Climate change can lead to changes in the frequency, intensity, and duration of extreme Climate events in different parts of the world. The purpose of this research is to investigate temperature and precipitation extremes in Lorestan Province. The data used in this study included precipitation and the maximum and minimum daily temperature of nine synoptic stations in Lorestan Province during a 28-year (1990-2017) common period. The matrices of minimum and maximum temperatures and precipitation daily data for each station were prepared and used to compute the extreme Climate indices (26 precipitation and temperature extreme indices based on the recommendation of CLIVAR \ CLL expert group) using the R programming software. The results of studying the trend of cold and hot extreme weather indicators during the period 1990-2017 in the province using the Mann-Kendall trend test showed that for all stations, the hot indices have increased and more cold indices have decreased. In different regions of the province, positive and negative trends of hot and cold indices with different intensities have occurred. The highest upward trend of the warm extreme indices has occurred for the hot night’s index. Among the cold indicators, the greatest decrease occurred for several frost days and cold days indices. The decreasing trend of ice days is significant for 45% of stations at 99% level and the decreasing trend of cold days for 77% of stations at different levels of 90, 95, and 99%. The results of the study of the frequency of occurrence and trend of precipitation extreme indicators in Lorestan province showed that the total rainfall of this province, like many regions of the country, has decreased. In contrast, the occurrence of maximum rainfall in addition to being significant in the province has an increasing trend during the period 2017-1990. These conditions can indicate an increase in the number of occurrences of heavy and short-term rainfall events and, shorten the period of the rainfall season in the region.

Identifying the climatic indices and study of teleconnection is an important method in association with the purpose of synoptic climatology science. What are the most important indicators for Iran' s Climate? For this aim, digital data of daily height for important atmospheric levels from the National Centers for environmental prediction and Atmospheric research for a period of 63 years (1948-2010) and daily temperature and precipitation data for 43 synoptic stations was received in 30years (since 1977-2008). from meteorological organization of Iran. By using the analysis method of main component the important atmospheric indices in the geographical limit of northern latitude of 10 to 70 degrees and 10 to 80 degrees of geographical east longitude for the cold half of year (fall and winter) were identified and through correlation method, the manner and importance of each one of these indices in Iran's Climate were determined. The results showed that there are seven climatic indexes in the under study geographic area and the most important of them for Iran's Climate from beginning of cold season up to the middle of March include the indices of Central Asia, North Siberia, Western Europe, Anatolia and the Western Mediterranean respectively. Indicators have the greatest impacts on the temperature of Iran’s Climate and their precipitation effect is zonal. Most of the precipitation relationship is between the Caspian Sea coastal area and the index of Central Asia and Scandinavia - Central Siberia respectively in autumn and winter and whole of Iran with western Mediterranean. Thus, according to tobler’s principal in geography, the centers and the most important indicators of Iran's Climate are near Iran and their importance is more than the climatic indices located in far away distances.

Although most dust storms, especially those prevailing in Iran, are often regional in nature, But local centers dust, the dust belt and specifically in Iran, played a significant role. One of the origins of local dust in this belt is Yazd city and salt domes northeast of Ardakan. Understanding the characteristics of dust storms is important in terms of type, frequency, location and time of occurrence. Due to the dry climatic conditions and strong and erosive winds on it, the city of Yazd is faced with various dust events every year, which causes significant damage to the economic and biological resources of the city. In addition to climatic parameters and indicators, some indicators reveal that Climate change can also affect the course of changes in dust occurrence. In addition to climatic parameters and indicators, some indicators reveal that Climate change can also affect the process of change in the occurrence of dust in order to identify these indicators from the software ClimPACT is based on RClimDEX software and runs in R 2.10 software environment. In addition to identifying the trend of dust changes, the purpose of this study is to determine the importance of each factor affecting the occurrence of dust in Yazd. For this purpose, analysis and comparison of different functions of neural network, multilayer perceptron was used and finally the model with the least error rate and the highest correlation coefficient, as the optimal model to investigate the share of climatic factors affecting the occurrence of dust originating around and outside the station was estimated based on the optimal model. The occurrence of internal and external dust (dependent variable) was modeled and analyzed and the most important determining factors in the occurrence of internal and external dust were determined

