Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model – CUACE/Aero

Abstract

CUACE/Aero, the China Meteorological Administration (CMA) Unified Atmospheric Chemistry Environment for aerosols, is a comprehensive numerical aerosol module incorporating emissions, gaseous chemistry and size-segregated multi-component aerosol algorithm. On-line coupled into a meso-scale weather forecast model (MM5), its performance and improvements for aerosol chemical and optical simulations have been evaluated using the observations data of aerosols/gases from the intensive observations and from the CMA Atmosphere Watch network, plus aerosol optical depth (AOD) data from CMA Aerosol Remote Sensing network (CARSNET) and from Moderate Resolution Imaging Spectroradiometer (MODIS). Targeting Beijing and North China region from July 13 to 31, 2008, when a heavy hazy weather system occurred, the model captured the general variations of PM10 with most of the data within a factor of 2 from the observations and a combined correlation coefficient (r) of 0.38 (significance level = 0.05). The correlation coefficients are better at rural than at urban sites, and better at daytime than at nighttime. Chemically, the correlation coefficients between the daily-averaged modelled and observed concentrations range from 0.34 for black carbon (BC) to 0.09 for nitrates with sulphate, ammonium and organic carbon (OC) in between. Like the PM10, the values of chemical species are higher for the daytime than those for the nighttime. On average, the sulphate, ammonium, nitrate and OC are underestimated by about 60, 70, 96.0 and 10.8%, respectively. Black carbon is overestimated by about 120%. A new size distribution for the primary particle emissions was constructed for most of the anthropogenic aerosols such as BC, OC, sulphate, nitrate and ammonium from the observed size distribution of atmospheric aerosols in Beijing. This not only improves the correlation between the modelled and observed AOD, but also reduces the overestimation of AOD simulated by the original model size distributions of primary aerosols. The normalised mean error has been reduced to 62% with the CARSNET observations and 76% with MODIS, from the original 111% and 143%, respectively. The factors resulting in the underestimation of aerosol concentrations and other discrepancies in the model are explored, and improvements in enhancing the model performance are proposed from the analysis. It is found that the accuracy in meteorological predictions plays a critical role on the simulation of the occurrence and accumulation of heavy pollution episode, especially the circulation winds and the treatment of Planetary Boundary Layer (PBL).