<i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

Abstract

Abstract.Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

Multi-Months Cycles Observed in Climatic Data

“…These figures show that most of the cycles associated with c 1 and c 2 can be considered as significant. The cycle observed in the climatic indices are also significant, since one always get P < 0.1 (see Nicolay et al (2010)). Finally, let us remark that c 1 and c 2 can also be detected through the Fourier transform, if the time series are preprocessed in order to free the corresponding spectrum from the dominating cycle corresponding to one year (for more details, see Nicolay et al (2010)).…”

“…It is shown in Nicolay et al (2010) that such a methodology yields reliable data. The probability values concerning c 1 and c 2 are displayed in Fig.…”

“…In Nicolay et al (2010), to check if the cycles observed in the time series can be trusted, the scale spectra of the NCEP/NCAR reanalysis data have been compared with the spectra of signals made of an autoregressive model of the first order (AR(1) model, see e.g. Janacek (2001)), in which maxima could occur fortuitously.…”

The Adaptive Analysis of Shock Signals on the Basis of Improved Morlet Wavelet Clusters

“…ere is a 90-degree phase shift between its real and imaginary parts, which makes it easy to obtain the instantaneous frequency and phase of the signal, and it therefore has a wide range of applications. For example, Morlet wavelets have been applied to signal filtering and denoising [4], mechanical fault diagnosis [5][6][7][8], the analysis of medical signals [9], research on river runoff in natural environments [10], research on rainwater evaporation [11], problems of polymer pollution [12], the atmospheric system [13], the influence of cosmic rays on organisms [14], and the motion of celestial bodies [15].…”

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

“…Other studies [3,4] analyzed δ 18 O stalagmites records through the Morlet wavelet in order to determine the 5 frequency and temporal variability of isotopic and climatic anomalies. Some other recent applications of the wavelet transform in similar fields include airborne CO 2 measurements [5], daily rainfall estimation [6], analysis of precipitation trends [7], time series modeling of river flows [8] or statistical analysis of air temperature data [9].…”