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Using Empirical Mode Decomposition to Correlate Paleoclimatic Time-series : Volume 7, Issue 2 (17/04/2007)

By Solé, J.

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Book Id: WPLBN0003990538
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: Using Empirical Mode Decomposition to Correlate Paleoclimatic Time-series : Volume 7, Issue 2 (17/04/2007)  
Author: Solé, J.
Volume: Vol. 7, Issue 2
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Turiel, A., Llebot, J. E., & Solé, J. (2007). Using Empirical Mode Decomposition to Correlate Paleoclimatic Time-series : Volume 7, Issue 2 (17/04/2007). Retrieved from

Description: Department of Physics, Universitat Autonoma de Barcelona. Campus de la UAB 08193 Bellaterra (Cerdanyola del Vallès), Catalunya, Spain. Determination of the timing and duration of paleoclimatic events is a challenging task. Classical techniques for time-series analysis rely too strongly on having a constant sampling rate, which poorly adapts to the uneven time recording of paleoclimatic variables; new, more flexible methods issued from Non-Linear Physics are hence required. In this paper, we have used Huang's Empirical Mode Decomposition (EMD) for the analysis of paleoclimatic series. We have studied three different time series of temperature proxies, characterizing oscillation patterns by using EMD. To measure the degree of temporal correlation of two variables, we have developed a method that relates couples of modes from different series by calculating the instantaneous phase differences among the associated modes. We observed that when two modes exhibited a constant phase difference, their frequencies were nearly equal to that of Milankovich cycles. Our results show that EMD is a good methodology not only for synchronization of different records but also for determination of the different local frequencies in each time series. Some of the obtained modes may be interpreted as the result of global forcing mechanisms.

Using empirical mode decomposition to correlate paleoclimatic time-series

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