Urban heat island (UHI) is an increasingly important public health problem which could be even more severe in a near future due to global warming. Air conditioning (A/C) is a key parameter for health problems in case of heat waves since, on one hand, it reduces mortality but, on the other hand, depending on the heat rejection management, it can increase the street temperature.
This communication, based on simulations, addresses the influence of A/C management on the street temperatures of Paris in the case of the severe heat wave which occurred in August 2003. Results show that A/C affects the UHI depending on its management. A detailed analysis on selected districts shows that the local temperature variation resulting from heat island is proportional to the sensible heat rejected locally by A/C, indicating that a clever A/C management is all the more important to provide comfort and to mitigate heat island. Moreover, the incidence of the sky view factor is also discussed.

This study addresses the use of classical regression techniques for predicting the monthly average global solar radiation in the city of Al-Ain, UAE. The mean daily recorded data used were supplied by the National Center of Meteorology and Seismology (NCMS) in Abu Dhabi , UAE or the years of 1995 till 2007. Available weather data included the mean air temperature (⁰C), mean wind speed (knots), daily sunshine hours, and percent relative humidity in addition to the daily global solar radiation (kWh/m²). The data was divided into two groups: one data group from 1995-2004 for the prediction model and the second group (2005-2007) for testing the model. The correlation between mean daily global solar radiation (GSR) and the other four variables suggested using all four variables for building the prediction model, although the GSR is highly correlated to the mean temperature and sunshine-hours variations.
The resulting model was tested using the three-year data of the period 2005-2007 yielding a deterministic coefficient R² = 90.77 % and RMSE= 0.417 as well as low MBE values. The model was also tested with Artificial Neural Network (ANN) model data yielding very favorable results.

Climate change will influence not only mean but also climate variability, in particular the frequency, intensity and the spatial distribution of droughts. The main objective of this paper is to project the extent to which future climate-change will induce changes in drought characteristics in Romania for the middle and the end of the 21st century. We use monthly precipitation data simulated by the regional climatic model ICTP_RegCM3 over Romania to calculate the Standardized Precipitation Index (SPI) at time scale of 1, 3 and 6 months. The simulations have been conducted at a horizontal resolution of 10 km in the framework of EU-FP6 project – CECILIA. The reference period of the control run is 1961-1990 and, the scenario runs were conducted for SRES A1B scenario for the periods 2021-2050 and 2071-2100. Considerable uncertainty in model skill in reproducing summer precipitation is observed when it is driven by ECHAM GCM. Future projections show increase in seasonal drought conditions over Romania, especially in the south and south-eastern part of the country, more pronounced for the end of the 21st century. The frequency of severe and extremely dry conditions of 3 and 6 months in duration is projected to slightly increase in comparison with severe and extremely wet conditions.

Earth's climate change is not a new phenomenon; it has occurred on all time scales throughout the Earth's history. However, climate change phenomena since the time of the mid eighteenth century are quite different from the changes occurred in the time of paleoclimatic and antiquity periods. The present day anthropogenically caused climate change is unique from its precedents in many respects and it has been bringing a serious challenge on life in different apocalyptic forms. This has a serious implication to Ethiopia since its economy is predominantly based on rain-fed agriculture. In this paper, climate change signals are detected at local scale in the country from analyses of precipitation data. Precipitation records from in-situ rain gauge measurements from over 190 stations are employed in the analysis which involves reconstruction of missing values, homogenization and gridding onto 0.5 x 0.5 degree. The monthly time series of girdded data are subjected to harmonic and singular spectrum analyses to see the seasonal cycles and trend components that predominantly account for the variability in the dataset. Harmonic analysis (HA) of the dataset reveals that the annual cycle accounts for 50 to 80% of the seasonal rainfall variability over northwestern and western parts as well as central and southeastern highlands of Ethiopia while the semiannual cycle accounts for 40% of seasonal rainfall variability over southern part of the country. A slow varying monotonic rainfall trend is detected using singular spectrum analysis (SSA). Mann- Kendall test is applied to determine statistical significance of the detected trend. The results reveal that almost all of the grid points show either significant increasing or decreasing trend at both 95% and 99% significant levels. Spatially, parts of southern Ethiopia with a bimodal rain type and some parts of western half of the country, which has a monomodal type long rainy season, have shown decreasing rainfall trend. Southeastern highlands and most of the southeastern lowlands have also exhibited long term decline in rainfall. The north and northeastern part of the country and very localized places of western Ethiopia have exhibited an increase in rainfall for the last 30 years. The rainfall over the central rift valley areas has been deceasing whereas it has been increasing over the southern rift valley areas.

Global warming, caused by increasing temperature of the Earth is a reality. Thus, the objective is to expose and analyze the various forms of cohabitation with in the variability of climates on the river basin Bridget, plus to create an analysis of climate variability and temperature for the year 2020. The analysis area has a semiarid climate and is located in northeastern region of Brazil, in the state of Pernambuco. Recent studies have shown noticeable changes in temperature, rainfall, water resources, surface runoff and agriculture, with severe consequences for populations. However, as water is poorly distributed and the intensification of droughts will worsen the situation increasingly reaching the point of entire families will migrate to other areas. Thus, the region will become an environment highly susceptible to changes in climate. The most vulnerable area of the basin is the northern portion, where the land use occurs in a disorganized and unprepared. While the southern portion is less vulnerable because it is more likely to receive higher rainfall. Thus, living with the climate changes like for example: capturing rainwater and agroecology are required options for the northen region.