Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-6077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2025
Research article |
| 21 Nov 2025
| 21 Nov 2025
Evaluation of wet snow dielectric mixing models for L-band radiometric measurement of liquid water content in Greenland's percolation zone
Alamgir Hossan
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States
Andreas Colliander
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States
Nicole-Jeanne Schlegel
NOAA/OAR Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, New Jersey, United States
Joel Harper
Department of Geosciences, University of Montana, Missoula, Montana, United States
Lauren Andrews
NASA Global Modeling and Assimilation Office, Goddard Space Flight Center (GSFC), Greenbelt, Maryland, United States
Jana Kolassa
NASA Global Modeling and Assimilation Office, Goddard Space Flight Center (GSFC), Greenbelt, Maryland, United States
Science Systems and Applications (SSAI), Berwyn Heights, Maryland, United States
Julie Z. Miller
EarthSAR, LLC, Salt Lake City, Utah, United States
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, United States
Richard Cullather
NASA Global Modeling and Assimilation Office, Goddard Space Flight Center (GSFC), Greenbelt, Maryland, United States
Earth System Science Interdisciplinary Center (ESSIC), University of Maryland at College Park, Maryland, United States
Related authors
Alamgir Hossan, Andreas Colliander, Baptiste Vandecrux, Nicole-Jeanne Schlegel, Joel Harper, Shawn Marshall, and Julie Z. Miller
The Cryosphere, 19, 4237–4258, https://doi.org/10.5194/tc-19-4237-2025, https://doi.org/10.5194/tc-19-4237-2025, 2025
Short summary
Short summary
We used L-band observations from the Soil Moisture Active Passive (SMAP) mission to quantify the surface and subsurface liquid water amounts (LWAs) 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 the Greenland ice sheet (GrIS), which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4069, https://doi.org/10.5194/egusphere-2025-4069, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
Renato Pardo Lara, Andreas Colliander, Erica Tetlock, Jarret Powers, Jaison Thomas Ambadan, and Aaron Berg
EGUsphere, https://doi.org/10.5194/egusphere-2025-3630, https://doi.org/10.5194/egusphere-2025-3630, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Frozen ground affects ecosystems, infrastructure, and farming, yet detecting it worldwide remains a challenge. To improve soil freeze/thaw detection from space, we adapted a laboratory tool called the soil freezing curve for use with NASA’s Soil Moisture Active Passive (SMAP) satellite and temperature records. This new “surface freezing curve” method can identify thresholds tied to moisture phase changes, improving the accuracy and stability of freeze/thaw monitoring in agricultural regions.
Luc Houriez, Eric Larour, Lambert Caron, Nicole-Jeanne Schlegel, Surendra Adhikari, Erik Ivins, Tyler Pelle, Hélène Seroussi, Eric Darve, and Martin Fischer
The Cryosphere, 19, 4355–4372, https://doi.org/10.5194/tc-19-4355-2025, https://doi.org/10.5194/tc-19-4355-2025, 2025
Short summary
Short summary
This work examines 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 study 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
The Cryosphere, 19, 4237–4258, https://doi.org/10.5194/tc-19-4237-2025, https://doi.org/10.5194/tc-19-4237-2025, 2025
Short summary
Short summary
We used L-band observations from the Soil Moisture Active Passive (SMAP) mission to quantify the surface and subsurface liquid water amounts (LWAs) 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 the Greenland ice sheet (GrIS), which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.
Kerttu Kouki and Andreas Colliander
Hydrol. Earth Syst. Sci., 29, 4791–4810, https://doi.org/10.5194/hess-29-4791-2025, https://doi.org/10.5194/hess-29-4791-2025, 2025
Short summary
Short summary
Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.
Zhimeng Zhang, Shannon Brown, and Andreas Colliander
The Cryosphere, 19, 4011–4026, https://doi.org/10.5194/tc-19-4011-2025, https://doi.org/10.5194/tc-19-4011-2025, 2025
Short summary
Short summary
Retrieving accurate water vapor and temperature profiles over land is challenging due to uncertainties in estimating surface emissions. To address this, we have developed an iterative method that combines atmospheric retrieval with surface emissions estimation. Using Advanced Technology Microwave Sounder (ATMS) data across various microwave frequencies, we successfully tracked atmospheric temperature and humidity changes. Testing against Radiosonde data showed our method is efficient and accurate, especially in detecting melting events.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
The Cryosphere, 19, 3571–3598, https://doi.org/10.5194/tc-19-3571-2025, https://doi.org/10.5194/tc-19-3571-2025, 2025
Short summary
Short summary
We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperature time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is extremely promising for monitoring ground temperature below the snowpack and studying the spatio-temporal variability thanks to 15 years of observations over the whole Arctic area.
Kirsten Gehl, Joel Harper, and Neil Humphrey
EGUsphere, https://doi.org/10.5194/egusphere-2025-3002, https://doi.org/10.5194/egusphere-2025-3002, 2025
Short summary
Short summary
The geometric form of snow grains governs snow compaction and the movement of air and water within the snow. We observed unexpectedly thick and deep layers of faceted snow grains in cores drilled along the flanks of the Greenland Ice Sheet. Based on field measurements and modeling, we find that meltwater infiltration and refreezing in the cold snow generates these grains. As more of the ice sheet is affected by melting, subsurface faceting of snow crystals may become increasingly important.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
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
Short summary
Short summary
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.
