Articles | Volume 15, issue 4
https://doi.org/10.5194/tc-15-1823-2021
© 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-15-1823-2021
© 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 Apr 2021
Research article |
| 13 Apr 2021
| 13 Apr 2021
Impact of updated radiative transfer scheme in snow and ice in RACMO2.3p3 on the surface mass and energy budget of the Greenland ice sheet
Christiaan T. van Dalum
CORRESPONDING AUTHOR
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Willem Jan van de Berg
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Michiel R. van den Broeke
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Related authors
Jessica M. A. Macha, Andrew N. Mackintosh, Felicity S. McCormack, Benjamin J. Henley, Helen V. McGregor, Christiaan T. van Dalum, and Ariaan Purich
The Cryosphere, 19, 1915–1935, https://doi.org/10.5194/tc-19-1915-2025, https://doi.org/10.5194/tc-19-1915-2025, 2025
Short summary
Short summary
Extreme El Niño–Southern Oscillation (ENSO) events have global impacts, but their Antarctic impacts are poorly understood. Examining Antarctic snow accumulation anomalies of past observed extreme ENSO events, we show that accumulation changes differ between events and are insignificant during most events. Significant changes occur during 2015/16 and in Enderby Land during all extreme El Niños. Historical data limit conclusions, but future greater extremes could cause Antarctic accumulation changes.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
EGUsphere, https://doi.org/10.5194/egusphere-2025-386, https://doi.org/10.5194/egusphere-2025-386, 2025
Short summary
Short summary
A realistic representation of Antarctic sea ice is crucial for accurate climate and ocean model predictions. We assessed how different models capture the sunlight reflectivity, snow cover, and ice thickness. Most performed well under mild weather conditions, but overestimated snow/ice reflectivity during cold, with patchy/thin snow conditions. High-resolution satellite imagery revealed spatial albedo variability that models failed to replicate.
René R. Wijngaard, Willem Jan van de Berg, Christaan T. van Dalum, Adam R. Herrington, and Xavier J. Levine
EGUsphere, https://doi.org/10.5194/egusphere-2025-1070, https://doi.org/10.5194/egusphere-2025-1070, 2025
Short summary
Short summary
We used the variable-resolution CESM to simulate present-day and future temperature and precipitation extremes in the Arctic by applying global grids (~111 km) with and without regional refinement (~28 km) and following a storyline approach. We found that global grids with (without) regional refinement generally perform better in simulating present-day precipitation (temperature) extremes, and that future high (low) temperature and wet precipitation extremes are projected to increase (decrease).
Christiaan T. van Dalum, Willem Jan van de Berg, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3728, https://doi.org/10.5194/egusphere-2024-3728, 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.
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.
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.
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.
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.
Tim van den Akker, William H. Lipscomb, Gunter R. Leguy, Willem Jan van de Berg, and Roderik S. W. van de Wal
EGUsphere, https://doi.org/10.5194/egusphere-2025-3380, https://doi.org/10.5194/egusphere-2025-3380, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The Antarctic Ice Sheet is currently thinning, especially at major outlet glaciers. Including present-day thinning rates in models is a modeller's choice and can affect future projections. This study quantifies the impact of current imbalance on forced future projections, revealing strong regional and short-term (up to 2100) effects when these mass change rates are included.
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.
Jessica M. A. Macha, Andrew N. Mackintosh, Felicity S. McCormack, Benjamin J. Henley, Helen V. McGregor, Christiaan T. van Dalum, and Ariaan Purich
The Cryosphere, 19, 1915–1935, https://doi.org/10.5194/tc-19-1915-2025, https://doi.org/10.5194/tc-19-1915-2025, 2025
Short summary
Short summary
Extreme El Niño–Southern Oscillation (ENSO) events have global impacts, but their Antarctic impacts are poorly understood. Examining Antarctic snow accumulation anomalies of past observed extreme ENSO events, we show that accumulation changes differ between events and are insignificant during most events. Significant changes occur during 2015/16 and in Enderby Land during all extreme El Niños. Historical data limit conclusions, but future greater extremes could cause Antarctic accumulation changes.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Peter Kuipers Munneke, and Michiel R. van den Broeke
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-88, https://doi.org/10.5194/essd-2025-88, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper describes the 154 station-years of in situ measurements from the 19 IMAU automatic weather stations that operated on the Antarctic ice sheet between 1995 and 2022. These stations also recorded all four components of net surface radiation and surface height change, which allows for the quantification of the surface energy-and-mass balance at hourly resolution. This data is invaluable for the evaluation of weather and climate models, and for the detection of climatological changes.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
EGUsphere, https://doi.org/10.5194/egusphere-2025-386, https://doi.org/10.5194/egusphere-2025-386, 2025
Short summary
Short summary
A realistic representation of Antarctic sea ice is crucial for accurate climate and ocean model predictions. We assessed how different models capture the sunlight reflectivity, snow cover, and ice thickness. Most performed well under mild weather conditions, but overestimated snow/ice reflectivity during cold, with patchy/thin snow conditions. High-resolution satellite imagery revealed spatial albedo variability that models failed to replicate.
