Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Sep 2020
Model description paper |
| 17 Sep 2020
| 17 Sep 2020
The Community Firn Model (CFM) v1.0
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Vincent Verjans
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Jessica M. D. Lundin
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Salesforce, San Francisco, CA, USA
Emma C. Kahle
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Annika N. Horlings
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Brita I. Horlings
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
now at: Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Edwin D. Waddington
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Related authors
Ingalise Kindstedt, Dominic Winski, C. Max Stevens, Emma Skelton, Luke Copland, Karl Kreutz, Mikaila Mannello, Renée Clavette, Jacob Holmes, Mary Albert, and Scott N. Williamson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3807, https://doi.org/10.5194/egusphere-2024-3807, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Atmospheric warming over mountain glaciers is leading to increased warming and melting of snow as it compresses into glacier ice. This affects both regional hydrology and climate records contained in the ice. Here we use field observations and modeling to show that surface melting and percolation at Eclipse Icefield (Yukon, Canada) is increasing with an increase in extreme melt events, and that compressing snow at Eclipse is likely to continue warming even if air temperatures remain stable.
Marissa E. Dattler, Brooke Medley, and C. Max Stevens
The Cryosphere, 18, 3613–3631, https://doi.org/10.5194/tc-18-3613-2024, https://doi.org/10.5194/tc-18-3613-2024, 2024
Short summary
Short summary
We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
Megan Thompson-Munson, Nander Wever, C. Max Stevens, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, https://doi.org/10.5194/tc-17-2185-2023, 2023
Short summary
Short summary
To better understand the Greenland Ice Sheet’s firn layer and its ability to buffer sea level rise by storing meltwater, we analyze firn density observations and output from two firn models. We find that both models, one physics-based and one semi-empirical, simulate realistic density and firn air content when compared to observations. The models differ in their representation of firn air content, highlighting the uncertainty in physical processes and the paucity of deep-firn measurements.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Michael J. MacFerrin, C. Max Stevens, Baptiste Vandecrux, Edwin D. Waddington, and Waleed Abdalati
Earth Syst. Sci. Data, 14, 955–971, https://doi.org/10.5194/essd-14-955-2022, https://doi.org/10.5194/essd-14-955-2022, 2022
Short summary
Short summary
The vast majority of the Greenland ice sheet's surface is covered by pluriannual snow, also called firn, that accumulates year after year and is compressed into glacial ice. The thickness of the firn layer changes through time and responds to the surface climate. We present continuous measurement of the firn compaction at various depths for eight sites. The dataset will help to evaluate firn models, interpret ice cores, and convert remotely sensed ice sheet surface height change to mass loss.
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.
Vincent Verjans, Amber A. Leeson, Christopher Nemeth, C. Max Stevens, Peter Kuipers Munneke, Brice Noël, and Jan Melchior van Wessem
The Cryosphere, 14, 3017–3032, https://doi.org/10.5194/tc-14-3017-2020, https://doi.org/10.5194/tc-14-3017-2020, 2020
Short summary
Short summary
Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
Abigail G. Hughes, Tyler R. Jones, Bo M. Vinther, Vasileios Gkinis, C. Max Stevens, Valerie Morris, Bruce H. Vaughn, Christian Holme, Bradley R. Markle, and James W. C. White
Clim. Past, 16, 1369–1386, https://doi.org/10.5194/cp-16-1369-2020, https://doi.org/10.5194/cp-16-1369-2020, 2020
Short summary
Short summary
An ice core drilled on the Renland ice cap (RECAP) in east-central Greenland contains a continuous climate record dating through the last glacial period. Here we present the water isotope record for the Holocene, in which high-resolution climate information is retained for the last 8 kyr. We find that the RECAP water isotope record exhibits seasonal and decadal variability which may reflect sea surface conditions and regional climate variability.
Tyler J. Fudge, David A. Lilien, Michelle Koutnik, Howard Conway, C. Max Stevens, Edwin D. Waddington, Eric J. Steig, Andrew J. Schauer, and Nicholas Holschuh
Clim. Past, 16, 819–832, https://doi.org/10.5194/cp-16-819-2020, https://doi.org/10.5194/cp-16-819-2020, 2020
Short summary
Short summary
A 1750 m ice core at the South Pole was recently drilled. The oldest ice is ~55 000 years old. Since ice at the South Pole flows at 10 m per year, the ice in the core originated upstream, where the climate is different. We made measurements of the ice flow, snow accumulation, and temperature upstream. We determined the ice came from ~150 km away near the Titan Dome where the accumulation rate was similar but the temperature was colder. Our measurements improve the interpretation of the ice core.
Achim Heilig, Olaf Eisen, Martin Schneebeli, Michael MacFerrin, C. Max Stevens, Baptiste Vandecrux, and Konrad Steffen
The Cryosphere, 14, 385–402, https://doi.org/10.5194/tc-14-385-2020, https://doi.org/10.5194/tc-14-385-2020, 2020
Short summary
Short summary
We investigate the spatial representativeness of point observations of snow accumulation in SW Greenland. Such analyses have rarely been conducted but are necessary to link regional-scale observations from, e.g., remote-sensing data to firn cores and snow pits. The presented data reveal a low regional variability in density but snow depth can vary significantly. It is necessary to combine pits with spatial snow depth data to increase the regional representativeness of accumulation observations.
Vincent Verjans, Amber A. Leeson, C. Max Stevens, Michael MacFerrin, Brice Noël, and Michiel R. van den Broeke
The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019, https://doi.org/10.5194/tc-13-1819-2019, 2019
Short summary
Short summary
Firn models rely on empirical approaches for representing the percolation and refreezing of meltwater through the firn column. We develop liquid water schemes of different levels of complexity for firn models and compare their performances with respect to observations of density profiles from Greenland. Our results demonstrate that physically advanced water schemes do not lead to better agreement with density observations. Uncertainties in other processes contribute more to model discrepancy.
