Application of online-coupled WRF/Chem-MADRID in East Asia: Model evaluation and climatic effects of anthropogenic aerosols

Abstract

Abstract The online-coupled Weather Research and Forecasting model with Chemistry with the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (referred to as WRF/Chem-MADRID) is applied to simulate meteorological fields, air quality, and the direct and indirect effects of anthropogenic aerosols over East Asia in four months (January, April, July, and October) in 2008. Model evaluation against available surface and satellite measurements shows that despite some model biases, WRF/Chem-MADRID is able to reproduce reasonably well the spatial and seasonal variations of most meteorological fields and chemical concentrations. Large model biases for chemical concentrations are attributed to uncertainties in emissions and their spatial and vertical allocations, simulated meteorological fields, imperfectness of model representations of aerosol formation processes, uncertainties in the observations based on air pollution index, and the use of a coarse grid resolution. The results show that anthropogenic aerosols can reduce net shortwave flux at the surface by up to 40.5–57.2 W m −2 , Temperature at 2-m by up to 0.5–0.8 °C, NO 2 photolytic rates by up to 0.06–0.1 min −1 and the planetary boundary layer height by up to 83.6–130.4 m. Anthropogenic aerosols contribute to the number concentrations of aerosols by up to 6.2–8.6 × 10 4 cm −3 and the surface cloud concentration nuclei at a supersaturation of 0.5% by up to 1.0–1.6 × 10 4 cm −3 . They increase the column cloud droplet number concentrations by up to 3.6–11.7 × 10 8 cm −2 and cloud optical thickness by up to 19.8–33.2. However, anthropogenic aerosols decrease daily precipitation in most areas by up to 3.9–18.6 mm during the 4 months. These results indicate the importance of anthropogenic aerosols in modulating regional climate changes in East Asia through aerosol direct and indirect effects, as well as the need to further improve the performance of online-coupled models.