فهرست مطالب مجید حبیبی نوخندان

The position of Iran between extratropical and subtropical latitudes, the location of a major part of Iran on the desert belt of the world from North Africa to Central Asia, and the role of various geographical phenomena in Iran have caused all kinds of atmospheric hazards in this region. To have an accident (Alijani, 2018). Such risks are inherent in Iran's climate and have occurred regularly since the distant past, as the natural environment and human activities in different regions of Iran have adapted to these phenomena. As the behavior of these phenomena has been repeated somewhat regularly and the climatic changes and short-term atmospheric processes have been less. These phenomena include droughts and droughts, heavy rains and floods, floods and dust storms, frosts, heat and cold waves (Omidvar-Kamal 2013). But in recent decades, with the increase in the temperature of the earth after the industrial revolution (1850), and with the intensification and rapid trend of temperature in the recent decades from 1970 onwards, the atmospheric hazards in the world, including Iran, have become abnormal. Such heavy and short-term rains cause a lot of damage related to agriculture, severe soil erosion, and destruction of transportation infrastructure and flooding of cities and villages. In recent years, in connection with global warming and changes in the behavior and anomaly of rainfall in the world, the amount of rainfall has decreased in this region, but the rainfall has mainly changed its behavior to heavy and short-term rainfall. Therefore, knowing the patterns that lead to the occurrence of these precipitations will be useful in the first place for predicting and managing precipitations. The aim of this research is to identify and analyze the atmospheric circulation patterns leading to the occurrence of heavy rain and short-term flooding based on the data of the stability rain gauge in Khorasan.

in this study, the daily rainfall data and the rain logger of the stability station were used. Among the stations in the region, those that have a proper distribution in terms of spatial distribution and also have long-term statistics (1993-2017) were selected. The synoptic stations were chosen to cover all the regions of the province with different topography and climate diversity. In order to determine heavy rains, the 95th percentile index of daily rains in each station was used. In the following, days with heavy rainfall and more than 95th percentile were extracted in each of the stations using these indicators. The spatial distribution of these rainfalls in the whole province was determined by using the number of these rainfalls in the entire region. , heavy rain. In connection with the purpose of this study, determining the atmospheric patterns of heavy rainfall in Khorasan, after determining the days with heavy rainfall in the previous stage, the atmospheric patterns of rainfall were determined and synoptically analyzed. For this purpose, reanalyzed daily grid data with a resolution of 2.5 degrees were extracted from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR) for the above days. The selected window for receiving network data in this study is 15 to 70 degrees north and 15 to 80 degrees east, so that the patterns affecting Khorasan precipitation in this range can be identified.

On February 17, 2017, in the geopotential height map of the middle surface of the atmosphere, a trough is drawn over the entire western half of Iran and the western coast of the Caspian Sea, and the center of this trough is a closed cell with a height of 5280 geopotential meters over the northeast of the Caspian Sea and Kazakhstan. This deep trough extends to Saudi Arabia and passes through the center of Saudi Arabia at a height of 5750 meters and shows the penetration of cold air in the upper atmosphere to the warm southern sea of Iran. At the same time, in the east of the Mediterranean Sea, there is a Rex-type blocking. These atmospheric conditions caused persistence and slow movement of the circulation system in the synoptic scale in Southwest Asia. At the same time, there is a low pressure center with a pressure of 1010 HP in front of the curved ship in the east of Iran. The east of this trough extends from the southwest and the western half along the southwest to the northeast direction of Iran, which caused instability in the northeast of Iran. On April 1, 2016, the arrangement of westerly winds in the entire Eurasia area shows a large anomaly in the westerly wind waves, naturally in late winter and early spring, due to the reduction of temperature and pressure differences in the northern hemisphere, westerly winds move meridian. And the number of long waves also increases hemispherical. In this map, there are two meridional patterns of westerly winds in the north and south, the first pattern in the north with a deep and curved channel with a west-east direction and closed with two equal heights, cut off low respectively over Western Russia and Kazakhstan respectively with There are 5280 and 5440 meters, That on the surface of the earth in these two areas, two low pressures with a pressure of 1003 and 1011 HP, respectively, indicate a strong rotation and instability in these areas.

Heavy rains and floods are one of the most important weather hazards that cause great damage to nature and humans every year. This type of precipitation, which is an inherent feature of arid and semi-arid climates, has increased significantly in recent years due to global warming and the increase in climate extremes.

One of the ways to reduce the water crisis in agriculture due to its low consumption efficiency and overuse of available resources is water management to meet the water needs of crops in arid areas where water resources are limited, and estimating the water needs of plants is essential. In this study, FAO Penman Montith method was used to estimate the evapotranspiration potential of Northeast Iran, and ArcGIS software was used to zonation of evapotranspiration. The data required for this study were collected from 15 meteorological stations affiliated to the Meteorological Organization located in Khorasan Province (Razavi, jonubi, Shomali). These data are for the years 1999 to 2019 and include daily maximum and minimum values of air temperature, relative humidity, sunshine hours and wind speed. Days with incomplete data were excluded from analysis in this study. The results of this study showed that the rate of evapotranspiration on average days of the year varies from 0 to 6.9 mm per day and the highest evapotranspiration in July (134.4) mm per month and the lowest evapotranspiration in January (18.7) mm has occurred in the month. Potential evapotranspiration zoning in ArcGIS software environment showed that the highest evapotranspiration was in the southwest of the study area (Tabas) and the lowest was in the northeast of the study area (Quchan and Bojnourd).

Seasonal forecasting has always been one of the challenges in forecasting Iran's diverse climate. In the last one or two decades, many efforts have been made to develop and improve the climate models of the restricted area and to minimize these challenges, but the problems and challenges still remain. Convective parameterization schemes are always one of the sources of error in regional climate models that have a significant impact on model outputs. Therefore, one of the most important issues in implementing the model is choosing the appropriate convective scheme from the existing schemes. One of the methods of forecasting precipitation in our country is the use of dynamical downscaling by RegCM model. Most of the studies that have been done for this purpose in the country so far have considered single convection schemes for the whole country, the results of which have not shown a significant improvement in rainfall forecasting.

