, C., Allison, I., Heil, P., and Xiao, C.: The PANDA automatic weather station network between the coast and Dome A, East Antarctica, Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, 2022. a, b
Drews, R., Martín, C., Steinhage, D., and Eisen, O.: Characterizing the glaciological conditions at Halvfarryggen ice dome, Dronning Maud Land, Antarctica, J. Glaciol., 59, 9–20, https://doi.org/10.3189/2013JoG12J134, 2013. a
Dunmire, D., Lenaerts, J. T., Banwell, A. F., Wever, N., Shragge, J., Lhermitte, S., Drews, R., Pattyn, F., Hansen, J. S., Willis, I. C., Miller, J., and Keenan, E.: Observations of Buried Lake Drainage on the Antarctic Ice Sheet, Geophys. Res. Lett., 47, e2020GL087970, https://doi.org/10.1029/2020GL087970, 2020. a
EPICA: Eight glacial cycles from an Antarctic ice core EPICA community members, Nature, 429, 623–628, 2004. a
EPICA: One-to-one coupling of glacial climate variability in Greenland and Antarctica, Nature, 444, 195–198, https://doi.org/10.1038/nature05301, 2006. a
Favier, V., Agosta, C., Parouty, S., Durand, G., Delaygue, G., Gallée, H., Drouet, A. S., Trouvilliez, A., and Krinner, G.: An updated and quality controlled surface mass balance dataset for Antarctica, The Cryosphere, 7, 583–597, https://doi.org/10.5194/tc-7-583-2013, 2013. a
Goldman, H. V.: From the editor: Halfway through the IPY – Halfway for an Antarctic traverse, Polar Res., 27, 1–6, https://doi.org/10.1111/j.1751-8369.2008.00048.x, 2008. a
Gorodetskaya, I. V., Van Lipzig, N. P., Van Den Broeke, M. R., Mangold, A., Boot, W., and Reijmer, C. H.: Meteorological regimes and accumulation patterns at Utsteinen, Dronning Maud Land, East Antarctica: Analysis of two contrasting years, J. Geophys. Res.-Atmos., 118, 1700–1715, https://doi.org/10.1002/jgrd.50177, 2013. a
Grigioni, P., Ciardini, V., De Silvestri, L., Iaccarino, A., Scarchilli, C., Camporeale, G., Dolci, S., Pellegrini, A., Proposito, M., and Schioppo, R.: La rete di stazioni meteorologiche dell' Osservatorio Meteo-Climatologico in Antartide, ENEA, https://iris.enea.it/retrieve/dd11e37c-d7bc-5d97-e053-d805fe0a6f04/RT-2016-35-ENEA.pdf (last access: 1 September 2025), 2016. a
Hakim, G. J., Bumbaco, K. A., Tardif, R., and Powers, J. G.: Optimal network design applied to monitoring and forecasting surface temperature in Antarctica, Mon. Weather Rev., 148, 857–873, https://doi.org/10.1175/MWR-D-19-0103.1, 2020. a
Holmlund, P., Gjerde, K., Gundestrup, N., Hansson, M., Isaksson, E., Karlöf, L., Nman, M., Pettersson, R., Pinglot, F., Reijmer, C. H., Stenberg, M., Thomassen, M., van de Wal, R., van der Veen, C., Wilhelms, F., and Winther, J.-G.: Spatial gradients in snow layering and 10 m temperatures at two EPICA-Dronning Maud Land (Antarctica) pre-site-survey drill sites, Ann. Glaciol., 30, 13–19, https://doi.org/10.3189/172756400781820796, 2000. a
Holtslag, A. A. M. and De Bruin, H. A. R.: Applied Modeling of the Nighttime Surface Energy Balance over Land, J. Appl. Meteorol. Clim., 27, 689–704, https://doi.org/10.1175/1520-0450(1988)027<0689:amotns>2.0.co;2, 1988. a
Howat, I., Porter, C., Noh, M.-J., Husby, E., Khuvis, S., Danish, E., Tomko, K., Gardiner, J., Negrete, A., Yadav, B., Klassen, J., Kelleher, C., Cloutier, M., Bakker, J., Enos, J., Arnold, G., Bauer, G., and Morin, P.: The Reference Elevation Model of Antarctica – Mosaics, Version 2, HARVARD Dataverse [data set], https://doi.org/10.7910/DVN/EBW8UC, 2022. 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, The Cryosphere, 15, 5639–5658, https://doi.org/10.5194/tc-15-5639-2021, 2021. a
