Articles | Volume 19, issue 10
https://doi.org/10.5194/gmd-19-4357-2026
© 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/gmd-19-4357-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 May 2026
Model description paper |
| 21 May 2026
| 21 May 2026
Biogeodynamics-Ice sheet-Geneva-MITgcm (BIG-MITgcm, v1.0): a simulation tool for exploring climate states with a representation of global ice sheets
Laure Moinat
CORRESPONDING AUTHOR
Group of Applied Physics, University of Geneva, Rue de l'École de Médecine 20, 1205 Geneva, Switzerland
Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
Centre pour la Vie dans l'Univers (CVU), University of Geneva, Geneva, Switzerland
Invited contribution by Laure Moinat, recipient of the Outstanding Student and PhD candidate Presentation (OSPP) Award 2025.
Florian Franziskakis
Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
Christian Vérard
Centre pour la Vie dans l'Univers (CVU), University of Geneva, Geneva, Switzerland
Section of Earth and Environmental Sciences, University of Geneva, Rue des Maraîchers 13, 1205 Geneva, Switzerland
Daniel Nathan Goldberg
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Group of Applied Physics, University of Geneva, Rue de l'École de Médecine 20, 1205 Geneva, Switzerland
Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
Centre pour la Vie dans l'Univers (CVU), University of Geneva, Geneva, Switzerland
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Jaime Otero, Daniel Goldberg, Peter Nienow, and Yefan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2298, https://doi.org/10.5194/egusphere-2026-2298, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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As climate change creates new lakes at the front of retreating glaciers, these ice bodies often accelerate and lose mass more quickly. We used a computer model to study this process at an Icelandic glacier. Our results show that thinning ice is the main driver for recent acceleration, with 37 % of the total ice loss resulting from the presence of the lake. These findings imply that as lakes grow globally, glaciers could vanish more rapidly than predicted, enhancing the rate of sea level rise.
Siddharth Bhatnagar, Francis Codron, Ehouarn Millour, Emeline Bolmont, Maura Brunetti, Jérôme Kasparian, Martin Turbet, and Guillaume Chaverot
Geosci. Model Dev., 19, 3285–3316, https://doi.org/10.5194/gmd-19-3285-2026, https://doi.org/10.5194/gmd-19-3285-2026, 2026
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We present an efficient ocean model coupled to a 3-D climate model (the Generic-PCM) that captures key features of ocean heat transport, matching well the global heat flows of more complex models. It closely reproduces Earth’s sea surface temperatures and sea ice, while influencing atmospheric circulation consistently. Balancing speed and accuracy, the model is ideal for exoplanet and paleoclimate studies, where observations are limited and broad parameter exploration is necessary.
Claire K. Yung, Xylar S. Asay-Davis, Alistair Adcroft, Christopher Y. S. Bull, Jan De Rydt, Michael S. Dinniman, Benjamin K. Galton-Fenzi, Daniel Goldberg, David E. Gwyther, Robert Hallberg, Matthew Harrison, Tore Hattermann, David M. Holland, Denise Holland, Paul R. Holland, James R. Jordan, Nicolas C. Jourdain, Kazuya Kusahara, Gustavo Marques, Pierre Mathiot, Dimitris Menemenlis, Adele K. Morrison, Yoshihiro Nakayama, Olga Sergienko, Robin S. Smith, Alon Stern, Ralph Timmermann, and Qin Zhou
The Cryosphere, 20, 2053–2088, https://doi.org/10.5194/tc-20-2053-2026, https://doi.org/10.5194/tc-20-2053-2026, 2026
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The second Ice Shelf-Ocean Model Intercomparison Project, ISOMIP+, compares 12 ice shelf-ocean models with a common, idealised, static configuration, aiming to assess inter-model variability. Models show similar basal melt rate patterns, ocean profiles and circulation but differ in ice-ocean boundary layer properties. Ice-ocean boundary layer representation is a key area for future work, as are realistic-domain ice sheet-ocean model intercomparisons.
Gillian M. A. Smith, Daniel N. Goldberg, Guillaume Jouvet, James R. Maddison, and Hamish D. Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2026-788, https://doi.org/10.5194/egusphere-2026-788, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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We estimate the thickness of a large glacier in the Himalaya, using recently available thickness measurements and a computational model which uses higher-order physics to match estimated thickness with satellite-observed velocity. Our velocity inversion achieves similar accuracy to leading thickness estimates, while thickness-constrained inversions show increased accuracy, but limited interpolative power. We make recommendations for future measurement locations and for choosing model parameters.
