گرمایش جهانی

Ecotourism as a part of the tourism industry is sensitive to environmental changes. Climate change and especially global warming can cause prosperity or stagnation. Reducing the consequences of future heat waves depends on identifying the mechanism of heat waves, and the meteorological systems that create them, predicting and providing ways to reduce the harmful effects of heat waves on public health, and tourism, and identifying vulnerable areas. It is related to heat waves. The present study was conducted to analyze heat waves and their effects on the ecotourism attractions of Khuzestan province. A descriptive-analytical method has been used in this study. Using the maximum temperature data in four stations located in the main geographical directions of Khuzestan province, for the statistical period of 1987 to 2020 and categorized using the 90th percentile index, the days with heat waves lasting 3 days or more were extracted. Charts of hot months were prepared and Arc GIS software was used for temperature zoning and map drawing. From the analysis of charts and maps, this result was obtained. Despite the existence of existing wetlands (Shimbar, Bamdej, Tembi) in the vicinity of these 4 stations, high temperature poses a significant threat to the water quality and quantity of the wetlands and their plant and animal life such as turtles. It's a frat. In addition, there has been a decrease in breeding and reproduction of the emperor salamander in Dezful station, an increase in fires in the Tembi forest areas of Masjid Sulaiman station, and a general decrease in biodiversity and the length and quality of tourist seasons. In terms of spatial distribution in the northwest and south coasts of Khuzestan province, the frequency and intensity of heat waves have been higher. According to the results of this research, the frequency of heat waves has been increasing in the statistical period, and in recent years, in addition, the frequency, intensity, and spatial expansion of its occurrence have increased.

Ozone in the atmosphere serves as a greenhouse gas and a shield against ultraviolet radiation, making it crucial for maintaining ecosystem balance and promoting human health. Variations in its levels can adversely impact climate change and air quality. Therefore, monitoring ozone concentrations is vital for creating effective environmental policies and strategies to mitigate these impacts. For this purpose, MERRA-2 data was used from 1980 to 2019. This study aims to understand the variations in ozone levels across different regions and seasons and is comprised of two main parts. First, we analyzed spatiotemporal variations in ozone in Iran’s atmosphere. We explored how ozone levels change throughout the year and across different geographical locations. We found that the highest ozone levels occurred during spring (May), whereas the lowest levels were observed in autumn (October). Geographically, the highest ozone concentrations are observed in the northern and northwestern regions of Iran, while the lowest levels are found in the southern areas. These variations undergo monthly fluctuations influenced by various factors. In the cold season, ozone concentration is primarily a function of latitude, whereas, in the warm season, the impact of altitude becomes significantly more pronounced than latitude. The annual ranges of ozone change were 294 and 354.47. Notably, the total amount of ozone in Iran’s atmosphere exhibited overall negative annual, seasonal, and monthly trends. This trend was particularly pronounced during the cold months, with statistical significance observed in winter (α=0.01). Spatially, the northwestern region of Iran, extending to its central side, exhibited a significant trend, whereas other areas showed a non-significant negative trend. This can contribute to a better understanding of ozone dynamics in Iran and provide valuable insights for policymakers and researchers working on climate change mitigation.

Land surface temperature is one of the key parameters in the study of climate change, which helps in the real assessment of the environmental situation at local to global scales. Investigating the trend of land surface temperature and its long-term changes is necessary to evaluate climate processes in arid and semi-arid regions. The MODIS probe was successfully launched by NASA on December 18, 1999. This sensor is installed on two satellites Terra and Aqua. These two satellites take pictures of the entire surface of the earth twice a day and collect data in 36 spectral bands (wavelengths of 0.4-14.4 micrometers) with a resolution of 250, 500 and 1000 meters. In this research, the daily data of the MYD11A1 product with a spatial resolution of 1000 meters was extracted from the NASA website for 7305 days from 1/1/2003 to 12/30/2023. By choosing this product and the Aqua satellite, due to the convenient time of its passage over the region, a more detailed investigation of the surface temperature of the Daranjir desert basin was carried out. Since Terra's Khurshid Song satellite passes over Iran at 10:30 am and 10:30 pm, it cannot provide an accurate picture of the energy balance, for this reason Aqua satellite monitors Iran at 1:30 pm and 1:30 pm. Slow is selected. The main reason for this choice is the high energy balance in the middle of the day and the middle of the night. Since the statistical period of Terra is longer than that of Aqua, this satellite should have been chosen for Al-Qaeda, but Aqua satellite data is used more for the reasons described (Wan, 2013).Examining the long-term surface temperature trend of the Daranjir desert during the last two decades shows that in a large part of the Daranjir desert (about 72%), the ground surface temperature trend has been positive (increasing). This trend of increasing temperature can be clearly seen especially in the bed of Shore Siriz River and Shore Rafsanjan River. It seems that the increased values of these areas are due to its drying and as a result, a sharp decrease in surface humidity in the long term. Also, Lale-Zar Mountains are among the areas that have shown a positive trend in surface temperature in the last two decades. Since this positive trend happened mostly in the cold seasons of the year (autumn and winter), one of the main reasons can be the reduction of snow cover in the last two decades (Key Khosravi Kiani and Masoudian, 2016). On the other hand, the presence of dams built in the south of Kerman, Rafsanjan and Chale Bafgh and their waterlogging in recent years shows the negative trend (decrease) of the surface temperature in these areas. These areas cover about 25% of the basin area. The rest of the surface of the Daranjir desert, which includes about 3% of the area of the basin, does not show any trend.The results of the long-term seasonal trend showed that in the winter season, a large part of the Daranjir desert (about 95% of the area) has increased the surface temperature trend. It seems that the increase in surface temperature in winter can be due to the decrease in rainfall, snow and vegetation in recent years. In summer, the situation is different. In this season, about 70% of the area of Daranjir desert has a negative (decreasing) surface temperature trend and about 30% of its area has a positive (increasing) trend. It seems that the positive trend in the axis of Kerman-Zarand and Rafsanjan-Anar can be caused by the drying of the Shor Siriz River and the Shor Rafsanjan River during the last two decades. This issue seems normal because with the drying of the river and the decrease in the humidity of the river bed, its surface temperature has increased drastically. In the autumn season, due to the recent droughts, in addition to the above-mentioned axes, the Bafaq hole also shows an increasing trend in surface temperature. The research conducted on the surface temperature of Iran is consistent with this idea (Moradi et al., 2015; Karbalaei et al., 2014; Abad et al., 2015). The results of the long-term annual trend showed that during the last two decades, the temperature of the earth's surface has been increasing in 75% of the surface of the Daranjir desert basin. Due to the drying of the rivers and as a result the decrease of their surface moisture, part of this increasing trend can be seen in the bed of the Shor Siriz and Shor Rafsanjan rivers. Another part of this increasing trend can be seen in the heights of Lale-Zar. One of the main reasons for the increasing trend of surface temperature in this region can be due to the decrease of snow cover during the last two decades (Keikhosravi-Kiani and Masoodian, 2016; Kefayat Mutlagh et al., 2024). Further investigations showed that the presence of dams built in the south of Kerman, Rafsanjan and Bafaq hole and their waterlogging in recent years is the cause of the negative trend (decrease) in the surface temperature in these areas (Azizi et al., 2016; Keihosravi Kayani and Masoodian, 1400).

The main greenhouse gases such as carbon dioxide (CO2) and methane (CH4) have a direct effect on the radiative balance of the atmosphere. They are the main driver of climate change, because their global average concentration during the industrial period has increased by about 47% and 156% for CO2 and CH4, respectively, as a result of human activities (IPCC, 2021).The importance of these gases, henceforth called greenhouse gases (GHGs), led to the creation of global monitoring networks to monitor their trends and variability. Terrestrial remote sensing networks such as the Atmospheric Composition Change Detection Network (NDACC) and the Total Carbon Column Observation Network (TCCON) are used and welcomed by researchers due to the long time span of accurate column observations (De Mazière et al., 2018). ; Wunch et al., 2011).Observational grid measurements of Fourier transform infrared (FTIR) spectrometers use direct sunlight to measure the absorption of rare gases along the line of sight and provide detailed information on the total column abundance or vertical profile of greenhouse gases and other species. These observations are used by scientists around the world to detect changes in atmospheric composition, to improve our understanding of the carbon cycle, or to provide validation for space-based measurements. Recently, low-cost mobile FTIR spectrometers have been used in the Collaborative Carbon Column Observing Network (COCCON) to validate fluxes in urban areas (Hase et al., 2015; Vogel et al., 2019; Makarova et al., 2021).In addition to FTIR observations, surface observations of these gases are performed for better management of springs and wells on a smaller scale. Both types of observations contain valuable information about the emission and atmospheric distribution of these species and complement each other. The need to estimate greenhouse gas emissions using independent approaches has encouraged scientists to develop policies based on different measurements (Dayalu et al., 2009).One of the important factors of global warming, the concentration of CO2 and its changes has aroused sensitivities all over the world. Establishing a clear understanding of the spatial and temporal distribution of CO2 concentrations at a regional scale is a critical technical challenge for climate change research. Modeling with the spatial resolution of the CO2 atmospheric concentration was done using the Weather Research and Forecasting-Chemistry (WRF-GHG) model and from the X CO2 data retrieved from the GOSAT satellite observations, as accuracy control information and Evaluation of the simulated data was used in CO2 column concentration. WRF-GHG is a model for simulating atmospheric chemical transport, designed for regional studies of CO2 concentration. The studied area, the Middle East region was designed as the first domain and Iran as the second domain with a resolution of 30 and 10 km, respectively. The performance of the simulations in predicting the concentration of greenhouse gas carbon dioxide (CO2), for the study period of February and August 2010, showed that the spatial and temporal variability of meteorological variables is well represented by the model with a correlation coefficient of 93%. -76%, 39%-47%, and 52-80% for temperature, wind, and relative humidity have been simulated. The most important sources of CO2 emissions include man-made emissions, biomass emissions, fire emissions, and ocean emissions. Among the emission sources of CO2 column concentration, human resources have the largest share in the emission of this pollutant with the amount of (38.33 and 23.70) percent in February and August. Biogenic emissions, after human emissions, with (24.08 and 46.64) percentages of absorption and (31.81 and 28.64) in February and August have the largest share in the production and absorption of this pollutant. Fire emissions with (5.76 and 2.04) percent and ocean with a value of (3.23-10 x 6) percent for CO2 pollutants are in third and fourth place respectively in the share of total CO2 emissions. From the analysis of the behavior of monthly average values from the advection field, in both February and August, it was determined that the maximum values of advection occurred at night to early morning. The evaluation results of the RMSE error indicate that the simulation has performed better in the cold season than in the hot season in the aforementioned pollutant simulations. CO2 column concentration was underestimated in winter (0.79 ppmv) and overestimated in summer (0.45 ppmv). This study showed that the WRF-GHG model is able to represent well many important features of observations (such as temperature, wind field, and humidity) in Southwest Asia (Middle East-Iran region) and the use of the model for future studies in It assures the region.

The current research aims to investigate the relationship between changes in the thickness of the atmosphere and the number of snow-covered days in Iran. For this purpose, the data of the geo-potential height values of 850, 700, 600, and 500 hpa levels were received from (ECMWF) daily and in a spatial resolution of 0.25 × 0.25 degrees of longitude and latitude for the period of 1379-1399. The thickness of the atmosphere is a variable that represents the temperature of the air layer, and the lower the thickness of the atmosphere, the lower the temperature of the air layer, and the higher the thickness of the atmosphere, the warmer the temperature of the air layer. The snow cover data of MODIS Terra and MODIS Aqua were taken daily between 1379 and 1399 and at a spatial resolution of 500 × 500 meters from the NASA website. Next, the spatial resolution of the snow cover data was changed to 0.25 × 0.25 degrees of latitude and longitude using the nearest neighbor method. The findings show that the highest values of the correlation coefficient can be seen in the winter season, so in many parts of the highlands in Iran, the values of the correlation coefficient are significant and generally less than -0.7, which indicates that the smaller values of the atmospheric thickness, the more snow-covered days throughout the country. Also, in spring and autumn, the number of cells that have a significant correlation is less than in winter. The increase in the thickness of the atmosphere over the past years in the Northern Hemisphere and Iran, which is a reflection of the increase in temperature in the atmospheric layers, can be a serious threat to the stability of snow cover, especially at high altitudes.

Due to its location in low latitudes, Iran is among the countries that has not been spared from the security consequences of climate change. The present study seeks to answer two questions: 1) What are the security consequences of climate change on Iran and which components have the greatest impact on Iran? 2) Which of the pillars of good governance as a solution has the greatest impact on reducing the effects of climate change on Iran? 
In this study, the study population is elites and experts familiar with the subject of the study, 100 of whom were selected for snowball sampling and a researcher-developed questionnaire was used to collect data. Data were evaluated using SPSS and MINITAB software. The results of the studies according to the obtained P-value which is equal to 0.000 and is less than 0.05 alpha, indicate the difference between the natural and human consequences of climate change in Iran. The results of the Tukey test indicate that the natural consequences of climate change with an average of 4.0584 are in group A and the human consequences with an average of 3.4460 are in group B. Also, the results of Friedman test on the effect of good governance in controlling and reducing the security consequences of climate change in Iran show that accountability with an average of 5.79 in the first place, rule of law with an average of 5.62 in the second place. and accountability of officials with an average rating of 5.32 are in third place.

In this study, for statistical studies to determine days whit temperature above 50°c, the reanalyzed data of the nineteenth, twentieth and twenty-first centuries for the West Asia region (12 to 42.5 degrees north latitude and 36 to 63.5 degrees east longitude) have been used. Also, for synoptic analysis of extreme temperatures, HGT, AIR, UWND, VWND and SLP data were used. To conduct this research, first, extreme temperature data above 50° during the last 185 years were extracted for the study area in the hot season (June, July, August and September). After identifying days whit above 50° c, HGT data at the level of 500 hp were extracted and WARD clustering was applied. Finally, after identifying the clusters, the days whit the highest temperature that occurred in each cluster were selected for synoptic analysis. It can be said that all altitude patterns of geopotential meters (HGT) at the level of 500 hp show that the main cause of occurrence and distribution of temperatures above 50°c in West Asia are high-altitude (high-pressure) subtropical West Asia, which due to the location of its high-pressure core on the Zagros and sometimes the Arabian Peninsula, it has been referred to as the Zagros or Saudi high-pressure in terms of interest and taste. What is certain, however, is the high-pressure independent identity of the subtropical Azores, which has been mentioned in numerous articles and is known to be the main cause of the heat in the West Asian region, especially Iran.

The greatest environmental threat today is global warming and Climate Change. Climate Change deined as dramatic and long-term change in the distribution of Atmospheric patterns in the long-term periods. What distinguishes the current Climate Change is that humans have played an important role in the global warming process. The issue is that human activities increase the amount of greenhouse gases in the atmosphere. This will increase the amount of intense absorbed radiation, and the amount that is emitted again to the Earth causes the Earth to warm up. The weather model forecasts in the summary report showed that during the 21st century, global temperatures increased from 0/3 to 1/7 degrees Celsius to 2/6 to 4/8 degrees Celsius (4/7 to 8/6 degrees Fahrenheit), and its amount depends on the amount of greenhouse gases and the effects of Climate feedback. (NASA, 2017)The main effect of Climate Change is the increase of global average temperature. The average surface temperature could increase from (approximately 1/67 to 5/56 degrees Celsius) by the end of the 21st. This causes a variety of secondary effects, namely, changes in patterns of precipitation, rising sea levels, agriculture pattern changes, extreme increase of weather events, the expansion of the range of tropical diseases, and the opening of new marine trade routes. Potential effects include sea level rise from 110 to 770 mm between 1990 to 2100, agriculture consequences, possible slowing of the ocean heat circulation, reductions in the ozone layer, increases of intensity and frequency of extreme weather events, lowering of ocean pH, and the spread of tropical diseases such as malaria and dengue fever (IPPC,2013). The country Iran, which is a spatial political unit in Southwest Asia, is not excluded from the Climate Change impacts. Undoubtedly, Iran will face the consequences of the Global Climate Change today or in the very near future. Environmental impacts of Climate Change in Iran, considering that 90% of Iran's land are dry and semi-arid lands, leads to destruction of the agricultural industry. For this purpose, the following questions have been asked: Which indices of sustainable development in Iran are subjected to and how is the status of these indices validated by the country? What are the indicators of the effects of Climate Change on the sustainable development of the country and what is the status of these indicators in the provinces of the country?

Historically, Iran has always been exposed to the consequences of Climate Change, given its Geographical location. Iran's location in the Earth's desert belt and the availability of a quarter of its water resources (precipitation and surface water) have revealed the need to pay attention to the various dimensions of Climate Change in Iran. The purpose of this study is to zoning the country's regions based on Sustainable Development indicators, the effects of Climate Change, and to provide a model in different regions of the country. The present study is descriptive-analytical in terms of purpose, application and method. Data collection and documentary and survey information have been done with questionnaire tools. Data collection and documentary and survey information have been done with questionnaire tools. The statistical population of the research is purposeful and includes experts and specialists in the field of Climate Change working in meteorological, environmental and passive defense organizations. To ensure the validity of the questionnaire, the opinions of 3 professors of Climatology and Political Geography of Tehran University and Research Sciences were used and after making corrections and adjustments, 48 questions were compiled for the final questionnaire. To measure the reliability of the questionnaires, Cronbach's alpha test scored 0/8 out of 16 questionnaires as a pretest. The questionnaire was designed based on the indicators of the effects of climate change and after distribution, 60 questionnaires were collected. The data were then entered into the SPSS software and Friedman's comparative test was used to prioritize the Sustainable Development indicators of the effects of Climate Change.

Then, Friedman's comparison test in SPSS software prioritizes the effective parameters of Climate Change on Sustainable Development, which arerespectively, they are: 1- Vulnerability to livelihood, poverty and weak government 2- Sustainable Agriculture and combating desertification and drought. 3- Health, Public Health 4- Maintaining the balance of the natural ecosystem.5- Justice and social security and citizenship will have the greatest impact on the sustainabledevelopment of the country. Then, using GIS software, the country's zoning was drawn based on the effects of Climate Change in the country's regions.Country zoning based on poverty and social anomalies (misery index), Sustainable Agriculture (Strategic Products), drought and agricultural destruction, public health indicator, ecosystem balance, Social Justice (Distributive Justice)

The results of this study show that the provinces of Sistan and Baluchestan, Hormozgan, Qom and Alborz are in line with the highest percentage of weakness and fragility against the effects of Climate Change in the country, which will have the greatest challenge with drought and agricultural degradation These challenges will increase the process of importing agricultural products to Iran, and Iran will become an importer of these products, and on the other hand, will cause migration, unemployment, poverty and increase social anomalies in Kermanshah, Sistan and Baluchestan provinces. The government is also authorized to use educational services and the ability to reconsider using the revision and change in re-status using the revision in the library of Sistan and Baluchestan, Bushehr, South Khorasan, Alborz and Qom. To this end, the government can use a well-written plan to reduce the challenges posed by the effects of climate change in these areas. Finally, the need to implement effective methods such as watershed management and clean energy use to reduce greenhouse gas emissions to adapt to Climate Change impacts is emphasized.

Sensible heat flux and latent heat flux are among the variables that are closely related to temperature and humidity and show heat transfer on a surface. So, their changes can be considered related to changes in temperature and humidity. In this regard, the current research aims to analyze and reveal the climatic changes of Iran by examining the course of changes in sensible heat flux and latent heat and the ratio between the two. For this purpose, NCEP/NCAR reanalysis data including sensible and latent heat flux during the period 1948-2020 was used in Iran. Bowen coefficient was calculated from the ratio of these two heat fluxes. Interpolation methods were used for their spatio-temporal analysis. In addition, by using the non-parametric methods of Mann-Kendall and Shibsen, spatial and temporal changes were also investigated. The first part of the results showed that, spatially, the Bowen coefficient is a function of latitude and roughness. And in terms of time, the lowest value corresponds to the month of January and the highest value corresponds to the month of July. The results of the second part show that the Bowen coefficient has a positive trend over time. Its upward trend indicates an increase in the dryness coefficient of the country. So that this situation can be seen in the positive trend and increase in temperature.

In recent years, land use changes in Isfahan city and its surroundings have been increasing, and these changes have been caused by factors such as population growth, economic interests, and changes in society's needs. The current research was conducted with the aim of investigating the temperature changes of Isfahan city and its surroundings in a forty-year period (1979-2018). The current research is based on the purpose of applied research and its method is descriptive-analytical according to the subject of the research and its nature, and to investigate the temperature changes affected by the changes in land use and urban development of Isfahan city, temperature data from The city of Isfahan and the area of ​​50 kilometers around the city of Isfahan have been collected from the database of the European Center for Medium-Range Weather Forecasts (ECMWF). To verify the accuracy, the data of meteorological stations of Isfahan, Isfahan Airport and Najaf-Abad have been compared and evaluated. became. The results of the research show that in this 40-year period, we have seen a significant increase in the average annual temperature from 0.06 degrees in the eastern area of ​​Isfahan city to 0.11 degrees in the western area. This means that the average temperature of Isfahan city and its surrounding areas has increased between 2.5 degrees and 4 degrees during a period of 40 years.

Land Surface Temperature (LST) is one of the most important parameters of environmental and climatic conditions. Therefore, this study is to investigate the trend change of day and nighttime LST in ​​Iran at a monthly and annual scale. To achieve this goal, the monthly products of MODIS sensor, Aqua satellite for day and nighttime with a spatial resolution of 5 km were obtained from USGS from 2020 through 2003. Then a three-dimensional matrix with dimensions of 216 × 297 × 388, in which 297 × 388, the number of cells and 216 months, was created. Finally, the monthly and annual changes of these two parameters were performed using Mann-Kendall test. Although the average annual trend of day and nighttime LST is increasing, on a monthly basis, different patterns are observed. The LST at nighttime increased significantly in May and September and during the daytime in December, but in June, July and January both day and nighttime temperatures increased. In contrast, both in March and October had a decreased trend. There was also a decreasing trend in nighttime LST in February and daytime in April and November. The results showed that the nighttime LST increased more than the daytime temperature, which led to a decrease in the 24h temperature range. In the spatial dimension, the highest increasing trend was observed in the western and southwestern regions and the highest decreasing trend was observed in the central plateaus and southeastern. Another point is that the water areas undergoing change, such as Lake Urmia and the lakes of Fars province, have experienced a significant increase (decrease) in LST during the day (night). In addition to decreasing the range of 24h temperatures, due to the fact that in the northern and western half of the country the upward trend was more and in the center, east and southeast the decreasing trend of temperature was predominant, the amplitude of spatial temperature gradient also decreased.

The impact of climate change on tourism has been the subject of many research studies. It is for this reason that tourism researchers have continued to explore the relationship between tourism and climate change and further explored response strategies among tourism stakeholders. The aim of this research is the evaluation of climate change and its impacts on thermal comfort and tourism climate of Iran. For this purpose daily maximum and minimum temperature, wind speed and relative humidity data of 91 synoptic stations during a period of 30 years (1987-2017) were used. In this research the (CDFt)[1] model is used to downscaling the GCMs data. ERA5 data was used to evaluate the performance of the downscaling model. The CanESM2 and GFDL_ESM2G daily data were used to draw future landscape changes of thermal comfort (based on the 2.6, 4.5, 8.5 RCPs). Then the thermal comfort for tourists was calculated using PET[2]. The thermal comfort results compared between the present and future. Thermal comfort of Iran was predicted for 2 periods (2021-2040) & (2041-2060). The results are displayed in ten-day classes. The number of stations that have optimal thermal comfort conditions in observation period indicates 0.83% increase compared to the futures perspective. The best conditions of climate comfort is predicted in the 24s and 25s decades and the most undesirable conditions are in the first to fifth and 34 to 36 decades. The most PET decrease is predicted in the northeast of Iran and the least increase of PET are occurred in the northwest. The results indicate an increasing on PET (between 0.06 to 1.56%) at future due to the increase in average temperature.

The daily variables of maximum and minimum temperature, wind speed and relative humidity of Synoptic stations as well as the ERA5 database in a 30-year period (1987-2017) and GCMs data were used to simulate the PET from 2021 to 2060. The CDFt model was validated using the Pearson correlation coefficient and the Kolmogorov-Smirnov tests along with a climatic RMSE. There are often abrupt changes in the climate time series. The homogeneity of observed data evaluated using the RhtestV4 software package based on the maximal penalized T and F (MPF, MPT) tests.  Data imputation was performed using Sequential K-Nearest Neighbor Method.Then, the research process was done as follows:The variables of maximum and minimum temperature, relative humidity and wind speed was simulated by CDFt statistical downscaling. The CDFt can be considered as an approach to the Quintile Mapping (QM) method by providing cumulative distribution functions (CDFs). Assuming that the ERA5 data are converted to the cumulative distribution function of the local climate variable as predictand at the desired station. In this study the CDFt software package has been used in R software. Validation of the simulated data was performed by Pearson correlation coefficient, Kolmogorov-Smirnov test and tidal error of the mean square squares.The RayMan model was used to calculate the thermal comfort based on PET index using both the observed and the GCMs downscaled data (CanESM2 & GFDL_ESM2G). This model is able to calculate the effect of short and long wave radiation flux on the human body, which is required in the human energy balance model.

After making sure that the CDFt, in general, showed a good performance in downscaling of the variable applied to calculation of climate comfort in the study area. Therefore, it is reliable to project the future thermal comfort of the region under the climate change conditions.Then  the historical period of CanESM2 & GFDL_ESM2G Models were used to show a comparison between PET observations and PET simulations. The results of the thermal comfort verification calculated based on the historical period simulated values ​​in comparison to the observed values ​​are as follows:There is a high correlation between the time series of the PET observed and PET simulated the historical period of GCM Models. However, a linear and positive relationship was observed for all-time series. According to the Kolmogorov-Smirnov test, the simulated values ​​of all PET time series showed good fit with the observed data at the 0.01 significance level. The calculated value of RMSE test for PET indicates the high performance of the downscaling method in simulation of historical period of GCM Models. The results showed that the CanESM2 model with minor differences performed better than the GFDL_ESM2G model. Incremental changes of physiological equivalent temperature haveoccurred in higher latitudes and in the high places like Alborz and Zagros. Also the PET decreasing changes are in low latitudes and central parts of Iran.Therefore, it is expected that more area of Iran will be at the desired threshold of thermal comfort at the future.The results showed that in the rest of the year, the largest utilitys are in the 24 and 25 th decades. Regardless of the model type towards the Rcp4.5 and Rcp8.5 and the 2041 to 2060 period increasing the values of the Physiological Equivalent Temperature will proceed. Also, the length of comfort decades is increasing in future periods.

This study was performed in order to provide infrastructure for tourism in future periods from 2021 to 2060. For this purpose, the climate comfort of Iran was predicted by GCMs data. Currently, most parts of the Iran are in the range of mild to severe cold stress. It is predicted that wider parts of study area will have optimal comfort conditions in future. Also, the number of optimal comfort decades is increasing compared to the observation period. The results of the climate comfort study showed that mild to severe cold stress conditions are predicted in More than half of this country at different decades of the year. So it will create opportunities and constraints in the future which requires long-term planning and strategies in this area.

Projection of summer monsoon rains in Southeast Iran based on Ensemble model the precipitation factor has a variable and random nature and has a different behavior in terms of space and time. Therefore, the prediction of precipitation has more uncertainty compared to other meteorological variables. In the current study, the data output of Cordex database and CMIP5 models were used using the neural network method to reduce the uncertainty and estimate precipitation properly. The results showed that due to the high correlation of temperature, humidity and air pressure with precipitation, the use of these variables is beneficial in reducing the uncertainty of precipitation forecast. In addition, the non-linear method of artificial neural networks can be used to bias the rainfall data of Cordex database and CMIP5 to forecast the rainfall in the southeast of the country. Another result of this research is the increasing trend of rainfall in the southeast of Iran, especially in the coastal areas. This can be considered as a result of the increase in the level under the influence of rains affected or simultaneous with the southwest monsoon of India. The increasing trend of precipitation in the southern coasts is also related to the increase in the storage capacity of moisture content. The interannual variability of India's monsoon rainfall also shows a steady positive trend under continued global warming. Since both the increase in the duration of monsoon rains and the increase in interannual variability in the future are seen in most models, we can be confident in these predicted trends. Indian summer monsoon rainfall is also predicted to be higher under global warming in the 2050s compared to the baseline.

The continuous and expanding process of global warming, especially in the Asian region, has provided the conditions for increasing drought and the spread of desertification. Many deserts had ecologically balanced soil conservation conditions that until recently have become new sources of dust generation now. Numerous examples have occurred in Iran due to its special geographical location among some of the most important deserts in the world. Temperature anomaly (about 8º C) last winter in the Caspian Sea basin has created new dust sources for the southern coastal of the Caspian Sea. On 30-31 May 1400, dust emission was recorded in meteorological stations of Gilan province in terms of area and concentration. The implementation of HYSPLIT chemical backward models shows the emission of dust from the northwestern region of the Caspian Sea to the southern coastal of the Caspian Sea (Guilan province) for the first time with such intensity. The source and origin of this dust was identified in the Rhine desert in the northwest of the Caspian Sea. Continuous and unprecedented warming in the region and accompanied by strong north-south currents provided the conditions for the emission of this dust. Due to the origin of the emitted dust as well as the geographical and topographical conditions of the Caspian Sea basin, the level of this dust was assessed from the ground level to an altitude of less than 1500 meters. Analysis of synoptic conditions using NCEP / NCAR analysis data with 1 degree horizontal resolution indicates the establishment of high pressure air mass with a center of 1018 hPa on the northwestern parts of the Caspian Sea and the penetration of high pressure to the southern coastal areas of the Caspian Sea. Due to the appropriate pressure gradient and increasing wind speed, dust-producing springs are formed on the desert areas of the Rhine and with the dominance of the northern currents (south-south), the dust mass is sent to Gilan province.

