Search

Large‐scale human activity changes in megacities during Chinese New Year (CNY) are believed to significantly affect urban heat islands (UHIs). However, the urban‐rural gradient in UHI variations responsive to human activity changes in cities remains largely unclear. Using in‐situ surface air temperature obtained from a meteorological network that includes 3000‐plus stations, we show that the mean UHI intensity (UHII) in 31 Chinese capitals is 0.52 ± 0.23 K during the CNY holiday and 0.77 ± 0.29 K in the reference period, indicating a UHII reduction of 0.25 ± 0.20 K during the holiday. The reduced UHII decreased more from city core (0.54 K) to city periphery (0.071 K). We find that these UHII reductions were larger at night than during the day and were larger in northern subtropical and warm temperate climates than in other climates. These UHII reductions were mainly attributable to the decline in anthropogenic heat release. Plain Language Summary: Human activity changes during the Chinese New Year (CNY) holiday can lead to various impacts on urban climate and environment and particularly the urban heat island (UHI). However, the spatial pattern of UHI intensity (UHII) changes during the CNY holiday for cities within different background climates is still not clear. Based on in situ observations from 2017 to 2019, we find that the mean UHII during the CNY holiday is reduced by 33% (UHII of 0.52 ± 0.23 K in the CNY holiday vs. UHII of 0.77 ± 0.29 K in the reference period) over 31 capitals in China. During the CNY holiday, the UHII reduction reaches 57% (0.15 ± 0.11 K vs. 0.34 ± 0.16 K) in the day and 25% (0.90 ± 0.31 K vs. 1.20 ± 0.36 K) at night. The CNY‐holiday impact also differs greatly by climate zone and hour of day. The UHII reductions are more notable in northern subtropical and warm temperate climates than in other climates. These reductions are mainly caused by the substantial decrease in human activities during the CNY holiday. Our findings can help better understand urban climate changes during the CNY holiday. Key Points: The urban heat island intensity (UHII) decreases by −0.19 K in the day and −0.30 K at night during the Chinese New Year (CNY) holiday over 31 capitals in ChinaThe UHII reduction is more pronounced in city core (0.54 K) than in city periphery (0.071 K)The CNY‐holiday impacts on UHII differ greatly by climate zone and by hour of day [ABSTRACT FROM AUTHOR]

The significant reduction in human activities during COVID‐19 lockdown is anticipated to substantially influence urban climates, especially urban heat islands (UHIs). However, the UHI variations during lockdown periods remain to be quantified. Based on the MODIS daily land surface temperature and the in‐situ surface air temperature observations, we reveal a substantial decline in both surface and canopy UHIs over 300‐plus megacities in China during lockdown periods compared with reference periods. The surface UHI intensity (UHII) is reduced by 0.25 (one S.D. = 0.22) K in the daytime and by 0.23 (0.20) K at night during lockdown periods. The reductions in canopy UHII reach 0.42 (one S.D. = 0.26) K in the daytime and 0.39 (0.29) K at night. These reductions are mainly due to the near‐unprecedented drop in human activities induced by strict lockdown measures. Our results provide an improved understanding of the urban climate variations during the global pandemic. Plain Language Summary: The outbreak of COVID‐19 has destabilized the global economy and resulted in various impacts on the climate and environment. However, the changes in urban climate during lockdown periods, and particularly in urban heat islands (UHIs), remain largely not known. Using satellite and ground‐based measurements, we find that the surface UHI intensity (UHII) during lockdown periods is reduced by 25% in the daytime and by 20% at night. The reductions in canopy UHII are 36% and 42% during the daytime and nighttime, respectively. These reductions almost certainly result from the significant decrease in human activities induced by strict lockdown measures. Our results provide a fresh perspective of understanding urban climate changes during COVID‐19 lockdown periods. Key Points: Reduced human activities during COVID‐19 lockdown cause a significant decline in both surface and canopy urban heat islands (UHI) intensity over ChinaSurface (canopy) UHI intensity is reduced by 0.25 K (0.42 K) and 0.23 K (0.39 K) on average during the day and night, respectivelyImpacts of COVID‐19 lockdown UHI intensity depend on hour of day [ABSTRACT FROM AUTHOR]

