The land ice contribution to global mean sea level rise has not yet been predicted1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models2,3,4,5,6,7,8, but primarily used previous-generation scenarios9 and climate models10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.
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All global climate, simple climate, ice sheet and glacier model data used as inputs to this study are provided with the code as described above. Main and risk-averse projections at 2100 from the analysis are provided in Supplementary Information for each of the 23 regions, and the Antarctic, glacier and land ice sums.
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We thank J. Rougier for providing advice and support throughout, and writing the original random effects model. We also thank B. Fox-Kemper, H. Hewitt, R. Kopp, S. Drijfhout and J. Rohmer for discussions, suggestions and support. We thank N. Barrand, W. Chang, V. Volodina and D. Williamson for their thorough and constructive comments, which greatly improved the manuscript. We thank the Climate and Cryosphere (CliC) Project, which provided support for ISMIP6 and GlacierMIP through sponsoring of workshops, hosting the websites and ISMIP6 wiki, and promotion. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and the ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6. This is ISMIP6 contribution no. 13. This publication was supported by PROTECT, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869304. This is PROTECT contribution number 12. T.L.E. was supported by PROTECT and the UK Natural Environment Research Council grant NE/T007443/1. F.T. was supported by PROTECT. J.F.O’N. was supported by the UK Natural Environment Research Council London Doctoral Training Partnership. R. Gladstone’s contribution was supported by Academy of Finland grants 286587 and 322430, and T. Zwinger’s by grant 322430. W.H.L. and G.R.L. 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. Support for X.A.-D., M.J.H., S.F.P. and T. Zhang was provided through the Scientific Discovery through Advanced Computing (SciDAC) programme funded by the US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research programmes. N.R.G., D.P.L. and B.A. were supported by New Zealand Ministry for Business, Innovation and Employment contracts RTUV1705 (‘NZSeaRise’) and ANTA1801 (‘Antarctic Science Platform’). J.M.G. and R.S.S. were supported by the National Centre for Atmospheric Science, funded by the UK National Environment Research Council. R. Calov was funded by the PalMod project of the Bundesministerium für Bildung und Forschung (BMBF) with the grants FKZ 01LP1502C and 01LP1504D. D.F.M. and C.S. were supported by the Director, Office of Science, Offices of Advanced Scientific Computing Research (ASCR) and Biological and Environmental Research (BER), of the US Department of Energy under contract no. DE-AC02-05CH11231, as a part of the ProSPect SciDAC Partnership. BISICLES simulations used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under contract no. DE-AC02-05CH11231. C.Z. and B.K.G.-F. were supported under the Australian Research Council’s Special Research Initiative for Antarctic Gateway Partnership (project ID SR140300001) and received grant funding from the Australian Government for the Australian Antarctic Program Partnership (project ID ASCI000002). Work was performed by E.L., N.-J.S. and H.S. at the California Institute of Technology’s Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration; support was provided by grants from NASA’s Cryospheric Science, Sea Level Change Team, and Modeling, Analysis and Prediction (MAP) programmes. They acknowledge computational resources and support from the NASA Advanced Supercomputing Division. The CMIP5 and CMIP6 projection data were processed by C.M.M. with funding from the European Union’s CONSTRAIN project as part of the Horizon 2020 Research and Innovation Programme under grant agreement number 820829. A. Barthel was supported by the DOE Office of Science HiLAT-RASM project and Early Career Research programme. T.A. and R.W. are supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme ‘Antarctic research with comparative investigations in Arctic ice areas’ by grants 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). R.R. is supported by the Deutsche Forschungsgemeinschaft (DFG) by grant WI4556/3-1 and through the TiPACCs project that receives 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 NNX17AG65G and NSF grants PLR-1603799 and PLR-1644277. The authors gratefully acknowledge the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources for the high-performance computer system at the Potsdam Institute for Climate Impact Research. Computer resources for this project have also been provided by the Gauss Centre for Supercomputing, Leibniz Supercomputing Centre (http://www.lrz.de, last access: 16 July 2020) under project IDs pr94ga and pn69ru. R. Greve and C.C. were supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grant nos JP16H02224 and JP17H06323. R. Greve was supported by JSPS KAKENHI grant no. JP17H06104, by a Leadership Research Grant of Hokkaido University’s Institute of Low Temperature Science (ILTS), and by the Arctic Challenge for Sustainability (ArCS, ArCS II) project of the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) (programme grant nos JPMXD1300000000, JPMXD1420318865). F.P. and S. Sun were supported by the MIMO project within the STEREO III programme of the Belgian Science Policy Office, contract SR/00/336 and the Fonds de la Recherche Scientifique (FNRS) and the Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) under the EOS project no. O0100718F. A. Shepherd was supported by the UK Natural Environment Research Council in partnership with the Centre for Polar Observation and Modelling and the British Antarctic Survey and by the European Space Agency Climate Change Initiative. D.F. was supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. R.v.d.W. acknowledges the support of the Future Deltas programme of Utrecht University. C.J.S. was supported by a NERC/IIASA Collaborative Research Fellowship (NE/T009381/1). H.G. has received funding from the programme of the Netherlands Earth System Science Centre (NESSC), financially supported by the Dutch Ministry of Education, Culture and Science (OCW) under grant no. 024.002.001 and from the Research Council of Norway under projects INES (270061) and KeyClim (295046). F.S. acknowledges support from DOE Office of Science grant no. DE-SC0020073. High-performance computing and storage resources were provided by the Norwegian Infrastructure for Computational Science through projects NN9560K, NN9252K, NS9560K, NS9252K and NS5011K.
