Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Revisiting Antarctic ice loss due to marine ice-cliff instability


Predictions for sea-level rise this century due to melt from Antarctica range from zero to more than one metre. The highest predictions are driven by the controversial marine ice-cliff instability (MICI) hypothesis, which assumes that coastal ice cliffs can rapidly collapse after ice shelves disintegrate, as a result of surface and sub-shelf melting caused by global warming. But MICI has not been observed in the modern era and it remains unclear whether it is required to reproduce sea-level variations in the geological past. Here we quantify ice-sheet modelling uncertainties for the original MICI study and show that the probability distributions are skewed towards lower values (under very high greenhouse gas concentrations, the most likely value is 45 centimetres). However, MICI is not required to reproduce sea-level changes due to Antarctic ice loss in the mid-Pliocene epoch, the last interglacial period or 1992–2017; without it we find that the projections agree with previous studies (all 95th percentiles are less than 43 centimetres). We conclude that previous interpretations of these MICI projections over-estimate sea-level rise this century; because the MICI hypothesis is not well constrained, confidence in projections with MICI would require a greater range of observationally constrained models of ice-shelf vulnerability and ice-cliff collapse.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Probabilistic projections of the Antarctic contribution to sea level at 2100.
Fig. 2: Emergence of ice-cliff instability.
Fig. 3: Long-term projections of Antarctic sea-level contribution.
Fig. 4: Multi-model comparison.

Data availability

All projections from this study are available from the corresponding author on request. Simulations of the last interglacial, Pliocene and 1992–2017 and 2000–2100 sea-level contributions for all DP16 ensemble members are available on Code Ocean ( Simulations at 2500 for the subset of the DP16 ensemble that pass their calibration are available in the supplementary information of DP16.


  1. IPCC. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the IPCC Fifth Assessment Report (Cambridge Univ. Press, Cambridge, 2013).

  2. Little, C. M., Oppenheimer, M. & Urban, N. M. Upper bounds on twenty-first-century Antarctic ice loss assessed using a probabilistic framework. Nat. Clim. Chang. 3, 654–659 (2013).

    Article  ADS  Google Scholar 

  3. Levermann, A. et al. Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models. Earth Syst. Dynam. 5, 271–293 (2014).

    Article  ADS  Google Scholar 

  4. Ritz, C. et al. Potential sea-level rise from Antarctic ice-sheet instability constrained by observations. Nature 528, 115–118 (2015).

    ADS  CAS  PubMed  Google Scholar 

  5. Ruckert, K. L. et al. Assessing the impact of retreat mechanisms in a simple Antarctic Ice Sheet model using Bayesian calibration. PLoS One 12, e0170052 (2017).

    Article  Google Scholar 

  6. DeConto, R. M. & Pollard, D. Contribution of Antarctica to past and future sea-level rise. Nature 531, 591–597 (2016).

    Article  ADS  CAS  Google Scholar 

  7. Pollard, D., DeConto, R. M. & Alley, R. B. Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure. Earth Planet. Sci. Lett. 412, 112–121 (2015).

    Article  ADS  CAS  Google Scholar 

  8. Hellmer, H. H., Kauker, F., Timmermann, R., Determann, J. & Rae, J. Twenty-first-century warming of a large Antarctic ice-shelf cavity by a redirected coastal current. Nature 485, 225–228 (2012).

    Article  ADS  CAS  Google Scholar 

  9. Kuipers Munneke, P., Ligtenberg, S. R. M., van den Broeke, M. R. & Vaughan, D. G. Firn air depletion as a precursor of Antarctic ice-shelf collapse. J. Glaciol. 60, 205–214 (2014).

    Article  ADS  Google Scholar 

  10. Trusel, L. D. et al. Divergent trajectories of Antarctic surface melt under two twenty-first-century climate scenarios. Nat. Geosci. 8, 927–932 (2015).

    Article  ADS  CAS  Google Scholar 

  11. Vaughan, D. G. West Antarctic Ice Sheet collapse – the fall and rise of a paradigm. Clim. Change 91, 65–79 (2008).

    Article  Google Scholar 

  12. Schoof, C. Ice sheet grounding line dynamics: steady states, stability, and hysteresis. J. Geophys. Res. 112, F03S28 (2007).

    Article  ADS  Google Scholar 

  13. Rignot, E., Mouginot, J., Morlighem, M., Seroussi, H. & Scheuchl, B. Widespread, rapid grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, from 1992 to 2011. Geophys. Res. Lett. 41, 3502–3509 (2014).

    Article  ADS  Google Scholar 

  14. Favier, L. et al. Retreat of Pine Island Glacier controlled by marine ice-sheet instability. Nat. Clim. Chang. 4, 117–121 (2014).

