نشریه پژوهش های اقلیم شناسی
پیاپی 52 (زمستان 1401)

Atmospheric circulation has a variety of patterns in terms of time and space, However, drastic changes in atmospheric factors and elements have caused anomalies in these patterns, which result in changes in temperature, humidity, pressure and precipitation in different parts of the world, one of which can be the snowfall in snow-covered areas. Snow cover has significant effects on climate, such as the effect of radiant energy in the region and atmospheric and thermal circulation, and any changes in climate may have long-term environmental and economic consequences for the time, amount and distribution of snow. Cold, rainy and snowy winters are a feature of the mountainous climate of the Central Zagros region of Iran, which is the gateway for various humid air masses to enter Iran from the Mediterranean and Eastern Europe. With this feature and being mountainous, the region is witnessing low temperatures and snowfall in the mountainous areas of this region. In this research, snowfall in the Middle Zagros, which is the source, the most important catchment area of the country and the source of the most important and largest rivers in Iran to the south and southwest of the country is investigated. This is especially necessary in the mountainous areas of the Zagros, where water from snowmelt plays an important role in runoff, which can be considered a pivotal operation in the field of water resources management and flood control in the Zagros. Therefore, the present study tries to investigate the relationship between changes in snow cover in the Middle Zagros region and changes in atmospheric circulation patterns using synoptic methods. The purpose of this study is to analyze the synoptic characteristics of the Middle Zagros snow cover in relation to atmospheric circulation patterns and to explain the spatial and temporal characteristics of this phenomenon using the statistics of synoptic stations in the study area. The results can help in forecasting and planning surface and groundwater resources as well as drought and prevent damage due to flooding of rivers in this region, most of which is due to snowmelt in the highlands of this region. The data required for the study include hourly and daily meteorological statistics of snowy days at synoptic stations in the three provinces of Ilam, Kermanshah and Lorestan, which were obtained from the Meteorological Organization. Geopotential altitude data of 500 and 850 and sea level were also obtained from the website of the National Center for Environmental Prediction and the National Meteorological Research Center (NCEP / NCAR). These statistics cover the long-term period (2018-1989). After extracting the snow day codes (including codes 70, 71, 72, 73, 74, 75), it was found that among the stations in the region, 14 stations had days with snow to be checked, which in terms of time distribution. And have a statistical period of 30 years and cover the entire Middle Zagros. In this study, after collecting statistics and data on snow-related days during the statistical period (1989-2018) in synonymous stations located in three provinces of Kermanshah, Ilam and Lorestan, as well as data on different atmospheric levels from the site of NCEP/NCAR center in Middle Zagros, they were processed using factor analysis and hierarchical clustering techniques and The results were analyzed and finally different snow-generating companion patterns in the study area were studied in detail. The results of studying circulatory coping patterns after analyzing the data at the levels of 500 and 850 hpa were entered using factor analysis technique and hierarchical clustering method, four models of companionship governing the snowy days of middle Zagros were identified and determined. These results showed that four distinct patterns for snowfall in middle Zagros can be identified. Accordingly, the first and third patterns with a total of 64.5% of the dominant pattern of snowfall in middle Zagros are mainly due to the establishment of high pressure center in the Mediterranean and Black Sea and the study area in the front of the deep landing at a high level Also, in most cases of heavy snowfall, a cold hole is formed at the level of 850 hpe which covers the study area and In most cases, a strong carrier is located in Eastern Europe or stretched diagonally from the west of the Caspian Sea to the south of the Red Sea. The study of patterns shows that the high-elevated center of the western Mediterranean and tibetan plateau is strengthened and by moving to higher latitudes, it helps to create strong graphs with north-south axis in Europe and Asia. The creation of these crypts and the existence of low-lying or cold centers in western Iran strengthens the eastern Mediterranean frigate and stretches to low latitudes and causes cold escape in the study area. On such days, the polar vortex fold extends to low latitudes such as the north of the Caspian and western Iran with the north-south or northeast-southwest axis, causing cold air to fall in the region. In the study of patterns and days of heavy snowfall, it is necessary to penetrate the polar vertex fold into low latitudes for heavy snowfall.

On April 01th , 2019 Iranian calendar, heady damages was inflicted on Lorestan province due to the arising of the heavy rainfall and consequent destructive flood . Present study was carried out to investigate the synoptic mechanisms associated with the occurrence of this rainfall. At first, the data related to the daily rainfall in different research stations was received from the metrological organizations. Afterwards, data related to atmospheric levels including Sea Level Pressure (SLP), the altitude geopotential of 850 and 500 hectopascal levels, vertical atmosphere speed , wind stream in 1000, 850, and 500 hectopascal, specific humidity of 700 hectopascal level , and the river flow of the 250 hectopascal level during the studied days were prepared and received from National Center of the Environmental Prediction (NCEP) and National Center for Atmospheric Research(NCAR). The created maps were drawn and interpreted in Grads Software.The results of the study indicated that, on April 01th when the flood hit the region, synoptic investigation of the Sea Level Press (SLP) shows a low-pressure system with a central core equals to 1206 hectopascal is located in Northern Europe with its braces covered the north of the Black Sea. Furthermore, the low-pressure system having central core of 1026 hectopascal covered wide regions surrounding Aral lake to North-east of Afghanistan. Accordingly, a low-pressure system having central core of 1008 hectopascal moved to eastern parts compared to previous day and dominated wide regions from Mediterranean through all investigated areas to Central Iran. Given that low pressure system spreads north-east Russia in line with northeast-southwest through Red Sea and East Africa, a high-pressure shave was created which was accompanied by instability and intense ascending of the weather on desired April 01th along with the arising of the rainfall in investigated region. Consequently, with the rise in the altitude and in 850 hectopascal level, low pressure system having central core equaling to 1300 hectopascal dominates North-Russia through lower latitudes. Subsequently, lower altitude closed system corresponding to 1420 geopotential meters resides Eastern Mediterranean and, compared to previous day headed eastwards and spread West and North-west of Iran including investigated region in the north-south direction.Moreover, high altitude system with central core which equals 1520 geopotential meters has spread North Europe up to 48-degree latitudes, which, given to high altitude system located on Afghanistan and Pakistan, a more intense shave arose compared to previous day. Afterwards, at 500 hectopascal level, central core having 5540 geopotential meters places on East-Mediterranean Sea in its Northeast-southwest direction, which resulted in deeper and wider trough so much that central trough spreads west Iran. The placement of its positive vorticity covers wide investigated region and results in ascending motions. The deepening of the cyclone and penetration of trough to the lower latitudes can gives rise to the precipitation of cold weather. Synoptic investigation of vertical atmospheric speed of 1000 hectopascal level revealed that maximum negative omega equals -0/2 pascal / second that is located on the North-west of Caspian Sea in Northeast-Southwest direction, and, on its movement toward Lorestan, it decreases in its intensity and reaches -0/05 pascal/ sec. Given that the second negative omega equaling to -0/15 pascal/sec is placed on Persian Gulf and omega is negative throughout the investigated region, the required conditions are really for the occurrence of instability, ascending of the weather, and rainfall. Subsequently, with the rise of altitude in the atmospheric levels, the maximum negative omega has increased and reached 0/3 pascal/sec. Within 500 hectopascal, the direction of negative omega changed from Red Sea towards investigated region which, accordingly, has been strengthened over Caspian Sea and spread the intended region in North-South direction. Given the increasing altitude of the level of the atmosphere, the maximum omega has risen and reached -0/4 pascal/sec, which accordingly intensified the conditions for atmospheric instability. Synoptic investigation of specific humidity indicated that most of the humidity advection happened from the Southern water reserves to the investigated regions. Maximum humidity core is 24 g/kgs over Red Sea and is decreasing in extent toward Iran, which, subsequently, passed over Persian Gulf and reached 18 g/kgs in Oman Sea and spread intended area in Northwest-Southeast direction.Humidity injection, particularly from southern water reserves, led to arising of the heavy rainfall during the specified day in investigated region. With the rise in the altitude at the level of 700 hectopascal, maximum specific humidity, which equal 10g/kgs, residing in East Africa and got the investigated region having passed over Caspian Sea. So , the sources for the heavy cloud rainfall on Farvardin 12th are Red Sea, Mediterranean Sea, Arab Sea, and Persian Gulf.Key Words: Heavy rainfall, Geopotential, Omega, Synopsis, Lorestan.

