A simple decomposition of European temperature variability capturing the variance from days to a decade

Abstract

We analyze European temperature variability from station data with the method of detrended fluctuation analysis. This method is known to give a scaling exponent indicating long range correlations in time for temperature anomalies. However, by a more careful look at the fluctuation function we are able to explain the emergent scaling behaviour by short time relaxation, the yearly cycle and one additional process. It turns out that for many stations this interannual variability is an oscillatory mode with a period length of approximately 7-8 years, which is consistent with results of other methods. We discuss the spatial patterns in all parameters and validate the finding of the 7-8 year period by comparing stations with and without this mode.

Regional changes of surface air temperature annual cycle in the Northern Hemisphere land areas

“…Besides the long‐term trends of the annual cycle amplitudes, one can also find that there exist some consistent quasi‐periods in the amplitude variations. Almost all of them have a period of 6–8 years (Figure S5), which is well known in the previous studies on the surface air temperature in Europe (Grieser et al ., 2002; Paluš, 2014; Jajcay et al ., 2016; Meyer and Kantz, 2019).…”

“…The truth is that the changes in AC amplitude and phase are nonlinear (Paluš et al ., 2005; Deng et al ., 2018) and fluctuating. A significant quasi‐period of 7–8 years has been found in changes of AC amplitude and phase in Europe (Grieser et al ., 2002; Paluš, 2014; Jajcay et al ., 2016; Meyer and Kantz, 2019). Therefore, the more precise way is to analyse the original time‐varying amplitude and phase series.…”

Time scales in the dynamics of political opinions and the voter model

“…where σ 2 η is the average variance of the noise over the whole trajectory. The decomposition of δ 2 X is analogous to the decomposition of fluctuation functions presented in [54,65]. It allows one to separate the signals δ 2 X (t) and σ 2 η .…”

“…x (∆) is, therefore, characteristic of the underlying dynamics of the process under investigation [50]. In this sense, the TAMSD is a similar tool to detrended fluctuation analysis [52], that can be used equivalently [53,54]. If x(t), e.g.…”

MFDFA: Efficient Multifractal Detrended Fluctuation Analysis in Python

“…[18]. MFDFA has found application in various fields, such as the analysis of heartbeat rate [19], arterial pressure [10], EEG sleep data [11,13], physiology [20], keystroke time series from Parkinson's disease patients [21], cosmic microwave radiation [22,23], seismic activity [24,25], sunspot activity [26], atmospheric scintillation [27], temperature variability [28], meteorology [29], precipitation levels [30], streamflow and sediment movement [7,[31][32][33][34][35][36], protein folding [37], finance and econophysics [38][39][40][41][42], electricity prices [43,44], power-grid frequency [45,46], epidemiology [47], music [48][49][50], ethology [51,52], multifractal harmonic signals [53], and microrheology [54].…”