TJ8.2 Communicating Projections of Weather and Climate Extremes for Urban Decision-Makers

Wednesday, 10 January 2018: 1:45 PM
Ballroom F (ACC) (Austin, Texas)
Andrew P. Ballinger, North Carolina State Univ., Asheville, NC; and K. E. Kunkel

The Urban Resilience to Extremes Sustainability Research Network (UREx SRN) is a 5-year NSF-funded multi-institutional project whose mission is to link inter-disciplinary scholars with city practitioners and decision makers in order to produce resilient infrastructure and accelerate innovative urban sustainability knowledge and application. A suite of nine pilot cities have been chosen across the United States and Latin America, reflecting a variety of unique challenges and exposure/vulnerability to different weather and climate extreme events.

For the initial phase of the project the utmost importance has been placed on first listening to partners and practitioners in each of the pilot cities in order to assess the pertinent needs and priorities with respect to weather and climate information, including both historical records and future model projections. Based on this feedback from city practitioners the Climate and Hydrological Extremes Working Group of the SRN has been analyzing downscaled global climate model data for each of the locations, and disseminating this information among the other SRN working groups and city partners.

Here we will present “Scenarios Lite”, a city-focused summary of the past and future climate extreme statistics based on our analysis of 32 models from the CMIP5 database, downscaled using the Localized Constructed Analogs (LOCA) methodology. A number of techniques have been employed in order to analyze the anticipated occurrence of a variety of extreme indices within the model ensemble, including GEV-analysis and bootstrapping methods for estimating confidence intervals. With examples from our interaction with city practitioners within the SRN, we’ll share some preliminary avenues for translating climate model projections of future climate extremes into usable information for meeting decision-maker needs.

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