Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World
Yang Zhang, firstname.lastname@example.org (North Carolina State University (NCSU))
L. Ruby Leung (Pacific Northwest National Laboratory), David Streets (University of Chicago), Michelle Bell (Yale)
NCSU (Raleigh, NC), Pacific Northwest National Laboratory (Richland, WA), University of Chicago (Chicago, IL), Yale (New Haven, CT)
We hypothesize that the spatial/temporal variations of air pollutants will have significant impacts on predicted exposure and health effects, and compound climate extremes will significantly impact air quality and health. Using advanced 3-D regional models, novel bias reduction and uncertainty quantification techniques, and satellite/surface data, we will improve characterization of the temporal and spatial variability of energy-related multipollutants and identify the most important modifiable factors contributing to their regional differences under current and future energy/emission and global change scenarios.
Using emissions related to energy transitions (Project 1) over the U.S., we will improve and apply 3 advanced 3-D online coupled regional climate-air quality models, WRF/CMAQ, WRF/Chem, and WRF/CAM5, to accurately characterize spatio-temporal variations of pollutants under current and future scenarios. Innovative techniques such as chemical data assimilation, inverse modeling, bias correction, and ensemble modeling coupled with observations from surface networks, satellites, and Project 2, will be used to minimize model biases in inputs, simulations, and outputs. An advanced global Earth system model (CESM/CAM5) will be used to generate boundary conditions for regional simulations. We will examine impacts of modifiable factors such as changes in energy/emissions, climate, land use/cover, and wildfire, individually and collectively. We will analyze compound extreme events and quantify uncertainty in projected changes of extreme air quality episodes.
The large ensemble of 20-25 year simulations of coupled regional climate- air quality models utilizing petascale supercomputers will produce 3-D concentration estimates of multipollutants along with climate projections and impacts of compound climate extremes on air quality from a diverse set of scenarios over North America during 2008-2052. These scenarios encompass multiple combinations of modifiable factors of future air quality changes consisting of 7+ energy transitions, 2 climate scenarios representing different pathways and carbon policies, 3 global climate models capturing the wide range of large-scale circulation changes, and 3 regional models accounting for air quality-climate feedbacks. Results will provide unprecedented information for Project 4 to estimate health effects and for policy makers to develop robust integrated control strategies to effectively improve air quality and human health.