Climate Dynamics

, Volume 41, Issue 3–4, pp 1099–1116 | Cite as

CFSv2 ensemble prediction of the wintertime Arctic Oscillation

  • Emily E. RiddleEmail author
  • Amy H. Butler
  • Jason C. Furtado
  • Judah L. Cohen
  • Arun Kumar
Part of the following topical collections:
  1. Topical Collection on Climate Forecast System Version 2 (CFSv2)


Lagged ensembles from the operational Climate Forecast System version 2 (CFSv2) seasonal hindcast dataset are used to assess skill in forecasting interannual variability of the December–February Arctic Oscillation (AO). We find that a small but statistically significant portion of the interannual variance (>20 %) of the wintertime AO can be predicted at leads up to 2 months using lagged ensemble averages. As far as we are aware, this is the first study to demonstrate that an operational model has discernible skill in predicting AO variability on seasonal timescales. We find that the CFS forecast skill is slightly higher when a weighted ensemble is used that rewards forecast runs with the most accurate representations of October Eurasian snow cover extent (SCE), hinting that a stratospheric pathway linking October Eurasian SCE with the AO may be responsible for the model skill. However, further analysis reveals that the CFS is unable to capture many important aspects of this stratospheric mechanism. Model deficiencies identified include: (1) the CFS significantly underestimates the observed variance in October Eurasian SCE, (2) the CFS fails to translate surface pressure anomalies associated with SCE anomalies into vertically propagating waves, and (3) stratospheric AO patterns in the CFS fail to propagate downward through the tropopause to the surface. Thus, alternate boundary forcings are likely contributing to model skill. Improving model deficiencies identified in this study may lead to even more skillful predictions of wintertime AO variability in future versions of the CFS.


Arctic Oscillation Stratosphere–troposphere coupling Seasonal forecasting Eurasian snow cover Climate prediction Modes of climate variability 



Work for this project was supported by NOAA Grant #NA10OAR4310163. The observational snow cover dataset was provided by Rutgers University Global Snow Lab. We greatly appreciate helpful editorial comments provided by Dan Collins, Craig Long and two anonymous reviewers.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emily E. Riddle
    • 1
    • 2
    Email author
  • Amy H. Butler
    • 1
  • Jason C. Furtado
    • 3
  • Judah L. Cohen
    • 3
  • Arun Kumar
    • 1
  1. 1.Climate Prediction Center, NCEP/NWS/NOAACollege ParkUSA
  2. 2.Wyle Science Technology and Engineering GroupHoustonUSA
  3. 3.Atmospheric and Environmental ResearchLexingtonUSA

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