Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6421-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6421-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2025
Research article |
| 02 Dec 2025
| 02 Dec 2025
Lessons for multi-model ensemble design drawn from emulator experiments: application to a large ensemble for 2100 sea level contributions of the Greenland ice sheet
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
Heiko Goelzer
NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
Tamsin L. Edwards
Department of Geography, King's College London, Bush House, North East Wing, 40 Aldwych, London, WC2B 4BG, London, UK
Goneri Le Cozannet
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
Gael Durand
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
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Konstanze Haubner, Heiko Goelzer, and Andreas Born
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Sam Sherriff-Tadano, Ruza Ivanovic, Lauren Gregoire, Charlotte Lang, Niall Gandy, Jonathan Gregory, Tamsin L. Edwards, Oliver Pollard, and Robin S. Smith
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Ensemble simulations of the climate and ice sheets of the Last Glacial Maximum (LGM) are performed with a new coupled climate–ice sheet model. Results show a strong sensitivity of the North American ice sheet to the albedo scheme, while the Greenland ice sheet appeared more sensitive to basal sliding schemes. Our result implies a potential connection between the North American ice sheet at the LGM and the future Greenland ice sheet through the albedo scheme.
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Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Violaine Coulon, Ann Kristin Klose, Christoph Kittel, Tamsin Edwards, Fiona Turner, Ricarda Winkelmann, and Frank Pattyn
The Cryosphere, 18, 653–681, https://doi.org/10.5194/tc-18-653-2024, https://doi.org/10.5194/tc-18-653-2024, 2024
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We present new projections of the evolution of the Antarctic ice sheet until the end of the millennium, calibrated with observations. We show that the ocean will be the main trigger of future ice loss. As temperatures continue to rise, the atmosphere's role may shift from mitigating to amplifying Antarctic mass loss already by the end of the century. For high-emission scenarios, this may lead to substantial sea-level rise. Adopting sustainable practices would however reduce the rate of ice loss.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
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Emily A. Hill, Benoît Urruty, Ronja Reese, Julius Garbe, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Ricarda Winkelmann, Mondher Chekki, David Chandler, and Petra M. Langebroek
The Cryosphere, 17, 3739–3759, https://doi.org/10.5194/tc-17-3739-2023, https://doi.org/10.5194/tc-17-3739-2023, 2023
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The grounding lines of the Antarctic Ice Sheet could enter phases of irreversible retreat or advance. We use three ice sheet models to show that the present-day locations of Antarctic grounding lines are reversible with respect to a small perturbation away from their current position. This indicates that present-day retreat of the grounding lines is not yet irreversible or self-enhancing.
Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann
The Cryosphere, 17, 3761–3783, https://doi.org/10.5194/tc-17-3761-2023, https://doi.org/10.5194/tc-17-3761-2023, 2023
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We use an ice sheet model to test where current climate conditions in Antarctica might lead. We find that present-day ocean and atmosphere conditions might commit an irreversible collapse of parts of West Antarctica which evolves over centuries to millennia. Importantly, this collapse is not irreversible yet.
Jeremy Rohmer, Remi Thieblemont, Goneri Le Cozannet, Heiko Goelzer, and Gael Durand
The Cryosphere, 16, 4637–4657, https://doi.org/10.5194/tc-16-4637-2022, https://doi.org/10.5194/tc-16-4637-2022, 2022
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To improve the interpretability of process-based projections of the sea-level contribution from land ice components, we apply the machine-learning-based
SHapley Additive exPlanationsapproach to a subset of a multi-model ensemble study for the Greenland ice sheet. This allows us to quantify the influence of particular modelling decisions (related to numerical implementation, initial conditions, or parametrisation of ice-sheet processes) directly in terms of sea-level change contribution.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
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We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
Jeremy Rohmer, Deborah Idier, Remi Thieblemont, Goneri Le Cozannet, and François Bachoc
Nat. Hazards Earth Syst. Sci., 22, 3167–3182, https://doi.org/10.5194/nhess-22-3167-2022, https://doi.org/10.5194/nhess-22-3167-2022, 2022
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We quantify the influence of wave–wind characteristics, offshore water level and sea level rise (projected up to 2200) on the occurrence of flooding events at Gâvres, French Atlantic coast. Our results outline the overwhelming influence of sea level rise over time compared to the others. By showing the robustness of our conclusions to the errors in the estimation procedure, our approach proves to be valuable for exploring and characterizing uncertainties in assessments of future flooding.