In this study, a relatively new approach was adopted, so that convection schemes were selected appropriate to the climate of the region, and then the final forecast of the entire country by regional integration of each climate zone was presented. In this paper a relatively new perspective of the climatic zones of the regions, was used for optimum configuration of the RegCM4.5 model; The study area in this study is Iran, which includes 25 to 41 degrees north latitude and 47 to 63 degrees east longitude, but the model area ranges from 30 to 70 degrees east longitude and 10 to 55 degrees north latitude. It covers important geographical features, including mountains and seas. In this study, the output of the CFSv2 global climate model originating from November 1 in each year as the boundary condition data has been used and the CRU precipitation data has been used as reanalysis data to test the output of the RegCM model. Because CRU data are averaged monthly, they are suitable for studies that examine monthly averages. CRU data have already been used by various researchers in the country to validate the output of the RegCM model. After selecting the schemes of the planetary boundary layer and surface layer, the selection of the appropriate Cumulus Parametrization Schemas(CPS) was done based on Iran's climatic classification using the Demarten index. This method is the simplest and most common method for climate classification that precipitation and temperature variables are effective in calculating climate index, and precipitation and temperature data have also been used from the CRU database. For this purpose, Iran was first divided into seven very humid, humid, semi-humid, Mediterranean, semi-arid and arid climates based on the Demarten index, and each grid points of the study area were assigned the relevant climate index. The share of each climatic class in zoning was obtained as follows; Arid 32.4%, Semi-Arid 30.1%, Mediterranean 7.6%, Semi-humid 7.6%, Humid 10.5% and Highly humid 11.8%. The study period was 5 rainy seasons 2019-2014 (November to May) that the beginning of each simulation with the initial condition data on the first of November and its end at the end of May (as the end of the rainy season in the country) in each year. The horizontal resolution considered to be 30 km regional model, the planetary boundary layer schemes and the surface layer Holslag and BATS were considered, respectively. Kuo, Grell, Emanuel, Tiedtke and Kain convection schemes were tested during this period to achieve optimal configuration.

In the first stage, mean precipitation and its RMSE from individual and integrated schemas were calculated, but due to the fact that the Emanuel and Kain schemes did not rank higher in any of the model experiments in terms of climatic classes and have more errors than others, theywere removed from the configuration selection process. The results showed that in very humid, humid, semi-humid and humid climates the Tiedtke convection scheme, in the semi-arid regions the Grell scheme and in the arid areas of the Kuo scheme had the least bias compared to other convection schemes. Therefore, the seasonal forecast of the country was presented by combining regional schemas, the average bias of which was calculated at 0.45, 0.79, 1.01 and 0.69 mm in the integrated schemes of Tiedtke, Grell and Kuo, respectively. On the other hand, in addition to calculating the ability of different schemas to predict precipitation using the RMSE index, the area under the ROC curve was also calculated in three classes less than normal (BN), normal (NN) and more than normal (AN) for different climates. For this purpose, in each precipitation layer, the number of schemas that predicted precipitation in different climates and had the largest area under the curve compared to other schemes was extracted. ROC diagrams of different schemas showed that Tiedtke and Grell schemas have the highest ability to predict less than normal, normal and more than normal rainfall classes. The results showed that the regional integrated scheme (TGK: Tiedtke, Grell and Kuo) showed an improvement of 54 to 126% compared to the individual schemas. In general, it can be said that choosing the optimal configuration based on the idea of climate-based convection scheme can increase the performance of the RegCM4.5 regional model in seasonal precipitation forecast in Iran.

Although a study with a regional climatic zones perspective was not found on Iran, but some studies have found the Tiedtke scheme suitable for our country (Alizadeh Choubari et al., 1398), which with the findings of this study in which the Tiedke scheme for four of the six climates used in this study are considered appropriate. On the other hand, Zarrin and Dadashi (1399) used the Grell scheme to study the events of the partial rainfall in Iran by RegCM4 model, which in this study was found to be suitable for semi-arid climate. In addition, it was observed that in the study period of seven months, the most RMSE error occurred in April, which is the month of transition from cold to warm season.

To investigate the mechanism governing the formation of cyclones in Iran over a period of 18 years (2017-2000) using the National Geological Survey's Center for Environmental Prediction / Atmospheric Sciences (NCEP / NCAR) 2.5 Geographic Resolution And the European Center for Medium Term Forecasting (ECMWF), with a resolution of 0.125 °, identified the phenomenon in the Zagros Mountains. Two potential equilibrium parameters on the convective surfaces and relative equilibrium advection were also used to investigate the dynamic structure. The results showed that during this 18 year period, only three examples of the above mentioned phenomenon occurred in Iran. The major mechanism governing the formation of these cyclones in the windward part of the mountain with the formation of a high-pressure core and an increase in relative relative tropics at the ground level and the positioning of a quasi-stationary stack in the troposphere mid-level as a major condition of the mountainous winds and the presence of a dune. With the strong geopotential rotations over the mountainous altitudes that continue from the middle to upper troposphere, these conditions are accompanied by a positive torrential flow. Thus, the first and foremost condition for the occurrence of windsurfing is the existence of these two conditions on the windward and mountainous slopes. At the level of 500 hPa in central Iran, the relative tropical advection values ​​increase, indicating a positive relative tropical increase both in time and location in the region.

Evapotranspiration is significantly affected by global climate changes as an essential component of both climate and hydrological cycles. Comprehensive analyses of the spatiotemporal changes of ETo enhance the understanding of hydrological processes and improve water resource management. The main objective of this study is to investigate and predict the temporal trend and spatial distributions of the mean maximum temperature (Tmax), the mean minimum temperature (Tmin) and reference evapotranspiration (ETo) during 1961-2014, 2021-2050 and 2051-2080 over Khorasan Razavi Province using CRUTS3.23 dataset and the outputs of four CMIP5 climate models. The results were as follows: (i) the ability of CRU dataset in simulating monthly mean of Tmax and Tmin is suitable, (ii) generally, ETo increased from north to south across the province (ii) from 1961 to 2014, annual ETo exhibited an increasing continuous trend across the area under study (iii) the mean annual minimum temperature projected to increase by 1.6 under RCP4.5 and RCP8.5 scenarios during two future periods. During 2051-2080, this variable will have an increase by 3ᵒ C under RCP8.5 scenario. The maximum temperature will increase by 4ᵒ C during the middle future period under RCP8.5 scenario. (v) The difference between mean annual ETo values of two periods was statistically significant in all grid points covering this province. The results showed that these increases may lead to the increase in crop water requirements and aggravate the water shortage in this area in view of the increase in ETo in response to ongoing climate change.

This project is an updated estimate of the climate change trend in Iran. The time series of monthly and annually mean, maximum and minimum temperature (°C), relative humidity (%) at 2 m height, wind speed (m.sat 10 m height, sun hours (h), precipitation (mm), radiation (Mj.m-2day-1) and cloudiness data at 27 weather stations over Iran are collected from IRIMO for the period 1958-2017. Quality control, detection and modification of non-climate heterogeneity of data was performed. Due to missing data, the Sun hours trend was calculated for the period 1992-2017.

Methodology and data:

Among all stations of Iran, 27 stations have 60-yearly data in the period of 1958-2017. In this research the changes in temperature, precipitation, humidity, radiation, cloudiness and wind during the period of 2017-1958 (for sunshine the period was 1992-2017) were investigated. At first, the data were checked for quality controlling. Then their non-climatic heterogeneities were fixed. The slope of the trend was determined using the least squares method and the slope estimator and their significance was assessed using nonparametric Man-Kendal test and regression.

