Climatic Change

, Volume 127, Issue 3–4, pp 579–586 | Cite as

Probabilistic projections of the Atlantic overturning

  • Carl-Friedrich Schleussner
  • Anders Levermann
  • Malte Meinshausen
Letter

Abstract

Changes in the Atlantic overturning circulation have a strong influence on European temperatures, North American sea level and other climate phenomena worldwide. A meaningful assessment of associated societal impacts needs to be based on the full range of its possible future evolution. This requires capturing both the uncertainty in future warming pathways and the inherently long-term response of the ocean circulation. While probabilistic projections of the global mean and regional temperatures exist, process-based probabilistic assessments of large-scale dynamical systems such as the Atlantic overturning are still missing. Here we present such an assessment and find that a reduction of more than 50 % in Atlantic overturning strength by the end of the 21 s t century is within the likely range under an unmitigated climate change scenario (RCP8.5). By combining linear response functions derived from comprehensive climate simulations with the full range of possible future warming pathways, we provide probability estimates of overturning changes by the year 2100. A weakening of more than 25 % is found to be very unlikely under a climate protection scenario (RCP2.6), but likely for unmitigated climate change. The method is able to reproduce the modelled recovery caused by climatic equilibration under climate protection scenarios which provides confidence in the approach. Within this century, a reduction of the Atlantic overturning is a robust climatic phenomena that intensifies with global warming and needs to be accounted for in global adaptation strategies.

Keywords

Atlantic Meridional Overturning Circulation Model Ensemble Linear Response Function Atlantic Meridional Overturning Circulation Strength Atlantic Meridional Overturning Circulation Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We acknowledge the World Climate Research Programmes Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP the US Department of Energys Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement No. 603864. The authors would like to thank the reviewers for their comments that helped to improve the manuscript.

Supplementary material

10584_2014_1265_MOESM1_ESM.pdf (832 kb)
(PDF 831 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Carl-Friedrich Schleussner
    • 1
    • 2
  • Anders Levermann
    • 1
    • 3
  • Malte Meinshausen
    • 1
    • 4
  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Climate AnalyticsBerlinGermany
  3. 3.Institute of PhysicsPotsdam UniversityPotsdamGermany
  4. 4.School of Earth SciencesUniversity of MelbourneVictoriaAustralia

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