Lower Urban Humidity Moderates Outdoor Heat Stress

Abstract

Surface temperature is often used to examine heat exposure in multi‐city studies and for informing urban heat mitigation efforts due to scarcity of urban air temperature measurements. Cities also have lower relative humidity, traditionally not accounted for in large‐scale observational urban heat risk assessments. Here, using crowdsourced measurements from over 40,000 weather stations in ≈600 urban clusters in Europe, we show the moderating effect of this urbanization‐induced humidity reduction on outdoor heat stress during the 2019 heatwave. We demonstrate that daytime differences in heat index between urban clusters and their surroundings are weak, and associations of this urban‐rural difference with background climate, generally examined from the surface temperature perspective, are diminished due to moisture feedbacks. We also examine the spatial variability of surface temperature, air temperature, and heat index within these clusters—relevant for detecting hotspots and potential disparities in heat exposure—and find that surface temperature is a poor proxy for the intra‐urban distribution of heat index during daytime. Finally, urban vegetation shows much weaker (∼1/6th as strong) associations with heat index than with surface temperature, which has broad implications for optimizing urban heat stress mitigation strategies. These findings are valid for operational metrics of heat stress for shaded conditions (apparent temperature and humidex), thermodynamic proxies (wet‐bulb temperature), and empirical heat indices. Based on this large‐scale empirical evidence, surface temperature, used due to the lack of better alternatives, may not be suitable for accurately informing heat mitigation strategies within and across cities, necessitating more urban‐scale observations and better urban‐resolving models.

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

“…Moreover, it is also one of the first to use these methods to predict the spatial variability of RH, and thus estimates of ambient moist heat stress. We find that, when combined with other ancillary information, satellite-derived LST can be a strong predictor of ambient air temperature, even though using LST directly as a proxy for urban heat exposure may be misleading (Chakraborty et al 2022, Turner et al 2022. Overall, since our ML model identified LST from both the Landsat two-month average and the closest time period as being the variables contributing the most predictive power to our model, this means that the air temperature variability is embedded within LST variability, as reflected in the feature importance scores (figure 2).…”

“…Studies have found positive correlation between satellite-derived LST and air temperature (Mildrexler et al 2011, Zhang et al 2014, particularly during nighttime (Chakraborty et al 2022), and LST data have been widely used as a proxy for urban heat exposure in the past (Chakraborty et al 2019, Manoli et al 2019, Hoffman et al 2020, Hsu et al 2021, McDonald et al 2021, Mentaschi et al 2022. In this study, the LST data from satellite sensors Landsat 8 and MODIS were examined, since both data sources have their advantages and trade-offs.…”

Unequal exposure to heatwaves in Los Angeles: Impact of uneven green spaces

“…Hence, fine-scale LST observations can still provide valuable insights into intracity temperature gradients and complexities at detailed levels that are difficult to achieve with meteorological temperature data alone. Moving forward, more urban-scale in situ observations of air temperature and other relevant meteorological variables are urgently needed to better understand urban heat exposure ( 26 ).…”

Comparing Approximated Heat Stress Measures Across the United States

“…Additionally, different data processing techniques applied to this study compared to the previous studies might have resulted in different outcomes. t is challenging to explain heat measures' relationship with the individual variable comparison results directly since the effect of RH on HI is nonlinear according to Ta increase, and WBGT is the weighted sum of natural wet bulb temperature, black globe temperature, and air temperature (Chakraborty et al, 2022).…”