Huwald, H., Higgins, C. W., Boldi, M. O., Bou-Zeid, E., Lehning, M., and Parlange, M. B.: Albedo effect on radiative errors in air temperature measurements, Water Resour. Res., 45, 1–13, https://doi.org/10.1029/2008WR007600, 2009. a
Jacobs, A. F. and McNaughton, K. G.: The excess temperature of a rigid fast-response thermometer and its effects on measured heat flux, J. Atmos. Ocean. Tech., 11, 680–686, https://doi.org/10.1175/1520-0426(1994)011<0680:TETOAR>2.0.CO;2, 1994. a
Jakobs, C. L., Reijmer, C. H., Smeets, C. J., Trusel, L. D., Van De Berg, W. J., Van Den Broeke, M. R., 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, 2020. a, b, c, d
Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P. V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021. a
Knap, W.: Basic and other measurements of radiation at station Cabauw (2005-02 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940531, 2022. 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, 353–363, https://doi.org/10.5194/tc-6-353-2012, 2012. a, b
Kuipers Munneke, P., Luckman, A. J., Bevan, S. L., Smeets, C. J. P. P., Gilbert, E., van den Broeke, M. R., 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, 2018. a, b
Kurita, N., Kameda, T., Motoyama, H., Hirasawa, N., Mikolajczyk, D., Welhouse, L. J., Keller, L. M., Weidner, G. A., and Lazzara, M. A.: Near-Surface Air Temperature Records over the Past 30 Years in the Interior of Dronning Maud Land, East Antarctica, J. Atmos. Ocean. Tech., 41, 179–188, https://doi.org/10.1175/JTECH-D-23-0092.1, 2024. a
Laepple, T., Werner, M., and Lohmann, G.: Synchronicity of Antarctic temperatures and local solar insolation on orbital timescales, Nature, 471, 91–94, https://doi.org/10.1038/nature09825, 2011. a
Lazzara, M. A., Weidner, G. A., Keller, L. M., Thom, J. E., and Cassano, J. J.: Antarctic automatic weather station program: 30 years of polar observations, B. Am. Meteorol. Soc., 93, 1519–1537, https://doi.org/10.1175/BAMS-D-11-00015.1, 2012. a, b
Le Moigne, P., Bazile, E., Cheng, A., Dutra, E., Edwards, J. M., Maurel, W., Sandu, I., Traullé, O., Vignon, E., Zadra, A., and Zheng, W.: GABLS4 intercomparison of snow models at Dome C in Antarctica, The Cryosphere, 16, 2183–2202, https://doi.org/10.5194/tc-16-2183-2022, 2022. a
Lenaerts, J. T., Lhermitte, S., Drews, R., Ligtenberg, S. R., Berger, S., Helm, V., Smeets, C. J., Broeke, M. R. D., 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
Lupi, A., Lanconelli, C., and Vitale, V.: Basic and other measurements of radiation at Concordia station (2006-01 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.935421, 2021. a
Makkonen, L. and Laakso, T.: Humidity measurements in cold and humid environments, Bound.-Lay. Meteorol., 116, 131–147, https://doi.org/10.1007/s10546-004-7955-y, 2005. a
Morlighem, M., Rignot, E., Binder, T., Blankenship, D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W. S., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., den Broeke, M. R., Ommen, T. D., van Wessem, M., and Young, D. A.: Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, Nat. Geosci., 13, 132–137, https://doi.org/10.1038/s41561-019-0510-8, 2020. 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
Nakamura, R. and Mahrt, L.: Air temperature measurement errors in naturally ventilated radiation shields, J. Atmos. Ocean. Tech., 22, 1046–1058, https://doi.org/10.1175/JTECH1762.1, 2005. a
