Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2613-2024
© Author(s) 2024. 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-18-2613-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Biases in ice sheet models from missing noise-induced drift
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
Vincent Verjans
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
Center for Climate Physics, Institute for Basic Science, Busan, Republic of Korea
Aminat A. Ambelorun
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
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In this work, we simulate estuary-like seawater intrusions into the subglacial hydrologic system for marine outlet glaciers. We find the largest controls on seawater intrusion are the subglacial space geometry and meltwater discharge velocity. Further, we highlight the importance of extending ocean-forced ice loss to grounded portions of the ice sheet, which is currently not represented in models coupling ice sheets to ocean dynamics.
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We predicted how sea level changed in the Ross Sea (Antarctica) due to glacial isostatic adjustment, or solid Earth ice sheet interactions, over the last deglaciation (20 000 years ago to present) and calculated how these changes in bathymetry impacted ice stream stability. Glacial isostatic adjustment shifts stability from where ice reached its maximum 20 000 years ago, at the continental shelf edge, to the modern grounding line today, reinforcing ice-age climate endmembers.
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The rate of ice loss from ice sheets is controlled by the flow of ice from the center of the ice sheet and by the internal fracturing of the ice. These processes are coupled; fractures reduce the viscosity of ice and enable more rapid flow, and rapid flow causes the fracturing of ice. We present a simplified way of representing damage that is applicable to long-timescale flow estimates. Using this model, we find that including fracturing in an ice sheet simulation can increase the loss of ice by 13–29 %.
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
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Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
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Marine-terminating glaciers have recently retreated dramatically, but the role of anthropogenic forcing remains uncertain. We use idealized model simulations to develop a framework for assessing the probability of rapid retreat in the context of natural climate variability. Our analyses show that century-scale anthropogenic trends can substantially increase the probability of retreats. This provides a roadmap for future work to formally assess the role of human activity in recent glacier change.
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Warm seawater may intrude as a thin layer below glaciers in contact with the ocean. Mathematical theory predicts that this intrusion may extend over distances of kilometers under realistic conditions. Computer models demonstrate that if this warm seawater causes melting of a glacier bottom, it can cause rates of glacier ice loss and sea level rise to be up to 2 times faster in response to potential future ocean warming.
Madeline S. Mamer, Alexander A. Robel, Chris C. K. Lai, Earle Wilson, and Peter Washam
The Cryosphere, 19, 3227–3251, https://doi.org/10.5194/tc-19-3227-2025, https://doi.org/10.5194/tc-19-3227-2025, 2025
Short summary
Short summary
In this work, we simulate estuary-like seawater intrusions into the subglacial hydrologic system for marine outlet glaciers. We find the largest controls on seawater intrusion are the subglacial space geometry and meltwater discharge velocity. Further, we highlight the importance of extending ocean-forced ice loss to grounded portions of the ice sheet, which is currently not represented in models coupling ice sheets to ocean dynamics.
Samuel T. Kodama, Tamara Pico, Alexander A. Robel, John Erich Christian, Natalya Gomez, Casey Vigilia, Evelyn Powell, Jessica Gagliardi, Slawek Tulaczyk, and Terrence Blackburn
The Cryosphere, 19, 2935–2948, https://doi.org/10.5194/tc-19-2935-2025, https://doi.org/10.5194/tc-19-2935-2025, 2025
Short summary
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We predicted how sea level changed in the Ross Sea (Antarctica) due to glacial isostatic adjustment, or solid Earth ice sheet interactions, over the last deglaciation (20 000 years ago to present) and calculated how these changes in bathymetry impacted ice stream stability. Glacial isostatic adjustment shifts stability from where ice reached its maximum 20 000 years ago, at the continental shelf edge, to the modern grounding line today, reinforcing ice-age climate endmembers.
Paul T. Summers, Rebecca H. Jackson, and Alexander A. Robel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1555, https://doi.org/10.5194/egusphere-2025-1555, 2025
Short summary
Short summary
We develop a method that allows numerical ocean models to include drag from icebergs, even for icebergs smaller than the model grid scale. This builds upon previous models that have either neglected iceberg drag, or required higher resolution to model individual icebergs. We test our model against higher resolution models, as well as models without iceberg drag, and show that including drag from icebergs is important for capturing realistic ocean circulation, temperature, and ice melt rates.