Brad Reed, Jan De Rydt, Kaitlin A. Naughten, and Daniel N. Goldberg
EGUsphere, https://doi.org/10.5194/egusphere-2026-931, https://doi.org/10.5194/egusphere-2026-931, 2026
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Melting beneath Antarctic ice shelves drives ice loss and raises sea levels, but the computer models we use to predict this are poorly constrained. We improved how these models are set up by using multiple real-world observations of ice thinning and glacier speed, rather than melt rates alone. This better captures how the ice and ocean interact. Our projections show this approach adds 14 mm of extra sea-level rise by 2100, which is more than switching to a warmer climate scenario alone.
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor
Geosci. Model Dev., 19, 1301–1319, https://doi.org/10.5194/gmd-19-1301-2026, https://doi.org/10.5194/gmd-19-1301-2026, 2026
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To improve large-scale understanding of glaciers, we developed a new data assimilation method that integrates available observations in a dynamically consistent way, while taking their timestamps into account. It is designed to flexibly include new glacier data as it becomes available. We tested the method with idealized experiments and found promising results in terms of accuracy and efficiency, showing strong potential for real-world applications.
Jowan M. Barnes, G. Hilmar Gudmundsson, Daniel N. Goldberg, and Sainan Sun
The Cryosphere, 20, 777–790, https://doi.org/10.5194/tc-20-777-2026, https://doi.org/10.5194/tc-20-777-2026, 2026
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Calving is where ice breaks off the front of glaciers. It has not been included widely in modelling as it is difficult to represent. We use our ice flow model to investigate the effects of calving floating ice shelves in West Antarctica. More calving leads to more ice loss and greater sea level rise, with local differences due to the shape of the bedrock. We find that ocean forcing and calving should be considered equally when trying to improve how models represent the real world.
Hamish D. Pritchard, Edward C. King, David J. Goodger, Douglas Boyle, Daniel N. Goldberg, Beatriz Recinos, Andrew Orr, and Dhananjay Regmi
Earth Syst. Sci. Data, 18, 199–217, https://doi.org/10.5194/essd-18-199-2026, https://doi.org/10.5194/essd-18-199-2026, 2026
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We present a new and uniquely extensive dataset of glacier thickness from the Khumbu Himal around Mount Everest that stretches for 119 km, doubling the extent of thickness measurements in High Mountain Asia. Such measurements are key inputs for models that estimate how much ice is stored on the whole mountain range scale and for models that predict how this ice reserve will change in future, and what impact this will have on water supply for the large populations living downstream.
Florian Franziskakis, Christian Vérard, Sébastien Castelltort, and Grégory Giuliani
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W13-2025, 111–118, https://doi.org/10.5194/isprs-archives-XLVIII-4-W13-2025-111-2025, https://doi.org/10.5194/isprs-archives-XLVIII-4-W13-2025-111-2025, 2025
Justin Gérard, Loïc Sablon, Jarno J. C. Huygh, Anne-Christine Da Silva, Alexandre Pohl, Christian Vérard, and Michel Crucifix
Clim. Past, 21, 239–260, https://doi.org/10.5194/cp-21-239-2025, https://doi.org/10.5194/cp-21-239-2025, 2025
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We used cGENIE, a climate model, to explore how changes in continental configuration, CO2 levels, and orbital configuration affected ocean oxygen levels during the Devonian period (419–359 million years ago). Key factors contributing to ocean anoxia were identified, highlighting the influence of continental configurations, atmospheric conditions, and orbital changes. Our findings offer new insights into the causes and prolonged durations of Devonian ocean anoxic events.
David T. Bett, Alexander T. Bradley, C. Rosie Williams, Paul R. Holland, Robert J. Arthern, and Daniel N. Goldberg
The Cryosphere, 18, 2653–2675, https://doi.org/10.5194/tc-18-2653-2024, https://doi.org/10.5194/tc-18-2653-2024, 2024
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A new ice–ocean model simulates future ice sheet evolution in the Amundsen Sea sector of Antarctica. Substantial ice retreat is simulated in all scenarios, with some retreat still occurring even with no future ocean melting. The future of small "pinning points" (islands of ice that contact the seabed) is an important control on this retreat. Ocean melting is crucial in causing these features to go afloat, providing the link by which climate change may affect this sector's sea level contribution.
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
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This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
Beatriz Recinos, Daniel Goldberg, James R. Maddison, and Joe Todd
The Cryosphere, 17, 4241–4266, https://doi.org/10.5194/tc-17-4241-2023, https://doi.org/10.5194/tc-17-4241-2023, 2023
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Ice sheet models generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, capturing the complete range of possible projections of mass loss is of critical societal importance. Here we add to data assimilation techniques commonly used in ice sheet modelling (a Bayesian inference approach) and fully characterize calibration uncertainty. We successfully propagate this type of error onto sea level rise projections of three ice streams in West Antarctica.