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.
Ryota Wada, Jeremy Rohmer, Yann Krien, and Philip Jonathan
Nat. Hazards Earth Syst. Sci., 22, 431–444, https://doi.org/10.5194/nhess-22-431-2022, https://doi.org/10.5194/nhess-22-431-2022, 2022
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Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near Guadeloupe is difficult because cyclones rarely pass near the location of interest. STM-E (space-time maxima and exposure) model utilizes wave data during cyclones on a spatial neighbourhood. Long-duration wave data generated from a database of synthetic tropical cyclones are used to evaluate the performance of STM-E. Results indicate STM-E provides estimates with small bias and realistic uncertainty.
Rémi Thiéblemont, Gonéri Le Cozannet, Jérémy Rohmer, Alexandra Toimil, Moisés Álvarez-Cuesta, and Iñigo J. Losada
Nat. Hazards Earth Syst. Sci., 21, 2257–2276, https://doi.org/10.5194/nhess-21-2257-2021, https://doi.org/10.5194/nhess-21-2257-2021, 2021
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Sea level rise and its acceleration are projected to aggravate coastal erosion over the 21st century. Resulting shoreline projections are deeply uncertain, however, which constitutes a major challenge for coastal planning and management. Our work presents a new extra-probabilistic framework to develop future shoreline projections and shows that deep uncertainties could be drastically reduced by better constraining sea level projections and improving coastal impact models.
Constantijn J. Berends, Heiko Goelzer, and Roderik S. W. van de Wal
Geosci. Model Dev., 14, 2443–2470, https://doi.org/10.5194/gmd-14-2443-2021, https://doi.org/10.5194/gmd-14-2443-2021, 2021
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The largest uncertainty in projections of sea-level rise comes from ice-sheet retreat. To better understand how these ice sheets respond to the changing climate, ice-sheet models are used, which must be able to reproduce both their present and past evolution. We have created a model that is fast enough to simulate an ice sheet at a high resolution over the course of an entire 120 000-year glacial cycle. This allows us to study processes that cannot be captured by lower-resolution models.
Gonéri Le Cozannet, Déborah Idier, Marcello de Michele, Yoann Legendre, Manuel Moisan, Rodrigo Pedreros, Rémi Thiéblemont, Giorgio Spada, Daniel Raucoules, and Ywenn de la Torre
Nat. Hazards Earth Syst. Sci., 21, 703–722, https://doi.org/10.5194/nhess-21-703-2021, https://doi.org/10.5194/nhess-21-703-2021, 2021
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Chronic flooding occurring at high tides under calm weather conditions is an early impact of sea-level rise. This hazard is a reason for concern on tropical islands, where coastal infrastructure is commonly located in low-lying areas. We focus here on the Guadeloupe archipelago, in the French Antilles, where chronic flood events have been reported for about 10 years. We show that the number of such events will increase drastically over the 21st century under continued growth of CO2 emissions.
Cited articles
Altmann, A., Tolosi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, 2010.
Aschwanden, A. and Brinkerhoff, D. J.: Calibrated mass loss predictions for the Greenland ice sheet. Geophysical Research Letters, 49, e2022GL099058, https://doi.org/10.1029/2022GL099058, 2022.
Aschwanden, A., Bartholomaus, T. C., Brinkerhoff, D. J., and Truffer, M.: Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level, The Cryosphere, 15, 5705–5715, https://doi.org/10.5194/tc-15-5705-2021, 2021.