Baptiste Vandecrux, Michael MacFerrin, Horst Machguth, William T. Colgan, Dirk van As, Achim Heilig, C. Max Stevens, Charalampos Charalampidis, Robert S. Fausto, Elizabeth M. Morris, Ellen Mosley-Thompson, Lora Koenig, Lynn N. Montgomery, Clément Miège, Sebastian B. Simonsen, Thomas Ingeman-Nielsen, and Jason E. Box
The Cryosphere, 13, 845–859, https://doi.org/10.5194/tc-13-845-2019, https://doi.org/10.5194/tc-13-845-2019, 2019
Short summary
Short summary
The perennial snow, or firn, on the Greenland ice sheet each summer stores part of the meltwater formed at the surface, buffering the ice sheet’s contribution to sea level. We gathered observations of firn air content, indicative of the space available in the firn to retain meltwater, and find that this air content remained stable in cold regions of the firn over the last 65 years but recently decreased significantly in western Greenland.
David E. Shean, Knut Christianson, Kristine M. Larson, Stefan R. M. Ligtenberg, Ian R. Joughin, Ben E. Smith, C. Max Stevens, Mitchell Bushuk, and David M. Holland
The Cryosphere, 11, 2655–2674, https://doi.org/10.5194/tc-11-2655-2017, https://doi.org/10.5194/tc-11-2655-2017, 2017
Short summary
Short summary
We used long-term GPS data and interferometric reflectometry (GPS-IR) to measure velocity, strain rate and surface elevation for the PIG ice shelf – a site of significant mass loss in recent decades. We combined these observations with high-res DEMs and firn model output to constrain surface mass balance and basal melt rates. We document notable spatial variability in basal melt rates but limited temporal variability from 2012 to 2014 despite significant changes in sub-shelf ocean heat content.
Ingalise Kindstedt, Dominic Winski, C. Max Stevens, Emma Skelton, Luke Copland, Karl Kreutz, Mikaila Mannello, Renée Clavette, Jacob Holmes, Mary Albert, and Scott N. Williamson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3807, https://doi.org/10.5194/egusphere-2024-3807, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Atmospheric warming over mountain glaciers is leading to increased warming and melting of snow as it compresses into glacier ice. This affects both regional hydrology and climate records contained in the ice. Here we use field observations and modeling to show that surface melting and percolation at Eclipse Icefield (Yukon, Canada) is increasing with an increase in extreme melt events, and that compressing snow at Eclipse is likely to continue warming even if air temperatures remain stable.
Marissa E. Dattler, Brooke Medley, and C. Max Stevens
The Cryosphere, 18, 3613–3631, https://doi.org/10.5194/tc-18-3613-2024, https://doi.org/10.5194/tc-18-3613-2024, 2024
Short summary
Short summary
We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
Tyler J. Fudge, Raphael Sauvage, Linh Vu, Benjamin H. Hills, Mirko Severi, and Edwin D. Waddington
Clim. Past, 20, 297–312, https://doi.org/10.5194/cp-20-297-2024, https://doi.org/10.5194/cp-20-297-2024, 2024
Short summary
Short summary
We use the oldest Antarctic ice core to estimate the rate of diffusion of sulfuric acid. Sulfuric acid is a marker of past volcanic activity and is critical in developing ice core timescales. The rate of diffusion is uncertain and is important to know, both for selecting future ice core locations and interpreting ice core records. We find the effective diffusivity of sulfate is 10 times smaller than previously estimated, indicating that the sulfuric acid signals will persist for longer.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Megan Thompson-Munson, Nander Wever, C. Max Stevens, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, https://doi.org/10.5194/tc-17-2185-2023, 2023
Short summary
Short summary
To better understand the Greenland Ice Sheet’s firn layer and its ability to buffer sea level rise by storing meltwater, we analyze firn density observations and output from two firn models. We find that both models, one physics-based and one semi-empirical, simulate realistic density and firn air content when compared to observations. The models differ in their representation of firn air content, highlighting the uncertainty in physical processes and the paucity of deep-firn measurements.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
Short summary
Short summary
We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Michael J. MacFerrin, C. Max Stevens, Baptiste Vandecrux, Edwin D. Waddington, and Waleed Abdalati
Earth Syst. Sci. Data, 14, 955–971, https://doi.org/10.5194/essd-14-955-2022, https://doi.org/10.5194/essd-14-955-2022, 2022
Short summary
Short summary
The vast majority of the Greenland ice sheet's surface is covered by pluriannual snow, also called firn, that accumulates year after year and is compressed into glacial ice. The thickness of the firn layer changes through time and responds to the surface climate. We present continuous measurement of the firn compaction at various depths for eight sites. The dataset will help to evaluate firn models, interpret ice cores, and convert remotely sensed ice sheet surface height change to mass loss.
Thomas James Barnes, Amber Alexandra Leeson, Malcolm McMillan, Vincent Verjans, Jeremy Carter, and Christoph Kittel
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-214, https://doi.org/10.5194/tc-2021-214, 2021
Revised manuscript not accepted
Short summary
Short summary
We find that the area covered by lakes on George VI ice shelf in 2020 is similar to that seen in other years such as 1989. However, the climate conditions are much more in favour of lakes forming. We find that it is likely that snowfall, and the build up of a surface snow layer limits the development of lakes on the surface of George VI ice shelf in 2020. We also find that in future, snowfall is predicted to decrease, and therefore this limiting effect may be reduced in future.
Jenna A. Epifanio, Edward J. Brook, Christo Buizert, Jon S. Edwards, Todd A. Sowers, Emma C. Kahle, Jeffrey P. Severinghaus, Eric J. Steig, Dominic A. Winski, Erich C. Osterberg, Tyler J. Fudge, Murat Aydin, Ekaterina Hood, Michael Kalk, Karl J. Kreutz, David G. Ferris, and Joshua A. Kennedy
Clim. Past, 16, 2431–2444, https://doi.org/10.5194/cp-16-2431-2020, https://doi.org/10.5194/cp-16-2431-2020, 2020
Short summary
Short summary
A new ice core drilled at the South Pole provides a 54 000-year paleo-environmental record including the composition of the past atmosphere. This paper describes the gas chronology for the South Pole ice core, based on a high-resolution methane record. The new gas chronology, in combination with the existing ice age scale from Winski et al. (2019), allows a model-independent reconstruction of the delta age record.
