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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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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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
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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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We present the daily and seasonal variability in black carbon (BC) in surface snow inferred from two specific experiments based on the hourly and daily time resolution sampling during the Arctic spring in Svalbard. These unique data sets give us, for the first time, the opportunity to evaluate the associations between the observed surface snow BC mass concentration and a set of predictors corresponding to the considered meteorological and snow physico-chemical parameters.
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.
Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4–8 August 2019, https://doi.org/10.1145/3292500.3330701, 2019. a
Bueso-Bello, J.-L., Martone, M., González, C., Sica, F., Valdo, P., Posovszky, P., Pulella, A., and Rizzoli, P.: The global water body layer from TanDEM-X interferometric SAR data, Remote Sens., 13, 5069, https://doi.org/10.3390/rs13245069, 2021. a
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. a, b
Clarke, G. K., Berthier, E., Schoof, C. G., and Jarosch, A. H.: Neural networks applied to estimating subglacial topography and glacier volume, J. Climate, 22, 2146–2160, 2009. a
Cuffey, K. M. and Paterson, W. S. B.: The physics of glaciers, J. Glaciol., 57, 383–384, https://doi.org/10.3189/002214311796405906, 2010. a, b
Duan, T., Anand, A., Ding, D. Y., Thai, K. K., Basu, S., Ng, A., and Schuler, A.: Ngboost: Natural gradient boosting for probabilistic prediction, in: International Conference on Machine Learning, 13–18 July 2020, online, PMLR, 2690–2700, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.03225, 9 June 2020. a
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H., and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11, 949–970, https://doi.org/10.5194/tc-11-949-2017, 2017. a, b
Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I., Ruiz, L., Sallée, J.-B., Slangen, A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a
Friedman, J. H.: Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189–1232, 2001. a
GlaThiDa Consortium: Glacier Thickness Database 3.1.0, World Glacier Monitoring Service, Zurich, Switzerland, https://doi.org/10.5904/wgms-glathida-2020-10, 2020. a, b
González, C., Bachmann, M., Bueso-Bello, J.-L., Rizzoli, P., and Zink, M.: A fully automatic algorithm for editing the tandem-x global dem, Remote Sens., 12, 3961, https://doi.org/10.3390/rs12233961, 2020. a
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data?, Adv. Neur. In., 35, 507–520, 2022. a
Hagberg, A. A., Schult, D. A., and Swart, P. J.: Exploring Network Structure, Dynamics, and Function using NetworkX, in: Proceedings of the 7th Python in Science Conference, SciPy 2008, Pasadena, California, USA, 19–24 August 2008, edited by: Varoquaux, G., Vaught, T., and Millman, J., 11–15, https://doi.org/10.25080/TCWV9851, 2008. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Huss, M. and Farinotti, D.: Distributed ice thickness and volume of all glaciers around the globe, J. Geophys. Res.-Earth, 117, F04010, https://doi.org/10.1029/2012JF002523, 2012. a, b
Huss, M. and Hock, R.: Global-scale hydrological response to future glacier mass loss, Nat. Clim. Change, 8, 135–140, 2018. a
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a
Joughin, I., Smith, B., Howat, I., and Scambos, T.: MEaSUREs Multi-year Greenland Ice Sheet Velocity Mosaic (NSIDC-0670, Version 1), NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/QUA5Q9SVMSJG, 2016. a, b, c, d
Jouvet, G.: Inversion of a Stokes glacier flow model emulated by deep learning, J. Glaciol., 69, 13–26, 2023. a
Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.: Deep learning speeds up ice flow modelling by several orders of magnitude, J. Glaciol., 68, 651–664, 2022. a
Le Meur, E., Gagliardini, O., Zwinger, T., and Ruokolainen, J.: Glacier flow modelling: a comparison of the Shallow Ice Approximation and the full-Stokes solution, C. R. Phys., 5, 709–722, 2004. a
Liu, Z., Koo, Y., and Rahnemoonfar, M.: Physics-Informed Machine Learning On Polar Ice: A Survey, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.19536, 30 April 2024. a
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems, edited by: Guyon, I., Von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Curran Associates, Inc., https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf (last access: 16 October 2024), 2017. a, b
Maffezzoli, N.: IceBoost – a Gradient Boosted Tree global framework for glacier ice thickness retrieval (v1.1), Zenodo [data set], https://doi.org/10.5281/zenodo.13145836, 2024. a