Charline Ragon, Christian Vérard, Jérôme Kasparian, and Maura Brunetti
EGUsphere, https://doi.org/10.5194/egusphere-2023-1808, https://doi.org/10.5194/egusphere-2023-1808, 2023
Preprint archived
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Deep-time climate simulations are challenging since sedimentary data are too sparse and uncertain to fully constrain initial and boundary conditions. By exploring a wide range of initial conditions, we find three alternative steady states with a temperature difference of around 10 °C in simulations using the Permian-Triassic paleogeography. This opens the possibility of explaining climatic variations in Early Triassic geological records through tipping mechanisms between steady states.
Helen Ockenden, Robert G. Bingham, Andrew Curtis, and Daniel Goldberg
The Cryosphere, 16, 3867–3887, https://doi.org/10.5194/tc-16-3867-2022, https://doi.org/10.5194/tc-16-3867-2022, 2022
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Hills and valleys hidden under the ice of Thwaites Glacier have an impact on ice flow and future ice loss, but there are not many three-dimensional observations of their location or size. We apply a mathematical theory to new high-resolution observations of the ice surface to predict the bed topography beneath the ice. There is a good correlation with ice-penetrating radar observations. The method may be useful in areas with few direct observations or as a further constraint for other methods.
Alexander Robinson, Daniel Goldberg, and William H. Lipscomb
The Cryosphere, 16, 689–709, https://doi.org/10.5194/tc-16-689-2022, https://doi.org/10.5194/tc-16-689-2022, 2022
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Here we investigate the numerical stability of several commonly used methods in order to determine which of them are capable of resolving the complex physics of the ice flow and are also computationally efficient. We find that the so-called DIVA solver outperforms the others. Its representation of the physics is consistent with more complex methods, while it remains computationally efficient at high resolution.
Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison
Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, https://doi.org/10.5194/gmd-14-5843-2021, 2021
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Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Cited articles
Ackermann, L., Danek, C., Gierz, P., and Lohmann, G.: AMOC recovery in a multicentennial scenario using a coupled atmosphere-ocean-ice sheet model, Geophys. Res. Lett., 47, e2019GL086810, https://doi.org/10.1029/2019GL086810, 2020. a
Adcroft, A., Campin, J.-M., Hill, C., and Marshall, J.: Implementation of an atmosphere ocean general circulation model on the expanded spherical cube, Mon. Weather Rev., 132, 2845–2863, https://doi.org/10.1175/MWR2823.1, 2004. a
Allen, B. J., Hill, D. J., Burke, A. M., Clark, M., Marchant, R., Stringer, L. C., Williams, D. R., and Lyon, C.: Projected future climatic forcing on the global distribution of vegetation types, Philos. T. Roy. Soc. B, 379, 20230011, https://doi.org/10.1098/rstb.2023.0011, 2024. a
Balaji, V., Maisonnave, E., Zadeh, N., Lawrence, B. N., Biercamp, J., Fladrich, U., Aloisio, G., Benson, R., Caubel, A., Durachta, J., Foujols, M.-A., Lister, G., Mocavero, S., Underwood, S., and Wright, G.: CPMIP: measurements of real computational performance of Earth system models in CMIP6, Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, 2017. a
Bartos, M.: pysheds: simple and fast watershed delineation in python, Zenodo [code], https://doi.org/10.5281/zenodo.3822494, 2020. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.5195, 2018. a
Braithwaite, R. J.: Air Temperature and Glacier Ablation – A Parametric Approach, PhD thesis, McGill University, PhD thesis, Interdisciplinary Studies in Glaciology, https://escholarship.mcgill.ca/concern/theses/tq57nr644 (last access: 19 May 2026), 1977. a
Brunetti, M. and Ragon, C.: Attractors and bifurcation diagrams in complex climate models, Phys. Rev. E, 107, 054214, https://doi.org/10.1103/PhysRevE.107.054214, 2023. a, b
Brunetti, M. and Vérard, C.: How to reduce long-term drift in present-day and deep-time simulations?, Clim. Dynam., 50, 4425–4436, https://doi.org/10.1007/s00382-017-3883-7, 2018. a, b
