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2014 | 51 | 1 | 37-44
Tytuł artykułu

Global annual average temperature – a precise modelling

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Global annual average temperature (GAAT) is regarded as a precise indicator of the warming of the globe over the centuries, and its spectre is looming large with the passage of time and with the advancement of civilization. Global warming, caused by the accumulation of greenhouse gases in the atmosphere, has become the worst environmental threat to mankind. The phase 1981 to 2012 was the most crucial phase, and the impact of global warming in that phase indeed points to a disaster if not controlled now. Work on the building of appropriate models to represent the GAAT data can be found in the literature, although the precision levels (in terms of R2 values) of such models do not exceed 0.86. In this paper, six models are developed by using different combinations of mathematical functions. The developed models are superior to existing models in terms of their precision. In fact, to generate such models, extensive simulation work has been carried out not only with respect to the types of mathematical functions, but also with respect to the choices of initial values of the coefficients involved in each model. The models developed here have attained R2 values as high as 0.896.
Wydawca
Czasopismo
Rocznik
Tom
51
Numer
1
Strony
37-44
Opis fizyczny
Daty
wydano
2014-06-01
online
2014-06-06
Twórcy
  • Former Dean, Post Graduate Studies and Professor of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Faculty of Agriculture, Mohanpur, Nadia, West Bengal, 741252, India, satyabrata.pal@hotmail.com
  • Uttar Banga Krishi Viswavidyalaya, Department of Agricultural Statistics, Pundibari, Coochbehar, West Bengal, 736165, India, arunava_ubkv@yahoo.co.in
Bibliografia
  • Casey K.S., Cornillon P. (2001): Global and regional sea surface temperature trends. J. Climate 14: 3801-3818.[Crossref]
  • Draper N.R., Smith H. (1998): Applied Regression Analysis, John Wiley and Sons.
  • Fomby T.B., Vogelsang T.J. (2001): The Application of Size-Robust Trend Statistics to Global-Warming Temperature Series. J. Climate 14.
  • Houghton J.T., Ding Y., Griggs D.J., Noguer M., van der Linden P.J., Dai X., Maskell K., Johnson C.A., Eds. (2001): Climate Change: The Scientific Basis. Cambridge University.
  • Jones P.D., Osborn T.J., Briffa K.R., Folland C.K., Horton E.B., Alexander L.V., Parker D.E., Rayner N.A. (2001): Adjusting for sampling density in grid box land and ocean surface temperature time series. J. Geophys. Res. 106: 3371-3380.
  • Lawrence S.P., Llewellyn-Jones D.T., Smith S.J. (2004): The measurement of climate change using data from the Advanced Very High Resolution and Along Track Scanning Radiometers. J. Geophys. Res. 109.
  • Pal S., Pal S. (2011): A Revisit to the Global Warming Phenomenon. Proceedings (published online) of the 58th World Statistics Congress organised by the International Statistical Institute, Hague, held at Dublin, Ireland.
  • Pal S., Pal S., Kageyama S. (2013): Modeling the Global Mean Temperature. Bull. Hiroshima Inst. Tech. Research 47: 149-152.
  • Pal S., Ghosh A., Kageyama S. (2014): Revisit to Modelling Global Annual Average Temperature - A Parametric Approach. Bull. Hiroshima Inst. Tech. Research 48 (to appear).
  • Woodward W.A., Gray H.L. (1993): Global warming and the problem of testing for trend in time series data. J. Climate 6: 953-962.[Crossref]
  • Zheng X., Basher R.E. ( 1999): Structural time series models and trend detection in global and regional temperature series. J. Climate 12: 2347-2358.[Crossref]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_2478_bile-2014-0003
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