Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2545-2025
© 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/gmd-18-2545-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
09 May 2025
Model description paper |
| 09 May 2025
| 09 May 2025
A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)
Niccolò Maffezzoli
CORRESPONDING AUTHOR
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Institute of Polar Sciences, National Research Council, Venice, Italy
Eric Rignot
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Jet Propulsion Laboratory, Pasadena, CA, USA
Carlo Barbante
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy
Institute of Polar Sciences, National Research Council, Venice, Italy
Troels Petersen
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
Sebastiano Vascon
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy
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Multiple lines of research in ice core science are limited by manually intensive and time-consuming optical microscopy investigations for the detection of insoluble particles, from pollen grains to volcanic shards. To help overcome these limitations and support researchers, we present a novel methodology for the identification and autonomous classification of ice core insoluble particles based on flow image microscopy and neural networks.
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Azzurra Spagnesi, Elena Barbaro, Matteo Feltracco, Federico Scoto, Marco Vecchiato, Massimiliano Vardè, Mauro Mazzola, François Yves Burgay, Federica Bruschi, Clara Jule Marie Hoppe, Allison Bailey, Andrea Gambaro, Carlo Barbante, and Andrea Spolaor
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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
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Niccolò Maffezzoli, Eliza Cook, Willem G. M. van der Bilt, Eivind N. Støren, Daniela Festi, Florian Muthreich, Alistair W. R. Seddon, François Burgay, Giovanni Baccolo, Amalie R. F. Mygind, Troels Petersen, Andrea Spolaor, Sebastiano Vascon, Marcello Pelillo, Patrizia Ferretti, Rafael S. dos Reis, Jefferson C. Simões, Yuval Ronen, Barbara Delmonte, Marco Viccaro, Jørgen Peder Steffensen, Dorthe Dahl-Jensen, Kerim H. Nisancioglu, and Carlo Barbante
The Cryosphere, 17, 539–565, https://doi.org/10.5194/tc-17-539-2023, https://doi.org/10.5194/tc-17-539-2023, 2023
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Multiple lines of research in ice core science are limited by manually intensive and time-consuming optical microscopy investigations for the detection of insoluble particles, from pollen grains to volcanic shards. To help overcome these limitations and support researchers, we present a novel methodology for the identification and autonomous classification of ice core insoluble particles based on flow image microscopy and neural networks.
François Burgay, Rafael Pedro Fernández, Delia Segato, Clara Turetta, Christopher S. Blaszczak-Boxe, Rachael H. Rhodes, Claudio Scarchilli, Virginia Ciardini, Carlo Barbante, Alfonso Saiz-Lopez, and Andrea Spolaor
The Cryosphere, 17, 391–405, https://doi.org/10.5194/tc-17-391-2023, https://doi.org/10.5194/tc-17-391-2023, 2023
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The paper presents the first ice-core record of bromine (Br) in the Antarctic plateau. By the observation of the ice core and the application of atmospheric chemical models, we investigate the behaviour of bromine after its deposition into the snowpack, with interest in the effect of UV radiation change connected to the formation of the ozone hole, the role of volcanic deposition, and the possible use of Br to reconstruct past sea ice changes from ice core collect in the inner Antarctic plateau.
Romain Millan, Jeremie Mouginot, Anna Derkacheva, Eric Rignot, Pietro Milillo, Enrico Ciraci, Luigi Dini, and Anders Bjørk
The Cryosphere, 16, 3021–3031, https://doi.org/10.5194/tc-16-3021-2022, https://doi.org/10.5194/tc-16-3021-2022, 2022
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We detect for the first time a dramatic retreat of the grounding line of Petermann Glacier, a major glacier of the Greenland Ice Sheet. Using satellite data, we also observe a speedup of the glacier and a fracturing of the ice shelf. This sequence of events is coherent with ocean warming in this region and suggests that Petermann Glacier has initiated a phase of destabilization, which is of prime importance for the stability and future contribution of the Greenland Ice Sheet to sea level rise.
Paolo Gabrielli, Theo Manuel Jenk, Michele Bertó, Giuliano Dreossi, Daniela Festi, Werner Kofler, Mai Winstrup, Klaus Oeggl, Margit Schwikowski, Barbara Stenni, and Carlo Barbante
Clim. Past Discuss., https://doi.org/10.5194/cp-2022-20, https://doi.org/10.5194/cp-2022-20, 2022
Revised manuscript not accepted
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We present a methodology that reduces the chronological uncertainty of an Alpine ice core record from the glacier Alto dell’Ortles, Italy. This chronology will allow the constraint of the Holocene climatic and environmental histories emerging from this archive of Central Europe. This method will allow to obtain accurate chronologies also from other ice cores from-low latitude/high-altitude glaciers that typically suffer from larger dating uncertainties compared with well dated polar records.
Federico Dallo, Daniele Zannoni, Jacopo Gabrieli, Paolo Cristofanelli, Francescopiero Calzolari, Fabrizio de Blasi, Andrea Spolaor, Dario Battistel, Rachele Lodi, Warren Raymond Lee Cairns, Ann Mari Fjæraa, Paolo Bonasoni, and Carlo Barbante
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Our work showed how the adoption of low-cost technology could be useful in environmental research and monitoring. We focused our work on tropospheric ozone, but we also showed how to make a general purpose low-cost sensing system which may be adapted and optimised to be used in many other case studies. Given the importance of providing quality data, we put a lot of effort in the sensor's calibration, and we believe that our results show how to exploit the potential of the low-cost technology.
Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor
Atmos. Chem. Phys., 21, 12479–12493, https://doi.org/10.5194/acp-21-12479-2021, https://doi.org/10.5194/acp-21-12479-2021, 2021
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Delia Segato, Maria Del Carmen Villoslada Hidalgo, Ross Edwards, Elena Barbaro, Paul Vallelonga, Helle Astrid Kjær, Marius Simonsen, Bo Vinther, Niccolò Maffezzoli, Roberta Zangrando, Clara Turetta, Dario Battistel, Orri Vésteinsson, Carlo Barbante, and Andrea Spolaor
Clim. Past, 17, 1533–1545, https://doi.org/10.5194/cp-17-1533-2021, https://doi.org/10.5194/cp-17-1533-2021, 2021
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Human influence on fire regimes in the past is poorly understood, especially at high latitudes. We present 5 kyr of fire proxies levoglucosan, black carbon, and ammonium in the RECAP ice core in Greenland and reconstruct for the first time the fire regime in the high North Atlantic region, comprising coastal east Greenland and Iceland. Climate is the main driver of the fire regime, but at 1.1 kyr BP a contribution may be made by the deforestation resulting from Viking colonization of Iceland.
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
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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.
François Burgay, Andrea Spolaor, Jacopo Gabrieli, Giulio Cozzi, Clara Turetta, Paul Vallelonga, and Carlo Barbante
Clim. Past, 17, 491–505, https://doi.org/10.5194/cp-17-491-2021, https://doi.org/10.5194/cp-17-491-2021, 2021
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We present the first Fe record from the NEEM ice core, which provides insight into past atmospheric Fe deposition in the Arctic. Considering the biological relevance of Fe, we questioned if the increased eolian Fe supply during glacial periods could explain the marine productivity variability in the Fe-limited subarctic Pacific Ocean. We found no overwhelming evidence that eolian Fe fertilization triggered any phytoplankton blooms, likely because other factors play a more relevant role.
Tuukka Petäjä, Ella-Maria Duplissy, Ksenia Tabakova, Julia Schmale, Barbara Altstädter, Gerard Ancellet, Mikhail Arshinov, Yurii Balin, Urs Baltensperger, Jens Bange, Alison Beamish, Boris Belan, Antoine Berchet, Rossana Bossi, Warren R. L. Cairns, Ralf Ebinghaus, Imad El Haddad, Beatriz Ferreira-Araujo, Anna Franck, Lin Huang, Antti Hyvärinen, Angelika Humbert, Athina-Cerise Kalogridis, Pavel Konstantinov, Astrid Lampert, Matthew MacLeod, Olivier Magand, Alexander Mahura, Louis Marelle, Vladimir Masloboev, Dmitri Moisseev, Vaios Moschos, Niklas Neckel, Tatsuo Onishi, Stefan Osterwalder, Aino Ovaska, Pauli Paasonen, Mikhail Panchenko, Fidel Pankratov, Jakob B. Pernov, Andreas Platis, Olga Popovicheva, Jean-Christophe Raut, Aurélie Riandet, Torsten Sachs, Rosamaria Salvatori, Roberto Salzano, Ludwig Schröder, Martin Schön, Vladimir Shevchenko, Henrik Skov, Jeroen E. Sonke, Andrea Spolaor, Vasileios K. Stathopoulos, Mikko Strahlendorff, Jennie L. Thomas, Vito Vitale, Sterios Vratolis, Carlo Barbante, Sabine Chabrillat, Aurélien Dommergue, Konstantinos Eleftheriadis, Jyri Heilimo, Kathy S. Law, Andreas Massling, Steffen M. Noe, Jean-Daniel Paris, André S. H. Prévôt, Ilona Riipinen, Birgit Wehner, Zhiyong Xie, and Hanna K. Lappalainen
Atmos. Chem. Phys., 20, 8551–8592, https://doi.org/10.5194/acp-20-8551-2020, https://doi.org/10.5194/acp-20-8551-2020, 2020
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The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. Here we summarize initial results from our integrative project exploring the Arctic environment and pollution to deliver data products, metrics, and indicators for stakeholders.
Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-574, https://doi.org/10.5194/acp-2020-574, 2020
Preprint withdrawn
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We present the daily and seasonal variability of Black carbon inferred from two specific experiment based on the hourly and daily time resolution sampling strategy. These unique datasets give us for the first time the opportunity to evaluate the associations between the observed surface snow rBC mass concentration and a set of predictors corresponding to the considered meteorological and snow physico-chemical parameters, via a multiple linear regression approach.
Niccolò Maffezzoli, Paul Vallelonga, Ross Edwards, Alfonso Saiz-Lopez, Clara Turetta, Helle Astrid Kjær, Carlo Barbante, Bo Vinther, and Andrea Spolaor
Clim. Past, 15, 2031–2051, https://doi.org/10.5194/cp-15-2031-2019, https://doi.org/10.5194/cp-15-2031-2019, 2019
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This study provides the first ice-core-based history of sea ice in the North Atlantic Ocean, reaching 120 000 years back in time. This record was obtained from bromine and sodium measurements in the RECAP ice core, drilled in east Greenland. We found that, during the last deglaciation, sea ice started to melt ~ 17 500 years ago. Over the 120 000 years of the last glacial cycle, sea ice extent was maximal during MIS2, while minimum sea ice extent exists for the Holocene.
Juan Pablo Corella, Niccolo Maffezzoli, Carlos Alberto Cuevas, Paul Vallelonga, Andrea Spolaor, Giulio Cozzi, Juliane Müller, Bo Vinther, Carlo Barbante, Helle Astrid Kjær, Ross Edwards, and Alfonso Saiz-Lopez
Clim. Past, 15, 2019–2030, https://doi.org/10.5194/cp-15-2019-2019, https://doi.org/10.5194/cp-15-2019-2019, 2019
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This study provides the first reconstruction of atmospheric iodine levels in the Arctic during the last 11 700 years from an ice core record in coastal Greenland. Dramatic shifts in iodine level variability coincide with abrupt climatic transitions in the North Atlantic. Since atmospheric iodine levels have significant environmental and climatic implications, this study may serve as a past analog to predict future changes in Arctic climate in response to global warming.
Andrea Spolaor, Elena Barbaro, David Cappelletti, Clara Turetta, Mauro Mazzola, Fabio Giardi, Mats P. Björkman, Federico Lucchetta, Federico Dallo, Katrine Aspmo Pfaffhuber, Hélène Angot, Aurelien Dommergue, Marion Maturilli, Alfonso Saiz-Lopez, Carlo Barbante, and Warren R. L. Cairns
Atmos. Chem. Phys., 19, 13325–13339, https://doi.org/10.5194/acp-19-13325-2019, https://doi.org/10.5194/acp-19-13325-2019, 2019
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The main aims of the study are to (a) detect whether mercury in the surface snow undergoes a daily cycle as determined in the atmosphere, (b) compare the mercury concentration in surface snow with the concentration in the atmosphere, (c) evaluate the effect of snow depositions, (d) detect whether iodine and bromine in the surface snow undergo a daily cycle, and (e) evaluate the role of metereological and atmospheric conditions. Different behaviours were determined during different seasons.
