Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition

Authors: Md. Khademul Islam Molla, Akimasa Sumi, M. Sayedur Rahman

Abstract:

The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on the properties exhibited by the data. In this paper, EMD is applied to explore the properties of the multi-year air temperature and to observe its effects on climate change under global warming. This method decomposes the original time-series into intrinsic time scale. It is capable of analyzing nonlinear, non-stationary climatic time series that cause problems to many linear statistical methods and their users. The analysis results show that the mode of EMD presents seasonal variability. The most of the IMFs have normal distribution and the energy density distribution of the IMFs satisfies Chi-square distribution. The IMFs are more effective in isolating physical processes of various time-scales and also statistically significant. The analysis results also show that the EMD method provides a good job to find many characteristics on inter annual climate. The results suggest that climate fluctuations of every single element such as temperature are the results of variations in the global atmospheric circulation.

Keywords: Empirical mode decomposition, instantaneous frequency, Hilbert spectrum, Chi-square distribution, anthropogenic impact.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056525

References:

[1] Radic, V., Z. Pasaric and N. Sinik, "Analysis of Zagreb climatological data series using empirically decomposed intrinsic mode functions," Geofizika, 21, pp. 15-36, 2004.
[2] R. Voss, W. May and E. Roeckner, "Enhanched resolution modeling study on anthropogenic climate change: changes in extremes of the hydrological cycle", International Journal of Climatology, 22, pp. 755-777, 2002.
[3] K. E. Kunkel, R. A. Pielke and S. A. Changnon, "Temporal fluctuations in weather and climate extremes that cause economic and human health impacts: a review", Bulletin of the American Meteorological Society, 80, pp. 1077-1098, 1999.
[4] IPCC, Climate change 1995: The science of climate change, Cambridge University press, Cambridge, 1996.
[5] K. Dairaku, S. Emori, T. Nozawa, N. Yamazaki, M. Hara and H. Kawase, "Hydrological change under the global warming in Asia with a regional climate model nested in a general circulation model", 3rd International Workshop on Monsoons (IWM-III), 56, 2004.
[6] A. Markham, N. Dudley and S. Stolton, "Some like it hot. WWF International CH-1196", Gland Switzerland, reprint, 1994.
[7] B. C. Bates, S. P. Charles and J. P. Hughes, "Stochastic downscaling of numerical climate model simulations", Environmental Modeling Software, 13(3-4), pp. 325-331, 1998.
[8] B. Rajagopalan, U. Lall and M. A. Cane, "Anomalous ENSO occurrences: an alternative view", Journal of Climate, 10, pp. 2351-2357, 1997.
[9] B. Rajagopalan, U. Lall and M. A. Cane, "Comment on Reply to the Comments of Trenberth and Hurrell", Bulletin of American Meteorological Society, 80, pp. 2724-2726, 1999.
[10] D. E. Harrison and N. K. Larkin, "Darwin sea level pressure, 1876-1996: Evidence for climate change?" Geophysics Review Letter, 24, pp. 1779-1782, 1997.
[11] C. Wunsch, "The interpretation of short climate records, with comments on the North atlantic and Southern Oscillations", Bulletin of American Meteorological Society, 80, pp. 245-255, 1999.
[12] F. S. Mpelasoka, A. B. Mullan and R. G. Heerdegen, "New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches", International Journal of Climatology, 21, pp. 1415-1433, 2001.
[13] D. R. Easterling, G. A. Meehl, C. Permesan, S. A. Changnon, T. R. Karl and L. O. Mearns, "Climate extremes: observations, modeling and impacts", Science, 289, pp. 2068-2074, 2000.