Temperature: The results showed that all stations in the country face a significant increase in the annual minimum temperature. The minimum temperature increasing rate per decade calculated to be between 0.2 and 0.4  degree of Celsius in Bandar-Abbas and Tehran, alternatively. In general, minimum temperatures rise were detected at all stations and in all seasons, especially in autumn and winter. The annual maximum temperature trend is also increasing, but the rate of increase in maximum temperature is less than the minimum temperature. The increasing rate per decade calculated to be between 0.08 and 0.3  degree of Celsius in Zanjan and Ahwaz, alternativelyPrecipitation: The 60, 30, and 10-years averaged annual precipitation of Iran calculated to be 230.8, 222.4 and 199.3 mm, respectively. The results showed that during the 60-year period, the average rainfall of the Iran decreases, with a rate of 0.43 mm per year (4.3 mm per decade), although the precipitation decline is not significant at 95% level.However, during the last 30 years (2017-1988), the average precipitation of the country has dropped by 2.2 millimeter per year (22 mm per decade), which is significant in 95% confidence level; meaning that precipitation reduction in the most recent 30 years is about four times higher than that of past 60 years.In Figures 2 and 3, the all-country time series of rainfall and temperature changes are shown in the 60- and 30-yearly basis.The country's declining precipitation for the most recent 30 years is about four times faster than the decline of most recent 60 years. The rapid decline in country precipitation over the past 30 years, which is significant   in 95% level, is consistent with the intensification of global warming in the most recent 30 years period.Other parameters: The average wind speed in many parts of the country has increasing trend, which is significant in many stations located in the west, center, and northern part of the country. Average relative humidity has decreased in many regions of the country. The most decreasing trend was observed in southwest and west of the country. Of course, in a few cases such as Gorgan and Rasht, there was an increase, which was not statistically significant. Sunny hours trend was calculated in the period of 1992-1992, and interestingly, seasonal and annual trends at most stations indicate an increase in the number of sunny hours. The total number of days with sky overcast in the west of the Caspian Sea, western part of the country, and stations such as Kerman, Sabzevar and Shahrood has decreased significantly..

Evapotranspiration is significantly affected by global climatic changes as an essential component of both climate and hydrological cycles. Comprehensive analyses of the spatiotemporal changes of ETo enhance the understanding of hydrological processes and improve water resource management. The main objective of this study are to investigate and predict the temporal trend and spatial distributions of the reference evapotranspiration (ET0) during 1961-2014, 2021-2050 and 2051-2080 over Razavi Khorasan Province using observed grided dataset named CRU and four GCMs outputs. Data and methods: 2.1. study area
Razavi Khorasan Province is located in northeastern Iran. This province is located within the longitude and latitude of 56° 19” to 61° 16” E and 33° 52” to 37° 42” N. The climate of this area can be characterized as arid and semiarid with a long term annual precipitation of about 207.5 mm and a long term annual maximum and minimum temperture of 18.3°C and 9.3°C , respectivily.
2.2.data
Monthly maximum and minimum temperature data were collected from updated highr esolution (0.5°× 0.5°) gridded dataset of CRU initially released in 1999 by New et al., total 42 grid points in Razavi Khorasan province. After assessing the accuracy of the CRU data using 11 synoptic stations over this province, the monthly ETo values were calculated using Hargrives-Samani method to study the spatiotemporal variations in this variable. The impact of climate change on future spatiotemporal Eto, evaluated using four coupled atmosphere-ocean general circulation models (AOGCMs) under RCP8.5 scenarios. The parametric t-test and nonparametric Mann-Kendall test methods were used to analyze the temporal characteristics of annual ET during 1961-2005, 2021-2050 and 2051-2080. Results and Conclusions: The results were as follows: (i) generally, ETo increased from north to south across the province (ii) from 1961 to 2014, annual ET exhibited an increasing continuous trend across the area under study (iii) ETo also displayed a significantly increasing temporal trend during two future periods across Razavi Khorasan Province. (v). The difference between mean annual ETo values of two periods was statistically significant in all grid points covering this province. The results showed that these increases may lead to the increase in crop water requirements and aggravate the water shortage in this area in view of the increase in ET0 in response to ongoing climate change.
ETo play an important role in the agricultural and water resources management, and its accurate prediction will signify better planning and management of the water, agriculture and other sectors, hence, using outputs of GCMs can facilitate the sectors by reliably predicting the future climate change impact on ETo in this province.

Transportation is one of the subs tractor parts in Economy of each country. Road transportation depends on atmospheric conditions. Weather is one environmental risk factor that is known to affect road crash rates in IRAN and elsewhere. Weather that reduces road friction, impairs visibility and/or makes vehicle handling more difficult creates a serious road safety threat Weather and climate, as represented by several indicators (e.g. rain, storms, winds, etc), contribute to several hazards or sensitivities within the transportation sector (e.g. landslides, reduced visibility, road traction, etc).Unfavorable atmospheric conditions may damage the soil of levees and make the road unused. In addition, adverse weather conditions make travel dangerous for humans and impose financial risks on travelers and those who use the roads. A number of factors measured in road weather stations, such as destruction of atmospheric phenomena, effective elements in reduced visibility, analysis of rainfalls, showers, snow and avalanches, dust storms, frost, freezing, and etc. Environmental Sensor Station categories provide information to identify roadway weather conditions of interest. ESS data are collected in a remote processing unit (RPU) and transmitted to a central processor. Resulting road weather information is used to activate automated warning systems and provide decision support to managers in traffic management centers, road maintenance facilities, and emergency operations centers. By monitoring road conditions using pavement sensors or video, operations and maintenance personnel are able to assess how well their traffic management or winter maintenance strategies are performing, or to determine what additional actions are required. The purpose of this project is to identify the different climatic conditions over Iran in order to detect the most suitable meteorological road sensors for each region according to its climate. The results of this survey would be useful for the road transportation management and appropriate use of sensors according to climate zones. The aim of this study is to identify reputable companies producing sensors and road weather sensors.

Since long time ago, prediction of precipitation status and investigation of drought hazards in catchment areas of North West of Iran, due to the critical importance of discharge rate of related catchments for Lake Uromia, has been one of the most important challenging issues in efficient management of water resources; management of vast capital of water resources and energy production of the country is highly affected by the aforesaid factors. Therefore, application of dynamic methods may play significant role in adjustment of such conditions concerning the frequencies of climate parameters and occurrence of imbalance behaviors in precipitation pattern of the country. In this regard, application of Aphrodite data can greatly reduce calculation errors and considers proper distribution of time and place in the category of post-processing framework. A wide range of methods has been used in the post-processing analysis of the output of dynamic methods; Therefore, application of post-processing technique may develop outputs of dynamic models to be used in subscales and the outputs of these models may be implemented in macro-environment management with a more comprehensive approach. Main objective of the present research consisted of developing the accuracy of seasonal predictions of precipitation of North-west of the country using dynamic model output post-processing method used toward managing the drought hazard.

