Simulating aerosol-radiation-cloud feedbacks on meteorology and air quality over eastern China under severe haze conditionsin winter

Abstract

The aerosol-radiation-cloud feedbacks on meteorology and air quality over eastern China under severe winter haze conditions in January 2013 are simulated using the fully coupled online Weather Research and Forecasting/Chemistry (WRF-Chem) model. Three simulation scenarios including different aerosol configurations are undertaken to distinguish the aerosol's radiative (direct and semi-direct) and indirect effects. Simulated spatial and temporal variations of PM2.5 are generally consistent with surface observations, with a mean bias of -18.9 μg m-3 (-15.0%) averaged over 71 big cities in China. Comparisons between different scenarios reveal that aerosol radiative effects (direct effect and semi-direct effects) result in reductions of downward shortwave flux at the surface, 2 m temperature, 10 m wind speed and planetary boundary layer (PBL) height by up to 84.0 W m-2, 3.2°C, 0.8 m s-1, and 268 m, respectively. The simulated impact of the aerosol indirect effects is comparatively smaller. Through reducing the PBL height and stabilizing lower atmosphere, the aerosol effects lead to increases in surface concentrations of primary pollutants (CO and SO2). Surface O3 mixing ratio is reduced by up to 6.9 ppb (parts per billion) due to reduced incoming solar radiation and lower temperature, while the aerosol feedbacks on PM2.5 mass concentrations show some spatial variations. Comparisons of model results with observations show that inclusion of aerosol feedbacks in the model significantly improves model performance in simulating meteorological variables and improves simulations of PM2.5 temporal distributions over the North China Plain, the Yangtze River delta, the Pearl River delta, and central China. Although the aerosol-radiation-cloud feedbacks on aerosol mass concentrations are subject to uncertainties, this work demonstrates the significance of aerosol-radiation-cloud feedbacks for real-time air quality forecasting under haze conditions.

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