AJCC  Vol.1 No.3 , September 2012
Restoration of Time-Spatial Scales in Global Temperature Data
Author(s) Igor Zurbenko, Ming Luo*
ABSTRACT
The objective of this paper is to utilize images of spatial and temporal fluctuations of temperature over the Earth to study the global climate variation. We illustrated that monthly temperature observations from weather stations could be decomposed as components with different time scales based on their spectral distribution. Kolmogorov-Zurbenko (KZ) filters were applied to smooth and interpolate gridded temperature data to construct global maps for long-term (≥ 6 years) trends and El Nino-like (2 to 5 years) movements over the time period of 1893 to 2008. Annual temperature seasonality, latitude and altitude effects have been carefully accounted for to capture meaningful spatiotemporal patterns of climate variability. The result revealed striking facts about global temperature anomalies for specific regions. Correlation analysis and the movie of thermal maps for El Nino-like component clearly supported the existence of such climate fluctuations in time and space.

Cite this paper
I. Zurbenko and M. Luo, "Restoration of Time-Spatial Scales in Global Temperature Data," American Journal of Climate Change, Vol. 1 No. 3, 2012, pp. 154-163. doi: 10.4236/ajcc.2012.13013.
References
[1]   M. G. New, M. Hulme and P. D. Jones, “Representing Twentieth-Century Space-Time Climate Variability, Part I: Development of A 1961-1990 Mean Monthly Terrestrial Climatology,” Journal of Climate, Vol. 12, No. 3, 1999, pp. 829-856. Hdoi:10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2

[2]   M. G. New, M. Hulme and P. D. Jones, “Representing Twentieth-Century Space-Time Climate Variability, Part II: Development of 1901-1996 Monthly Grids of Terrestrial Surface Climate,” Journal of Climate, Vol. 13, No. 13, 2000, pp. 2217-2238. Hdoi:10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2

[3]   J. L. Caesar and R. Vose, “Large Scale Changes in Observed Daily Maximum and Minimum Temperatures: Creation and Analysis of a New Gridded Data Set,” Journal of Geophysical Research, Vol. 111, No. D05101, 2006, 10 p. Hdoi:10.1029/2005JD006280

[4]   J. H. Lawrimore, M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose and J. Rennie, “An Overview of the Global Historical Climatology Network Monthly Mean Temperature Data Set, Version 3,” Journal of Geophysical Research, Vol. 116, No. D19121, 2011, 18 p. Hdoi:10.1029/2011JD016187

[5]   J. Hansen, R. Ruedy, M. Sato and K. Lo, “2010: Global Surface Temperature Change,” Reviews of Geophysics, Vol. 48, No. RG4004, 2010, 29 p. Hdoi:10.1029/2010RG000345

[6]   S. Solomon, et al., “Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,” Cambridge University Press, New York, 2007.

[7]   S. Huang, P. M. Rich, R. L. Crabtree, C. S. Potter and P. Fu, “Modeling Monthly Near-Surface Air Temperature from Solar Radiation and Lapse Rate: Application over Complex Terrain in Yellowstone National Park,” Physical Geography, Vol. 29, No. 2, 2008, pp. 158-178. Hdoi:10.2747/0272-3646.29.2.158

[8]   E. T. Linacre, “Climate Data & Resources,” Routledge, London, 1992.

[9]   B. Finkenstadt, L. Held and V. Isham, “Statistical Methods for Spatio-Temporal Systems,” Chapman & Hall/ CRC, Boca Raton, 2007.

[10]   E. Porcu, J. M. Montero and M. Schlather, “Advances and Challenges in Space-time Modeling of Natural Events,” Springer-Verlag Berlin Heidelberg, New York, 2012. Hdoi:10.1007/978-3-642-17086-7

[11]   D. D. Cyr, “A Spline Kernel Based Smoothing Algorithm: A Comparison of Methods and a Spatiotemporal Application to Global Climate Fluctuations,” Ph.D. Thesis, State University of New York, Albany, 2010.

