Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme
Segado-Moreno, L., Montavez, J. P., Garnes-Morales, G., Raluy-Lopez, E., Jimenez-Guerrero, P., et al. (2026). Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme. Atmospheric Chemistry and Physics, doi:https://doi.org/10.5194/acp-26-12641-2026
| Title | Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme |
|---|---|
| Genre | Article |
| Author(s) | L. Segado-Moreno, J. P. Montavez, G. Garnes-Morales, E. Raluy-Lopez, P. Jimenez-Guerrero, Rajesh Kumar |
| Abstract | Mineral dust is a major atmospheric aerosol influencing climate, air quality, and human health through radiative and microphysical processes. The Iberian Peninsula is frequently affected by North African dust intrusions, leading to episodic PM10 exceedances that challenge air quality forecasting. However, accurate representation of dust emissions remains limited by uncertainties in soil erodibility, land surface properties, and meteorological forcing.This study evaluates the impact of two high-resolution soil erodibility datasets on dust simulations using WRF-Chem with the GOCART scheme. The first dataset, EROD-HR, integrates fine-resolution topography to improve dust source representation at 0.0625 degrees (about 5 km) globally and 1 km over the Iberian Peninsula. The second dataset, SOILHD, further refines dust source characterization by incorporating high-resolution soil texture (sand, silt, and clay fractions) and removing misclassified bare-soil areas, resulting in a 1 km global resolution. Both datasets aim to better capture the spatial heterogeneity of dust sources.Simulations are conducted for five dust episodes between 2022 and 2025, covering local and long-range transport conditions. Model performance is evaluated against PM10 observations from the SINQLAIR network in the Region of Murcia. Results show improved representation of dust emissions, with better agreement in magnitude and timing of PM10 peaks at inland stations. Improvements are more limited at coastal and anthropogenically influenced sites, although statistical metrics (correlation, bias, RMSE) indicate consistent gains.Overall, high-resolution erodibility datasets enhance dust simulations by reducing biases and improving variability representation, highlighting the importance of detailed land-surface information for regional dust forecasting systems. |
| Publication Title | Atmospheric Chemistry and Physics |
| Publication Date | Sep 1, 2026 |
| Publisher's Version of Record | https://doi.org/10.5194/acp-26-12641-2026 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7t72p1g |
| OpenSky Listing | View on OpenSky |
| RAL Affiliations | RALAO |