Abstract
Abstract. 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 timescales of the two phases. As a spin-off of the modelling procedure, we can deduce that the interannual dependence of the NAO mostly affects the positive phase, and that timescales of 1 to 3 weeks are more dominant for the negative phase. Furthermore, the statistical properties of the model make it useful for the generation of realistic climate noise.
Highlights
The large-scale atmospheric flow has attracted the attention of climate scientists since the days of Gilbert Walker almost a century ago
In a previous article (Önskog et al, 2018), we studied the properties of the time series of the daily North Atlantic Oscillation (NAO) index, in particular the station-based time series published by Cropper et al (2015), and found that the distribution of the NAO index has clear non-Gaussian features and long-range dependence (Franzke et al, 2020)
In this study we have shown that the NAO can be well described by nonlinear autoregressive models
Summary
The large-scale atmospheric flow has attracted the attention of climate scientists since the days of Gilbert Walker almost a century ago (see, e.g., Walker and Bliss, 1932, Rossby, 1940, Horel and Wallace, 1981, and the review of Hannachi et al, 2017, and the references therein). The NAO is a nonlinear phenomenon and is related to synoptic Rossby wave-breaking (Benedict et al, 2004; Franzke et al, 2004) and regime behaviour (Hannachi et al, 2017) Based on this observation, it is reasonable to expect that nonlinear probabilistic models can be better suited to fit the NAO time series and be able to reproduce the properties mentioned above. In a previous article (Önskog et al, 2018), we studied the properties of the time series of the daily NAO index, in particular the station-based time series published by Cropper et al (2015), and found that the distribution of the NAO index has clear non-Gaussian features and long-range dependence (Franzke et al, 2020).
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