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As urban population is forecast to exceed 60% of the world’s population by 2050, urban growth can be expected. However, research on spatial projections of urban growth at a global scale are limited. We constructed a framework to project global urban growth based on the SLEUTH urban growth model and a database with a resolution of 30 arc-seconds containing urban growth probabilities from 2020 to 2050. Using the historical distribution of the global population from LandScan TM as a proxy for urban land cover, the SLEUTH model was calibrated for the period from 2000 to 2013. This model simulates urban growth using two layers of 50 arc-minutes grids encompassing global urban regions. While varying growth rates are observed in each urban area, the global urban cover is forecast to reach 1.7 × 10 6 km 2 by 2050, which is approximately 1.4 times that of the year 2012. A global urban growth database is essential for future environmental planning and assessments, as well as numerical investigations of future urban climates.

Air temperature trends (1960-2009) based on stations in cities, minus those based on global surface temperature datasets, are defined herein as urban heat island (UHI) trends. Urban climate was examined globally by comparing UHI trends with indices of geophysical factors, including background climate, latitude, and diurnal temperature range (DTR) and indices of artificial factors, including anthropogenic heat emission (AHE) and population indices. Surprisingly, a better relationship was found between UHI trends and DTR-an integrated geophysical index representing thermal inertia-than with the indices of artificial factors. Thus, while an increase in sensible heat (mechanism 1) triggers UHI formation, this study infers that large thermal inertia (mechanism 2) contributes significantly on UHI. The correlation of UHI trends with other indices can be explained by both mechanisms.

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Urban dwellers are at risk of heat-related mortality in the onset of climate change. In this study, future changes in heat-related mortality of elderly citizens were estimated while considering the combined effects of spatially-varying megacity’s population growth, urbanization, and climate change. The target area is the Jakarta metropolitan area of Indonesia, a rapidly developing tropical country. 1.2 × 1.2 km2 daily maximum temperatures were acquired from weather model outputs for the August months from 2006 to 2015 (present 2010s) and 2046 to 2055 (future 2050s considering pseudo-global warming of RCP2.6 and RCP8.5). The weather model considers population-induced spatial changes in urban morphology and anthropogenic heating distribution. Present and future heat-related mortality was mapped out based on the simulated daily maximum temperatures. The August total number of heat-related elderly deaths in Jakarta will drastically increase by 12~15 times in the 2050s compared to 2010s because of population aging and rising daytime temperatures under “compact city” and “business-as-usual” scenarios. Meanwhile, mitigating climate change (RCP 2.6) could reduce the August elderly mortality count by up to 17.34%. The downwind areas of the densest city core and the coastal areas of Jakarta should be avoided by elderly citizens during the daytime.

Numerical weather prediction models are progressively used to downscale future climate in cities at increasing spatial resolutions. Boundary conditions representing rapidly growing urban areas are imperative to more plausible future predictions. In this work, 1-km global anthropogenic heat emission (AHE) datasets of the present and future are constructed. To improve present AHE maps, 30 arc-second VIIRS satellite imagery outputs such as nighttime lights and night-fires were incorporated along with the LandScanTM population dataset. A futuristic scenario of AHE was also developed while considering pathways of radiative forcing (i.e. representative concentration pathways), pathways of social conditions (i.e. shared socio-economic pathways), a 1-km future urbanization probability map, and a model to estimate changes in population distribution. The new dataset highlights two distinct features; (1) a more spatially-heterogeneous representation of AHE is captured compared with other recent datasets, and (2) consideration of future urban sprawls and climate change in futuristic AHE maps. Significant increases in projected AHE for multiple cities under a worst-case scenario strengthen the need for further assessment of futuristic AHE.

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