Improper waste management threatens the sustainability of the environment. Defining an optimal and sustainable system for waste disposal requires the examination of all processes within the system, their impact on the environment, economic aspects, implementation, and energy consumption in each part. The present study was conducted with the goal of modeling the municipal solid waste management system of Rasht County based on the life cycle assessment approach. In this regard, two existing disposal scenarios and the development plan for the future were compared. The first scenario (existing disposal) includes the sale of recycled waste, biological processing (compost production), and landfilling. The second scenario (development plan) involves the use of an incinerator in addition to previous disposal methods. After collecting data using the IWM-2 model, the environmental impacts of each scenario were assessed. The results show that if the second scenario assumptions are applied, the amount of energy consumption, pollutants, and global warming will decrease relative to the first scenario. Total energy consumption in the first and second scenarios is respectively +3891 and -1683234 Gj. In addition, the global warming potential is reduced due to the reduction of landfills, reduced transportation and fuel consumption, improved equipment, and energy recovery in the second scenario. In general, environmental performance in the second scenario will be improved if appropriate action is taken. Reducing landfill and greenhouse gases, increasing recycling, etc., are the main reasons. Although incineration reduces landfilling in Saravan, it is still the proper design of the landfill, and energy utilization from it provides a favorable life cycle of waste management based on the second scenario. Moreover, the use of waste incineration is justified only if pollution and energy recovery standards are respected. Modifying the components and capacity of each disposal option produces new results. In this context, it is suggested to use alternative models.

By assessing the trend of air temperature changes, it is possible to explore traces of climatic changes in the area of Iran. Climate change and temperature increase are important human-environmental issues. Based on the change point method, it is possible to identify the onset time of changes in basic variables such as minimum and maximum temperature. Therefore, the present research aims to analyze the change point of temperature thresholds of heat and cold waves in Iran. The temperature threshold means the 95th percentile for maximum temperature values and the 5th percentile for minimum values. For this purpose, the temperature data (minimum and maximum) of 43 synoptic stations in Iran, which have a long statistical period (1966-2018) and suitable distribution, were used. To identify the temperature threshold change time, three change point methods - Pettitt, SNHT, and Buishand's Range Test were used. The results showed that the temperature threshold of heat and cold waves has been increasing over the past few decades. The rising growth rate is equal to 0.019 and 0.052 degrees Celsius per year, respectively. Therefore, in the current study, it was found that the increasing trend of the temperature threshold led to an increase in the frequency of maximum temperatures, and on the other hand, it will lead to an increase in hot days and a decrease in cold nights, in addition to the higher frequency of events, it can be stated that the frequencies will move towards higher values. Based on the results of the spatial-temporal analysis of the thresholds, the increasing trend of the minimum temperature in the northwest, Zagros, and southeast regions of Iran is quite evident. And from the other results of this study, we can emphasize the time of the change point or jump of the temperature threshold of cold and heat waves around 1991. The mentioned year is in line with global studies

The zero-carbon or low-carbon city is a new approach, which is rooted in sustainable urban planning. The main goal of this model is to reduce greenhouse gas emissions and carbon dioxide in urban areas. Sari, which is the capital of Mazandaran, contains natural resources and is located in an especial geographical location. As a consequence of recent years' development, the city has gone out of the way of harmonious development with nature. This study aims to compile indicators and criteria of the zero-carbon model for Sari city. Thus, the document study method is used as the tool to review the general condition of the city and basic indicators and criteria. Then, the depth interviews were conducted with the experts and the analysis has done by MXQDA to find out the indigenous criteria and indicators. After that, the indicators and criteria were scored and prioritized by experts. As a result, this study would place applied and descriptive-analytical in terms of purpose. The final result has shown that land use, management, and policymaking are the top three indicators, respectively. The means of sustainable transportation, reusable energy sources for neighborhoods, and green infrastructure are the main three indicators. In conclusion, the physical variables are not the only effective elements in planning a zero-carbon city, raising awareness and making suitable policies for encouraging citizens' participation are also efficient.

In the present study, the temperature parameter has been reconstructed by using the annual growth rings of juniper trees (Juniperus polycopos) in Tengel Raver habitat in the north of Kerman province. Therefore, samples of 95 trees were taken using a growth gauge drill. Counting the number, measuring the width of the rings and matching the time between the growth curves of the trees was done by LINTAB desktop and TSAP Win software with an accuracy of 0.01 mm. The chronology of the region was constructed, detrended and standardized in ARSTAN software for 517 years (1500-2017) and its quality was checked with Cumulative Signal Statistics (EPS) and finally Residual chronology was selected for reconstruction. The relationship between climate and the width of the rings was measured using data from Kerman and Zarand stations and CRU TS4.01 data for the last 116 years of Iran. The results showed that the temperature of the months before the growing season and the month of March at the beginning of the growing season have a positive effect on the width of the rings, and the temperature of the months of April, May and June have a negative effect. Also, the investigations showed the occurrence of the issue of divergence between temperature and growth rings in the last 25 years and the region being affected by global warming in the last two decades. The reconstructed temperature showed a general decrease of 0.5 to 1.5 degrees in the two periods of 1750-1800 and 1700-1500 AD simultaneously with the Little Ice Age event in Europe for the studied area.