Heavy rains are a risk factor for natural disasters such as floods. Therefore, identifying and predicting their occurrence is one of the measures that can reduce the damage caused by it. This study was with purpose analyze and explain heavy rainfall using statistical-synoptic methods. For this purpose, precipitation data from 14 synoptic and rainfall stations of Karkheh and Dez basins in a period of 60 years (1959-2019) were used. In this regard, first, using the probability distribution function of the final limit of type one (Gamble), the heavy rain threshold was determined. The results obtained from heavy rainfall thresholds showed that the average rainfall of more than 40.7 mm in Karkheh basin and the average rainfall of more than 47 mm in Dez basin are considered as heavy rainfall. The average number of days of heavy rainfall in Karkheh basin is 118 days and Dez basin is 81 days. The flood threshold of Karkheh basin is lower than Dez basin. Therefore, Karkheh rains occur more suddenly and irregularly, while the rains in the Dez basin have more normal time series. As a result, Karkheh basin is more vulnerable to floods than Dez basin in terms of increased heavy rainfall. The results of synoptic analysis showed that in all models, synoptic systems of Sudanese and Sudanese-Mediterranean integration systems have played an effective role in creating heavy rainfall simultaneously in both Karkheh and Dez basins. The deepening of the Mediterranean submarine also activates the humid low-pressure system on Sudan and the Red Sea. The Sudanese system causes heavy rainfall by moving north and northeast of the Mediterranean coast over the study area. The main source of moisture is the Arabian Sea, the Oman Sea, the Red Sea and the Mediterranean.Heavy rains are a risk factor for natural disasters such as floods. Therefore, identifying and predicting their occurrence is one of the measures that can reduce the damage caused by it. This study was with purpose analyze and explain heavy rainfall using statistical-synoptic methods. For this purpose, precipitation data from 14 synoptic and rainfall stations of Karkheh and Dez basins in a period of 60 years (1959-2019) were used. In this regard, first, using the probability distribution function of the final limit of type one (Gamble), the heavy rain threshold was determined. The results obtained from heavy rainfall thresholds showed that the average rainfall of more than 40.7 mm in Karkheh basin and the average rainfall of more than 47 mm in Dez basin are considered as heavy rainfall. The average number of days of heavy rainfall in Karkheh basin is 118 days and Dez basin is 81 days. The flood threshold of Karkheh basin is lower than Dez basin. Therefore, Karkheh rains occur more suddenly and irregularly, while the rains in the Dez basin have more normal time series. As a result, Karkheh basin is more vulnerable to floods than Dez basin in terms of increased heavy rainfall. The results of synoptic analysis showed that in all models, synoptic systems of Sudanese and Sudanese-Mediterranean integration systems have played an effective role in creating heavy rainfall simultaneously in both Karkheh and Dez basins. The deepening of the Mediterranean submarine also activates the humid low-pressure system on Sudan and the Red Sea. The Sudanese system causes heavy rainfall by moving north and northeast of the Mediterranean coast over the study area. The main source of moisture is the Arabian Sea, the Oman Sea, the Red Sea and the Mediterranean.Heavy rains are a risk factor for natural disasters such as floods. Therefore, identifying and predicting their occurrence is one of the measures that can reduce the damage caused by it. This study was with purpose analyze and explain heavy rainfall using statistical-synoptic methods. For this purpose, precipitation data from 14 synoptic and rainfall stations of Karkheh and Dez basins in a period of 60 years (1959-2019) were used. In this regard, first, using the probability distribution function of the final limit of type one (Gamble), the heavy rain threshold was determined. The results obtained from heavy rainfall thresholds showed that the average rainfall of more than 40.7 mm in Karkheh basin and the average rainfall of more than 47 mm in Dez basin are considered as heavy rainfall. The average number of days of heavy rainfall in Karkheh basin is 118 days and Dez basin is 81 days. The flood threshold of Karkheh basin is lower than Dez basin. Therefore, Karkheh rains occur more suddenly and irregularly, while the rains in the Dez basin have more normal time series. As a result, Karkheh basin is more vulnerable to floods than Dez basin in terms of increased heavy rainfall. The results of synoptic analysis showed that in all models, synoptic systems of Sudanese and Sudanese-Mediterranean integration systems have played an effective role in creating heavy rainfall simultaneously in both Karkheh and Dez basins. The deepening of the Mediterranean submarine also activates the humid low-pressure system on Sudan and the Red Sea. The Sudanese system causes heavy rainfall by moving north and northeast of the Mediterranean coast over the study area. The main source of moisture is the Arabian Sea, the Oman Sea, the Red Sea and the Mediterranean.