Brunetti, M., Vérard, C., and Baumgartner, P. O.: Modeling the Middle Jurassic ocean circulation, Journal of Palaeogeography, 4, 371–383, https://doi.org/10.1016/j.jop.2015.09.001, 2015. a, b
Brunetti, M., Kasparian, J., and Vérard, C.: Co-existing climate attractors in a coupled aquaplanet, Clim. Dynam., 53, 6293–6308, https://doi.org/10.1007/s00382-019-04926-7, 2019. a, b
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., and Saba, V.: Observed fingerprint of a weakening Atlantic Ocean overturning circulation, Nature, 556, 191–196, https://doi.org/10.1038/s41586-018-0006-5, 2018. a
Carissimo, B. C., Oort, A. H., and Haar, T. H. V.: Estimating the meridional energy transports in the atmosphere and ocean, J. Phys. Oceanogr., 15, 82–91, https://doi.org/10.1175/1520-0485(1985)015<0082:ETMETI>2.0.CO;2, 1985. a
Claussen, M.: On coupling global biome models with climate models, Clim. Res., 4, 203–221, 1994. a
Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M.-F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R., Ganopolski, A., Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I. I., Petoukhov, V., Stone, P., and Wang, Z.: Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models, Clim. Dynam., 18, 579–586, 2002. a
Copernicus Climate Change Service: ORAS5 global ocean reanalysis monthlydata from 1958 to present, Copernicus Climate Change Service [data set], https://doi.org/10.24381/cds.67e8eeb7, 2021. a
de Noblet-Ducoudré, N., Claussen, M., and Prentice, C.: Mid-Holocene greening of the Sahara: first results of the GAIM 6000year BP Experiment with two asynchronously coupled atmosphere/biome models, Clim. Dynam., 16, 643–659, https://doi.org/10.1007/s003820000074, 2000. a
Dinh, T. L. A., Goll, D., Ciais, P., and Lauerwald, R.: Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model, Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, 2024. a
Drótos, G., Bódai, T., and Tél, T.: On the importance of the convergence to climate attractors, Eur. Phys. J.-Spec. Top., 226, 2031–2038, https://doi.org/10.1140/epjst/e2017-70045-7, 2017. a
Drüke, M., von Bloh, W., Petri, S., Sakschewski, B., Schaphoff, S., Forkel, M., Huiskamp, W., Feulner, G., and Thonicke, K.: CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model, Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, 2021. a
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015. a
ETOPO: 15 Arc-Second Global Relief Model, ETOPO [data set], https://doi.org/10.25921/fd45-gt74, 2022. a
ETOPO2: 2-minute Gridded Global Relief Data (ETOPO2) v2, ETOPO2 [data set], https://doi.org/10.7289/V5J1012Q, 2006. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Farinotti, D., Huss, M., Fürst, J. J., Landmann, J., Machguth, H., Maussion, F., and Pandit, A.: A consensus estimate for the ice thickness distribution of all glaciers on Earth, Nat. Geosci., 12, 168–173, https://doi.org/10.1038/s41561-019-0300-3, 2019. a
Ferreira, D., Marshall, J., and Rose, B.: Climate determinism revisited: multiple equilibria in a complex climate model, J. Climate, 24, 992–1012, https://doi.org/10.1175/2010JCLI3580.1, 2011. a
Ferreira, D., Marshall, J., Ito, T., and McGee, D.: Linking glacial-interglacial states to multiple equilibria of climate, Geophys. Res. Lett., 45, 9160–9170, https://doi.org/10.1029/2018GL077019, 2018. a
Fetterer, F., Knowles, K., Meier, W., and Savoie, M.: Sea Ice Index, updated daily [Indicate subset used],NSIDC [data set], https://doi.org/10.7265/N5QJ7F7W, 2002. a
Gent, P. R. and McWilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–160, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Goldberg, D. N. and Heimbach, P.: Parameter and state estimation with a time-dependent adjoint marine ice sheet model, The Cryosphere, 7, 1659–1678, https://doi.org/10.5194/tc-7-1659-2013, 2013. a
Greve, R. and Blatter, H.: Glacial Isostasy, Springer Berlin Heidelberg, Berlin, Heidelberg, ISBN 978-3-642-03415-2, https://doi.org/10.1007/978-3-642-03415-2_8, 185–201, 2009. a, b, c
Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy, R., and Travis, L.: Efficient three-dimensional global models for climate studies: models I and II, Mon. Weather Rev., 111, 609–662, https://doi.org/10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2, 1983. a
