Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2277-2023
© Author(s) 2023. 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-16-2277-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Alex S. Gardner
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Nicole-Jeanne Schlegel
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Eric Larour
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Related authors
Alex S. Gardner, Chad A. Greene, Joseph H. Kennedy, Mark A. Fahnestock, Maria Liukis, Luis A. López, Yang Lei, Ted A. Scambos, and Amaury Dehecq
The Cryosphere, 19, 3517–3533, https://doi.org/10.5194/tc-19-3517-2025, https://doi.org/10.5194/tc-19-3517-2025, 2025
Short summary
Short summary
The NASA MEaSUREs Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project provides glacier and ice sheet velocity products for the full Landsat, Sentinel-1, and Sentinel-2 satellite archives and will soon include data from the NISAR satellite. This paper describes the ITS_LIVE processing chain and gives guidance for working with the cloud-optimized glacier and ice sheet velocity products.
Johan Nilsson and Alex S. Gardner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-311, https://doi.org/10.5194/essd-2024-311, 2024
Revised manuscript has not been submitted
Short summary
Short summary
Integrating data from multiple satellite altimetry missions, we analyzed Greenland’s peripheral glaciers and Ice Sheet (GrIS) from 1992–2023. Our methodology ensures consistent, reliable elevation change data, now publicly available via NASA's ITS_LIVE project. The GrIS lost an average of -173 ± 19 Gt a-1 and peripheral glaciers -23 ± 5 Gt a-1 from 1992–2022. The study highlights the importance of continued monitoring to understand climate change impacts on Earth's Cryosphere.
Brian Menounos, Alex Gardner, Caitlyn Florentine, and Andrew Fountain
The Cryosphere, 18, 889–894, https://doi.org/10.5194/tc-18-889-2024, https://doi.org/10.5194/tc-18-889-2024, 2024
Short summary
Short summary
Glaciers in western North American outside of Alaska are often overlooked in global studies because their potential to contribute to changes in sea level is small. Nonetheless, these glaciers represent important sources of freshwater, especially during times of drought. We show that these glaciers lost mass at a rate of about 12 Gt yr-1 for about the period 2013–2021; the rate of mass loss over the period 2018–2022 was similar.
Youngmin Choi, Helene Seroussi, Mathieu Morlighem, Nicole-Jeanne Schlegel, and Alex Gardner
The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, https://doi.org/10.5194/tc-17-5499-2023, 2023
Short summary
Short summary
Ice sheet models are often initialized using snapshot observations of present-day conditions, but this approach has limitations in capturing the transient evolution of the system. To more accurately represent the accelerating changes in glaciers, we employed time-dependent data assimilation. We found that models calibrated with the transient data better capture past trends and more accurately reproduce changes after the calibration period, even with limited observations.
Fernando S. Paolo, Alex S. Gardner, Chad A. Greene, Johan Nilsson, Michael P. Schodlok, Nicole-Jeanne Schlegel, and Helen A. Fricker
The Cryosphere, 17, 3409–3433, https://doi.org/10.5194/tc-17-3409-2023, https://doi.org/10.5194/tc-17-3409-2023, 2023
Short summary
Short summary
We report on a slowdown in the rate of thinning and melting of West Antarctic ice shelves. We present a comprehensive assessment of the Antarctic ice shelves, where we analyze at a continental scale the changes in thickness, flow, and basal melt over the past 26 years. We also present a novel method to estimate ice shelf change from satellite altimetry and a time-dependent data set of ice shelf thickness and basal melt rates at an unprecedented resolution.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Yang Lei, Alex S. Gardner, and Piyush Agram
Earth Syst. Sci. Data, 14, 5111–5137, https://doi.org/10.5194/essd-14-5111-2022, https://doi.org/10.5194/essd-14-5111-2022, 2022
Short summary
Short summary
This work describes NASA MEaSUREs ITS_LIVE project's Version 2 Sentinel-1 image-pair ice velocity product and processing methodology. We show the refined offset tracking algorithm, autoRIFT, calibration for Sentinel-1 geolocation biases and correction of the ionosphere streaking problems. Validation was performed over three typical test sites covering the globe by comparing with other similar global and regional products.
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels J. Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, https://doi.org/10.5194/tc-16-3215-2022, 2022
Short summary
Short summary
Terminus traces have been used to understand how Greenland's glaciers have changed over time; however, manual digitization is time-intensive, and a lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39 000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
Johan Nilsson, Alex S. Gardner, and Fernando S. Paolo
Earth Syst. Sci. Data, 14, 3573–3598, https://doi.org/10.5194/essd-14-3573-2022, https://doi.org/10.5194/essd-14-3573-2022, 2022
Short summary
Short summary
The longest observational record available to study the mass balance of the Earth’s ice sheets comes from satellite altimeters. This record consists of multiple satellite missions with different measurements and quality, and it must be cross-calibrated and integrated into a consistent record for scientific use. Here, we present a novel approach for generating such a record providing a seamless record of elevation change for the Antarctic Ice Sheet that spans the period 1985 to 2020.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
Short summary
Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Chad A. Greene, Alex S. Gardner, and Lauren C. Andrews
The Cryosphere, 14, 4365–4378, https://doi.org/10.5194/tc-14-4365-2020, https://doi.org/10.5194/tc-14-4365-2020, 2020
Short summary
Short summary
Seasonal variability is a fundamental characteristic of any Earth surface system, but we do not fully understand which of the world's glaciers speed up and slow down on an annual cycle. Such short-timescale accelerations may offer clues about how individual glaciers will respond to longer-term changes in climate, but understanding any behavior requires an ability to observe it. We describe how to use satellite image feature tracking to determine the magnitude and timing of seasonal ice dynamics.