Climatology study of fog at Mashhad airportAbstract Low horizontal visibility caused by fog can affect air traffic and in some cases is the main cause of air accidents. Fog is a condition in which water droplets or ice crystals in the air layer near the Earth's surface reduce horizontal visibility to less than 1000 meters. Fog is one of the major causes of flight delays and accidents. In fact, fog is the second hazardous weather event affecting aviation activities (Gultepe et al., 2019). The effects of fog in the aviation industry can cost hundreds of millions of dollars due to flight delays and cancelations (Gultepe et al., 2017). Fog event at Mashhad airport has repeatedly delayed or canceled flights. Therefore, study the climatology of the fog event at this airport helps to better understand this phenomenon and improve fog forecasting. For this purpose, all types of fog events were detected and analyzed using the hourly observation data of METAR during the statistical period from 2001 to 2020 and based on Tardif and Rasmussen (2007) algorithm. Then the fog climate was studied during the period. The results showed that CBL fog is the most common type of fog in terms of frequency with 43.94% of all fog occurrences at Mashhad Airport. Precipitation fog is also the rarest type of fog among fog types with 22.72% of all fog occurrences. Advection fog, which is formed under the influence of the marine environment, was not observed at this airport. Given that radiation fog usually forms at night and usually dissipates after sunrise, this type of fog has been the longest-lasting type of fog at the airport during 16 years of study. Also, the duration of CBL fog was the shortest one compared to other types of fog events.The minimum visibility of radiation and CBL fog events at this airport was lower than other types of fog. Also, precipitation fog had the lowest concentration compared to other types of fog events, which is consistent with the results of Tardif and Rasmussen (2007). The most reports of fog occurrence at Mashhad Airport during the studied years was at midnight and before sunrise, which could be due to the radiation cooling at night and before sunrise, which reaches its maximum (Hoch et al., 2011; Cséplő et al., 2019; Zouzoua et al., 2021; Wærsted et al., 2017). This result is consistent with the study of Cséplő et al. (2019) and Tardif and Rasmussen (2007). Tajbakhsh (2015) has also reported the maximum occurrence of fog at 21, 00 and 03 UTC using 20-years synoptic data at Mashhad Airport. Also, the frequency of fog occurrence decreases rapidly after 03 UTC.The monthly distribution of fog types in Mashhad Airport showed that radiation fog events often occur in winter and CBL fog events often occur from late autumn to mid-spring. Boundary layer cooling is the most important process that causes fog event in spring, while winter fog occurrences can be related to large-scale atmospheric systems (Tardif and Rasmussen, 2007). The maximum monthly frequency of precipitation fog event at this airport is in the winter. The fog events in the autumn can also be related to these weather systems. Since precipitation fog events depend on large-scale factors (Tardif and Rasmussen, 2007), the maximum occurrence of this type of fog is observed in winter. In general, the frequency of fogs in December and January is higher than other months of the year. This result is consistent with Tajbakhsh (2015).In terms of annual changes in fog occurrences, there was no significant trend in the number of fog hours during the studied years (2001-2020). In terms of fog concentration, in all months of the year, the number of semi-dense fog events with minimum visibility of 100 to 500 meters was more than other types. Also, the number of fog events with minimum visibility of 100 meters and then 200 meters had the highest number. It shows the importance of climate investigation and prediction of the fog event at this airport from the point of view of the aviation industry, because dense fog events cause difficulties in the landing and takeoff of airplanes. Keywords: Fog climatology, Fog type, Radiation fog, Precipitation fog, CBL fog.

Predicting the quantity and quality of climate change is one of the complex issues that has occupied the minds of climatologists. Now, with the help of access to new technologies and having multiple series of necessary data from climate variables and with the help of knowledge of understanding the relationships between these variables, basic steps in understanding and predicting climate trends have emerged. As now, computer models all respond to climate prediction issues and factors affecting climate change to the best of their ability. Completely accurate predictions with zero error, regardless of the field and subject matter, are very difficult and almost impossible, especially since the prediction process is in a very complex environment and in a dense cloud of uncertainties and actors and drivers. Numerous and effective effects on the environment and the data and information used in forecasting also have vague and gray features . Among climatic elements, temperature and precipitation are of special importance due to their wide effect on other factors and especially the effects they have on human activities. because human systems are dependent on climatic elements. Like agriculture, industries and the like are designed and operate on the basis of climate stability.Time series models are experimental models and a powerful tool for simulating and predicting the behavior of climatic and hydrological systems such as temperature and precipitation. The classical approach in terms of time series modeling depends on the static and non-static time series according to the application of the Jenkins box approach. If such series show long-term memory properties, the prediction value based on the moving average self-regression (ARMA) and stacked moving average (ARIMA) models will not be valid. If there is long-term memory in time series, there will be a significant correlation between series observations at very long distances, apart and far from each other, which indicates that observations are not independent of each other, there is a correlation between them and past observations. They will help to predict the data. With these descriptions, the existence of long-term memory in atmospheric parameters weakly violates market efficiency, and then changes in the capital market will not be accidental and will be predictable. In the early 1980s, Granger and Jokes proposed an alternative approach to long-term memory modeling by creating their own model of partial stack moving average (ARFIMA), since the ARFIMA model between the short-term memory process and the long-term memory process in series. When differentiated, it creates a distinct advantage over classical S / R analysis, which has a very high tendency to accept the assumption of zero long-term memory despite the short-term memory process .The data required in this article include the average monthly temperature and precipitation in the last half century (1969-2018), which was prepared by the Meteorological Organization. In time series data analysis, time series models including ARFIMA model have been used for modeling and simulation of the mentioned parameters.Changes in temperature and precipitation time series are among the most important climatic parameters in studying hydrological, agricultural, environmental, health, industrial, and economic processes. Assessing and forecasting temperature and precipitation will be of great help to managers and planners of agriculture and water resources. One way to examine time-series data is to use statistical models. Due to the importance of the subject, in this article, the amount of temperature and monthly rainfall in the last half-century (1969-2018) of Tabriz Synoptic Meteorological Station is examined using the ARFIMA model, and to fit the model, R / S and GPH models have been used. To investigate the statics of the model, autocorrelation (ADF), partial autocorrelation (PACF) functions, and differentiation methods have been used. However, since the synoptic meteorological station data is evaluated for the first time, the evaluation is based on the ARFIMA model, the Bayesian Information Criteria (BIC), root-mean-square error (RMSE), and the Akaike information criterion (AIC). The results of R/S and GPH tests show that the model with long-term memory of Tabriz temperature and precipitation time series is approved at a 95% level. The only difference is that in the case of precipitation, this act seems fragile. In addition, the analysis of different structures showed that the temperature and precipitation data have the best fit or performance using the ARFIMA (3,0.2,1) and the ARFIMA (1,0.0004,4), respectively. It should be noted that the RMSE value of the fitting models between the observed values and the simulation of temperature and precipitation was equal to 2.2 and 38.4, respectively, indicating the appropriate accuracy of the model and its applicability for forecasting.

Climate has a major impact on all human activities, including military operations, and should be carefully considered in any military activity. This means using the principles and concepts of climate in a practical context. Many of the common effects of atmospheric elements can be due to the conditions of operation and any military operation without coordination with the climatic conditions of the place is doomed to failure. Geographical criteria, especially climate and geomorphology, have a great impact on natural hazards. Due to this importance, military climatology is one of the important topics in military geography. This field discusses the climate of operational areas. The experiences of the imposed war and other world wars have shown that the element of temperature, which is one of the elements of climate, can be effective along with humidity and wind speed in the fate of a great power involved in war. Military climate is one of the branches of military geography that studies the effects of climate on military affairs at different levels of operation. For example, Operation Typhoon 1341, which was carried out in the Soviet Union, and the German army was confident of victory over the Stalinist regime and victory in Moscow, but the weather conditions and the scorching cold of Russia caused the defeat of Germany. According to the public opinion, cold, along with biting winds, and heat, along with high humidity, are the worst combination of climates. Dry temperatures below zero degrees weaken frostbite among people with poor clothing and poor training. In the winter of 1942-1941, one hundred thousand German armed forces were injured in Russia for this reason, of which 15 thousand were forced to amputate. Wet cold in some cases, even more severe, causes weakness and exhaustion of forces. Disability due to prolonged exposure of the feet to trench water, at temperatures slightly above zero degrees, is an old case of injury. During World War II, the disease became a rampant among American infantry in the operational arenas of Europe, because in the last days, they had to jump into the water instead of walking on dry land and pass through icy mud and into trenches. They lived full of water and did not have access to shelter or dry shoes and socks. In the dampening heat, the armed forces face another set of problems. To prevent the body from losing weight, water consumption increases.

area of study Khorasan Razavi province is located in northeastern Iran and is the fourth largest province in the country. This province covers an area of 127,700 square kilometers, 7.7% of the area of Iran and is located between 34 to 38 degrees' north latitude and 57 to 61 degrees' east longitude of the Greenwich meridian. Khorasan Razavi province shares 531.6 km border with Turkmenistan and 302 km with Afghanistan and is limited to South Khorasan from the south, North Khorasan from the northwest and Semnan and Yazd provinces from the west.

