Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 17 3 2010 10.5194/npg-17-269-2010 http://www.nonlin-processes-geophys.net/17/269/2010/ http://www.nonlin-processes-geophys.net/17/269/2010/npg-17-269-2010.html http://www.nonlin-processes-geophys.net/17/269/2010/npg-17-269-2010.pdf 269 272 2010-05-20 <i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method" S. Nicolay s.nicolay@ulg.ac.be G. Mabille X. Fettweis M. Erpicum Département de mathématique, University of Liège, Liège, Belgium Département de géographie, University of Liège, Liège, Belgium 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. Allen, M. R. and Robertson, A. W.: Distinguishing modulated oscillations from coloured noise in multivariate datasets, Clim. Dynam., 12, 775–784, 1996. Arneodo, A., Argoul, F., Elezgaray, J., and Grasseau, G.: Wavelet transform analysis of fractals: application to nonequilibrium phase transitions, in: Nonlinear Dynamics, edited by: Turchetti, G., World Scientific, Singapore, 1988. Daubechies, I.: Ten lectures on wavelets, SIAM, Philadelphia, 1992. Freysz, E., Pouligny, B., Argoul, F., and Arneodo, A.: Optical wavelet transform of fractal aggregates, Phys. Rev. Lett., 64, 745–748, 1990. Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. C., Shih, H. H., Zheng, Q. N., Yen, N. C., Tung, C. C., and Liu, H. H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lon. Ser A, 454, 903–995, 1998. Kalney, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year reanalysis project, B. Am. Meteorol. Soc., 104, 437–471, 1996. Mabille, G. and Nicolay, S.: Multi-year cycles observed in air temperature data and proxy series, Eur. Phys. J.-Spec. Top., 174, 135–145, 2009. Mann, M. E. and Lees, J.: Robust estimation of background noise and signal detection, Climatic Change, 33, 409–445, 1996. Mann, M. E., Rutherford, S., Wahl, E., and Ammann, C.: Robustness of proxy-based climate field reconstruction methods, J. Geophys. Res., 112, D12109, doi:10.1029/2006JD008272, 2007. Maraun, D. and Kurths, J.: Cross wavelet analysis: significance testing and pitfalls, Nonlin. Processes Geophys., 11, 505–514, doi:10.5194/npg-11-505-2004, 2004. Matyasovszky, I.: Improving the methodology for spectral analysis of climatic time series, Theor. Appl. Climatol., doi:10.1007/s00704-009-0212-z, in press, 2010. Nicolay, S., Mabille, G., Fettweis, X., and Erpicum, M.: 30 and 43 months period cycles found in air temperature time series using the Morlet wavelet method, Clim. Dynam., 33, 1117–1129, 2009. Palu$\check\rm s$, M. and Novotná, D.: Quasi-biennial oscillations extracted from the monthly NAO index and temperature records are phase-synchronized, Nonlin. Processes Geophys., 13, 287–296, doi:10.5194/npg-13-287-2006, 2006. Percival, D. B. and Walden, A. T.: Spectral analysis for physical applications, Cambridge University Press, Cambridge, 1993. Smith, D., Cusack, S., Colman, A., Folland, C., Harris, G., and Murphy, J.: Improved surface temperature prediction for the coming decade from global climate model, Science, 317, 796–799, 2007.