Mathieu Morlighem, Michael Wood, Hélène Seroussi, Youngmin Choi, and Eric Rignot
The Cryosphere, 13, 723–734, https://doi.org/10.5194/tc-13-723-2019, https://doi.org/10.5194/tc-13-723-2019, 2019
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Many glaciers along the coast of Greenland have been retreating. It has been suggested that this retreat is triggered by the presence of warm water in the fjords, and surface melt at the top of the ice sheet is exacerbating this problem. Here, we quantify the vulnerability of northwestern Greenland to further warming using a numerical model. We find that in current conditions, this sector alone will contribute more than 1 cm to sea rise level by 2100, and up to 3 cm in the most extreme scenario.
Hongju Yu, Eric Rignot, Helene Seroussi, and Mathieu Morlighem
The Cryosphere, 12, 3861–3876, https://doi.org/10.5194/tc-12-3861-2018, https://doi.org/10.5194/tc-12-3861-2018, 2018
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Thwaites Glacier, West Antarctica, has experienced rapid grounding line retreat and mass loss in the past decades. In this study, we simulate the evolution of Thwaites Glacier over the next century using different model configurations. Overall, we estimate a 5 mm contribution to global sea level rise from Thwaites Glacier in the next 30 years. However, a 300 % uncertainty is found over the next 100 years, ranging from 14 to 42 mm, depending on the model setup.
Alice Callegaro, Dario Battistel, Natalie M. Kehrwald, Felipe Matsubara Pereira, Torben Kirchgeorg, Maria del Carmen Villoslada Hidalgo, Broxton W. Bird, and Carlo Barbante
Clim. Past, 14, 1543–1563, https://doi.org/10.5194/cp-14-1543-2018, https://doi.org/10.5194/cp-14-1543-2018, 2018
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Holocene fires and vegetation are reconstructed using different molecular markers with a single analytical method, applied for the first time to lake sediments from Tibet. The early Holocene shows oscillations between grasses and conifers, with smouldering fires represented by levoglucosan peaks, and high-temperature fires represented by PAHs. The lack of human FeSts excludes local human influence on fire and vegetation changes. Late Holocene displays an increase in local to regional combustion.
Luca Naitza, Davide Putero, Angela Marinoni, Francescopiero Calzolari, Fabrizio Roccato, Maurizio Busetto, Damiano Sferlazzo, Eleonora Aruffo, Piero Di Carlo, Mariantonia Bencardino, Francesco D'Amore, Francesca Sprovieri, Nicola Pirrone, Federico Dallo, Jacopo Gabrieli, Massimiliano Vardè, Carlo Barbante, Paolo Bonasoni, and Paolo Cristofanelli
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-245, https://doi.org/10.5194/amt-2018-245, 2018
Revised manuscript not accepted
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We implemented a prototype of a centralized system to support atmospheric observatories in data production and submission. By using the “R” Language, for several near-surface ECVs, we developed specific routines for data filtering, flagging, formatting, and creation of data products for detecting instrumental problems or special atmospheric events. Our effort would improve atmospheric data quality, accelerate the process of data submission and make the data flagging more “objective".
Dario Battistel, Natalie M. Kehrwald, Piero Zennaro, Giuseppe Pellegrino, Elena Barbaro, Roberta Zangrando, Xanthi X. Pedeli, Cristiano Varin, Andrea Spolaor, Paul T. Vallelonga, Andrea Gambaro, and Carlo Barbante
Clim. Past, 14, 871–886, https://doi.org/10.5194/cp-14-871-2018, https://doi.org/10.5194/cp-14-871-2018, 2018
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From the analysis of an Antarctic ice core we showed that during the mid- to late Holocene (6000–750 BP) the long-term fire activity increased with higher rates starting at ~ 4000 BP and, more surprisingly, peaked between 2500 and 1500 BP. The anomalous increase in biomass burning centered at about 2000 BP is due to a complex interaction between changes in atmospheric circulation and biomass availability, with the main contribution coming from southern South America.
Konstanze Haubner, Jason E. Box, Nicole J. Schlegel, Eric Y. Larour, Mathieu Morlighem, Anne M. Solgaard, Kristian K. Kjeldsen, Signe H. Larsen, Eric Rignot, Todd K. Dupont, and Kurt H. Kjær
The Cryosphere, 12, 1511–1522, https://doi.org/10.5194/tc-12-1511-2018, https://doi.org/10.5194/tc-12-1511-2018, 2018
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We investigate the effect of neglecting calving on Upernavik Isstrøm, West Greenland, between 1849 and 2012.
Our simulation is forced with observed terminus positions in discrete time steps and is responsive to the prescribed ice front changes.