Au, T. C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem. Journal of Machine Learning Research, 19, 1–30, 2018.
Barthel, A., Agosta, C., Little, C. M., Hattermann, T., Jourdain, N. C., Goelzer, H., Nowicki, S., Seroussi, H., Straneo, F., and Bracegirdle, T. J.: CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica, The Cryosphere, 14, 855–879, https://doi.org/10.5194/tc-14-855-2020, 2020.
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J. M., and McBratney, A.: Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, TrAC Trends in Analytical Chemistry, 29, 1073–1081, 2010.
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A.-L., Deng, D., and Lindauer, M.: Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13, e1484, https://doi.org/10.1002/widm.1484, 2023.
Bracher, J., Ray, E. L., Gneiting, T., and Reich, N. G.: Evaluating epidemic forecasts in an interval format, PLoS computational biology, 17, e1008618, https://doi.org/10.1371/journal.pcbi.1008618, 2021.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and regression trees, Wadsworth, California, ISBN 9780534980542, 1984.
Buriticá, G. and Engelke, S.: Progression: an extrapolation principle for regression, arXiv [preprint], arXiv:2410.23246, https://doi.org/10.48550/arXiv.2410.23246, 2024.
Ettema, J., van den Broeke, M. R., van Meijgaard, E., van de Berg, W. J., Box, J. E., and Steffen, K.: Climate of the Greenland ice sheet using a high-resolution climate model – Part 1: Evaluation, The Cryosphere, 4, 511–527, https://doi.org/10.5194/tc-4-511-2010, 2010.
Evin, G., Hingray, B., Blanchet, J., Eckert, N., Morin, S., and Verfaillie, D.: Partitioning uncertainty components of an incomplete ensemble of climate projections using data augmentation, Journal of Climate, 32, 2423–2440, 2019.
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017.
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021.
Gneiting, T., Raftery, A. E., Westveld III, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum crps estimation, Monthly Weather Review, 133, 1098–1118, 2005.
Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec'h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433–1460, https://doi.org/10.5194/tc-12-1433-2018, 2018.
Goelzer, H., Nowicki, S., Payne, A., Larour, E., Seroussi, H., Lipscomb, W. H., Gregory, J., Abe-Ouchi, A., Shepherd, A., Simon, E., Agosta, C., Alexander, P., Aschwanden, A., Barthel, A., Calov, R., Chambers, C., Choi, Y., Cuzzone, J., Dumas, C., Edwards, T., Felikson, D., Fettweis, X., Golledge, N. R., Greve, R., Humbert, A., Huybrechts, P., Le clec'h, S., Lee, V., Leguy, G., Little, C., Lowry, D. P., Morlighem, M., Nias, I., Quiquet, A., Rückamp, M., Schlegel, N.-J., Slater, D. A., Smith, R. S., Straneo, F., Tarasov, L., van de Wal, R., and van den Broeke, M.: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6, The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, 2020.
Goelzer, H., Berends, C. J., Boberg, F., Durand, G., Edwards, T., Fettweis, X., Gillet-Chaulet, F., Glaude, Q., Huybrechts, P., Le clec'h, S., Mottram, R., Noël, B., Olesen, M., Rahlves, C., Rohmer, J., van den Broeke, M., and van de Wal, R. S. W.: Extending the range and reach of physically-based Greenland ice sheet sea-level projections, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3098, 2025.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Berlin/Heidelberg, Germany, https://doi.org/10.1007/978-0-387-84858-7, 2009.
Hinkel, J., Church, J. A., Gregory, J. M., Lambert, E., Le Cozannet, G., Lowe, J., McInnes, K. L., Nicholls, R. J., van Der Pol, T. D., and Van De Wal, R.: Meeting user needs for sea level rise information: A decision analysis perspective, Earth's Future, 7, 320–337, 2019.
