Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem)

摘要

The chemistry-aerosol-cloud-radiation-climate feedbacks are simulated using WRF/Chem over the continental U.S. in January and July 2001. Aerosols can reduce incoming solar radiation by up to-9% in January and -16% in July and 2-m temperatures by up to 0.16 ℃ in January and 0.37 ℃ in July over most of the continental U.S. The NO_2 photolysis rates decrease in July by up to -8% over the central and eastern U.S. where aerosol concentrations are high but increase by up to 7% over the western U.S. in July and up to 13% over the entire domain in January. Planetary boundary layer (PBL) height reduces by up to -23% in January and -24% in July. Temperatures and wind speeds in July in big cities such as Atlanta and New York City reduce atear surface but increase at higher altitudes. The changes in PBL height, temperatures, and wind speed indicate a more stable atmospheric stability of the PBL and further exacerbate air pollution over areas where air pollution is already severe. Aerosols can increase cloud optical depths in big cities in July, and can lead to 500-5000 cm~(-3) cloud condensation nuclei (CCN) at a supersaturation of 1% over most land areas and 10-500 cm~(-3) CCN over ocean in both months with higher values over most areas in July than in January, particularly in the eastern U.S. The total column cloud droplet number concentrations are up to 4.9 × 10~6 cm~(-2) in January and up to 11.8 × 10~6 cm~(-2) in July, with higher values over regions with high CCN concentrations and sufficient cloud coverage. Aerosols can reduce daily precipitation by up to 1.1 mm day~(-1) in January and 19.4 mm day~(-1) in July thus the wet removal rates over most of the land areas due to the formation of small CCNs, but they can increase precipitation over regions with the formation of large/giant CCN. These results indicate potential importance of the aerosol feedbacks and an urgent need for their accurate representations in current atmospheric models to reduce uncertainties associated with climate change predictions.