Parameterization schemes on dust deposition in northwest China: model validation and implications for the global dust cycle

Authors: ZHANG, XIAOXIAO - Chinese Academy Of Sciences; Sharratt, Brenton; LEI, JIA-QIANG - Chinese Academy Of Sciences; WU, CHENG-LAI - Chinese Academy Of Sciences; ZHANG, JIE - Lanzhou University; ZHAO, CHUN - University Of Science And Technology Of China; WANG, ZI-FA - Chinese Academy Of Sciences; WU, SHI-XIN - Chinese Academy Of Sciences; LI, SHENG-YU - Chinese Academy Of Sciences; LIU, LIAN-YOU - Beijing Normal University; HUANG, SHUANG-YAN - Chinese Academy Of Sciences; GUO, YU-HONG - Chinese Academy Of Sciences; MAO, RUI - Beijing Normal University; LI, JIE - Chinese Academy Of Sciences; TANG, XIAO - Chinese Academy Of Sciences; HAO, JIAN-QI - Chinese Academy Of Sciences

Submitted to: Atmospheric Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/8/2019
Publication Date: 4/11/2019
Citation: Zhang, X., Sharratt, B.S., Lei, J., Wu, C., Zhang, J., Zhao, C., Wang, Z., Wu, S., Li, S., Liu, L., Huang, S., Guo, Y., Mao, R., Li, J., Tang, X., Hao, J. 2019. Parameterization schemes on dust deposition in northwest China: model validation and implications for the global dust cycle. Atmospheric Environment. 209:1-13. https://doi.org/10.1016/j.atmosenv.2019.04.017.
DOI: https://doi.org/10.1016/j.atmosenv.2019.04.017

Interpretive Summary: Accurate predictions of dust emission and deposition from arid and semiarid regions is vital to air quality and global climate change assessments. The WRF-Chem global climate model, coupled with either the GOCART or SHAO dust emission schemes, was used to predict dust deposition at 14 meteorological stations in Xinjiang Province, China. Dust deposition was better estimated using the SHAO emission scheme. However, dust deposition was underestimated by an order of magnitude using either the GOCART or SHAO emission schemes. Scientists must acquire the necessary field data and develop a better understanding of dust emissions, transport, and deposition that will enable better predictions of the fate of dust in the atmosphere and improve air quality.

Technical Abstract: Accurate estimation of dust deposition is of significance for modelling global radiation and the biochemical carbon cycle in the earth system. However, the paucity of dust deposition data precludes our ability to adequately verify estimations of dust deposition. Based on the environmental monitoring records in Xinjiang Province, northwest China, we conducted a numerical simulation of dust deposition using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and compared observed and modelled deposition during the spring dust season (March-May). The performance of WRF-Chem on modelling dust deposition was tested and validated with adoption of Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) and Shao et al. (2011b) dust emission schemes. Our results indicate that the dry deposition schemes are sensitive to several parameters and have the capability to predict size-resolved dust deposition intensity. However, modelled and measured dust deposition differed by more than two orders of magnitude. The modelled dust dry deposition does not satisfactorily agree well with field measurements. This study suggests significant distinctions exist among these two dust emission schemes when simulating mineral dust dry deposition in northwest China. The schemes underestimated measured dust deposition by the factors of 3-18. Uncertainties in estimating the dry dust deposition are in a range of 77-96%. These uncertainties imply that parameterization in the current dust deposition schemes need to be further improved to better simulate dust deposition in the complex terrain. This work systematically evaluated and quantitatively validated the performance of deposition schemes against in situ data over the source region, which would contribute to in-depth knowledge on dust emission, transport and deposition for dust modelling community. We found that the estimation of dust deposition is highly underestimated by the GCM/RCM model. Thus global dust cycles and dust deposition may exceed our current estimates.