Impacts of climatic zones on urban heat island: Spatiotemporal variations, trends, and drivers in China from 2001–2020
Urban heat island (UHI) adversely impacts the human-earth system. However, the impact of
climatic zones on UHI remains unclear. This study, therefore, aimed at investigating how
climatic zones affect spatiotemporal variations, trends and potential drivers of surface UHI
intensity (SUHII) based upon 253 Chinese cities in five climatic zones: middle temperate
zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical
zone (MSZ), and south subtropical zone (SSZ) during 2001–2020. The results showed that …
climatic zones on UHI remains unclear. This study, therefore, aimed at investigating how
climatic zones affect spatiotemporal variations, trends and potential drivers of surface UHI
intensity (SUHII) based upon 253 Chinese cities in five climatic zones: middle temperate
zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical
zone (MSZ), and south subtropical zone (SSZ) during 2001–2020. The results showed that …
Abstract
Urban heat island (UHI) adversely impacts the human-earth system. However, the impact of climatic zones on UHI remains unclear. This study, therefore, aimed at investigating how climatic zones affect spatiotemporal variations, trends and potential drivers of surface UHI intensity (SUHII) based upon 253 Chinese cities in five climatic zones: middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ) during 2001–2020. The results showed that the SUHII ranged from -2.59 to 6.20 °C, average daytime SUHII showed higher seasonal fluctuations and larger variation than nighttime and summer daytime had the highest seasonal and annual SUHII variation (SUHIISAV). Cities in NSZ, MSZ, and SSZ had high average daytime SUHIISAV and a large proportion had significant increasing trends (TrendSI) (P<0.05) from 2001–2020, while the nighttime showed opposite rule in general. Random forest (RF) model explained 68% of day/night SUHIISAV on average. Except for city area, which greatly impacted day/night SUHIISAV in all periods and climatic zones, other drivers more significantly influenced SUHII in certain periods and climatic zones, e.g. aerosol optical depth (AOD) in winter nighttime in MTZ and STZ. Compared with single or serval drivers’ determination of UHI effect, we highlighted the multi-factor driven of day/night SUHIISAV. Specific suggestions, e.g. controlling haze pollution can bring co-benefits on urban air quality and UHI mitigation were proposed. These findings could help to provide valuable reference for future climatic adaptive strategy.
Elsevier
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