The occurrence of any possible change in the future Climate will seriously change agricultural production at various levels and will be able to significantly change the crop systems that have evolved under the current climatic conditions. The aim of this study is to investigate the changes in key indicators of agricultural Climate based on temperature in the context of Climate change. This achieve the aim, the spatio-temporal variations in extreme temperature indices in the current conditions (1986–2016) and under scenarios of RCP4.5 and RCP8.5 (2020–2070) was analyzed based on observed data and Climate models in Lorestan province. The agro-Climate indices related to temperature are consists of Daily Temperature Range, Frost Days, Days with temperatures below -20 °C, Length of Growing Season and Growing Degree Days. The results indicate that the Daily Temperature Range is approximately -0.4 °C decreasing and 0.3–0.9 °C increasing in the current situation and in the RCP8.5, respectively. indices of Frost Days and Days with temperatures below -20 °C will decrease by about 20 days in the future compared to the current condition. Compared with the current situation, Length of Growing Season and Growing Degree Days will increase by an average of 60 days a year and by about 300 degrees in the future, respectively. In general, in the future, the warm and cold periods of the year will increase and decrease, respectively. This leads to an increase in evapotranspiration and a decrease in soil moisture and a reason to reduce water storage and ultimately reduce crop yield.

Study of weather as the fleeting atmospheric conditions and Climate as the prevailing weather of a region, can be effective as a local index for area attractions and activities and comfort of tourists. Checking the bio- Climate of the regions gives tourists possibility to choose and survey their ideal eras from the view point of comfortableness in specific sessions. In this study, using bioclimatic indices such as Backer (CPI), stress, Steadman-Tom (THI), non-comfort (Humidex) (HU), Tom thermo hygrometric  (THI), tourism Climate of Urmia city have been evaluated. In order to calculate each of the criteria stated in the monthly time scale, the parameters of temperature, relative humidity, wind, vapor pressure of synoptic stations of Urmia during 1951 to 2005 were used. As a result, in Baker index, the months of April, May, November and December are suitable for tourism. In stress index both July and August, at the Steadman-Tom index August, September and June, in temperature-humidity index, months of May, June, July, August and September are comfortable and suitable for tourism. In the Non Comfort index, there is no month of the year having non comfortableness qualification.

Agroclimatic indices are based on climatic factors effective on crop growth and development. Under Climate change, these indices are also changed, therefore the pattern of these changes could be associated with crop growth and yield. The purpose of the present investigation was to calculate agroclimatic indices under Climate change and compare the changes with the indices of the present climatic conditions and hence prediction changes on crop productivity that may occur. For this purpose 14 hey variables were disaggregated to 55 new variables which were indictors of agroclimatic seasonal changes. These indices were calculated for the present and the year 2025 and 2050. Results showed that the first occurrence of autumn freezing day will be delayed by 5-9 to 8-15 days for 2025 and 2050, respectively and the magnitude of these changes will be higher from the North to South and from West to East of the country. However, occurrence of last spring freezing day will be earlier by 4-8 and 7-12 days for these target years. However, again the spatial trend will increase from the West to the East and from the North to the South. Based on these two events, length of growth period will increase by 5-23 to 16-42 days for years 2025 and 2050, respectively.