René R. Wijngaard, Willem Jan van de Berg, Christaan T. van Dalum, Adam R. Herrington, and Xavier J. Levine
EGUsphere, https://doi.org/10.5194/egusphere-2025-1070, https://doi.org/10.5194/egusphere-2025-1070, 2025
Short summary
Short summary
We used the variable-resolution CESM to simulate present-day and future temperature and precipitation extremes in the Arctic by applying global grids (~111 km) with and without regional refinement (~28 km) and following a storyline approach. We found that global grids with (without) regional refinement generally perform better in simulating present-day precipitation (temperature) extremes, and that future high (low) temperature and wet precipitation extremes are projected to increase (decrease).
Tim van den Akker, William H. Lipscomb, Gunter R. Leguy, Willem Jan van de Berg, and Roderik S. W. van de Wal
EGUsphere, https://doi.org/10.5194/egusphere-2025-441, https://doi.org/10.5194/egusphere-2025-441, 2025
Short summary
Short summary
Ice sheet models to simulate future sea level rise require parameterizations, like for the friction at the bedrock. Studies have quantified the effect of using different parameterizations, and some have concluded that projections are sensitive to the choice of the specific parameterization. In this study, we show that you can make an ice sheet model sensitive to the basal friction parameterization, and that for equally defendable modellers choices you can also make the model insensitive to this.
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.
Tim van den Akker, William H. Lipscomb, Gunter R. Leguy, Jorjo Bernales, Constantijn J. Berends, Willem Jan van de Berg, and Roderik S. W. van de Wal
The Cryosphere, 19, 283–301, https://doi.org/10.5194/tc-19-283-2025, https://doi.org/10.5194/tc-19-283-2025, 2025
Short summary
Short summary
In this study, we present an improved way of representing ice thickness change rates in an ice sheet model. We apply this method using two ice sheet models of the Antarctic Ice Sheet. We found that the two largest outlet glaciers on the Antarctic Ice Sheet, Thwaites Glacier and Pine Island Glacier, will collapse without further warming on a timescale of centuries. This would cause a sea level rise of about 1.2 m globally.
Anneke Louise Vries, Willem Jan van de Berg, Brice Noël, Lorenz Meire, and Michiel R. van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2024-3735, https://doi.org/10.5194/egusphere-2024-3735, 2025
Short summary
Short summary
Freshwater enters Greenland's fjords from various sources. Solid ice discharge dominates freshwater input into fjords in the southeast and northwest. In contrast, in the southwest, runoff from the ice sheet and tundra are most significant. Seasonally resolved data revealed that fjord precipitation and tundra runoff contribute up to 11 % and 35 % of the total freshwater influx, respectively. Our results provide valuable input for ocean models and for researchers studying fjord ecosystems.
Christiaan T. van Dalum, Willem Jan van de Berg, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3728, https://doi.org/10.5194/egusphere-2024-3728, 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.
Srinidhi Gadde and Willem Jan van de Berg
The Cryosphere, 18, 4933–4953, https://doi.org/10.5194/tc-18-4933-2024, https://doi.org/10.5194/tc-18-4933-2024, 2024
Short summary
Short summary
Blowing-snow sublimation is the major loss term in the mass balance of Antarctica. In this study we update the blowing-snow representation in the Regional Atmospheric Climate Model (RACMO). With the updates, results compare well with observations from East Antarctica. Also, the continent-wide variation of blowing snow compares well with satellite observations. Hence, the updates provide a clear step forward in producing a physically sound and reliable estimate of the mass balance of Antarctica.
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.
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.
Raf M. Antwerpen, Marco Tedesco, Xavier Fettweis, Patrick Alexander, and Willem Jan van de Berg
The Cryosphere, 16, 4185–4199, https://doi.org/10.5194/tc-16-4185-2022, https://doi.org/10.5194/tc-16-4185-2022, 2022
Short summary
Short summary
The ice on Greenland has been melting more rapidly over the last few years. Most of this melt comes from the exposure of ice when the overlying snow melts. This ice is darker than snow and absorbs more sunlight, leading to more melt. It remains challenging to accurately simulate the brightness of the ice. We show that the color of ice simulated by Modèle Atmosphérique Régional (MAR) is too bright. We then show that this means that MAR may underestimate how fast the Greenland ice is melting.
