Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-715-2021
© Author(s) 2021. 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-15-715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity of ice sheet surface velocity and elevation to variations in basal friction and topography in the full Stokes and shallow-shelf approximation frameworks using adjoint equations
Department of Information Technology, Uppsala University, P.O. Box 337, 751 05 Uppsala, Sweden
Nina Kirchner
Department of Physical Geography, Stockholm University, 106 91 Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
Per Lötstedt
Department of Information Technology, Uppsala University, P.O. Box 337, 751 05 Uppsala, Sweden
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE (Physics-Informed Neural Networks for Ice and CLimatE), an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
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The study simulates the 21st-century evolution of Great Aletsch Glacier and Hintereisferner using full-Stokes ice dynamics and surface mass balance under different emission scenarios. Results show significant ice loss, with Hintereisferner expected to disappear by mid-century. Great Aletsch Glacier vanish by the end of the century under high-emission scenarios, but persist under lower-emission scenarios. These trends agree with large-scale models except some variability.
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Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
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The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and be useful for GIS ice sheet modeling.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
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We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Joel A. Wilner, Mathieu Morlighem, and Gong Cheng
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We use numerical modeling to study iceberg calving off of ice shelves in Antarctica. We examine four widely used mathematical descriptions of calving (
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE (Physics-Informed Neural Networks for Ice and CLimatE), an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
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The study simulates the 21st-century evolution of Great Aletsch Glacier and Hintereisferner using full-Stokes ice dynamics and surface mass balance under different emission scenarios. Results show significant ice loss, with Hintereisferner expected to disappear by mid-century. Great Aletsch Glacier vanish by the end of the century under high-emission scenarios, but persist under lower-emission scenarios. These trends agree with large-scale models except some variability.
Felicity A. Holmes, Jamie Barnett, Henning Åkesson, Mathieu Morlighem, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
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Northern Greenland contains some of the ice sheet's last remaining glaciers with floating ice tongues. One of these is Ryder Glacier, which has been relatively stable in recent decades, in contrast to nearby glaciers. Here, we use a computer model to simulate Ryder Glacier until 2300 under both a low- and a high-emissions scenario. Very high levels of surface melt under a high-emissions future lead to a sea level rise contribution that is an order of magnitude higher than under a low-emissions future.
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Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, Alicia Bråtner, and Javed Hassan
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Jamie Barnett, Felicity Alice Holmes, Joshua Cuzzone, Henning Åkesson, Mathieu Morlighem, Matt O'Regan, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
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Understanding how ice sheets have changed in the past can allow us to make better predictions for the future. By running a state-of-the-art model of Ryder Glacier, North Greenland, over the past 12,000 years we find that both a warming atmosphere and ocean play a key role in the evolution of the Glacier. Our conclusions stress that accurately quantifying the ice sheet’s interactions with the ocean are required to predict future changes and reliable sea level rise estimates.
Adrian Dye, Robert Bryant, Francesca Falcini, Joseph Mallalieu, Miles Dimbleby, Michael Beckwith, David Rippin, and Nina Kirchner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2510, https://doi.org/10.5194/egusphere-2024-2510, 2024
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Thermal undercutting of the terminus has driven recent rapid retreat of an Arctic glacier. Water temperatures (~4 °C) at the ice front were warmer than previously assumed and thermal undercutting was over several metres deep. This triggered phases of high calving activity, playing a substantial role in the rapid retreat of Kaskasapakte glacier since 2012, with important implications for processes occurring at glacier-water contact points and implications for hydrology and ecology downstream.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
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We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Abhay Prakash, Qin Zhou, Tore Hattermann, and Nina Kirchner
The Cryosphere, 17, 5255–5281, https://doi.org/10.5194/tc-17-5255-2023, https://doi.org/10.5194/tc-17-5255-2023, 2023
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Sea ice arch formation in the Nares Strait has shielded the Petermann Glacier ice shelf from enhanced basal melting. However, with the sustained decline of the Arctic sea ice predicted to continue, the ice shelf is likely to be exposed to a year-round mobile and thin sea ice cover. In such a scenario, our modelled results show that elevated temperatures, and more importantly, a stronger ocean circulation in the ice shelf cavity, could result in up to two-thirds increase in basal melt.
Joel A. Wilner, Mathieu Morlighem, and Gong Cheng
The Cryosphere, 17, 4889–4901, https://doi.org/10.5194/tc-17-4889-2023, https://doi.org/10.5194/tc-17-4889-2023, 2023
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We use numerical modeling to study iceberg calving off of ice shelves in Antarctica. We examine four widely used mathematical descriptions of calving (
calving laws), under the assumption that Antarctic ice shelf front positions should be in steady state under the current climate forcing. We quantify how well each of these calving laws replicates the observed front positions. Our results suggest that the eigencalving and von Mises laws are most suitable for Antarctic ice shelves.
Felicity A. Holmes, Eef van Dongen, Riko Noormets, Michał Pętlicki, and Nina Kirchner
The Cryosphere, 17, 1853–1872, https://doi.org/10.5194/tc-17-1853-2023, https://doi.org/10.5194/tc-17-1853-2023, 2023
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Glaciers which end in bodies of water can lose mass through melting below the waterline, as well as by the breaking off of icebergs. We use a numerical model to simulate the breaking off of icebergs at Kronebreen, a glacier in Svalbard, and find that both melting below the waterline and tides are important for iceberg production. In addition, we compare the modelled glacier front to observations and show that melting below the waterline can lead to undercuts of up to around 25 m.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, https://doi.org/10.5194/esd-13-457-2022, 2022
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Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge about the effects of global warming on past and future changes in the climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere.
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Short summary
We present an inverse modeling approach to improve the understanding of spatiotemporally variable processes at the inaccessible base of an ice sheet by determining the sensitivity of direct surface observations to perturbations of basal conditions. Time dependency is proved to be important in these types of problems. The effect of perturbations is analyzed based on analytical and numerical solutions.
We present an inverse modeling approach to improve the understanding of spatiotemporally...