Hough, A. and Wong, T. E.: Analysis of the evolution of parametric drivers of high-end sea-level hazards, Advances in Statistical Climatology, Meteorology and Oceanography, 8, 117–134, 2022.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G.: Challenges in combining projections from multiple climate models, J. Climate, 23, 2739–2758, 2010.
Langen, P. L., Fausto, R. S., Vandecrux, B., Mottram, R. H., and Box, J. E.: Liquid water flow and retention on the Greenland ice sheet in the regional climate model HIRHAM5: Local and large-scale impacts, Frontiers in Earth Science, 4, 110, https://doi.org/10.3389/feart.2016.00110, 2017.
Le Cozannet, G., Thiéblemont, R., Rohmer, J., and Capderrey, C.: Sea-level scenarios aligned with the 3rd adaptation plan in France, Comptes Rendus Géoscience, 357, 105–123, 2025.
Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J. P., Engelbrecht, F., Fischer, E., Fyfe, J. C., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O., Panickal, S., and Zhou, T.: Future Global Climate: Scenario-Based Projections and NearTerm Information, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 553–672, https://doi.org/10.1017/9781009157896.006, 2021.
Levermann, A., Winkelmann, R., Albrecht, T., Goelzer, H., Golledge, N. R., Greve, R., Huybrechts, P., Jordan, J., Leguy, G., Martin, D., Morlighem, M., Pattyn, F., Pollard, D., Quiquet, A., Rodehacke, C., Seroussi, H., Sutter, J., Zhang, T., Van Breedam, J., Calov, R., DeConto, R., Dumas, C., Garbe, J., Gudmundsson, G. H., Hoffman, M. J., Humbert, A., Kleiner, T., Lipscomb, W. H., Meinshausen, M., Ng, E., Nowicki, S. M. J., Perego, M., Price, S. F., Saito, F., Schlegel, N.-J., Sun, S., and van de Wal, R. S. W.: Projecting Antarctica's contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2), Earth Syst. Dynam., 11, 35–76, https://doi.org/10.5194/esd-11-35-2020, 2020.
Lipscomb, W. H., Price, S. F., Hoffman, M. J., Leguy, G. R., Bennett, A. R., Bradley, S. L., Evans, K. J., Fyke, J. G., Kennedy, J. H., Perego, M., Ranken, D. M., Sacks, W. J., Salinger, A. G., Vargo, L. J., and Worley, P. H.: Description and evaluation of the Community Ice Sheet Model (CISM) v2.1, Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, 2019.
Mastrandrea, M. D., Field, C. B., Stocker, T. F., Edenhofer, O., Ebi, K. L., Frame, D. J., Held, H., Kriegler, E., Mach, K. J., Matschoss, P. R., Plattner, G.-K., Yohe, G. W., and Zwiers, A. F. W.: Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties, Intergovernmental Panel on Climate Change (IPCC), https://www.ipcc.ch (last access: 28 November 2025), 2010.
Marzeion, B., Hock, R., Anderson, B., Bliss, A., Champollion, N., Fujita, K., Huss, M., Immerzeel, W. W., Kraaijenbrink, P., J.-H., Malles, Maussion, F., Radic, V., Rounce, D. R., Sakai, A., Shannon, S., van de Wal, R., and Zekollari, H.: Partitioning the uncertainty of ensemble projections of global glacier mass change, Earth's Future, 8, e2019EF001470, https://doi.org/10.1029/2019EF001470, 2020.
Meinshausen, N.: Quantile regression forests, Journal of Machine Learning Research, 7, 983–999, 2006.
Meinshausen, M., Schleussner, C.-F., Beyer, K., Bodeker, G., Boucher, O., Canadell, J. G., Daniel, J. S., Diongue-Niang, A., Driouech, F., Fischer, E., Forster, P., Grose, M., Hansen, G., Hausfather, Z., Ilyina, T., Kikstra, J. S., Kimutai, J., King, A. D., Lee, J.-Y., Lennard, C., Lissner, T., Nauels, A., Peters, G. P., Pirani, A., Plattner, G.-K., Pörtner, H., Rogelj, J., Rojas, M., Roy, J., Samset, B. H., Sanderson, B. M., Séférian, R., Seneviratne, S., Smith, C. J., Szopa, S., Thomas, A., Urge-Vorsatz, D., Velders, G. J. M., Yokohata, T., Ziehn, T., and Nicholls, Z.: A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs), Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, 2024.