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.
Tiago Silva, Jakob Abermann, Brice Noël, Sonika Shahi, Willem Jan van de Berg, and Wolfgang Schöner
The Cryosphere, 16, 3375–3391, https://doi.org/10.5194/tc-16-3375-2022, https://doi.org/10.5194/tc-16-3375-2022, 2022
Short summary
Short summary
To overcome internal climate variability, this study uses k-means clustering to combine NAO, GBI and IWV over the Greenland Ice Sheet (GrIS) and names the approach as the North Atlantic influence on Greenland (NAG). With the support of a polar-adapted RCM, spatio-temporal changes on SEB components within NAG phases are investigated. We report atmospheric warming and moistening across all NAG phases as well as large-scale and regional-scale contributions to GrIS mass loss and their interactions.
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.
Ruth Mottram, Nicolaj Hansen, Christoph Kittel, J. Melchior van Wessem, Cécile Agosta, Charles Amory, Fredrik Boberg, Willem Jan van de Berg, Xavier Fettweis, Alexandra Gossart, Nicole P. M. van Lipzig, Erik van Meijgaard, Andrew Orr, Tony Phillips, Stuart Webster, Sebastian B. Simonsen, and Niels Souverijns
The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
Short summary
Short summary
We compare the calculated surface mass budget (SMB) of Antarctica in five different regional climate models. On average ~ 2000 Gt of snow accumulates annually, but different models vary by ~ 10 %, a difference equivalent to ± 0.5 mm of global sea level rise. All models reproduce observed weather, but there are large differences in regional patterns of snowfall, especially in areas with very few observations, giving greater uncertainty in Antarctic mass budget than previously identified.
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.
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.
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.
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.
Thore Kausch, Stef Lhermitte, Jan T. M. Lenaerts, Nander Wever, Mana Inoue, Frank Pattyn, Sainan Sun, Sarah Wauthy, Jean-Louis Tison, and Willem Jan van de Berg
The Cryosphere, 14, 3367–3380, https://doi.org/10.5194/tc-14-3367-2020, https://doi.org/10.5194/tc-14-3367-2020, 2020
Short summary
Short summary
Ice rises are elevated parts of the otherwise flat ice shelf. Here we study the impact of an Antarctic ice rise on the surrounding snow accumulation by combining field data and modeling. Our results show a clear difference in average yearly snow accumulation between the windward side, the leeward side and the peak of the ice rise due to differences in snowfall and wind erosion. This is relevant for the interpretation of ice core records, which are often drilled on the peak of an ice rise.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, https://doi.org/10.5194/tc-14-3071-2020, 2020
Short summary
Short summary
In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Cited articles
Ackermann, M., Ahrens, J., Bai, X., Bartelt, M., Barwick, S. W., Bay, R. C.,
Becka, T., Becker, J. K., Becker, K.-H., Berghaus, P., Bernardini, E.,
Bertrand, D., Boersma, D. J., Böser, S., Botner, O., Bouchta, A., Bouhali,
O., Burgess, C., Burgess, T., Castermans, T., Chirkin, D., Collin, B.,
Conrad, J., Cooley, J., Cowen, D. F., Davour, A., De Clercq, C., de los
Heros, C. P., Desiati, P., DeYoung, T., Ekström, P., Feser, T., Gaisser,
T. K., Ganugapati, R., Geenen, H., Gerhardt, L., Goldschmidt, A., Groß, A.,
Hallgren, A., Halzen, F., Hanson, K., Hardtke, D. H., Harenberg, T.,
Hauschildt, T., Helbing, K., Hellwig, M., Herquet, P., Hill, G. C., Hodges,
J., Hubert, D., Hughey, B., Hulth, P. O., Hultqvist, K., Hundertmark, S.,
Jacobsen, J., Kampert, K. H., Karle, A., Kestel, M., Kohnen, G., Köpke, L.,
Kowalski, M., Kuehn, K., Lang, R., Leich, H., Leuthold, M., Liubarsky, I.,
Lundberg, J., Madsen, J., Marciniewski, P., Matis, H. S., McParland, C. P.,
Messarius, T., Minaeva, Y., Miočinović, P., Morse, R., Münich, K.,
Nahnhauer, R., Nam, J. W., Neunhöffer, T., Niessen, P., Nygren, D. R.,
Olbrechts, P., Pohl, A. C., Porrata, R., Price, P. B., Przybylski, G. T.,
Rawlins, K., Resconi, E., Rhode, W., Ribordy, M., Richter, S.,
Rodríguez Martino, J., Sander, H.-G., Schlenstedt, S., Schneider, D.,
Schwarz, R., Silvestri, A., Solarz, M., Spiczak, G. M., Spiering, C.,
Stamatikos, M., Steele, D., Steffen, P., Stokstad, R. G., Sulanke, K.-H.,
Taboada, I., Tarasova, O., Thollander, L., Tilav, S., Wagner, W., Walck, C.,
Walter, M., Wang, Y.-R., Wiebusch, C. H., Wischnewski, R., Wissing, H., and
Woschnagg, K.: Optical properties of deep glacial ice at the South Pole,
J. Geophys. Res.-Atmos., 111, D13203,
https://doi.org/10.1029/2005JD006687, 2006. a
Alexander, P. M., Tedesco, M., Koenig, L., and Fettweis, X.: Evaluating a
Regional Climate Model Simulation of Greenland Ice Sheet Snow and Firn
Density for Improved Surface Mass Balance Estimates, Geophys. Res.
