the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Apr 2026
Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme
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 (GMTED2010) to improve dust source representation at 0.0625 ° (~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, clay fractions) and removing misclassified bare soil areas, reaching 1 km global resolution. Both datasets aim to better capture spatial heterogeneity of dust sources in semi-arid environments.
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 WRF-Chem dust simulations by reducing biases and improving variability representation, highlighting the importance of detailed land-surface information for regional dust forecasting systems.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 03 Jun 2026)
- RC1: 'Comment on egusphere-2026-2127', I. Pérez, 08 May 2026 reply
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RC2: 'Comment on egusphere-2026-2127', Anonymous Referee #2, 11 May 2026
reply
This manuscript introduces two new erodibility maps to be used for dust emission within WRF-Chem configured to use GOCART for aerosols, building upon prior work by using a finer spatial resolution and considering soil type. These new dust emission configurations were evaluated against a control scenario using observations of PM10 from SINQLAIR network in southeastern Spain. It was evident that a great deal of effort went into generating the dust source maps, completing the modeling simulations, and comparing the output to observations. The manuscript itself still needs some work to better organize the content, explain to the reader what the figure is showing, and then tying everything together. There are figures barely mentioned in the text, and a good example of where the writing could be improved on this is Figure 11. The text jumps right in to say which configuration was best before introducing the timing of the event or discussing that all three simulations underestimate PM10 for the three days prior to maximum. After looking at the Appendix, it turns out on the first day, a bias is present at all stations, irrelevant of the dust source. I would encourage the authors to take a step back and re-read the paper from start to finish with a fresh set of eyes to ultimately improve the readability and help the reader understand the bigger picture.
Line 44: The Ginoux 2001 reference is specific to dust in GOCART coupled with GEOS. A more appropriate reference for GOCART itself should be used here, in addition to a reference on the coupling of GOCART to WRF-Chem.
-Line 51: Is EROD an acronym that should be defined?
-Line 53: Where is IP? Can the study region be introduced further?
-Paragraph beginning on like 59: Another recent paper that can be cited is at https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JD045333?utm_source=researchgate.net&utm_medium=article/.
-Line 133-135: Friedl et al. 2002 is a relatively old reference for MODIS land surface characterization. What exactly was used to generate regions where does cannot be emitted? Can the surface type vary in time, for example from glaciers that retreat annually?
-Figures 1 -3: Can a colorbar be added for sand, silt, and clay erodibility? Maybe consider to an appendix or supplemental doc as they are not discussed in the text.
-Table 3: Are the mean and max temporal, spatial, or both?
-Equations 4-6: It is not necessary to include these equations as they are standard metrics.
-Figure 5: Can a reference box for the domains be added to at least one of the panels?
-Section 3.1: The organization of this section needs to be improved. The text jumps around from Figure 5 to 6, and then back to 5, with references to Figures 2 and 3 thrown in, then 7, and back to 5.
-Figure 8 is never referenced in the text. I would recommend taking this one out using a figure along the same lines for domain d03 showing the spatial map of PM10 but adding dots over the surface stations to get a visual representation of where and when the model performed well in section 3.2.
-Section beginning on line 329: Can a rationale be given for the purpose of looking at the vertical profiles? Can anything in the vertical profiles be connected back to the erodibility or emissions difference in the previous figures?
-Section 3.2/Figure 10: The interannual and spatial variability of these results should be further discussed more coherently and elaborated upon.
Figure 11: I am intrigued about the underestimate of PM10 from all dust emission configurations for August 23 through 25. Does this indicate that WFR-Chem could be underestimating something else that happens to be compensated?
Citation: https://doi.org/10.5194/egusphere-2026-2127-RC2
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This paper considers two improved databases that are used in a simulation model to investigate the particulate matter concentration. The study region considers the Iberian Peninsula and Northern Africa, although the analysis is focused on the southeast of the Iberian Peninsula. Five short simulation periods during the years 2022 and 2025were considered. Observations were obtained from 8 stations and the comparison between measured and modelled values was established with the correlation coefficient, MBE and RMSE. Since the paper is quite complete, only some minor changes should be introduced in the manuscript.
The authors should indicate the reason for the selection of such specific periods since there is a gap, from 2023 to 2024, which is not covered by this study. Moreover, a comment about the study representativeness would be acknowledged.
If selected periods correspond to pollution events, representative synoptic charts could be introduced to present the atmospheric circulation in such episodes.
The Pearson correlation coefficient may be affected by outliers. Fig. 11b presents some accused outliers in some simulations. A comment about the response of the correlation coefficient could be introduced, i.e., the authors could comment if successful or poor correlations may be considered spurious due to the outlier influence. Perhaps, supplementary graphs could show the outlier weight in the correlations.
The authors could indicate the simulation that leads to the best results or, alternatively, the conditions to select one simulation type against the rest.