Merrifield, A. L., Brunner, L., Lorenz, R., Medhaug, I., and Knutti, R.: An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles, Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, 2020.
Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.
Nowicki, S. M. J., Payne, A., Larour, E., Seroussi, H., Goelzer, H., Lipscomb, W., Gregory, J., Abe-Ouchi, A., and Shepherd, A.: Ice Sheet Model Intercomparison Project (ISMIP6) contribution to CMIP6, Geosci. Model Dev., 9, 4521–4545, https://doi.org/10.5194/gmd-9-4521-2016, 2016.
Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.
Nowicki, S., Payne, A. J., Seroussi, H. L., Goelzer, H., Smith, R. S., Asay-Davis, X., Lipscomb, W. H., Abe-Ouchi, A., and Csatho, B. M.: Plans for the Ice Sheet Model Intercomparison Project for CMIP7, in: AGU Fall Meeting Abstracts, 2023, G52A-07, https://ui.adsabs.harvard.edu/abs/2023AGUFM.G52A..07N/abstract (last access: 28 November 2025), 2023.
Probst, P., Wright, M. N., and Boulesteix, A. L.: Hyperparameters and tuning strategies for random forest, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9, e1301, https://doi.org/10.1002/widm.1301, 2019.
Rahlves, C., Goelzer, H., Born, A., and Langebroek, P. M.: Historically consistent mass loss projections of the Greenland ice sheet, The Cryosphere, 19, 1205–1220, https://doi.org/10.5194/tc-19-1205-2025, 2025.
Rohmer, J., Thieblemont, R., Le Cozannet, G., Goelzer, H., and Durand, G.: Improving interpretation of sea-level projections through a machine-learning-based local explanation approach, The Cryosphere, 16, 4637–4657, https://doi.org/10.5194/tc-16-4637-2022, 2022.
Rohmer, J.: Codes supporting the manuscript “Lessons for multi-model ensemble design drawn from emulator experiments: application to a large ensemble for 2100 sea level contributions of the Greenland ice sheet” (zenodo_release), Zenodo [data set], https://doi.org/10.5281/zenodo.17761003, 2025.
Rohmer, J., Idier, D., Paris, F., Pedreros, R., and Louisor, J.: Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach, Environmental Modelling & Software, 104, 64–80, 2018.
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.
Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023.
Seroussi, H., Pelle, T., Lipscomb, W. H., Abe-Ouchi, A., Albrecht, T., Alvarez-Solas, J., Asay-Davis, X., Barre, J.-B., Berends, C. J., Bernales, J., Blasco, J., Caillet, J., Chandler, D. M., Coulon, V., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Garbe, J., Gillet-Chaulet, F., Gladstone, R., Goelzer, H., Golledge, N., Greve, R., Gudmundsson, G. H., Han, H. K., Hillebrand, T. R., Hoffman, M. J., Huybrechts, P., Jourdain, N. C., Klose, A. K., Langebroek, P. M.; Leguy, G. R., Lowry, D. P., Mathiot, P., Montoya, M., Morlighem, M., Nowicki, S., Pattyn, F., Payne, A. J., Quiquet, A., Reese, R., Robinson, A., Saraste, L., Simon, E. G., Sun, S., Twarog, J. P., Trusel, L. D., Urruty, B., Van Breedam, J., van de Wal, R. S. W., Wang, Y., Zhao, C., and Zwinger, T.: Evolution of the Antarctic Ice Sheet over the next three centuries from an ISMIP6 model ensemble, Earth's Future, 12, e2024EF004561, https://doi.org/10.1029/2024EF004561, 2024.