Haq, B. U.: Triassic eustatic variations reexamined, GSA Today, 28, 4–9, https://doi.org/10.1130/GSATG381A.1, 2018. a
Hawkins, E., Smith, R. S., Allison, L. C., Gregory, J. M., Woollings, T. J., Pohlmann, H., and de Cuevas, B.: Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport, Geophys. Res. Lett., 38, https://doi.org/10.1029/2011GL047208, 2011. a
Haxeltine, A. and Prentice, I. C.: BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochem. Cy., 10, 693–709, https://doi.org/10.1029/96GB02344, 1996. a, b
Haywood, A. M., Valdes, P. J., Sellwood, B. W., and Kaplan, J. O.: Antarcticclimate during the middle Pliocene: model sensitivity to ice sheet variation, Palaeogeogr. Palaeocl., 182, 93–115, https://doi.org/10.1016/S0031-0182(01)00454-0, 2002. a
Haywood, A. M., Dowsett, H. J., Otto-Bliesner, B., Chandler, M. A., Dolan, A. M., Hill, D. J., Lunt, D. J., Robinson, M. M., Rosenbloom, N., Salzmann, U., and Sohl, L. E.: Pliocene Model Intercomparison Project (PlioMIP): experimental design and boundary conditions (Experiment 1), Geosci. Model Dev., 3, 227–242, https://doi.org/10.5194/gmd-3-227-2010, 2010. a, b
Herrington, A. R. and Poulsen, C. J.: Terminating the last interglacial: the role of ice sheet–climate feedbacks in a GCM asynchronously coupled to an ice sheet model, J. Climate, 25, 1871–1882, https://doi.org/10.1175/JCLI-D-11-00218.1, 2011. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Climate Data Store [data set], https://doi.org/10.24381/cds.f17050d7, 2023. a
Hock, R. and Holmgren, B.: A distributed surface energy-balance model for complex topography and its application to Storglaciären, Sweden, J. Glaciol., 51, 25–36, https://doi.org/10.3189/172756505781829566, 2005. a
Hoffman, P. F. and Schrag, D. P.: The snowball Earth hypothesis: testing the limits of global change, Terra Nova, 14, 129–155, https://doi.org/10.1046/j.1365-3121.2002.00408.x, 2002. a
Holden, P. B., Edwards, N. R., Fraedrich, K., Kirk, E., Lunkeit, F., and Zhu, X.: PLASIM–GENIE v1.0: a new intermediate complexity AOGCM, Geosci. Model Dev., 9, 3347–3361, https://doi.org/10.5194/gmd-9-3347-2016, 2016. a
Hou, Y., Guo, H., Yang, Y., and Liu, W.: Global evaluation of runoff simulation from climate, hydrological and land surface models, Water Resour. Res., 59, e2021WR031817, https://doi.org/10.1029/2021WR031817, 2023. a
Kaplan, J. O., Bigelow, N. H., Prentice, I. C., Harrison, S. P., Bartlein, P. J., Christensen, T. R., Cramer, W., Matveyeva, N. V., McGuire, A. D., Murray, D. F., Razzhivin, V. Y., Smith, B., Walker, D. A., Anderson, P. M., Andreev, A. A., Brubaker, L. B., Edwards, M. E., and Lozhkin, A. V.: Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD002559, 2003. a, b, c
Kirschvink, J. L.: Late proterozoic low-latitude global glaciation: the snowball Earth, in: The Proterozoic Biosphere: A Multidisciplinary Study, edited by: Schopf, J. W. and Klein, C., Cambridge University Press, Cambridge, UK, 51–52, ISBN 9780521366151, 1992. a
Kucharski, F., Molteni, F., King, M. P., Farneti, R., Kang, I.-S., and Feudale, L.: On the need of intermediate complexity general circulation models: a “SPEEDY” example, B. Am. Meteorol. Soc., 94, 25–30, https://doi.org/10.1175/BAMS-D-11-00238.1, 2013. a, b
Lan, X., Tans, P., and Thoning, K. W.: Trends in globally-averaged CO2 determined from NOAA Global Monitoring Laboratory measurements, version Monday, 5-May-2025 16:38:58 MDT, NOAA [data set], https://doi.org/10.15138/9N0H-ZH07, 2025. a
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, Technical note, National Center for Atmospheric Research (NCAR), Boulder, CO, https://doi.org/10.5065/D6KK98Q6, 2004. a
Lembo, V., Lunkeit, F., and Lucarini, V.: TheDiaTo (v1.0) – a new diagnostic tool for water, energy and entropy budgets in climate models, Geosci. Model Dev., 12, 3805–3834, https://doi.org/10.5194/gmd-12-3805-2019, 2019. a
Levitus, S.: Climatological Atlas of the World Ocean, NOAA Professional Paper 13, US Government Printing Office, Washington, D.C., https://books.google.ch/books?id=_x0IAQAAIAAJ (last access: 19 May 2026), 1982. a
Lucarini, V. and Bódai, T.: Edge states in the climate system: exploring global instabilities and critical transitions, Nonlinearity, 30, R32–R66, https://doi.org/10.1088/1361-6544/aa6b11, 2017. a
