With the seriousness of the climate change debate in the world, the study of parameters and elements of the climate has been widely considered. With changes in climate patterns and changes in temperature and precipitation patterns, other components such as runoff and soil moisture, which are important for natural and human systems, will undergo metamorphosis. Therefore, long-term prediction of climatic variables has been considered by many scientific communities worldwide in order to know about their changes and considering the necessary measures to moderate the adverse effects of climate change. The phenomenon of climate change is of increasing importance due to its scientific and practical dimensions, since human systems dependent on climatic elements such as agriculture, industry and the like are designed and operated on the basis of the stability and stability of the climate. Accordingly, general circulation models (GCMs) have been developed. Although these models represent significant results on the atmospheric and continental spatial scales, they combine a large part of the complexity of the planet's system, but they are inherently unable to control the dynamics and forms with a fine grid Local scalability. Therefore, an assessment of the effect of climate change on a local scale requires an interim and spatial gap between large-scale climatic variables and meteorological variables with local scale, in which case the main approach is the same downscaling models. The SDSM model is one of the most widely used statistical microscopic instruments, which has many uses in meteorological, hydrological, geographic and environmental studies. Because in this method, large-scale daily circulation patterns are used on a stationary scale; and when used for the rapid and cost-effective estimation of climate change, and for randomized meteorological generators and modified functions, have given acceptable results. Given that global models have generally simulated climatic elements until the year 2100, it is possible to use global model data to simulate the desired variables such as precipitation and temperature on a station scale. The Intergovernmental Panel on Climate Change (IPCC) has used its latest assessment report (AR5) on new scenarios for the RCP as representatives of different levels of greenhouse gas emissions. The new emission scenarios have four key paths RCP2. 6, RCP4. 5, RCP6 and RCP8. 5, which are named after their radiation in 2100, Future Perspective. The variation of the maximum temperatures of the synoptic station of Urmia during the period (2021-2050) of the CanESM2 global model has been used under three scenarios RCP2. 6, RCP4. 5 and RCP8. 5.

Introduction: As global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & Methods: Data collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period. Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2. 6, RCP4. 5, and RCP8. 5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & Discussion: Appropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6. 7 and the highest correlation coefficient of 0. 97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9. 9° C and January with 6. 6° C, respectively. The lowest error rate also belonged to December (-1. 5° C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8. 5, RCP4. 5 and RCP2. 6 scenarios, respectively. The increasing trend in RCP8. 5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6. 7° C) in RCP8. 5 scenario belongs to June and the highest temperature decrease (3° C) in the optimistic scenario (RCP2. 6) belongs to October. Conclusion: Selecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2. 6, RCP4. 5, and RCP8. 5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8. 5). The increasing trend of temperature begins with RCP2. 6 scenario and reaches its highest level in RCP8. 5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.