    Article  ADS  Google Scholar 

  15. Joughin, I., Smith, B. E. & Medley, B. Marine ice sheet collapse potentially under way for the Thwaites Glacier basin, West Antarctica. Science 344, 735–738 (2014).

    Article  ADS  CAS  Google Scholar 

  16. Waugh, D. W., Primeau, F., DeVries, T. & Holzer, M. Recent changes in the ventilation of the southern oceans. Science 339, 568–570 (2013).

    Article  ADS  CAS  Google Scholar 

  17. Previdi, M. & Polvani, L. M. Climate system response to stratospheric ozone depletion and recovery. Q. J. R. Meteorol. Soc. 140, 2401–2419 (2014).

    Article  ADS  Google Scholar 

  18. Bassis, J. N. & Walker, C. C. Upper and lower limits on the stability of calving glaciers from the yield strength envelope of ice. Proc. R. Soc. Lond. A. 468, 913–931 (2012).

    Article  ADS  Google Scholar 

  19. Sweeney, J., Salter-Townshend, M., Edwards, T., Buck, C. E. & Parnell, A. C. Statistical challenges in estimating past climate changes. Wiley Interdiscip. Rev. Comput. Stat. 10, e1437 (2018).

    Google Scholar 

  20. Gasson, E., DeConto, R. M. & Pollard, D. Modeling the oxygen isotope composition of the Antarctic ice sheet and its significance to Pliocene sea-level. Geology 44, 827–830 (2016).

    Article  ADS  CAS  Google Scholar 

  21. Williamson, D., Blaker, A., Hampton, C. & Salter, J. Identifying and removing structural biases in climate models with history matching. Clim. Dyn. 45, 1299–1324 (2015).

    Article  Google Scholar 

  22. McNeall, D. et al. The impact of structural error on parameter constraint in a climate model. Earth Syst. Dynam. 7, 917–935 (2016).

    Article  ADS  Google Scholar 

  23. Williamson, D., Blaker, A. T. & Sinha, B. Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model. Geosci. Model Dev. 10, 1789–1816 (2017).

    Article  ADS  Google Scholar 

  24. Gladstone, R. M. et al. Calibrated prediction of Pine Island Glacier retreat during the 21st and 22nd centuries with a coupled flowline model. Earth Planet. Sci. Lett. 333–334, 191–199 (2012).

    Article  ADS  Google Scholar 

  25. The IMBIE team. Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature 558, 219–222 (2018).

    Article  ADS  Google Scholar 

  26. Golledge, N. R. et al. Global environmental consequences of twenty-first-century ice-sheet melt. Nature 566, (2018).

  27. Le Bars, D., Drijfhout, S. & de Vries, H. A high-end sea-level rise probabilistic projection including rapid Antarctic ice sheet mass loss. Environ. Res. Lett. 12, 044013 (2017).

    Article  ADS  Google Scholar 

  28. Golledge, N. R. et al. The multi-millennial Antarctic commitment to future sea-level rise. Nature 526, 421–425 (2015).

    Article  ADS  CAS  Google Scholar 

  29. Cornford, S. L. et al. Century-scale simulations of the response of the West Antarctic Ice Sheet to a warming climate. Cryosphere 9, 1579–1600 (2015).

    Article  ADS  Google Scholar 

  30. Wise, M. G., Dowdeswell, J. A., Jakobsson, M. & Larter, R. D. Evidence of marine ice-cliff instability in PineIsland Bay from iceberg-keel plough marks. Nature 550, 506–510 (2017).

    Article  ADS  CAS  Google Scholar 

  31. Bell, R. E. et al. Antarctic ice shelf potentially stabilized by export of meltwater in surface river. Nature 544, 344–348 (2017).

    Article  ADS  CAS  Google Scholar 

  32. Kingslake, J., Ely, J. C., Das, I. & Bell, R. E. Widespread movement of meltwater onto and across Antarctic ice shelves. Nature 544, 349–352 (2017).

    Article  ADS  CAS  Google Scholar 

  33. Gourmelen, N. et al. Channelized melting drives thinning under a rapidly melting Antarctic ice shelf. Geophys. Res. Lett. 44, 9796–9804 (2017).

    Article  ADS  Google Scholar 

  34. Pattyn, F., Favier, L., Sun, S. & Durand, G. Progress in numerical modeling of Antarctic ice-sheet dynamics. Curr. Clim. Change Rep. 3, 174–184 (2017).

    Article  Google Scholar 

  35. Golledge, N. et al. Antarctic climate and ice-sheet configuration during the early Pliocene interglacial at 4.23 Ma. Clim. Past 13, 959–975 (2017).