MacAyeal, D.: EISMINT: Lessons in ice-sheet modeling, http://yokochi.uchicago.edu/pdfs/macayeal/lessons.pdf (last access: 19 May 2026), 1997. a
MacAyeal, D. R.: Binge/purge oscillations of the Laurentide Ice Sheet as a cause of the North Atlantic's Heinrich events, Paleoceanography, 8, 775–784, https://doi.org/10.1029/93PA02200, 1993. a
Madsen, M. S., Yang, S., Aðalgeirsdóttir, G., Svendsen, S. H., Rodehacke, C. B., and Ringgaard, I. M.: The role of an interactive Greenland ice sheet in the coupled climate-ice sheet model EC-Earth-PISM, Clim. Dynam., 59, 1189–1211, https://doi.org/10.1007/s00382-022-06184-6, 2022. a
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers, J. Geophys. Res.-Oceans, 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997a. a
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modeling, J. Geophys. Res.-Oceans, 102, 5733–5752, https://doi.org/10.1029/96JC02776, 1997b. a
Marshall, J., Adcroft, A., Campin, J.-M., and Hill, C.: Atmosphere–ocean modeling exploiting fluid isomorphisms, Mon. Weather Rev., 132, 2882–2894, https://doi.org/10.1175/MWR2835.1, 2004. a
Marshall, J., Ferreira, D., Campin, J.-M., and Enderton, D.: Mean climate and variability of the atmosphere and ocean on an aquaplanet, J. Atmos. Sci., 64, 4270–4286, https://doi.org/10.1175/2007JAS2226.1, 2007. a
Marshall, J., Donohoe, A., Ferreira, D., and McGee, D.: The ocean's role in setting the mean position of the Inter-Tropical Convergence Zone, Clim. Dynam., 42, 1967–1979, https://doi.org/10.1007/s00382-013-1767-z, 2014. a, b, c
Mehling, O., Vanderborght, E., and Dijkstra, H. A.: Critical freshwater forcing for AMOC tipping in climate models – compensation matters, Earth Syst. Dynam., 17, 563–579, https://doi.org/10.5194/esd-17-563-2026, 2026. a
Merdith, A. S., Williams, S. E., Collins, A. S., Tetley, M. G., Mulder, J. A., Blades, M. L., Young, A., Armistead, S. E., Cannon, J., Zahirovic, S., and Müller, R. D.: Extending full-plate tectonic models into deep time: linking the Neoproterozoic and the Phanerozoic, Earth-Sci. Rev., 214, 103477, https://doi.org/10.1016/j.earscirev.2020.103477, 2021. a, b
Moat, B. I., Smeed, D. A., Rayner, D., Johns, W. E., Smith, R., Volkov, D., Elipot, S., Petit, T., Kajtar, J., Baringer, M. O., and Collins, J.: Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2023 (v2023.1a), NERC EDS British Oceanographic Data Centre NOC [data set], https://doi.org/10.5285/33826d6e-801c-b0a7-e063-7086abc0b9db, 2025. a
Moinat, L., Kasparian, J., and Brunetti, M.: Tipping detection using climate networks, Chaos: An Interdisciplinary Journal of Nonlinear Science, 34, 123161, https://doi.org/10.1063/5.0230848, 2024. a, b
Moinat, L., Franziskakis, F., Vérard, C., Goldberg, D. N., and Brunetti, M.: Repository for: biogeodyn-MITgcmIS (v1): a biogeodynamical tool for exploratory climate modelling], Zenodo [code and data set], https://doi.org/10.5281/zenodo.18723952, 2025. a, b, c
Molteni, F.: Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments, Clim. Dynam., 20, 175–191, https://doi.org/10.1007/s00382-002-0268-2, 2003. a, b, c
Moreno-Parada, D., Alvarez-Solas, J., Blasco, J., Montoya, M., and Robinson, A.: Simulating the Laurentide Ice Sheet of the Last Glacial Maximum, The Cryosphere, 17, 2139–2156, https://doi.org/10.5194/tc-17-2139-2023, 2023. a
Morlighem, M.: MEaSUREs BedMachine Antarctica, Version 3, NSIDC [data set], https://doi.org/10.5067/FPSU0V1MWUB6, 2022. a, b, c, d
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), NSIDC [data set], https://doi.org/10.5067/GMEVBWFLWA7X, 2022. a, b, c, d
Mottram, R., Hansen, N., Kittel, C., van Wessem, J. M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates, The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, 2021. a
Muntjewerf, L., Petrini, M., Vizcaino, M., Ernani da Silva, C., Sellevold, R., Scherrenberg, M. D. W., Thayer-Calder, K., Bradley, S. L., Lenaerts, J. T. M., Lipscomb, W. H., and Lofverstrom, M.: Greenland ice sheet contribution to 21st century sea level rise as simulated by the coupled CESM2.1-CISM2.1, Geophys. Res. Lett., 47, e2019GL086836, https://doi.org/10.1029/2019GL086836, 2020. a
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020. a