Introduction Climate plans are used in macro-planning, especially for natural disasters (Rezaei et al., 2015). Statistical micro-distribution methods are more efficient due to easy and cheap calculations (Diersing, 2009). One of the statistical models is the SDSM model, which is the relationship between large-scale predictors and local-scale predictors based on multiple linear regression methods. According to research, the SDSM model has an acceptable accuracy in clustering micro data. On the other hand, due to the importance of Yasuj station and its location in Karun catchment area and the need for planning to manage water resources in this basin, the present study uses CanESM2 output, which is one of the climate change models of CMIP5. The IPCC, simulates and examines climate and temperature variables at Yasuj station from 2020 to 2067. ​​Study Area Yasuj is a city in southwestern Iran, the capital of Kohgiluyeh and Boyer-Ahmad Province, located on the banks of Bashar River, at the hillsides of the Dena Peak. Yasuj city is located in a cold climate region and has a temperate climate that tends to be cold. Study Method The SDSM microcontroller model was developed in 2002 in the UK by Wilby and Dawson. This model is based on daily local climate data (temperature and precipitation) and large-scale NCEP regional data. The canESM2 model is the fourth generation of climate models developed by the Canadian Center for Climate Modeling and Analysis (CCCMA) and networks the earth in the form of cells measuring 128 x 64 (Charron 2016). In this study, NCEP observational data were used to compile monthly models and canESM2 model outputs were used to predict the amount of variables using SDSM software. NCEP atmospheric variables enter the regression equation of the SDSM model. After selecting the predictors, the observational data of the Yasuj Synoptic Station and the data of the National Center for Predicting Environmental Variables of Canada (NCEP) were calibrated. Then, in order to ensure the calibrated model, temperature and precipitation for the period 2035-2020 were simulated and by comparing the observed and simulated data, the efficiency of the model for Yasuj station was investigated. Results and Discussion In this study, based on the observed data and the global model canESM2, the mean minimum and maximum temperature and average precipitation during the three periods of 2035-2020, 2051-2036 and 2067-2052 were compared with the base period of 2005-2007 under three RCP2 scenarios. RCP2. 6, RCP4. 5 and RCP8. 5 were simulated for Yasuj station and the accuracy of the model was evaluated. The maximum agreement is at the minimum and maximum temperatures of the observed and simulated data, which show the appropriate and acceptable efficiency of simulating the desired climatic parameters for the period. In general, the amount of precipitation will increase in all studied future periods. This increase will be more evident than RCP2. 6 according to the RCP4. 5 and RCP8. 5 scenarios. In general, the maximum minimum temperature during the period 2035-2020 shows an increasing anomaly of about 0. 5 degrees and in the future periods 2051-2036 and 2067-2052 it shows a decrease compared to the base period. The lowest minimum temperature is estimated for January 2035-2020 under the RCP8. 5 scenario. The maximum temperature of Yasuj station during the periods shows an increase. Incremental changes are less in June and August and more in January to May as well as in October, November and December. Of course, these changes are more noticeable in November and April. The highest temperature in the coming years will be related to July of 2051-2036 under the RCP4. 5 scenario. Conclusion According to the results, it was found that precipitation in the coming years will show an increasing anomaly, which is faster in the first period and slower in the final periods. Slight changes in precipitation along with increasing temperature have affected the quality of water resources. This is due to the importance of this station in Karun catchment. Therefore future planning of water resources management should deal with the least quantitative and qualitative effects of water resources in the basin.

Greenhouse gases and the occurrence of climate change have occurred with the development of technology and the industrialization of human societies. long-term forecasting of climate parameters has always been interesting due to the importance of climate change for the earth and its inhabitants. General Circulation Models (GCMs) are one of the most widely used methods for evaluating future climate conditions. In the present study, the results of three general circulation models including the American model of GFDL-CM3, the Canadian model of CanESM2, and the Russian model of inmcm4ncml for the study area were evaluated and the CanESM2 model was selected as the superior model. The RCP scenarios 2.6, 4.5, and RCP 8.5 were used with the CanESM2 model to assess climate change conditions across the Hablehroud River basin for the period 2020-2051. According to the results, the total monthly precipitation shows an increasing trend in the coming decades 2020-2051 period compared to the period 1986-2017. The results of the study of temperature changes in the period 2020-2051 in the Hablehroud River basin also indicate an increase in the monthly average of maximum and minimum temperatures in the coming decades. The consequences of these conditions are of great hydrological importance in the study area, this condition necessitates the adoption of climate change adaptation policies in this watershed.

Wetlands are one of the most important natural ecosystems in arid and semi-arid regions that their moisture content has decreased in recent years due to the adverse effects of climate changes and droughts. It is necessary to evaluate the impact of such events on different ecosystems, especially these valuable ecosystems. In this study, the future drought status of Jazmourian wetland under elevated temperature was evaluated using the output of the IPCC Fifth Report Model. For this purpose, the standardized precipitation-evapotranspiration index and reconnaissance drought index, which take into account the impact of rising temperatures due to future climate change on droughts, were used. The effect of increasing of temperature on drought risk was studied by using the return period of drought. Results showed that the standardized precipitation-evapotranspiration index was better able to show the effect of increasing of the evapotranspiration and temperature on drought than the reconnaissance drought index. Therefore, Standardized precipitation-evapotranspiration was used to evaluate the future droughts under RCP scenarios. Based on the drought time series, by increasing temperatures, the future long-term droughts might be more intense and longer period than the historical period. Analysis of drought showed that drought risk would decrease on 3, 6 and 12-monthly time scales, respectively. Under the RCP2. 6, RCP4. 5 and RCP8. 5 scenarios, the risk of drought will increase at all study time scales. In general, these results can be useful for assessing climate change and managing of water resources in arid regions.