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.
Vincent Verjans, Amber A. Leeson, Christopher Nemeth, C. Max Stevens, Peter Kuipers Munneke, Brice Noël, and Jan Melchior van Wessem
The Cryosphere, 14, 3017–3032, https://doi.org/10.5194/tc-14-3017-2020, https://doi.org/10.5194/tc-14-3017-2020, 2020
Short summary
Short summary
Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
Abigail G. Hughes, Tyler R. Jones, Bo M. Vinther, Vasileios Gkinis, C. Max Stevens, Valerie Morris, Bruce H. Vaughn, Christian Holme, Bradley R. Markle, and James W. C. White
Clim. Past, 16, 1369–1386, https://doi.org/10.5194/cp-16-1369-2020, https://doi.org/10.5194/cp-16-1369-2020, 2020
Short summary
Short summary
An ice core drilled on the Renland ice cap (RECAP) in east-central Greenland contains a continuous climate record dating through the last glacial period. Here we present the water isotope record for the Holocene, in which high-resolution climate information is retained for the last 8 kyr. We find that the RECAP water isotope record exhibits seasonal and decadal variability which may reflect sea surface conditions and regional climate variability.
Tyler J. Fudge, David A. Lilien, Michelle Koutnik, Howard Conway, C. Max Stevens, Edwin D. Waddington, Eric J. Steig, Andrew J. Schauer, and Nicholas Holschuh
Clim. Past, 16, 819–832, https://doi.org/10.5194/cp-16-819-2020, https://doi.org/10.5194/cp-16-819-2020, 2020
Short summary
Short summary
A 1750 m ice core at the South Pole was recently drilled. The oldest ice is ~55 000 years old. Since ice at the South Pole flows at 10 m per year, the ice in the core originated upstream, where the climate is different. We made measurements of the ice flow, snow accumulation, and temperature upstream. We determined the ice came from ~150 km away near the Titan Dome where the accumulation rate was similar but the temperature was colder. Our measurements improve the interpretation of the ice core.
Achim Heilig, Olaf Eisen, Martin Schneebeli, Michael MacFerrin, C. Max Stevens, Baptiste Vandecrux, and Konrad Steffen
The Cryosphere, 14, 385–402, https://doi.org/10.5194/tc-14-385-2020, https://doi.org/10.5194/tc-14-385-2020, 2020
Short summary
Short summary
We investigate the spatial representativeness of point observations of snow accumulation in SW Greenland. Such analyses have rarely been conducted but are necessary to link regional-scale observations from, e.g., remote-sensing data to firn cores and snow pits. The presented data reveal a low regional variability in density but snow depth can vary significantly. It is necessary to combine pits with spatial snow depth data to increase the regional representativeness of accumulation observations.
Dominic A. Winski, Tyler J. Fudge, David G. Ferris, Erich C. Osterberg, John M. Fegyveresi, Jihong Cole-Dai, Zayta Thundercloud, Thomas S. Cox, Karl J. Kreutz, Nikolas Ortman, Christo Buizert, Jenna Epifanio, Edward J. Brook, Ross Beaudette, Jeffrey Severinghaus, Todd Sowers, Eric J. Steig, Emma C. Kahle, Tyler R. Jones, Valerie Morris, Murat Aydin, Melinda R. Nicewonger, Kimberly A. Casey, Richard B. Alley, Edwin D. Waddington, Nels A. Iverson, Nelia W. Dunbar, Ryan C. Bay, Joseph M. Souney, Michael Sigl, and Joseph R. McConnell
Clim. Past, 15, 1793–1808, https://doi.org/10.5194/cp-15-1793-2019, https://doi.org/10.5194/cp-15-1793-2019, 2019
Short summary
Short summary
A deep ice core was recently drilled at the South Pole to understand past variations in the Earth's climate. To understand the information contained within the ice, we present the relationship between the depth and age of the ice in the South Pole Ice Core. We found that the oldest ice in our record is from 54 302 ± 519 years ago. Our results show that, on average, 7.4 cm of snow falls at the South Pole each year.
Vincent Verjans, Amber A. Leeson, C. Max Stevens, Michael MacFerrin, Brice Noël, and Michiel R. van den Broeke
The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019, https://doi.org/10.5194/tc-13-1819-2019, 2019
Short summary
Short summary
Firn models rely on empirical approaches for representing the percolation and refreezing of meltwater through the firn column. We develop liquid water schemes of different levels of complexity for firn models and compare their performances with respect to observations of density profiles from Greenland. Our results demonstrate that physically advanced water schemes do not lead to better agreement with density observations. Uncertainties in other processes contribute more to model discrepancy.
Baptiste Vandecrux, Michael MacFerrin, Horst Machguth, William T. Colgan, Dirk van As, Achim Heilig, C. Max Stevens, Charalampos Charalampidis, Robert S. Fausto, Elizabeth M. Morris, Ellen Mosley-Thompson, Lora Koenig, Lynn N. Montgomery, Clément Miège, Sebastian B. Simonsen, Thomas Ingeman-Nielsen, and Jason E. Box
The Cryosphere, 13, 845–859, https://doi.org/10.5194/tc-13-845-2019, https://doi.org/10.5194/tc-13-845-2019, 2019
Short summary
Short summary
The perennial snow, or firn, on the Greenland ice sheet each summer stores part of the meltwater formed at the surface, buffering the ice sheet’s contribution to sea level. We gathered observations of firn air content, indicative of the space available in the firn to retain meltwater, and find that this air content remained stable in cold regions of the firn over the last 65 years but recently decreased significantly in western Greenland.
