Abstract

Fine particulate matter (PM2.5) is a criteria pollutant. Its sensitivity to meteorology implies its distribution will likely change with climate shifts. Limited availability of global climate models with full chemistry complicates efforts to assess rigorously the uncertainties in the PM2.5 respon... Show moreFine particulate matter (PM2.5) is a criteria pollutant. Its sensitivity to meteorology implies its distribution will likely change with climate shifts. Limited availability of global climate models with full chemistry complicates efforts to assess rigorously the uncertainties in the PM2.5 response to a warming climate. We evaluate the potential for PM2.5 distributions in a chemistry-climate model under current-day and warmer climate conditions over the Northeastern United States to be represented by a Synthetic Aerosol tracer (SAt). The SAt implemented into the Geophysical Fluid Dynamics Laboratory chemistry-climate model (AM3) follows the protocol of a recent multimodel community effort (HTAP), with CO emissions, 25-day chemical lifetime, and wet deposition rate of sulfate. Over the Northeastern United States, the summer daily time series of SAt correlates strongly with that of PM2.5, with similar cumulative density functions under both present and future climate conditions. With a linear regression model derived from PM2.5 and SAt in the current-day simulation, we reconstruct both the current-day and future PM2.5 daily time series from the simulated SAt. This reconstruction captures the summer mean PM2.5, the incidence of days above the 24-h mean PM2.5 NAAQS, and PM2.5 responses to climate change. This reconstruction also works over other polluted Northern Hemispheric regions and in spring. Our proof-of-concept study demonstrates that simple tracers can be developed to mimic PM2.5, including its response to climate change, as an easy-to-implement and low-cost addition to physical climate models that should help air quality managers to reap the benefits of climate models that have no chemistry. Show less