Introduction: Evapotranspiration is one of the important components of water balance. Estimation of evapotranspiration has been the focus of many researchers in Iran and the world. Accurate estimation of evapotranspiration is very important in hydrological modeling, irrigation design, and water resources management. This variable is one of the most important and effective components in the water balance. Evapotranspiration after rainfall is considered the second largest component of the earth's water cycle on a global scale. The assessment of the future climate and climate change and its effects is done through the output of climate models. Materials and Methods: In this research, the average daily minimum and maximum temperature data were scaled according to the CanESM2 model under RCP scenarios using the SDSM for 6 synoptic stations in the southern part of the Aras River basin to draw the perspective of ETp using the Hargreaves-Samani method until the 2050s. For this purpose, observational data of stations and reanalysis data (NCEP) in the daily period (1985-2005) as well as historical data of the CanESM2 model (historical-2005) under RCP scenarios (for the period 2006-2100) has been used. Results and Discussion: Estimated ETp values for the Aras basin during the coming period based on the downscaled temperature data of the CanESM2 model under RCP scenarios showed that the value of this variable under the RCP2.6 scenario compared to the base period, slightly decreased and under the RCP4.5 and RCP8 scenarios will have a slight increase. The amount of ETp in this basin will have a decreasing change in the Ardabil, Ahar, and Khoi stations and an increasing change in the Pars-Abad and Jolfa stations. The monthly ETp value of the Aras basin in the future period in January, April to June, and August was estimated to increase with a range between 0.1 to a maximum of 24.3 mm compared to the base period. Comparing the estimated ETp values of the future and the past period showed that the ETp estimated by the Hargreaves-Samani method in the past period compared to the evaporation data of stations except Pars-Abad and Khoi was overestimated by more than 100 mm per year, and it was less in other stations. Hargreaves-Samani ETp values, except for Mako, which is higher from 1985 to 2005 than in 1992-2005, in the other stations in the period of 1992-2005 are greater than the values of the base period. Conclusion: The estimated ETp values for the Aras basin during the coming period showed that the value of this variable at the annual basin level will increase slightly compared to the ETp of the base period (by the Hargreaves-Samani method), which means that the water requirement of plants will increase in the future in the growing season and this increase means an increase in the water requirement of plants in the future in the growing season, a decrease in infiltration and an increase in evaporation of water resulting from rainfall and snow melting, and a decrease in the feeding of aquifers.

This study aimed to predict flood zone in climate change conditions based on the fifth assessment report of the intergovernmental panel on climate change (IPCC) scenarios in the Talar watershed (Zirab city). To investigate the effect of climate change from six synoptic stations were used. Among the general circulation models (GCM), CanESM2 based on RCP 2. 6, RCP4. 5, and RCP8. 5 scenarios were applied for statistical downscaling of the maximum daily rainfall. To hydrologic and hydraulic simulation of flood were used from HEC-HMS and HEC-RAS models in the recent decades and the future. The results indicated that maximum daily rainfall will increase in the watershed. The results also showed that the increase in maximum daily rainfall in humid climate (the North) is more than dry climate (the South). In general, maximum daily rainfall will increase the minimum and maximum 8 and 33 mm, respectively in the watershed. The simulation results in terms of flood hydrograph indicate that flood increase in the all periods. The RCP 4. 5 scenario will produce at minimum and maximum flood discharge in 2020-2040 (374 m3/s) and 2020-2100 (1209 m3/s), respectively. Flood zoning map showed that floodplain area is the base period in the river basin, but climate change will increase the flood zone in this region. Besides, the results showed that at least 0. 18 percent and at most 8. 7 percent of the total Zirab city will effect on flood under climate change conditions.