Lett., 46, 12073–12082, https://doi.org/10.1029/2019GL084101, 2019. a
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox,
C. J., Kulie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland
melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86,
https://doi.org/10.1038/nature12002, 2013. a
Bevis, M., Harig, C., Khan, S. A., Brown, A., Simons, F. J., Willis, M.,
Fettweis, X., Van den Broeke, M. R., Madsen, F. B., Kendrick, E., Caccamise,
D. J., Van Dam, T., Knudsen, P., and Nylen, T.: Accelerating changes in ice
mass within Greenland, and the ice sheet's sensitivity to
atmospheric forcing, P. Natl. Acad. Sci. USA, 116,
1934–1939, https://doi.org/10.1073/pnas.1806562116, 2019. a
Bigg, G. R., Wei, H.-L., Wilton, D. J., Zhao, Y., Billings, S. A., Hanna, E.,
and Kadirkamanathan, V.: A century of variation in the dependence of
Greenland iceberg calving on ice sheet surface mass balance and regional
climate change, P. Roy. Soc. A-Math. Phy., 470, 20130662, https://doi.org/10.1098/rspa.2013.0662, 2014. a
Box, J. E. and Colgan, W.: Greenland Ice Sheet Mass Balance Reconstruction.
Part III: Marine Ice Loss and Total Mass Balance (1840–2010), J.
Climate, 26, 6990–7002, https://doi.org/10.1175/JCLI-D-12-00546.1, 2013. a
Chylek, P., Johnson, B., and Wu, H.: Black carbon concentration in a Greenland Dye-3 Ice Core, Geophys. Res. Lett., 19, 1951–1953,
https://doi.org/10.1029/92GL01904, 1992. a
Cogley, J. G., Hock, R., Rasmussen, L. A., Arendt, A. A., Bauder, A., Braithwaite, R. J., Jansson, P., Kaser, G., Möller, M., Nicholson, L., and Zemp, M.: Glossary of Glacier Mass Balance and Related Terms, IHP-VII Technical Documents in Hydrology No. 86, IACS Contribution No. 2, UNESCO-IHP, Paris, France, 2011. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow:
experimental results in a cold laboratory, Ann. Glaciol., 26, 64–68,
https://doi.org/10.3189/1998AoG26-1-64-68, 1998. a
Cooper, M. G., Smith, L. C., Rennermalm, A. K., Tedesco, M., Muthyala, R., Leidman, S. Z., Moustafa, S. E., and Fayne, J. V.: First spectral measurements of light attenuation in Greenland Ice Sheet bare ice suggest shallower subsurface radiative heating and ICESat-2 penetration depth in the ablation zone, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-53, in review, 2020. a, b
Dadic, R., Mullen, P. C., Schneebeli, M., Brandt, R. E., and Warren, S. G.:
Effects of bubbles, cracks, and volcanic tephra on the spectral albedo of
bare ice near the Transantarctic Mountains: Implications for sea glaciers on
Snowball Earth, J. Geophys. Res.-Earth, 118,
1658–1676, https://doi.org/10.1002/jgrf.20098, 2013. a
Dang, C., Brandt, R. E., and Warren, S. G.: Parameterizations for narrowband
and broadband albedo of pure snow and snow containing mineral dust and black
carbon, J. Geophys. Res.-Atmos., 120, 5446–5468,
https://doi.org/10.1002/2014JD022646, 2015. a, b
De Ridder, K. and Gallée, H.: Land Surface-Induced Regional Climate Change in
Southern Israel, J. Appl. Meteorol., 37, 1470–1485,
https://doi.org/10.1175/1520-0450(1998)037<1470:LSIRCC>2.0.CO;2, 1998. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., Van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Doherty, S. J., Warren, S. G., Grenfell, T. C., Clarke, A. D., and Brandt, R. E.: Light-absorbing impurities in Arctic snow, Atmos. Chem. Phys., 10, 11647–11680, https://doi.org/10.5194/acp-10-11647-2010, 2010. a