Ziad Rashed, Alexander A. Robel, and Hélène Seroussi
The Cryosphere, 19, 1775–1788, https://doi.org/10.5194/tc-19-1775-2025, https://doi.org/10.5194/tc-19-1775-2025, 2025
Short summary
Short summary
Sermeq Kujalleq, Greenland's largest glacier, has significantly retreated since the late 1990s in response to warming ocean temperatures. Using a large-ensemble approach, our simulations show that the retreat is mainly initiated by the arrival of warm water but sustained and accelerated by the glacier's position over deeper bed troughs and vigorous calving. We highlight the need for models of ice mélange to project glacier behavior under rapid calving regimes.
Meghana Ranganathan, Alexander A. Robel, Alexander Huth, and Ravindra Duddu
The Cryosphere, 19, 1599–1619, https://doi.org/10.5194/tc-19-1599-2025, https://doi.org/10.5194/tc-19-1599-2025, 2025
Short summary
Short summary
The rate of ice loss from ice sheets is controlled by the flow of ice from the center of the ice sheet and by the internal fracturing of the ice. These processes are coupled; fractures reduce the viscosity of ice and enable more rapid flow, and rapid flow causes the fracturing of ice. We present a simplified way of representing damage that is applicable to long-timescale flow estimates. Using this model, we find that including fracturing in an ice sheet simulation can increase the loss of ice by 13–29 %.
Vincent Verjans, Alexander A. Robel, Lizz Ultee, Helene Seroussi, Andrew F. Thompson, Lars Ackerman, Youngmin Choi, and Uta Krebs-Kanzow
EGUsphere, https://doi.org/10.5194/egusphere-2024-4067, https://doi.org/10.5194/egusphere-2024-4067, 2025
Short summary
Short summary
This study examines how random variations in climate may influence future ice loss from the Greenland Ice Sheet. We find that random climate variations are important for predicting future ice loss from the entire Greenland Ice Sheet over the next 20–30 years, but relatively unimportant after that period. Thus, uncertainty in sea level projections from the effect of climate variability on Greenland may play a role in coastal decision-making about sea level rise over the next few decades.
Jason M. Amundson, Alexander A. Robel, Justin C. Burton, and Kavinda Nissanka
The Cryosphere, 19, 19–35, https://doi.org/10.5194/tc-19-19-2025, https://doi.org/10.5194/tc-19-19-2025, 2025
Short summary
Short summary
Some fjords contain dense packs of icebergs referred to as ice mélange. Ice mélange can affect the stability of marine-terminating glaciers by resisting the calving of new icebergs and by modifying fjord currents and water properties. We have developed the first numerical model of ice mélange that captures its granular nature and that is suitable for long-timescale simulations. The model is capable of explaining why some glaciers are more strongly influenced by ice mélange than others.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
Short summary
Short summary
The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
Short summary
Short summary
We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
John Erich Christian, Alexander A. Robel, and Ginny Catania
The Cryosphere, 16, 2725–2743, https://doi.org/10.5194/tc-16-2725-2022, https://doi.org/10.5194/tc-16-2725-2022, 2022
Short summary
Short summary
Marine-terminating glaciers have recently retreated dramatically, but the role of anthropogenic forcing remains uncertain. We use idealized model simulations to develop a framework for assessing the probability of rapid retreat in the context of natural climate variability. Our analyses show that century-scale anthropogenic trends can substantially increase the probability of retreats. This provides a roadmap for future work to formally assess the role of human activity in recent glacier change.
Alexander A. Robel, Earle Wilson, and Helene Seroussi
The Cryosphere, 16, 451–469, https://doi.org/10.5194/tc-16-451-2022, https://doi.org/10.5194/tc-16-451-2022, 2022
Short summary
Short summary
Warm seawater may intrude as a thin layer below glaciers in contact with the ocean. Mathematical theory predicts that this intrusion may extend over distances of kilometers under realistic conditions. Computer models demonstrate that if this warm seawater causes melting of a glacier bottom, it can cause rates of glacier ice loss and sea level rise to be up to 2 times faster in response to potential future ocean warming.
