It is challenging to estimate how the climate of a region will change under global warming in the future. Among the essential climate variables, temperature, relative humidity and wind speed are very important, and their changes in relation to each other can have many consequences, such as increasing heat stress and evapotranspiration.    In this research, we investigated future temperature, wind speed, and relative humidity in Iran. Five models, namely GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have been evaluated versus observations for 1990-2014. A multi-model ensemble was generated with the Integrated Weighted Mean (IWM) method from selected CMIP6 models, and the performances of individual CMIP6 and CMIP6-MME models in estimating temperature, wind speed, and relative humidity were investigated. The results showed that all five selected models are capable to reproduce the variables; however, the CMIP6-MME significantly improved the results. The CMIP6-MME showed good agreement with observational data both in terms of climatology and spatial distribution of each variable, and its performance is higher compared to individual models.   We have used two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, and three-time periods, namely near-term (2026-2050), midterm (2051-2075), and long-term (2076-2100) relative to the historical period (1990-2014).The results of this study showed that Iran will undergo changes in temperature, wind speed, and relative humidity spatial distribution in the future. The decrease in relative humidity and dryness of the air in the future can have important consequences for agriculture, food security, and water resources management. The findings of this study, in agreement with previous studies, emphasize the significant increase in temperature throughout Iran. Under the SSP2-4.5 (SSP5-8.5) scenario, the average annual temperature of the country increases by 1.40 (1.83), 2.34 (3.58), and 2.99 (5.58) degrees Celsius in the near (2026-2050), middle (2051-2075), and far (2076-2100) future, respectively.    Along with the increasing trend of temperature in Iran, wind speed will decrease in most regions of the country in the middle and end of the 21st century under two SSP scenarios. This decreasing trend can be a result of decreasing atmospheric instability and increasing potential temperature. The northwest of Iran has shown the maximum increasing temperature and the maximum decreasing wind speed. The decrease in relative humidity in Iran has been evident since the 1990s, and the projection results indicate that it will decrease in large parts of Iran in the future. However, the relative humidity in the southeast of Iran shows an increasing rate in the future. Results of this study show that the heat stress will be significantly higher through SSP5-8.5 than SSP2-4.5 in the 21st century due to the decrease in wind speed and increase in temperature throughout Iran. Therefore, Iran should quickly move on to formulate and implement long-term adaptation plans for resilience against climate change.

ABSTRACTDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. The purpose of this research is to identify the key variables affecting the relations and spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. This research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). sampling method is based on purposive sampling and Micmac software was used to analyze the data. Output of Micmac software shows the position of the instability of the system. Therefore, the position of each variables in the conceptual model can be categorized to driver, linkage, dependent, and autonomous factors. The research findings indicate 4 variables (like as “income, cost and investmnets in port-city”) can be introduced as a driver factor, 5 variables (like as “advanced multimodal transportation in the port”) can be introduced as a linkage factor, 6 variables (like as “quality of living environment and desirable city”) can be introduced as a dependent factor, 3 variables (like as “environmental protection and sustainable development of the city-port”) can be introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourismExtended AbstractIntroductionDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. The global increase in oil demand since the 1950s led to the creation of discovery of new oil wells and large industrial areas. With the construction of new oil or gas facilities in the port areas, increasing oil revenues contributed to the countries of the Persian Gulf region and form a new geographical relationship between ports and coastal cities which had an impact on the spatial structure and relation of them. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. In the 1950s, Containerization (container loading technology) was based in some of the world's ports and created a huge transformation in the shipping industry, which had a significant impact on the development of ports. Although the ports of the Persian Gulf have made a significant contribution to the economic growth and physical development of other ports in the world, but few researches have been done on the variables and factors affecting the development of the spatial structure of the important ports of the Persian Gulf. This region, due to its unique geographical location, has always been considered as a most important corridor in the field of economic and transportation throughout history. Some of the most important energy and commercial ports in this region are: Dubai, Manama, Kuwait, Dammam, Doha, Bushehr, Bandar-Abbas and Assaluyeh. In this regard, the purpose of this research is to identify the key variables affecting the relations and spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. MethodologyThis research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). The method used in this research is quantitative and the sampling method is based on purposive sampling. the variables affecting the spatial structure of port-city relations were identified in the form of a review of 31 prominent articles. 