Ebert, E. E., Schramm, J. L., and Curry, J. A.: Disposition of solar radiation in sea ice and the upper ocean, J. Geophys. Res.-Oceans,
100, 15965–15975, https://doi.org/10.1029/95JC01672, 1995. a
ECMWF: Part IV: Physical Processes, IFS Documentation, ECMWF, operational
implementation 3 June 2008, https://doi.org/10.21957/8o7vwlbdr, 2009. a
Enderlin, E. M., Howat, I. M., Jeong, S., Noh, M.-J., Van Angelen, J. H., and
Van den Broeke, M. R.: An improved mass budget for the Greenland ice sheet,
Geophys. Res. Lett., 41, 866–872, https://doi.org/10.1002/2013GL059010, 2014. a
Fausto, R. S., van As, D., Box, J. E., Colgan, W., and Langen, P. L.:
Quantifying the Surface Energy Fluxes in South Greenland during the 2012 High
Melt Episodes Using In situ Observations, Front. Earth Sci., 4, 82,
https://doi.org/10.3389/feart.2016.00082, 2016. a
Fettweis, X.: Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using the regional climate model MAR, The Cryosphere, 1, 21–40, https://doi.org/10.5194/tc-1-21-2007, 2007. a
Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J.: Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models, The Cryosphere, 5, 359–375, https://doi.org/10.5194/tc-5-359-2011, 2011. a
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017. a, b
Fukusako, S.: Thermophysical properties of ice, snow, and sea ice,
Int. J. Thermophys., 11, 353–372, 1990. a
Gallée, H., van Ypersele, J. P., Fichefet, T., Tricot, C., and Berger, A.:
Simulation of the last glacial cycle by a coupled, sectorially averaged
climate–ice sheet model: 1. The climate model, J. Geophys.
Res.-Atmos., 96, 13139–13161, https://doi.org/10.1029/91JD00874, 1991. a
Goelzer, H., Huybrechts, P., Fürst, J. J., Nick, F., Andersen, M. L.,
Edwards, T. L., Fettweis, X., Payne, A. J., and Shannon, S.: Sensitivity of
Greenland ice sheet projections to model formulations, J. Glaciol.,
59, 733–749, https://doi.org/10.3189/2013JoG12J182, 2013. a
Granskog, M. A., Vihma, T., Pirazzini, R., and Cheng, B.: Superimposed ice
formation and surface energy fluxes on sea ice during the spring melt–freeze
period in the Baltic Sea, J. Glaciol., 52, 119–127,
https://doi.org/10.3189/172756506781828971, 2006. a
He, C., Takano, Y., Liou, K.-N., Yang, P., Li, Q., and Chen, F.: Impact of Snow Grain Shape and Black Carbon-Snow Internal Mixing on Snow Optical Properties: Parameterizations for Climate Models, J. Climate, 30,
10019–10036, https://doi.org/10.1175/JCLI-D-17-0300.1, 2017. a, b
He, C., Flanner, M. G., Chen, F., Barlage, M., Liou, K.-N., Kang, S., Ming, J., and Qian, Y.: Black carbon-induced snow albedo reduction over the Tibetan Plateau: uncertainties from snow grain shape and aerosol–snow mixing state based on an updated SNICAR model, Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, 2018. a, b
IPCC: Index, book section Index, Cambridge University Press,
Cambridge, UK and New York, NY, USA, 1523–1535,
https://doi.org/10.1017/CBO9781107415324, 2013. a
Jakobs, C. L., Reijmer, C. H., Kuipers Munneke, P., König-Langlo, G., and van den Broeke, M. R.: Quantifying the snowmelt–albedo feedback at Neumayer Station, East Antarctica, The Cryosphere, 13, 1473–1485, https://doi.org/10.5194/tc-13-1473-2019, 2019. a
Jiménez-Aquino, J. I. and Varela, J. R.: Two stream approximation to
radiative transfer equation: An alternative method of solution, Rev.