    Article  Google Scholar 

  36. Pukelsheim, F. The three sigma rule. Am. Stat. 48, 88–91 (1994).

    MathSciNet  Google Scholar 

  37. Miller, K. G. et al. High tide of the warm Pliocene: implications of global sea-level for Antarctic deglaciation. Geology 40, 407–410 (2012).

    Article  ADS  CAS  Google Scholar 

  38. Raymo, M. E. et al. The accuracy of mid-Pliocene δ18O-based ice volume and sea level reconstructions. Earth Sci. Rev. 177, 291–302 (2018).

    Article  CAS  Google Scholar 

  39. Kopp, R. E., Simons, F. J., Mitrovica, J. X., Maloof, A. C. & Oppenheimer, M. A probabilistic assessment of sea-level variations within the last interglacial stage. Geophys. J. Int. 193, 711–716 (2013).

    Article  ADS  Google Scholar 

  40. Düsterhus, A., Tamisiea, M. E. & Jevrejeva, S. Estimating the sea-level highstand during the last interglacial: a probabilistic massive ensemble approach. Geophys. J. Int. 206, 900–920 (2016).

    Article  ADS  Google Scholar 

  41. Austermann, J., Mitrovica, J. X., Huybers, P. & Rovere, A. Detection of a dynamic topography signal in last interglacial sea-level records. Sci. Adv. 3, e1700457 (2017).

    Article  ADS  Google Scholar 

  42. Nias, I., Cornford, S. L. & Payne, A. J. Contrasting model sensitivity of the Amundsen Sea embayment ice streams. J. Glaciol. 62, 552–562 (2016).

    Article  ADS  Google Scholar 

  43. Holden, P. B., Edwards, N. R., Oliver, K. I. C., Lenton, T. M. & Wilkinson, R. D. A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1. Clim. Dyn. 35, 785–806 (2010).

    Article  Google Scholar 

  44. Edwards, N. R., Cameron, D. & Rougier, J. Precalibrating an intermediate complexity climate model. Clim. Dyn. 37, 1469–1482 (2011).

    Article  Google Scholar 

  45. Nowicki, S. M. J. et al. Ice Sheet Model Intercomparison Project (ISMIP6) contribution to CMIP6. Geosci. Model Dev. 9, 4521–4545 (2016).

    Article  ADS  Google Scholar 

  46. O’Hagan, A. Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91, 1290–1300 (2006).

    Article  Google Scholar 

  47. Sweet, W. V. et al. Global and Regional Sea-Level Rise Scenarios for the United States. Report No. NOS CO-OPS 083 (NOAA, 2017).

  48. Williamson, D. et al. History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble. Clim. Dyn. 41, 1703–1729 (2013).

    Article  Google Scholar 

  49. Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).

    Article  CAS  Google Scholar 

  50. Vernon, I., Goldstein, M. & Bower, R. G. Galaxy formation: a Bayesian uncertainty analysis. Bayesian Anal. 5, 619–669 (2010).

    Article  MathSciNet  Google Scholar 

Download references


T.L.E., N.R.E. and P.B.H. were supported by EPSRC Research on Changes of Variability and Environmental Risk (ReCoVER: EP/M008495/1) under the Quantifying Uncertainty in Antarctic Ice Sheet Instability (QUAntIS) project (RFFLP 006). T.L.E. was also supported by the EPSRC-funded Past Earth Network (EP/M008363/1) and the Université Joseph Fourier–Grenoble International visitor fund ‘Campagne INVITES’. N.R.G. is supported by contract VUW1501 from the Royal Society Te Aparangi. I.J.N. was supported by the NERC iSTAR-C project Dynamical control on the response of Pine Island Glacier (NE/J005738/1) and now by the NASA Sea Level Change programme. A.W. is supported by the Open University Faculty of Science, Technology, Engineering and Mathematics. We thank R. DeConto for running additional simulations necessary for the analysis, suggestions and discussions, and K. Ruckert and A. Levermann for providing data. We thank the statisticians who attended the Past Earth Network events ‘Assessing Palaeoclimate Uncertainty’ (held jointly with the Environmental Statistics Section of the Royal Statistics Society, Cambridge, August 2016), ‘Emulators workshop’ (Leeds, June 2017) and writing retreat (Callow Hall, August 2017) for advice, particularly I. Vernon, P. Challenor and J. Rougier. We thank D. Le Bars, D. McNeall, D. Demeritt and the King’s Geography Hazards, Risk and Regulation reading group (G. Adamson, F. Liu, A. Razli, L. Ball and A. Heilbron) for comments on the manuscript. We also thank K.-K. Shiu and D. Campbell for supporting T.L.E. in this work.