In this study, in order to evaluate the role of climatic elements on the activity of military forces in Khorasan Razavi province, among the synoptic stations in the region, 9 stations that had data and a suitable statistical period (20 years) were selected. Then, data related to climatic parameters including dry air temperature and relative humidity in the monthly period during the statistical period (1999-2019) were received from the Meteorological Organization.One of the most important indicators to identify climatic conditions, in line with the activities of military forces, the Wind chill index was used. Burn index can be a good indicator of cold sensitivity and is very useful in deciding on the type of clothing or planning for outdoor activities. Low levels of burnout are associated with risks, one of which is a sudden drop in body temperature or hypothermia. Under these conditions, the body temperature drops to such an extent that the muscles and the brain are damaged. In some countries, the Wind chill index is used to issue forecasts and warnings. When this indicator reaches the point where frost and frost occur, it is predicted and issued a warning in case of dangerous conditions. Another factor of human comfort is the wind speed index or in fact the cooling effect of wind. This index represents the energy dissipation in terms of kilograms per hour from the surface of one square meter of the body and in normal conditions, in inactivity and normal skin temperature, ie about 33 degrees Celsius.

Shows the day and night comfort coefficient based on Wind chill index in the stations of the province. Accordingly, the months of January, February and December show cool conditions in all stations except Sarakhs and Kashmar stations, and very cool physiological conditions in all other stations. In March, Sabzevar, Sarakhs, Kashmar and Gonabad stations show cool conditions and the rest of the stations show very cool conditions. Cool weather prevails in all stations in April and November. During May and October, favorable conditions for military activity in Sabzevar, Sarakhs (May and October) and Gonabad (May) prevail, and in other stations, cold conditions prevail. In June and September, Torbat Heydariyeh and Mashhad stations (June and September), Kashmar (June), Gonabad (September) have comfortable conditions, and other stations have cold to warm conditions. During July, comfort conditions are limited to only 3 stations of Golmkan, Quchan and Neishabour and the rest of the stations are in hot condition. Finally, in September in all stations except Sabzevar, Sarakhs and Kashmar (warm) and Golmkan (cool) There is desirable. The results of day-to-day dispersal maps of Wind chill index show that Wind chill climate index is favorable for the development of the province's military activity in the center, south and southeast of the province and the appropriate period in these parts is longer than other parts of the province.Key words: Military Climate, Thermal Stress, Wind chill index, Khorasan Razavi Province

Climate change is one of the current and future challenges facing agricultural producers, ie it affects the type and amount of products produced and ultimately the income of farmers. Therefore, it should be predicted or simulated in a way using various tools in the field of management of this work, so that according to it, the necessary planning and policy-making can be done, finally, while providing food security for consumption. Maximum welfare of producers should also be provided. Considering the high economic value of saffron crop and recent climate change, the need to provide an alternative cultivation pattern for current crops in our country, especially in Lorestan province, such as saifi and horticulture, which need plenty of water, is felt more than ever. Although Lorestan province ranks first in the country in terms of grain production and in terms of figs, pomegranates and other horticultural products is one of the relatively well-ranked provinces, but the climatic conditions of this province is a good ground for expanding and cultivating more productive and profitable crops. It also has another such as saffron.

The length of the statistical period required for climate analysis is at least 25-30 years. The statistical period from 1990 to 2016 was selected using meteorological data from synoptic stations. For spatial-temporal analysis of indices in the future, first, the average of 2050 statistical periods was calculated for two scenarios: RCP 4.5 and RCP 8.5 for temperature and precipitation indices. Then, each of the means was compared with the statistical period of the current situation. Finally, with Spline geostatistical method in GIS10.5 software, zoning maps of 2050 period were prepared based on two optimistic and pessimistic scenarios and future positive and negative phases were analyzed. Also, after preparing the data, the score of each component The weights of descriptive information of each component were determined according to their importance in saffron cultivation in the region. The weighting range of descriptive information was considered between 1-5. In the last stage, the components were combined with each other in GIS environment according to the score and weight of their inner layers, and the optimal areas of saffron cultivation in the current and future situation were identified based on pessimistic (RCP 8.5) and optimistic (RCP 4.5) scenarios. .

In the current situation, 15.8% of the province's area is in high priority areas, followed by relatively high and medium with 25.5% and 20.9%, respectively. These areas have different environmental constraints such as soil type, land use and unsuitable temperature. These areas can have saffron cultivation conditions with careful studies and adaptation measures. Relatively low priorities for saffron cultivation include mountainous areas with unsuitable temperature, slope and land restrictions. In the current situation, the most suitable cultivation areas for this crop are concentrated in the western and somewhat central areas of the province. Scattered areas in the northeast and north of the province are also suitable. Inappropriate areas include relatively low and low priorities in the eastern and southeastern areas. It is noteworthy that scattered areas in the southwest and north are also seen in two priorities. Future simulations show that climate change has a significant effect on the spatial change of suitable areas for saffron cultivation. By 2070, in the best conditions, according to the RCP scenario, 4.5 high-potential areas (high priority) will decrease by 1.5 percent, and areas with medium priority will increase by 4.8 and 6.3 percent, respectively. Without considering adaptation strategies and adopting appropriate management approaches in accordance with the RCP 8.5 scenario, saffron yield decreases due to climate change when temperature thresholds increase due to climate change. Suitable areas for its cultivation by 2070, areas with high and relatively high priorities will decrease by about 9 percent and areas with relatively low and low priorities will decrease by about 15.5 percent. With the reduction of these middle class areas, the priorities increase by 24.4% compared to the current situation. Scenario RCP 4.5 shows more balanced conditions than RCP 8.5 and moving from optimal conditions by considering adaptation strategies to create conditions without adopting adaptation strategies in saffron cultivation. In the simulation performed in RCP 4.5 scenario, with increasing temperature thresholds in three reproductive, vegetative and stagnation phenological periods of saffron growth, the desired areas from the west and the center of the province in the current situation have been transferred as a strip from north to southeast. In this scenario, the areas with moderate and relatively low priority are more scattered in the west, center and southeast. And low-priority areas are concentrated in the southwest of the province.

