In recent decades, changes in climate have caused impacts on natural and human systems on all continents and across the oceans (Stocker 2014). The United Arab Emirates (UAE) and the Arabian Gulf region are no exception. The UAE, covering an area of 83000 Km2 5 , with extended coastlines and small islands, is consequently more prone to the impact of climate change, especially that it is also undergoing rapid growth and huge urban developments (Yearbook 2010). In Abu Dhabi, for instance, by 2050, average temperature will increase by around 2.5 degrees C, while rainfall will change by between -21.2% and +10.3% using General circulation Models or GCMs (Dougherty et al. 2009). The urgency of climate adaptations and mitigations require the use of these types of models to predict the impact of climate change (Merryeld et al. 2013). GCMs are known to produce hypothesis based predictions relying on well established and demonstrated physical principles (Randall et al. 2007). In order for any climate models to be realistic and applicable, at regional as well as global scales, some type of downscaling is necessary. Statistically based downscaling approaches (referred to as SDSM) of climate data have been around for more than a decade (Wilby and Dawson 2013).
In the present attempt, therefore, data from MarksimGCM is used to (1) assess the different Representative Concentration Pathways (RCPs) which are best compatible with the UAE future temperature and rainfall scenarios, and (2) pinpoint specific UAE regions and ecosystems where most likely climate change, as it relates to both temperature and rainfall abnormalities, will occur.
Results/Conclusions
The results of this study show a strong correlation between the present maximum temperature (Tmax) and Tmax2020, Tmax2040, Tmax2060, Tmax 2080 and Tmax 2095 for both RCP4.5 and RCP8.5. These two RCPs predict mean global warming of 1.4 and 2.0 Degrees C; respectively. The correlation coefficients ranged between 96.8% and 99.6%; which indicate very high correlations between the present maximum temperature and the projected maximum temperatures. The projected Tmax of 2020 RCP4.5, 2080 RCP4.5, 2020 RCP8.5 and 2060 RCP8.5 showed the highest correlation coefficients.