25 experts (who had comprehensive knowledge and information about the development process of the city-port of Dubai) participated to determine the effectiveness or influence of variables. Finally, the output of data analysis was done by the Micmac software. Results and discussionOutput of Micmac software shows the position of the variables in the diagram that it indicates their status in the system and their role in the dynamics and changes of the system. The method of distribution and dispersion of the variables in the spatial structure of port-city relations indicates the instability of the system. Therefore, based on the output of the system, the position of each variables in the conceptual model can be identified in five categories (driver, linkage, dependent, autonomous and regulatory variables). According to the results, some variables such as “the changes in the strategic positions” and “geopolitics of port-city”, “the modification of the management method” and “regional planning of the ports”, “the promotion of local governance in the relations between port-city” and “the way of national and international management of ports have been introduced as driver variables. On the other hand, five variables consist of “advanced multimodal transportation in the port” and “income, cost and investment in the port-city”, “the contrast and physical integration of the port - city space”, “the development of various industrial and production activities in the ports and their local hinterland” and “their qualities Financial and commercial policies of ports” have been introduced as linkage variables. Findings of research also shows that some variables consist of “quality of living environment and desirable city”, “quantity and quality of transit corridors (rail, road and air) to hinterland”, “construction and launching the new port (sea port or dry port)”, “terminal facilities and infrastructure and warehousing”, “conflict or convergence between the city and the port” and “increase in population growth rate and migration to the port - city” categorized as dependent variables. Three variables including “cultural and historical background of ports”, “use of cheaper energy, Less-polluted, clean and renewable energies in the city-port” and “environmental protection and sustainable development of the city-port” categorized as autonomous variables. Finally, a variable with the title of “improvement in management and information technology and development in loading, unloading and storage of commodities” have been introduced as regulatory variables. The findings of this research have a remarkable similarity with other researches carried out in the field of city-port relation and clearly emphasize the direct impact of the "investment in port-city" in the development of them. For example, Grossmann (2008) emphasized that city-port of Hamburg has become one of the largest ports in Northern Europe during the last few decades due to huge domestic and foreign investment. ConclusionThis research carried out in order to introduce and explain the key variables affecting the development of the spatial structure of the port-city of Dubai. On this basis, 21 variables have been extracted by systematic reviews of prominent articles. In the following, 25 experts were selected with the purposive sampling method. Among the 21 variables extracted, 4 variables (like as “geopolitics of port-city”) introduced as a driver factor, 5 variables (like as “advanced multimodal transportation in the port”) introduced as a linkage factor, 6 variables (like as “quality of living environment and desirable city”) introduced as a dependent factor, 3 variables (like as “environmental protection and sustainable development of the city-port”) introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourism. A part of these revenues has been directed towards investment in important transportation infrastructures such as airports and ports of this city. These strategies have a wide impact on improving the economic growth of the city and the development of the port in order to adapt with the global trade and advanced technologies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

In this study, the average climate data of meteorological stations including temperatures of the warmest and coldest months of the year, precipitation regime, potential evapotranspiration and UNEP aridity index has been used to map Iran's climate zones. In this regard, climate variable of air temperature, relative humidity, potential evapotranspiration, and precipitation characteristics of 303 meteorological stations throughout Iran was used on monthly and annual time’ s scales. The annual aridity index in each site was calculated using the United Nations Environment Program (UNEP) index. Then, the temperature characteristics of the warmest and coldest months of the year were coded. Results show a very high climatic diversity throughout Iran. In this model, Iran's climate was divided into 27 categories. Based on aridity index, Iran has seven climates zones. There are 30 cities with hyper-arid climate. This climate type has three climate territories and occupies about 3. 4 percent of the country's territory. The arid zone with five climate territories cover about 23. 7 percent of the country and dominates 95 cities. The semi-arid climate with 6 climatic territories accounts for about 39. 6 percent and dominates 113 cities. The dry sub-humid zone with four climate territories and covers about 17. 3 percent and dominates 30 cities. Nine cities have a semi-humid climate with three climatic territories which accounts for about 8. 9%. The humid climate with four climate territories covers about 5. 8 % and dominates 13 cities. The very humid climate with 2 climatic territories accounts for about 1. 3% and represents the climate type of the 12 cities.