The reproductive period of saffron growth in Lorestan province is October. Spatial variations in the priorities of suitable areas for the reproductive period Saffron growth based on the average temperature in the reproductive period in the current situation is 2% of the total area, which is located in the northern and eastern regions of the province. The growing season of saffron in Lorestan province is from November to May. The area of priorities of suitable areas for the growing period of saffron, based on the average temperature index in the current situation, is the highest area with high priority (66%), followed by medium priorities (26%) and low priorities (8%). The period of stagnation of saffron growth in Lorestan province is from April to September. The area of priorities of suitable areas for the period of stagnation of saffron growth based on the average temperature index is the dominant spatial pattern with low priority and its area is 99%. High priority areas with 1% are scattered in the north and east.

Calculating the Q vector and preparing its output products can be very effective for meteorology and weather forecasting. The vector and its convergence determine the upward motions of the atmosphere, so maps involving the vector and its convergence at different pressure levels, along with other meteorological maps, for weather forecasts and related warnings, increases the accuracy of this process. In order for the omega equation to have a more appropriate description and analysis, and also to combine sentences in the equation that neutralize each other to some extent, the Q vector has been defined and replaced in the equation. Thus, convergence and divergence for the Q vector show the ascending and descending motions of the atmosphere, respectively. In this study, Q vector values and their convergence were calculated at different pressure levels and the accuracy of the results was studied for two incoming meteorological systems with heavy rainfall. A period of time includes February 25 and 26, 2020, during which regions in the west of the Iran and the southern slopes of the Alborz mountain range received significant rainfall. The second period is July 13 and 14, 2020, with relatively heavy rainfall in parts of the north of the Iran.To perform the calculation, global GFS model output data with a horizontal resolution of 0.5 degrees and 3-hour forecast time step, temperature values and geopotential height at grid points were extracted. the Q vector and its convergence were calculated using the central second-order finite difference method and the numerical noise arising from high spatial resolution was reduced using a discrete spatial filter. Convergence maps of the Q vector representing the upward motion were analyzed by the accumulated precipitation, ground surface information, satellite images, and synoptic maps. The results of vector calculation and its convergence had considerable accuracy and consistency with the time and place of heavy rainfall leading to flooding in the two cases studied. In the first case, the precipitation system was imported from the west of the Iran with a dynamic active trough, and there was a good humidity flux from lower altitudes, which caused heavy rainfall, especially in Ilam province and areas of the southern slopes of the Alborz mountain range, including Tehran province. Studying of synoptic maps, cumulative precipitation and analyzes performed and their comparison with the results of Q vector calculation and its convergence in different forecasting hours, shows the appropriate accuracy of the Q vector calculation. The convergence of Q vector, corresponds to the hours and areas of heavy rainfall in the west of the country and the southern slopes of the Alborz mountain range. In the second case, the existing conditions from the analysis of synoptic maps indicate that the influence of the high pressure system from the north to the shores of the Caspian Sea, which is a common cause of mechanically ascending due to precipitation in this region, does not have a strong presence and being located at the cold output of the jet, the presence of the trough in the middle of the atmosphere in the northwest of the Iran and the flux of moisture from the eastern Mediterranean have caused instability and precipitation. These rains caused floods and damage in areas of the northern slopes of the Alborz mountain range, especially in the western and eastern heights of Gilan province. Despite the differences in precipitation forecasts by GFS and ARPEGE models for this time period, the studies show a significant temporal and spatial agreement between the results of Q vector calculation and its convergence with the analysis of synoptic maps and land surface information.The results showed that the Q vector results for the western regions and along the Zagros mountain range at the level of 500 hPa, for the southern areas of the Alborz mountain range at the level of 700 hPa and for Gilan province and the southern shores of the Caspian Sea at the level of 850 hPa, was more consistent with the cumulative precipitation measurements and synoptic analyzes. Considering more cases continuously and operationally, can provide the better estimate the conditions of forecasting by Q vector and its accuracy, according to the specific conditions of each region, such as altitude, moisture resources, location of roughness and mountain ranges and other factors.

The issue of global warming, climate change and drought are among the major challenges facing the world today, which can cause widespread fluctuations in the Earth's climate. Areas with Mediterranean and semi-arid climates are highly dependent on temperature and precipitation and as a result are affected by climate change. Iran is one of the countries in the semi-arid and arid regions of the world that is more sensitive to climate change. Global warming due to increasing greenhouse gas concentrations and land use change has caused obvious changes in Iran's climatic parameters, including increasing temperature, decreasing rainfall and increasing the incidence of destructive atmospheric-climatic phenomena in the country. This research has been carried out with the aim of detecting future changes in temperature and precipitation in Liqvan watershed. For this purpose, statistical Downscaling of SDSM model and CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 climate change scenarios on long-term data of Tabriz Synoptic Station (1951-2020) has been used. The results show that in four periods of 20 years (2020-2100) and based on the three existing scenarios, the temperature will increase and the precipitation will decrease. This temperature increase for the minimum temperature will sometimes be up to 14.35 ° C (January with RCP8.5 scenario and time period (2100-2081). Examination of rainfall in four periods and three scenarios shows that the amount of rainfall in October, November, December and April will increase and in the remaining months will decrease.In the present study, climatic parameters of temperature and precipitation were simulated using multiple linear model of SDSM and general circulation models of barley using data of Tabriz city for Liqvan watershed. In this research, the output of canESM2 model under scenarios RCP8.5, RCP4.5, RCP2.6 has been used for future periods.The results showed that temperature data correlated better with observational data (compared to rainfall data), this is because temperature variability is less than rainfall and temperature is a parameter with a normal possible distribution. One of the reasons for the decrease in rainfall correlation is that different factors affect rainfall and on the other hand, rainfall is a discrete variable. These results are consistent with the results of Sajjad Khan et al. (2006), Sarvar et al. (2010) and Nouri and Alam (2014). Therefore, solving the correlation problem in the development of future climate change models should be considered.Also, the results of this research are in line with the results of most researchers such as trainee et al. (2009), Sajjad Khan et al. (2006), Sarvar et al. (2010), Mino et al. (2012), Chima et al. (2013), Dehghanipour et al. (2011). And Shakeri et al. (2021) agree that the SDSM model has a good ability to small-scale temperature and precipitation data.Climate change can cause temporal and spatial changes in climate variables. The characteristics of these variables can have detrimental effects on ecosystem components. According to the results, it was found that during the 21st century, temperature is increasing and precipitation is decreasing.In Tabriz station, in general, precipitation will decrease in the three scenarios studied and in only one scenario and for the period 2100-2080, there will be an increase in precipitation. Also, rainfall will generally increase in winter and the rest of the seasons will decrease. These results are consistent with the results of Golmohammadi and Masah Bavani (2011) which introduced the period 2069-2040 as a period with increased rainfall in Qarasu basin but with the results of Rajabi and Shabanloo (2012) using the SDSM model in Kermanshah region in Western Iran has considered the period 2070-2070 to be drier, there is a difference, so that according to the results of the present study at Tabriz station, from May to September, we will have a decrease in rainfall for all periods and scenarios. Figures 8 and 9 show the changes in monthly and annual rainfall in the RCP scenarios and the four periods compared to the base period, which show a decrease in precipitation in most months except October, November and December compared to the base period.Changes in the average minimum temperature of Tabriz station in all months except October, November and December will increase in future periods. Figures 11 and 12 show the monthly and annual minimum temperature changes in the RCP scenarios and the four periods compared to the base period, which show an increase in the minimum temperature compared to the observation period, which is more than the other scenarios in RCP 8.5. .