• SUHI and SUCI are both augmented in heat wave (HW) periods. • Augmentation of SUHI and SUCI is regulated by rural vegetation coverage. • SUHIs in north subtropical climate exhibit the largest augmentation. • SUHIs are strengthened with an intensified HW intensity in most climate zones. The impact of heat waves (HWs) on surface urban heat islands (SUHIs) has been widely studied, but the spatial pattern of SUHI responsiveness to HWs across various climates remains unclear, and the influence of HW intensity on SUHI responsiveness has not been systematically quantified. Using MODIS land surface temperature data, here we investigated the responsiveness of SUHI to HWs (quantified as ∆ I) as well as its variations with HW intensity in 354 cities in seven climate zones across China. We find that during HW periods, the SUHI and surface urban cool island are augmented in the humid and arid regions of China, respectively. The inter-city heterogeneity in rural vegetation coverage accounts for such a spatial pattern. In eastern China, the ∆ I peaks in the north subtropical climate (0.72 ± 0.54 K for daytime and 0.29 ± 0.23 K for the nighttime) probably for its specific rural farming method. With the intensification of HWs, the augmentation effect can be further enhanced for the north subtropical, warm temperate, and arid temperate climates during the day and for almost all the climates at night. These findings can help advance the understanding of the responsiveness of SUHI to extreme climatic events. [ABSTRACT FROM AUTHOR]

Separate impacts of building and tree on the urban thermal environment have been studied extensively, but their combined impacts, especially from both the horizontal (i.e., two-dimensional (2D)) and vertical (i.e., three-dimensional (3D)) perspectives remain largely unclear. Based on satellite thermal data and elaborate 2D and 3D urban morphology, herein we simultaneously investigate the separate and combined impacts of building and tree over Nanjing in China from both the 2D and 3D perspectives. We further examine the day–night contrast together with the sensitivity of such impacts to scale. Our results show that, when compared with urban structures from a single dimension, the combination of 2D and 3D structures is more capable of predicting urban land surface temperatures (LSTs) for both day and night. The assessments further illustrate that the separate and combined impacts of building and tree on LSTs are usually more significant when the spatial scale increases. As for the separate impacts of building and tree, 2D structure affects more urban thermal environment than 3D structure at all spatial scales during the day, but an opposite trend occurs at night. Moreover, for the combined impact of building and tree on LST across different scales, daytime and nighttime LSTs are respectively dominated by 2D and 3D building structures. Combining 2D and 3D structures improves the explained LST variation by 7.3%–11.1% and 25.3%–37.7% for day and night, respectively when compared to using 2D structures only. These findings emphasize the need to incorporate both 2D and 3D urban morphology to improve the urban thermal environment. [Display omitted] • Separate and combined impacts of building and tree on LST are analyzed from both 2D and 3D perspectives. • Sensitivity of the relative importance of 2D and 3D structures on LST to scale is examined. • Combining 2D and 3D structures better predicts LST than features of a single dimension. • 2D building structure affects more LST than 3D building structure during the day, but opposite at night. [ABSTRACT FROM AUTHOR]

Urban vegetation, a harbinger of future global vegetation change, is controlled by complex urban environments. The urban-rural gradient in vegetation greenness trends and their responses to biogeochemical drivers (e.g. elevated atmospheric CO 2 concentration and climate warming) and land-cover changes, however, remain unclear. Here we used satellite-derived enhanced vegetation index to examine the greenness trends for 1500-plus cities in China for 2000–2019. We developed a conceptual framework to differentiate between the contributions of four key drivers to the greenness trends: two biogeochemical drivers, a background biogeochemical driver (BBD) and an urban biogeochemical driver (UBD), and two drivers of land-cover changes, urban expansion or densification (UED) and urban green recovery (UGR). We find that the greening trends gradually decreased from urban cores to urban new towns and then to browning trends in urban fringes. The significant greening in urban cores was mainly contributed by BBD (25.6%) and UBD (52.3%). While the minor greening in urban new towns was contributed by both BBD (33.1%) and UBD (24.1%) and weakened by UED (−39.7%). The UED (−64.4%) dominated the browning in urban fringes. These results suggest that biogeochemical drivers and land-cover changes jointly regulated the urban-rural gradient in greenness trends, which contributes to the assessment of future global vegetation change driven by complex environmental changes. • The urban-rural gradient in greenness trends and its drivers were analyzed. • The urban-rural gradient in greenness trends is vividly featured by a 'V-shape'. • Biogeochemical drivers and land-cover changes jointly regulate the 'V-shape'. • Urban biogeochemical drivers lead to the significant greening in urban cores. • Urban expansion or densification dominates the browning in urban fringes. [ABSTRACT FROM AUTHOR]