Abstract
Abstract. Mineral dust plays a significant role in climate change and air quality, but large uncertainties remain in terms of dust emission prediction. In this study, we improved treatment of the dust emission process in a global 3-D chemical transport model (GEOS-Chem v12.6.0), by incorporating the geographical variation of aerodynamic roughness length (Z0), smooth roughness length (Z0s) and soil texture and by introducing the Owen effect and the formulation of the sandblasting efficiency α by Lu and Shao (1999). To investigate the impact of the modifications incorporated in the model, several sensitivity simulations were performed for a severe dust storm during 27 March to 2 April 2015 over northern China. Results show that simulated threshold friction velocity is very sensitive to the updated Z0 and Z0s field, with the relative difference ranging from 10 % to 60 % compared to the original model with a uniform value. The inclusion of the Owen effect leads to an increase in surface friction velocity, which mainly occurs in the arid and semi-arid regions of northwest China. The substitution of a fixed value of α assumed in the original scheme with one varying with friction velocity and soil texture based on observations reduces α by 50 % on average, especially over regions with sand texture. Comparisons of sensitivity simulations and measurements show that the revised scheme with the implementation of updates provides more realistic threshold friction velocities and PM10 mass concentrations. The performance of the improved model has been evaluated against surface PM10 observations as well as MODIS aerosol optical depth (AOD) values, showing that the spatial and temporal variation of mineral dust are better captured by the revised scheme. Due to the inclusion of the improvement, average PM10 concentrations at observational sites are more comparable to the observations, and the average mean bias (MB) and normalized mean bias (NMB) values are reduced from −196.29 µg m−3 and −52.79 % to −47.72 µg m−3 and −22.46 % respectively. Our study suggests that the erodibility factor, sandblasting efficiency and soil-related properties which are simply assumed in the empirical scheme may lack a physical mechanism and spatial–temporal representativeness. Further study and measurements should be conducted to obtain a more realistic and detailed map of these parameters in order to improve dust representation in the model.
Highlights
IntroductionMineral dust is typically produced by wind erosion from regions with arid and semi-arid surfaces in the world and exerts significant impacts on the atmospheric radiation balance (Tegen et al, 1996; DeMott et al, 2010; Kumar et al, 2014; Saidou Chaibou et al, 2020a), climate (DeMott et al, 2003; Mahowald and Kiehl, 2003; Zhao et al, 2012; Chen et al., 2014; Chin et al, 2014), air quality (Giannadaki et al, 2014; Tian et al, 2019) and human health (Goudie, 2014; Tong et al, 2017)
Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) values, showing that the spatial and temporal variation of mineral dust are better captured by the revised scheme
Due to the inclusion of the improvement, average PM10 concentrations at observational sites are more comparable to the observations, and the average mean bias (MB) and normalized mean bias (NMB) values are reduced from -196.29μg m−3 and -52.79% to -47.72μg m−3 and -22.46% respectively
Summary
Mineral dust is typically produced by wind erosion from regions with arid and semi-arid surfaces in the world and exerts significant impacts on the atmospheric radiation balance (Tegen et al, 1996; DeMott et al, 2010; Kumar et al, 2014; Saidou Chaibou et al, 2020a), climate (DeMott et al, 2003; Mahowald and Kiehl, 2003; Zhao et al, 2012; Chen et al., 2014; Chin et al, 2014), air quality (Giannadaki et al, 2014; Tian et al, 2019) and human health (Goudie, 2014; Tong et al, 2017). Dust emission process has been recognized as a leading contributor to dust aerosol loading. With East Asia (including the Gobi and Taklimakan deserts) accounting for ~20% of the global dust emission (Ginoux et al, 2004; Nagashima et al, 2016). In order to properly reproduce dust emission process, many dust emission schemes have been developed and implemented in both global and regional chemical transport models (CTMs) (e.g., Marticorena and Bergametti, 1995; Lu and Shao, 1999; Alfaro and Gomes, 2001; Shao, 2001, 2004; Shao et al, 2011; Zender et al, 2003; Kok, 2011a, 2011b)
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