The occurrence of precipitation in an area requires several conditions within the Earth's atmosphere. Moisture availability, deep instability, and cooling availability are the three basic conditions for precipitation to occur. Precipitation, no matter what happens, requires a source to supply the moisture pluvial systems. This moisture can be supplied from the site itself or nearby or more remote areas. The processes and factors involved in the phenomenon of precipitation have long been considered by researchers and have been studied and researched from various aspects. Due to the location of Iran in the transition from a tropical climate to mid-latitude climate and severe spatial and temporal variability of precipitation, issues related to precipitation and water resources have long been one of the most important issues in this land. There are no significant sources of moisture inside Iran. Inland lakes or rivers are not large enough to provide moisture precipitation to adjacent areas. They are mostly local and change the Absolute and Relative humidity of the air. As a result, Iran's moisture precipitations are supplied from nearby water sources such as the Caspian Sea and southern waters or more distant sources such as the Mediterranean Sea, the Indian Ocean, etc. According to the rich research literature on the dynamic, physical and synoptic properties of moisture flux convergence on local, regional and global scales, there are still some questions, especially about the spatial and temporal characteristic of moisture transfer in the form of moisture flux convergence functions during pervasive wet and dry periods in the Middle East, especially in Iran and this study is trying to answer some of them. Therefore, the most important questions in this study that are to be answered are as follows:• What are the vertical variations of moisture flux convergence in the vertical dimension during pervasive wet and dry periods?• What are the vertical variations of moisture flux convergence in the horizontal dimension during pervasive wet and dry periods?This research investigates the characteristics of moisture flux convergence at the time of occurrence of pervasive wet and dry periods in Iran. To achieve this goal, two different databases were used. The first database was monthly precipitation data of 63 synoptic stations in Iran for a period of 30 years (1986-2016), which was obtained from the Iran Meteorological Organization. The second database included gridded data of atmospheric variables such as the geopotential height, sea level pressure, zonal component of wind, meridional component of wind, temperature, and specific humidity which were obtained from the website of the European Center for Medium-Range Weather Forecasting (ECMWF) as monthly observations. The Standardized Precipitation Index (SPI) was used to analyze Iran's droughts on three-time scales: monthly, seasonal and annual. Then, based on a spatial index, wet and dry periods, which affected about 75% or more of the stations studied, were defined as pervasive wet and dry periods. Finally, with the identification of pervasive wet and dry periods at different time scales, moisture flux convergence variations during pervasive wet and dry periods were determined in both vertical and horizontal dimensions. The results of analysis of moisture flux convergence profiles on the studied time scales showed that the height and thickness of the moisture transmission layers inside Iran play a very important role in the occurrence of pervasive wet and dry periods. So that in pervasive wet periods, moisture is transported into Iran from lower levels and with greater thickness, and in pervasive dry periods, this transition takes place from higher levels and with less thickness. The spatial arrangement of moisture flux convergence in the horizontal dimension at different levels also showed that the 850 hPa level can reveal the cause of pervasive wet and dry periods more than other levels studied. At the 850 hPa level, pervasive wet periods are observed as a relatively continuous strip of moisture flux convergence with a west-east direction from the Mediterranean Sea between two latitudes of 35-40 degrees to the northwest of Iran. At the 850 hPa level of pervasive wet periods, we observe a relatively continuous strip of moisture flux convergence with a west-east direction is extended from on the Mediterranean Sea between two latitudes of 35-35 degrees to the northwest of Iran. From the south, a relatively wide and strong strip of moisture flux convergence with the source of moisture the Arabian Sea extends in a south-north direction to the western half of Iran. These two strips of moisture flux convergence are connected in northwestern Iran. But in pervasive dry periods, we see almost different spatial patterns than moisture flux convergence at the 850 hPa level on the study area. The continuous west-east strip of moisture flux convergence, which was from the Mediterranean Sea during the pervasive wet periods, moves to higher latitudes in dry pervasive Octobers, between 40 and 50 degrees latitude. The same displacements range from 40-35 to 50-40 degrees, diverting the entry of precipitation systems of the Mediterranean Sea into Iran.

The phenomenon of climate change has caused abnormal changes in the Earth's atmosphere and has a variety of consequences, including rising global average temperatures, rising sea levels, changes in rainfall, impact on the global economy, financial systems, etc. Given the impact of this phenomenon on different sectors, adaptation and mitigation strategies should be used to better understand the effects of climate change and reduce vulnerability.The wide consequences of climate change in the last 25 years in developing countries, including 95% of the world's deaths and the doubling of direct economic losses in low-income countries compared to high-income countries, have led to different plans with the focus on adaptation. Strategies for adapting to the effects of climate change include a wide range of non-engineering or non-structural strategies (such as education and insurance) and engineering or structural strategies (such as structural measures and management practices). The insurance industry reduces the catastrophic effects of climate and natural disasters and enables timely recovery by distributing losses and also reduces the burden of damage, as a measure of adaptation to climate change. It provides timely post-disaster liquidity and is associated with preventive and risk reduction activities. It also plays an important role in reducing the vulnerability of society to natural disasters related to climate change by supporting sustainable development goals.

The research method was based on the qualitative research method. Therefore, related scientific reports and sources were extracted and written documents were reviewed to extract the experiences of developing countries according to a comparative approach. A general understanding of the effects of climate change, underwriting, and product development were the three main classifications in developed countries that were analyzed to present the results. The selection of countries was based on the value of the Human Development Index in 2021. This index ranges between 0 and 1 and is used by the United Nations to determine the development of countries. Finally, Ukraine, Bangladesh, Thailand, Taiwan, Syria, Colombia, Morocco, Nigeria, Indonesia, Brazil, Pakistan, China, Sri Lanka, Kenya, Mexico, India, and Afghanistan were selected among developing countries.

The experience of developing countries in the general understanding of the effects of climate change shows that in recent years. global warming has led to rising temperatures in all countries and rising sea levels in coastal countries. In addition, the number of natural disasters caused by climate change, especially floods, droughts, and Tropical storms has increased in developing countries, which in many cases have been catastrophic, resulting in significant deaths and economic losses.Studying the underwriting and insurance programs in selected countries shows that in general, some countries do not have a practical plan in this field, some are researching and testing solutions in this field, and some establish microfinance institutions, insurance pools, and government agencies, etc., and have paid particular attention to agricultural insurance.With the increasingly negative consequences of climate change, a wide range of insurance products including index-based insurance, agricultural insurance, microinsurance, weather derivatives, green insurance, catastrophic bonds, and other types of securities has been considered by the government to secure financing post-disaster damages.

