Global patterns and determinants of year-to-year variations in surface urban heat islands

Abstract

Investigations on year-to-year variations in surface urban heat island intensity (ΔI_s, the change in urban heat island intensity between consecutive years) are crucial for capturing the dynamics of urban climates at mid-term scales. While the patterns and underlying drivers of I_s have been extensively studied, their year-to-year variability remains poorly understood, especially across global cities. Using MODIS land surface temperature observations from March 2003 to February 2024, here we examined the spatiotemporal patterns of ΔI_s across 1,642 cities worldwide, by removing the interannual component from yearly I_s observations. We also analyzed the impacts from various background climate and urban surface property factors on these patterns. Additionally, we simulated the ΔI_s by integrating the advanced Light Gradient Boosting Machine (LightGBM) model with various controlling factors. Our analysis yielded three key findings: (1) The global mean absolute ΔI_s (i.e., ΔI_{s_mean}) was 0.30 ± 0.02 K (mean ± S.D.) during the day and 0.18 ± 0.01 K at night, accounting for approximately 19.40 % and 13.57 % of overall I_s observations. Spatially, both daytime and nighttime ΔI_{s_mean} were notably higher in snow climates compared to equatorial, arid, and warm climates. (2) In terms of controlling factors, global daytime ΔI_{s_mean} showed strong negative correlations with year-to-year variations in both urban–rural EVI contrast (r = −0.69, p < 0.01) and background surface air temperature (r = −0.62, p < 0.01). By comparison, these correlations became less significant at night. (3) The LightGBM model demonstrated high accuracy in estimating the ΔI_s across global cities, with r values exceeding 0.96 and MAE values below 0.09 K for both daytime and nighttime. These findings are critical for enriching our understanding of urban heat island patterns at multiple temporal scales. They also provide an efficient approach for identifying abrupt urban climate changes due to extreme climate events or anthropogenic activities.