David E. Shean, Knut Christianson, Kristine M. Larson, Stefan R. M. Ligtenberg, Ian R. Joughin, Ben E. Smith, C. Max Stevens, Mitchell Bushuk, and David M. Holland
The Cryosphere, 11, 2655–2674, https://doi.org/10.5194/tc-11-2655-2017, https://doi.org/10.5194/tc-11-2655-2017, 2017
Short summary
Short summary
We used long-term GPS data and interferometric reflectometry (GPS-IR) to measure velocity, strain rate and surface elevation for the PIG ice shelf – a site of significant mass loss in recent decades. We combined these observations with high-res DEMs and firn model output to constrain surface mass balance and basal melt rates. We document notable spatial variability in basal melt rates but limited temporal variability from 2012 to 2014 despite significant changes in sub-shelf ocean heat content.
Adam J. Campbell, Betzalel Massarano, Edwin D. Waddington, and Stephen G. Warren
The Cryosphere, 11, 1141–1148, https://doi.org/10.5194/tc-11-1141-2017, https://doi.org/10.5194/tc-11-1141-2017, 2017
Short summary
Short summary
How could plant life, that needs light to survive, live on a planet covered with ice? Such a situation is thought to have existed during what are called the Snowball Earth events over 600 million years ago. Here we find that
ice shadows, regions where ice has difficulty flowing into, may have a played a role in that survival of early plant life.
T. J. Fudge, E. D. Waddington, H. Conway, J. M. D. Lundin, and K. Taylor
Clim. Past, 10, 1195–1209, https://doi.org/10.5194/cp-10-1195-2014, https://doi.org/10.5194/cp-10-1195-2014, 2014
Related subject area
Cryosphere
Clustering simulated snow profiles to form avalanche forecast regions
SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance
Tuning parameters of a sea ice model using machine learning
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
Towards deep learning solutions for classification of automated snow height measurements (CleanSnow v1.0.0)
Quantitative Sub-Ice and Marine Tracing of Antarctic Sediment Provenance (TASP v1.0)
Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
Short summary
Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
Short summary
Short summary
We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
Short summary
Short summary
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
Short summary
Short summary
Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
Short summary
Short summary
Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
Short summary
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
Short summary
Short summary
We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
Short summary
Short summary
We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
Short summary
Short summary
In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
Short summary
Short summary
Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
Short summary
Short summary
openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
Short summary
Short summary
Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
Short summary
Short summary
Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Anton Korosov, Yue Ying, and Einar Olason
EGUsphere, https://doi.org/10.5194/egusphere-2024-2527, https://doi.org/10.5194/egusphere-2024-2527, 2024
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
Short summary
Short summary
Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1752, https://doi.org/10.5194/egusphere-2024-1752, 2024
Short summary
Short summary
Accurately measuring snow height is key for modeling approaches in climate sciences, snow hydrology and avalanche forecasting. Erroneous snow height measurements often occur when the snow height is low or changes, for instance, during a snowfall in the summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep learning approaches. Our approach can be easily implemented into a data pipeline for snow modeling.
Jim Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin Siegert, and Liam Holder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-104, https://doi.org/10.5194/gmd-2024-104, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output, plus other data, to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-37, https://doi.org/10.5194/gmd-2024-37, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluated its performance in simulating snow cover, snow depth, and concentrations of black carbon (BC), dust, and nitrate using new observations from Northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
Short summary
Short summary
In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
Short summary
Short summary
The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
Short summary
Short summary
We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
Short summary
Short summary
Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
Short summary
Short summary
Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
Short summary
Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
Short summary
Short summary
We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
Short summary
Short summary
Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
Short summary
Short summary
Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
Short summary
Short summary
Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
Short summary
Short summary
Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
Short summary
Short summary
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
Short summary
Short summary
The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
Short summary
Short summary
This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
Short summary
Short summary
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
Short summary
Short summary
Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
Short summary
Short summary
We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
Short summary
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
Short summary
Short summary
We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
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.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
Short summary
Short summary
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
Short summary
Short summary
The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
Short summary
Short summary
We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
Short summary
Short summary
Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
Short summary
Short summary
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
Short summary
Short summary
The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
Short summary
Short summary
We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
Short summary
Short summary
We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
Short summary
Short summary
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
Short summary
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Cited articles
Adolph, A. C. and Albert, M. R.: Gas diffusivity and permeability through the firn column at Summit, Greenland: measurements and comparison to microstructural properties, The Cryosphere, 8, 319–328, https://doi.org/10.5194/tc-8-319-2014, 2014. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a
Alexander, P., 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
Alley, R. B.: The Younger Dryas cold interval as viewed from central Greenland,
Quatern. Sci. Rev., 19, 213–226,
https://doi.org/10.1016/S0277-3791(99)00062-1, 2000. a
Anderson, E. A.: A point energy and mass balance model of a snow cover, NOAA
technical report NWS; 19, Office of Hydrology, National Weather Service,
1976. a
Bader, H.: Sorge's Law of Densification of Snow on High Polar Glaciers, J, Glaciol,, 2, 319–323, https://doi.org/10.3189/S0022143000025144, 1954. a
Barnola, J. M., Pimienta, P., Raynaud, D., and Korotkevich, Y. S.:
CO2-climate relationship as deduced from the Vostok ice core: a
re-examination based on new measurements and on a re-evaluation of the air
dating, Tellus B, 43, 83–90, https://doi.org/10.1034/j.1600-0889.1991.t01-1-00002.x,
1991. a, b, c
Battle, M., Bender, M., Sowers, T., Tans, P. P., Butler, J. H., Elkins, J. W., Ellis, J. T., Conway, T., Zhang, N., Lang, P., and Clarket, A. D.:
Atmospheric gas concentrations over the past century measured in air from
firn at the South Pole, Nature, 383, 231–235, https://doi.org/10.1038/383231a0, 1996. a
Birner, B., Buizert, C., Wagner, T. J. W., and Severinghaus, J. P.: The influence of layering and barometric pumping on firn air transport in a 2-D model, The Cryosphere, 12, 2021–2037, https://doi.org/10.5194/tc-12-2021-2018, 2018. a, b
Brandt, R. E. and Warren, S. G.: Temperature measurements and heat transfer in near-surface snow at the South Pole, J. Glaciol., 43, 339–351,
https://doi.org/10.3189/S0022143000003294, 1997. a
Brun, E.: Investigation on Wet-Snow Metamorphism in Respect of Liquid-Water
Content, Ann. Glaciol., 13, 22–26, https://doi.org/10.3189/S0260305500007576,
1989. a
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate
snow-cover stratigraphy for operational avalanche forecasting, J.