Mex. Fis., 51, 82–86, available at:
http://www.scielo.org.mx/pdf/rmf/v51n1/v51n1a13.pdf (last access: 12 April 2021), 2005. a
Kjeldsen, K. K., Korsgaard, N. J., Bjørk, A. A., Khan, S. A., Box, J. E.,
Funder, S., Larsen, N. K., Bamber, J. L., Colgan, W., Van den Broeke, M.,
Siggaard-Andersen, M.-L., Nuth, C., Schomacker, A., Andresen, C. S., Willerslev, E., and Kjær, K. H.: Spatial and temporal distribution of mass loss from the Greenland Ice
Sheet since AD 1900, Nature, 528, 396–400, https://doi.org/10.1038/nature16183, 2015. a, b
Kokhanovsky, A. A.: Light scattering Media Optics: Problems and Solutions,
Springer, Berlin, Germany, 2004. a
Kuipers Munneke, P., van den Broeke, M. R., Reijmer, C. H., Helsen, M. M., Boot, W., Schneebeli, M., and Steffen, K.: The role of radiation penetration in the energy budget of the snowpack at Summit, Greenland, The Cryosphere, 3, 155–165, https://doi.org/10.5194/tc-3-155-2009, 2009. a, b
Langen, P. L., Fausto, R. S., Vandecrux, B., Mottram, R. H., and Box, J. E.:
Liquid Water Flow and Retention on the Greenland Ice Sheet in the Regional
Climate Model HIRHAM5: Local and Large-Scale Impacts, Front. Earth
Sci., 4, 110, https://doi.org/10.3389/feart.2016.00110, 2017. a
Leeson, A. A., Eastoe, E., and Fettweis, X.: Extreme temperature events on Greenland in observations and the MAR regional climate model, The Cryosphere, 12, 1091–1102, https://doi.org/10.5194/tc-12-1091-2018, 2018. a, b
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013. a
Loewe, F.: Screen Temperatures and 10m Temperatures, J. Glaciol., 9,
263–268, https://doi.org/10.3189/S0022143000023571, 1970. a
Lucas-Picher, P., Wulff-Nielsen, M., Christensen, J. H., Aðalgeirsdóttir, G.,
Mottram, R., and Simonsen, S. B.: Very high resolution regional climate model
simulations over Greenland: Identifying added value, J. Geophys.
Res.-Atmos., 117, D02108, https://doi.org/10.1029/2011JD016267, 2012. a
Machguth, H., Thomsen, H., Weidick, A., Ahlstrøm, A. P., Abermann, J., Andersen, M. L., Andersen, S., Bjørk, A. A., Box,J. E., Braithwaite, R. J., Bøggild, C. E., Citterio, M., Clement,P., Colgan, W., Fausto, R. S., Gubler, K. G. S., Hasholt, B.,Hynek, B., Knudsen, N., Larsen, S., Mernild, S., Oerlemans, J., Oerter, H., Olesen, O., Smeets, C., Steffen, K., Stober,
M., Sugiyama, S., van As, D., van den Broeke, M. R., and van de Wal, R. S.: Greenland surface mass-balance observations from the
ice-sheet ablation area and local glaciers, J. Glaciol., 62,
861–887, https://doi.org/10.1017/jog.2016.75, 2016. a, b
Meirold-Mautner, I. and Lehning, M.: Measurements and model calculations of the solar shortwave fluxes in snow on Summit, Greenland, Ann. Glaciol.,
38, 279–284, https://doi.org/10.3189/172756404781814753, 2004. a, b
Mouginot, J., Rignot, E., Bjørk, A. A., Van den Broeke, M., Millan, R.,
Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years of
Greenland Ice Sheet mass balance from 1972 to 2018, P.
Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.1073/pnas.1904242116,
2019. a
Nghiem, S. V., Hall, D. K., Mote, T. L., Tedesco, M., Albert, M. R., Keegan,
K., Shuman, C. A., DiGirolamo, N. E., and Neumann, G.: The extreme melt
across the Greenland ice sheet in 2012, Geophys. Res. Lett., 39, L20502,
https://doi.org/10.1029/2012GL053611, 2012. a
Niwano, M., Aoki, T., Hashimoto, A., Matoba, S., Yamaguchi, S., Tanikawa, T., Fujita, K., Tsushima, A., Iizuka, Y., Shimada, R., and Hori, M.: NHM–SMAP: spatially and temporally high-resolution nonhydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet, The Cryosphere, 12, 635–655, https://doi.org/10.5194/tc-12-635-2018, 2018. a
Noël, B., van de Berg, W. J., van Meijgaard, E., Kuipers Munneke, P., van de Wal, R. S. W., and van den Broeke, M. R.: Evaluation of the updated regional climate model RACMO2.3: summer snowfall impact on the Greenland Ice Sheet, The Cryosphere, 9, 1831–1844, https://doi.org/10.5194/tc-9-1831-2015, 2015. a
Noël, B., Van de Berg, W. J., Lhermitte, S., Wouters, B., Schaffer, N., and
Van den Broeke, M. R.: Six Decades of Glacial Mass Loss in the Canadian
Arctic Archipelago, J. Geophys. Res.-Earth, 123,
1430–1449, https://doi.org/10.1029/2017JF004304, 2018a. a
Noël, B., van de Berg, W. J., van Wessem, J. M., van Meijgaard, E., van As, D., Lenaerts, J. T. M., Lhermitte, S., Kuipers Munneke, P., Smeets, C. J. P. P., van Ulft, L. H., van de Wal, R. S. W., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016), The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, 2018b. a, b, c, d, e, f, g
Noël, B., Van de Berg, W. J., Lhermitte, S., and Van den Broeke, M. R.:
Rapid ablation zone expansion amplifies north Greenland mass loss, Sci.
