Abstract
Abstract. The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF) are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1) simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1) processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.
Highlights
The analysis of climate time series provides insight for understanding and predicting climate variability
The most pronounced patterns in the Northern Hemisphere are the North Atlantic Oscillation (NAO) and the Pacific-North America (PNA) pattern whose surface imprint is known as the North Pacific (NP) index
We describe the results of applying the Empirical Mode Decomposition (EMD) method to atmospheric teleconnection indices and testing the significance of the intrinsic mode functions (IMF) and trends
Summary
The analysis of climate time series provides insight for understanding and predicting climate variability. An important topic in climate research is the existence of dynamically relevant and statistically significant modes of variability and trends. Most of the atmospheric mid-latitude variability can be described by just a few large-scale teleconnection patterns Wallace and Gutzler, 1981) which explain most of the variance and exert a huge influence on regional surface climate and seasonal climate conditions. The most pronounced patterns in the Northern Hemisphere are the North Atlantic Oscillation (NAO) and the Pacific-North America (PNA) pattern whose surface imprint is known as the North Pacific (NP) index. The Southern Hemisphere is dominated by the Southern Annular Mode (SAM). The study of univariate teleconnection indices provides insight into climate dynamics and global climate change
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