Zachary Fair, Mark Flanner, Kelly M. Brunt, Helen Amanda Fricker, and Alex Gardner
The Cryosphere, 14, 4253–4263, https://doi.org/10.5194/tc-14-4253-2020, https://doi.org/10.5194/tc-14-4253-2020, 2020
Short summary
Short summary
Ice on glaciers and ice sheets may melt and pond on ice surfaces in summer months. Detection and observation of these meltwater ponds is important for understanding glaciers and ice sheets, and satellite imagery has been used in previous work. However, image-based methods struggle with deep water, so we used data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Airborne Topographic Mapper (ATM) to demonstrate the potential for lidar depth monitoring.
Alex S. Gardner, Chad A. Greene, Joseph H. Kennedy, Mark A. Fahnestock, Maria Liukis, Luis A. López, Yang Lei, Ted A. Scambos, and Amaury Dehecq
The Cryosphere, 19, 3517–3533, https://doi.org/10.5194/tc-19-3517-2025, https://doi.org/10.5194/tc-19-3517-2025, 2025
Short summary
Short summary
The NASA MEaSUREs Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project provides glacier and ice sheet velocity products for the full Landsat, Sentinel-1, and Sentinel-2 satellite archives and will soon include data from the NISAR satellite. This paper describes the ITS_LIVE processing chain and gives guidance for working with the cloud-optimized glacier and ice sheet velocity products.
Alamgir Hossan, Andreas Colliander, Nicole-Jeanne Schlegel, Joel Harper, Lauren Andrews, Jana Kolassa, Julie Z. Miller, and Richard Cullather
EGUsphere, https://doi.org/10.5194/egusphere-2025-2681, https://doi.org/10.5194/egusphere-2025-2681, 2025
Short summary
Short summary
Microwave L-band radiometry offers a promising tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.
Lambert Caron, Erik Ivins, Eric Larour, Surendra Adhikari, and Laurent Metivier
EGUsphere, https://doi.org/10.5194/egusphere-2024-3414, https://doi.org/10.5194/egusphere-2024-3414, 2025
Short summary
Short summary
Presented here is a new model of the solid-Earth response to tides and mass changes in ice sheets, oceans, and groundwater, in of terms of gravity change and bedrock motion. The model is capable simulating mantle deformation including elasticity, transient and steady-state viscous flow. We detail our approach to numerical optimization, and report the accuracy of results with respect to community benchmarks. The resulting coupled system features kilometer-scale resolution and fast computation.
Luc Houriez, Eric Larour, Lambert Caron, Nicole-Jeanne Schlegel, Surendra Adhikari, Erik Ivins, Tyler Pelle, Hélène Seroussi, Eric Darve, and Martin Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2024-4136, https://doi.org/10.5194/egusphere-2024-4136, 2025
Short summary
Short summary
We studied how interactions between the ice sheet and the Earth’s evolving surface affect the future of Thwaites Glacier in Antarctica. We find that small features in the bedrock play a major role in these interactions which can delay the glacier’s retreat by decades or even centuries. This can significantly reduce sea-level rise projections. Our work highlights resolution requirements for similar ice—earth models, and the importance of bedrock mapping efforts in Antarctica.
Alamgir Hossan, Andreas Colliander, Baptiste Vandecrux, Nicole-Jeanne Schlegel, Joel Harper, Shawn Marshall, and Julie Z. Miller
EGUsphere, https://doi.org/10.5194/egusphere-2024-2563, https://doi.org/10.5194/egusphere-2024-2563, 2024
Short summary
Short summary
We used L-band observations from the SMAP mission to quantify the surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland ice sheet. The algorithm is described, and the validation results are provided. The results demonstrate the potential for creating an LWA data product across GrIS, which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.
Johan Nilsson and Alex S. Gardner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-311, https://doi.org/10.5194/essd-2024-311, 2024
Revised manuscript has not been submitted
Short summary
Short summary
Integrating data from multiple satellite altimetry missions, we analyzed Greenland’s peripheral glaciers and Ice Sheet (GrIS) from 1992–2023. Our methodology ensures consistent, reliable elevation change data, now publicly available via NASA's ITS_LIVE project. The GrIS lost an average of -173 ± 19 Gt a-1 and peripheral glaciers -23 ± 5 Gt a-1 from 1992–2022. The study highlights the importance of continued monitoring to understand climate change impacts on Earth's Cryosphere.
Brian Menounos, Alex Gardner, Caitlyn Florentine, and Andrew Fountain
The Cryosphere, 18, 889–894, https://doi.org/10.5194/tc-18-889-2024, https://doi.org/10.5194/tc-18-889-2024, 2024
Short summary
Short summary
Glaciers in western North American outside of Alaska are often overlooked in global studies because their potential to contribute to changes in sea level is small. Nonetheless, these glaciers represent important sources of freshwater, especially during times of drought. We show that these glaciers lost mass at a rate of about 12 Gt yr-1 for about the period 2013–2021; the rate of mass loss over the period 2018–2022 was similar.
Youngmin Choi, Helene Seroussi, Mathieu Morlighem, Nicole-Jeanne Schlegel, and Alex Gardner
The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, https://doi.org/10.5194/tc-17-5499-2023, 2023
Short summary
Short summary
Ice sheet models are often initialized using snapshot observations of present-day conditions, but this approach has limitations in capturing the transient evolution of the system. To more accurately represent the accelerating changes in glaciers, we employed time-dependent data assimilation. We found that models calibrated with the transient data better capture past trends and more accurately reproduce changes after the calibration period, even with limited observations.