In recent years, different climatic events such as floods and drought in the Babul-rod watershed and province of Mazandaran emphasis have been the need to investigate further the impact of climate change on hydrologic and meteorological watershed factors such as precipitation and temperature. Implement this action the using general circulation models of the atmosphere. Due to of large scale computational grid for general circulation models of the atmosphere, they are able to forecast weather and meteorological parameters do not point scale, based on the interface tool called WG developed by and help out Numerical models can scale climate change point and the desired station evaluated. In this study, using this method, the data model HadCM3 general circulation of the atmosphere with the use of LARS-WG A2 and B1 for the time periods 2046-2065 and 2080-2099 were downscaled of LARS-WG also introduced four climatic variables temperature, minimum temperature, maximum temperature and sunshine hours related to the statistical base period 1982-2011 was about calibration and verification and assurance of the ability of the model was used to simulate future periods. The results of the assessment of climate change indicate changes in precipitation in the watershed basin Babul-rod from -43 to percent, and changes of range mid was increase of precipitation in the months of high rainfall and decrease of rainfall in the low rainfall month during the summer. Also, the average annual temperature is about 1.4 °C to 3.6 °C, especially in the warmer months of the year that causes on decreases of snowfall (solid precipitation) and change of precipitation regime is more. Increased rainfall in the months of cold and warm temperatures rise during in warm month that subsequent the causes early in the snow mass and increase runoff will bring, impact on regional climate in the near future, likely the number and intensity of extreme events will increase greatly.

The proper study of atmospheric phenomena and reporting the weather in accordance with current standards and extracting the average of climatic elements in different periods in a region in order to recognize local climate and micro climate of a geographical units, requires building a network of enough weather stations. This problem is very important, due to creating exclusive conditions in urban areas that are created because of the physical difference between cities with their surrounding environment. Therefore in this research after studying effective parameters on urban weather stations site selection under mentioned circumstances and the qualities of the studied area, positioning indexes including: dispersion of climatic elements in the city, dispersion of physical qualities of the city (crossings and land uses) and dispersion of environmental jeopardies in relation with the climate of the city were selected and after gathering required information of each index such as: climatic data in reported form, matrix data related to physical qualities of the city and environmental jeopardies reports, required layers were extracted using GIS. Setting priorities of indexes related to each other was done using experts’ verdicts and analytical hierarchy process technique and binary comparison. Then, the obtained weights of layers normalized with Fuzzy membership function in AHP are compounded and overlapped using weighted average approach and the weight of the layers were determined and finally the most appropriate parts including 11 parts are prioritized and located with a suitable distance from the main station in order to expand Mashhad and replace urban weather stations .

The present paper aimed to determine the criteria for understanding climate and ranking factors influencing saffron and assess its impact on Roshtkhoar city of Khorasan Razavi province. The city of Roshtkhar has potential for increasing saffron cultivation; therefore، the main hypothesis during the research was، which climatic factors had the most influence on the cultivation of saffron in the city Roshtkhar. The research methodology was based on a period of ten years cross-sectional data collected from meteorological stations in the studied area. In this study، with taking into account of climatic factors affecting the cultivation of saffron، Analytical Hierarchy Process (AHP) method was used to prioritize rural and regional municipalities Roshtkhar cultivated land. Research process included data collection، analysis، statistical analysis، data entry software Expert choice، clustering and selection criteria، and integrating information. The results showed that، among environmental factors، precipitation index (0. 281) and temperature coefficient (0. 137) had the greatest impact in saffron cultivation. Water resources and evaporation of water had the lowest score in the survey accounted. In the municipalities of the city، Hossein-Abad districts had favorable conditions for growing saffron.

In recent decades increasing the green house gases caused damage to the Earth climate balance which is named Climate Change. Most important effects are increasing in mean temperature، flood، storm، hail and sea levels. In this article، synoptic stations in Mazandaran and Tehran provinces is investigated from 1988 to 2005 and by LARS-WG modeling and by downscaling GCM data which is predicted for 2010-2039. The main purpose of this research was comparing changes between Northern and Sothern hillside of Central Alborz Mountain. Finally، according to the rain and temperature incensement in both provinces، development of deserts happens and this negative expanding in the Southern side of Alborz will be more extensive. Also، heavy rain falls may increase in both sides، but in Mazandaran more sever rain fall will occur and in Tehran rain fall will happen in short period of times and more frequently.

Dust storm events carry a huge amount of aerosols to the atmosphere. These particles reduce air quality and can cause breathing, allergic, cordial problems. This environmental disaster increasingly outbreaks in arid and semiarid areas (e.g. Middle East) in recent years. Monitoring from space using remote sensing is one of the most effective and extensive techniques for dust storm detection. Terra MODerate resolution Imaging Spectroradiometer (MODIS) data have been utilized in this study for dust detection and classification in the Middle East. Dust particles emissivity differences in thermal infrared signals can discriminate the dusty from non-dusty pixels. D-parameter is calculated by combination of visible reflectance properties and Brightness Temperature Difference (BTD) between 11 and 12 µm channels. D-parameter is examined were using 28 satellite images (expanded in 2008 to 2009) and showed that it is very effective approach for dust area detection. Relation between D parameter and visibility (obtained from 41 synoptic stations) was investigated. The results showed that there is an exponential relation with R=0.68 between them. This relationship can retrieved the visibility from D parameter with Mean Percent Absolute Error=45.6%. According to the complicacy of problem and vast study region, R=0.68 and MPAE=45.6% seems very good. Hence we utilized this developed exponential relationship to retrieved visibility maps in Middle East from D-parameter maps. Then these generated visibility maps were classified to the different dust storm classes. This classification method was developed using the combination of different dust classification methods according to the visibility values. In this classification method, the dust storms were classified to 5 different classes (Sever Dust Storm¡ Medium Dust Storm¡ Light Dust Storm¡ Blowing Dust and Dust in Suspension).

