Thursday, 16 January 2020: 3:30 PM
104B (Boston Convention and Exhibition Center)
The interaction of greenhouse gas (GHG) climate change, local urban climate effects, population growth, and adaptation will control the US population exposure to hot and cold extremes during the 21st century. We use regional climate simulations to account for these factors by dynamically downscaling global climate projections to 20 km resolution across the Continental US. We quantify changes in population exposure to thermal extremes in 47 US metropolitan areas during the 21st century. The average proportion of US urban population potentially exposed to locally defined hot conditions throughout the summer will increase from 3.7% for the first decade of the 21st century to 17.6–39.8% by the year 2100. The largest relative change in summer metro-area exposure is projected across cities in the US Sunbelt. The total population exposure in person hours (i.e., population × number of hot hours) will increase from 66 billion person hours per year at the start of century to 139–234 billion by the end of century with the largest absolute change in the New York/Newark/Long Island metro-area (27 billion extra person hours per year added). High-intensity implementation of infrastructure adaptation strategies, including cool and green roofs and street trees, will reduce US average end of century summer exposure from 17.6–39.8% to 3.6–17.7%, largely offsetting the future impacts of GHG-induced climate warming on population exposure in many cities. Our findings will be of particular interest to planners and policy-makers; we identify specific metro-areas where population exposure will increase most distinctly and quantify where commonly used infrastructure adaptation measures will be most (in)effective at reducing population exposure to extreme temperatures. Our results highlight the potential for cities with similar characteristics and growth trajectories to collaborate with each other and maximize the efficacy of infrastructure adaptation proposals.
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