A wedge strategy for mitigation of urban warming in future climate scenarios

Zhao, Lei; Lee, Xuhui; Schultz, Natalie M.

doi:10.5194/acp-17-9067-2017

Cited by 45 publications

(35 citation statements)

References 51 publications

(100 reference statements)

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“…This indicates negative feedbacks between UHI 2m and HWs in a future warmer world. Although our analysis and previous studies ( Oleson 2012, Zhao et al 2017 have shown that different RCP scenarios have different impacts on the UHI, we find only slight difference in UHI 2m -HW interactions between the two climate scenarios (figure 3(a)). The reason might lie in the calculation of the synergistic effects of HW and UHI.…”

Section: Interactions Between Uhi 2m and Hwscontrasting

confidence: 90%

“…We computed UHI 2m (ΔT 2m ) and UHI s (ΔT s ) from the variables generated by the urban and rural sub-grids in the grid cells where our selected cities are located (please see the supplementary information for more details). Although the UHI 2m and UHI s differ in various aspects and their magnitudes are not directly comparable (Arnfield 2003), the two UHIs show consistency in terms of the linear supposition property of their biophysical contributions (Zhao et al 2017). One merit of UHI s compared to UHI 2m is that the former has a firm and yet simple theoretical basis derived from the surface energy balance principle that underpins the biophysical factorization (described in the next section).…”

Section: Uhi Analysismentioning

confidence: 99%

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Interactions between urban heat islands and heat waves

Zhao

Oppenheimer

Zhu

et al. 2018

Environ. Res. Lett.

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301

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Heat waves (HWs) are among the most damaging climate extremes to human society. Climate models consistently project that HW frequency, severity, and duration will increase markedly over this century. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs. Here we use a climate model to investigate the interactions between the UHI and HWs in 50 cities in the United States under current climate and future warming scenarios. We examine UHI 2m (defined as urban-rural difference in 2m-height air temperature) and UHI s (defined as urban-rural difference in radiative surface temperature). Our results show significant sensitivity of the interaction between UHI and HWs to local background climate and warming scenarios. Sensitivity also differs between daytime and nighttime. During daytime, cities in the temperate climate region show significant synergistic effects between UHI and HWs in current climate, with an average of 0.4 K higher UHI 2m or 2.8 K higher UHI s during HWs than during normal days. These synergistic effects, however, diminish in future warmer climates. In contrast, the daytime synergistic effects for cities in dry regions are insignificant in the current climate, but emerge in future climates. At night, the synergistic effects are similar across climate regions in the current climate, and are stronger in future climate scenarios. We use a biophysical factorization method to disentangle the mechanisms behind the interactions between UHI and HWs that explain the spatial-temporal patterns of the interactions. Results show that the difference in the increase of urban versus rural evaporation and enhanced anthropogenic heat emissions (air conditioning energy use) during HWs are key contributors to the synergistic effects during daytime. The contrast in water availability between urban and rural land plays an important role in determining the contribution of evaporation. At night, the enhanced release of stored and anthropogenic heat during HWs are the primary contributors to the synergistic effects.

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Section: Interactions Between Uhi 2m and Hwscontrasting

confidence: 90%

Section: Uhi Analysismentioning

confidence: 99%

Interactions between urban heat islands and heat waves

Zhao

Oppenheimer

Zhu

et al. 2018

Environ. Res. Lett.

Self Cite

301

159

View full text Add to dashboard Cite

show abstract

“…UHI mitigation strategies may need to be targeted at populations that have the greatest need due to their disproportionate exposure or low capacity to cope with the associated health effects. There is evidence that combining multiple UHI mitigation strategies can effectively negate the localized warming due to both UHI and climate change (Zhao et al 2017). In an increasingly urbanized future, intertwining different UHI mitigation strategies can potentially transform cities into havens of climate adaptation, since city-level policy making can bypass constraints often present at other government levels (Hoffmann 2011, Hsu et al 2015.…”

Section: Discussionmentioning

confidence: 99%

“…While many others factors, like anthropogenic heat flux, longwave trapping by urban canyons, aerosols, etc can modulate the UHI (Taha 1997, Zhao et al 2014, Li et al 2018, two of the factors considered in this study account for the vast majority of UHI mitigation measures; green roofs and green spaces modulate NDVI, while white roofs and reflective pavements alter α (Rizwan et al 2008, Zhao et al 2017. The third factor, NDBI, is the UHI's most direct cause since it is a proxy for the degree of urbanization.…”

Section: Data Processingmentioning

confidence: 92%

Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective

Chakraborty

Hsu

Manya

et al. 2019

Environ. Res. Lett.

158

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A growing literature documents the effects of heat stress on premature mortality and other adverse health outcomes. Urban heat islands (UHI) can exacerbate these adverse impacts in cities by amplifying heat exposure during the day and inhibiting the body's ability to recover at night. Since the UHI intensity varies not only across, but also within cities, intra-city variation may lead to differential impact of urban heat stress on different demographic groups. To examine these differential impacts, we combine satellite observations with census data to evaluate the relationship between distributions of both UHI and income at the neighborhood scale for 25 cities around the world. We find that in most (72%) cases, poorer neighborhoods experience elevated heat exposure, an incidental consequence of the intra-city distribution of income in cities. This finding suggests that policymakers should consider designing city-specific UHI reduction strategies to mitigate its impacts on the most socioeconomically vulnerable populations who may be less equipped to adapt to environmental stressors. Since the strongest contributor of intra-urban UHI variability among the physical characteristics considered in this study is a neighborhood's vegetation density, increasing green space in lower income neighborhoods is one strategy urban policymakers can adopt to ameliorate some of UHI's inequitable burden on economically disadvantaged residents.

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“…Such measurements may be especially useful in situations where monitoring with conventional radiometers is not feasible. For example, white roofs are proposed as a strategy to mitigate the urban heat island in the city landscape [41]. However, white paint on building roofs can suffer from erosion and dust deposition, and thus, its albedo can quickly decrease, from the original high values of 0.7 to 0.8 to values of 0.2 to 0.3 after a few years [42].…”

Section: Potential Applicationsmentioning

confidence: 99%

Measuring Landscape Albedo Using Unmanned Aerial Vehicles

et al. 2018

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Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation.

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A wedge strategy for mitigation of urban warming in future climate scenarios

Cited by 45 publications

References 51 publications

Interactions between urban heat islands and heat waves

Interactions between urban heat islands and heat waves

Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective

Measuring Landscape Albedo Using Unmanned Aerial Vehicles

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