Abstract
The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.
Original language | English |
---|---|
Pages (from-to) | 5197-5217 |
Number of pages | 21 |
Journal | Cryosphere |
Volume | 17 |
Issue number | 12 |
DOIs | |
Publication status | Published - 7 Dec 2023 |
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In: Cryosphere, Vol. 17, No. 12, 07.12.2023, p. 5197-5217.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty
AU - Seroussi, Hélène
AU - Verjans, Vincent
AU - Nowicki, Sophie
AU - Payne, Antony J.
AU - Goelzer, Heiko
AU - Lipscomb, William H.
AU - Abe-Ouchi, Ayako
AU - Agosta, Cécile
AU - Albrecht, Torsten
AU - Asay-Davis, Xylar
AU - Barthel, Alice
AU - Calov, Reinhard
AU - Cullather, Richard
AU - Dumas, Christophe
AU - Galton-Fenzi, Benjamin K.
AU - Gladstone, Rupert
AU - Golledge, Nicholas R.
AU - Gregory, Jonathan M.
AU - Greve, Ralf
AU - Hattermann, Tore
AU - Hoffman, Matthew J.
AU - Humbert, Angelika
AU - Huybrechts, Philippe
AU - Jourdain, Nicolas C.
AU - Kleiner, Thomas
AU - Larour, Eric
AU - Leguy, Gunter R.
AU - Lowry, Daniel P.
AU - Little, Chistopher M.
AU - Morlighem, Mathieu
AU - Pattyn, Frank
AU - Pelle, Tyler
AU - Price, Stephen F.
AU - Quiquet, Aurélien
AU - Reese, Ronja
AU - Schlegel, Nicole Jeanne
AU - Shepherd, Andrew
AU - Simon, Erika
AU - Smith, Robin S.
AU - Straneo, Fiammetta
AU - Sun, Sainan
AU - Trusel, Luke D.
AU - Van Breedam, Jonas
AU - Van Katwyk, Peter
AU - van de Wal, Roderik S.W.
AU - Winkelmann, Ricarda
AU - Zhao, Chen
AU - Zhang, Tong
AU - Zwinger, Thomas
N1 - Funding information: Helene Seroussi was supported by grants from the NASA Sea Level Change Team and Cryospheric Science programs (grant nos. 80NSSC21K1939 and 80NSSC22K0383). Research was carried out by Nicole Schlegel and Eric Larour at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Sophie Nowicki was supported by NASA Sea Level Change Team and Cryospheric Science programs (grant nos. 80NSSC21K0915 and 80NSSC21K0322). Support for Xylar Asay-Davis, Alice Barthel, Matthew Hoffman, and Stephen Price was provided by the Scientific Discovery Through Advanced Computing and Earth System Model Development programs, funded by the U.S. Department of Energy, Office of Science. MALI simulations were performed on machines at the National Energy Research Scientific Computing Center, a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under contract no. DE-AC02-05CH11231. Rupert Gladstone and Thomas Zwinger were supported by Academy of Finland (grant nos. 322430 and 286587) and wish to acknowledge CSC – IT Center for Science, Finland, for computational resources. Chen Zhao and Ben Galton-Fenzi received grant funding from the Australian Government as part of the Antarctic Science Collaboration Initiative program (ASCI000002; Australian Antarctic Program Partnership). Ralf Greve was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI (grant nos. JP16H02224, JP17H06104, and JP17H06323). Gunter Leguy and William Lipscomb were supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977. Computing and data storage resources for CISM simulations, including the Cheyenne supercomputer ( 10.5065/D6RX99HX ), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. The work of Thomas Kleiner has been conducted in the framework of the PalMod project (FKZ: 01LP1511B), supported by the German Federal Ministry of Education and Research (BMBF) as part of the Research for Sustainability initiative (FONA). Torsten Albrecht and Ricarda Winkelmann were supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority program “Antarctic Research with comparative investigations in Arctic ice areas” by grant nos. WI4556/2-1 and WI4556/4-1 and within the framework of the PalMod project (FKZ: 01LP1925D) supported by the German Federal Ministry of Education and Research (BMBF) as a Research for Sustainability initiative (FONA). Ronja Reese was supported by the Deutsche Forschungsgemeinschaft (DFG) by grant no. WI4556/3-1 and through the TiPACCs project that received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 820575. Development of PISM is supported by NASA grant nos. 20-CRYO2020-0052 and 80NSSC22K0274 and NSF grant no. OAC-2118285. Heiko Goelzer received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 869304 and used resources provided by Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway through projects NS5011K, NN8085K, and NS8085K. Nicolas Jourdain is supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 101003536 (ESM2025). Peter Van Katwyk was supported by the National Science Foundation Graduate Research