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Applying detrended fluctuation analysis to long term atmospheric data

PI: Steve Brenner
Institution: Bar Ilan University, Israel
Additional Investigators: Armin Bunde (Justus-Liebig-Universitat Giessen, Germany), Shlomo Havlin (Bar Ilan University, Israel), and Hans-Joachim Schellnhuber Armin Bunde (Justus-Liebig-Universitat Giessen, Germany), Shlomo Havlin (Bar Ilan University, Israel), Hans-Joachim Schellnhuber (Potsdam Institute for Climate Impact Research, Germany)
By applying time series analysis methods developed for modern statistical physics to long-term atmospheric data records, it has been shown that the near surface temperature exhibits universal long-term power law correlations. The main tools used were Fluctuation Analysis (FA) and Detrended Fluctuation Analysis (DFA). We have also applied these methods to the analysis of climate model simulations previously available through the IPCC web site (models from various centers and for various forcing scenarios) as well as for a series of 10 simulation conducted with the NCAR PCM. Through the application of this methodology we have been able to assess the ability of the various models to reproduce the observed atmospheric variability. The objective of our work is to apply DFA (and possibly other methodologies such as Discrete Wavelet Transform) to the newest multi-model data set that will become available and thereby assess these climate simulations. Application of these methods can help identify the models' strengths and weaknesses. For our work the primary data set needed consists of near surface (2 m) temperature as well as sea surface temperature from coupled AOGCMs.

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