Changes in means and extremities of temperature and precipitation in Hungary: One empirical and two dynamical model approaches with special reference to northeast Hungary

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Abstract

Regional climate changes are still one of the most difficult problems of the climate change issue. Weather extremes are even more problematic components of the projected regional climate changes, since, as our analysis demonstrates, no unequivocal similarity exists for them. Results from three scientific approaches, the raw General Circulation Models (GCM), the mesoscale models compiled from the PRUDENCE project, and an empirical method called Natural experiment are compared. The latter approach provides regression coefficients between the local and global variables in the continually warming period 1976-2005. The global model results comprise results of 9 AOGCMs, while in PRUDENCE a set of 5 model outputs are analysed in detail. According to the experience of our analyses, there is a fair agreement of the three approaches in the temperature extremes of the warm half-year in Hungary, with a much less unequivocal picture in the frequency of frozen days in the cold half of the year. For precipitation, again, the summer maxima of diurnal totals behave similarly in all regions of the country, namely they exhibit unequivocal increase, whereas no clear picture is seen for frequency of wet/dry days. These results are further scaled down, towards the northeastern part of Hungary. Results of the empirical analysis are specified to six stations of the region. Uncertainties of the analogy in space vs. analogy in time are also demonstrated, as methodical alternatives to get time details. At the end of the paper, statistical independence of NE Hungary is demonstrated by factor analysis of Palmer Drought Severity Index.

Original languageEnglish
Pages (from-to)443-457
Number of pages15
JournalThaiszia Journal of Botany
Volume19
Issue numberSUPPL. 1
Publication statusPublished - Dec 1 2009

Keywords

  • Climate change, GCM
  • Hungary
  • Mesoscale modelling
  • Statistical downscaling

ASJC Scopus subject areas

  • Plant Science

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