Cited articles
Berends, C. J., van de Wal, R. S. W., van den Akker, T., and Lipscomb, W. H.: Compensating errors in inversions for subglacial bed roughness: same steady state, different dynamic response, The Cryosphere, 17, 1585–1600, https://doi.org/10.5194/tc-17-1585-2023, 2023. a
Bintanja, R., van der Wiel, K., Van der Linden, E., Reusen, J., Bogerd, L., Krikken, F., and Selten, F.: Strong future increases in Arctic precipitation variability linked to poleward moisture transport, Science Advances, 6, eaax6869, https://doi.org/10.1126/sciadv.aax6869, 2020. a
Budd, W., Keage, P., and Blundy, N.: Empirical studies of ice sliding, J. Glaciol., 23, 157–170, 1979. a
Choi, Y., Seroussi, H., Morlighem, M., Schlegel, N.-J., and Gardner, A.: Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland, The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, 2023. a
Christian, J. E., Robel, A. A., and Catania, G.: A probabilistic framework for quantifying the role of anthropogenic climate change in marine-terminating glacier retreats, The Cryosphere, 16, 2725–2743, https://doi.org/10.5194/tc-16-2725-2022, 2022. a, b, c
Ettema, J., van den Broeke, M. R., van Meijgaard, E., van de Berg, W. J., Bamber, J. L., Box, J. E., and Bales, R. C.: Higher surface mass balance of the Greenland ice sheet revealed by high-resolution climate modeling, Geophys. Res. Lett., 36, L12501, https://doi.org/10.1029/2009GL038110, 2009. a
Felikson, D., Nowicki, S., Nias, I., Morlighem, M., and Seroussi, H.: Seasonal Tidewater Glacier Terminus Oscillations Bias Multi-Decadal Projections of Ice Mass Change, J. Geophys. Res.-Earth, 127, e2021JF006249, https://doi.org/10.1029/2021JF006249, 2022. a
Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, T., Berends, C. J., Born, A., Box, J. E., Delhasse, A., Fujita, K., Gierz, P., Goelzer, H., Hanna, E., Hashimoto, A., Huybrechts, P., Kapsch, M.-L., King, M. D., Kittel, C., Lang, C., Langen, P. L., Lenaerts, J. T. M., Liston, G. E., Lohmann, G., Mernild, S. H., Mikolajewicz, U., Modali, K., Mottram, R. H., Niwano, M., Noël, B., Ryan, J. C., Smith, A., Streffing, J., Tedesco, M., van de Berg, W. J., van den Broeke, M., van de Wal, R. S. W., van Kampenhout, L., Wilton, D., Wouters, B., Ziemen, F., and Zolles, T.: GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet, The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, 2020. a
Glen, J. W.: The creep of polycrystalline ice, P. R. Soc. A-Math. Phy., 228, 519–538, 1955. a
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. a, b, c
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
Hanna, E., Huybrechts, P., Cappelen, J., Steffen, K., Bales, R. C., Burgess, E., McConnell, J. R., Peder Steffensen, J., Van den Broeke, M., Wake, L., Bigg, G., Griffiths, M., and Savas, D.: Greenland Ice Sheet surface mass balance 1870 to 2010 based on Twentieth Century Reanalysis, and links with global climate forcing, J. Geophys. Res.-Atmos., 116, D24121, https://doi.org/10.1029/2011JD016387, 2011. a
Hindmarsh, R. C. and Le Meur, E.: Dynamical processes involved in the retreat of marine ice sheets, J. Glaciol., 47, 271–282, 2001. a
Kloeden, P. E. and Platen, E.: Numerical Solutions of Stochastic Differential Equations, ISBN 978-3-642-08107-1, 1995. a
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM), J. Geophys. Res.-Earth, 117, F01022, https://doi.org/10.1029/2011JF002140, 2012. a
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Ice sheet and Sea Level System Model, ISSM [data set], https://issm.ess.uci.edu/svn/issm/issm/trunk, last access: 24 May 2024.
