American Association for Aerosol Research - Abstract Submission

AAAR 38th Annual Conference
October 5 - October 9, 2020

Virtual Conference

Abstract View


Dynamical Downscaling of a Global Chemistry-Climate Model to Study the Influence of Climate Change and Variability on Mid-21st Century PM2.5 in the Continental US

SURENDRA KUNWAR, Jared Bowden, George Milly, Previdi Michael, Arlene Fiore, Jason West, University of North Carolina at Chapel Hill

     Abstract Number: 508
     Working Group: Remote and Regional Atmospheric Aerosol

Abstract
Anthropogenically induced climate change and associated feedbacks from natural emissions (biogenic VOCs, wildfires) have the potential to alter air quality in the coming decades, but noise from climate variability can confound the climate change signal. Here, we aim to quantify the impacts of climate change and variability on US PM2.5 levels at fine spatial resolution, by statistically combining probability distributions from multi-year global model ensembles with dynamical downscaling over the continental US.

We use a 3-member ensemble of simulations varying only in initial conditions from the GFDL-CM3 global chemistry-climate model for the period 2006-2100 under the RCP8.5 scenario. The GFDL-CM3 simulations fix aerosol and O3 precursor emissions at 2005 levels to isolate the impact of climate change on air quality. Empirical Orthogonal Function analysis of the simulations identifies eastern US regions that vary coherently, from which we carefully select four present (2006-2020) and four future (2050-2060) years that include several high and medium annual mean PM2.5 levels in each region.

We dynamically downscale the GFDL-CM3 meteorology and chemistry to 12-km with the regional models WRF and CMAQ for the selected years. The 3-member GFDL-CM3 model simulations (at 2o spatial resolution) from 2006 to 2060 provide a broader context for the downscaled CMAQ simulations. Additionally, another global model NCAR CESM (12-member ensemble, 1o spatial resolution) run under the same future scenario as GFDL-CM3 also contributes to the context and statistics. From the downscaled CMAQ simulations, we construct mean annual PM2.5 probability distributions for the present and the future in individual 12 km grid cells using distributions from the global models. Finally, we examine the differences in fine scale mean annual PM2.5 distributions between the present and the future to quantify the effects of climate change and climate variability on PM2.5.