Simulated frontal retreat is needed to obtain a realistic ice surface elevation and velocity evolution of Upernavik.
Using the prescribed terminus position change we gain insight to mass loss partitioning during different time periods.
Niccolò Maffezzoli, Andrea Spolaor, Carlo Barbante, Michele Bertò, Massimo Frezzotti, and Paul Vallelonga
The Cryosphere, 11, 693–705, https://doi.org/10.5194/tc-11-693-2017, https://doi.org/10.5194/tc-11-693-2017, 2017
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Sea ice is a crucial parameter within Earth's climate system. Understanding its dynamics and its response to other climatic variables is therefore of primary importance in view of a warming climate and sea ice decline. In this work we investigate some features of a chemical parameter in ice cores, bromine enrichment, which is linked to sea ice and can therefore be used to reconstruct sea ice in the past.
Francesca Sprovieri, Nicola Pirrone, Mariantonia Bencardino, Francesco D'Amore, Helene Angot, Carlo Barbante, Ernst-Günther Brunke, Flor Arcega-Cabrera, Warren Cairns, Sara Comero, María del Carmen Diéguez, Aurélien Dommergue, Ralf Ebinghaus, Xin Bin Feng, Xuewu Fu, Patricia Elizabeth Garcia, Bernd Manfred Gawlik, Ulla Hageström, Katarina Hansson, Milena Horvat, Jože Kotnik, Casper Labuschagne, Olivier Magand, Lynwill Martin, Nikolay Mashyanov, Thumeka Mkololo, John Munthe, Vladimir Obolkin, Martha Ramirez Islas, Fabrizio Sena, Vernon Somerset, Pia Spandow, Massimiliano Vardè, Chavon Walters, Ingvar Wängberg, Andreas Weigelt, Xu Yang, and Hui Zhang
Atmos. Chem. Phys., 17, 2689–2708, https://doi.org/10.5194/acp-17-2689-2017, https://doi.org/10.5194/acp-17-2689-2017, 2017
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The results on total mercury (THg) wet deposition flux obtained within the GMOS network have been presented and discussed to understand the atmospheric Hg cycling and its seasonal depositional patterns over the 2011–2015 period. The data set provides new insight into baseline concentrations of THg concentrations in precipitation particularly in regions where wet deposition and atmospheric Hg species were not investigated before, opening the way for additional measurements and modeling studies.
Feras Habbal, Eric Larour, Mathieu Morlighem, Helene Seroussi, Christopher P. Borstad, and Eric Rignot
Geosci. Model Dev., 10, 155–168, https://doi.org/10.5194/gmd-10-155-2017, https://doi.org/10.5194/gmd-10-155-2017, 2017
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This work presents the results from testing a suite of numerical solvers on a standard ice sheet benchmark test. We note the relevance of this test to practical simulations and identify the fastest solvers for the transient simulation. The highlighted solvers show significant speed-ups in relation to the default solver (~1.5–100 times faster) and enable a new capability for solving massive, high-resolution models that are critical for improving projections of ice sheets and sea-level change.
Paolo Gabrielli, Carlo Barbante, Giuliano Bertagna, Michele Bertó, Daniel Binder, Alberto Carton, Luca Carturan, Federico Cazorzi, Giulio Cozzi, Giancarlo Dalla Fontana, Mary Davis, Fabrizio De Blasi, Roberto Dinale, Gianfranco Dragà, Giuliano Dreossi, Daniela Festi, Massimo Frezzotti, Jacopo Gabrieli, Stephan P. Galos, Patrick Ginot, Petra Heidenwolf, Theo M. Jenk, Natalie Kehrwald, Donald Kenny, Olivier Magand, Volkmar Mair, Vladimir Mikhalenko, Ping Nan Lin, Klaus Oeggl, Gianni Piffer, Mirko Rinaldi, Ulrich Schotterer, Margit Schwikowski, Roberto Seppi, Andrea Spolaor, Barbara Stenni, David Tonidandel, Chiara Uglietti, Victor Zagorodnov, Thomas Zanoner, and Piero Zennaro
The Cryosphere, 10, 2779–2797, https://doi.org/10.5194/tc-10-2779-2016, https://doi.org/10.5194/tc-10-2779-2016, 2016
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New ice cores were extracted from Alto dell'Ortles, the highest glacier of South Tyrol in the Italian Alps, to check whether prehistoric ice, which is coeval to the famous 5300-yr-old Tyrolean Iceman, is still preserved in this region. Dating of the ice cores confirms the hypothesis and indicates the drilling site has been glaciated since the end of the Northern Hemisphere Climatic Optimum (7000 yrs BP). We also infer that an unprecedented acceleration of the glacier flow has recently begun.