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.
Fernando S. Paolo, Alex S. Gardner, Chad A. Greene, Johan Nilsson, Michael P. Schodlok, Nicole-Jeanne Schlegel, and Helen A. Fricker
The Cryosphere, 17, 3409–3433, https://doi.org/10.5194/tc-17-3409-2023, https://doi.org/10.5194/tc-17-3409-2023, 2023
Short summary
Short summary
We report on a slowdown in the rate of thinning and melting of West Antarctic ice shelves. We present a comprehensive assessment of the Antarctic ice shelves, where we analyze at a continental scale the changes in thickness, flow, and basal melt over the past 26 years. We also present a novel method to estimate ice shelf change from satellite altimetry and a time-dependent data set of ice shelf thickness and basal melt rates at an unprecedented resolution.
Mattia Poinelli, Michael Schodlok, Eric Larour, Miren Vizcaino, and Riccardo Riva
The Cryosphere, 17, 2261–2283, https://doi.org/10.5194/tc-17-2261-2023, https://doi.org/10.5194/tc-17-2261-2023, 2023
Short summary
Short summary
Rifts are fractures on ice shelves that connect the ice on top to the ocean below. The impact of rifts on ocean circulation below Antarctic ice shelves has been largely unexplored as ocean models are commonly run at resolutions that are too coarse to resolve the presence of rifts. Our model simulations show that a kilometer-wide rift near the ice-shelf front modulates heat intrusion beneath the ice and inhibits basal melt. These processes are therefore worthy of further investigation.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Yang Lei, Alex S. Gardner, and Piyush Agram
Earth Syst. Sci. Data, 14, 5111–5137, https://doi.org/10.5194/essd-14-5111-2022, https://doi.org/10.5194/essd-14-5111-2022, 2022
Short summary
Short summary
This work describes NASA MEaSUREs ITS_LIVE project's Version 2 Sentinel-1 image-pair ice velocity product and processing methodology. We show the refined offset tracking algorithm, autoRIFT, calibration for Sentinel-1 geolocation biases and correction of the ionosphere streaking problems. Validation was performed over three typical test sites covering the globe by comparing with other similar global and regional products.
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels J. Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, https://doi.org/10.5194/tc-16-3215-2022, 2022
Short summary
Short summary
Terminus traces have been used to understand how Greenland's glaciers have changed over time; however, manual digitization is time-intensive, and a lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39 000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
Johan Nilsson, Alex S. Gardner, and Fernando S. Paolo
Earth Syst. Sci. Data, 14, 3573–3598, https://doi.org/10.5194/essd-14-3573-2022, https://doi.org/10.5194/essd-14-3573-2022, 2022
Short summary
Short summary
The longest observational record available to study the mass balance of the Earth’s ice sheets comes from satellite altimeters. This record consists of multiple satellite missions with different measurements and quality, and it must be cross-calibrated and integrated into a consistent record for scientific use. Here, we present a novel approach for generating such a record providing a seamless record of elevation change for the Antarctic Ice Sheet that spans the period 1985 to 2020.
Joshua K. Cuzzone, Nicolás E. Young, Mathieu Morlighem, Jason P. Briner, and Nicole-Jeanne Schlegel
The Cryosphere, 16, 2355–2372, https://doi.org/10.5194/tc-16-2355-2022, https://doi.org/10.5194/tc-16-2355-2022, 2022
Short summary
Short summary
We use an ice sheet model to determine what influenced the Greenland Ice Sheet to retreat across a portion of southwestern Greenland during the Holocene (about the last 12 000 years). Our simulations, constrained by observations from geologic markers, show that atmospheric warming and ice melt primarily caused the ice sheet to retreat rapidly across this domain. We find, however, that iceberg calving at the interface where the ice meets the ocean significantly influenced ice mass change.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
Short summary
Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Blake A. Castleman, Nicole-Jeanne Schlegel, Lambert Caron, Eric Larour, and Ala Khazendar
The Cryosphere, 16, 761–778, https://doi.org/10.5194/tc-16-761-2022, https://doi.org/10.5194/tc-16-761-2022, 2022
Short summary
Short summary
In the described study, we derive an uncertainty range for global mean sea level rise (SLR) contribution from Thwaites Glacier in a 200-year period under an extreme ocean warming scenario. We derive the spatial and vertical resolutions needed for bedrock data acquisition missions in order to limit global mean SLR contribution from Thwaites Glacier to ±2 cm in a 200-year period. We conduct sensitivity experiments in order to present the locations of critical regions in need of accurate mapping.
Kevin Bulthuis and Eric Larour
Geosci. Model Dev., 15, 1195–1217, https://doi.org/10.5194/gmd-15-1195-2022, https://doi.org/10.5194/gmd-15-1195-2022, 2022
Short summary
Short summary
We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
Daniel Cheng, Wayne Hayes, Eric Larour, Yara Mohajerani, Michael Wood, Isabella Velicogna, and Eric Rignot
The Cryosphere, 15, 1663–1675, https://doi.org/10.5194/tc-15-1663-2021, https://doi.org/10.5194/tc-15-1663-2021, 2021
Short summary
Short summary
Tracking changes in Greenland's glaciers is important for understanding Earth's climate, but it is time consuming to do so by hand. We train a program, called CALFIN, to automatically track these changes with human levels of accuracy. CALFIN is a special type of program called a neural network. This method can be applied to other glaciers and eventually other tracking tasks. This will enhance our understanding of the Greenland Ice Sheet and permit better models of Earth's climate.