Glaciol., 38, 13–22, https://doi.org/10.3189/S0022143000009552, 1992. a
Buizert, C.: The influence of firn air transport processes and radiocarbon
production on gas records from polar firn and ice, PhD thesis, University of
Copenhagen, 2011. a
Buizert, C., Martinerie, P., Petrenko, V. V., Severinghaus, J. P., Trudinger, C. M., Witrant, E., Rosen, J. L., Orsi, A. J., Rubino, M., Etheridge, D. M., Steele, L. P., Hogan, C., Laube, J. C., Sturges, W. T., Levchenko, V. A., Smith, A. M., Levin, I., Conway, T. J., Dlugokencky, E. J., Lang, P. M., Kawamura, K., Jenk, T. M., White, J. W. C., Sowers, T., Schwander, J., and Blunier, T.: Gas transport in firn: multiple-tracer characterisation and model intercomparison for NEEM, Northern Greenland, Atmos. Chem. Phys., 12, 4259–4277, https://doi.org/10.5194/acp-12-4259-2012, 2012. a, b, c, d, e
Buizert, C., Cuffey, K. M., Severinghaus, J. P., Baggenstos, D., Fudge, T. J., Steig, E. J., Markle, B. R., Winstrup, M., Rhodes, R. H., Brook, E. J., Sowers, T. A., Clow, G. D., Cheng, H., Edwards, R. L., Sigl, M., McConnell, J. R., and Taylor, K. C.: The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference, Clim. Past, 11, 153 173, https://doi.org/10.5194/cp-11-153-2015, 2015. a, b, c
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
Cuffey, K. M. and Clow, G. D.: Temperature, accumulation, and ice sheet
elevation in central Greenland through the last deglacial transition, J. Geophys. Res.-Oceans, 102, 26383–26396,
https://doi.org/10.1029/96JC03981, 1997. a, b
Cuffey, K. M. and Paterson, W. S. B.: The physics of glaciers, Elsevier, 2010. a
Cullather, R. I., Nowicki, S. M. J., Zhao, B., and Koenig, L. S.: A
Characterization of Greenland Ice Sheet Surface Melt and Runoff in
Contemporary Reanalyses and a Regional Climate Model, Front. Earth
Sci., 4, 10 pp., https://doi.org/10.3389/feart.2016.00010, 2016. a
Fausto, R. S., Box, J. E., Vandecrux, B., van As, D., Steffen, K., Macferrin,
M. J., Machguth, H., Colgan, W., Koenig, L. S., McGrath, D., Charalampidis,
C., and Braithwaite, R. J.: A snow density dataset for improving surface
boundary conditions in Greenland ice sheet firn modeling, Front. Earth
Sci., 6, 51 pp., https://doi.org/10.3389/feart.2018.00051, 2018. 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, c
Freitag, J., Dobrindt, U., and Kipfstuhl, J.: A new method for predicting
transport properties of polar firn with respect to gases on the pore-space
scale, Ann. Glaciol., 35, 538–544, https://doi.org/10.3189/172756402781816582,
2002. a
Gkinis, V., Simonsen, S., Buchardt, S., White, J., and Vinther, B.: Water
isotope diffusion rates from the NorthGRIP ice core for the last 16,000 years
– Glaciological and paleoclimatic implications, Earth Planet. Sci.
Lett., 405, 132–141, https://doi.org/10.1016/j.epsl.2014.08.022, 2014. a
Goujon, C., Barnola, J.-M., and Ritz, C.: Modeling the densification of polar
firn including heat diffusion: Application to close-off characteristics and
gas isotopic fractionation for Antarctica and Greenland sites, J.
