Accurate representation of aerosol optical properties is essential for the modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, the use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate-nitrate-Ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (rCombining double low line0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM and the ratio between the two components (OM/SNA) are the major indicators for SNA and OM dry aerosol size. A parameterization of the dry effective radius (Reff) for SNA and OM aerosol is designed to represent the airborne measurements (R2Combining double low line0.74; slopeĝ€¯Combining double low lineĝ€¯1.00) and the GEOS-Chem-TOMAS simulation (R2Combining double low line0.72; slopeĝ€¯Combining double low lineĝ€¯0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R2 from 0.68 to 0.73 and slope from 0.75 to 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically.
CITATION STYLE
Zhu, H., Martin, R. V., Croft, B., Zhai, S., Li, C., Bindle, L., … Weinheimer, A. (2023). Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties. Atmospheric Chemistry and Physics, 23(9), 5023–5042. https://doi.org/10.5194/acp-23-5023-2023
Mendeley helps you to discover research relevant for your work.