Riley Culberg, Roger J. Michaelides, and Julie Z. Miller
The Cryosphere, 18, 2531–2555, https://doi.org/10.5194/tc-18-2531-2024, https://doi.org/10.5194/tc-18-2531-2024, 2024
Short summary
Short summary
Ice slabs enhance meltwater runoff from the Greenland Ice Sheet. Therefore, it is important to understand their extent and change in extent over time. We present a new method for detecting ice slabs in satellite radar data, which we use to map ice slabs at 500 m resolution across the entire ice sheet in winter 2016–2017. Our results provide better spatial coverage and resolution than previous maps from airborne radar and lay the groundwork for long-term monitoring of ice slabs from space.
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.
Celia Trunz, Kristin Poinar, Lauren C. Andrews, Matthew D. Covington, Jessica Mejia, Jason Gulley, and Victoria Siegel
The Cryosphere, 17, 5075–5094, https://doi.org/10.5194/tc-17-5075-2023, https://doi.org/10.5194/tc-17-5075-2023, 2023
Short summary
Short summary
Models simulating water pressure variations at the bottom of glaciers must use large storage parameters to produce realistic results. Whether that storage occurs englacially (in moulins) or subglacially is a matter of debate. Here, we directly simulate moulin volume to constrain the storage there. We find it is not enough. Instead, subglacial processes, including basal melt and input from upstream moulins, must be responsible for stabilizing these water pressure fluctuations.
Anja Løkkegaard, Kenneth D. Mankoff, Christian Zdanowicz, Gary D. Clow, Martin P. Lüthi, Samuel H. Doyle, Henrik H. Thomsen, David Fisher, Joel Harper, Andy Aschwanden, Bo M. Vinther, Dorthe Dahl-Jensen, Harry Zekollari, Toby Meierbachtol, Ian McDowell, Neil Humphrey, Anne Solgaard, Nanna B. Karlsson, Shfaqat A. Khan, Benjamin Hills, Robert Law, Bryn Hubbard, Poul Christoffersen, Mylène Jacquemart, Julien Seguinot, Robert S. Fausto, and William T. Colgan
The Cryosphere, 17, 3829–3845, https://doi.org/10.5194/tc-17-3829-2023, https://doi.org/10.5194/tc-17-3829-2023, 2023
Short summary
Short summary
This study presents a database compiling 95 ice temperature profiles from the Greenland ice sheet and peripheral ice caps. Ice viscosity and hence ice flow are highly sensitive to ice temperature. To highlight the value of the database in evaluating ice flow simulations, profiles from the Greenland ice sheet are compared to a modeled temperature field. Reoccurring discrepancies between modeled and observed temperatures provide insight on the difficulties faced when simulating ice temperatures.
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.
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.
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.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
Short summary
Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Lauren C. Andrews, Kristin Poinar, and Celia Trunz
The Cryosphere, 16, 2421–2448, https://doi.org/10.5194/tc-16-2421-2022, https://doi.org/10.5194/tc-16-2421-2022, 2022
Short summary
Short summary
We introduce a model for moulin geometry motivated by the wide range of sizes and shapes of explored moulins. Moulins comprise 10–14 % of the Greenland englacial–subglacial hydrologic system and act as time-varying water storage reservoirs. Moulin geometry can vary approximately 10 % daily and over 100 % seasonally. Moulin shape modulates the efficiency of the subglacial system that controls ice flow and should thus be included in hydrologic models.
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.
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.
Julie Z. Miller, Riley Culberg, David G. Long, Christopher A. Shuman, Dustin M. Schroeder, and Mary J. Brodzik
The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, https://doi.org/10.5194/tc-16-103-2022, 2022
Short summary
Short summary
We use L-band brightness temperature imagery from NASA's Soil Moisture Active Passive (SMAP) satellite to map the extent of perennial firn aquifer and ice slab areas within the Greenland Ice Sheet. As Greenland's climate continues to warm and seasonal surface melting increases in extent, intensity, and duration, quantifying the possible rapid expansion of perennial firn aquifers and ice slab areas has significant implications for understanding the stability of the Greenland Ice Sheet.
Chang-Hwan Park, Aaron Berg, Michael H. Cosh, Andreas Colliander, Andreas Behrendt, Hida Manns, Jinkyu Hong, Johan Lee, Runze Zhang, and Volker Wulfmeyer
Hydrol. Earth Syst. Sci., 25, 6407–6420, https://doi.org/10.5194/hess-25-6407-2021, https://doi.org/10.5194/hess-25-6407-2021, 2021
Short summary
Short summary
In this study, we proposed an inversion of the dielectric mixing model for a 50 Hz soil sensor for agricultural organic soil. This model can reflect the variability of soil organic matter (SOM) in wilting point and porosity, which play a critical role in improving the accuracy of SM estimation, using a dielectric-based soil sensor. The results of statistical analyses demonstrated a higher performance of the new model than the factory setting probe algorithm.
