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1. Urban Heat Islands during Heat Waves: A Comparative Study between Boston and Phoenix

   https://doi.org/10.1175/JAMC-D-20-0132.1  

   Wang, Liang ; Li, Dan ( May 2021 , Journal of Applied Meteorology and Climatology)

   null (Ed.)

   Abstract In this study, we simulate the magnitude of urban heat islands (UHIs) during heat wave (HWs) in two cities with contrasting climates (Boston, Massachusetts, and Phoenix, Arizona) using the Weather Research and Forecasting (WRF) Model and quantify their drivers with a newly developed attribution method. During the daytime, a surface UHI (SUHI) is found in Boston, which is mainly caused by the higher urban surface resistance that reduces the latent heat flux and the higher urban aerodynamic resistance r a that inhibits convective heat transfer between the urban surface and the lower atmosphere. In contrast, a daytime surface urban cool island is found in Phoenix, which is mainly due to the lower urban r a that facilitates convective heat transfer. In terms of near-surface air UHI (AUHI), there is almost no daytime AUHI in either city. At night, an SUHI and an AUHI are identified in Boston that are due to the stronger release of heat storage in urban areas. In comparison, the lower urban r a in Phoenix enhances convective heat transfer from the atmosphere to the urban surface at night, leading to a positive SUHI but no AUHI. Our study highlights that the magnitude of UHIs or urban cool islands is strongly controlled by urban–rural differences in terms of aerodynamic features, vegetation and moisture conditions, and heat storage, which show contrasting characteristics in different regions. 

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2. Attributing the Urban–Rural Contrast of Heat Stress Simulated by a Global Model

   https://doi.org/10.1175/JCLI-D-22-0436.1  

   Qin, Yue ; Liao, Weilin ; Li, Dan ( March 2023 , Journal of Climate)

   Abstract The two-resistance mechanism (TRM) attribution method, which was designed to analyze the urban–rural contrast of temperature, is improved to study the urban–rural contrast of heat stress. The improved method can be applied to diagnosing any heat stress index that is a function of temperature and humidity. As an example, in this study we use it to analyze the summertime urban–rural contrast of simplified wet bulb globe temperature (SWBGT) simulated by the Geophysical Fluid Dynamics Laboratory land model coupled with an urban canopy model. We find that the urban–rural contrast of SWBGT is primarily caused by the lack of evapotranspiration in urban areas during the daytime and the release of heat storage during the nighttime, with the urban–rural differences in aerodynamic features playing either positive or negative roles depending on the background climate. Compared to the magnitude of the urban–rural contrast of temperature, the magnitude of the urban–rural contrast of SWBGT is damped due to the moisture deficits in urban areas. We further find that the urban–rural contrast of 2-m air temperature/SWBGT is fundamentally different from that of canopy air temperature/SWBGT. Turbulent mixing in the surface layer leads to much smaller urban–rural contrasts of 2-m air temperature/SWBGT than their canopy air counterparts. Significance Statement Heat leads to serious public health concerns, but urban and rural areas have different levels of heat stress. Our study explains the magnitude and pattern of the simulated urban–rural contrast in heat stress at the global scale and improves an attribution method to quantify which biophysical processes are mostly responsible for the simulated urban–rural contrast in heat stress. We highlight two well-known causes of higher heat stress in cities: the lack of evapotranspiration and the stronger release of heat storage. Meanwhile, we draw attention to the vegetation types in rural areas, which determine the urban–rural difference in surface roughness and significantly affect the urban–rural difference in heat stress. Last, we find the urban–rural contrasts of 2-m air temperature/SWBGT are largely reduced relative to their canopy air counterparts due to the turbulent mixing effect. 

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3. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

   https://doi.org/10.3390/rs11010048  

   Zhou, Decheng ; Xiao, Jingfeng ; Bonafoni, Stefania ; Berger, Christian ; Deilami, Kaveh ; Zhou, Yuyu ; Frolking, Steve ; Yao, Rui ; Qiao, Zhi ; Sobrino, José ( January 2019 , Remote Sensing)

   The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling. 

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4. Estimating Heat‐Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave

   https://doi.org/10.1029/2021GH000535  

   Chen, Kaiyu ; Newman, Andrew J. ; Huang, Mengjiao ; Coon, Colton ; Darrow, Lyndsey A. ; Strickland, Matthew J. ; Holmes, Heather A. ( January 2022 , GeoHealth)

   Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts.

    

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5. Seasonal hysteresis of surface urban heat islands

   https://doi.org/10.1073/pnas.1917554117  

   Manoli, Gabriele ; Fatichi, Simone ; Bou-Zeid, Elie ; Katul, Gabriel G. ( March 2020 , Proceedings of the National Academy of Sciences)

   Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences ($\mathrm{\Delta }{T}_s$) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of$\mathrm{\Delta }{T}_s$. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce$\mathrm{\Delta }{T}_s$during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation.

    

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