Chad A. Greene, Alex S. Gardner, and Lauren C. Andrews
The Cryosphere, 14, 4365–4378, https://doi.org/10.5194/tc-14-4365-2020, https://doi.org/10.5194/tc-14-4365-2020, 2020
Short summary
Short summary
Seasonal variability is a fundamental characteristic of any Earth surface system, but we do not fully understand which of the world's glaciers speed up and slow down on an annual cycle. Such short-timescale accelerations may offer clues about how individual glaciers will respond to longer-term changes in climate, but understanding any behavior requires an ability to observe it. We describe how to use satellite image feature tracking to determine the magnitude and timing of seasonal ice dynamics.
Zachary Fair, Mark Flanner, Kelly M. Brunt, Helen Amanda Fricker, and Alex Gardner
The Cryosphere, 14, 4253–4263, https://doi.org/10.5194/tc-14-4253-2020, https://doi.org/10.5194/tc-14-4253-2020, 2020
Short summary
Short summary
Ice on glaciers and ice sheets may melt and pond on ice surfaces in summer months. Detection and observation of these meltwater ponds is important for understanding glaciers and ice sheets, and satellite imagery has been used in previous work. However, image-based methods struggle with deep water, so we used data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Airborne Topographic Mapper (ATM) to demonstrate the potential for lidar depth monitoring.
Eric Larour, Lambert Caron, Mathieu Morlighem, Surendra Adhikari, Thomas Frederikse, Nicole-Jeanne Schlegel, Erik Ivins, Benjamin Hamlington, Robert Kopp, and Sophie Nowicki
Geosci. Model Dev., 13, 4925–4941, https://doi.org/10.5194/gmd-13-4925-2020, https://doi.org/10.5194/gmd-13-4925-2020, 2020
Short summary
Short summary
ISSM-SLPS is a new projection system for future sea level that increases the resolution and accuracy of current projection systems and improves the way uncertainty is treated in such projections. This will pave the way for better inclusion of state-of-the-art results from existing intercomparison efforts carried out by the scientific community, such as GlacierMIP2 or ISMIP6, into sea-level projections.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, https://doi.org/10.5194/tc-14-3071-2020, 2020
Short summary
Short summary
In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Hélène Seroussi, 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, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, https://doi.org/10.5194/tc-14-3033-2020, 2020
Short summary
Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
Cited articles
Alexander, P. M., Tedesco, M., Fettweis, X., van de Wal, R. S. W., Smeets, C. J. P. P., and van den Broeke, M. R.: Assessing spatio-temporal variability and trends in modelled and measured Greenland Ice Sheet albedo (2000–2013), The Cryosphere, 8, 2293–2312, https://doi.org/10.5194/tc-8-2293-2014, 2014.
Alley, R. B.: Firn densification by grain-boundary sliding: a first model,
Le Journal de Physique Colloques, 48, C1–249, 1987.
Arthern, R. J. and Wingham, D. J.: The Natural Fluctuations of Firn
Densification and Their Effect on the Geodetic Determination of Ice Sheet
Mass Balance, Clim. Change, 40, 605–624,
https://doi.org/10.1023/A:1005320713306, 1998.
Arthern, R. J., Vaughan, D. G., Rankin, A. M., Mulvaney, R., and Thomas, E. R.:
In situ measurements of Antarctic snow compaction compared with predictions
of models, J. Geophys. Res., 115, F03011, https://doi.org/10.1029/2009JF001306, 2010.
Baker, I.: NEEM Firn Core 2009S2 Density and Permeability, Arctic Data Center [data set],
https://doi.org/10.18739/A2Q88G, 2016.
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss
avalanche warning Part I: Numerical model, Cold Reg. Sci.
Technol., 35, 123–145, 2002.
Bassford, R. P.: Geophysical and numerical modelling investigations of the
ice caps in Severnaya Zemlya, PhD thesis, University of Bristol, England,
2002.
Beljaars, A. C. M. and Holtslag, A. A. M.: Flux parameterization over land
surfaces for atmospheric models, J. Appl. Meteorol., 30,
327–341, 1991.
Benson, C.: Greenland Snow Pit and Core Stratigraphy (Analog and Digital
Formats), National Snow and Ice Data Center, Boulder, Colorado USA [data set],
https://doi.org/10.7265/N5RN35SK, 2013.
Benson, C.: Greenland Snow Pit and Core Stratigraphy, Carl S. Benson
Collection, Coll. 2010011, Roger G. Barry Archives and Resource Center,
National Snow Data Center, 2017.
Bolton, D.: The computation of equivalent potential temperature, Mon.
Weather Rev., 108, 1046–1953, 1980.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2 Site
15, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55511, 1999a.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 73, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55516, 1999b.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 37, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55513, 1999c.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 31, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55512, 1999d.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 13, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55510, 1999e.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 51, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.55514, 1999f.
Bolzan, J. F. and Strobel, M.: Oxygen isotope data from snowpit at GISP2
Site 571, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.59996, 2001.
Bougamont, M. and Bamber, J. L.: A surface mass balance model for the
Greenland Ice Sheet, J. Geophys. Res.-Earth Surf., 110, F04018,
https://doi.org/10.1029/2005JF000348, 2005.