Joel Harper, Toby Meierbachtol, Neil Humphrey, Jun Saito, and Aidan Stansberry
The Cryosphere, 15, 5409–5421, https://doi.org/10.5194/tc-15-5409-2021, https://doi.org/10.5194/tc-15-5409-2021, 2021
Short summary
Short summary
We use surface and borehole measurements to investigate the generation and fate of basal meltwater in the ablation zone of western Greenland. The rate of basal meltwater generation at borehole study sites increases by up to 20 % over the winter period. Accommodation of all basal meltwater by expansion of isolated subglacial cavities is implausible. Other sinks for water do not likely balance basal meltwater generation, implying water evacuation through a connected drainage system in winter.
Kristin Poinar and Lauren C. Andrews
The Cryosphere, 15, 1455–1483, https://doi.org/10.5194/tc-15-1455-2021, https://doi.org/10.5194/tc-15-1455-2021, 2021
Short summary
Short summary
This study addresses Greenland supraglacial lake drainages. We analyze ice deformation associated with lake drainages over 18 summers to assess whether
precursorstrain-rate events consistently precede lake drainages. We find that currently available remote sensing data products cannot resolve these events, and thus we cannot predict future lake drainages. Thus, future avenues for evaluating this hypothesis will require major field-based GPS or photogrammetry efforts.
Ian E. McDowell, Neil F. Humphrey, Joel T. Harper, and Toby W. Meierbachtol
The Cryosphere, 15, 897–907, https://doi.org/10.5194/tc-15-897-2021, https://doi.org/10.5194/tc-15-897-2021, 2021
Short summary
Short summary
Ice temperature controls rates of internal deformation and the onset of basal sliding. To identify heat transfer mechanisms and englacial heat sources within Greenland's ablation zone, we examine a 2–3-year continuous temperature record from nine full-depth boreholes. Thermal decay after basal crevasses release heat in the near-basal ice likely produces the observed cooling. Basal crevasses in Greenland can affect the basal ice rheology and indicate a potentially complex basal hydrologic system.
Nataniel M. Holtzman, Leander D. L. Anderegg, Simon Kraatz, Alex Mavrovic, Oliver Sonnentag, Christoforos Pappas, Michael H. Cosh, Alexandre Langlois, Tarendra Lakhankar, Derek Tesser, Nicholas Steiner, Andreas Colliander, Alexandre Roy, and Alexandra G. Konings
Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, https://doi.org/10.5194/bg-18-739-2021, 2021
Short summary
Short summary
Microwave radiation coming from Earth's land surface is affected by both soil moisture and the water in plants that cover the soil. We measured such radiation with a sensor elevated above a forest canopy while repeatedly measuring the amount of water stored in trees at the same location. Changes in the microwave signal over time were closely related to tree water storage changes. Satellites with similar sensors could thus be used to monitor how trees in an entire region respond to drought.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
Short summary
Short summary
NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
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.
Cited articles
Abdalati, W. and Steffen, K.: Snowmelt on the Greenland ice sheet as derived from passive microwave satellite data, J. Climate, 10, 165–175, https://doi.org/10.1175/1520-0442(1997)010<0165:SOTGIS>2.0.CO;2, 1997.
Birchak, J. R., Gardner, C. G., Hipp, J. E., and Victor, J. M.: High Dielectric Constant Microwave Probes for Sensing Soil Moisture, Proc. IEEE, 62, 93–98, https://doi.org/10.1109/PROC.1974.9388, 1974.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets, ISPRS International Journal of Geo-Information, 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: Correction: Brodzik, M.J., et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets, ISPRS International Journal of Geo-Information 2012, 1, 32–45, ISPRS International Journal of Geo-Information, 3, 1154–1156, https://doi.org/10.3390/ijgi3031154, 2014.
Brodzik, M. J., Long, D. G., and Hardman, M. A.: SMAP Radiometer Twice-Daily rSIR-Enhanced EASE-Grid 2.0 Brightness Temperatures (NSIDC-0738, Version 2), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/YAMX52BXFL10 [data set], 2021.
Chaubell, J., Yueh, S., Peng, J., Dunbar, S., Chan, S., Chen, F., Piepmeier, J., Bindlish, R., Entekhabi, D., and O'Neill, P.: Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document: SMAP L1(B/C) Enhanced Radiometer Brightness Temperature Data Product, 1, 1–32, 2018.
Chaubell, J., Chan, S., Dunbar, R. S., Peng, J., and Yueh, S.: SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/XB8K63YM4U8O, 2020.
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.: A theory for water flow through a layered snowpack, Water Resour. Res., 11, 261–266, https://doi.org/10.1029/WR011i002p00261, 1975.
Colbeck, S. C.: Liquid distribution and the dielectric constant of wet snow, Microw. Remote Sens. Snowpack Prop. Goddard Sp. Flight Cent., 21–40, 1980.
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.
Colliander, A., Mousavi, M., Marshall, S., Samimi, S., Kimball, J. S., Miller, J. Z., Johnson, J., and Burgin, M.: Ice Sheet Surface and Subsurface Melt Water Discrimination Using Multi-Frequency Microwave Radiometry, Geophys. Res. Lett., 49, https://doi.org/10.1029/2021GL096599, 2022a.
Colliander, A., Mousavi, M., Misra, S., Brown, S., Kimball, J. S., Miller, J., Johnson, J., and Burgin, M.: Ice Sheet Melt Water Profile Mapping Using Multi-Frequency Microwave Radiometry, Int. Geosci. Remote Sens. Symp., 2022, 4178–4181, https://doi.org/10.1109/IGARSS46834.2022.9883717, 2022b.