Shiogama, H., Fujimori, S., Hasegawa, T., Hayashi, M., Hirabayashi, Y., Ogura, T., Iizumi, T., Takahashi, K., and Takemura, T.: Important distinctiveness of SSP3–7.0 for use in impact assessments, Nature Climate Change, 13, 1276–1278, https://doi.org/10.1038/s41558-023-01883-2, 2023.
Sim, R. H. L., Xu, X., and Low, B. K. H.: Data Valuation in Machine Learning: “Ingredients”, Strategies, and Open Challenges, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Survey Track, 5607–5614, https://doi.org/10.24963/ijcai.2022/782, 2022.
Slater, D. A., Straneo, F., Felikson, D., Little, C. M., Goelzer, H., Fettweis, X., and Holte, J.: Estimating Greenland tidewater glacier retreat driven by submarine melting, The Cryosphere, 13, 2489–2509, https://doi.org/10.5194/tc-13-2489-2019, 2019.
Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.
Smith, H. L., Biggs, P. J., French, N. P., Smith, A. N., and Marshall, J. C.: Lost in the Forest: Encoding categorical variables and the absent levels problem, Data Mining and Knowledge Discovery, 38, 1889–1908, 2024.
Storlie, C. B., Swiler, L. P., Helton, J. C., and Sallaberry, C. J.: Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models, Reliability Engineering & System Safety, 94, 1735–1763, 2009.
Tadesse, M., Wahl, T., and Cid, A.: Data-Driven Modeling of Global Storm Surges, Frontiers in Marine Science, 7, 260, https://doi.org/10.3389/fmars.2020.00260, 2020.
Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in probabilistic climate projections, Philos. T. Roy. Soc. A, 365, 2053–2075, https://doi.org/10.1098/rsta.2007.2076, 2007.
Turner, F., Rougier, J., Edwards, T., Coulon, V., and Klose, A. K.: Building probabilistic projections of the Antarctic contribution to global sea level rise using a random forests emulato, EGU General Assembly 2024, Vienna, Austria, 14–19 April 2024, EGU24-16820, https://doi.org/10.5194/egusphere-egu24-16820, 2024.
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910, 2019.
Van Breedam, J., Huybrechts, P., and Crucifix, M.: A Gaussian process emulator for simulating ice sheet–climate interactions on a multi-million-year timescale: CLISEMv1.0, Geosci. Model Dev., 14, 6373–6401, https://doi.org/10.5194/gmd-14-6373-2021, 2021.
Van Katwyk, P., Fox-Kemper, B., Nowicki, S., Seroussi, H., and Bergen, K. J.: ISEFlow v1.0: A Flow-Based Neural Network Emulator for Improved Sea Level Projections and Uncertainty Quantification, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-870, 2025.
Verjans, V., Robel, A. A., Ultee, L., Seroussi, H., Thompson, A. F., Ackermann, L., Choi, Y., and Krebs-Kanzow, U.: The Greenland Ice Sheet Large Ensemble (GrISLENS): simulating the future of Greenland under climate variability, The Cryosphere, 19, 3749–3783, https://doi.org/10.5194/tc-19-3749-2025, 2025.
Wright, M. N. and König, I. R.: Splitting on categorical predictors in random forests, PeerJ, 7, e6339, https://doi.org/10.7717/peerj.6339, 2019.
Yoo, M., Gopalan, G., Hoffman, M., Coulson, S., Han, H. K., Wikle, C. K., and Hillebrand, T.: Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheet, Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000349, https://doi.org/10.1029/2024JH000349, 2025.
Short summary
Developing robust protocols to design multi-model ensembles is of primary importance for the uncertainty quantification of sea level projections. Here, we set up a series of computer experiments to reflect design decisions for the prediction of future sea level contribution of the Greenland ice sheet in 2100. We show the importance of including the most extreme climate scenario and the implications of using a single type of numerical model for ice sheets or regional climate.
Developing robust protocols to design multi-model ensembles is of primary importance for the...
