Tourism is a rapidly growing international sector and relies intrinsically on an amenable climate to attract visitors. Climate change is likely to influence the locations preferred by tourists and the time of year of peak travel. This study investigates the effect of climate change on the Tourism Climate Index (TCI) for Iran. The paper first calculates the monthly TCI for 40 cities across Iran for each year from 1961 to 2010. Changes in the TCI over the study period for each of the cities are then explored. Increases in TCI are observed for at least one station in each month, whilst for some months no decreases occurred. For October, the maximum of 45% of stations demonstrated significant changes in TCI, whilst for December only 10% of stations demonstrated change. The stations Kashan, Orumiyeh, Shahrekord, Tabriz, Torbat-e-Heidarieh and Zahedan experienced significant increases in TCI for over 6 months. The beginning of the change in TCI is calculated to have occurred from 1970 to 1980 for all stations. Given the economic dependence on oil exports, the development of sustainable tourism in Iran is of importance. This critically requires the identification of locations most suitable for tourism, now and in the future, to guide strategic investment.

This study presents a spatiotemporal analysis of bioclimatic comfort conditions for Iran using mean daily meteorological data from 1995 to 2014, analyzed through Physiological Equivalent Temperature (PET) index and Universal Thermal Climate Index (UTCI) indices, and bioclimatic clustering. The results of this study demonstrate that due to the climate variability across Iran during the year, there is at any point in time a location with climatic condition suitable for tourism. Mean values demonstrate maxima in bioclimatic comfort indices for the country in late winter and spring and minima for summer. Seven statistically significant clusters in bioclimatic indices were identified. Comparing these with clustering performed on PET and UTCI, the maximum overlaps between the two indices. In the following, the outputs of this research showed that most appropriate bioclimatic clustering for Iran includes seven clusters. These clustering locations according to climatic suitability for tourism provide a valuable contribution to tourism management in the country, particularly through marketing destinations to maximize tourist flow.

As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades' climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran's bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960-2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to midspring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations across the studied period, an increase in the frequency of thermal comfort condition is observed in almost half of the stations. Moreover, based on Pearson and Mann-Kendall methods, the trend of changes in monthly averages of PET has decreased in most stations and months, which can lead to different consequences in each month and station. Thus, it is expected that due to PET changes in recent decades and to the intensified global warming conditions, Iran's bioclimatic conditions change in a way that transfers the days with comfort to early spring and late autumn.

Background and purpose: Human health is affected by a variety of human and natural phenomena that surround the environment. Atmospheric pollutants and thermal comfort conditions concern the quality of surrounding air. Given the influential role of lakes on the climatic conditions of their surrounding environment, the effect of different scenarios of Maharlu Lake in the southeastern part of Shiraz on the changes of thermal comfort conditions was modeled. Materials and Methods: In this study, cooling power index and temperature humidity index were used to explore climate comfort conditions according to the long-term observational data from 1960 to 2010. Results: It was found that temperature humidity index has a declining trend in most months of the year. Maximum decreasing changes were observed in November and May with means of −0.31 and −0.29, respectively. However, the maximum of decade and significant changes of cooling power index belonged to April and November with means of 1.36 and 1.22, respectively. Conclusion: Low relative humidity was seen in all the seasons; maximum decrease was observed during summer and in August with 11% decrease. Also, the dried lake outputs showed that the temperature during hot seasons increased, and the temperature during cold seasons