Colliander, A., Mousavi, M., Kimball, J. S., Miller, J. Z., and Burgin, M.: Spatial and temporal differences in surface and subsurface meltwater distribution over Greenland ice sheet using multi-frequency passive microwave observations, Remote Sens. Environ., 295, 113705, https://doi.org/10.1016/j.rse.2023.113705, 2023.
Cummings, W. A.: The dielectric properties of ice and snow at 3.2 cm, J. Appl. Phys., 23, 768–773, https://doi.org/10.1063/1.1702299, 1952.
de Loor, G. P.: Dielectric properties of heterogeneous mixtures containing water, J. Microw. Power, 3, 67–73, 1968.
Denoth, A.: The pendular-fenicular liquid transition and snow metamorphism., J. Glaciol., 28, 357–364, https://doi.org/10.3189/s0022143000011692, 1982.
Denoth, A.: Snow dielectric measurements, Adv. Sp. Res., 9, 233–243, https://doi.org/10.1016/0273-1177(89)90491-2, 1989.
Denoth, A.: An electronic device for long-term snow wetness recording, Ann. Glaciol., 19, 104–106, https://doi.org/10.3189/s0260305500011058, 1994.
Denoth, A., Foglar, A., Weiland, P., Mätzler, C., Aebischer, H., Tiuri, M., and Sihvola, A.: A comparative study of instruments for measuring the liquid water content of snow, J. Appl. Phys., 56, 2154–2160, https://doi.org/10.1063/1.334215, 1984.
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.
Early, D. S. and Long, D. G.: Image reconstruction and enhanced resolution imaging from irregular samples, IEEE Trans. Geosci. Remote Sens., 39, 291–302, https://doi.org/10.1109/36.905237, 2001.
Ebrahimi, S. and Marshall, S. J.: Surface energy balance sensitivity to meteorological variability on Haig Glacier, Canadian Rocky Mountains, The Cryosphere, 10, 2799–2819, https://doi.org/10.5194/tc-10-2799-2016, 2016.
Elachi, C. and Van Zyl, J. J.: Introduction to the Physics and Techniques of Remote Sensing, John Wiley & Sons, 159–189, https://doi.org/10.1002/9781119523048.ch5, 2021.
Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021.
Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, T., Berends, C. J., Born, A., Box, J. E., Delhasse, A., Fujita, K., Gierz, P., Goelzer, H., Hanna, E., Hashimoto, A., Huybrechts, P., Kapsch, M.-L., King, M. D., Kittel, C., Lang, C., Langen, P. L., Lenaerts, J. T. M., Liston, G. E., Lohmann, G., Mernild, S. H., Mikolajewicz, U., Modali, K., Mottram, R. H., Niwano, M., Noël, B., Ryan, J. C., Smith, A., Streffing, J., Tedesco, M., van de Berg, W. J., van den Broeke, M., van de Wal, R. S. W., van Kampenhout, L., Wilton, D., Wouters, B., Ziemen, F., and Zolles, T.: GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet, The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, 2020.
Gardner, A. S., Schlegel, N.-J., and Larour, E.: Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research, Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, 2023.
Garnett, J. C. M.: XII. Colours in metal glasses and in metallic films, Philos. Trans. R. Soc. London. Ser. A, Contain. Pap. a Math. or Phys. Character, 203, 385–420, 1904.
Greene, C. A., Gwyther, D. E., and Blankenship, D. D.: Antarctic Mapping Tools for MATLAB, Comput. Geosci., 104, 151–157, https://doi.org/10.1016/j.cageo.2016.08.003, 2017.
Greene, C. A., Gardner, A. S., Wood, M., and Cuzzone, J. K.: Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022, Nature, 625, https://doi.org/10.1038/s41586-023-06863-2, 2024.
Hallikainen, M. T., Ulaby, F. T., and Abdelrazik, M.: Dielectric Properties of Snow in the 3 To 37 Ghz Range., IEEE Trans. Antennas Propag., AP-34, 1329–1340, https://doi.org/10.1109/tap.1986.1143757, 1986.
Hossan, A.: Dataset for Evaluating Wet Snow Dielectric Models with L-Band Radiometry in Greenland Firn [data set], Zenodo, https://doi.org/10.5281/zenodo.17195725, 2025a.
Hossan, A.: DielectricModels4WetSnow: Revised Main Script to Generate All Figures, Zenodo [code], https://doi.org/10.5281/zenodo.17478663, 2025b.
Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland ice sheet using L-band radiometry, The Cryosphere, 19, 4237–4258, https://doi.org/10.5194/tc-19-4237-2025, 2025.
Houtz, D., Mätzler, C., Naderpour, R., Schwank, M., and Steffen, K.: Quantifying Surface Melt and Liquid Water on the Greenland Ice Sheet using L-band Radiometry, Remote Sens. Environ., 256, 112341, https://doi.org/10.1016/j.rse.2021.112341, 2021.
Houtz, D., Naderpour, R., Schwank, M., and Steffen, K.: Snow wetness and density retrieved from L-band satellite radiometer observations over a site in the West Greenland ablation zone, Remote Sens. Environ., 235, 111361, https://doi.org/10.1016/j.rse.2019.111361, 2019.