Bougamont, M., Bamber, J. L., Ridley, J. K., Gladstone, R. M., Greuell, W.,
Hanna, E., Payne, A. J., and Rutt, I.: Impact of model physics on
estimating the surface mass balance of the Greenland Ice Sheet, Geophys.
Res. Lett., 34, L17501, https://doi.org/10.1029/2007GL030700, 2007.
Brils, M., Kuipers Munneke, P., van de Berg, W. J., and van den Broeke, M.: Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G, Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, 2022.
Brun, E.: Investigation on wet-snow metamorphism in respect of liquid-water
content, Ann. Glaciol., 13, 22–26, 1989.
Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C.: An energy and
mass model of snow cover suitable for operational avalanche forecasting,
J. Glaciol., 35, 333–342, 1989.
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, 1992.
Calonne, N., Flin, F., Morin, S., Lesaffre, B., du Roscoat, S. R., and Geindreau, C.: Numerical and experimental investigations of the effective
thermal conductivity of snow, Geophys. Res. Lett., 38, L23501,
https://doi.org/10.1029/2011GL049234, 2011.
Calonne, N., Milliancourt, L., Burr, A., Philip, A., Martin, C. L., Flin,
F., and Geindreau, C.: Thermal Conductivity of Snow, Firn, and Porous Ice
From 3-D Image-Based Computations, Geophys. Res. Lett., 46,
13079–13089, 2019.
Colbeck, S. C.: Theory of metamorphism of wet snow, Cold Regions Research and Engineering Laboratory (U.S.) Engineer Research and Development Center (U.S.), http://hdl.handle.net/11681/5894, 1973.
Colbeck, S. C.: The capillary effects on water percolation in homogeneous
snow, J. Glaciol., 13, 85–97, https://doi.org/10.3189/S002214300002339X, 1974.
Colbeck, S. C.: An overview of seasonal snow metamorphism, Rev.
Geophys. Space Phys., 20, 45–61, 1982.
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.
Conway, H.: Roosevelt Island Ice Core Density and Beta Count Data, U.S. Antarctic Program (USAP) Data Center [data set],
https://doi.org/10.7265/N55718ZW, 2003.
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate. Copernicus Climate Change
Service Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, last access: 25 January 2019.
Courant, R., Friedrichs, K., and Lewy, H.: Über die partiellen
Differenzengleichungen der mathematischen Physik, Mathematische Annalen,
100, 32–74, 1928.
Culberg, R., Schroeder, D. M., and Chu, W.: Extreme melt season ice layers
reduce firn permeability across Greenland, Nat. Commun., 12,
1–9, 2021.
Ding, M., Yang, D., van den Broeke, M., Allison, I., Xiao, C., Qin, D., and Huai, B.: The surface energy balance at Panda 1 Station, Princess Elizabeth
Land: A typical katabatic wind region in East Antarctica, J.
Geophys. Res.-Atmos., 125, e2019JD030378,
https://doi.org/10.1029/2019JD030378, 2020.
Dunmire, D., Banwell, A. F., Wever, N., Lenaerts, J. T. M., and Datta, R. T.: Contrasting regional variability of buried meltwater extent over 2 years across the Greenland Ice Sheet, The Cryosphere, 15, 2983–3005, https://doi.org/10.5194/tc-15-2983-2021, 2021.
Fausto, R. S., Box, J. E., Vandecrux, B., van As, D., Steffen, K.,
MacFerrin, M., 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 modelling,
Front. Earth Sci., 6, https://doi.org/10.3389/feart.2018.00051,
2018.
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res.-Atmos., 111, D12208,
https://doi.org/10.1029/2005JD006834, 2006.
Foken, T.: Micrometeorology, Springer Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-74666-9, 2008.
Forster, R. R., Box, J. E., van den Broeke, M. R., Miège, C., Burgess,
E. W., van Angelen, J. H., Lenaerts, J. T. M., Koenig, L. S., Paden, J.,
Lewis, C., Gogineni, S. P., Leuschen, C., and McConnell, J. R.: Extensive
liquid meltwater storage in firn within the Greenland ice sheet, Nat.
Geosci., 7, 95–98, https://doi.org/10.1038/ngeo2043, 2014.
Gardner, A. S.: Glacier Energy and Mass Balance – MATLAB (v0.1), Zenodo [code],
https://doi.org/10.5281/zenodo.6975252, 2022.
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the
development of a new physically based broadband albedo parameterization,
J. Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010.
Greuell, W. and Konzelmann, T.: Numerical modelling of the energy balance
and the englacial temperature of the Greenland Ice Sheet, Calculations for
the ETH-Camp location (West Greenland, 1155 m a.s.l.), Global Planet.
Change, 9, 91–114, 1994.
Greuell, W. and Oerlemans, J.: The Evolution of the Englacial Temperature
Distribution in the Superimposed Ice Zone of a Polar Ice Cap During a Summer
Season BT – Glacier Fluctuations and Climatic Change, edited by: Oerlemans, J.,
289–303, Springer Netherlands, 1989.
Guyomarc'h, G. and Merindol, L.: Validation of an application for
forecasting blowing snow, Ann. Glaciol., 26, 138–143, 1998.
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.
Herron, M. and Langway, C.: Firn Densification: An Empirical Model, J. Glaciol., 25, 373–385, https://doi.org/10.3189/S0022143000015239, 1980.
Hirashima, H., Avanzi, F., and Yamaguchi, S.: Liquid water infiltration into a layered snowpack: evaluation of a 3-D water transport model with laboratory experiments, Hydrol. Earth Syst. Sci., 21, 5503–5515, https://doi.org/10.5194/hess-21-5503-2017, 2017.