IntroductionDust storms are natural events and are common in the dry land areas. The severe droughts can increase the number of dust storms, particularly during the summer months. Dust storms reduce air quality and may have adverse effects on health, particularly for people who already have breathing-related problems. The most common experienced symptoms during a dust storm are irritation to the eyes and upper airways. The most vulnerable people are infants and young children, the elderly and people with respiratory conditions (e.g. asthma, bronchitis and emphysema) and heart diseases. For these people, exposure to a dust storm may, trigger allergic reactions and asthma attacks, cause serious breathing-related problems, contribute to cardiovascular or heart diseases, contribute to reduced life span. Visibility deteriorates very quickly during a dust storm. Low visibility has important effects on transportation. In these days, dust storm is one of the major environmental disasters in the Middle East. Dust storms happen in the Middle East with very high frequency. According to the mentioned dust storm effects, it is vital to study the dust storms in the Middle East. The first step toward the study on of dust storms is to identify the point sources of dust storms. In addition, it is necessary to find the properties and the characteristics of the point sources and to determine the most important regions in Middle East with high density of point sources. Remote sensing is an appropriate tool for these investigations. Many different dust indices have been developed for the dust identification from remotely sensed images. In this study, a new false color composite method is developed for the identification of the point sources of dust storms in the Middle East using the dust indices. Then, the properties of the point sources are determined and finally, the share of different countries in the dust storm generation is investigated.In this study, MODIS images were utilized as remotely sensed images. MODIS images have been used successfully for dust storms detection. 28 MODIS-Level 1b images from 2008 to 2009 were selected and received from the archive of Iranian Space Agency. The software package for the image processing was ILWIS 3.7 (Integrated Land and Water Information System). The digital number of satellite images converted to the radiance and reflectance data. Then the different famous dust indices were developed using the MODIS images. These indices were BTD3132 (Brightness Temperature Difference in band number 31 and 32), BTD2931 (Brightness Temperature Difference in band number 31 and 32), NDDI (Normalized Difference Dust Index) and D (Roskovensky and Liou, 2005). Different false color composite maps were generated using these Indices, bands 3 and band 4 of MODIS images. Then the performance of different color composite map was evaluated using visual interpretation and the best color composite method for dust enhancement was selected. Then using the selected color composite method, the point sources in 28 MODIS images were identified and after that, all of the identified point sources were combined in GIS environment. Then the share of each country in Middle East in dust storms generation was determined according to the number of point sources in their territories. For determination of the characteristics of the point sources, the point sources map was combined with DEM map, Geological map and NDVI map of Middle East in GIS environment. Finally, we tried to determine the regions with high density of point sources and major role in dust storm generation using the point sources map and IDW (Inverse Distance Weighting) interpolation technique.The visual interpretation of the generated False Color Composite maps demonstrated that the best combination for dust identification is the utilization of D, BTD3132 and NDDI indices as the Red, Green and Blue bands of color composite map, respectively. Using this color composite method, about 420 point sources were extracted from 28 MODIS images and point sources map was generated. The point sources map showed that about 39.2, 23, 14.5, 13.8, 5.7 and 3.7 percent of the point sources of dust storms have been located in Iraq, Syria, Saudi Arabia, Iran, Jordan and Turkey territories, respectively. These results mean that more than 60% of dust storms are generated in Iraq and Syria. Combination of NDVI map with point sources showed that the point sources often have been located in the region with low NDVI values (low vegetation covers). Almost all of the point sources have been located in the planes with low altitude (elevation less than 400 meters). In addition, combination of geological map with the point sources map showed that the surface of the point sources regions often have been covered by non-rigorous soil formations. Therefore, the main characteristics of the dust point sources in Middle East have been determined. To determine the density map of the point sources, the gridded point sources map was interpolated by IDW and an indicator map for density of point sources in the Middle East was generated. The indicator map demonstrated that the main regions of dust storm generation are two regions in western Iraq and eastern Syria.MODIS images were very useful for the identification and detection of dust storms. In this study, a new false color composite map was developed for dust storms and point sources detection using the famous dust indices. This color composite was generated by the combination of D, BTD3132 and NDDI indices. The point sources of dust storms in the Middle East have been located in the regions with low altitude, low vegetation cover and non-rigorous soil surface. More than 60% of dust storms are generated in Iraq and Syria (especially in the western Iraq and eastern Syria) and transferred to the other parts of the region by the wind. The share of Iran in dust storm generation is about 13.8 %.

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 lands. The 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. So, it could be so difficult to predict climate change over whole of country. In this case, using new methods for solving weather equations and having climate prediction because of its long term temporal has so many important rolls for massive management. In climate change studies, the global circulation models (GCMs) are usually used to simulate the past and future global climate. Unfortunately, despite the advancement in GCM research and modern computing technology, the most recent generation of general circulation models still have serious problems due to their low spatial resolutions (with the field variables being represented on grid points 300 km apart). So, because of its serious limitation and resolution, using them in long term forecast couldn’t predict actual weather in station scale or small scale and it is important to assess the accuracy and uncertainty of GCMs in various climatic and geographical regions. To employ output of Global Climate Models and accesses to good resolution, "Downscaling Methods" are used that are divided in two dynamical and statistical groups and some when syncretistic of them. A thorough evaluation of the current generation of GCMs has only started recently and the evaluation of a rich spectrum of indices on extremes is new. In this, calibrating downscaled data is very important as a main parameter to reach best resolution and for analyzing long term forecast. Two different approaches to downscaling have been employed. It has adopted a methodology that exploits mean inter station correlations to correct the statistics of grid-box means. The method, closely related to block-kriging, is demonstrated to remove the sample size sensitivity of statistics in daily grid-point precipitation. It has adopted a direct downscaling by distance and direction weighted average of point observations. At this filed, SDSM is a Statistical down scaling model that distributed both of these aggregation techniques to the consortium. Several data of selected stations have been started applying to a dataset and coding study area. These datasets will provide a valuable reference for model evaluation simulate predictor variables across selected region. The SDSM model run on selected period and reached amount of precipitation, minimum and Maximum of temperature and their standard deviations. By using statistical methods we could evaluate SDSM outputs to reach the best conclusion and selecting best data. With acceptable results, we could use them for climate prediction over region. Materials: In this paper, at the first we tried to select 41 synoptic stations that have 41 years climate data (1961-2001). These stations distributed to whole country with several climates. These data applied our observation dada. At this method we used third version of the coupled global Hadley Centre Climate Model (HadCM3) Outputs as predictor of method and A2 scenario that is one of the most probable emission scenarios. Then we down scaled them by using SDSM model version4.2 that could downscale general circulation models to station scales. Then by using statistical methods and reaching differential coefficients could analyses downscaled data by base data and present suitable correlation of them. Results was shown, there is no significant deference with 0.5 critical errors and correlation of data and accepted at 0.01 significant levels. And there is a good accepted correlation between modeled data and observing minimum and maximum temperature and precipitation data. So, using Downscaled data is acceptable with suitable efficiency to correct future data at station scale. This study should help to fill in the knowledge gap in GCM downscaling research of climate and add an important piece in the global climatic assessment jigsaw puzzle.

The main aim of this research is the assessment of change trend in agro climatological parameters that influence the growth of citrus in the north of Iran. Thereby the daily data of min and max temperature, sunshine and precipitation for 6 number stations of the area under study provided from Iranian meteorological organization. At the next stage, the other parameters including temperature range, mean temperature, Growing Degree Day (GDD), Heliothermal Units (HTU) and Photothermal Units (PTU) has been calculated in the monthly, seasonal and annually scales. Then, Man Kendal trend test and linear method have been used for trend analysis in the above mentioned scales. The results of this research show the significant positive trend in min, max and mean temperature, GDD, HTU and PTU. The temperature range had significant negative trend. Precipitation has positive trend in 5% level of significant in a few stations in the monthly scale and it hasnt significant trend in the seasonal and annually scales. If the variables such as minimum temperature, max temperature, mean temperature, Heliothermal Units, Photothermal Units and temperature difference continue theirs increased and decreased rations respectively, would have harmful effect in the Citrus growth.