Francesca Sprovieri, Nicola Pirrone, Mariantonia Bencardino, Francesco D'Amore, Francesco Carbone, Sergio Cinnirella, Valentino Mannarino, Matthew Landis, Ralf Ebinghaus, Andreas Weigelt, Ernst-Günther Brunke, Casper Labuschagne, Lynwill Martin, John Munthe, Ingvar Wängberg, Paulo Artaxo, Fernando Morais, Henrique de Melo Jorge Barbosa, Joel Brito, Warren Cairns, Carlo Barbante, María del Carmen Diéguez, Patricia Elizabeth Garcia, Aurélien Dommergue, Helene Angot, Olivier Magand, Henrik Skov, Milena Horvat, Jože Kotnik, Katie Alana Read, Luis Mendes Neves, Bernd Manfred Gawlik, Fabrizio Sena, Nikolay Mashyanov, Vladimir Obolkin, Dennis Wip, Xin Bin Feng, Hui Zhang, Xuewu Fu, Ramesh Ramachandran, Daniel Cossa, Joël Knoery, Nicolas Marusczak, Michelle Nerentorp, and Claus Norstrom
Atmos. Chem. Phys., 16, 11915–11935, https://doi.org/10.5194/acp-16-11915-2016, https://doi.org/10.5194/acp-16-11915-2016, 2016
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This work presents atmospheric Hg concentrations recorded within the GMOS global network analyzing Hg measurement results in terms of temporal trends, seasonality and comparability within the network. The over-arching benefit of this coordinated Hg monitoring network would clearly be the production of high-quality measurement datasets on a global scale useful in developing and validating models on different spatial and temporal scales.
E. Barbaro, R. Zangrando, M. Vecchiato, R. Piazza, W. R. L. Cairns, G. Capodaglio, C. Barbante, and A. Gambaro
Atmos. Chem. Phys., 15, 5457–5469, https://doi.org/10.5194/acp-15-5457-2015, https://doi.org/10.5194/acp-15-5457-2015, 2015
M. Schaefer, H. Machguth, M. Falvey, G. Casassa, and E. Rignot
The Cryosphere, 9, 25–35, https://doi.org/10.5194/tc-9-25-2015, https://doi.org/10.5194/tc-9-25-2015, 2015
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We use a meteorological-glaciological multi-model approach to quantify, for the first time, melt and accumulation of snow on the Southern Patagonia Icefield (SPI). We were able to reproduce the high measured accumulation of snow of up to 15.4 m water equivalent per year as well as the high measured ablation of up to 11 m water equivalent per year. Mass losses of the SPI due to calving of icebergs strongly increased from 1975-2000 to 2000-2011 and were higher than losses due to surface melt.
H. Seroussi, M. Morlighem, E. Larour, E. Rignot, and A. Khazendar
The Cryosphere, 8, 2075–2087, https://doi.org/10.5194/tc-8-2075-2014, https://doi.org/10.5194/tc-8-2075-2014, 2014
P. Zennaro, N. Kehrwald, J. R. McConnell, S. Schüpbach, O. J. Maselli, J. Marlon, P. Vallelonga, D. Leuenberger, R. Zangrando, A. Spolaor, M. Borrotti, E. Barbaro, A. Gambaro, and C. Barbante
Clim. Past, 10, 1905–1924, https://doi.org/10.5194/cp-10-1905-2014, https://doi.org/10.5194/cp-10-1905-2014, 2014
H. Seroussi, M. Morlighem, E. Rignot, J. Mouginot, E. Larour, M. Schodlok, and A. Khazendar
The Cryosphere, 8, 1699–1710, https://doi.org/10.5194/tc-8-1699-2014, https://doi.org/10.5194/tc-8-1699-2014, 2014
A. Spolaor, P. Vallelonga, J. Gabrieli, T. Martma, M. P. Björkman, E. Isaksson, G. Cozzi, C. Turetta, H. A. Kjær, M. A. J. Curran, A. D. Moy, A. Schönhardt, A.-M. Blechschmidt, J. P. Burrows, J. M. C. Plane, and C. Barbante
Atmos. Chem. Phys., 14, 9613–9622, https://doi.org/10.5194/acp-14-9613-2014, https://doi.org/10.5194/acp-14-9613-2014, 2014
E. Barbaro, R. Zangrando, M. Vecchiato, R. Piazza, G. Capodaglio, C. Barbante, and A. Gambaro
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-17067-2014, https://doi.org/10.5194/acpd-14-17067-2014, 2014
Revised manuscript not accepted
S. Schüpbach, U. Federer, P. R. Kaufmann, S. Albani, C. Barbante, T. F. Stocker, and H. Fischer
Clim. Past, 9, 2789–2807, https://doi.org/10.5194/cp-9-2789-2013, https://doi.org/10.5194/cp-9-2789-2013, 2013
C. P. Borstad, E. Rignot, J. Mouginot, and M. P. Schodlok
The Cryosphere, 7, 1931–1947, https://doi.org/10.5194/tc-7-1931-2013, https://doi.org/10.5194/tc-7-1931-2013, 2013
A. Spolaor, J. Gabrieli, T. Martma, J. Kohler, M. B. Björkman, E. Isaksson, C. Varin, P. Vallelonga, J. M. C. Plane, and C. Barbante
The Cryosphere, 7, 1645–1658, https://doi.org/10.5194/tc-7-1645-2013, https://doi.org/10.5194/tc-7-1645-2013, 2013
A. Spolaor, P. Vallelonga, J. M. C. Plane, N. Kehrwald, J. Gabrieli, C. Varin, C. Turetta, G. Cozzi, R. Kumar, C. Boutron, and C. Barbante
Atmos. Chem. Phys., 13, 6623–6635, https://doi.org/10.5194/acp-13-6623-2013, https://doi.org/10.5194/acp-13-6623-2013, 2013
C. Barbante, N. M. Kehrwald, P. Marianelli, B. M. Vinther, J. P. Steffensen, G. Cozzi, C. U. Hammer, H. B. Clausen, and M.-L. Siggaard-Andersen
Clim. Past, 9, 1221–1232, https://doi.org/10.5194/cp-9-1221-2013, https://doi.org/10.5194/cp-9-1221-2013, 2013
P. Vallelonga, C. Barbante, G. Cozzi, J. Gabrieli, S. Schüpbach, A. Spolaor, and C. Turetta
Clim. Past, 9, 597–604, https://doi.org/10.5194/cp-9-597-2013, https://doi.org/10.5194/cp-9-597-2013, 2013
Related subject area
Cryosphere
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
Tuning parameters of a sea ice model using machine learning
WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
Clustering simulated snow profiles to form avalanche forecast regions
SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Computationally efficient subglacial drainage modelling using Gaussian Process emulators: GlaDS-GP v1.0
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
A Flexible Snow Model (FSM 2.1.0) including a forest canopy
Anisotropic metric-based mesh adaptation for ice flow modelling in Firedrake
A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance
CMIP6 models overestimate sea ice melt, growth & conduction relative to ice mass balance buoy estimates
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
The Utrecht Finite Volume Ice-Sheet Model (UFEMISM version 2.0) – part 1: description and idealised experiments
Coupling framework (1.0) for the Úa (2023b) ice sheet model and the FESOM-1.4 z-coordinate ocean model in an Antarctic domain
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
Description and validation of the ice sheet model Nix v1.0
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Alex Edward West and Edward William Blockley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-121, https://doi.org/10.5194/gmd-2024-121, 2024
Revised manuscript accepted for GMD
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This study uses ice mass balance buoys – temperature and height-measuring devices frozen into sea ice – to find how well climate models simulate the melt & growth of, and conduction of heat through, Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find models tend to show more melt, growth or conduction for a given ice thickness than the buoys, though the difference is smaller for models with more physically realistic thermodynamics.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Constantijn J. Berends, Victor Azizi, Jorge Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-5, https://doi.org/10.5194/gmd-2024-5, 2024
Revised manuscript accepted for GMD
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution, which can resolve a lot of detail in areas where lots of changes happen in the ice, and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
EGUsphere, https://doi.org/10.5194/egusphere-2024-648, https://doi.org/10.5194/egusphere-2024-648, 2024
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The new coupled ice sheet-ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean/Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial time scales.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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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.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
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Short summary
In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
In this work we introduce IceBoost, a machine learning framework to model the ice thickness...
