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Upper bounds on twenty-first-century Antarctic ice loss assessed using a probabilistic framework

Nature Climate Change volume 3, pages 654659 (2013) | Download Citation


Climate adaptation and flood risk assessments1,2 have incorporated sea-level rise (SLR) projections developed using semi-empirical methods3,4,5 (SEMs) and expert-informed mass-balance scenarios2,6. These techniques, which do not explicitly model ice dynamics, generate upper bounds on twenty-first century SLR that are up to three times higher than Intergovernmental Panel on Climate Change estimates7. However, the physical basis underlying these projections, and their likelihood of occurrence, remain unclear8,9,10. Here, we develop mass-balance projections for the Antarctic ice sheet within a Bayesian probabilistic framework10, integrating numerical model output11 and updating projections with an observational synthesis12. Without abrupt, sustained, changes in ice discharge (collapse), we project a 95th percentile mass loss equivalent to 13 cm SLR by 2100, lower than previous upper-bound projections. Substantially higher mass loss requires regional collapse, invoking dynamics that are likely to be inconsistent with the underlying assumptions of SEMs. In this probabilistic framework, the pronounced sensitivity of upper-bound SLR projections to the poorly known likelihood of collapse is lessened with constraints on the persistence and magnitude of subsequent discharge. More realistic, fully probabilistic, estimates of the ice-sheet contribution to SLR may thus be obtained by assimilating additional observations and numerical models11,13.

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C.M.L. is grateful for financial support from the Science, Technology and Environmental Policy programme in the Woodrow Wilson School of Public and International Affairs at Princeton University and the Carbon Mitigation Initiative in the Princeton Environmental Institute. The authors thank K. Keller, O. Sergienko and Y. Liu for many helpful suggestions. We also thank A. Shepherd and the Ice Sheet Mass Balance Exercise team for promptly providing data.

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  1. Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, USA

    • Christopher M. Little
    • , Michael Oppenheimer
    •  & Nathan M. Urban
  2. Department of Geosciences, Princeton University, Princeton, New Jersey 08544, USA

    • Michael Oppenheimer
  3. Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA

    • Nathan M. Urban


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C.M.L, N.M.U and M.O. designed the research. C.M.L. conducted the data analysis and wrote the manuscript. M.O and N.M.U. contributed extensively to the paper writing, editing and revision.

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The authors declare no competing financial interests.

Corresponding author

Correspondence to Christopher M. Little.

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