In this study, to predict the effect of climate change on hydro-climate variables in the study area, based on downscaled outputs from atmosphere general circulation models, the most appropriate scenarios were used. For this purpose at first, by valid trend tests, climate change in the case study was valued during the base period (1976-2005). Then the CanESM2 model under RCP2.6, RCP4.5, and RCP8.5 scenarios in three future periods (2039-2010) (2040-2069) and (2070-2099) was used, and the SDSM model was used to downscale the climatic data. The results of the simulation for future periods and comparison the results with the base period indicated that average temperature increase between 1.3 to 5.7 (ºC), the mean annual precipitation under the RCP4.5 scenario for all three future periods decrease and for RCP8.5 scenario for all three periods increase and also for RCP2.6 it has a reduction of precipitation for 2010-2039 and precipitation increase for other two future periods. The IHACRES model was used to simulate runoff. The results showed that for all periods and RCPs scenarios, the runoff decreases between 10 to 38% in comparison with the basic period. Based on the results of this study, increasing temperature and decreasing precipitation over the next years will reduce the runoff and water resources of the region due to increased evaporation and transpiration, which in the future will increase the probability of increasing drought and flood in the region. Therefore, to adapt to climate change, appropriate managerial approaches at the watershed should be considered.

Introduction: As proven by some researches, the atmosphere’ s general circulation models (GCMs) well predict the temporal and special variations in climatic variables such as temperature and precipitation at a global scale. However, although these models can simulate the global climate in a three-dimensional grid for the whole world, the resolution of their images cannot display the details of the climate changes at regional scale. Therefore, to predict climate changes at regional and regional scales, downscaling tools are needed to be developed. This study, thus, sought to investigate the variations of temperature and precipitation in future periods in a semi-arid region in Iran. Materials and methods: To conduct this study, the Kermanshah province with average annual precipitation of 402. 27 mm and mean temperature of 15. 9° C was selected. To investigate the future climate change, we need a base period as an evidence or reference (1961-2005). The data used in this study were collected from station observations based on the required output and large-scale data NCEP and GCM gained from the nearest global network to the study area. To estimate the future periods’ temperature and precipitation data, the GCM model of the CanESM2 was applied under three scenarios including RCP 2. 6, RCP 4. 5 and RCP 8. 5, and SDSM 4. 2. 9 model was used in this regard for downscaling the output data. The SDSM is a multivariate regression model for the production of climatic data via statistical downscaling techniques, seeking to generate high-resolution climatic data from GCM’ s large-scale simulations data. This model is used when rapid and low-cost estimation of the climate is required. Results: This study evaluated the model’ s performance in predicting climatic parameters based on R2, RMSE, and NS. As confirmed by the results of RCP2. 6, RCP4. 5 and, RCP8. 5, the values regarding the climatic parameters were modeled with acceptable accuracy. However, precipitation prediction was less accurate than temperature which could be attributed to the inaccuracy of the precipitation data and their unconditional nature. The study’ s results showed that the annual mean temperature values in the period 2020-2049 under RCP2. 6, RCP4. 5 and RCP8. 5 increased by 0. 6, 0. 7, and 0. 9° C, respectively, compared to the base period. Moreover, the investigation of the prospective temperature changes in 2050-2069 period under the above-mentioned scenarios suggested that the temperature would increase throughout the year except in September, October, November, and December. The highest increase would occur in June by 6. 6° C under RCP8. 5, and the lowest increase would happen in October by 4° C under RCP2. 6 scenario. Furthermore, the annual mean temperature values would increase by 0. 8, 1. 4 and 2. 4° C in 2050-2069 under RCP2. 6, RCP4. 5 and RCP8. 5 scenarios compared to the base period, respectively. It was also found that the temperature would increase in all season but autumn throughout 2070-2099, with the annual mean temperature values getting increased by 4, 1. 9 and 4° C, respectively, under RCP2. 6, RCP4. 5, and, RCP8. 5 scenarios. Precipitation values and its variations in 2020-2049 period indicated that the highest decrease in precipitation value would occur in March by 38. 2 mm under RCP2. 6 scenario and the highest increase in this parameter would occur in October by 127. 5 mm. Moreover, the annual mean precipitation rata would be 6. 4 mm lower in 2020– 2049 period than the observed value based on RCP2. 6 scenario, and it would increase by 2. 6 mm in the same period under the RCP4. 5 scenario compared to the baseline period, and it would decrease by 2. 6 mm under RCP8. 5 scenario. Precipitation values for the period 2050-2069 show that the highest decrease in precipitation in March was 38. 3 mm under RCP2. 6 and the highest increase in October to 127. 5 mm under RCP4. 5. Furthermore, according to the annual mean precipitation values for 2050-2069 period, it was found that the highest decrease in precipitation rate would occur in March by 38. 