عنوان مقاله [English]
نویسندگان [English]چکیده [English]
The aim of this study was to simulate the impact of climate change on frost phenomena in the Zabol station. For this purpose, the method of comparing and selecting the best model fitted to the series of general circulation models were used. At First the daily data of Zabol synoptic station in period (1966-2008) was prepared. Then the general circulation model data in two separate periods (1988- 2004 and 2010- 2039) to develop a climate change scenario were used.
After the providing the basic scenario ,four circulation models including HADCM3, BCM2, HADGEM NCPCM were selected and these models evaluated by statistical methods.
The bias , mean absolute error , means and standard deviations of each model was calculated and finally generate daily data until 2037. By selecting the best model, artificial data for future climate of Zabol station was generated.
The results showed that the component of future climatic temperature will increase compared to the previous period. Increase the maximum temperature for spring and autumn more than winter and summer. Maximum increase in low temperature is observed from August to February. Frost range in the observed climate period are about six months and continued from November to April.In the future climate period this time reduce to five months from November to March.
The results showed that all three types of frost are Non-static .The trend of weak frost will increase in the future and trend of moderate and severe frosts will decreases . Increasing the number of weak frosts and increasing of moderate and severe frosts in future indicates the sensitivity of frost in Zabol station to global warming.
4- Dubrovsky M., 1998: Estimating climate change impacts on crop yields with use of crop growth model and weather generator. Proc. 14th Conf. Prob. Stat., AMS.
5 -Dubrovsky, M., 1996: Met&Roll: the stochastic generator of daily weather series for the crop growth model. Meteorological Bulletin 49, 97-105.
6 -Dubrovsky, M., 1996: Validation of the stochastic Weather Generator Met&ROLL, Meteorogickeo Zpravy, Vol49, pp. 12q – 1380.
7-Elshamy, M.E., Wheater, .H.S., Gedney, .N., Huntingford, .C.,2005: Evaluation of the rainfall component of weather generator for climate change studies. journal of hydrology, 326:1-24.
8- Harmel,R.D, et.al, 2002: Evaluating the Adequecy of Simulating Maximum and Minimum Daily Air Temprature With the Normal Distributian, Canadian Society for engineering , 26pp.
9- Johnson, G.L., Hanson, C.L., Hardegree, S.P., and Ballard, E.B., 1996: Stochastic weather simulation: overview and analysis of two commonly used models. J. Applied Meteorology 35, 1878-1896.
10- Mc kague,k., et. Al 2003: Clim Gen- A ZGnvenient weather Genera Tion Tool for Canadian climat stations, proceeding of CCAE/SCGR 2003 Meeting, Montreal, Canada.
11- Rasco, P., Szeidl, L., and Semenov, M.A., 1991: A serial approach to local stochastic models. J. Ecological Modeling 57, 27-41.
12- Richardson, C.W., and Wright, D.A., 1984: WGEN: A model for generating daily weather variables. U.S. Dept. Agr., Agricultural Research Service, Publ. ARS-8, 83 pp.
13- Semenov, M.A., and Barrow, E.M., 2002: LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual, Version3.0.
14- Semenov, M.A., Brooks, R.J., Barrow, E.M., and Richardson, C.W., 1998: Comparison of the WGEN and LARS-WG stochastic weather generators in divers climates. Climate Research 10, 95-107.
15- Semenov, M.A., and Barrow, E.M., 1997: Use of a stochastic Weather Generator in the development of Climate Change Scenarios.Climatic Change 35, 397-414.
16- Semenov, M.A.,and Brooks, R.J.,1999: Spatial interpolation of the LARS-WG Stochastic Weather Generator in Great Britain. Climate Research 11, 137-148.
17- Thompson, C.S, and Mullan, A.B, 1995: Weather Generators. NIWA Internal report, 115- 120.
18- Wilks, D.S. 1992: Adapting stochastic weather generation algorithms for climate change studies. Climate Change. 22, 67-84.
19- Wilks, D.S. and Wilby, R.L. 1999: The Weather Generation game: a review of Stochastic Weather Models. Progress in Physical Geography 23, 329-357.
20-Willby,R.L,Dawson,C.W,Barrow,EM,2001:SDSM Version 3.1 –A decision support tool for the assessment of regional climate change impacts.