Articles | Volume 16, issue 10
https://doi.org/10.5194/tc-16-3971-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3971-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulations of firn processes over the Greenland and Antarctic ice sheets: 1980–2021
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, 20771, USA
Thomas A. Neumann
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, 20771, USA
H. Jay Zwally
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, 20771, USA
Benjamin E. Smith
Applied Physics Laboratory, University of Washington, Seattle, WA,
98105, USA
C. Max Stevens
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, 20771, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, MD, 20740, USA
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Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
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This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the V and H SMAP brightness temperature time series well. This model provides a potential to study the aquifer liquid water content with radiometry.
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
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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.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
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The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
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
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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.
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
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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.
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We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
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The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
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Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Tessa Gorte, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 14, 4719–4733, https://doi.org/10.5194/tc-14-4719-2020, https://doi.org/10.5194/tc-14-4719-2020, 2020
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Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
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We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Benjamin E. Smith, Michael Studinger, Tyler Sutterley, Zachary Fair, and Thomas Neumann
The Cryosphere, 19, 975–995, https://doi.org/10.5194/tc-19-975-2025, https://doi.org/10.5194/tc-19-975-2025, 2025
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This study investigates errors (biases) that may result when green lasers are used to measure the elevation of glaciers and ice sheets. These biases are important because if the snow or ice on top of the ice sheet changes, it can make the elevation of the ice appear to change by the wrong amount. We measure these biases over the Greenland Ice Sheet with a laser system on an airplane and explore how the use of satellite data can let us correct for the biases.
Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
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Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
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Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
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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.
Beata Csatho, Tony Schenk, and Tom Neumann
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Allison M. Chartrand, Ian M. Howat, Ian R. Joughin, and Benjamin E. Smith
The Cryosphere, 18, 4971–4992, https://doi.org/10.5194/tc-18-4971-2024, https://doi.org/10.5194/tc-18-4971-2024, 2024
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This study uses high-resolution remote-sensing data to show that shrinking of the West Antarctic Thwaites Glacier’s ice shelf (floating extension) is exacerbated by several sub-ice-shelf meltwater channels that form as the glacier transitions from full contact with the seafloor to fully floating. In mapping these channels, the position of the transition zone, and thinning rates of the Thwaites Glacier, this work elucidates important processes driving its rapid contribution to sea level rise.
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
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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.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
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The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
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.
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.
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
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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.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
Christian J. Taubenberger, Denis Felikson, and Thomas Neumann
The Cryosphere, 16, 1341–1348, https://doi.org/10.5194/tc-16-1341-2022, https://doi.org/10.5194/tc-16-1341-2022, 2022
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Outlet glaciers are projected to account for half of the total ice loss from the Greenland Ice Sheet over the 21st century. We classify patterns of seasonal dynamic thickness changes of outlet glaciers using new observations from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Our results reveal seven distinct patterns that differ across glaciers even within the same region. Future work can use our results to improve our understanding of processes that drive seasonal ice sheet changes.
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
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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.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
Short summary
Short summary
Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Tessa Gorte, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 14, 4719–4733, https://doi.org/10.5194/tc-14-4719-2020, https://doi.org/10.5194/tc-14-4719-2020, 2020
Short summary
Short summary
In this paper, we analyze several spatial and temporal criteria to assess the ability of models in the CMIP5 and CMIP6 frameworks to recreate past Antarctic surface mass balance. We then compared a subset of the top performing models to all remaining models to refine future surface mass balance predictions under different forcing scenarios. We found that the top performing models predict lower surface mass balance by 2100, indicating less buffering than otherwise expected of sea level rise.
Andrew O. Hoffman, Knut Christianson, Daniel Shapero, Benjamin E. Smith, and Ian Joughin
The Cryosphere, 14, 4603–4609, https://doi.org/10.5194/tc-14-4603-2020, https://doi.org/10.5194/tc-14-4603-2020, 2020
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The West Antarctic Ice Sheet has long been considered geometrically prone to collapse, and Thwaites Glacier, the largest glacier in the Amundsen Sea, is likely in the early stages of disintegration. Using observations of Thwaites Glacier velocity and elevation change, we show that the transport of ~2 km3 of water beneath Thwaites Glacier has only a small and transient effect on glacier speed relative to ongoing thinning driven by ocean melt.
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
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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.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
Short summary
Short summary
We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
C. Max Stevens, Vincent Verjans, Jessica M. D. Lundin, Emma C. Kahle, Annika N. Horlings, Brita I. Horlings, and Edwin D. Waddington
Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, https://doi.org/10.5194/gmd-13-4355-2020, 2020
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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.
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
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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.
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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, https://doi.org/10.1029/2019GL085228, 2019.
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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.
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order...