Future projections of human thermal comfort in the United Arab Emirates

Global warming is intensifying the frequency and severity of extreme heat events, significantly impacting human thermal comfort (HTC), particularly in vulnerable regions such as the United Arab Emirates (UAE). Heat waves rank among the most dangerous natural hazards, directly affecting public health and well-being. Vulnerable populations, including children, the elderly, and individuals with pre-existing health conditions, particularly face heightened risks. The UAE, classified as a hyper-arid desert region with most of its major cities located along the coast, experiences a hot and humid climate. This makes it imperative to develop robust estimates of future HTC to implement effective measures against potential adverse health outcomes.

 

This study examines past trends and future projections of human thermal comfort (HTC) in selected cities across the UAE. Two 10-year simulations were conducted: a historical run (2005–2014) driven by ERA5 reanalysis data and a Pseudo Global Warming (PGW) simulation using perturbation derived from the CMIP6 CCSM4 model under the Representative Concentration Pathway (RCP) 8.5 (business-as-usual) emission scenario. Key heat indices, Universal Thermal Climate Index (UTCI), Physiologically Equivalent Temperature (PET), wet-bulb temperature, and apparent temperature, were calculated and compared between the historical and future scenarios to evaluate changes in HTC.

 

The results reveal a substantial increase in all heat indices under future climate conditions, with UTCI showing the highest rise of over 5°C, while wet-bulb temperature exhibited the smallest increase. Heat index values were most pronounced from June to August, with Abu Dhabi recording the highest values among studied cities, followed by Dubai, Al Fujairah, and Al Ain. This research provides critical insights for developing intervention strategies to address future HTC challenges in the UAE. Future work aims to refine projections using ensemble modeling and alternative emission scenarios to reduce uncertainties.