Geophys. Res.-Atmos., 108, 4792, https://doi.org/10.1029/2002JD003319, 2003. a, b, c, d, e, f, g, h, i
Gow, A. J.: On the Rates of Growth of Grains and Crystals in South Polar Firn,
J. Glaciol., 8, 241–252, https://doi.org/10.3189/S0022143000031233, 1969. a
Gow, A. J.: Time-temperature dependence of sintering in perennial isothermal
snowpacks, in: IAHS-AISH Publication No. 114, 25–41, 1975. a
Gow, A. J., Meese, D. A., and Bialas, R. W.: Accumulation variability, density
profiles and crystal growth trends in ITASE firn and ice cores from West
Antarctica, Ann. Glaciol., 39, 101–109,
https://doi.org/10.3189/172756404781814690, 2004. a
Gregory, S. A., Albert, M. R., and Baker, I.: Impact of physical properties and accumulation rate on pore close-off in layered firn, The Cryosphere, 8, 91–105, https://doi.org/10.5194/tc-8-91-2014, 2014. a
Helsen, M. M., van den Broeke, M. R., van de Wal, R. S. W., van de Berg, W. J., van Meijgaard, E., Davis, C. H., Li, Y., and Goodwin, I.: Elevation Changes in Antarctica Mainly Determined by Accumulation Variability, Science, 320, 1626–1629, https://doi.org/10.1126/science.1153894, 2008. a, b, c
Hughes, A. G., Jones, T. R., Vinther, B. M., Gkinis, V., Stevens, C. M., Morris, V., Vaughn, B. H., Holme, C., Markle, B. R., and White, J. W. C.: High-frequency climate variability in the Holocene from a coastal-dome ice core in east-central Greenland, Clim. Past, 16, 1369–1386, https://doi.org/10.5194/cp-16-1369-2020, 2020. a
Hulbe, C. L. and Whillans, I. M.: A method for determining ice-thickness change at remote locations using GPS, Annal. Glaciol., 20, 263–268,
https://doi.org/10.3189/172756494794587348, 1994. a
Jiawen, R., Dahe, Q., and Maohuan, H.: Thermal properties and temperature
distribution of snow/firn on the Law Dome ice cap, Antarctica, Chinese
J. Polar Sci., 2, 38–46, 1991. a
Johnsen, S. J., Clausen, H. B., Cuffey, K. M., Hoffmann, G., Schwander, J., and Creyts, T.: Diffusion of stable isotopes in polar firn and ice: the isotope
effect in firn diffusion, in: Physics of ice core records,
Hokkaido University Press, 121–140, 2000. a
Jones, T., Cuffey, K., White, J., Steig, E., Buizert, C., Markle, B.,
McConnell, J., and Sigl, M.: Water isotope diffusion in the WAIS Divide ice
core during the Holocene and last glacial, J. Geophys. Res.-Ea. Surf., 122, 290–309, https://doi.org/10.1002/2016JF003938, 2017. a
Katsushima, T., Kumakura, T., and Takeuchi, Y.: A multiple snow layer model
including a parameterization of vertical water channel process in snowpack,
Cold Reg. Sci. Technol., 59, 143–151,
https://doi.org/10.1016/j.coldregions.2009.09.002, 2009. a
Kawamura, K., Severinghaus, J. P., Ishidoya, S., Sugawara, S., Hashida, G.,
Motoyama, H., Fujii, Y., Aoki, S., and Nakazawa, T.: Convective mixing of air
in firn at four polar sites, Earth Planet. Sc. Lett., 244, 672–682, https://doi.org/10.1016/j.epsl.2006.02.017, 2006. 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
Kuipers Munneke, P., Ligtenberg, S. R. M., Noël, B. P. Y., Howat, I. M., Box, J. E., Mosley-Thompson, E., McConnell, J. R., Steffen, K., Harper, J. T., Das, S. B., and van den Broeke, M. R.: Elevation change of the Greenland Ice Sheet due to surface mass balance and firn processes, 1960–2014, The Cryosphere, 9, 2009–2025, https://doi.org/10.5194/tc-9-2009-2015, 2015. a, b, c, d, e, f, g, h
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 pp., https://doi.org/10.3389/feart.2016.00110, 2017. a, b
Li, J. and Zwally, H. J.: Modeled seasonal variations of firn density induced
by steady-state surface air-temperature cycle, Ann. Glaciol., 34,
299–302, https://doi.org/10.3189/172756402781817707, 2002. a, b
Li, J. and Zwally, H. J.: Modeling the density variation in the shallow firn
layer, Anna. Glaciol., 38, 309–313, 2004. a
Li, J. and Zwally, H. J.: Modeling of firn compaction for estimating ice-sheet
mass change from observed ice-sheet elevation change, Ann. Glaciol.,
52, 1–7, https://doi.org/10.3189/172756411799096321, 2011. a, b, c, d
Li, J. and Zwally, H. J.: Response times of ice-sheet surface heights to
changes in the rate of Antarctic firn compaction caused by accumulation and
temperature variations, J. Glaciol., 61, 1037–1047,
https://doi.org/10.3189/2015JoG14J182, 2015. a, b, c
Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere, 5, 809–819, https://doi.org/10.5194/tc-5-809-2011, 2011. a, b, c, d
Ligtenberg, S. R. M., Kuipers Munneke, P., and van den Broeke, M. R.: Present and future variations in Antarctic firn air content, The Cryosphere, 8, 1711–1723, https://doi.org/10.5194/tc-8-1711-2014, 2014. a
Linow, S., Hörhold, M., and Freitag, J.: Grain-size evolution of polar
firn: A new empirical grain growth parameterization based on X-ray
microcomputer tomography measurements, J. Glaciol., 58, 1245–1252,
https://doi.org/10.3189/2012JoG11J256, 2012. a
Lundin, J. M., Stevens, C. M., Arthern, R., Buizert, C., Orsi, A., Ligtenberg,
S. R., Simonsen, S. B., Cummings, E., Essery, R., Leahy, W., Harris, P.,
Helsen, M. M., and Waddington, E. D.: Firn Model Intercomparison Experiment
(FirnMICE), J. Glaciol., 63, 401–422, https://doi.org/10.1017/jog.2016.114,
2017. a, b, c, d, e, f
Lüthi, M. P. and Funk, M.: Modelling heat flow in a cold, high-altitude
glacier: interpretation of measurements from Colle Gnifetti, Swiss Alps,
J. Glaciol., 47, 314–324, 2001. a
Maeno, N. and Ebinuma, T.: Pressure sintering of ice and its implication to the
densification of snow at polar glaciers and ice sheets, J. Phys. Chem., 87, 4103–4110, https://doi.org/10.1021/j100244a023, 1983. a
Martinerie, P., Lipenkov, V. Y., Raynaud, D., Chappellaz, J., Barkov, N., and
Lorius, C.: Air content paleo record in the Vostok ice core (Antarctica): A
mixed record of climatic and glaciological parameters, J. Geophys.
