Projecting Future Air Quality Under Energy Transition Scenarios over the U.S. using Online-Coupled Models

 

Online-coupled meteorology-chemistry models provide powerful tools for more realistically simulation of current and future air quality with feedbacks between atmospheric composition and meteorology that cannot be considered in offline-coupled models. In this work, several state-of-science online-coupled models are applied to generate the best possible predictions of surface ozone (O₃) and fine particulate matter (PM_2.5) concentrations under current emission and climate conditions. Two ensemble methods are used to further reduce the model biases and errors including a simple ensemble mean based on an average of ensemble members, and a weighted ensemble mean based on the multi-linear regression. The skills of individual models and their ensembles are evaluated using available surface network data.  Compared to individual models and the simple ensemble mean, the weighted ensemble predictions based on the multi-linear regression perform the best overall for both O₃ and PM_2.5. The model with best performance is selected to apply for future years to project the changes in air quality under various energy transition scenarios to support the development of emission control strategies. These results illustrate the current capability of the online-coupled models and the potential of weighted ensemble in generating the best possible estimates of air pollutant concentrations under current and future atmospheric conditions.