Evaluation of WRF-Chem (version 4.7.1) aeolian dust emission and land surface models over the dust belt

Abstract. Aeolian dust is a key component of the Earth system, influencing biogeochemical cycles, cloud microphysics, and the radiative energy budget and atmospheric dynamics, while also degrading air quality around major source regions. The representation of mineral dust remains a major challenge for regional and global atmospheric models due to uncertainties in emission processes, land-surface interactions, together with limited availability of observations. In this study, we present the first comprehensive, year-long evaluation of the simulated dust with the WRF-Chem model (v4.7.1) over the dust belt spanning North Africa, the Middle East, and Central Asia. We evaluate an ensemble of six simulations using three widely applied dust emission schemes (GOCART, GOCART-AFWA, and University of Cologne – UoC) combined with two advanced land surface models (LSM): Noah-MP and CLM4. The model performance is evaluated through a set of observations, including the MODIS-derived MIDAS dust optical depth product, AERONET aerosol optical depth (AOD), ERA5-Land surface soil moisture and wind speed, and EMEP coarse particulate matter (PM10-PM2.5) measurements. We find that among the dust emission schemes, GOCART provides the most robust agreement with MIDAS and AERONET, closely followed by AFWA but with a wider spread, while UoC systematically diverges from observations failing to represent realistic column dust optical depth. Evaluation of surface drivers reveals that land-surface representation exerts a strong influence on dust emission magnitude and spatial distribution, with Noah-MP yielding systematically better agreement with observed meteorology and AOD, whereas CLM4 introduces more pronounced regional discrepancies. UoC exhibits improved alignment with coarse particulate matter measurements at the EMEP stations compared to GOCART and AFWA. Finally, we provide empirical scaling factors derived for each emissions mechanism–LSM pairing, applicable for WRF-Chem dust simulations, offering guidance for improved dust and air quality, and climate modelling applications.