Introduction General atmospheric circulation models show an increase in greenhouse gas concentrations. These models predict global climate change by simulating the earth's climate. General atmospheric circulation models are not accurate enough for hydrological and water resources studies due to the large temporal and spatial scale of the simulated climate variables compared to the case model. Therefore, they must be scaled. There are different methods for exponential downscale of large-scale variables, and general atmospheric circulation models. In this research, the SDSM model is used to downscaling climatic data. Materials and Methods Boroujerd Synoptic Station with an altitude of 1629 meters above sea level is located at latitude 33◦,55′,East and longitude 48◦,45′,North. Boroujerd city is located at the foot of the highest wall of the Zagros at an altitude of 1550 to 1571 meters above sea level and the highest point is in the Garin mountain range with an altitude of 3623 meters in the west and its lowest area is in Silakhor plain with an altitude of 1500 meters. In this study, the CanESM2 climate model under three scenarios RCP26, RCP45, and RCP85 in three time periods 2040-2021, 2060-2041, and 2080-2061,and SDSM model version 4. 2 were used for downscale (micro-scale) exponential climatic data. For calibration and validation of SDSM microscale model, R 2 and RMSE calibration indices were used. In this study, 30% of the data were used for validation and 70% of the data were used for calibration. Result and Discussion In the SDSM model, the maximum and minimum temperature values are better predicted than the precipitation values, and the simulated data are closer to the observational values. In all scenarios and periods, the precipitation trend is decreasing. The largest decrease in precipitation is related to January in the period 2021-2040 and the RCP8. 5scenario, with a decrease of 69. 22 percent. The temperature in all scenarios and periods had an increasing trend compared to the base period. The highest increase in the minimum temperature data is related to the RCP4. 5 scenario in October for the period 2061-2080 and it was equal to 4. 90 °,C, respectively, and in the maximum temperature data related to the RCP4. 5 scenario in October for the period, 2061-2080 was predicted to be equal to 7. 02 °,C. Calibration of SDSM model for Boroujerd station for each of the minimum and maximum temperature and precipitation showed the mean values of coefficient of determination 0. 99, 0. 98 and 0. 67, respectively. Conclusions The highest decrease in rainfall is related to January in the period 2040-2021 and the RCP8. 5 scenario, with a decrease of 69. 22%. Also, the SDSM exponential downscale (microscale) model for the minimum and maximum temperature parameters predicted an upward trend. Calibration of SDSM model for Boroujerd synoptic station shows the efficiency of SDSM model in microcompalation of parameters. As a result, we will face a decrease in hydrological reserves in future periods.

In this study, the effects of climate change on meteorological parameters in the Horrood river in lorestan Province, using CanESM5 model under RCP scenarios (RCP2. 6, RCP4. 5 and RCP8. 5) was evaluated based on the sixth IPCC report in three time periods including the near future (2026-2050), the middle future (2051-2075) and the distant future (2076-2100). The statistical downscaling model (SDSM) was used to predict precipitation and mean temperature in the baseline period 1970-2005 at Kaka Reza and Dehno synoptic stations. The results of this study, in both Kaka Reza and Dehno stations, indicated a decreasing in precipitation and an increasing in average temperature under the all three RCP scenarios over the future period, so that in the distant future (2076-2100), under the RCP8. 5 scenario (i. e. the pessimistic one), at Kaka Reza and Dehno synoptic stations, the precipitation showed the highest decreased by 36 and 39 percent in monthly scale and 30. 36 and 33. 35 percent in annual scale respectively and the mean temperature showed the highest increased by 17. 5 and 17. 1 in monthly scale and 9. 32 and 9. 06 in annual scale respectively. Finally, the results of this study showed that the climate change will affect precipitation and temperature in the Horrood river basin.

Iran's location in the latitude of 25 to 40º N in the northern hemisphere has made the subtropical jet stream a factor in regulating humidity systems in Iran, making it easy for humidity systems to enter the country when this jet stream is in Southern Iran. But as it moves northward, its strength decreases. In recent years, it has been reported that the positioning of subtropical jet streams in the Northern Hemisphere is shifting. Therefore, the purpose of this study is to evaluate the position of subtropical jet stream location and its variability over Iran. In this study, data on zoning wind velocity ranged between 30 and 80º E in the Northern Hemisphere, at levels between 1000 and 10 hPa from NOAA, as well as outputs of circulation models including CanESM2 and GFDLCM3 for the historical period 1948 to 2005 and periods Future from 2006 to 2100 were received from IPCC in two scenarios RCP4. 5 and RCP8. 5. In this study, the main components of the jet stream include the central core velocity of the jet stream and its latitude position. Investigation of the position and velocity of the jet stream indicates that the subtropical jet position changes in Iran and its eastern regions are followed by significant incremental changes. Whereas in west Iran, there is a significant decline in jet stream velocity changes. The future of Jet Stream positioning in Iran based on the CanESM2 and GFDL-CM3 climate models in both rcp4. 5 and rcp8. 5 scenarios indicates that relative to the base period in both scenarios as well as the near and far future of its position to the north Moves.