Preprints
https://doi.org/10.5194/egusphere-2026-1962
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-1962
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
18 May 2026
| 18 May 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).
The implementation and effectiveness of Calving Algorithms in numerical ice models (CalvingMIP)
Abstract. Ice calving plays a significant role in ice sheet mass loss. Predictions of future ice sheet mass balance require accurate representations of the calving process in numerical ice models to determine rates of future global mean sea level rise. Whilst calving has recently begun to have been implemented in numerical models there has not been a systematic investigation into how this implementation compares between different ice flow models. The Calving Model Intercomparison Project (CalvingMIP) has been established to address this question by providing a framework of experiments to investigate the accuracy of simulated calving rates and how simulated properties at the calving front evolve over time. Our initial focus has been on how calving is implemented in models, therefore focusing on calving algorithms, rather than on how much ice should be calved at a particular time (calving law). Our results, from thirteen different numerical modelling groups, show that the majority of calving algorithms implemented are able to accurately implement a given calving rate with an ice front that evolves smoothly throughout the calving process. These results show that we can have confidence in the models capacity to accurately implement calving laws in the future.
How to cite. Jordan, J. R., Pattyn, F., Abele, D., Albrecht, T., Alvarez-Solas, J., Barnes, J. M., Berends, T., Bernales, J. A., Blasco, J., Cheng, G., Choi, Y., Cornford, S. L., Garcia-Molina, C., Gillet-Chaulet, F., Gudmundsson, G. H., Humbert, A., Leguy, G. R., Lipscomb, W. H., Montoya, M., Moreno-Parada, D., Morlighem, M., Pelle, T., Robinson, A., Rückamp, M., Seroussi, H., Tian, Y., Wagner, L., van de Wal, R. S. W., Zhao, L., and Zwinger, T.: The implementation and effectiveness of Calving Algorithms in numerical ice models (CalvingMIP), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-1962, 2026.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
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CalvingMIP – Calving algorithm simulations James R. Jordan, Frank Pattyn, and The CalvingMIP Team https://zenodo.org/records/20041205
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Department of Geography, Swansea University, Swansea, United Kingdom
Frank Pattyn
Laboratoire de Glaciologie, Université libre de Bruxelles (ULB), Brussels, Belgium
Daniel Abele
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven/Potsdam, Germany
Institute of Software Technology, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
School of Computation, Information and Technology, Technical University Munich (TUM), Munich, Germany
Torsten Albrecht
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Department of Integrative Earth System Science, Max Planck Institute of Geoanthropology, Jena, Germany
Jorge Alvarez-Solas
Instituto de Geociencias, Consejo Superior de Investigaciones Científicas, Universidad Complutense de Madrid, Madrid, Spain
Jowan M. Barnes
School of Geography and Natural Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
Tijn Berends
Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht University, Utrecht, The Netherlands
Jorge A. Bernales
Danish Meteorological Institute (DMI), Copenhagen, Denmark
Javier Blasco
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven/Potsdam, Germany
Gong Cheng
Department of Earth Sciences, Dartmouth College, Hanover, NH, USA
Youngmin Choi
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Stephen L. Cornford
School of Geographical Sciences, University of Bristol, Bristol, UK
Cruz Garcia-Molina
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, France
Fabien Gillet-Chaulet
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, France
G. Hilmar Gudmundsson
School of Geography and Natural Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
Angelika Humbert
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven/Potsdam, Germany
Faculty of Geosciences, University of Bremen, Bremen, Germany
Gunter R. Leguy
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
William H. Lipscomb
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Marisa Montoya
Instituto de Geociencias, Consejo Superior de Investigaciones Científicas, Universidad Complutense de Madrid, Madrid, Spain
Departamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain
Daniel Moreno-Parada
Laboratoire de Glaciologie, Université libre de Bruxelles (ULB), Brussels, Belgium
Mathieu Morlighem
Department of Earth Sciences, Dartmouth College, Hanover, NH, USA
Tyler Pelle
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
Alexander Robinson
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven/Potsdam, Germany
Martin Rückamp
Bavarian Academy of Sciences and Humanities, Munich, Germany
Hélène Seroussi
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Yanmei Tian
State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Luisa Wagner
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven/Potsdam, Germany
Bavarian Academy of Sciences and Humanities, Munich, Germany
Roderick S. W. van de Wal
Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht University, Utrecht, The Netherlands
Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
Liyun Zhao
State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Thomas Zwinger
CSC – IT Center for Science, Espoo, Finland
Short summary
Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test of how well it has been implemented. We show that models can accurately represent a given rate of ice calving, properties at the ice front evolve smoothly during calving, and that there are no consistent differences in ice behaviour between various approaches to representing calving in numerical ice models. This gives us confidence in their ability for use in sea level rise prediction simulations.
Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test...
This is a valuable community paper and an important first step for systematic evaluation of calving in numerical ice-sheet models. The manuscript is well motivated, the experimental design is appropriate for a phase-1 algorithm-focused MIP, and the results will be useful for model developers and users. I recommend publication after following revision.
1. Line 245, "floating cells adjacent to ....", does this mean CISM only capture floating shelf calving? Can it capture grounded glacier calving activity?
2. Table3, there is a clear resolution mismatch for IMAU UFEMISM (20km) coarser than all the other models. Can results be replaced with 5km resolution simulation instead? In fact, shouldn't IMAU be consistent with NCAR CISM's results with the same resolution (at least for circular domain where calving rate direction doesn't make diffrence)?
3. Have all these models conducted convergence analysis on grid size? Might be a good idea to put it in SI and make sure the paper presents converged result from each model.
4. In conclusion text, the author claims calving algorithm does not make a noticeable difference for the result. To justify that, the author should first justify in table3, other variations do not skew simulation results. (for instance, is it safe to claim in current configureation, HO and SSA has similar results, any scaling argument to justify this? The author has already justified for the circular domain, calving rate direction shouldn't make any difference on the results)
5. Line 385, when interpreting Figure3, I can see the outliers are IMAU & AWI YELMO, is it a typo that the author emphasize PIK PISM intead?
6. Fig.3 and Fig.9 are not consistent. The outlier changes from IMAU & AWI YELMO to IMAU & PIK PISM, why?
7. For each figure, after stating which models deviate from theory/other models, the authors can add more explanation of why that happens, and mitigation strategies to fix that (if possible).
8. The project is very valuable as stated by the author "whilst undertaking the work for this project most models significatnly imporved their results for those initially submitted, both in terms of accuracy as well as spotting previously unnoticed errors in their code". Can the author highlight what bugs in the code for different models were fixed during this project (maybe summarize in a table and attach as SI)? I think the list of "common mistakes to make" is very valuable for students who just start learning ice sheet models. The table can also highlight the mitigation strategy to be implemented in the future, for those models that exhibit limitations during this project.