Myong-In Lee - 613 Seminar Series

Ulsan National Institute of Science & Technology (UNIST)

P { margin-bottom: 0.08in; direction: ltr; text-align: justify; }P.western { font-family: "Times New Roman",serif; font-size: 8pt; }P.cjk { font-family: "바탕체"; font-size: 8pt; }P.ctl { font-family: "Times New Roman"; font-size: 10pt; }This study first 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 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 2-months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal timescales. 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). This study further explores why the prediction skill has increased in the reforecasts after the mid-1990s by most EPSs. It is suggested that the enhancement of prediction skill is mainly contributed by enhanced connection between AO and the El Niño and Southern Oscillation (ENSO) in the recent period. Results from the data analysis and simple barotropic model experiments tend to support the hypothesis.