Fellowship Program under grant no. 2040433. Aurélien Quiquet was funded by the project EIS ANR-19-CE1-0015 of the Agence Nationale de la Recherche. Jonas Van Breedam and Philippe Huybrechts acknowledge support from project G091820N, funded by the Research Foundation Flanders (FWO Vlaanderen). Nicholas Golledge and Daniel Lowry were supported by the New Zealand Ministry for Business, Innovation and Employment contract nos. RTVU2206 (“Our Changing Coast”) and ANTA1801 (“Antarctic Science Platform”). Tore Hatterman was supported by the Research Council of Norway, project no. 332635. Funding Information: Financial support. Helene Seroussi was supported by grants from the NASA Sea Level Change Team and Cryospheric Science programs (grant nos. 80NSSC21K1939 and 80NSSC22K0383). Research was carried out by Nicole Schlegel and Eric Larour at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Sophie Nowicki was supported by NASA Sea Level Change Team and Cryospheric Science programs (grant nos. 80NSSC21K0915 and 80NSSC21K0322). Support for Xylar Asay-Davis, Alice Barthel, Matthew Hoffman, and Stephen Price was provided by the Scientific Discovery Through Advanced Computing and Earth System Model Development programs, funded by the U.S. Department of Energy, Office of Science. MALI simulations were performed on machines at the National Energy Research Scientific Computing Center, a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under contract no. DE-AC02-05CH11231. Rupert Gladstone and Thomas Zwinger were supported by Academy of Finland (grant nos. 322430 and 286587) and wish to acknowledge CSC – IT Center for Science, Finland, for computational resources. Chen Zhao and Ben Galton-Fenzi received grant funding from the Australian Government as part of the Antarctic Science Collaboration Initiative program (ASCI000002; Australian Antarctic Program Partnership). Ralf Greve was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI (grant nos. JP16H02224, JP17H06104, and JP17H06323). Gunter Leguy and William Lipscomb were supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977. Computing and data storage resources for CISM simulations, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. The work of Thomas Kleiner has been conducted in the framework of the PalMod project (FKZ: 01LP1511B), supported by the German Federal Ministry of Education and Research (BMBF) as part of the Research for Sustainability initiative (FONA). Torsten Albrecht and Ricarda Winkelmann were supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority program “Antarctic Research with comparative investigations in Arctic ice areas” by grant nos. WI4556/2-1 and WI4556/4-1 and within the framework of the PalMod project (FKZ: 01LP1925D) supported by the German Federal Ministry of Education and Research (BMBF) as a Research for Sustainability initiative (FONA). Ronja Reese was supported by the Deutsche Forschungsgemeinschaft (DFG) by grant no. WI4556/3-1 and through the TiPACCs project that received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 820575. Development of PISM is supported by NASA grant nos. 20-CRYO2020-0052 and 80NSSC22K0274 and NSF grant no. OAC-2118285. Heiko Goelzer received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 869304 and used resources provided by Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway through projects NS5011K, NN8085K, and NS8085K. Nicolas Jourdain is supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 101003536 (ESM2025). Peter Van Katwyk was supported by the National Science Foundation Graduate Research Fellowship Program under grant no. 2040433. Aurélien Quiquet was funded by the project EIS ANR-19-CE1-0015 of the Agence Nationale de la Recherche. Jonas Van Breedam and Philippe Huybrechts acknowledge support from project G091820N, funded by the Research Foundation Flanders (FWO Vlaanderen). Nicholas Golledge and Daniel Lowry were supported by the New Zealand Ministry for Business, Innovation and Employment contract nos. RTVU2206 (“Our Changing Coast”) and ANTA1801 (“Antarctic Science Platform”). Tore Hatterman was supported by the Research Council of Norway, project no. 332635.
PY - 2023/12/7
Y1 - 2023/12/7
N2 - The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.
AB - The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.
UR - http://www.scopus.com/inward/record.url?scp=85180560833&partnerID=8YFLogxK
U2 - 10.5194/tc-17-5197-2023
DO - 10.5194/tc-17-5197-2023
M3 - Article
AN - SCOPUS:85180560833
SN - 1994-0416
VL - 17
SP - 5197
EP - 5217
JO - Cryosphere
JF - Cryosphere
IS - 12
ER -