Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition
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.
[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.
[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.
@article{"International Journal of Earth, Energy and Environmental Sciences:51268", author = "Md. Khademul Islam Molla and Akimasa Sumi and M. Sayedur Rahman", title = "Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition", 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.", volume = "1", number = "7", pages = "69-9", }