Because most compatibility options have long execution times, long-term planning and acceleration are crucial to closing compliance gaps. With the advent of the insurance industry as one of the largest industries in the world, it is expected that disaster safety nets will be provided to discuss regime adaptation and climate change. Insurance and alternative tools are effective in risk transfer and can play an important role in financing and reducing the vulnerability of human and natural systems. The experiences of developing countries in adopting effective approaches and tools to deal with climate change showed that in terms of recognizing and understanding the necessity of the issue, these countries, following the developed countries, realized the importance of the climate change issue and seek to compensate for the gap created between the existing level of capacity utilization and its acceptable level. Regarding underwriting and existing measures, there is a major difference in the approach of selected countries, which has led to a wide range of practical measures. These differences are due to the prioritization of different sectors and financing capacities in the country, which in some cases have used the help of international institutions. In reviewing the programs and development of insurance products, it was observed that in all selected countries, according to priority, the development of insurance products has been considered and, in some countries, leading to the development of index-based insurance and issuance of various securities to secure financing post-disaster damages.

One of the main challenges currently facing arid and desert areas is the phenomenon of dust. Although most dust storms, especially dust storms in Iran, are often regional in nature, local hotspots play an important role in the dust belt, especially in Iran. Yazd province has been one of the most prone areas with dust in the country since ancient times, so that one of the origins of local dust in this belt is the city of Yazd and salt domes northeast of Ardakan.Many studies have used the DSI index to evaluate the frequency and intensity of dust storms; Although more studies have used the DSI index to identify the intensity and frequency of dust, fewer studies have used the horizontal visibility to determine the intensity of the dust index. In this study, the behavior of dust phenomenon in Yazd province based on horizontal visibility and dust storm index (DSI) has been investigated. The DSI index is a convenient way to monitor wind erosion on a large scale using meteorological records that calculate the frequency and intensity of dust storms in the area. Using a horizontal visibility is also a way to evaluate and estimate the frequency and intensity of dust storms. In this research, it has been tried to reveal the compatibility of these two methods in estimating the frequency and intensity of dust storms, and in the absence of data from one method, data replacement and other methods have been used in conducting research projects.

order to study the dust phenomenon in Yazd province, a joint statistical period from 2000 to 2018 was selected. In this study, the aim is to investigate the compatibility of the two methods of using horizontal visibility and using dust storm intensity index to investigate the status of dust storms in an area. In order to extract the dust event from 100 phenomenon codes (0-99), the codes related to the dust event were extracted. After extracting the dust events, the horizontal visibility parameter related to each event was extracted and classified on the data. The classification was done and the dust events were divided into 5 floors based on the horizontal visibility and zoned in the GIS software environment.The DSI index is a combination of the following three indices that were extracted for all synoptic stations in the study area.SDS = days with Sever Dust Storm Days, Total Dust Code Observations Maximum Daily Code 35-33.MDS = days with Meduim Dust Storm days, total observations of dust codes maximum daily 30, 32 and 98.LDE = days with local dust, total observations of dust codes maximum daily 07 and 09.Finally, the following equation was used to calculate the DSI index To prepare maps of different classes of horizontal field of view, the statistics of hourly dust events and kriging method were used. Finally, based on the spatial distribution of dust storms using both methods, the degree of conformity and the possibility of replacing each method with another method was investigated and analyzed.

Examination of the frequency map of the occurrence of a horizontal visibility of 100-200 meters in Yazd province showed that there are two maximum dust occurrence cores separately in Meybod and Bafgh areas. Occurrence of 1000 m horizontal visibility indicates critical conditions in a dusty area. Examination of the frequency map of the occurrence of 1000 meters horizontal visibility in Yazd province shows the existence of 2 maximum cores in the area of Meybod, Yazd and Bafgh. Check the frequency map of 3000 and 500 horizontal field of view. 900 meters in Yazd province shows that one core occurs in the area of Yazd and the second maximum area is located in Meybod. Minimal polynuclear cells with small surface area can be seen in Herat, Gariz and Mehriz. Central, western and northern have the highest frequency.Spatial distribution of dust occurrence intensity using DSI index showed that the highest value of this index occurred in the wind corridor with northwest-southeast direction in Meybod-Yazd. The other core of this index is located in Bafgh. The maximum core of the DSI index in terms of spatial distribution corresponds to the spatial distribution of the horizontal visibility 100-200 meters (supercritical) and the horizontal visibility 1000 meters (critical). In general, the intensity of the DSI index in the west of the province is higher than in the east. The south of the province also has the minimum values of the DSI index.

Although most dust storms, especially dust storms in Iran, are often regional in nature, local hotspots play an important role in the dust belt, especially in Iran. Yazd province is one of the central arid regions of the country and due to the presence of desert and desert areas, it is a region prone to dust. In order to evaluate the appropriate indicators for monitoring and evaluating the frequency and intensity of dust, two indices of horizontal visibility and dust storm intensity index were used. The results of the studies showed that the field of view of 100-200 meters, which represents the supercritical conditions, had the lowest frequency. The wider the field of view, the greater the frequency of occurrence. Comparison of dust storm intensity based on two indicators showed that the spatial distribution of the field of view related to severe dust with a field of view of 100-200 meters and also the field of view of 1000 meters and less is completely consistent with the spatial distribution of dust storm index resulting from DSI method. And monitoring the intensity of the dust storm. If the data related to the dust phenomenon code is not available, the horizontal visibility data can be used and vice versa

Global warming increases the frequency and severity of extreme weather events. These changes have major social and environmental consequences. The extensive social and economic effects of extreme values in arid and semi-arid regions such as Iran due to having a very vulnerable climate are high, and their sudden changes may lead to devastating events. Its destructive effects, including floods and droughts, have been very large in recent years in Iran. The World Meteorological Organization has defined a joint project between the Commission for Climatology (CCL), Climate Variability and Predictability (CLIVAR) and the World Climate Research Program (WCRP) in order to detect and monitor climate change and its profiles. Calculation methods of different profiles were presented with several softwares, including ClimDex and RClimDex. In this research, we investigate the changes in temperature and precipitation profiles according to CCL/CLIVAR in Kohgiluyeh and Boyer Ahmad province.

Due to the importance of Kohgiluyeh and Boyer Ahmad province in terms of water, agriculture, industry and human resources, this region was selected for study. In order to ensure that there is no spatial and environmental change in the investigated stations and to have the appropriate statistical quality, the data and information of all meteorological stations of the province were checked since their establishment. Finally, the stations of Yasouj in cold climate and Dogunbadan in warm climate were selected for this study because they had a suitable statistical period (1986-2018) and the least single or consecutive missing data.Extreme indices based on long-term and homogeneous data express the status of extreme events. These indices show aspects of the climate change event and its effects. The expert team of ETCCDM has classified extreme indices into five groups, which are based on percentiles, absolute extremes, threshold extremes and other profiles. In order to study the existence of a trend and its significance, the non-parametric Mann-Kendall test was used. This test is developed based on the ranking of data in a time series. The null hypothesis of this test means that there is no trend in the data series and assumption one indicates the existence of a trend in the data series.