Author information

Authors and Affiliations



T.L.E. conceived the idea, carried out the analysis, produced the figures and wrote the manuscript. A.W. and P.B.H. performed preliminary analyses. A.J.P., A.W., C.R., G.D., I.J.N., M.A.B. and N.R.G. contributed ideas on glaciological and oceanic aspects; A.W., N.R.E. and P.B.H. contributed ideas on statistical aspects. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Tamsin L. Edwards.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Sensitivity of DP16 RCP8.5 projections to the lower bound of the Pliocene data.

a, b, Bias-uncorrected (a) and bias-corrected (b) DP16 projections6 for Antarctic sea-level contribution by 2100 under RCP8.5 as a function of the lower bound of the Pliocene data range. The solid red line and pink shading show the mean ± 1 s.d.; the dotted lines indicate ±2 s.d. c, Sensitivity of our emulated projections for RCP8.5 at 2100 with MICI: lines show 5th, 25th, 50th, 75th and 95th percentiles; asterisks indicate the mode.

Extended Data Fig. 2 DP16 RCP8.5 projection distributions.

ad, DP16 ensemble projections6 for Antarctic sea-level contribution by 2100 under RCP8.5 for their four variants—low-Pliocene bias-uncorrected (a), low-Pliocene bias-corrected (b), high-Pliocene bias-uncorrected (c) and high-Pliocene bias-corrected (d)—showing the full 64-member ensemble (grey) and the subset selected by calibrating with Pliocene and last interglacial sea-level reconstructions (red). The solid red lines and pink shading show the mean ± 1 s.d.; the horizontal black lines indicate the ≥68% probability interval (see main text and Methods for more details).

Extended Data Fig. 3 Relationships between RCP8.5 projections at 2100 and past sea-level changes.

ac, Sea-level contribution at 2100 under RCP8.5 versus sea-level contribution during the Pliocene (a), last interglacial (b) and from 1992 to 2017 (c), for the emulator (small grey dots) and DP16 simulator (large open circles; ref. 6 and R. DeConto, personal communication), with ocean bias correction off (blue) and on (red). Grey shading indicates the DP16 palaeodata range (a, b) or observational mean ± 3 s.d.25 (c); the dashed lines additionally include model error.

Extended Data Fig. 4 Relationship between past and future sea-level changes with and without MICI.

a, b, Simulator ensemble (large circles; ref. 6 and R. DeConto, personal communication) and emulated ensembles (small circles) with (a) and without (b) MICI, showing Pliocene versus last interglacial sea-level changes, with the colour scale indicating the sea-level equivalent (SLE) contribution at 2100 under RCP8.5. Large emulator points and filled simulator points are those that pass the 1992–2017 calibration25. The shaded rectangle indicates the bounds of the DP16 low-Pliocene and last interglacial palaeodata constraints; the dashed rectangle shows constraints in this study (which include model error).

Extended Data Fig. 5 Sensitivity of RCP8.5 projections to MICI and calibration choices.

Projections for RCP8.5 at 2100 are shown with and without MICI, for different combinations of calibration eras (‘palaeo’, Pliocene and last interglacial; present, 1992–2017) and model discrepancy (with, without or double). Box and whiskers show the 5th, 25th, 50th, 75th and 95th percentiles; asterisks show the mode. Numbers alongside each plot indicate the median, the 5%–95% probability interval and the mode (in parentheses and asterisked).

Extended Data Fig. 6 Emulator validation.

Left column, emulator prediction versus simulation for each ensemble member, with the emulator fitted to the other ensemble members, for each of the outputs used for building and validating emulator structure: RCP8.5, RCP4.5 and RCP2.6 sea-level contribution at 2100; 1992–2017 contribution; last interglacial; and Pliocene. Vertical error bars show 95% credibility intervals. Right column, difference between emulator predictions and simulations, standardized by emulator error, for the same six outputs. Values falling mostly between ±2 indicate that the emulator has adequate uncertainty estimates. Simulation data from ref. 6 and R. DeConto (personal communication).

Extended Data Fig. 7 Sensitivity of RCP8.5 projections to model parameters.

ac, Sea-level contribution at 2100 under RCP8.5 versus VCLIF (a), CREVLIQ (b) and OCFAC (c) parameters for the emulator (small grey dots with error bars) and simulator (large open circles; blue, bias-uncorrected; red, bias-corrected). Simulation data from ref. 6 and R. DeConto (personal communication).

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Edwards, T.L., Brandon, M.A., Durand, G. et al. Revisiting Antarctic ice loss due to marine ice-cliff instability. Nature 566, 58–64 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing