چكيده لاتين :
Introduction
It into one of the most important issues of climate change and variability of atmospheric sciences, ocean and environment has become. Nowadays, many researchers have been attracted to global warming and climate change. One of the aspects of climate change is global warming. Today, evidence of global, including an increase in temperature measurement, fluctuations in energy input to Earth, increasing ocean temperatures, changes in the melting lakes and changes in settlement patterns of plants and animals are at a higher latitude .Modeling the past, present, and future weather parameters, especially the day of fundamental importance in relation to climate change and variability for more accurate algorithm parameters studied, RegCM4 regional climate model used for Downscaling data to lower resolution. Using local climate is thriving in the last two decades, these models based on dynamic relations atmosphere boundary layer, surface topography, atmospheric chemistry and aerosols, ocean current flow, aqueous coating, plants and soil surface Downscaling local climate data to their low resolution. One of these climatic parameters influencing the occurrence of global warming, temperature, especially the day. the average daily temperature is measured using the threshold at which the selected temperature thresholds for calculating cooling degree days, depending on the specific objectives are One of the methods of data analysis, spatial analysis.
Materials and method
First, the average daily temperature model, (EH5OM) was simulated. Given that this area of research later (Iran), the data in the fourth edition of the regional climate model (RegCM4) that are better suited for small scale micro-scale processes output Downscaling model with dimensions of 0/27 * 0/27. The latitude is about where the dimensions of 30 x 30 km area covering Iran. After the simulation, the average daily temperature for a period of 36 years (2050 -2015) was extracted by model. In this study, cluster analysis and analysis is used to study cooling degree days. Cluster analysis and the local Moran insulin index is also known, is a model optimized for displaying statistical distribution of phenomena in space.
Results and discussion
that require cooling in warm months for the whole country increases, the size of these parameters has been in the country for neighboring units. But in the cold months cooling needs in the country is decreased significantly. In the months April, May and June (spring) this parameter is the highest merit. This is because almost similar matches leaps cooling degree days is the total area of the country. Moran spatial autocorrelation showed the world just sort of pattern is clear. In order to show the spatial distribution pattern of spatial distribution of cooling degree days during the period of the local Moran has been used. In the spring, the plains and the southern coast, Dasht , bar and pits Jazmurian South East (at a significance level of 99%) were positive correlation with the pattern of hot and humid or the points at April 20, May 28 and June 29% of the range across the country are low and warm, moist air of the need for this parameter in these areas is maximized. Bar mountainous, foothills and plains interior and north coast in April also 47, is a 43-June by 36% (at the 99% significance level) negative correlation with the pattern of dry and hot or requires less cooling than areas have the South. The outer foothills and desert in this season are no significant pattern.
Conclusion
The results showed that the method of global Moran Moran index for warm months (March, April, May, June, July, August, September) is higher than 90 per cent of this represent a broadly based index World Moran, cooling degree days in Iran during the study period in the warmer months of the year, with high cluster pattern on the surface of 90, 95 and 99 percent. While in the cold months down the cluster pattern indicative of the low index levels are mentioned. As the global Moran index only determines the type of pattern, so to change the spatial autocorrelation cooling patterns of the local Moran index () and analysis of hot spots () was used. It can be said that the countryʹs mountainous strip in the first half year saw a negative correlation pattern (cluster down) and the plains and the southern coast has a pattern of positive correlation (positive cluster) are. In the cold months, the majority of regions of the country except the southern coastal areas are no specific pattern. In the spring and summer plain and the southern coast, , bar and pits Jazmurian South East (at a significance level of 99%) have a positive correlation pattern, which is hot and humid or hot and humid weather of the maximum width of the bottom and the need for this parameters are in the area. Bar mountainous, foothills and plains interior and north coast in April also 47, is a 43-June by 36% (at the 99% significance level) negative correlation with the pattern of dry and hot or requires less cooling than areas have the South. The results of this study could be a model for other climatic parameters of Space Studies. For spatial statistical studies before the new valves open .
