Sensitivity of dust event simulation to dust emission schemes and meteorological forcing datasets in the Belt and Road regions: A case study

This study investigates the sensitivity of dust event simulation to dust emission schemes and meteorological forcing datasets in the Belt and Road regions, which we divided into three dust-affected regions: North Africa, the Middle East-central Asia, and East Asia. The Weather Research and Forecasting model with online coupled chemistry (WRF-Chem) is used to simulate a dust event in the Belt and Road regions from June 15 to 20, 2016. Nine experiments were conducted using three sets of dust emission schemes (GOCART, Goddard Global Ozone Chemistry Aerosol Radiation and Transport; AFWA, Air Force Weather Agency; UoC, University of Cologne) and three meteorological forcing datasets (NCEP FNL, National Centers for Environmental Prediction Final; ECMWF ERA5, European Centre for Medium-Range Weather Forecasts Reanalysis v5; JMA JRA-55, Japan Meteorological Agency 55-year Reanalysis). The accuracy and applicability of the schemes and datasets were verified and evaluated by the global atmospheric reanalysis data, Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), as well as observed data from the AErosol RObotic NETwork (AERONET) and CloudAerosol Lidar with Orthogonal Polarization (CALIOP). The main conclusions drawn from the simulation of this dust event are as follows: In the Middle East-central Asia and East Asia, the uncertainty in the dust emission schemes is greater than that in the meteorological forcing datasets, while in North Africa, they are comparable. Among the schemes, GOCART is generally more applicable across the three dust-affected regions compared to AFWA and UoC. Additionally, JRA-55 shows a slight advantage over FNL and ERA5 in North Africa and the Middle East-central Asia. Significant differences in the spatial distribution of dust emissions are observed among experiments using different dust emission schemes, whereas slight differences are noted among those with different meteorological forcing datasets; notably, dust optical depth (DOD) distribution closely aligns with dust emissions.