Res.-Atmos., 99, 10565–10576, https://doi.org/10.1029/93JD03223, 1994. a
Meyer, C. R. and Hewitt, I. J.: A continuum model for meltwater flow through compacting snow, The Cryosphere, 11, 2799–2813, https://doi.org/10.5194/tc-11-2799-2017, 2017. a
Montgomery, L., Koenig, L., and Alexander, P.: The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland, Earth Syst. Sci. Data, 10, 1959–1985, https://doi.org/10.5194/essd-10-1959-2018, 2018. 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, 2018. a, b
Patankar, S. V.: Numerical heat transfer and fluid flow, CRC Press, Boca Raton,
https://doi.org/10.1201/9781482234213, 1980. a
Pimienta, P.: Etude du comportement mécanique des glaces polycristallines
aux faibles contraintes: applications aux glaces des calottes polaires, PhD
thesis, Université Scientifique, Technique et Médicale de Grenoble,
1987. a
Rasmussen, S. O., Bigler, M., Blockley, S. P., Blunier, T., Buchardt, S. L.,
Clausen, H. B., Cvijanovic, I., Dahl-Jensen, D., Johnsen, S. J., Fischer, H.,
Gkinis, V., Guillevic, M., Hoek, W. Z., Lowe, J. J., Pedro, J. B., Popp, T.,
Seierstad, I. K., Steffensen, J. P., Svensson, A. M., Vallelonga, P.,
Vinther, B. M., Walker, M. J., Wheatley, J. J., and Winstrup, M.: A
stratigraphic framework for abrupt climatic changes during the Last Glacial
period based on three synchronized Greenland ice-core records: refining and
extending the INTIMATE event stratigraphy, Quatern. Sci. Rev., 106,
14–28, https://doi.org/10.1016/j.quascirev.2014.09.007, 2014. a
Riche, F. and Schneebeli, M.: Thermal conductivity of snow measured by three independent methods and anisotropy considerations, The Cryosphere, 7, 217–227, https://doi.org/10.5194/tc-7-217-2013, 2013. a
Robin, G. d. Q.: Glaciology III: Seismic shooting and related investigations,
in: Norwegian-British-Swedish Antarctic Expedition, 1949–52, Scientific
Results, vol. 5, Norsk Polarinstit, Oslo, 1958. a
Schwander, J. and Stauffer, B.: Age difference between polar ice and the air
trapped in its bubbles, Nature, 311, 45–47, https://doi.org/10.1038/311045a0, 1984. a
Schwander, J., Stauffer, B., and Sigg, A.: Air Mixing in Firn and the Age of
the Air at Pore Close-Off, Ann. Glaciol., 10, 141–145,
https://doi.org/10.3189/S0260305500004328, 1988. a
Schwander, J., Sowers, T., Barnola, J.-M., Blunier, T., Fuchs, A., and
Malaizé, B.: Age scale of the air in the summit ice: Implication for
glacial-interglacial temperature change, J. Geophys. Res.-Atmos., 102, 19483–19493, https://doi.org/10.1029/97JD01309, 1997. a
Schwerdtfeger, P.: Theoretical derivation of the thermal conductivity and
diffusivity of snow, Int. Assoc. Sci. Hydrol. Publ, 61, 75–81, 1963. a
Schytt, V.: Glaciology II: Snow Studies at Maudheim: Snow Studies Inland: the
Inner Structure of the Ice Shelf at Maudheim as Shown by Core Drilling, in:
Norwegian-British-Swedish Antarctic Expedition, 1949–52, Scientific Results,
vol. 4, Norsk Polarinstitutt, Oslo, 1958. a
Seierstad, I. K., Abbott, P. M., Bigler, M., Blunier, T., Bourne, A. J., Brook,
E., Buchardt, S. L., Buizert, C., Clausen, H. B., Cook, E., Dahl-Jensen, D.,
Davies, S. M., Guillevic, M., Johnsen, S. J., Pedersen, D. S., Popp, T. J.,
Rasmussen, S. O., Severinghaus, J. P., Svensson, A., and Vinther, B. M.: Consistently dated records from the Greenland GRIP, GISP2 and NGRIP ice cores
for the past 104 ka reveal regional millennial-scale δ18O gradients
with possible Heinrich event imprint, Quatern. Sci. Rev., 106, 29– 46, https://doi.org/10.1016/j.quascirev.2014.10.032, 2014. a
Severinghaus, J. P., Grachev, A., and Battle, M.: Thermal fractionation of air in polar firn by seasonal temperature gradients, Geochemistry, Geophysics, Geosystems, 2, 1048, https://doi.org/10.1029/2000GC000146, 2001. a, b
Severinghaus, J. P., Albert, M. R., Courville, Z. R., Fahnestock, M. A.,
Kawamura, K., Montzka, S. A., Mühle, J., Scambos, T. A., Shields, E.,
Shuman, C. A., Suwa, M., Tans, P., and Weiss, R. F.: Deep air convection in
the firn at a zero-accumulation site, central Antarctica, Earth Planet.
Sci. Lett., 293, 359–367, https://doi.org/10.1016/j.epsl.2010.03.003, 2010. a
Shepherd, A., Ivins, E. R., A, G., Barletta, V. R., Bentley, M. J., Bettadpur, S., Briggs, K. H., Bromwich, D. H., Forsberg, R., Galin, N., Horwath, M.,
Jacobs, S., Joughin, I., King, M. A., Lenaerts, J. T. M., Li, J., Ligtenberg,
S. R. M., Luckman, A., Luthcke, S. B., McMillan, M., Meister, R., Milne, G.,
Mouginot, J., Muir, A., Nicolas, J. P., Paden, J., Payne, A. J., Pritchard,
H., Rignot, E., Rott, H., Sørensen, L. S., Scambos, T. A., Scheuchl, B.,
Schrama, E. J. O., Smith, B., Sundal, A. V., van Angelen, J. H., van de Berg,
W. J., van den Broeke, M. R., Vaughan, D. G., Velicogna, I., Wahr, J.,
Whitehouse, P. L., Wingham, D. J., Yi, D., Young, D., and Zwally, H. J.: A
Reconciled Estimate of Ice-Sheet Mass Balance, Science, 338, 1183–1189,
https://doi.org/10.1126/science.1228102, 2012. a
Simonsen, S. B., Stenseng, L., Adalgeirsdottir, G., Fausto, R. S., Hvidberg,
C. S., and Lucas-Picher, P.: Assessing a multilayered dynamic firn-compaction
model for Greenland with ASIRAS radar measurements, J. Glaciol., 59, 545–558,
https://doi.org/10.3189/2013JoG12J158, 2013. a, b, c, d
Sowers, T., Bender, M., Raynaud, D., and Korotkevich, Y. S.: δ15N of N2 in air trapped in polar ice: A tracer of gas transport in the firn and a
possible constraint on ice age-gas age differences, J. Geophys.
