Projecting Future Air Temperature of Hong Kong for the 21st Century and its Implications on Urban Planning and Design

The effects of global climate change on urban environment have been widely discussed in recent decades. In particular, changes in urban climate have received much attention as they affect the living quality of urban dwellers. However, the coarse spatial scales employed in recent climate change studies were found to be insufficient in the context of urban planning and design. It leads to the lack of information on the changing urban climate and insufficient consideration of climate change in urban planning and design processes. In high-density cities like Hong Kong, the complex urban environment requires climatic data at very fine temporal resolution in order to formulate effective adaptation and mitigation strategies for future climate change.;The present study employed regression techniques to establish empirical relationship between large-scale predictor variables and local predictands in order to obtain future air temperature of urban and rural areas of Hong Kong. 40-year baseline conditions of local air temperature were obtained from both the observational and statistically extended temperature record. Monthly means of daily mean, maximum, and minimum air temperatures for both daytime and night-time were calculated for establishing statistical downscaling (SD) models to project future air temperature of urban and rural areas of Hong Kong.;The results suggest that regression-based downscaling techniques are able to capture the relationship between large-scale atmospheric conditions and station-scale meteorological parameters. The SD models performed particularly well in winter and considerably satisfactory results were obtained in spring and autumn. Night-time temperature trends generally exhibited greater increases than daytime trends. Seasonal variations were present with greatest increases observed in winter. Rural areas would likely experience greater warming than the urban areas in the future. With urbanization effect incorporated into the projected temperature series, it was found that air temperature projected for suburban stations would exceed that for the urban core. Rural warming also exhibited a higher rate than those observed in suburban and urban stations.;The present study shows that statistical downscaling approach provides a method to obtain information about future climatic conditions at local scale by using GCM outputs which are widely accepted to be useful tools to assist climate change studies. Despite of the limitations that historical climate would persist in projected climatic series, it allows a low-cost but effective measure for climate impact assessments, particularly in the context of urban planning and design, which requires extensive data for a wide range of studies. Based on the projected air temperature, implications of future climate change on urban planning and design of potential development were discussed and recommendations on potential adaptation and mitigation measures at different planning levels were also presented.