Spatial distribution of aerosol microphysical and optical properties and direct radiative effect from the China Aerosol Remote Sensing Network

Multi-year observations of aerosol microphysical and optical properties, obtained through ground-based remote sensing at 50 China Aerosol Remote Sensing Network (CARSNET) sites, were used to characterize the aerosol climatology for representative remote, rural, and urban areas over China to assess effects on climate. The annual mean effective radii for total particles (R-effT) decreased from north to south and from rural to urban sites, and high total particle volumes were found at the urban sites. The aerosol optical depth at 440 nm (AOD(440 nm)) increased from remote and rural sites (0.12) to urban sites (0.79), and the extinction Angstrom exponent (EAE(440-870 nm)) increased from 0.71 at the arid and semi-arid sites to 1.15 at the urban sites, presumably due to anthropogenic emissions. Single-scattering albedo (SSA(440 nm)) ranged from 0.88 to 0.92, indicating slightly to strongly absorbing aerosols. Absorption AOD(440 nm) values were 0.01 at the remote sites versus 0.07 at the urban sites. The average direct aerosol radiative effect (DARE) at the bottom of atmosphere increased from the sites in the remote areas (-24.40 W m-2) to the urban areas (-103.28 W m(-2)), indicating increased cooling at the latter. The DARE for the top of the atmosphere increased from -4.79 W m(-2) at the remote sites to -30.05 W m(-2) at the urban sites, indicating overall cooling effects for the Earth-atmosphere system. A classification method based on SSA(440 nm), fine-mode fraction (FMF), and EAE(440-870 nm) showed that coarse-mode particles (mainly dust) were dominant at the rural sites near the northwestern deserts, while light-absorbing, fine-mode particles were important at most urban sites. This study will be important for understanding aerosol climate effects and regional environmental pollution, and the results will provide useful information for satellite validation and the improvement of climate modelling.