Urbanization-induced Earth's surface energy alteration and warming: A global spatiotemporal analysis

REMOTE SENSING OF ENVIRONMENT(2023)

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摘要
As both drivers and first responders, urban areas are rapidly increasing in importance of shaping global climate change. The global imprint of urbanization on surface energy balance (SEB) remains, however, largely unknown. Here, we undertake a global spatiotemporal analysis on urbanization-induced Earth's surface energy alteration using annually dynamic maps of impervious surface and satellite-derived surface energy fluxes and land surface temperature (LST), alongside space-for-time (spatial) and time-for-time (temporal) approaches. Relying on space-for-time substitution, we estimate that global urbanization-driven surface warming of annual mean temperature has reached 0.054 degrees C (95% CI, 0.009-0.214 degrees C) between 2003 and 2018 (with temporal stability), especially pronounced in summer daytime (0.122 degrees C, -0.038-0.495 degrees C), largely attributed to decline in local latent heat cooling, enlarged long-wave heat dissipation and anthropogenic heat. Temporal quantification of urbanization effects demonstrates consistence with spatial gradient approach, implying that space can substitute time in understanding and predicting future urbanization imprint on SEB and temperature (i.e., cities as harbingers of climate change). We further predict that during the first 35 years of this century annual warming magnitude will be nearly double that of the period 1985-2018, and particularly, urbanization perturbation to SEB will be more intense across countries or regions in the arid and warm temperate climates under global warming. This research provides science-based foundation that can help inform the IPCC special report on cities and climate change, and emphasize urgency to develop tailored mitigation and adaptation strategies against rapidly warming based on SEB attribution and climate background.
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关键词
Urbanization effects,Surface energy balance,Surface warming,Space-for-time substitution,Temporal analysis,Future prediction,Climate background
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