Högström, U.: Review of some basic characteristics of the
atmospheric surface layer, Bound.-Lay. Meteorol., 78, 215–246, 1996.
Horlings, A. N., Christianson, K., Holschuh, N., Stevens, C. M., and Waddington, E. D.: Effect of horizontal divergence on estimates of firn-air
content, J. Glaciol., 67, 287–296, https://doi.org/10.1017/jog.2020.105, 2021.
ISSM Team: Ice-sheet and Sea-level System Model source code, v4.21 r27238
(4.21), Zenodo [code], https://doi.org/10.5281/zenodo.7026445, 2022.
Janssens, I. and Huybrechts, P.: The treatment of meltwater retention in
mass-balance parameterizations of the Greenland ice sheet, Ann.
Glaciol., 31, 133–140, https://doi.org/10.3189/172756400781819941,
2000.
Kaspers, K. A., van de Wal, R. S. W., van den Broeke, M. R., Schwander, J., van Lipzig, N. P. M., and Brenninkmeijer, C. A. M.: Model calculations of the age of firn air across the Antarctic continent, Atmos. Chem. Phys., 4, 1365–1380, https://doi.org/10.5194/acp-4-1365-2004, 2004.
Keenan, E., Wever, N., Dattler, M., Lenaerts, J. T. M., Medley, B., Kuipers Munneke, P., and Reijmer, C.: Physics-based SNOWPACK model improves representation of near-surface Antarctic snow and firn density, The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, 2021.
Koenig, L. S., Miège, C., Forster, R. R., and Brucker, L.: Initial in
situ measurements of perennial meltwater storage in the Greenland firn
aquifer: Measurements of Greenland Aquifer, Geophys. Res. Lett.,
41, 81–85, https://doi.org/10.1002/2013GL058083, 2014.
Kreutz, K.: Microparticle, Conductivity, and Density Measurements from the
WAIS Divide Deep Ice Core, Antarctica, U.S. Antarctic Program (USAP) Data Center [data set], https://doi.org/10.7265/N5K07264, 2011.
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.
Larour, E., Morlighem, M., Seroussi, H., Schiermeier, J., and Rignot, E.: Ice
flow sensitivity to geothermal heat flux of Pine Island Glacier, Antarctica,
J. Geophys. Res., 117, F04023, https://doi.org/10.1029/2012JF002371, 2012a.
Larour, E., Schiermeier, J., Rignot, E., Seroussi, H., and Morlighem, M.:
Sensitivity Analysis of Pine Island Glacier ice flow using ISSM and DAKOTA,
J. Geophys. Res., 117, F02009, https://doi.org/10.1029/2011JF002146, 2012b.
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM), J. Geophys. Res.-Earth Surf., 117, F01022, https://doi.org/10.1029/2011JF002140, 2012c.
Lefebre, F., Gallée, H., van Ypersele, J.-P., and Greuell, W.:
Modeling of snow and ice melt at ETH Camp (West Greenland): A study of
surface albedo, J. Geophys. Res., 108, 4231, https://doi.org/10.1029/2001JD001160,
2003.
Lenaerts, J. T. M., van den Broeke, M. R., Déry, S. J., König-Langlo, G., Ettema, J., and Munneke, P. K.: Modelling snowdrift sublimation on an Antarctic ice shelf, The Cryosphere, 4, 179–190, https://doi.org/10.5194/tc-4-179-2010, 2010.
Li, J. and Zwally, H.: Modeling the density variation in the shallow firn
layer, Ann. Glaciol., 38, 309–313, https://doi.org/10.3189/172756404781814988,
2004.
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.
Ligtenberg, S. R. M., Kuipers Munneke, P., Noël, B. P. Y., and van den Broeke, M. R.: Brief communication: Improved simulation of the present-day Greenland firn layer (1960–2016), The Cryosphere, 12, 1643–1649, https://doi.org/10.5194/tc-12-1643-2018, 2018.
Liston, G. E. and Elder, K.: A Distributed Snow-Evolution Modeling System
(SnowModel), J. Hydrometeorol., 7, 1259–1276,
https://doi.org/10.1175/JHM548.1, 2006.
Lundin, J. M. D., Stevens, C. M., Arthern, R., Buizert, C., Orsi, A.,
Ligtenberg, S. R. M., 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.
MacFerrin, M., Machguth, H., van As, D., Charalampidis, C., Stevens, C. M., Heilig, A., Vandecrux, B., Langen, P. L., Mottram, R., Fettweis, X., van den Broeke, M. R., Pfeffer, W. T., Moussavi, M. S., and Abdalati, W.: Rapid expansion of Greenland's
low-permeability ice slabs, Nature, 573, 403–407, 2019.
Male, D. H. and Granger, R. J.: Snow surface energy exchange, Water
Resour. Res., 17, 609–627, 1981.
Marbouty, D.: An experimental study of temperature-gradient metamorphism,
J. Glaciol., 26, 303–312, 1980.
Marchenko, S., van Pelt, W. J. J., Claremar, B., Pohjola, V., Pettersson,
R., Machguth, H., and Reijmer, C.: Parameterizing Deep Water Percolation
Improves Subsurface Temperature Simulations by a Multilayer Firn Model,
Front. Earth Sci., 5, 16, https://doi.org/10.3389/feart.2017.00016, 2017.
Marsh, P. and Woo, M. K.: Wetting front advance and freezing of meltwater
within a snow cover: 2. A simulation model, Water Resour. Res.,
20, 1865–1874, https://doi.org/10.1029/WR020i012p01865,
1984.
