Climatic Change

, Volume 144, Issue 2, pp 347–364 | Cite as

Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense

  • Tony E. Wong
  • Alexander M. R. Bakker
  • Klaus Keller


Strategies to manage the risks posed by future sea-level rise hinge on a sound characterization of the inherent uncertainties. One of the major uncertainties is the possible rapid disintegration of large fractions of the Antarctic ice sheet in response to rising global temperatures. This could potentially lead to several meters of sea-level rise during the next few centuries. Previous studies have typically been silent on two coupled questions: (i) What are probabilistic estimates of this “fast dynamic” contribution to sea-level rise? (ii) What are the implications for strategies to manage coastal flooding risks? Here, we present probabilistic hindcasts and projections of sea-level rise to 2100. The fast dynamic mechanism is approximated by a simple parameterization, designed to allow for a careful quantification of the uncertainty in its contribution to sea-level rise. We estimate that global temperature increases ranging from 1.9 to 3.1 °C coincide with fast Antarctic disintegration, and these contributions account for sea-level rise of 21–74 cm this century (5–95% range, Representative Concentration Pathway 8.5). We use a simple cost-benefit analysis of coastal defense to demonstrate in a didactic exercise how neglecting this mechanism and associated uncertainty can (i) lead to strategies which fall sizably short of protection targets and (ii) increase the expected net costs.


Sea-level projections West Antarctic ice sheet Coastal flood defense Climate impacts Cliff failure and hydrofracturing 



We thank Dave Pollard for lending his insight into reasonable prior ranges and central values for the Antarctic ice sheet fast dynamic parameters and for comments on the initial version of the manuscript. We thank Kelsey Ruckert and Yawen Guan for their assistance in providing and interpreting codes relevant to the original DAIS model and its calibration. We also thank Kelsey Ruckert for manuscript formatting assistance. We thank Aimée Slangen for supplying and assistance interpreting the regional sea-level fingerprinting data. We gratefully acknowledge Richard Alley, Murali Haran, Chris and Bella Forest, Rob Nicholas, Patrick Reed, Michael Oppenheimer, Tad Pfeffer, Rob Lempert, David Johnson, Roger Cooke, and Dale Jennings for invaluable inputs. This work was partially supported by the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 as well as the Penn State Center for Climate Risk Management. Any conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. Any errors and opinions are, of course, those of the authors.

Code availability

The BRICK model (with the added fast dynamic module) and analysis codes are freely available from Large parameter files and model results files are available from Code examples and a routine for fingerprinting sea-level rise projections to other locations (aside from New Orleans, as presented in this manuscript) are provided at The physical models are coded in Fortran 90 and called from driver scripts coded in the R Programming Language. The analysis was performed using RStudio (version 0.99.903).

Author contributions

T.W. and K.K. initiated the study. T.W., A.B., and K.K. designed the research. T.W. and A.B. produced the model simulations. T.W. designed the initial figures and wrote the first draft. All contributed to the final text.

Supplementary material

10584_2017_2039_MOESM1_ESM.pdf (695 kb)
Online Resource 1 (ESM_1.pdf) Supplementary figures are provided, with captions. (PDF 694 kb)
10584_2017_2039_MOESM2_ESM.pdf (27 kb)
Online Resource 2 (ESM_2.pdf) Page 1 table contains the following: parameter names (column 1); model of origin (column 2); ensemble median, 5% quantile, and 95% quantile (columns 3, 4, and 5), from the experiment using the gamma priors for the fast dynamics; prior range lower and upper bounds, when assigned uniform priors (columns 6 and 7); and units (column 8). Page 2 table contains the following: parameter names (column 1), model of origin (column 2), and brief description (column 3). (PDF 26 kb)


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Tony E. Wong
    • 1
  • Alexander M. R. Bakker
    • 1
    • 2
  • Klaus Keller
    • 1
    • 3
    • 4
  1. 1.Earth and Environmental Systems Institute2217 EESB Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Rijkswaterstaat, Ministry of Infrastructure and EnvironmentThe HagueThe Netherlands
  3. 3.Department of GeosciencesPennsylvania State UniversityUniversity ParkUSA
  4. 4.Department of Engineering and Public PolicyCarnegie Mellon UniversityPittsburghUSA

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