Climate Dynamics

, Volume 45, Issue 1–2, pp 539–556 | Cite as

Contrasting interannual and multidecadal NAO variability

  • T. Woollings
  • C. Franzke
  • D. L. R. Hodson
  • B. Dong
  • E. A. Barnes
  • C. C. Raible
  • J. G. Pinto


Decadal and longer timescale variability in the winter North Atlantic Oscillation (NAO) has considerable impact on regional climate, yet it remains unclear what fraction of this variability is potentially predictable. This study takes a new approach to this question by demonstrating clear physical differences between NAO variability on interannual-decadal (<30 year) and multidecadal (>30 year) timescales. It is shown that on the shorter timescale the NAO is dominated by variations in the latitude of the North Atlantic jet and storm track, whereas on the longer timescale it represents changes in their strength instead. NAO variability on the two timescales is associated with different dynamical behaviour in terms of eddy-mean flow interaction, Rossby wave breaking and blocking. The two timescales also exhibit different regional impacts on temperature and precipitation and different relationships to sea surface temperatures. These results are derived from linear regression analysis of the Twentieth Century and NCEP-NCAR reanalyses and of a high-resolution HiGEM General Circulation Model control simulation, with additional analysis of a long sea level pressure reconstruction. Evidence is presented for an influence of the ocean circulation on the longer timescale variability of the NAO, which is particularly clear in the model data. As well as providing new evidence of potential predictability, these findings are shown to have implications for the reconstruction and interpretation of long climate records.


North Atlantic Oscillation Jet variability Atmosphere–ocean interaction Climate reconstructions 



NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. CF is supported by the German Research Foundation through the cluster of excellence CliSAP. DLRH was supported by the National Centre for Atmospheric Science (NCAS) and the Natural Environmental Research Council (NERC) during this work. CCR is supported by the Swiss National Science Foundation under the grant CRSI122-130642 (FUPSOL). JGP was partially supported by the German Federal Ministry of Education and Research (BMBF) under the project Probabilistic Decadal Forecast for Central and western Europe (MIKLIP-PRODEF, contract 01LP1120A). The authors would like to thank Len Shaffrey and the HiGEM project for use of the HiGEM data in this study, and two anonymous reviewers for constructive comments.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • T. Woollings
    • 1
  • C. Franzke
    • 2
  • D. L. R. Hodson
    • 3
  • B. Dong
    • 3
  • E. A. Barnes
    • 4
  • C. C. Raible
    • 5
  • J. G. Pinto
    • 6
    • 7
  1. 1.Department of PhysicsAtmospheric, Oceanic and Planetary PhysicsOxfordUK
  2. 2.Meteorological Institute and Center for Earth System Research and SustainabilityUniversität HamburgHamburgGermany
  3. 3.NCAS-Climate and Department of MeteorologyUniversity of ReadingReadingUK
  4. 4.Department of Atmospheric ScienceColorado State UniversityFort CollinsUSA
  5. 5.Climate and Environmental Physics and Oeschger Centre for Climate Change ResearchUniversity of BernBernSwitzerland
  6. 6.Department of MeteorologyUniversity of ReadingReadingUK
  7. 7.Institute for Geophysics and MeteorologyUniversity of CologneCologneGermany

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