AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Modeling Ambient Air Quality at Exposure Relevant Scales using the Community Earth System Model
FORREST LACEY, Rebecca Schwantes, Simone Tilmes, Colin Zarzycki, Louisa Emmons, Marsh Daniel, Walters Stacy, Francis Vitt, Gabriele Pfister, Peter Lauritzen, Alma Hodzic, National Center for Atmospheric Research
Abstract Number: 49 Working Group: Aerosol Exposure
Abstract Modeling ambient air pollution is difficult due to the complex interactions between the atmosphere and other Earth systems. This is especially challenging when considering how changes in anthropogenic activity and subsequently climate will shift the formation and fate of aerosols throughout the next century. Commonly, chemical transport models (CTMs) are used to estimate human exposure to trace pollutants, such as fine particulate matter (PM2.5) and ozone, although these methods do not include many of the climate and Earth system feedbacks that are necessary to accurately predict future changes in ambient air quality. Here we will present comparisons of observed aerosol concentrations over the United States during 2013 with modeled concentrations using a newly developed fully coupled global state-of-the-science Earth system model with variable resolution (“CAM-chem-SE-RR”). This model is a configuration of version 2 of the Community Earth System Model (CESM2) that allows us to isolate the benefits of increasing model resolution, emission spatial and temporal resolution, and model chemical complexity. These results are then combined with exposure response functions to estimate the human health impacts from ambient air pollution for 2013 including sector and region-specific source attribution for these impacts. The conclusions from this analysis show that the combination of exposure-relevant resolution (~14km over the contiguous U.S.), detailed atmospheric chemistry, and the inclusion of earth system feedbacks, all within a global model, allows for more accurate predictions of air quality than previous methods by considering the bi-directional interactions over local to global scales making this model a new and powerful tool for future air quality predictions.