How, P., Lund, M. C., Ahlstrøm, A. P., Andersen, S. B., Box, J. E., Citterio, M., Colgan, W. T., Fausto, R. S., Karlsson, N. B., Jakobsen, J., Jakobsgaard, H. T., Larsen, S. H., Mankoff, K. D., Nielsen, R. B., Rutishauser, A., Shield, C. L., Solgaard, A. M., Stevens, I. T., van As, D., Vandecrux, B., Abermann, J., Bjørk, A. A., Langley, K., Lea, J., Messerli, A., and Prinz, R.: PROMICE and GC-Net automated weather station data in Greenland, GEUS Dataverse, V28 [data set], https://doi.org/10.22008/FK2/IW73UU, 2022.
Humphrey, N. F., Harper, J. T., and Pfeffer, W. T.: Thermal tracking of meltwater retention in Greenland's accumulation area, J. Geophys. Res.-Earth Surf., 117, 1–11, https://doi.org/10.1029/2011JF002083, 2012.
Jezek, K. C., Johnson, J. T., Tan, S., Tsang, L., Andrews, M. J., Brogioni, M., MacElloni, G., Durand, M., Chen, C. C., Belgiovane, D. J., Duan, Y., Yardim, C., Li, H., Bringer, A., Leuski, V., and Aksoy, M.: 500-2000-MHz Brightness Temperature Spectra of the Northwestern Greenland Ice Sheet, IEEE Trans. Geosci. Remote Sens., 56, 1485–1496, https://doi.org/10.1109/TGRS.2017.2764381, 2018.
Johnson, J. T., Jezek, K. C., and Tsang, L.: UWBRAD: Ultra-Wideband Software-Defined Microwave Radiometer for Ice Sheet Subsurface Temperature Sensing, https://research.byrd.osu.edu/rsl/UWBRAD/documents/technical.pdf (last access: 27 October 2025, 2014.
Jones, S. B. and Friedman, S. P.: Particle shape effects on the effective permittivity of anisotropic or isotropic media consisting of aligned or randomly oriented ellipsoidal particles, Water Resour. Res., 36, 2821–2833, https://doi.org/10.1029/2000WR900198, 2000.
Khan, S. A., Aschwanden, A., Bjørk, A. A., Wahr, J., Kjeldsen, K. K., and Kjaær, K. H.: Greenland ice sheet mass balance: A review, Reports Prog. Phys., 78, https://doi.org/10.1088/0034-4885/78/4/046801, 2015.
Khan, S. A., Bamber, J. L., Rignot, E., Helm, V., Aschwanden, A., Holland, D. M., van den Broeke, M., King, M., Noël, B., Truffer, M., Humbert, A., Colgan, W., Vijay, S., and Kuipers Munneke, P.: Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020, J. Geophys. Res.-Earth Surf., 127, 1–20, https://doi.org/10.1029/2021JF006505, 2022.
Leduc-Leballeur, M., Picard, G., Macelloni, G., Mialon, A., and Kerr, Y. H.: Melt in Antarctica derived from Soil Moisture and Ocean Salinity (SMOS) observations at L band, The Cryosphere, 14, 539–548, https://doi.org/10.5194/tc-14-539-2020, 2020.
Leduc-Leballeur, M., Picard, G., Zeiger, P., and Macelloni, G.: Empirical classification of dry-wet snow status in Antarctica using multi-frequency passive microwave observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-732, 2025.
Liebe, H. J., Hufford, G. A., and Manabe, T.: A model for the complex permittivity of water at frequencies below 1 THz, Int. J. Infrared Milli Waves, 12, 659–675, https://doi.org/10.1007/BF01008897, 1991.
Long, D. G.: Scatterometer Backscatter Imaging Using Backus-Gilbert Inversion, IEEE Trans. Geosci. Remote Sens., 57, 3179–3190, https://doi.org/10.1109/TGRS.2018.2882136, 2019.
Long, D. G. and Brodzik, M. J.: Optimum Image Formation for Spaceborne Microwave Radiometer Products, IEEE Trans. Geosci. Remote Sens., 54, 2763–2779, https://doi.org/10.1109/TGRS.2015.2505677, 2016.
Long, D. G., Brodzik, M. J., and Hardman, M. A.: Enhanced-Resolution SMAP Brightness Temperature Image Products, IEEE T. Geosci. Remote, 57, 4151–4163, https://doi.org/10.1109/TGRS.2018.2889427, 2019.
Long, D. G., Brodzik, M. J., and Hardman, M.: Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products, Front. Remote Sens., 4, 1–10, https://doi.org/10.3389/frsen.2023.1073765, 2023.
Long, D. G. and Daum, D. L.: Spatial resolution enhancement of SSM/I data, IEEE Trans. Geosci. Remote Sens., 36, 407–417, https://doi.org/10.1109/36.662726, 1998.
Long, D. G., Hardin, P. J., and Whiting, P. T.: Resolution Enhancement of Spaceborne Scatterometer Data, IEEE Trans. Geosci. Remote Sens., 31, 700–715, https://doi.org/10.1109/36.225536, 1993.
Looyenga, H.: Dielectric constants of heterogeneous mixtures, Physica, 31, 401–406, https://doi.org/10.1016/0031-8914(65)90045-5, 1965.