[12]   K. G. Tsakiri and I. G. Zurbenko, “Prediction of Ozone Concentrations Using Atmospheric Variables,” Air Quality, Atmosphere & Health, Vol. 4, No. 2, 2011, pp. 111- 120. Hdoi:10.1007/s11869-010-0084-5

[13]   S. T. Rao, I. G. Zurbenko, R. Neagu, P. S. Porter, J. Y. Ku and R. F. Henry, “Space and Time Scales in Ambient Ozone Data,” Bulletin of the American Meteorological Society, Vol. 78, No. 10, 1997, pp. 2153-2166. Hdoi:10.1175/1520-0477(1997)078<2153:SATSIA>2.0.CO;2

[14]   Organization for Economic Co-Operation and Devel- opment (OECD), “Section 4: Guidelines for the Reporting of Different Forms of Data,” In: Data and Metadata Reporting and Presentation Handbook, OECD Publishing, Paris, 2007, pp. 45-57.

[15]   W. Yang and I. G. Zurbenko, “Kolmogorov-Zurbenko Filters,” Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 3, 2010, pp. 340-351. Hdoi:10.1002/wics.71

[16]   T. R. Karl, H. F. Diaz and G. Kukla, “Urbanization: Its Detection and Effect in the United States Climate Record,” Journal of Climate, Vol. 1, No. 11, 1988, pp. 1099- 1123. Hdoi:10.1175/1520-0442(1988)001<1099:UIDAEI>2.0.CO;2

[17]   I. Zurbenko and D. Cyr, “Climate Fluctuations in Time and Space,” Climate Research, Vol. 46, No. 1, 2011, pp. 67-76. Hdoi:10.3354/cr00956

[18]   P. E. Thornton, S. W. Running, and M. A. White, “Generating Surfaces of Daily Meteorology Variables over Large Regions of Complex Terrain,” Journal of Hy- drology, Vol. 190, No. 3, 1997, pp. 214-251.

[19]   T. R. Blandford, K. S. Humes, B. J. Harshburger, B. C. Moore and V. P. Walden, “Seasonal and Synoptic Varia- tions in Near-Surface Air Temperature Lapse Rates in a Mountainous Basin,” Journal of Applied Meteorology and Climatology, Vol. 47, No. 1, 2008, pp. 249-261. doi:10.1175/2007JAMC1565.1

[20]   R. Dodson and D. Marks, “Daily Air Temperature Interpolated at High Spatial Resolution over A Large Mountainous Region,” Climate Research, Vol. 8, No. 1, 1997, pp. 1-20. Hdoi:10.3354/cr008001

[21]   C. Rolland, “Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions,” Journal of Climate, Vol. 16, No. 7, 2003, pp. 1032-1046. Hdoi:10.1175/1520-0442(2003)016<1032:SASVOA>2.0.CO;2

[22]   I. G. Zurbenko, S. T. Rao and R. Henry, “Mapping Ozone in the Eastern United States,” Environmental Manager, Vol. 1, No. 2, 1995, pp. 24-30.

[23]   B. Close and I. Zurbenko, “KZA: Kolmogorov-Zurbenko Adaptive Filters,” R package, ver. 2.01, 2011.

[24]   D. Cyr and I. Zurbenko, “Kzs: Kolmogorov-Zurbenko Spatial Smoothing and Applications,” R package, ver. 1.4, 2008.

[25]   K. Stahl, R. D. Moore, J. A. Floyer, M. G. Asplin and I. G. McKendry, “Comparison of Approaches for Spatial Interpolation of Daily Air Temperature in a Large Region with Complex Topography and Highly Variable Station Density,” Agricultural and Forest Meteorology, Vol. 139, No. 3-4, 2006, pp. 224-236. Hdoi:10.1016/j.agrformet.2006.07.004

[26]   I. G. Zurbenko, “The Spectral Analysis of Time Series,” North-Holland Series in Statistics and Probability, Elsevier, Amsterdam, 1986.

[27]   W. D. Sellers, “Physical Climatology,” University of Chi- cago Press, Chicago, 1965. http://www4.uwsp.edu/geo/faculty/ritter/geog101/textbook/energy/global_patterns_of_heat_transfer.html

[28]   I. Zurbenko and A. Potrzeba, “Tides in the Atmosphere,” Air Quality, Atmosphere and Health, 2011. Hdoi:10.1007/s11869-011-0143-6

[29]   NASA/Goddard Space Flight Center, “Five-Year Average Global Temperature Anomalies from 1880 to 2010,” Scientific Visualization Studio. http://svs.gsfc.nasa.gov/vis/a000000/a003800/a003817

 
 
Top