Ogawa, Y., Tanaka, Y., Ogihara, H., Fukuda, M., Kawashima, K., Doi, M., and Yamanouchi, T.: Basic and other measurements of radiation at station Syowa (1994-01 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.956748, 2024. a
Reijmer, C., Greuell, W., and Oerlemans, J.: The annual cycle of meteorological variables and the surface energy balance on Berkner Island, Antarctica, Ann. Glaciol., 29, 49–54, https://doi.org/10.3189/172756499781821166, 1999. a
Reijmer, C. H. and van den Broeke, M. R.: Temporal and spatial variability of the surface mass balance in Dronning Maud Land, Antarctica, as derived from automatic weather stations, J. Glaciol., 49, 512–520, https://doi.org/10.3189/172756503781830494, 2003. a, b
Riihimaki, L., Long, C. E., Dutton, E. G., and Michalsky, J.: Basic and other measurements of radiation at station South Pole (1992-01 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.956847, 2023. a
Schmithüsen, H.: Basic and other measurements of radiation at Neumayer Station (1992-04 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.932418, 2021. a, b, c
Smeets, P. C. J. P., Kuipers Munneke, P., van As, D., van den Broeke, M. R., Boot, W., Oerlemans, H., 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, S100002, https://doi.org/10.1080/15230430.2017.1420954, 2018. a, b, c
Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J. T., Medley, B., Gorodetskaya, I. V., Vanden Broucke, S., and van Lipzig, N. P.: A New Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-Year Hindcast with COSMO-CLM 2 Over Antarctica, J. Geophys. Res.-Atmos., 124, 1405–1427, https://doi.org/10.1029/2018JD028862, 2019. a
Thiery, W., Gorodetskaya, I. V., Bintanja, R., Van Lipzig, N. P., Van Den Broeke, M. R., Reijmer, C. H., and Kuipers Munneke, P.: Surface and snowdrift sublimation at Princess Elisabeth station, East Antarctica, The Cryosphere, 6, 841–857, https://doi.org/10.5194/tc-6-841-2012, 2012. a
Trusel, L. D., Frey, K. E., Das, S. B., Munneke, P. K., and Van Den Broeke, M. R.: Satellite-based estimates of Antarctic surface meltwater fluxes, Geophys. Res. Lett., 40, 6148–6153, https://doi.org/10.1002/2013GL058138, 2013. a
Turner, J., Lu, H., White, I., King, J. C., Phillips, T., Hosking, J. S., Bracegirdle, T. J., Marshall, G. J., Mulvaney, R., and Deb, P.: Absence of 21st century warming on Antarctic Peninsula consistent with natural variability, Nature, 535, 411–415, https://doi.org/10.1038/nature18645, 2016. 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, The Cryosphere, 18, 4065–4088, https://doi.org/10.5194/tc-18-4065-2024, 2024. a
Van den Broeke, M., van As, D., Reijmer, C., and van de Wal, R.: Assessing and Improving the Quality of Unattended Radiation Observations in Antarctica, J. Atmos. Ocean. Tech., 21, 1417–1431, https://doi.org/10.1175/1520-0426(2004)021<1417:AAITQO>2.0.CO;2, 2004. a, b
Van den Broeke, M. R., Winther, J. G., Isaksson, E., Pinglot, J. F., Karlöf, L., Eiken, T., and Conrads, L.: Climate variables along a traverse line in Dronning Maud Land, East Antarctica, J. Glaciol., 45, 295–302, https://doi.org/10.3189/s0022143000001799, 1999. a
Van Liefferinge, B., Pattyn, F., Cavitte, M. G., Karlsson, N. B., Young, D. A., Sutter, J., and Eisen, O.: Promising oldest ice sites in east Antarctica based on thermodynamical modelling, The Cryosphere, 12, 2773–2787, https://doi.org/10.5194/tc-12-2773-2018, 2018. a