Macelloni, G., Brogioni, M., Leduc-Leballeur, M., Montomoli, F., Bartsch, A., Mialon, A., Ritz, C., Closa Soteras, J., Stammer, D., Picard, G., De Carolis, G., Boutin, J., Johnson, J. T., Nicholls, K. W., Jezek, K. C., Rautiainen, K., Kaleschke, L., Bertino, L., Tsang, L., Van Den Broeke, M., Skou, N., and Tietsche, S.: Cryorad: A low frequency wideband radiometer mission for the study of the cryosphere, Int. Geosci. Remote Sens. Symp., 2018, 1998–2000, https://doi.org/10.1109/IGARSS.2018.8519172, 2018.
MacFerrin, M., Machguth, H., As, D. van, Charalampidis, C., Stevens, C. M., Heilig, A., Vandecrux, B., Langen, P. L., Mottram, R., Fettweis, X., Broeke, M. R. va. den, Pfeffer, W. T., Moussavi, M. S., and Abdalati, W.: Rapid expansion of Greenland's low-permeability ice slabs, Nature, 573, 403–407, https://doi.org/10.1038/s41586-019-1550-3, 2019.
Machguth, H., Macferrin, M., Van As, D., Box, J. E., Charalampidis, C., Colgan, W., Fausto, R. S., Meijer, H. A. J., Mosley-Thompson, E., and Van De Wal, R. S. W.: Greenland meltwater storage in firn limited by near-surface ice formation, Nat. Clim. Chang., 6, 390–393, https://doi.org/10.1038/nclimate2899, 2016.
Marsh, P. and Woo, M.-K: Wetting front advance and freezing of meltwater within a snow cover: 1. Observations in the Canadian Arctic, Water Resour. Res., 20, 1853–1864, https://doi.org/10.1029/WR020i012p01853, 1984.
Marshall, S.: MATLAB code for firn thermodynamic and hydrological modelling, Borealis, V2 [code], https://doi.org/10.5683/SP2/WRWJAZ, 2021.
Mätzler, C.: Microwave permittivity of dry snow, IEEE Transactions on Geoscience and Remote Sensing, 34, 2, 573–581, https://doi.org/10.1109/36.485133, 1996.
Mätzler, C.: Microwave dielectric properties of ice, Transportation (Amst), 1, 21–30, https://doi.org/10.1002/ejoc.201200111, 2006.
Mätzler, C. and Wiesmann, A.: Documentation for MEMLS, Version 3 Microwave Emission Model of Layered Snowpacks, Tech. rep., Institute for Applied Physics, University of Bern, Switzerland, 1–25, 2012.
Matzler, C., Aebischer, H., and Schanda, E.: Microwave dielectric properties of surface snow, IEEE J. Ocean. Eng., 9, 366–371, 1984.
Miller, J. Z., Long, D. G., Jezek, K. C., Johnson, J. T., Brodzik, M. J., Shuman, C. A., Koenig, L. S., and Scambos, T. A.: Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series, The Cryosphere, 14, 2809–2817, https://doi.org/10.5194/tc-14-2809-2020, 2020a.
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, 2022a.
Miller, J. Z., Long, D. G., Shuman, C. A., Culberg, R., Hardman, M., and Brodzik, M. J.: Mapping Firn Saturation Over Greenland Using NASA's Soil Moisture Active Passive Satellite, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 3714–3729, https://doi.org/10.1109/JSTARS.2022.3154968, 2022b.
Miller, O., Solomon, D. K., Miège, C., Koenig, L., Forster, R., Schmerr, N., Ligtenberg, S. R. M., Legchenko, A., Voss, C. I., Montgomery, L., and McConnell, J. R.: Hydrology of a Perennial Firn Aquifer in Southeast Greenland: An Overview Driven by Field Data, Water Resour. Res., 56, https://doi.org/10.1029/2019WR026348, 2020b.
Moon, T., Harper, J., Colliander, A., Hossan, A., and Humphrey, N.: L-Band Radiometric Measurement of Liquid Water in Greenland's Firn: Comparative Analysis with In Situ Measurements and Modeling, Annals of Glaciology, 66:e15, https://doi.org/10.1017/aog.2025.10012, 2025.
Mote, T. L. and Anderson, M. R.: Variations in snowpack melt on the Greenland ice sheet based on passive-microwave measurements, J. Glaciol., 41, 51–60, https://doi.org/10.1017/S0022143000017755, 1995.
Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R., Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018, P. Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.1073/pnas.1904242116, 2019.
Mousavi, M., Colliander, A., Miller, J. Z., Entekhabi, D., Johnson, J. T., Shuman, C. A., Kimball, J. S., and Courville, Z. R.: Evaluation of Surface Melt on the Greenland Ice Sheet Using SMAP L-Band Microwave Radiometry, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 11439–11449, https://doi.org/10.1109/JSTARS.2021.3124229, 2021.
Mousavi, M., Colliander, A., Miller, J., and Kimball, J. S.: A Novel Approach to Map the Intensity of Surface Melting on the Antarctica Ice Sheet Using SMAP L-Band Microwave Radiometry, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 1724–1743, https://doi.org/10.1109/JSTARS.2022.3147430, 2022.
Naderpour, R., Houtz, D., and Schwank, M.: Snow wetness retrieved from close-range L-band radiometry in the western Greenland ablation zone, J. Glaciol., 67, 27–38, https://doi.org/10.1017/jog.2020.79, 2021.
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.
Pfeffer, W. T. and Humphrey, N. F.: Determination of timing and location of water movement and ice-layer formation by temperature measurements in sub-freezing snow, Journal of Glaciology, 42, 292–304. https://doi.org/10.3189/S0022143000004159, 1996.
Picard, G., Leduc-Leballeur, M., Banwell, A. F., Brucker, L., and Macelloni, G.: The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack, The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, 2022.
Piepmeier, J. R., Focardi, P., Horgan, K. A., Knuble, J., Ehsan, N., Lucey, J., Brambora, C., Brown, P. R., Hoffman, P. J., French, R. T., Mikhaylov, R. L., Kwack, E. Y., Slimko, E. M., Dawson, D. E., Hudson, D., Peng, J., Mohammed, P. N., De Amici, G., Freedman, A. P., Medeiros, J., Sacks, F., Estep, R., Spencer, M. W., Chen, C. W., Wheeler, K. B., Edelstein, W. N., O'Neill, P. E., and Njoku, E. G.: SMAP L-Band Microwave Radiometer: Instrument Design and First Year on Orbit, IEEE Trans. Geosci. Remote Sens., 55, 1954–1966, https://doi.org/10.1109/TGRS.2016.2631978, 2017.
Poinar, K., Mankoff, K., Fausto, R., Fettweis, X., Loomis, B., Wehrlé, A., and Mote, T.: Greenland Ice Sheet in: Arctic Report Card 2023, https://doi.org/10.25923/yetx-rs76, 2023.
Polder, D. and van Santen, J. H.: The effective permeability of mixtures of solids, Physica, 12, 257–271, https://doi.org/10.1016/S0031-8914(46)80066-1, 1946.
Rennermalm, Å.K., Hock, R., Covi, F., Xiao, J., Corti, G., Kingslake, J., Leidman, S. Z., Miège, C., Macferrin, M., Machguth, H., Osterberg, E., Kameda, T., and McConnell, J. R.: Shallow firn cores 1989–2019 in southwest Greenland's percolation zone reveal decreasing density and ice layer thickness after 2012, Journal of Glaciology, 68, 431–442, https://doi.org/10.1017/jog.2021.102, 2022.
Samimi, S., Marshall, S. J., and MacFerrin, M.: Meltwater Penetration Through Temperate Ice Layers in the Percolation Zone at DYE-2, Greenland Ice Sheet, Geophys. Res. Lett., 47, 1–9, https://doi.org/10.1029/2020GL089211, 2020.
Samimi, S., Marshall, S. J., Vandecrux, B., and MacFerrin, M.: Time-Domain Reflectometry Measurements and Modeling of Firn Meltwater Infiltration at DYE-2, Greenland, J. Geophys. Res.-Earth Surf., 126, e2021JF006295, https://doi.org/10.1029/2021JF006295, 2021.
Schwank, M. and Naderpour, R.: Snow density and ground permittivity retrieved from L-band radiometry: Melting effects, Remote Sens., 10, 1–26, https://doi.org/10.3390/rs10020354, 2018.
Schwank, M., Rautiainen, K., Mätzler, C., Stähli, M., Lemmetyinen, J., Pulliainen, J., Vehviläinen, J., Kontu, A., Ikonen, J., Ménard, C. B., Drusch, M., Wiesmann, A., and Wegmüller, U.: Model for microwave emission of a snow-covered ground with focus on L band, Remote Sens. Environ., 154, 180–191, https://doi.org/10.1016/j.rse.2014.08.029, 2014.
Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, T., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V. R., Bjørk, A. A., Blazquez, A., Bonin, J., Colgan, W., Csatho, B., Cullather, R., Engdahl, M. E., Felikson, D., Fettweis, X., Forsberg, R., Hogg, A. E., Gallee, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath, A., Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P. L., Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild, S., Mohajerani, Y., Moore, P., Mottram, R., Mouginot, J., Moyano, G., Muir, A., Nagler, T., Nield, G., Nilsson, J., Noël, B., Otosaka, I., Pattle, M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H., Sandberg Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E., Schröder, L., Seo, K. W., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T., Talpe, M., Tarasov, L., van de Berg, W. J., van der Wal, W., van Wessem, M., Vishwakarma, B. D., Wiese, D., Wilton, D., Wagner, T., Wouters, B., and Wuite, J.: Mass balance of the Greenland Ice Sheet from 1992 to 2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020.
Sihvola, A., Nyfors, E., and Tiuri, M.: Mixing Formulae and Experimental Results for the Dielectric Constant of Snow, J. Glaciol., 31, 163–170, https://doi.org/10.3189/s0022143000006419, 1985.
Sihvola, A.: Electromagnetic mixing formulas and applications, The Institution of Engineering and Technology, London, https://doi.org/10.1049/PBEW047E, 1999.
Tedesco, M.: Snowmelt detection over the Greenland ice sheet from SSM/I brightness temperature daily variations, Geophys. Res. Lett., 34, 1–6, https://doi.org/10.1029/2006GL028466, 2007.
Tedesco, M., Abdalati, W., and Zwally, H. J.: Persistent surface snowmelt over Antarctica (1987–2006) from 19.35 GHz brightness temperatures, Geophys. Res. Lett., 34, 1–6, https://doi.org/10.1029/2007GL031199, 2007.
