3C.6 Assessing Climate Variability and Change in an Ensemble Simulation of Climate Impacts on U.S. Air Quality and Public Health (Invited Presentation)

Monday, 13 January 2020: 3:30 PM
151A (Boston Convention and Exhibition Center)
Fernando Garcia Menendez, North Carolina State Univ., Raleigh, NC; and J. East, R. K. Saari, and E. Monier

The effects of climate change on air pollution and its associated public health burden are important components of impacts projections and policy assessments. However, the noise associated with internal variability makes it challenging to distinguish the signal of anthropogenic climate change in simulated air quality and estimates of related greenhouse gas mitigation benefits. Few studies have systematically investigated internal variability in projections of climate change effects on air quality. Here we assess the influence of internal variability within an ensemble simulation of future economic activity, climate, air pollution, and human health impacts. The ensemble, which includes multiple initial condition members and multidecadal atmospheric chemistry simulations, projects climate-induced impacts on U.S. ozone and fine particulate matter pollution, as well as associated mortality and morbidity outcomes. We compare the magnitude of the ensemble-mean impacts to the degree of internal variability under different greenhouse gas emissions scenarios, levels of climate model response, and time periods. Additionally, we investigate the influence of internal variability in occurrences of extreme air pollution and intraseasonal variations in air quality. The complete ensemble, consisting of 2,850 years of simulated air quality, demonstrates the important role internal variability can play in climate change impacts projections across different sectors. Based on this assessment, we share recommendations for future air quality and health projections using extended modeling, initial condition ensembles, or increased the spatial-scales. Finally, we place internal variability in the larger context of climate policy decision-making by comparing the range of predicted outcomes under internal variability to uncertainties related to health response and economic valuation.
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