Norby, R. J., DeLucia, E. H., Gielen, B., Calfapietra, C., Giardina, C. P., King, J. S., Ledford, J., McCarthy, H. R., Moore, D. J. P., Ceulemans, R., Angelis, P. D., Finzi, A. C., Karnosky, D. F., Kubiske, M. E., Lukac, M., Pregitzer, K. S., Scarascia-Mugnozza, G. E., Schlesinger, W. H., and Oren, R.: Forest response to elevated CO2 is conserved across a broad range of productivity, P. Natl. Acad. Sci. USA, 102, 18052–18056, https://doi.org/10.1073/pnas.0509478102, 2005. a
Paxman, G. J. G., Austermann, J., and Hollyday, A.: Total isostatic response to the complete unloading of the Greenland and Antarctic Ice Sheets, Sci. Rep.-UK, 12, 11399, https://doi.org/10.1038/s41598-022-15440-y, 2022a. a
Paxman, P., Austermann, J., and Hollyday, A.: Grid files of the total isostatic response to the complete unloading of the Greenland and Antarctic Ice Sheets (version 1), Arctic Data Center [data set], https://doi.org/10.18739/A2280509Z, 2022b. a
Plach, A., Nisancioglu, K. H., Le clec'h, S., Born, A., Langebroek, P. M., Guo, C., Imhof, M., and Stocker, T. F.: Eemian Greenland SMB strongly sensitive to model choice, Clim. Past, 14, 1463–1485, https://doi.org/10.5194/cp-14-1463-2018, 2018. a
Pohl, A., Donnadieu, Y., Le Hir, G., Ladant, J.-B., Dumas, C., Alvarez-Solas, J., and Vandenbroucke, T. R. A.: Glacial onset predated Late Ordovician climate cooling, Paleoceanography, 31, 800–821, https://doi.org/10.1002/2016PA002928, 2016. a
Prentice, C., Guiot, J., Huntley, B., Jolly, D., and Cheddadi, R.: Reconstructing biomes from palaeoecological data: a general method and its application to European pollen data at 0 and 6 ka, Clim. Dynam., 12, 185–194, https://doi.org/10.1007/BF00211617, 1996. a
Quiquet, A., Dumas, C., Ritz, C., Peyaud, V., and Roche, D. M.: The GRISLI ice sheet model (version 2.0): calibration and validation for multi-millennial changes of the Antarctic ice sheet, Geosci. Model Dev., 11, 5003–5025, https://doi.org/10.5194/gmd-11-5003-2018, 2018. a
Ragon, C., Lembo, V., Lucarini, V., Vérard, C., Kasparian, J., and Brunetti, M.: Robustness of competing climatic states, J. Climate, 35, 2769–2784, https://doi.org/10.1175/JCLI-D-21-0148.1, 2022. a, b
Ragon, C., Vérard, C., Kasparian, J., Nowak, H., Kustatscher, E., and Brunetti, M.: Comparison between plant fossil assemblages and simulated biomes across the Permian-Triassic Boundary, Front. Earth Sci., 13, https://doi.org/10.3389/feart.2025.1520846, 2025. a, b, c, d
Ragon, C. N.: Modelling the Permian-Triassic climate changes: new insights from the multistability framework, PhD thesis, University of Geneva, https://doi.org/10.13097/archive-ouverte/unige:178745, 2024. a, b, c
Rose, B. E.: Stable “Waterbelt” climates controlled by tropical ocean heat transport: a nonlinear coupled climate mechanism of relevance to snowball Earth, J. Geophys. Res.-Atmos., 120, 1404–1423, https://doi.org/10.1002/2014jd022659, 2015. a
Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the Alfred Wegener Institute (AWI) contribution to ISMIP6 Greenland using the Ice-sheet and Sea-level System Model (ISSM), The Cryosphere, 14, 3309–3327, https://doi.org/10.5194/tc-14-3309-2020, 2020. a
Ruggieri, P., Abid, M. A., García-Serrano, J., Grancini, C., Kucharski, F., Pascale, S., and Volpi, D.: SPEEDY-NEMO: performance and applications of a fully-coupled intermediate-complexity climate model, Clim. Dynam., 62, 3763–3781, https://doi.org/10.1007/s00382-023-07097-8, 2024. a, b, c, d
Salzmann, U., Haywood, A. M., Lunt, D. J., Valdes, P. J., and Hill, D. J.: A new global biome reconstruction and data-model comparison for the Middle Pliocene, Global Ecol. Biogeogr. 17, 432–447, https://doi.org/10.1111/j.1466-8238.2008.00381.x, 2008. a
Schoof, C. and Hewitt, I.: Ice-sheet dynamics, Annu. Rev. Fluid Mech., 45, 217–239, https://doi.org/10.1146/annurev-fluid-011212-140632, 2013. a
Scotese, C. R.: An atlas of phanerozoic paleogeographic maps: the seas come in and the seas go out, Annu. Rev. Earth Pl. Sc., 49, 679–728, https://doi.org/10.1146/annurev-earth-081320-064052, 2021. a, b
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a
Sellwood, B. W. and Valdes, P. J.: Jurassic climates, P. Geologist. Assoc., 119, 5–17, https://doi.org/10.1016/S0016-7878(59)80068-7, 2008. a