Adv., 5, eaaw0123, https://doi.org/10.1126/sciadv.aaw0123, 2019. a, b
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The Weather Research and Forecasting
Model: Overview, System Efforts, and Future Directions, B.
Am. Meteorol. Soc., 98, 1717–1737,
https://doi.org/10.1175/BAMS-D-15-00308.1, 2017. a
Rae, J. G. L., Aðalgeirsdóttir, G., Edwards, T. L., Fettweis, X., Gregory, J. M., Hewitt, H. T., Lowe, J. A., Lucas-Picher, P., Mottram, R. H., Payne, A. J., Ridley, J. K., Shannon, S. R., van de Berg, W. J., van de Wal, R. S. W., and van den Broeke, M. R.: Greenland ice sheet surface mass balance: evaluating simulations and making projections with regional climate models, The Cryosphere, 6, 1275–1294, https://doi.org/10.5194/tc-6-1275-2012, 2012. a
Reijmer, C. H., Bintanja, R., and Greuell, W.: Surface albedo measurements over snow and blue ice in thematic mapper bands 2 and 4 in Dronning Maud Land, Antarctica, J. Geophys. Res.-Atmos., 106, 9661–9672,
https://doi.org/10.1029/2000JD900718, 2001. a
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch,M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh,L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric general circulation model ECHAM 5. PART I:
Model description, Report/Max-Planck-Institut für Meteorologie, 349,
available at: http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf (last access: 12 April 2021),
2003. a
Schaaf, C. B. and Wang, Z.: MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3
Global – 500m V006, Distributed by NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MODIS/MCD43A3.006, 2015. a
Shepherd, A., Ivins, E., Rignot, E., Smith, B., Van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, T., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V. R., Bjørk, A. A., Blazquez, A., Bonin, J., Colgan, W., Csatho, B., Cullather, R., Engdahl, M. E., Felikson, D., Fettweis, X., Forsberg, R., Hogg, A. E., Gallee, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath, A., Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P. L., Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild, S., Mohajerani, Y., Moore, P., Mottram, R., Mouginot, J., Moyano, G., Muir, A., Nagler, T., Nield, G., Nilsson, J., Noël, B., Otosaka, I., Pattle, M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H., Sandberg Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E., Schröder, L., Seo, K.-W., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T., Talpe, M., Tarasov, L., Van de Berg, W. J., Van der Wal, W., Van Wessem, M., Vishwakarma, B. D., Wiese, D., Wilton, D., Wagner, T., Wouters, B., Wuite, J., and Team, T. I.: Mass balance of the Greenland Ice Sheet from 1992 to 2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020. a, b
Smeets, P. C. J. P., Munneke, P. K., As, D. V., den Broeke, M. R. V., Boot, W.,
Oerlemans, H., Snellen, H., Reijmer, C. H., and de Wal, R. S. W. V.: 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
Steger, C. R., Reijmer, C. H., Van den Broeke, M. R., Wever, N., Forster,
R. R., Koenig, L. S., Kuipers Munneke, P., Lehning, M., Lhermitte, S.,
Ligtenberg, S. R. M., Miège, C., and Noël, B. P. Y.: Firn Meltwater
Retention on the Greenland Ice Sheet: A Model Comparison, Front. Earth
Sci., 5, 3, https://doi.org/10.3389/feart.2017.00003, 2017. a
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal
conductivity of seasonal snow, J. Glaciol., 43, 26–41,
https://doi.org/10.3189/S0022143000002781, 1997. a
Tedesco, M. and Fettweis, X.: Unprecedented atmospheric conditions (1948–2019) drive the 2019 exceptional melting season over the Greenland ice sheet, The Cryosphere, 14, 1209–1223, https://doi.org/10.5194/tc-14-1209-2020, 2020. a
