Introduction:Iran is located in the south-west of Asia and is in the arid belt of the world and about 60% of the extent of the country is mountainous and the remaining part (1/3) is desert and arid land. Climate of the country can be divided into three main categories: -Warm temperate, rainy with dry summer in a narrow strip in the north, -Dry, hot desert in the central plateau, -Dry, hot steppe covering the rest of the country. Then, it could be so difficult to predict climate change over whole of the country.

Using the outputs of Global Circulation models (GCM) has nowadays developed in meteorological and climatology studies and could compensate more computational vacuity. The resolution of global and local circulation models have recently increased, but none of these models could predict actual weather in station and micro scale. Thus, several methods are presented for using outputs of these models. These outputs are divided into statistical and dynamic groups and are known as Down Scaling Methods. In this paper, the researcher has tried to recognize and select the best climate scenario that fits the observation data of the selected area (stations of Khorasan Razavi Province) and using special statistical and dynamic methods to produce optimum data that fits the actual data. Then, by Down Scaling then, we could extract improved forecast data. Finally, by using Standard Precipitation Index (SPI), we can present drought status in future.

In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN) based downscaling for mean monthly precipitation and temperature of two stations (Tabriz and Ardabil in North West of Iran) were evaluated using general circulation models (GCMs). PIs were constructed by novel Upper Lower Bound Estimation (LUBE) method in which an ANN with two outputs was constructed for estimating the prediction bounds. Also, Bootstrap method as a classic technique for assessing uncertainty of ANN was used to further examine the proposed LUBE method. In this way, the accuracy of PIs was quantified by coverage and width criteria. Three GCMs, Can-ESM2, BNU-ESM, INM-CM4 and ensemble-GCM (ensemble of mentioned models) were used in four grid points around each of station for evaluating ANNbased downscaling of precipitation and temperature parameters. Comparison between the results of two methods indicated that LUBE method could lead to more reliable results than the Bootstrap method. PIs width and coverage probability were 10% to 40% lower and 2% to 10% higher than the Bootstrap method for different GCMs, respectively. Ensemble-GCM led to more accurate results so that computed PIs width and coverage probability were 10% to 60% lower and 2% to 20% higher than those for the single GCMs.

 Research over recent years has shown that climate change exerts varying effects on climatic and hydrological components. Scientific findings indicate that the impacts of climate change will become more pronounced in the future; therefore, analyzing and predicting these changes is of significant importance. This study, given its subject and nature, is a descriptive-analytical and applied research with an emphasis on quantitative methods, conducted using the General Circulation model (GCM). To achieve the study objectives—including modeling climate change in the Alagol, Ajigol, and Almagol wetlands—data from the Incheh-Borun meteorological station were used. This station provides 20 years of monthly and annual average precipitation and temperature data (2001–2020). Forecasting of precipitation and temperature was carried out using outputs from the HadCM3 model in this study. The simulated average precipitation data, obtained from HadCM3 and downscaled in LARS-WG under the SRA1B scenario, were used to generate synthetic data for the future period (2021–2050) based on three IPCC-approved scenarios: B1 (optimistic), A2 (pessimistic), and A1B (moderate). To determine the best interpolation method, statistical indicators such as RMSE, ME, and MSE were applied. One of the most effective strategies in data weighting is the application of quantitative and mathematical techniques. According to the study results, the homogeneity tests demonstrated a high level of consistency. When trend analysis indicated a statistically significant trend in the time series, the homogeneity tests confirmed the non-homogeneity of those series. The precipitation series at Incheh-Borun station showed a generally decreasing trend, while both maximum and minimum temperatures exhibited increasing trends.

Dust is an important atmospheric phenomena that occurs in spring and summer in many regions, including Iran and its neighboring countries. Considered one of the most important challenges of the last century, this phenomenon occurs on a global scale in arid and semi-arid regions. Because of changes in climate and vegetation as well as progressive processes of soil erosion and the disturbances resulting from them, the sensitivity of regions to rapid erosion will have important reactions on the region's climate and desertification. Therefore, the current research investigated the concentration and distribution of fine dust under the influence of meteorological parameters using the GCM climate model and attempted to determine the effect of climate change on the concentration of the relevant pollutant in the coming years. In this study, the CALPUFF model considered the temporal and spatial effects of weather conditions on the transfer, transformation, and removal of atmospheric pollutants. The emission rate of the PM10 pollutant was estimated. The results indicated that the increase in greenhouse gas emissions and changes in climate variables in the near future will cause the distribution of suspended particles one of the important pollutants to increase significantly. The results also revealed a significant relationship between the degradation of air quality and the trend of air warming during the period 2046-2065.

Introduction:The historical trend of Iran annual average temperature of shows a 0.05oC.year-1 increase which indicates that future emissions of greenhouse gases will continue to increase temperature and consequently cause to climatic change in the country. This change in environment will have a serious impact on different growth and development processes of crops. Increasing temperature could affect physiological processes like photosynthesis, respiration and partitioning of photo-assimilates. The negative impacts of climate change on potato production are reported in the literature. The present study was conducted to quantify the potential impacts of climate change on phenology, growth and tuber yield of potato. …

In this study we have used the monthly data resulted from the calculation of temperature and precipitation for a 52-year period (1951-2003).The doubling effect of carbon dioxide (CO2) on temperature and precipitation was modeled as the most important climatic parameters. The resulting Hythergraph and Ambrothermic models for Tabriz indicate monthly increases of temperature and precipitation during the CO2 doubling periods. A comparison of the present and simulated Ambrothermic models applying Goddard Institute of Space Studies (GISS) shows precipitationand temperaturechanges in Tabriz. A decrease in the duration and intensity of cold period and freezing days, a reduction in spring and winter precipitation, a shift in precipitation from snow to rain, the extension of growth season, the increase in temperature and the resulting dryness are the signs of such changes.