Thompson-Munson, M., Wever, N., Stevens, C. M., Lenaerts, J. T. M., and Medley, B.: An evaluation of a physics-based firn model and a semi-empirical firn model across the Greenland Ice Sheet (1980–2020), The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, 2023.
Tinga, W. R., Voss, W. A. G., and Blossey, D. F.: Generalized approach to multiphase dielectric mixture theory, J. Appl. Phys., 44, 3897–3902, https://doi.org/10.1063/1.1662868, 1973.
Tiuri, M. E., Sihvola, A. H., Nyfors, E. G., and Hallikaiken, M. T.: The Complex Dielectric Constant of Snow at Microwave Frequencies, IEEE J. Ocean. Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Torinesi, O., Fily, M., and Genthon, C.: Variability and trends of the summer melt period of Antartic ice margins since 1980 from microwave sensors, J. Climate, 16, 1047–1060, https://doi.org/10.1175/1520-0442(2003)016<1047:VATOTS>2.0.CO;2, 2003.
Tsang, L., Kong, J. A., and Shin, R. T.: Theory of microwave remote sensing, Wiley, ISBN 9780471888604, 1985.
Ulaby, F. Long, D., Blackwell, W., Elachi, C., Fung, A., Ruf, C., Sarabandi, K., Zyl, J., and Zebker, H.: Microwave Radar and Radiometric Remote Sensing, The University of Michigan Press, Chapter 4, 140–145 p., https://doi.org/10.3998/0472119356, 2014.
Van Den Broeke, M., Bus, C., Ettema, J., and Smeets, P.: Temperature thresholds for degree-day modelling of Greenland ice sheet melt rates, Geophys. Res. Lett., 37, 1–5, https://doi.org/10.1029/2010GL044123, 2010.
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.
Vandecrux, B., Amory, C., Ahlstrøm, A. P., Akers, P. D., Albert, M., Alley, R. B., Alves de Castro, M., Arnaud, L., Baker, I., Bales, R., Benson, C., Box, J. E., Brucker, L., Buizert, C., Chandler, D., Charalampidis, C., Cherblanc, C., Clerx, N., Colgan, W., Covi, F., Dattler, M., Denis, G., Derksen, C., Dibb, J. E., Ding, M., Dixon, D., Eisen, O., Fahrner, D., Fausto, R., Favier, V., Fernandoy, F., Freitag, J., Gerland, S., Harper, J., Hawley, R. L., Heuer, J., Hock, R., Hou, S., How, P., Humphrey, N.,Hubbard, B., Iizuka, Y., Isaksson, E., Kameda, T., Karlsson, N. B., Kawakami, K., Kjær, H. A., Kreutz, K., Kuipers Munneke, P., Lazzara, M., Lemeur, E., Lenaerts, J. T. M., Lewis, G., Ley Lindau, F. G., Lindsey-Clark, J., MacFerrin, M., Machguth, H., Magand, O., Mankoff, K. D., Marquetto, L., Martinerie, P., McConnell, J. R., Medley, B., Miège, C., Miles, K. E., Miller, O., Miller, H., Montgomery, L., Morris, E., Mosley-Thompson, E., Mulvaney, R., Niwano, M., Oerter, H., Osterberg, E., Otosaka, I., Picard, G., Polashenski, C., Reijmer, C., Rennermalm, A., Rutishauser, A., Scanlan, K., Simoes, J. C., Simonsen, S. B., Smeets, P. C. J. P., Smith, A., Solgaard, A., Spencer, M., Steen-Larsen, H. C., Stevens, C. M., Sugiyama, S., Svensson, J., Tedesco, M., Thomas, E., Thompson-Munson, M., Tsutaki, S., van As, D., Van den Broeke, M. R., van Tiggelen, M., Wang, Y., Wilhelms, F., Winstrup, M., Xiao, J., and Xiao, C.: The SUMup collaborative database: Surface mass balance, subsurface temperature and density measurements from the Greenland and Antarctic ice sheets (2024 release), version: urn:uuid:35eb18e0-8107-4458-bc62-3cb194bb395f, Arctic Data Center, https://doi.org/10.18739/A2M61BR5M, 2024.
Verjans, V., Leeson, A. A., Stevens, C. M., MacFerrin, M., Noël, B., and van den Broeke, M. R.: Development of physically based liquid water schemes for Greenland firn-densification models, The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019, 2019.
Zeiger, P., Picard, G., Richaume, P., and Mialon, A.: Remote Sensing of Environment Resolution enhancement of SMOS brightness temperatures: Application to melt detection on the Antarctic and Greenland ice sheets, Remote Sens. Environ., 315, 114469, https://doi.org/10.1016/j.rse.2024.114469, 2024.
Zhang, Z., Zheng, L., Leng, W., Zhao, T., Li, T., and Liang, Q.: Toward a real validation of passive microwave snowmelt detection algorithms over the Antarctic Ice sheet, Int. J. Appl. Earth Obs. Geoinf., 125, 103600, https://doi.org/10.1016/j.jag.2023.103600, 2023.
Zwally, H. J. and Fiegles, S.: Extent and duration of Antarctic surface melting, J. Glaciol., 40, 463–476, https://doi.org/10.3189/s0022143000012338, 1994.
Short summary
Microwave L-band radiometry offers a promising new 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.
Microwave L-band radiometry offers a promising new tool for estimating the total...