Van Tiggelen, M., Smeets, P. C., Reijmer, C. H., van den Broeke, M. R., van As, D., Box, J. E., and Fausto, R. S.: Observed and Parameterized Roughness Lengths for Momentum and Heat Over Rough Ice Surfaces, J. Geophys. Res.-Atmos., 128, e2022JD036970, https://doi.org/10.1029/2022JD036970, 2023. a
Van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Kuipers Munneke, P., van den Broeke, M. R., Oerter, H., Eisen, O., Steinhage, D., Kipfstuhl, S., van Lipzig, N. P. M., Mangold, A., Lhermitte, S., and Lenaerts, J. T. M.: IMAU Antarctic automatic weather station data, including surface radiation balance (1995–2022), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.974080, 2024. a, b
Van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Kuipers Munneke, P., and van den Broeke, M. R.: MATLAB scripts used to process the IMAU Antarctic automatic weather station data, including surface radiation balance (1995–2022), Zenodo [code], https://doi.org/10.5281/zenodo.15101447, 2025a. a
Van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Kuipers Munneke, P., and van den Broeke, M. R.: IMAU-ice-and-climate/IMAU-IceEddie, Zenodo [code], https://doi.org/10.5281/zenodo.15058514, 2025b. a
Van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Kuipers Munneke, P., and van den Broeke, M. R.: IMAU-ice-and-climate/IMAU-pyEBM, Zenodo [code], https://doi.org/10.5281/zenodo.15082294, 2025c. a
Van Wessem, J. M., Van De Berg, W. J., Noël, B. P., Van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T., Lhermitte, S., Ligtenberg, S. R., 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
Wang, W., Zender, C. S., van As, D., Smeets, P. C. J. P., and van den Broeke, M. R.: A Retrospective, Iterative, Geometry-Based (RIGB) tilt-correction method for radiation observed by automatic weather stations on snow-covered surfaces: application to Greenland, The Cryosphere, 10, 727–741, https://doi.org/10.5194/tc-10-727-2016, 2016. a
Wang, Y., Ding, M., Reijmer, C. H., Smeets, P. C., Hou, S., and Xiao, C.: The AntSMB dataset: A comprehensive compilation of surface mass balance field observations over the Antarctic Ice Sheet, Earth Syst. Sci. Data, 13, 3057–3074, https://doi.org/10.5194/essd-13-3057-2021, 2021. a, b, c
Wang, Y., Zhang, X., Ning, W., Lazzara, M. A., Ding, M., Reijmer, C. H., Smeets, P. C. J. P., Grigioni, P., Heil, P., Thomas, E. R., Mikolajczyk, D., Welhouse, L. J., Keller, L. M., Zhai, Z., Sun, Y., and Hou, S.: The AntAWS dataset: a compilation of Antarctic automatic weather station observations, Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, 2023. a, b, c, d
Wever, N., Keenan, E., Amory, C., Lehning, M., Sigmund, A., Huwald, H., and Lenaerts, J. T. M.: Observations and simulations of new snow density in the drifting snow-dominated environment of Antarctica, J. Glaciol., 69, 823–840, https://doi.org/10.1017/jog.2022.102, 2022. a
Winther, J., van den Broeke, M., Conrads, L., Eiken, T., Hurlen, R., Johnsrud, G., Karlöf, L., Onarheim, S., Richardson, C., and Schorno, R.: EPICA Dronning Maud Land pre site survey 1996/97, Report of the Norwegian Antarctic Research Expedition 1996, 97, 96–114, https://brage.npolar.no/npolar-xmlui/bitstream/handle/11250/173024/Meddelelser148.pdf (last access: 1 September 2025), 1997. a
WMO: Guide to Instruments and Methods of Observation Volume I – Measurement of Meteorological Variables, https://library.wmo.int/idurl/4/68695 (last access: 1 September 2025), 2023. a
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.
This paper describes the measurements from the 19 IMAU (Institute for Marine and Atmospheric...
Altmetrics
Final-revised paper
Preprint