Humans and the environment often react to changes in maximum and minimum values more than changes in average conditions. Therefore, analyzing the variability and examining the trend of extreme values are more important than average climatic conditions. In this research, extreme indices of temperature and precipitation were calculated and analyzed using the Rclimdex package in R software. The PRCPTOT index shows the total rainfall on days when the daily rainfall was equal to or more than 1 mm. It has a negative trend in both stations, which is not significant. The index of days with heavy rainfall, which means the number of days with daily rainfall equal to or greater than 10 mm, shows a significant decreasing trend in both stations. The index of consecutive wetter days, which means the maximum number of consecutive days of daily rainfall more than 1 mm, shows a decreasing trend in both stations, and its change trend is significant in Yasouj station, but not significant in Dogonbadan station.The index of the number of summer days, which is defined as the number of days with a maximum daily temperature of more than 25° C, showed a significant increasing trend in both stations. No ice day observed in Dogonbadan station during the statistical period. However, this index has a negative trend in Yasouj station, which is not significant. The index of warm nights or the number of days with a daily minimum temperature of more than 20° C shows a significant increasing trend in Dogonbadan, and although it has a decreasing trend in Yasuj, the trend of changes wad not significant. Due to the prevailing climate in that region, Dogonbadan station has not experienced zero temperature during the statistical period. In Yasouj, the trend of frost days is increasing, but it is not significant. The Growing season Length index of Dogonbadan has shown a significant increase during the 1986-2018 period. The index of the warmest day in both stations showed a significant increasing trend and the coldest day changes in both stations is positive but not significant. The warmest night in Dogonbadan showed a positive trend and in Yasouj it showed a decreasing trend, which is not significant in any of them. The coldest night showed a decreasing trend in Dogonbadan and an increasing trend in Yasouj, which was not significant in any of them. The average difference between the maximum and minimum daily temperature in Yasouj station is increasing and significant, but this index was decreasing in Dogonbadan, although the trend of its changes was not significant.

Extreme indices of temperature and precipitation and their changes were investigated using the Rclimdex. The results indicate the decreasing trend of all precipitation indices in the statistical period. In the Dogonbadan station, the index of days with heavy rainfall above 10 mm had a significant decreasing trend, and the rest of the changes in other indices were not significant. In the Yasouj station, the profiles of days with heavy rainfall above 10 mm and consecutive wet days had a significant decreasing trend. The frequency of warm events, such as summer days and warm nights, the length of the growing season and the range of day and night temperature changes showed an increasing trend during the study period. In addition, the frequency of cold events, such as cold days and nights and freezing days, was reduced.

Today, tourism as a dynamic and vast industry has become one of the largest economic sectors in the world. In a way that includes all the basic elements of the world community and accounts for a significant share of national and local economies (Scott et al., 2016; Masoudi, 2021). Empirical evidence suggests that climate resources directly affect destination choice, season length and, quality of tourism, as well as destination costs (Li et al. 2017; Wilkins et al. 2018; Yu et al. 2021 ). Climate change and extreme events such as heat and cold waves, droughts, storms and heavy rainfall can also affect tourism demand. Therefore, it is important to study the climatic conditions of an area in terms of climate comfort and determine suitable periods for all elements of tourism, such as planning, implementation, accommodation, etc. Climatic conditions and weather are essential components of the holiday experience and are among the main motivations for travel (Goh, 2012). Providing threshold values and indices can give an idea of the comfort level of the climatic conditions of the environment. For this purpose, tourism climate indices have been prepared (Oztürk and Göral, 2018; Scott et al. 2016). tourism Climate indices, which are formed from indices designed for use in health and agriculture, are tools that use raw meteorological data to describe the suitability of a particular climate for tourism activities. Such indices have been widely used to compare climate resources and their impact on tourism for more than 35 years (Rutty et al., 2020).

In this study, in order to evaluate the tourism climate of Yazd province, the Tourism Climate Index (TCI) and the Holiday Climate Index (HCI) have been used. To calculate these indices from the data of maximum air temperature, mean air temperature, minimum relative humidity, mean relative humidity, precipitation, cloud cover, sunny hours and wind speed of synoptic stations of Yazd province and neighboring provinces between 2004 and 2021 used.Climate Tourism Index (TCI) The first composite index designed to assess tourism resources was the Tourism Climate Index (TCI) proposed by Mieczkowiski (1985) and it consists of five sub-indices including daytime comfort index (CID), daily comfort index (CIA), precipitation (P), sunshine (S) and wind (W) (Equation 1).TCI=2*(4 CID +CIA+2 P+2 S+W) (1) Holiday Climate Index (HCI) Despite the widespread use of TCI, this index has been significantly criticized. The four main shortcomings of the TCI index are: subjective rating and weighting system of climate variables, ignoring the possibility of the impact of physical climate variables, low temporal resolution of climate data and not paying attention to the different climatic needs of the main tourism sectors. (Scott et al., 2016). In response to these inherent weaknesses in TCI, the Holiday Climate Index (HCI) was proposed (Equation 2).HCI=4*(TC)+2(A)+(3*P+W) (2)Which TC ic a thermal comfort index, A index is cloud coverage, P and W are precipitation and wind respectively.

Using the IDW interpolation method and GIS software, Point data were converted to surface data and finally TCI and HCI index maps for Yazd province were drawn in different months of the year. The spacial distribution of TCI and HCI indices showes in April and May, according to the TCI index, most of the province's area is in the excellent and ideal class. In June, according to the TCI index, due to the gradual dominance of summer weather in the province, with the exception of mountainous areas, other areas are in good and very good class. According to the HCI index, in April, the eastern half of the province is in the excellent category and the western half is in the very good category. In May, all areas of the province are classified into excellent and ideal classes. But according to the HCI index, almost the entire area of the province in the months of June to September is in the ideal class for tourism and holidays. In October, according to the TCI index, most parts of the province are in ideal and excellent classes. During the autumn season, with the gradual decrease of temperature, the desirability of climatic conditions for tourism also decreases over time. The results of the HCI index are almost similar to the TCI index. The results of HCI and HCI indices show that in January, almost the entire area of the province is in good condition, and with the passage of time towards the end of winter and the improvement of temperature conditions, in March, the conditions for tourism in the province will improve. The temporal distribution of TCI and HCI indices also shows that in most stations in the second half of the year, October to March, as well as April and May, both indices are in good agreement with each other. This issue is more evident in the cold months of the year, December, January and February. Also, changes and fluctuations of both indices in these months are very limited.

The results of the study of these two indices showed that in general the quantitative values of the HCI index are larger than the values of the TCI index. The reason for this difference is due to the difference in the weight of the components, especially the thermal comfort component and the rating system of the variables. According to HCI index estimates, the warmest months of the year, June to September, are the most suitable months for tourism and leisure. During these months, all areas of the province have ideal conditions for tourism. But the results of the TCI index show that fewer months have ideal climatic conditions. Most stations have these conditions for a maximum of two or three months of the year, which includes the months of April, May and, October. In the cold season, in most stations, the results of the two indices are in complete agreement with each other, but in the warm season, the difference between the two indices is very large.