Abstract
Climate dynamics were assessed using multifractal detrended fluctuation analysis (MF-DFA) for sites in Finland, Germany and Spain across a latitudinal transect. Meteorological time series were divided into the two subsets (1980–2001 and 2002–2010) and respective spectra of these subsets were compared to check whether changes in climate dynamics can be observed using MF-DFA. Additionally, corresponding shuffled and surrogate time series were investigated to evaluate the type of multifractality. All time series indicated underlying multifractal structures with considerable differences in dynamics and development between the studied locations. The source of multifractality of precipitation time series was two-fold, coming from the width of the probability function to a greater extent than for other time series. The multifractality of other analyzed meteorological series was mainly due to long-range correlations for small and large fluctuations. These results may be especially valuable for assessing the change of climate dynamics, as we found that larger changes in asymmetry and width parameters of multifractal spectra for divided datasets were observed for precipitation than for other time series. This suggests that precipitation is the most vulnerable meteorological quantity to change of climate dynamics.
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Acknowledgements
This paper has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the projects: LCAgri, contract number: BIOSTRATEG1/271322/3/NCBR/2015 and GyroScan, contract number: BIOSTRATEG2/298782/11/NCBR/2016. We acknowledge Finnish Meteorological Institute (FMI) for delivering us data for Jokioinen site [37]. HH was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J).
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Krzyszczak, J., Baranowski, P., Hoffmann, H., Zubik, M., Sławiński, C. (2017). Analysis of Climate Dynamics Across a European Transect Using a Multifractal Method. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Advances in Time Series Analysis and Forecasting. ITISE 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55789-2_8
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