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

CUACE/Dust, an operational mesoscale sand and dust storm (SDS) forecasting system for East Asia, has been developed by online coupling a dust aerosol emission scheme and dust aerosol microphysics onto a regional meteorological model with improved advection and diffusion schemes and a detailed Northeast Asia soil erosion database. With improved initial dust aerosol conditions through a 3-DVar data assimilation system, CUACE/Dust successfully forecasted most of the 31 SDS processes in East Asia. A detailed comparison of the model predictions for the 8–12 March SDS process with surface network observations and lidar measurements revealed a robust forecasting ability of the system. The time series of the operationally forecasted dust concentrations for a number of representative stations for the whole spring 2006 (1 March–31 May) were evaluated against surface PM10 monitoring data, showing a good agreement in terms of the SDS timing and magnitudes at and near the source regions where dust aerosols dominate. For the operational forecasts of spring 2006 in East Asia, a TS (threat score) system evaluated the performance of CUACE/Dust against all available observations and rendered a spring averaged TS value of 0.31 for FT1 (24 h forecasts), 0.23 for FT2 (48 h forecasts) and 0.21 for FT3 (72 h forecasts).

A comprehensive aerosol–cloud–precipitation interaction (ACI) scheme has been developed under a China Meteorological Administration (CMA) chemical weather modeling system, GRAPES/CUACE (Global/Regional Assimilation and PrEdiction System, CMA Unified Atmospheric Chemistry Environment). Calculated by a sectional aerosol activation scheme based on the information of size and mass from CUACE and the thermal-dynamic and humid states from the weather model GRAPES at each time step, the cloud condensation nuclei (CCN) are interactively fed online into a two-moment cloud scheme (WRF Double-Moment 6-class scheme – WDM6) and a convective parameterization to drive cloud physics and precipitation formation processes. The modeling system has been applied to study the ACI for January 2013 when several persistent haze-fog events and eight precipitation events occurred.The results show that aerosols that interact with the WDM6 in GRAPES/CUACE obviously increase the total cloud water, liquid water content, and cloud droplet number concentrations, while decreasing the mean diameters of cloud droplets with varying magnitudes of the changes in each case and region. These interactive microphysical properties of clouds improve the calculation of their collection growth rates in some regions and hence the precipitation rate and distributions in the model, showing 24 to 48 % enhancements of threat score for 6 h precipitation in almost all regions. The aerosols that interact with the WDM6 also reduce the regional mean bias of temperature by 3 °C during certain precipitation events, but the monthly means bias is only reduced by about 0.3 °C.

Information regarding dust concentrations and size distributions is very important for determining air quality and aerosol–radiation–cloud interactions. Only by using a correct erosion database can the sectional dust emission scheme resolve detailed size distributions and determine where and how dust will be emitted. In this paper, the bias and reasons for dust emission in China Meteorological Administration Unified Atmospheric Chemistry Environment–Dust, an operational dust forecasting model, are analyzed using a heavy sand and dust storm episode. We used 18 years of routine sand and dust storm phenomena recorded at meteorological stations to retrieve and update the desertification details in the MBA sectional dust emission scheme adopted in China Meteorological Administration Unified Atmospheric Chemistry Environment–Dust. New desertification details include decreased erodibility in the area adjacent to Uzbekistan, Turkmenistan, and southern Kazakhstan, where Kyzylkum, Karakum, and Aralkum are located in central Asia, and in the Chinese deserts of Onqin Daga, Mu Us, and Gurbantungut. New desertification also results in increased erodibility in northern Mongolia. Comparisons show that the new desertification database decreases overestimation of dust emission in central Asia, including western Mongolia. It improves the underestimation of dust emission in northern Mongolia and the Gobi desert in southeast Mongolia, and the Taklimakan desert in China. Consequently, it corrects the overestimated dust cloud in the source area and in areas impacted by dust transportation. The timing, quantitative mass concentrations, and dust size distributions determined here are all more reasonable and rational than those of the original case.

A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mass and number size distributions of aerosols. In this paper, a parameterization scheme for aerosol size distribution in initial emission, which took into account the measured mass and number size distributions of aerosols, was developed in the GRAPES–CUACE [Global/Regional Assimilation and PrEdiction System–China Meteorological Administration (CMA) Unified Atmospheric Chemistry Environment model]—an online chemical weather forecast system that contains microphysical processes and emission, transport, and chemical conversion of sectional multi-component aerosols. In addition, the competitive mechanism between nucleation and condensation for secondary aerosol formation was improved, and the dry deposition was also modified to be in consistent with the real depositing length. Based on the above improvements, the GRAPES–CUACE simulations were verified against observational data during 1–31 January 2013, when a series of heavy regional haze–fog events occurred in eastern China. The results show that the aerosol number size distribution from the improved experiment was much closer to the observation, whereas in the old experiment the number concentration was higher in the nucleation mode and lower in the accumulation mode. Meanwhile, the errors in aerosol number size distribution as diagnosed by its sectional mass size distribution were also reduced. Moreover, simulations of organic carbon, sulfate, and other aerosol components were improved and the overestimation as well as underestimation of PM_2.5 concentration in eastern China was significantly reduced, leading to increased correlation coefficient between simulated and observed PM_2.5 by more than 70%. In the remote areas where bad simulation results were produced previously, the correlation coefficient grew from 0.35 to 0.61, and the mean mass concentration went up from 43% to 87.5% of the observed value. Thus, the simulation of particulate matters in these areas has been improved considerably.