Simmons, M., Miller, K., Ray, D., Davies, A., van Buchem, F., and Gréselle, B.: Phanerozoic eustasy, in: The Geologic Time Scale 2020, edited by: Gradstein, F. M., Ogg, J. G., Schmitz, M. D., and Ogg, G. M., Chap. 13, Elsevier, https://doi.org/10.1016/B978-0-12-824360-2.00013-9, 357–400, 2020. a
Smith, R. S., Mathiot, P., Siahaan, A., Lee, V., Cornford, S. L., Gregory, J. M., Payne, A. J., Jenkins, A., Holland, P. R., Ridley, J. K., and Jones, C. G.: Coupling the U.K. Earth system model to dynamic models of the Greenland and Antarctic Ice Sheets, J. Adv. Model. Earth Sy., 13, e2021MS002520, https://doi.org/10.1029/2021MS002520, 2021. a
Sommers, A. N., Otto-Bliesner, B. L., Lipscomb, W. H., Lofverstrom, M., Shafer, S. L., Bartlein, P. J., Brady, E. C., Kluzek, E., Leguy, G., Thayer-Calder, K., and Tomas, R. A.: Retreat and regrowth of the Greenland Ice Sheet during the last interglacial as simulated by the CESM2-CISM2 coupled climate–ice sheet model, Paleoceanography and Paleoclimatology, 36, e2021PA004272, https://doi.org/10.1029/2021PA004272, 2021. a
Valdes, P. J., Armstrong, E., Badger, M. P. S., Bradshaw, C. D., Bragg, F., Crucifix, M., Davies-Barnard, T., Day, J. J., Farnsworth, A., Gordon, C., Hopcroft, P. O., Kennedy, A. T., Lord, N. S., Lunt, D. J., Marzocchi, A., Parry, L. M., Pope, V., Roberts, W. H. G., Stone, E. J., Tourte, G. J. L., and Williams, J. H. T.: The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0, Geosci. Model Dev., 10, 3715–3743, https://doi.org/10.5194/gmd-10-3715-2017, 2017. a
Vérard, C.: Panalesis: towards global synthetic palaeogeographies using integration and coupling of manifold models, Geol. Mag., 156, 320–330, https://doi.org/10.1017/S0016756817001042, 2019. a, b, c
Wake, L. and Marshall, S.: Assessment of current methods of positive degree-day calculation using in situ observations from glaciated regions, J. Glaciol., 61, 329–344, https://doi.org/10.3189/2015JoG14J116, 2015. a
Willeit, M., Ganopolski, A., Robinson, A., and Edwards, N. R.: The Earth system model CLIMBER-X v1.0 – Part 1: Climate model description and validation , Geosci. Model Dev., 15, 5905–5948, https://doi.org/10.5194/gmd-15-5905-2022, 2022. a, b
Wiltshire, A. J., Duran Rojas, M. C., Edwards, J. M., Gedney, N., Harper, A. B., Hartley, A. J., Hendry, M. A., Robertson, E., and Smout-Day, K.: JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2, Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, 2020. a
Winton, M.: A reformulated three-layer sea ice model, J. Atmos. Ocean. Tech., 17, 525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2, 2000. a
Wunderling, N., von der Heydt, A. S., Aksenov, Y., Barker, S., Bastiaansen, R., Brovkin, V., Brunetti, M., Couplet, V., Kleinen, T., Lear, C. H., Lohmann, J., Roman-Cuesta, R. M., Sinet, S., Swingedouw, D., Winkelmann, R., Anand, P., Barichivich, J., Bathiany, S., Baudena, M., Bruun, J. T., Chiessi, C. M., Coxall, H. K., Docquier, D., Donges, J. F., Falkena, S. K. J., Klose, A. K., Obura, D., Rocha, J., Rynders, S., Steinert, N. J., and Willeit, M.: Climate tipping point interactions and cascades: a review, Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, 2024. a
Xie, Z., Dommenget, D., McCormack, F. S., and Mackintosh, A. N.: GREB-ISM v1.0: A coupled ice sheet model for the Globally Resolved Energy Balance model for global simulations on timescales of 100 kyr, Geosci. Model Dev., 15, 3691–3719, https://doi.org/10.5194/gmd-15-3691-2022, 2022. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhu, F. and Rose, B. E. J.: Multiple equilibria in a coupled climate–carbon model, J. Climate, 36, 547–564, https://doi.org/10.1175/JCLI-D-21-0984.1, 2023. a
Short summary
We describe a new tool, BIG-MITgcm, that consistently reproduces the global-scale dynamics of the ocean, atmosphere, vegetation and ice on multimillennial timescales at a low computational cost. Evaluated against observations and state-of-the-art Earth system models, it includes asynchronous coupling to models of vegetation, hydrology and a newly developed global-scale ice sheet. Using arbitrary continental configurations, it enables studies of past and present climates on Earth or exoplanets.
We describe a new tool, BIG-MITgcm, that consistently reproduces the global-scale dynamics of...