3 mm under the RCP2. 6 scenario, and its highest increase would occur in October by 127. 5 mm under RCP4. 5 scenario. Also, the annual precipitation rate in 2050-2069 period would increase by 2. 6 mm and 4. 2 mm under RCP8. 5 and RCP4. 5 scenarios, respectively, compared to the observation period, and it would decrease by 4. 9 mm under the RCP2. 6 scenario compared to the baseline period. The results of the precipitation rate for 2070-2099 period showed that the highest decrease would occur in March by 48. 1 mm under the RCP2. 6 scenario, and the highest increase would occur in September by 129. 5 mm under the RCP8. 5 scenario. Moreover, in 2070-2099 period, the average annual precipitation values would decrease by 0. 6 and 7. 4 mm under RCP2. 6 and RCP4. 5 scenarios, respectively, and it would increase by 7. 8 mm under the RCP8. 5 scenario compared to the base period. Discussion and Conclusion: The climate changes observed in the 20th and 21st centuries are incompatible with those of the past millennium. Arid and semi-arid regions are extremely vulnerable to climate changes. Therefore, identifying and comprehending the relationship between climate variables, and knowing their future changes are important for sustainable and efficient management of resources in such areas. According to studies conducted in this regard, climate change will inevitably occur in Iran. On the other hand, one of the most important issues in dealing with climate change in recent decades has been susceptibility to the climate changes. The investigation of the trends of the climatic data recorded in last decades, and the outputs of all climate models that predict future climates indicate that undeniable changes would occur in global climate. To conduct this study, the daily temperature and precipitation data of Kermanshah province’ s synoptic station were used. However, non-conditional data presented more acceptable results. The study’ s findings showed that in 2020-2049 period, the precipitation rate increased under RCP4. 5 scenario compared to the observation period (1961-2005), and it increased in the same period under the RCP2. 6 and RCP8. 5 scenarios. It was also found that in 2050– 2069 period, the precipitation rate decreased under RCP2. 6 scenario compared to the observation period (1961– 2005), and increased under RCP4. 5 and RCP8. 5 scenarios. Generally, it could be argued that the precipitation rate would increase in this period. Moreover, it could be said that the precipitation rate would be decreasing throughout the 2070-2099 period compared to the observation period, and temperature would experience an increasing trend during 2020-2049, 2050-2069, and 2070-2099 periods compared to the observed period. As indicated by the results, the Kermanshah province’ s climatic conditions in the prediction period would considerably differ from the current situation, suggesting serious changes in the region’ s climate status. Therefore, getting aware of the direct and indirect negative effects of the climate on different parts of the region and developing long– term strategic plans are necessary for dealing with such conditions.

Iran is located in a dry and semi-arid region, making its agriculture highly sensitive to climate changes. Iran has experienced the worst and longest drought events in the last few decades. This study examines the long- and short-run relationship between agricultural value added and the deriving factors including climate variables (temperature and precipitation) using data covering 1961 to 2021. The current study applies cointegration analysis known as Autoregressive Distributed Lag (ARDL) model. The results showed that weather variables account for a significant portion of the fluctuations in value added in the short run. In the long run, it was also found that an increase of 1.0°C will result in approximately a 5 percent fall in agricultural value added. However, precipitation did not significantly affect the value added. Among the capital inputs, land was found to be the most important driving factor. Labor was also found to be another significant factor contributing to agricultural output growth. The primary channels of global warming influencing agricultural output are the decline in labor productivity and the depreciation of physical capital. Therefore, enhancing the productivity of production factors is crucial in addressing these challenges.

Climate change from the previous period there have been, in recent decades, due to the intensification of human activities have created international concern and its effect on agricultural production worldwide. In this study, the combined data (panel) and the dynamic OLS (DOLS) was used to estimate. The results showed that temperature until the temperature returns over the long term, the positive impact and it has had a negative effect on the performance of cotton, the threshold temperature of 17.29°C, The weather temperature in long-term elasticity of 0.21% is obtained, ie, at constant average annual temperature averages if the other conditions of a percent increase, the average performance of cotton during the period under review 0.21% increase. In short model coefficient estimates ecm (-1), equivalent to -0.46 obtained show that in the period 0.46 amount of variables to converge to wards the long-run equilibrium. Finally, it is suggested to prevent temperature rise of human interference in nature (such as destroying pastures and forests) prevent and to deal with increasing temperature is recommended varieties resistant to temperature or change cropping pattern should be used.