Nonlinear time series models for the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models including both short and long lags perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution and the different time scales of the two phases. As a spinoff of the modelling procedure, we are able to deduce that the interannual dependence of the NAO mostly affects the positive phase and that timescales of one to three weeks are more dominant for the negative phase. The statistical properties of the model makes it useful for the generation of realistic climate noise.