[14] T. Uchiyama, A. Noda, S. Yukimoto and M. Chiba, "Study of the estimate of new climate change scenarios based on new emission scenarios- IPCC AR4 experiments", CGER-s Supercomputer Activity Report, Vol. 12-2003, pp. 51-58, 2005.
[15] M. J. Salinger and G. M. Griffiths, "Trends in New Zealand daily temperature and rainfall extremes", International Journal of Climatology, 21, pp. 1437-1452, 2001.
[16] K. Dairaku, S. Emori and T. Nozawa, "Hydrological projection over Asia under the global warming with a regional climate model nested in the CCSR/NIES AGCM", CGER-s Supercomputer Activity Report Vol.12-2003, pp. 13-20, 2005.
[17] K. Coughlin and K. K. Tung, "Eleven year solar cycle signal throughout the lower atmosphere", Journal of Geophysical Research, 109, D21105, 2004.
[18] M. H. Glantz, R. W. Katz and N. Nicholls, "Teleconnections linking of worldwide climate anomalies", Cambridge University Press, 535, 1991.
[19] A. V. Fedorov and S. G. Philander, "Is El Nino Changing?" Science, 288, 2000.
[20] Z. Wu, E. K. Schneider, Z. Z. Hu and L. Cao, "The impact of of global warming on ENSO variability in climate records", COLA Technical Report, CTR 110, 2001.
[21] K. Dairaku, S. Emori, T. Nozawa, N. Yamazaki, M. Hara and H. Kawase, "Regional climate simulation over Asia under the global warming nested in the CCSR/NIES AGCM", Symposium on Water Resource and Its Variability in Asia in the 21st Century, 90-93, 2004.
[22] K. Dairaku, S. Emori, T. Nozawa, N. Yamazaki, M. Hara and H. Kawase, "Regional climate simulation over Asia under the global warming nested in the CCSR/NIES AGCM", Symposium on Water Resource and Its Variability in Asia in the 21st Century, pp. 756-764, 2004.
[23] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. of Royal Society London, 454A, pp. 903-995, 1998.
[24] B. Z. Wu and N. E. Huang, "A study of the characteristics of white noise using the empirical mode decomposition", Proc. R. Soc. Lond., 460A, 1597-1611, 2004.
[25] I. Sadhukhan, and U. K. De, "Pre-monsoon consecutive developments over Gangetic West Bengal during 1980-1989", Indian Journal of Radio and Space Physics, 27, pp. 102-109, 1998.
[26] P. Gloerson and N. Huang, "Comparison of interanual intrinsic modes in hemispheric sea ice covers and others geophysical parameters", IEEE Trans. on Geosciences and Remote Sensing, 41(5), pp. 1062-1074, 2003.
[27] P. Flandrin, G. Rilling and P. Goncalves, "Empirical mode decomposition as a filter bank", IEEE Signal Processing Letters, 11(2), pp. 112-114, 2004.
[28] M. C. Ivan, and G. B. Richard, "Empirical mode decomposition based frequency attributes", Proceedings of the 69th SEG Meeting, Texas, USA, 1999.
[29] M. Cooke, Modeling Auditory Processing and Organisation, Cambridge University press, 1993.
[30] N. E. Huang et al., "Application of Hilbert-Huang transform to non-stationary financial time series analysis", Applied Stochastic Model in Business and Industry, 19, pp. 245-268, 2003.
[31] C. Y. Chang, N. E. Huang and Z. Shen, "A statistically significance periodicity in the homestake solar neutrino data", Chinese Journal of Physics, Vol. 35 (6-11), pp. 818-831, 1997.
[32] IPCC, "Climate change 2001: The specific Basis, contribution of working group to the third assessment report of the intergovernmental panel on climate change", Cambridge University Press, Cambridge, UK and New York, USA, 2001.
[33] A. Papoulies, Probability, Random Variable and Stochastic Processes, Second Edition, Mc-Graw Hill, 1986.
[34] N. E. Huang, Z. Shen, and S. R. Long, "A new view of non-linear water waves: the Hilbert spectrum", Annual Review of Fluid Mechanics, 31, pp. 417-457, 1999.