Introduction Although Climate change is a global challenge, its effects occur locally and differ from region to region (Filho et al., 2016; Leonard et al., 2014). Over the past few years, the large positive departures of temperatures from their mean values have become commonplace in many parts of the world. Surface temperature over land regions have warmed at a faster rate than over the oceans in both hemispheres (IPCC, 2007). Each of the last three decades has been successively warmer at the Earth’ s surface than any preceding decades since 1850. The warmth of the period from 1983 to 2012 is very likely higher than any 30-year period in last 1, 400 years in the Northern Hemisphere. The globally averaged temperature that results from combined land and ocean surface data shows a warming of 0. 85 ° C during the period 1880 to 2012, (IPCC, 2013). indices for Climate variability and extremes have been used for a long time, often by assessing daily temperature or precipitation observation above or below specific thresholds (Zhang et al., 2011). The dependence and thermodynamic relations between the precipitation and temperature have been addressed in numerous studies (Liu et al., 2012). Changes in extreme weather and Climate events have significant impacts and are among the most serious challenges to society in coping with a changing Climate (CCSP, 2008). The warming global Climate has increased the concurrent climatic extremes and the intensity of extreme weather events over different regions of the Earth, such as drought, heat waves, tropical cyclones, floods, and fires (AghaKouchak et al., 2014; Alexander et al., 2005; Leonard et al., 2014). The projections of extreme weather phenomena on the basis of temperature and precipitation indices in AR5 show a probable increase in the number and intensity of dry and hot periods in the summer time (Filho et al., 2016; Hao et al., 2013). Many practical problems require the knowledge of the behavior of extreme values. In particular, the infrastructures we depend upon for food, water, energy, shelter and transportation are sensitive to the high or low values of meteorological variables (WMO, 2009). The impact of these events can be due to a single variable in an extreme state, but more often it is the result of a combination of variables which are not all necessarily extreme (Leonard et al., 2014). The combination of variables leading to an extreme impact is referred to a compound event (Beniston, 2011). Recent studies of joint quantities of precipitation and temperature are often described in terms of warm/wet, warm/dry, cool/wet and cool/dry Climate combinations (e. g., Arsenovic et al., 2013; Beniston et al., 2009; Estralla & Menzel, 2012; Hao et al., 2013; Lopez-Moreno et al., 2011) and/or based on copula theory (see Miao et al., 2016). The combination of warm/wet, warm/dry, cool/wet and cool/dry modes reveals a systematic change at all the locations investigated with significant declines in the frequency of occurrence of the cool modes and a sharp rise in that of the warm modes. This article investigates the trends of combined temperature, precipitation, humidity, wind speed and sunshine statistic in spatial domain in Iran. Our investigation aims to use the joint quantities of temperature and precipitation and other variables of weather to offer some insights into the behavior of particular modes of heat and moisture which cannot be achieved by the analysis of the statistics of each individual variable. Over the last years, several extreme precipitation and temperature indices have been explained and analyzed in the literature (e. g., Parak et al., 2015; Rahimzadeh et al., 2009; Tabari et al., 2011); however, the Climate change has not been concerned with the use of joint extremes indices. Materials and Methods The study area in Iran lies approximately between 25oN and 40oN in latitude and between44oE and 64oE in longitude (see Fig. 1). Based on the Koppen Climate classification, most parts of Iran are categorized under arid (BW) and semi-arid (BS) Climates. Alborz and Zagros are the important mountains of Iran which play an important role in non-uniform spatiotemporal distribution of temperature and precipitation in Iran (Dinpashoh, 2006). The examination of Climate changes needs long and high quality records of climatic variables. In the present study, the dataset of daily maximum, the minimum and mean air temperatures and precipitation (P) for the period 1981-2015 from 47 synoptic stations from different geographic locations of Iran (Fig. 1) were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO) and were then analyzed. The homogeneity of the dataset was also assessed and approved by IRIMO. Most of the regions were covered by the corresponding data and the geographical location of the stations. Results and Discussion One solution for the better detection of Climate change is the use of compound indices; therefore, a set of compound indices derived using daily resolution climatic time series data with a major focus on extreme events were computed and analyzed to assess Climate changes in Iran. The compound indices consisted of cool/dry, cool/wet, warm/dry, warm/wet, TCI and UTCI which were examined for a 47 synoptic meteorological stations during 1981-2015. The main results of the research showed a substantial change in the behavior of the joint extremes of temperature and precipitation quantities associated with warming has occurred in the past three decades. More than 80 percent of Iran has experienced a decrease in the annual occurrence of the cold modes and an increase in the annual occurrence of the warm modes. Universal thermal Climate index (UTCI) change showed a widespread and significant increase in the annual occurrence of the strong heat stress (32– 38 ° C) and a significant decrease in the annual occurrence of the no thermal stress class (9-26 ° C); in fact, the changes are also spatially coherent as compared with joint extremes of temperature and precipitation changes. Trends in tourism climatic index (TCI), including the number of days with TCI≥ 60, and the number of days with TCI≥ 80 showed similar changes but with weak spatial coherence. The results of this study also allow researchers to preview the condition that may occur with greater frequency in the future throughout Iran. Conclusion This paper attempts to present a suite of compound extreme indices with a primary focus on extreme events. These indices were calculated and analyzed for a number of sites between the years of 1981-2015 to provide a general overview of Climate change in Iran. The results from non-parametric test showed statistically significant and spatially coherent trends in the compound extreme indices. It was found that in more than 80 percent of Iran the frequency of the warm modes has increased, while the frequency of cold modes has decreased but with smaller magnitudes. More than 97% of the country exhibited a positive trend for the annual WD index. The significant increasing trends of the annual WD varied from (+) 3. 7 to (+) 14. 5 days per decade respectively in Chabahar and Kish Island stations. Based on the results of the analysis, apart from a few stations and more specifically in Shahrekord station, joint quantities of temperature and precipitation indicates almost the same trend and responding to these differences definitely requires further investigation. At the same time, the occurrence of TCI and UTCI in the range strong heat stress (32-38 ° C) events has increased almost over the whole country, which is consistent with the increase of the frequency of warm modes. The distribution of the annual compound indices trends indicated that the negative and positive significant trends have mainly occurred in the northwest of Iran. The results also suggested the need for further investigation on local factors intervention in the environment, which could be one of the major causes of Climate change.

