Over the past half century, burgeoning urban areas such as Chicago have experienced elevated anthropogenic-induced alteration of local climates within urbanized regions. As a result, urban heat island (UHI) effect in these areas has intensified. Global climate change can further modulate UHI’s negative effects on human welfare and energy conservation. Various numerical models exist to understand, monitor, and predict UHI and its ramifications, but none can resolve all the relevant physical phenomena that span a wide range of scales. To this end, we have applied a comprehensive multi-scale approach to study UHI of Chicago.

The coupling of global, mesoscale, and micro-scale models has allowed for dynamical downscaling from global to regional to city and finally to neighborhood scales. The output of the Community Climate System Model (CCSM5), a general circulation model (GCM), provides future climate scenario, and its coupling with Weather Research and Forecasting (WRF) model enables studies on mesoscale behavior at urban scales. The output from the WRF model at 0.333 km resolution is used to drive a micro-scale model, ENVI-met. Through this coupling the bane of obtaining reliable initial and boundary conditions for the micro-scale model from limited available observational records has been aptly remedied. It was found that the performance of ENVI-met improves when WRF output, rather than observational data, is supplied for initial conditions. The success of the downscaling procedure allowed reasonable application of micro-scale model to future climate scenario provided by CCSM5 and WRF models. The fine (2 m) resolution of ENVI-met enables the study of two key effects of UHI at micro-scale: decreased pedestrian comfort and increased building-scale energy consumption. ENVI-met model’s explicit treatment of key processes that underpin urban microclimate makes it captivating for pedestrian comfort analysis. Building energy, however, is not modeled by ENVI-met so we have developed a simplified building energy model to estimate future cooling needs.

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