Mayewski, P. and Whitlow, S.: Snow Pit and Ice Core Data from Southern
Greenland, 1984, Version 1.0, Arctic Data Center [data set], https://doi.org/10.5065/D6S180MH, 2009.
Medley, B., Joughin, I., Das, S. B., Steig, E. J., Conway, H., Gogineni, S.,
Criscitiello, A. S., McConnell, J. R., Smith, B. E., van den Broeke, M. R.,
Lenaerts, J. T. M., Bromwich, D. H., and Nicolas, J. P.: Airborne-radar and
ice-core observations of annual snow accumulation over Thwaites Glacier,
West Antarctica confirm the spatiotemporal variability of global and
regional atmospheric models, Geophys. Res. Lett., 40, 3649–3654,
https://doi.org/10.1002/grl.50706, 2013.
Medley, B., Neumann, T. A., Zwally, H. J., Smith, B. E., and Stevens, C. M.: Simulations of firn processes over the Greenland and Antarctic ice sheets: 1980–2021, The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, 2022.
Miège, C., Forster, R. R., Box, J. E., Burgess, E. W., McConnell, J. R.,
Pasteris, D. R., and Spikes, V. B.: Southeast Greenland high accumulation
rates derived from firn cores and ground-penetrating radar, Ann.
Glaciol., 54, 322–332, https://doi.org/10.3189/2013AoG63A358, 2013.
Miller, H. and Schwager, M.: Density of ice core ngt37C95.2 from the North
Greenland Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.57798, 2000a.
Miller, H. and Schwager, M.: Density of ice core ngt42C95.2 from the North
Greenland Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.57655, 2000b.
Miller, J. Z., Culberg, R., Long, D. G., Shuman, C. A., Schroeder, D. M., and Brodzik, M. J.: An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry, The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, 2022.
Miller, N. B., Shupe, M. D., Cox, C. J., Noone, D., Persson, P. O. G., and Steffen, K.: Surface energy budget responses to radiative forcing at Summit, Greenland, The Cryosphere, 11, 497–516, https://doi.org/10.5194/tc-11-497-2017, 2017.
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.
Mosley-Thompson, E., McConnell, J. R., Bales, R. C., Li, Z., Lin, P.-N.,
Steffen, K., Thompson, L. G., Edwards, R., and Bathke, D.: Local to
regional-scale variability of annual net accumulation on the Greenland ice
sheet from PARCA cores, J. Geophys. Res., 106, 33839–33851,
https://doi.org/10.1029/2001JD900067, 2001.
Munro, D. S. and Davies, J. A.: An experimental study of the glacier
boundary layer over melting ice, J. Glaciol., 18, 425–436,
https://doi.org/10.3189/S0022143000021109, 1977.
Murphy, D. M. and Koop, T.: Review of the Vapour Pressures of Ice and
Supercooled Water for Atmospheric Applications, Q. J.
Roy. Meteor. Soc., 131, 1539–1565,
https://doi.org/10.1256/qj.04.94, 2005.
Murray, F. W.: On the Computation of Saturation Vapor Pressure. J. Appl.
Meteorol., 6, 203–204, 1967.
Noël, B., van de Berg, W. J., Machguth, H., Lhermitte, S., Howat, I., Fettweis, X., and van den Broeke, M. R.: A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10, 2361–2377, https://doi.org/10.5194/tc-10-2361-2016, 2016.
Parish, T. R. and Bromwich, D. H.: The surface windfield over the
Antarctic ice sheets, Nature, 328, 51–54,
https://doi.org/10.1038/328051a0, 1987.
Patankar, S.: Numerical Heat Transfer and Fluid Flow, 1st edn., CRC Press, https://doi.org/10.1201/9781482234213, 1980.
Paterson, W.: The Physics of Glaciers, 3rd edn., Pergamon Press, Oxford,
London, New York, 1994.
Pfeffer, W. T., Illangasekare, T. H., and Meier, M. F.: Analysis and
Modeling of Melt-Water Refreezing in Dry Snow, J. Glaciol.,
36, 238–246, https://doi.org/10.3189/S0022143000009497, 1990.
Pfeffer, W. T., Meier, M. F., and Illangasekare, T. H.: Retention of
Greenland Runoff by Refreezing – Implications for Projected Future Sea-Level
Change, J. Geophys. Res.-Oceans, 96, 22117–22124, 1991.
Schaaf, C. and Wang, Z.: MCD43C3 MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3
Global 0.05Deg CMG V006, NASA EOSDIS Land Processes
DAAC [data set], https://doi.org/10.5067/MODIS/MCD43C3.006, 2015.
Schlegel, N.-J., Larour, E., Seroussi, H., Morlighem, M., and Box, J. E.:
Decadal-scale sensitivity of northeast Greenland ice flow to errors in
surface mass balance using ISSM, J. Geophys. Res.-Earth Surf., 118, 667–680, https://doi.org/10.1002/jgrf.20062, 2013.
Schlegel, N.-J., Larour, E., Seroussi, H., Morlighem, M. and Box, J. E.: Ice
discharge uncertainties in Northeast Greenland from boundary conditions and
climate forcing of an ice flow model, J. Geophys. Res., 120, 29–54,
https://doi.org/10.1002/2014JF003359, 2015.
Schlegel, N.-J. and Larour, E. Y.: Quantification of surface forcing
requirements for a Greenland Ice Sheet model using uncertainty analyses,
Geophys. Res. Lett., 46, 9700–9709, https://doi.org/10.1029/2019GL083532, 2019.