The authors declare no competing interests.
Peer review information Nature thanks Nicholas Barrand, Won Chang, Victoria Volodina, Daniel Williamson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
a–l, Left: emulator predictions versus simulations for each regional sea level contribution in the year 2100, with percentage of predictions falling outside ±2 emulator standard deviations and mean absolute error in cm SLE. Right: standardized residuals (emulated minus simulated, divided by emulator standard deviation). Predictions falling outside ±2 emulator standard deviations are shown in orange.
As for Extended Data Fig. 1, but for the remaining glacier emulators.
a, b, Global surface air temperature projections under different greenhouse gas scenarios (see text) from the FaIR simple climate model ensemble (a; N = 5,000; same as Fig. 3a), and CMIP6 global climate model ensemble (b; N ≈ 30 models per scenario; see Methods) sampled with a kernel density estimate (N = 1,000).
Extended Data Fig. 4 Sensitivity of ice sheet projections at 2100 under SSP5-85 to uncertain inputs.
a, Greenland. b, West Antarctica. c, East Antarctica. d, Antarctic Peninsula. Box and whiskers show [5, 25, 50, 75, 95]th percentiles. Indices refer to test (see Extended Data Table 3). Sensitivity test 1, default; 2, CMIP6 global climate model ensemble projections of global mean surface air temperature, instead of FaIR simple climate model; 3, fixed global mean surface air temperature; 4, fixed glacier retreat (Greenland) or basal melt (Antarctica) parameter. Antarctic regions only: basal melt parameter has sensitivity test 5: ‘mean Antarctic’ distribution; 6, ‘Pine Island Glacier’ distribution; 7, uniform, high distribution; 8, uniform, very high distribution. Ice shelf collapse scenario: sensitivity test 9, off; 10, on. 11, Risk-averse projections using the high ‘Pine Island Glacier’ distribution for basal melt (test 6), ice shelf collapse on (test 10), and the ice sheet and climate models that give the highest sea level contributions (Extended Data Fig. 5; test 6, 7).
Extended Data Fig. 5 Robustness of ice sheet projections under NDCs to ice sheet/climate model simulation selection and treatment.
a, Greenland. b, West Antarctica. c, East Antarctica. d, Antarctic Peninsula. Box and whiskers show [5, 25, 50, 75, 95]th percentiles. Indices refer to test (see Extended Data Table 4). Robustness test 1, default; 2, higher-resolution ice sheet models; 3, ice sheet models with the most complete sampling of uncertainties (10 models for Greenland, four for Antarctica); 4, single ice sheet model with the most complete sampling of uncertainties and (coincidentally) high sensitivity to retreat or basal melting parameter. Antarctic regions only: robustness test 5, alternative single ice sheet model with nearly as complete sampling but low sensitivity to basal melt parameter; 6, ice sheet models with the highest sensitivity to basal melt parameter; 7, climate models that lead to highest sea level contributions. 8, ice sheet models with 2015–2020 mass change in the range 0–0.6 cm SLE; 9, only ice sheet models that use the standard ISMIP melt parameterizations; 10, higher basal melt value assigned to ice sheet models that do not use the standard ISMIP6 melt parameterizations.
Vertical lines show ice sheet models that do not use the ISMIP6 basal melt parameterization, and the basal melt value they are assigned. Ice sheet models include the high and low sensitivity models in Extended Data Fig. 5: test 4 (ILTS_PIK/SICOPOLIS) and test 5 (LSCE/GRISLI).
Additional sea level contribution at 2100 when using ice shelf collapse for six climate models, ordered by maximum impact on the Peninsula contribution. a, West Antarctica, b, East Antarctica and c, Antarctic Peninsula.
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Edwards, T.L., Nowicki, S., Marzeion, B. et al. Projected land ice contributions to twenty-first-century sea level rise. Nature 593, 74–82 (2021). https://doi.org/10.1038/s41586-021-03302-y
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