Introducing driving-force information increases the predictability of the North Atlantic Oscillation

Abstract

The North Atlantic Oscillation (NAO) is the most prominent mode of atmospheric variability in the Northern Hemisphere. Because of the close relationship between the NAO and regional climate in Eurasia, North Atlantic, and North America, improving the prediction skill for the NAO has attracted much attention. Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models. In this study, the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction. By computing the largest Lyapunov exponent, the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered. Furthermore, the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information. Results showed that the prediction skill for the NAO can be largely increased. In addition, results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers: the annual cycle (1.02 yr), the quasi-biennial oscillation (QBO) (2.44 yr), and the solar cycle (11.6 yr), which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.

Keywords

• North Atlantic Oscillation
• slow feature analysis
• driving force characteristics
• time series prediction