Tedesco, M., Fettweis, X., Mote, T., Wahr, J., Alexander, P., Box, J. E., and Wouters, B.: Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data, The Cryosphere, 7, 615–630, https://doi.org/10.5194/tc-7-615-2013, 2013. a
Undén, P., Rontu, L., Jarvinen, H., Lynch, P., Calvo Sánchez, F., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., García-Moya, J., and Jones, C.:
HIRLAM-5 Scientific Documentation, available at: http://www.hirlam.org/index.php/hirlam-documentation/cat_view/114-model-and-system-documentation/131-hirlam-documentation?limit=5&order=date&dir=ASC&start=10 (last access: 13 April 2021), 2002. a
Van As, D., Fausto, R., Ahlstrøm, A., Andersen, S., Andersen, M.,
Citterio, M., Edelvang, K., Gravesen, P., Machguth, H., Nick, F., Nielsen,
S., and Weidick, A.: Programme for Monitoring of the Greenland Ice Sheet
(PROMICE): first temperature and ablation record, Geological Survey of
Denmark and Greenland Bulletin, 73–76, https://doi.org/10.34194/geusb.v23.4876, 2011. a
van Dalum, C. T., van de Berg, W. J., Lhermitte, S., and van den Broeke, M. R.: Evaluation of a new snow albedo scheme for the Greenland ice sheet in the Regional Atmospheric Climate Model (RACMO2), The Cryosphere, 14, 3645–3662, https://doi.org/10.5194/tc-14-3645-2020, 2020a. a, b, c, d, e, f, g, h, i, j, k, l, m
van Dalum, C. T., van de Berg, W. J., and van den Broeke, M. R.: RACMO2.3p3 monthly SMB, SEB, t2m and t10m data for Greenland (September 2000–2018) [Data set], Zenodo, https://doi.org/10.5281/zenodo.4013856, 2020b.
a
van de Berg, W. J. and Medley, B.: Brief Communication: Upper-air relaxation in RACMO2 significantly improves modelled interannual surface mass balance variability in Antarctica, The Cryosphere, 10, 459–463, https://doi.org/10.5194/tc-10-459-2016, 2016. a
van de Berg, W. J., van Meijgaard, E., and van Ulft, L. H.: The added value of high resolution in estimating the surface mass balance in southern Greenland, The Cryosphere, 14, 1809–1827, https://doi.org/10.5194/tc-14-1809-2020, 2020. a
van den Broeke, M. R., Enderlin, E. M., Howat, I. M., Kuipers Munneke, P., Noël, B. P. Y., van de Berg, W. J., van Meijgaard, E., and Wouters, B.: On the recent contribution of the Greenland ice sheet to sea level change, The Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, 2016. a
Van de Wal, R. and Oerlemans, J.: An energy balance model for the Greenland ice sheet, Global Planet. Change, 9, 115–131,
https://doi.org/10.1016/0921-8181(94)90011-6, 1994. a
van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Wouters, B., Steiner, J. F., Nieuwstraten, E. J., Immerzeel, W. W., and van den Broeke, M. R.: Mapping the aerodynamic roughness of the Greenland ice sheet surface using ICESat-2: Evaluation over the K-transect, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-378, in review, 2021. 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
Warren, S. G.: Optical properties of ice and snow, Philos. T. Roy. Soc. A, 377,
20180161, https://doi.org/10.1098/rsta.2018.0161, 2019. a, b
Warren, S. G., Brandt, R. E., and Grenfell, T. C.: Visible and near-ultraviolet absorption spectrum of ice from transmission of solar radiation into snow,
Appl. Opt., 45, 5320–5334, https://doi.org/10.1364/AO.45.005320, 2006. a
Wientjes, I. G. M., Van de Wal, R. S. W., Reichart, G. J., Sluijs, A., and Oerlemans, J.: Dust from the dark region in the western ablation zone of the Greenland ice sheet, The Cryosphere, 5, 589–601, https://doi.org/10.5194/tc-5-589-2011, 2011. a
Wiesenekker, J. M., Kuipers Munneke, P., Van den Broeke, M. R., and Smeets, C. J. P. P.: A Multidecadal Analysis of Föhn Winds over Larsen C Ice Shelf from a Combination of Observations and Modeling, Atmosphere, 9, 172,
https://doi.org/10.3390/atmos9050172, 2018. a
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.
Absorption of solar radiation is often limited to the surface in regional climate models....
