Analysis of the Daytime Urban Heat Island Mechanism in East China

Abstract

Urban heat islands (UHIs) are one of the most typical changes to surface climate induced by human activities, which manifest as higher temperatures in urban areas than in their surroundings. In this study, we synthetically analyzed spatiotemporal features of daytime surface UHI intensity in east China over the period from 2009 to 2018 carried out with several offline simulations coupled with four land‐use data sets, and identified the causes of UHIs with two attribution methods. The results indicate that spatial variations of summer daytime UHI intensity in east China are dominated by variations in the efficiency of evaporating water (∼3°C) between urban and rural areas. Geographic variations of winter UHI intensity mainly depend on the urban‐rural contrast of heat storage capacity (∼1°C). Additionally, we found that forest‐covered rural areas have an extremely higher convection efficiency than urban areas and nonforest rural landscapes, resulting in a remarkable contribution to summer daytime UHI intensity (∼6°C). However, rural areas largely comprising croplands and grasslands evaporate water more efficiently, expanding summer daytime UHI intensity by approximately 3°C. The prominent capacity of heat storage in nonforest vegetation rural areas accounts for 1°C warming of winter daytime UHI intensity. The major discrepancy between the two attribution methods lies in the evapotranspiration performances of forest‐covered rural lands. Furthermore, the inter‐comparison of several modeling experiments for land‐use data sets suggests that the disparity in UHI intensity could increase by up to 0.4°C, which may exceed 1°C at the local scale.

Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models

“…Along with the EASM weakening, significant surface warming occurs over most of eastern China in both models, especially in the concentrated urbanization areas (Figures 5a and 5d). As indicated in previous studies (C. Li & Zhang, 2021; D. Li et al., 2019), the urban thermal forcing is mainly related to the decreased evaporation capability of urban impervious surfaces, which reduces surface latent heat flux but enhances sensible heat flux (Figures S7a–S7d in Supporting Information ). With combined near‐surface air temperature and moisture changes, the near‐surface equivalent potential temperature from the atmospheric energetic perspective (Song et al., 2022) decreases in the concentrated urbanization areas (Figures S7e and S7f in Supporting Information ).…”

Modeling Urban Heat Islands and Thermal Comfort During a Heat Wave Event in East China With CLM5 Incorporating Local Climate Zones

“…The surface energy balance equation expressed as: $$$Rn+{Q}_{ah}=H+LE+{Q}_{s}$$$ where $$Rn={S}_{{\downarrow}}+{L}_{{\downarrow}}-{S}_{{\uparrow}}-{L}_{{\uparrow}}$$ is the net all‐wave radiation, $${S}_{{\downarrow}}$$ and $${S}_{{\uparrow}}$$ denote the downward and upward shortwave radiation, respectively, $${L}_{{\downarrow}}$$ and $${L}_{{\uparrow}}$$ represent downward and upward longwave radiation, respectively, and $${Q}_{ah}$$, $$H$$, $$LE$$, and $${Q}_{s}$$ denote anthropogenic heat flux, sensible heat flux, latent heat flux, and heat storage, respectively. As demonstrated in Li and Zhang (2021), both two methods indicated the dominant factor of spatial variations of UHI intensity was the urban‐rural contrast of evaporation efficiency, which was modulated by a rural landscape. Therefore, in this study, the IBM method was utilized to estimate intra‐urban variability of attributing factors of SUHII, and the impacts of HW intensity and local climate on dominant factors.…”

“…At daytime, the spatial variations of SUHII are dominated by $${\increment}\beta $$ in the arid northern region (e.g., 3.4$$^{\circ}\mathrm{C}$$ in the WT climate zone) and $${\increment}{r}_{a}$$ in the humid southern region (e.g., 3.5$$^{\circ}\mathrm{C}$$ in the MS climate zone) during a HW period (Figure 18). It could be explained by the changes in climate‐rural vegetation control on SUHII over different climate zones (Li & Zhang, 2021). $${\increment}{Q}_{s}$$ and $${\increment}Rn$$ weakens SUHII, and this effect is −2.1$$^{\circ}\mathrm{C}$$ for $${\increment}{Q}_{s}$$ in the MS climate zone and −1.2$$^{\circ}\mathrm{C}$$ for $${\increment}Rn$$ in the WT climate zone.…”

“…Spatial variations of UHI intensity were modulated by rural background (Li & Zhang, 2021; Zhou et al., 2016), of which vegetation activities were mainly controlled by background climate. Therefore, we utilized several climate zones to represent different local climates in East China.…”

Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

“…However, these indices only evaluate one aspect of the environment and often reflect one-sided ecological conditions. For example, the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are used to reflect the growth status of plants in many study areas 18 – 20 , as well as the distribution of urbanized land 21 , 22 , while the land surface temperature (LST) has been used to study the effects of the urban thermal environment 23 – 25 . To cope with these challenges, Xu proposed a remote sensing ecological index (RSEI) that integrates the four indicators of greenness, dryness, humidity, and heat and extracts a single variable that can represent multiple variables without artificially setting the weight 26 .…”