One of the consequences of the increase and accumulation of greenhouse gases in the atmosphere is kown as global warming, which is undoubtedly one of the most important environmental challenges in the world, especially in the Middle East. Given the scarcity of water resources in recent years, the consequences of global warming and climate change in various countries have reached a very worrying level. Carbon dioxide and Methane are known as two of the most important human greenhouse gases in the atmosphere, accounting for 64% and 18%, respectively, of long-lived radiation induction (LLGHGs) (Forster et al, 2007). Methane is considered as the second most important anthropogenic greenhouse gas after Carbon Dioxide.The most important sources of Methane emissions include: biomass incineration, artificial human emissions, wetland emissions, and wastes. Despite the importance of Methane for physical and atmospheric conditions, the spatial distribution of global resources and Methane sinks is not well understood. With the launch of Methane measurement from satellites, knowledge about the global distribution of Methane in the atmosphere greatly increased.The Japanese Greenhouse Gas Satellite (GOSAT) is the only satellite that measures the column mixing ratio of atmospheric Methane. Since the 1990, various global models have been used to simulate CH4 concentrations. High-resolution simulation of CH4 at hourly intervals on Earth, with diverse ecosystems, due to the lack of intensive spatial and temporal measurements and the impossibility of reliable validations for chemical simulations are known as a serious challenge. The main purpose of this study is to understand the performance of the WRF-GHG model in simulation of Methane concentration and validation the results of medium-scale modeling output in total Methane concentration in comparison with GOSAT satellite observations over Iran.

Iran and some area of its surroundings is considered as a study area. This study focuses on two case periods of hot and dry (August 31-2010) and cold and wet (February 1-28, 2010). In order to provide the initial and boundary conditions of meteorological fields, ERA5 reanalysis data were used with a horizontal resolution of 0.25 ° with a time resolution of 6 hours. Different emission input data from three different global greenhouse gas emission databases EDGAR_v5.0 (anthropogenic emissions), GFAS emissions (fire emissions), and datasets (CMS_V01) (wetland emissions) have been used.Preliminary and boundary conditions for the chemical fields taken from atmospheric monitoring service data (CAMS) with a spatial resolution of 0.8º with 137 vertical levels and with 6 hours time resolution. To investigate and quantify the validity of meteorological fields simulated by the WRF-Chem numerical model, a set of observations of selective synoptic stations is used. For validation of CH4 WRF_Chem column concentration and statistical analysis, in the points that include remote sensing data (GOSAT sensor data), is used the set of level 2 products generated by the NIES algorithm. The local transit time of the GOSAT satellite Is approximated around 13: 00_9: 00, so the simulated concentration for this time is applied in the analysis. The first 15 days of the simulation are omitted to take into account the spin-up time. Statistical parameters of mean bias error (MBE), mean absolute error value (MAE), root mean square error (RMSE), and Pearson correlation coefficient (R) in meteorological and chemical variables are studied for validation of numerical simulations and quantification of error levels.

The model has been able to calculate temporal changes in surface temperature, relative humidity, and wind speed to some extent correctly. The general tendency of the model to simulate the observed temperature and relative humidity for the selected time period is evaluated. In general, model values are closer to summer observations than all thirty days in two selective months. The wind speed forecast is often consistent with the wind speed values obtained from the measurements, and in most cases, the wind speed is overestimated at around 1.2 m/s.Statistical evaluations of the WRF-Chem model, together with the GHG gas-phase chemistry mechanism, show the simulation of Methane concentration versus observations by the GOSAT satellite, and the estimation of the average monthly concentration in February and August 2010. The values of MAE, RMSE, RMSE_u, RMSE_s, BIAS and R are calculated equal to 42.92, 46.05, 7.82, 44.60, -24.99 and 0.63, for hot and equal to 12.01, 13.94, 7.09, 11.68, 7.50 and 0.76 ppb, for cold periods respectively. It can be seen that the WRF-Chem model performed better in Methane simulation in cold and wet periods (January) compared to the hot and dry seasons (August).

In this study, the WRF-Chem model was used to simulate meteorological variables and air pollutants (methane greenhouse gas) concentrations in the Middle East-Iran region during the study period of February and August 2010. The sensitivity of the model is considered using the GHG gas-phase chemistry scheme. The main findings of this study are: The model is able to reproduce temporal changes in surface temperature, relative humidity, and wind. The model underestimates the air temperature and relative humidity respectively around, 1/05 ⸰C -5% in the study area (Iran).In simulating of Methane concentration, and examining the results with related GOSAT satellite observations, the model overestimates around15 ppb of the Methane concentrations. The evaluation results show that the WRF-Chem model performs better in the cold season (January) than in the warm season (August). This uncertainty in CH4 simulation can be attributed to a deficiency in various input components of the CH4 emission in different categories. Improving the simulation for the various parameters reported to the model as the primary CH4 emission can generally help to improve the CH4 simulations.