Precipitation and temperature patterns have especial role in the accuracy of hydrologic models. The future patterns of rainfall and temperature can lead to better hydrological predictions. Hence, according to their importance, we try to derive the future rain and temperature patterns of the Gharehsoo River’s watershed. This watershed has been placed in the northwest of Iran in Ardebil province and it has high amount of agriculture productions. Interpolation schemes are utilized in this study to determine the rain and temperature patterns. The utilized software package is ArcGIS software. These interpolation techniques are included of Inverse Distance Weighting (IDW), Global polynomial, Local polynomial, Radial Basis Functions (RBF), ordinary Kriging and simple Kriging. Firstly, we gather the monthly temperature and precipitation data of 10 synoptic stations in 2004. Then, the interpolation schemes are evaluated in order to determine the best temperature and precipitation patterns. The evaluation criteria in this study were Root Mean Square Error (RMSE) and Mean Error (ME). The results of evaluation of different interpolation schemes demonstrated that IDW and RBF method are the best schemes for the spatial modeling of temperature and precipitation patterns, respectively. Using these patterns, it is straightforward to predict runoff using hydrological models. In this paper, a new algorithm is proposed for the prediction of temperature and precipitation patterns for future (2100). To predict temperature and precipitation pattern, it is necessary to utilize of a predictor model to predict the amount of precipitation and temperature. Then the amount of precipitation and temperature are converted to spatial pattern of precipitation and temperature using the developed algorithm in this study. PRECIS model that is a regional climate model was utilized as predictor model in this study. a) case study: The studied area (Gharehsoo river watershed) is located in the Northwest of Iran, between longitudes coordinates 47°45’ and 48°42’ E, and between latitude coordinates 37°46’ and 38°34’ N. The Gharehsoo river watershed area is approximately 4100 km2 and plays significant agricultural role in Iran. the mountainous areas have been located in the western and southeastern parts of watershed. Furthermore, there are many pasture and agriculture lands in this watershed. Watershed elevation varies from 1170 m in Gharehsoo river outflow to 4732 m in Sabalan mountainous. The precipitation in the watershed is highly related to the topography and wind in the watershed.. The sea fronts and orographic conditions are the main factors for precipitation in the western and eastern parts of watershed. In the winter, the cold front of Mediterranean Sea, coupled with the local effects of Sabalan Mountains lead to orographic rainfalls. In summer, weather conditions are predominant of Caspian Sea front is the major factor for precipitation in the eastern part of catchment. Autumn and spring rainfalls are the results of interaction between African-Mediterranean and Caspian Sea fronts. b) Data: Temperature and precipitation data are two basic climatologically variables, measured at meteorological stations. Monthly precipitation (mm) and temperature data for 2004 was provided through Iran Meteorological Organization. The number of stations in the watershed and near to watershed was 11 stations. c) PRECIS ModelPRECIS (Providing Regional Climates for Impacts Studies) is a regional modeling system that can be run over any area of the globe on a relatively inexpensive, fast PC to provide regional climate information for impacts studies. The idea of constructing a flexible regional modeling system originated from the growing demand of many countries for regional-scale climate projections. Only a few modeling centers in the world have been developed RCMs (Regional Climate Models) and utilize them to generate projections over specific areas, because it needs high amount of computational effort and time. The Hadley Centre has configured the third-generation of Hadley Centre RCM, named PRECIS that is easy to set up. The past (1961-1990) and future climate SRES B2 scenario (2071-2100) were simulated by PRECIS model at a spatial resolution of 50×50 km for Iran. It’s necessary to have a series of precipitation and temperature patterns to produce monthly patterns for future. These series of maps are generated using the precipitation and temperature patterns of 2004. The hyetograph maps are calculated by the ration of total volume of precipitation in January and the area of watershed. The calculated total volume of precipitation in January using the precipitation pattern map was about 490 million m3. The ration of volume and the area of watershed was about 0.117 m. This number shows the average precipitation of January. Similarly, these operations can be performed for the other months of 2004. The unit hyetograph and thermograph maps are generated by dividing the precipitation and temperature patterns in 2004 to their corresponding monthly precipitation and temperature values. The precipitation and temperature data were extracted from the PRECIS model for 2100. The monthly temperature data of 2100 shows an increase of temperature about 2 to 5 degrees in future, but there is no specific trend in precipitation data. If the amount of the monthly temperature and precipitation of 2100 are divided by these amounts in 2004, the amount of B parameters are calculated for precipitation and temperature in different months. Finally, the precipitation and temperature patterns will be obtained by the product value of B parameters and unit hyetograph or thermograph maps in each month, respectively. A new method was developed for reasonable prediction of spatial patterns of precipitation and temperature. This new method uses of the results of a Regional Climate Model (e.g. PRECIS model) coupled with the appropriate spatial modeling techniques (interpolation techniques). The derived precipitation and temperature patterns in 2100 in Gharehsoo River watershed show a reasonable similarity with the topography and the climate of the region, Hence This method can be introduced as an appropriate method for the hydrological forecasts and water resource management.

Introduction Daily increase of industrial activities causes effects like green house gases emission in the recent decades. This harmful effects cause considerable changes in whether condition in many parts of the planet earth. Most important effects are, increase in mean temperature, flood, storm, hail and sea level fluctuations. The forecasting and assessments of climate change have some difficulties at surface circulation of green house gases and large scales separate in general atmospheric circulation. Because of low spatial resolution of General Circulation Models, weather and climate cannot be predicted accurately. Assessment of the climate change in future needs to introduce new climate scenarios. For this purpose, the climate data should be predicted and simulated for coming periods by using outputs of Atmospheric- Ocean General Circulation Model. In this paper, LARS-WG model and A1 scenario from ECHO-G model are used for assessing and predicting climate change in Zagros (2010-2039). Materials and methodsThe region under study. The region in question is Zagros. In these research grided meteorological outputs of ECHO-G model including precipitation, maximum and minimum temperature and radiation have been downscaled over 18synoptic stations of Zagros during 2010-2039 with A1 scenario

Seasonal rainfall forecasts can have significant value for resources planning and management e. g.، reservoir operations، agricultural practices and flood emergency responses. To mitigate this، effective planning and management of water resources is necessary. In the short term، this requires a good idea of the upcoming season. In the long term، it needs realistic projections of scenarios of future variability and change. In this paper، we analyzed 38 years of rainfall data in Khorasan-e Razavi province that is located in the northeastern part of Iran situated at latitude-longitude pairs (34°-38°N، 56°- 62°E). We attempted to train Mamdani Fuzzy Inference system based on Tele-connection synoptically patterns with 38 years of rainfall data. For performance evaluation، the model predicted outputs were compared with the actual rainfall data. In this study، at the first step، the relationship between synoptically pattern variations including Sea Level Pressure (SLP)، Sea Surface Temperature (SST)، Sea Surface Pressure Difference (SLP)، Sea Surface Temperature Difference (SST)، air temperature at 700 hpa، thickness between 500and 1000 hpa level، relative humidity at 300 hpa and Precipitable water have been investigated. In the second step، model was calibrated from 1970 to 1997. Finally، rainfall prediction is performed from 1998 to 2007. Simulationresults reveal that Mamdani Fuzzy Inference system techniques and regression models are promising and efficient. Root mean square for Mamdani fuzzy inference system model and regression model was obtained 6. 34 and 5. 5 millimeter، respectively.