In many countries, Climate is a valuable asset for the tourist, since the Climate of the destination is oneof the information tourists need, and tourists plan their itinerary according to the destination's desired Climate. Climate is a major factor in the development of the tourism industry, and, considering the importance of the influence of Climate factors on tourism planning, determining the indicators oftourism comfort, tourism Climate and physiologically equivalent temperature is very important. In the present study, the Kerman province has been zoned monthly using tourism Climate indices and physiological equivalent temperature. For this purpose, the indicators were calculated for 12 synoptic stations within the Kerman province during the statistical period of 2003 to 2013. The calculated station results were generalized to the study area in the GIS environment using Inverse DistanceInterpolation method. The results of the Tourism Climate Index (TCI) showed that the best months to travel to Kerman province were April, October, December and March and also the best seasons for tourist attraction were early spring, autumn, and late winter. Also, according to the results of the Physiological Equivalent Temperature Index (PET), the best conditions for traveling to Kerman province were during April, October, November, and March, and the best seasons for touristattractions were early spring and autumn. Based on the results of the studied indices, it can be concluded that considering the conditions of the province and the climatic parameters used in each index, the results of Physiological Equivalent Temperature Index were more consistent with the conditions of Kerman province.

Background and Objective: In this research, it is tried to find the location of solar panels using Climate and geographic information systems in the province of Khuzestan. Material and Methodology: At first, climatic data (total annual precipitation, annual average, sunshine and number of days of dust) related to 21 meteorological stations and elevation, slope, tilt, fault, fault, land use and road layers as the most important climatic factors, Topography, environment and human environment, which were influenced by the amount of solar panels in GIS, were generated using the IDW method, then weighed according to the FTOPSIS model, and these layers were combined through the overlapping method and the layer layers To establish solar panels in the province was provided. Findings: After creating the layered layers, they were finally placed in the GIS environment by combining different layers of information and determining the weight of each information layer. The classification of the map of the solar panels in 5 highly desirable categories with (2. 020-3. 020-3. 050), in the desirable range (1. 540-2. 090), moderate (1. 220-1. 530), in the unfavorable range (941-1. 210) and very unfavorable (512-940). Discussion and Conclusion: The study showed that, by combining different information layers and applying limitations and potentials, the eastern boundary zones including the cities of Dahdz and Izeh have the highest degree of utility in the construction of solar panels. The results also showed that the GIS as a decision support system and fuzzy overhead analysis process (FTOPSIS) is a flexible model for locating data in the selection of suitable solar panels.