Drivers of global surface urban heat islands: Surface property, climate background, and 2D/3D urban morphologies

Local climate of the city is modulated by not only land surface properties and climate background but also urban morphology. While the modulations of the former two have been extensively studied, there is still a lack of understanding of the importance of urban morphology, especially the three-dimensional (3D) morphology at a global scale. Here we examine the surface urban heat island (SUHI) intensities in 688 urban agglomerations worldwide and explore their driving factors in terms of surface properties, climate background, and 2D/3D urban morphologies, by using stepwise multiple linear regression and interpretable machine learning method. The results show that the interpretable machine learning method performs better in modeling SUHIs than traditional linear regression. Notably, urban morphologies, particularly its 3D pattern, have significant impacts on SUHIs. The overall contribution of urban morphology is comparable to that of surface property, and even surpasses the latter during the daytime in autumn and winter. Specifically, increased urban patch size or higher urban density can significantly amplify the SUHI intensities, while urban expansion characterized by scattered or multi-centered development exhibits the potential in mitigating SUHIs. In terms of 3D urban morphology, building height emerges as a crucial factor in shaping SUHIs. A rougher urban surface characterized by high-rise buildings can yield a cooling effect during the daytime while exacerbating the warming effect at night. Overall, our research provides valuable insights into the role of urban morphology in shaping SUHIs, offering guidance for urban planning strategies that promote sustainable and resilient cities.