Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems

Daehyun Kang, Myong In Lee, Jungho Im, Daehyun Kim, Hye Mi Kim, Hyun Suk Kang, Siegfried D. Schubert, Alberto Arribas, Craig MacLachlan

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56 Scopus citations


This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

Original languageEnglish
Pages (from-to)3577-3585
Number of pages9
JournalGeophysical Research Letters
Issue number10
StatePublished - 28 May 2014


  • climate variability
  • coupled models of the climate system


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