Schlegel, N.-J., Gardner, A. S., and Larour, E.: Output from the Glacier
Energy and Mass Balance (GEMB v1.0) forced with 3-hourly RACMO fields,
Greenland and Antarctica 1979–2014 (1.1),
Zenodo [data set], https://doi.org/10.5281/zenodo.7430469, 2022.
Sjöblom, A.: Turbulent fluxes of momentum and heat over land in the
High-Arctic summer: the influence of observation techniques, Polar Res.,
33, 21567, https://doi.org/10.3402/polar.v33.21567, 2014.
Smith, B., Fricker, H. A., Gardner, A. S., Medley, B., Nilsson, J., Paolo,
F. S., Holschuh, N., Adusumilli, S., Brunt, K., Csatho, B., Harbeck, K., Markus, T.,
Neumann, T., Siegfried, M. R., and Zwally, H. J.: Pervasive ice sheet mass loss
reflects competing ocean and atmosphere processes, Science,
12, 1239–1242, 2020.
Steger, C. R., Reijmer, C. H., van den Broeke, M. R., Wever, N., Forster, R. R., Koenig, L. S., Kuipers Munneke, P., Lehning, M., Lhermitte, S., Ligtenberg, S. R. M., Miège, C., and Noël, B. P. Y.: Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison, Front. Earth Sci., 5, 3, https://doi.org/10.3389/feart.2017.00003, 2017.
Stevens, C. M., Verjans, V., Lundin, J. M. D., Kahle, E. C., Horlings, A. N., Horlings, B. I., and Waddington, E. D.: The Community Firn Model (CFM) v1.0, Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, 2020.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal
conductivity of seasonal snow, J. Glaciol., 43, 26–41, 1997.
Tennant, W: Considerations when using pre-1979 NCEP/NCAR reanalyses in the
southern hemisphere, Geophys. Res. Lett., 31, L11112,
https://doi.org/10.1029/2004GL019751, 2004.
US International Trans-Antarctic Scientific Expedition (US ITASE):
Glaciochemical Data, Version 2, edited by: Mayewski, P. A. and Dixon, D. A., US
International Trans-Antarctic Scientific Expedition (US ITASE)
Glaciochemical Data, Version 2, US_ITASE_Core
Info-SWE-Density_2013.xlsx, National
Snow and Ice Data Center, Boulder, Colorado USA, 2013.
van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., Fettweis, X., and van Meijgaard, E.: Rapid loss of firn pore space accelerates 21st century Greenland mass loss, Geophys. Res. Lett., 40, 2109–2113, https://doi.org/10.1002/grl.50490, 2013.
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.
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. R., Simonsen, S. B., Heilig, A., Samimi, S., Marshall, 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, 14, 3785–3810, https://doi.org/10.5194/tc-14-3785-2020, 2020.
van den Broeke, M. R., Duynkerke, P. G., and Henneken, E. A. C.: Heat,
momentum and moisture budgets of the katabatic layer over the melting zone
of the West Greenland Ice Sheet in summer, Bound.-Lay. Meteorol.,
71, 393–413, 1994.
Van Der Veen, C. J.: Interpretation of short-term ice-sheet elevation
changes inferred from satellite altimetry, Clim. Change, 23, 383–405,
https://doi.org/10.1007/BF01091624, 1993.
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.
Veldhuijsen, S. B. M., van de Berg, W. J., Brils, M., Kuipers Munneke, P., and van den Broeke, M. R.: Characteristics of the contemporary Antarctic firn layer simulated with IMAU-FDM v1.2A (1979–2020), The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-118, in review, 2022.
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.
Warren, S. G. and Wiscombe, W. J.: A model for the spectral albedo of
snow. II: Snow containing atmospheric aerosols, J. Atmos.
Sci., 37, 2734–2745, 1980.
Weinhart, A. H., Freitag, J., Hörhold, M., Kipfstuhl, S., and Eisen, O.: Representative surface snow density on the East Antarctic Plateau, The Cryosphere, 14, 3663–3685, https://doi.org/10.5194/tc-14-3663-2020, 2020.
Wilhelms, F.: Density of ice core ngt03C93.2 from the North Greenland
Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.56560, 2000a.
Wilhelms, F.: Density of ice core ngt06C93.2 from the North Greenland
Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.57153, 2000b.
Wilhelms, F.: Density of ice core ngt14C93.2 from the North Greenland
Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.56615, 2000c.
Wilhelms, F.: Density of ice core ngt27C94.2 from the North Greenland
Traverse, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.57296, 2000d.
Wiscombe, W. J. and Warren, S. G.: A model for the spectral albedo of
snow. I: Pure snow, J. Atmos. Sci., 37, 2712–2733,
1980.
Zwally, H. J., Giovinetto, M. B., Li, J., Cornejo, H. G., Beckley, M. A.,
Brenner, A. C., Saba, J. L., and Yi, D.: Mass changes of the Greenland and
Antarctic ice sheets and shelves and contributions to sea-level rise:
1992–2002, J. Glaciol., 51, 509–527, https://doi.org/10.3189/172756505781829007, 2005.
Zwally, H. J. A. Y., Bindschadler, R. A., Brenner, A. C., Major, J. A., and Marsh, J. G.: Growth of Greenland Ice Sheet: Measurement, Science,
246, 1587–1589, https://doi.org/10.1126/science.246.4937.1587,
1989.
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.
This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model....