Res.-Atmos., 97, 15683–15697, https://doi.org/10.1029/92JD01297, 1992. a
Steffen, K. and Box, J.: Surface climatology of the Greenland Ice Sheet:
Greenland Climate Network 1995–1999, J. Geophys. Res.-Atmos., 106, 33951–33964, https://doi.org/10.1029/2001JD900161, 2001. a
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, Frontiers in Earth
Science, 5, 3 pp., https://doi.org/10.3389/feart.2017.00003, 2017. a
Stevens, C. M., Verjans, V., Lundin, J. M., Kahle, E. C., Horlings, A. N.,
Horlings, B. I., and Waddington, E. D.: UWGlaciology/CommunityFirnModel:
Version 1.0.5 of the Community Firn Model, https://doi.org/10.5281/zenodo.3585885, 2019. 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
Tange, O.: GNU Parallel: the command-line power tool, login: The USENIX
Magazine, 36, 42–47, https://doi.org/10.5281/zenodo.16303, 2011. a
The IMBIE Team: Mass balance of the Antarctic Ice Sheet from 1992 to 2017,
Nature, 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y, 2018. a
Trudinger, C. M., Enting, I. G., Etheridge, D. M., Francey, R. J., Levchenko,
V. A., Steele, L. P., Raynaud, D., and Arnaud, L.: Modeling air movement and
bubble trapping in firn, J. Geophys. Res.-Atmos., 102,
6747–6763, https://doi.org/10.1029/96JD03382, 1997. a
Tusima, K.: Grain Coarsening of Ice Particles Immersed in Pure Water, J. Jpn. Soc. Snow Ice, 40, 155–165,
https://doi.org/10.5331/seppyo.40.155, 1978. a
Van Dusen, M. S.: Thermal conductivity of non-metallic solids, in:
International critical tables of numerical data, physics, chemistry and
technology, edited by: Washburn, E. W., 5, 216–217, McGraw-Hill,
1929. a
van Kampenhout, L., Lenaerts, J. T., Lipscomb, W. H., Sacks, W. J., Lawrence,
D. M., Slater, A. G., and van den Broeke, M. R.: Improving the Representation
of Polar Snow and Firn in the Community Earth System Model, J.
Adv. Model. Earth Sy., 9, 2583–2600,
https://doi.org/10.1002/2017MS000988, 2017. a, b
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
Vandecrux, B., Fausto, R. S., Langen, P. L., van As, D., MacFerrin, M., Colgan,
W. T., Ingeman-Nielsen, T., Steffen, K., Jensen, N. S., Møller, M. T., and
Box, J. E.: Drivers of Firn Density on the Greenland Ice Sheet Revealed by
Weather Station Observations and Modeling, J. Geophys. Res.-Ea. Surf. 123, 2563–2576, https://doi.org/10.1029/2017JF004597, 2018. a
Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W. T., van As, D., Heilig, A., Stevens, C. M., Charalampidis, C., Fausto, R. S., Morris, E. M., Mosley-Thompson, E., Koenig, L., Montgomery, L. N., Miège, C., Simonsen, S. B., Ingeman-Nielsen, T., and Box, J. E.: Firn data compilation reveals widespread decrease of firn air content in western Greenland, The Cryosphere, 13, 845–859, https://doi.org/10.5194/tc-13-845-2019, 2019. a
Vandecrux, B., Mottram, R., Langen, P. L., Fausto, R. S., Olesen, M., Stevens, C. M., Verjans, V., Leeson, A., Ligtenberg, S., Kuipers Munneke, P., Marchenko, S., van Pelt, W., Meyer, C., Simonsen, S. B., Heilig, A., Samimi, S., Machguth, H., MacFerrin, M., Niwano, M., Miller, O., Voss, C. I., and Box, J. E.: The firn meltwater Retention Model Intercomparison Project (RetMIP): Evaluation of nine firn models at four weather station sites on the Greenland ice sheet, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-331, in review, 2020. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b
Voller, V. R., Swaminathan, C. R., and Thomas, B. G.: Fixed grid techniques for phase change problems: A review, Int. J. Numer. Meth. Eng., 30, 875–898, https://doi.org/10.1002/nme.1620300419, 1990. a
Witrant, E., Martinerie, P., Hogan, C., Laube, J. C., Kawamura, K., Capron, E., Montzka, S. A., Dlugokencky, E. J., Etheridge, D., Blunier, T., and Sturges, W. T.: A new multi-gas constrained model of trace gas non-homogeneous transport in firn: evaluation and behaviour at eleven polar sites, Atmos. Chem. Phys., 12, 11465–11483, https://doi.org/10.5194/acp-12-11465-2012, 2012. a
Yen, Y.-C.: Review of thermal properties of snow, ice and sea ice, Tech. Rep.
Report 81-10, Cold Regions Research and Engineering Lab, 1981. a
Short summary
Understanding processes in snow (firn), including compaction and airflow, is important for calculating how much mass the ice sheets are losing and for interpreting climate records from ice cores. We have developed the open-source Community Firn Model to simulate these processes. We used it to compare 13 different firn compaction equations and found that they do not agree within 10 %. We also show that including firn compaction in a firn-air model improves the match with data from ice cores.
Understanding processes in snow (firn), including compaction and airflow, is important for...
