GC056-0011
Who’s carrying the burden of urban heat waves?

Thursday, 10 December 2020
Poster
Susanne Benz, University of California San Diego, School of Global Policy and Strategy, La Jolla, CA, United States and Jennifer Burney, University of California, San Diego, School of Global Policy and Strategy, La Jolla, United States
Abstract:
Due to a disturbed urban energy balance, temperatures in cities are typically elevated compared to their rural surrounding. Their intensity is primarily governed by building density and height, which trap radiation in the urban street canyons, and by vegetation or a lack thereof, which essentially reduces evapotranspiration and latent heat. Recently, we have developed a global, 1 × 1 km resolution data set of surface temperature anomalies and found that urban summer daytime temperatures are on average +3.2 °C warmer than their rural surrounding. However, this number varies greatly; both globally, where values range from -4.0 to +9.2°C, and within a single city, where the average standard deviation of urban temperature anomalies is 0.63°C. These elevated temperatures have dire consequences for the local population: they increase vulnerability to heat-related morbidity and mortality and affect efficiency and overall quality of life.

Environmental Justice describes the notion that no one should face inequitable environmental burdens. While this is being studied for air pollution, the impact that urban heat has on different communities has so far gone under-reported, particularly in large scale studies. Here we combine our global data set of temperature anomalies with census data from the US and European Union to assess the extent in which different disadvantaged communities are exposed to urban heat and how these disadvantages compare for different states and countries. For the US we find income to be a primary indicator of urban heat, with the poorest quartile of census tracts experiencing 1.5°C hotter summer heat waves than the richest quartile (average of all states). By controlling for local background conditions (e.g. weather), population density and income we are not only able to identify underlying inequalities but are furthermore able to link these findings back to proxies of the surface energy balance and determine how these disadvantages could best be mitigated.