Discrete Dynamics in Nature and Society
Volume 2011 (2011), Article ID 935034, 21 pages
Research Article

Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability

1Geophysical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2G7
2Department of Computer Science and Engineering, The University of Rajshahi, Rajshahi 6205, Bangladesh
3Department of Information and Communication Engineering, The University of Tokyo, Tokyo 113-0033, Japan

Received 7 January 2011; Accepted 25 May 2011

Academic Editor: M. De la Sen

Copyright © 2011 Md. Khademul Islam Molla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub-band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn's IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability.