Articles | Volume 12, issue 11
https://doi.org/10.5194/gmd-12-4603-2019
https://doi.org/10.5194/gmd-12-4603-2019
Model evaluation paper
 | 
06 Nov 2019
Model evaluation paper |  | 06 Nov 2019

Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1)

Siqi Ma, Xuelei Zhang, Chao Gao, Daniel Q. Tong, Aijun Xiu, Guangjian Wu, Xinyuan Cao, Ling Huang, Hongmei Zhao, Shichun Zhang, Sergio Ibarra-Espinosa, Xin Wang, Xiaolan Li, and Mo Dan

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Cited articles

Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas, J. Geophys. Res.-Atmos., 106, 18075–18084, https://doi.org/10.1029/2000JD900339, 2001. 
Alfaro, S. C., Gaudichet, A., Gomes, L., and Maillé, M.: Modeling the size distribution of a soil aerosol produced by sandblasting, J. Geophys. Res.-Atmos., 102, 11239–11249, https://doi.org/10.1029/97JD00403, 1997. 
Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012. 
Bagnold, R. A.: The physics of blown sand and desert dunes, Chapmann and Hall, Methuen, London, 265 pp., 1941. 
Basart, S., Pérez, C., Nickovic, S., Cuevas, E., and Baldasano, J.: Development and evaluation of the BSC-DREAM8b dust regional model over Northern Africa, the Mediterranean and the Middle East, Tellus B, 64, 18539, https://doi.org/10.3402/tellusb.v64i0.18539, 2012. 
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Short summary
Dust storms are thought to be a worldwide societal issue, and numerical modeling is an effective way to help us to predict dust events. Here we present the first comprehensive evaluation of dust emission modules in four commonly used air quality models for northeastern China. The results showed that most of these models were able to capture this dust event and indicated the dust source maps should be carefully selected or replaced with a new one that is constructed with local data.
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