Lauritzen, M., Aðalgeirsdóttir, Guðfinna, R. N., Grinsted, A., Noel, B., and Hvidberg, C. S.: The influence of inter-annual temperature variability on the Greenland Ice Sheet volume, Ann. Glaciol., 1–8, https://doi.org/10.1017/aog.2023.53, 2023. a, b, c, d
MacAyeal, D. R.: Large-scale ice flow over a viscous basal sediment: Theory and application to ice stream B, Antarctica, J. Geophys. Res.-Sol. Ea., 94, 4071–4087, 1989. a
Mantelli, E., Bertagni, M. B., and Ridolfi, L.: Stochastic ice stream dynamics, P. Natl. Acad. Sci. USA, 113, E4594–E4600, 2016. a
Mikkelsen, T. B., Grinsted, A., and Ditlevsen, P.: Influence of temperature fluctuations on equilibrium ice sheet volume, The Cryosphere, 12, 39–47, https://doi.org/10.5194/tc-12-39-2018, 2018. a, b, c, d
Millstein, J. D., Minchew, B. M., and Pegler, S. S.: Ice viscosity is more sensitive to stress than commonly assumed, Commun. Earth Environ., 3, 57, https://doi.org/10.1038/s43247-022-00385-x, 2022. a
Mulder, T., Baars, S., Wubs, F., and Dijkstra, H.: Stochastic marine ice sheet variability, J. Fluid Mech., 843, 748–777, 2018. a
Nguyen, A. T., Kwok, R., and Menemenlis, D.: Source and pathway of the Western Arctic upper halocline in a data-constrained coupled ocean and sea ice model, J. Phys. Oceanogr., 42, 802–823, 2012. a
Nias, I. J., Cornford, S. L., Edwards, T. L., Gourmelen, N., and Payne, A. J.: Assessing uncertainty in the dynamical ice response to ocean warming in the Amundsen Sea Embayment, West Antarctica, Geophys. Res. Lett., 46, 11253–11260, 2019. a
Nye, J. F.: The response of glaciers and ice-sheets to seasonal and climatic changes, P. R. Soc. Lond. A Mat., 256, 559–584, 1960. a
Oerlemans, J. and Van Der Veen, C. J.: Ice sheets and climate, Vol. 21, Springer, https://doi.org/10.1007/978-94-009-6325-2, 1984. a
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023. a
Penland, C.: Noise out of chaos and why it won't go away, B. Am. Meteorol. Soc., 84, 921–925, 2003. a
Robel, A. A., Pegler, S. S., Catania, G., Felikson, D., and Simkins, L. M.: Ambiguous stability of glaciers at bed peaks, J. Glaciol., 68, 1177–1184, 2022. a
Roe, G. H. and O’Neal, M. A.: The response of glaciers to intrinsic climate variability: observations and models of late-Holocene variations in the Pacific Northwest, J. Glaciol., 55, 839–854, https://doi.org/10.3189/002214309790152438, 2009. a
Sergienko, O. and Haseloff, M.: “Stable” and “unstable” are not useful descriptions of marine ice sheets in the Earth's climate system, J. Glaciol., 69, 1483–1499, https://doi.org/10.1017/jog.2023.40, 2023. a
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. a, b
Tsai, V. C., Stewart, A. L., and Thompson, A. F.: Marine ice-sheet profiles and stability under Coulomb basal conditions, J. Glaciol., 61, 205–215, 2015. a
Ultee, L., Robel, A. A., and Castruccio, S.: A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0), Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, 2024. a, b
Verjans, V.: Data for “The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)” by Verjans et al., Zenodo [data set], https://doi.org/10.5281/zenodo.7347470, 2022.
Verjans, V., Robel, A., Thompson, A. F., and Seroussi, H.: Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers, J. Adv. Model. Earth Sy., 15, e2023MS003610, https://doi.org/10.1029/2023MS003610, 2023. a, b
Wood, R. R., Lehner, F., Pendergrass, A. G., and Schlunegger, S.: Changes in precipitation variability across time scales in multiple global climate model large ensembles, Environ. Res. Lett., 16, 084022, https://doi.org/10.1088/1748-9326/ac10dd, 2021. a
Short summary
The average size of many glaciers and ice sheets changes when noise is added to the system. The reasons for this drift in glacier state is intrinsic to the dynamics of how ice flows and the bumpiness of the Earth's surface. We argue that not including noise in projections of ice sheet evolution over coming decades and centuries is a pervasive source of bias in these computer models, and so realistic variability in glacier and climate processes must be included in models.
The average size of many glaciers and ice sheets changes when noise is added to the system. The...