Climate Dynamics

, Volume 50, Issue 11–12, pp 4171–4188 | Cite as

North Atlantic observations sharpen meridional overturning projections

  • R. Olson
  • S.-I. AnEmail author
  • Y. Fan
  • J. P. Evans
  • L. Caesar


Atlantic Meridional Overturning Circulation (AMOC) projections are uncertain due to both model errors, as well as internal climate variability. An AMOC slowdown projected by many climate models is likely to have considerable effects on many aspects of global and North Atlantic climate. Previous studies to make probabilistic AMOC projections have broken new ground. However, they do not drift-correct or cross-validate the projections, and do not fully account for internal variability. Furthermore, they consider a limited subset of models, and ignore the skill of models at representing the temporal North Atlantic dynamics. We improve on previous work by applying Bayesian Model Averaging to weight 13 Coupled Model Intercomparison Project phase 5 models by their skill at modeling the AMOC strength, and its temporal dynamics, as approximated by the northern North-Atlantic temperature-based AMOC Index. We make drift-corrected projections accounting for structural model errors, and for the internal variability. Cross-validation experiments give approximately correct empirical coverage probabilities, which validates our method. Our results present more evidence that AMOC likely already started slowing down. While weighting considerably moderates and sharpens our projections, our results are at low end of previously published estimates. We project mean AMOC changes between periods 1960–1999 and 2060–2099 of −4.0 Sv and −6.8 Sv for RCP4.5 and RCP8.5 emissions scenarios respectively. The corresponding average 90% credible intervals for our weighted experiments are [−7.2, −1.2] and [−10.5, −3.7] Sv respectively for the two scenarios.


Atlantic Meridional Overturning Circulation Climate modeling Bayesian Model Averaging Model structural error Probabilistic projections 



For their roles in producing, coordinating, and making available the CMIP5 model output, we acknowledge the climate modeling groups (listed in Table 1), the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling (WGCM), and the Global Organization for Earth System Science Portals (GO-ESSP). Some data used here has been downloaded from the German Climate Computing Centre (DKRZ), with funding from the Federal Ministry for Education and Research. Jong-Soo Shin and Eun-Sook Heo provided technical assistance with downloading model output, and code debugging, respectively. Fruitful conversations with Stefan Rahmstorf and Axel Timmermann are gratefully acknowledged. S.-I. An and R. Olson were supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST, NRF-2009-0093069).

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesYonsei UniversitySeoulSouth Korea
  2. 2.School of Mathematics and StatisticsUNSWSydneyAustralia
  3. 3.Climate Change Research Centre, ARC Centre for Excellence in Climate System ScienceUNSWSydneyAustralia
  4. 4.Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationBerlinGermany
  5. 5.Institute for Physics and AstronomyUniversity of PotsdamPotsdamGermany

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