Maffezzoli, N.: IceBoost GitHub repository, GitHub [code], https://github.com/nmaffe/iceboost (last access: 16 October 2024), 2025. a
Martone, M., Rizzoli, P., Wecklich, C., González, C., Bueso-Bello, J.-L., Valdo, P., Schulze, D., Zink, M., Krieger, G., and Moreira, A.: The global forest/non-forest map from TanDEM-X interferometric SAR data, Remote Sens. Environ., 205, 352–373, 2018. a
Morlighem, M.: MEaSUREs BedMachine Antarctica (NSIDC-0756, Version 3), NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/FPSU0V1MWUB6, 2022. a, b, c
Morlighem, M., Williams, C., Rignot, E., An, L., Arndt, J. E., Bamber, J., Catania, G., Chauché, N., Dowdeswell, J. A., Dorschel, B., Fenty, I., Hogan, K., Howat, I., Hubbard, A., Jakobsson, M., Jordan, T. M., Kjeldsen, K. K., Millan, R., Mayer, L., Mouginot, J., Noël, B., O'Cofaigh, C., Palmer, S. J., Rysgaard, S., Seroussi, H., Siegert, M. J., Slabon, P., Straneo, F., van den Broeke, M. R., Weinrebe, W., Wood, M., and Zinglersen, K.: IceBridge BedMachine Greenland (IDBMG4, Version 5), NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/GMEVBWFLWA7X, 2022. a, b, c, d, e
Mouginot, J., Rignot, E., and Scheuchl, B.: MEaSUREs Phase-Based Antarctica Ice Velocity Map, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/PZ3NJ5RXRH10, 2019. a, b, c
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b
Noël, B. and van Kampenhout, L.: Downscaled 1 km RACMO2 data used in CESM2 Greenland SMB evaluation paper, Zenodo [data set], https://doi.org/10.5281/zenodo.3367211, 2019. a, b
Noël, B., van de Berg, W. J., van Wessem, J. M., van Meijgaard, E., van As, D., Lenaerts, J. T. M., Lhermitte, S., Kuipers Munneke, P., Smeets, C. J. P. P., van Ulft, L. H., van de Wal, R. S. W., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016), The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, 2018. a, b
Noël, B., van Wessem, M., Wouters, B., Trusel, L., Lhermitte, S., and van den Broeke, M.: Data set: “Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution”, Zenodo [data set], https://doi.org/10.5281/zenodo.10007855, 2023. a, b
Paden, J., Li, J., Leuschen, C., Rodriguez-Morales, F., and Hale, R.: IceBridge MCoRDS L2 Ice Thickness, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/GDQ0CUCVTE2Q, 2010. a
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B. H., Rich, J., and Sharp, M. J.: The Randolph Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014. a, b
Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N.: The ocean and cryosphere in a changing climate, IPCC special report on the ocean and cryosphere in a changing climate, Cambridge University Press, 1155, https://doi.org/10.1017/9781009157964, 2019. a
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A.: Catboost: Unbiased Boosting with Categorical Features, Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, 3–8 December 2018, 6639–6649, arXiv [preprint], https://doi.org/10.48550/arXiv.1706.09516, 31 October 2018. a, b
RAPIDS Development Team: RAPIDS: Libraries for End to End GPU Data Science, GitHub [code], https://github.com/rapidsai (last access: 16 October 2024), 2023. a
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J., Beaudoing, H. K., Landerer, F. W., and Lo, M.-H.: Emerging trends in global freshwater availability, Nature, 557, 651–659, 2018. a
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. a
Tober, B., Holt, J., Christoffersen, M., Truffer, M., Larsen, C., Brinkerhoff, D., and Mooneyham, S.: Comprehensive radar mapping of Malaspina Glacier (Sít'Tlein), Alaska – The world's largest piedmont glacier – Reveals potential for instability, J. Geophys. Res.-Earth, 128, e2022JF006898, https://doi.org/10.1029/2022JF006898, 2023. a, b
Uroz, L. L., Yan, Y., Benoit, A., Rabatel, A., Giffard-Roisin, S., and Lin-Kwong-Chon, C.: Using Deep Learning for Glacier Thickness Estimation at a Regional Scale, IEEE Geosci. Remote S., 21, 1–5, https://doi.org/10.1109/LGRS.2024.3353575, 2024. a
Welty, E., Zemp, M., Navarro, F., Huss, M., Fürst, J. J., Gärtner-Roer, I., Landmann, J., Machguth, H., Naegeli, K., Andreassen, L. M., Farinotti, D., Li, H., and GlaThiDa Contributors: Worldwide version-controlled database of glacier thickness observations, Earth Syst. Sci. Data, 12, 3039–3055, https://doi.org/10.5194/essd-12-3039-2020, 2020. a, b, c
Zekollari, H., Huss, M., Farinotti, D., and Lhermitte, S.: Ice-Dynamical Glacier Evolution Modeling – A Review, Rev. Geophys., 60, e2021RG000754, https://doi.org/10.1029/2021RG000754, 2022. a
Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J., Barandun, M., Machguth, H., Nussbaumer, S. U., Gärtner-Roer, I., Thomson, L., Paul, F., Maussion, F., Kutuzov, S., and Cogley, J. G.: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Nature, 568, 382–386, https://doi.org/10.1038/s41586-019-1071-0, 2019. a
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...