IntroductionSince 1960s, heat balance models of the human body have become more and more accepted in the assessment of thermal comfort. The basis for these models is the human energy balance equation. One of the first as well as still among the most popular heat balance models is the comfort equation defined by Fanger (1972).Climate and tourism have a great dependence to each other, so that existence of a desirable weather condition is an advantage and potential for tourism, and most of the travelers notice to weather conditions in selecting their travel place and time. Climate comforting conditions usually are expressed by indexes which a series of meteorological, human and environmental factors have been played important roles in, and the possibility of comparison among different places is provided by.Comfortable climate condition generally state by indexes that involve the sets of meteorology, humanities and environmental elements. Several thermal indices such as Predicted Mean Vote (PMV), Physiologically Equivalent Temperature (PET) and Standard Effective Temperature (SET*) may be calculated for the assessment of human bioclimatic in a physiologically relevant manner as shown in several applications (Matzarakis et al., 1999; Blazejczyk, Matzarakis, 2007; etc). All indices have the known grades of thermal perception for human beings and physiological stress (Höppe, 1999). PET is defined as a certain air temperature related to fixed standard indoor conditions at which the heat balance of the human body is maintained with core and skin temperature equal to those under the conditions being assessed. In this research, PET index has been used for several cities in different locations in Iran. Material and MethodsIn this research, touristy cities including Mashad, Rasht, Isfahan and Kish Island have been selected for comparative of comfortable climatic condition. In this research, the authors have used the PET index.The Munich energy balance model for individuals” (MEMI) (Höppe 1993) is one of the thermo-physiological heat balance models. It is the basis for the calculation of the physiologically equivalent temperature (PET).In detail the MEMI model is based on the energy balance equation (9.1) for the human body: M +W + R +C + E D + E Re + E Sw + S = 0The individual heat flows in Eq. 9.1, are controlled by the following meteorological parameters (Verein Deutscher Ingenieure 1998; Höppe 1999):- Air temperature: C, Ere – Air humidity: ED, ERe, ESw – Wind velocity: C, ESw – Mean radiant temperature: RThermo-physiological parameters are required in addition:- Heat resistance of clothing (clo units) – Activity of humans (in Watt)The following assumptions are made for the indoor reference climate:1– Mean radiant temperature equals air temperature (Tmrt = Ta). 2– Air velocity (wind speed) is fixed at v = 0.1 m/s. 3– Water vapor pressure is set to 12 hPa (approximately equivalent to a relative humidity of 50% at Ta = 20°C).The calculation of PET includes the following steps:Calculation of the thermal conditions of the body with MEMI for a given combination of meteorological parameters.And then Insertion of the calculated values for mean skin temperature and core temperature into the model MEMI and solving the energy balance equation system for the air temperature Ta (with v = 0.1 m/s, VP = 12 hPa and Tmrt = Ta).In this research, the requirement data have been used in the long-term period on the daily scale. The calculations of PET index have been done using Reymen 2.1 software. Results and Discussion The length of Climatic comfort period which is recommended to be the best time for tourism affairs is 35 days of a year in Mashhad and Esfahan, 37 days in Rasht and 85 days in Kish. The most important tourism limitation for Mashhad, Esfahan and Rasht cities is the existence of excessive cold stress during months Azar (November 22 until December 21), Day (22 December until 21January) and Bahman January 22 until February 21). The results of this research show that duration of climatic comfortable period in the selected cities is short and is located in the separated period on the early spring and autumn. Between of selected cities, Kish island in the cold months of the year and spring season has been the best comfortable climatic condition. The cold stress in the duration of cold season has been main limitation for Mashad, Isfahan and Rasht. Among the selected cities, Kish island has been the best comfortable climatic condition that can recommend for the entire travelers in the early spring. Isfahan town that is one of the most Famous Iranian touristy cities only during the months Ordibehesh (April 22 until May 21) and Mehr (September 22 until October 21) have suitable condition for traveling. ConclusionAccording to the results of this research, comfortable climatic period in the studied cities is short and is located in the second separated periods in the early of autumn and spring. The length of this period, in Isfahan, Mashhad, Rasht and Kish is 35, 35,37 and 85 days of years respectively. Result comparatives of this research show that the best destination for spending of Nowrooz holidays as well as winter travelling is Kish Island. For summer travelling, only Mashhad and Rasht cities have nearly suitable conditions on the second half of September.

Introduction Climate change is expected to affect agriculture very differently in various parts of the world. The resulting effects depend on current climatic and soil conditions, the direction of change and the availability of resources and infrastructure to cope with change. Biological systems are based primarily upon photosynthesis and are thus dependent on incoming radiation. However, the potential for production set by the radiation is greatly modified by temperature and rainfall. The main effect of temperature is to control the duration of the period when growth is possible in each year. Using statistical downscaling models in climate change studies provides possibility to generate the weather data on time and place appropriate scales. Increasing of Publication and human activities increase green house gases and carbon dioxide. This situation leads to the temperature increased to 0.6 Centigrade. Scientists believe that the extreme phenomenon is the main climatic index. This possibility is an important help in the climate change studying in local and regional scale. Intensity of extreme phenomenon must know as climate changes main branch. Materials and methods Extreme meteorological events, such as spells of high temperature, heavy storms, or droughts, can severely disrupt crop production. Recent studies have considered possible changes in the variability, as well as in the mean values of climatic variables. Where certain varieties of crops are grown near their limits of maximum temperature tolerance, heat spells can be particularly. In this research, growth season and freezing season length have been investigated as climate changes indices. Using statistical data for each station, the daily data bank including minimum and maximum temperature were marked in the Access software. Then these data in the various years using growth season length and freezing period length were extracted in the past and future period in the LARS-WG software. In the next step, Spirman’s correlation coefficient has been used for testing the hypothesis of the research. In definition, the first six daily period after latest freezing is as the starting of season period that means temperature is above of and the final six daily period is as the end of season period that the average temperature is below of. The interval between the first freezing in autumn and the final freezing in the spring is as freezing period. The changes assessment of indices have been done during two climate periods including the past (1976-2005) and the future (2010-2039) in three synoptically stations: Mashad, Torbat Heydarieh and Sabzevar in Khorasan Razavi province, in Iran. Results and Discussion Results showed that growth season length increases in Mashad and Sabzevar stations Results also showed that growth season length decreases in Torbat Heydarieh station. These changes in freezing season length indices are greater than the others. In three stations. We will observe that freezing season length decreases between 15-16 days as a result of global warming. Results of hypothesis test showed that there is no correlation between observed growth season length and estimated growth season length and also observed freezing season length and estimated freezing season length using Spearman’s correlation coefficient. Conclusion In this paper, with regarding to phenomenon including growth season length and freezing period length, the climatic fluctuations were estimated. The comparative of the past and future periods shows that growth season period will be increased and freezing period length will be decreased in the area under study. According to table 2, grow season length would have been increased in Mashad and Sabzevar stations. Also freezing period length would have been decresed in Torbat-e Heydarieh station. In the other words, premature freezing and late freezing would have been decreased in the area under study. The obtain results of this research emphasis the results of the other researches such as Sedaghat Kerdar and Rahimzadeh (2006), Hino et al (1999) and Bonsal (2000).

