Due to the growth of industries and factories, deforestation and other environmental degradation a well as greenhouse gases have been increasing greenhouse gases on the Earth's surface in recent decades. This increase disturbs the climate of the Earth and is called climate change. An Increase in greenhouse gases in the future could exacerbate the climate change phenomenon and have several negative consequences on different systems, including water resources, agriculture, environment, health and industry. On the other hand to evaluate the destructive effects of climate change on different systems, it is necessary to initially study the area affected by climate change phenomena. Although the effect of climate change on different fields has been studied up to now, most of these studies only used a downscaling method on the AOGCM output model. In climate change studies, using different AOGCM models, downscaling methods and scenarios of greenhouse gas emissions affect the final results. This paper aims to present a framework to assess the effect of using different AOGCM models and downscaling methods on the regional climate. One of the major problems in using the output of AOGCMs (Atmosphere-Ocean General circulation Model), is their low degree of resolution compared to the study area so to make them appropriate for use, downscaling methods are reguired. In this study the performance of kriging and IDW (Inverse Distance Weighting,) in downscaling monthly average of temperature and rainfall of 7 model AOGCM - presented in the IPCC Third Report- including CCSR / NIES: CGCM2, CSIRO MK2, ECHAM4 / OPOYC3, GFDL R30, HadCM3, and NCAR DOE PCM were evaluated using several computational cells around the desired position of the river basin. This performance was evaluated by the coefficient of determination (R2) and the Root Mean Square Error (RMSE) between observed and downscaled data. Finally, the IDW method with 8 computational cells was selected. Then accordingly, seasonal climate change scenarios of temperature and precipitation in the three periods 2039-2010, 2069-2040 and 2099-2070 from 7 AOGCM output models under the SRES (Special Report on Emission Scenario) were downscaled for the study area.
Based on the findings of this study the following conclusions are inferable. (1) Results of kriging and IDW with a different number of pixels around the original pixel did not show a significant difference. Therefore, because of its simplicity, the IDW method with 8 pixels was used to downscale the climate change scenarios of temperature and precipitation in future periods. (2) In all seasons and periods the average temperature of future increase was compared to the baseline period, so that in the period 2070 to 2099 the increase is more than the two other periods, while for rainfall, both reduction and increased amounts for future periods are predictable. According to the temperature results, in the 2070- 2099 period winter temperature would increase 2 to 7 ° C compared to baseline period in the study area, while for rainfall, this change is between -40 to +30 percent. On the other hand for the other seasons in the future period similar results were also derive. 3) The results indicate that the difference of climate change scenarios resulting from different AOGCM models under the same emission scenarios is more than the difference resulting from an AOGCM model under different emission. (4) Finally we can conclude that using data from only one AOGCM model and an emission scenario can force unrealistic results for related projects dealing with the destructive effects of climate change phenomena.