A Novel Technique for the Spatially and Automatic Estimation of Urban Heat Island from High-Resolution Climate Simulations

Natural areas, often referred to as rural areas, are characterized by minimal or non-existent urbanization. They serve as crucial benchmarks for impartial comparisons with urban environments. Urbanization leads to significant increases in surface and air temperatures in urban areas compared to their rural surroundings, a phenomenon known as the urban heat island (UHI) effect. This study employs high-resolution climate simulations conducted with the regional climate model CNRM-AROME46t1 (at 2.5 km resolution) to develop an innovative, fully automated method for spatializing UHIs. The CNRM-AROME46t1 model incorporates a land cover classification (ECOCLIMAP) derived from high-resolution Corine Land Cover data. This land cover mask enables the extraction of urban areas without requiring prior detailed knowledge of the region or predefined climate zones. The methodology developed, referred to as the M_Method, has been applied to a domain covering metropolitan France, encompassing urban areas of varying sizes, including the highly dense Paris metropolitan area and the medium-sized city of Dijon. These two urban centers, where local weather station data are available, were used to evaluate the automatic UHI spatialization approach. Results indicate the presence of thermal biases between the CNRM-AROME46t1 simulations and observational data. However, the application of the M_Method successfully identified and mapped areas most vulnerable to UHI effects with high accuracy. This methodology holds significant potential for future climate projections and scenario analyses, offering critical insights into key climate indicators. The spatialized diagnostics generated by this approach serve as a valuable strategic resource, enabling decision-makers to assess UHI conditions across metropolitan France and prioritize policies aimed at mitigating climate change impacts. Furthermore, the framework is designed to be adaptable to other geographical and climatic contexts and can be extended to analyze additional climate variables, such as the urban-rural moisture anomaly.