Introduction In the recent years, a CSG (Climate change Scenario Generators) model was developed and made a simple model for climate change that is called MAGICC and a scenario of climate information which organizes production that is called SCENGEN. M and SCENGEN models are divided into two main parts: parts that assess the climate change issued by the spreading circulation of green house gases (MAGICC) to organize sets of simple models. (Harvey et aL, 1997, 50) Magicc is not the GCM model, but it can simulate using GCM in several areas in the world (Wigley et al 2002, 2690). MAGICC can predict the annual average of ground temperature and mean annual sea surface temperature of spreading green house gases and CO2. This part includes the observation of climatic data and outputs of general circulation at atmospheric models that users can consider and assess in different situations. There are different couple of models that are mixed at the software Network, and users can use this network to change the concentration of CO2 and the mean temperature of ground and sea surface, to appoint climate circulation scenarios of CO2, CH4, N2O (PFC, HFC, HCFC).This model is used by IPCC in different assessment. Users can edit and update their scenarios into their models. Scenarios show base data of general circulation atmospheric model and global observation data of Europe, South Asia, America, and southern Africa. SCEGEN model has been developed in several years. Scenario of green house gases and SO2 can change by Magicc and SCENGEN and as well as the temperature of the ground and sea surface. Materials and Methods There are some limitations for modeling the climate in future decades using dynamically-developed models. Here we have used Magicc-Scengen statistical model, which uses ECHAM4 and HADCAM2 models data. In this model, Iran is divided into 9 parts and the precipitations and temperatures have been simulated for the decades of 2000, 2025 and 2075. Climate change scenarios have monthly, seasonal and yearly time scales. This model can use 16 GCM data. In this paper, calculations have been done on the basis of ECHAM4 and HADCAM2 outputs. Temperature and precipitation were analyzed during: 1986 to 2015 (2000 decade), 2011 to 2040 (2025 decade) 2036 to 2065(2050 decade), 2090 (2075 decade) and 2086 to 2115 (2100 decade) Results and Discussion mean precipitation The results of downscaling from HADCM2 data show that mean precipitation will reduce during the future decades. In contrast to the other decades, the largest decrease will happen during 2100 decade (figure 1) The largest decrease of precipitation is related to Is92D, which is about 6 percent. Precipitation will increase during the future decades in Mazandaran, Golestan, North khorasan, North of khorasan Razavi, Tehran, parts of Gillan, Ghazvin and Markazi. The largest increase in precipitation will happen in north east and east of khorasan Razavi province. We do not have a remarkable precipitation change on southern and eastern coasts of the Caspian sea. Also precipitation will decrease in southern and south-eastern provinces, parts of Sistan-O-Bluchestan, and kerman, bushehr, south Fars. The different changes of precipitation during the future decades are shown at figure 1. The results show that precipitation has increased and the highest precipitation will happen in the decade of 2100. On the basis of WKE450 and IS92E Scenarios, change in precipitation will occur at about 13.3 and 29.1 percent. The Results indicate that precipitation will decrease in the future decade in Mazandaran, Golestan, North of Khorasan, Parts of Khorasan Razavi north parts of Markazi, Tehran, parts of Gillan, Ghazvin and Markazi and the largest decrease of precipitation will happen on the southern and eastern coasts of Caspian sea, Markazi, Ghazvin, Semnan, Golestan, parts of Gillan and Mazandaran. In these provinces, precipitation will decrease about 9 to 18 percent. Precipitation will increase in sistan- O-Bluchestan, Hormozgan, Kerman, Fars, Bushehr, South- Khorasan, Yazd, Esfehan, Tehran, Kerman, Parts of Semnan, Southern parts of khorasan Razavi and Markazi, Persian Gulf plateau of Iran, and Oman sea. Certainly the highest precipitation will happen in Oman sea and Persian Gulf. The changes of precipitation with different scenarios are shown in figure 2. Mean Temperature The Whole mean temperatures were estimated and compared to the base data, the data of temperatures and the general circulation atmospheric model. (Figures 3 and 4). Mean Temperature has increased and the maximum increase will happen in 2100 decade. The results show the increase of temperature over Iran. On the other hand, we will have an increase of temperature of about 0.4 to 3 degrees in the HADCM2 model and of 5 to 4 degrees in the ECHAM4 model in the future decade; the maximum change in the temperature will happen at about 4.4 to 5.3 degrees in 2100 decade. Increases of temperature will occur in south of khorasan, Yazd, Esfehan, parts of khorasan razavi, Semnan, Tehran, Markazi, Ghazvin and Gillan, Hormozgan, Sistan o Baluchistan, Kerman and Fars. The largest increase will occur in Fars, Isfahan, Bushehr, Mazandarn, Tehran, Yazd, Semnan, Ghazvin, Gillan and Markazi. Conclusion According to the results of the Hadcm2 model, precipitation will decrease about 2.5 percent, but in the ECHAM4 model, in the same period of time, raining will decrease about 19.8 percent. The Hadcm2 model forecasted that precipitation will increase in Mazandaran, Golestan, north of khorasan, Semnan, Tehran and parts of Gillan, but will decrease in Kerman, Hormozgan, Bushehr, south of Fars and parts of Sistan- O - Baluchestan and according to ECHAM4, precipitation will increase in these areas. This model has predicted that the temperature increases about 3 to 3.6 (2100 decade). We suggest that using the general circulation model for precipitation and its assessment during the future decade over Iran is necessary.

The aim of this research is the assessment of the relation between rainfall and large scale synoptically patterns at Khorasan Razavi province. In this study, using adaptive neuro fuzzy inference system, the rainfall estimation has been done from April to June in the Area under study. Spring rainfall data including the information of 38 synoptic, Climatologic and rain gauge stations from 1970 to 2007 has been selected from Iranian Meteorological Organization and Ministry of Energy. In this paper, we are analyzed 38 years of rainfall data at Khorasan Razavi province located in northeastern part of Iran at latitude-longitude pairs (34°-38°N, 56°- 62°E). The Adaptive Neuro-Fuzzy Inference system based on synoptically patterns with 38 years of rainfall data was trained. For performance evaluation, network predicted outputs were compared with the actual rainfall data. In this Study, at the first step, the relationship Between synoptically pattern variations including Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (?SLP), Sea Surface Temperature Difference (?SST), air temperature at 700 hpa, thickness between 500 and 1000 hpa level, relative humidity at 300 hpa and precipitable water were investigated. As the second step, the model was calibrated from 1970 to 1997. Finally, rainfall prediction is performed from 1998 to 2007. The model that used in this research has an input layer, one hidden layer and an output layer. The number of neuron for input layer, hidden layer and output layer was 13-28-1, respectively. The results of simulation reveal that adaptive neuro fuzzy inference systems are promising and efficient.