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

Deb, Semontee; Louca, Elena; Violaris, Angelos; Kiriakidis, Pantelis; Proestos, Yannis; Kushta, Jonilda; Gkikas, Antonis; Pozzer, Andrea; Christoudias, Theodoros

doi:10.5194/egusphere-2026-1900

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https://doi.org/10.5194/egusphere-2026-1900

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19 Jun 2026

| 19 Jun 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

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

Semontee Deb, Elena Louca, Angelos Violaris, Pantelis Kiriakidis, Yannis Proestos, Jonilda Kushta, Antonis Gkikas, Andrea Pozzer, and Theodoros Christoudias

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.

Received: 03 Apr 2026 – Discussion started: 19 Jun 2026

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Semontee Deb, Elena Louca, Angelos Violaris, Pantelis Kiriakidis, Yannis Proestos, Jonilda Kushta, Antonis Gkikas, Andrea Pozzer, and Theodoros Christoudias

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RC1:
'Comment on egusphere-2026-1900', Anonymous Referee #1, 21 Jul 2026 reply
General Comments:
This study presents the evaluation of WRF-Chem simulations in dust events across a broad region covering North Africa, Middle East, Central Asia and Europe during 2021. It focuses on the comparison of the performance of three dust emission schemes (GOCART, AFWA, and UoC) and two land surface models (Noah-MP and CLM). The study has shown some noticeable differences in dust simulations among the three dust emission schemes and between the two land surface models. It also summarizes some key differences and similarities. This study could provide some important information on the applicability of the dust emission schemes and land surface models in the simulation and forecast of dust events, and thus it could potentially serve as a useful reference for the relevant community. However, due to unclear statements and unreasonable interpretation in some key results, I suggest denial of the manuscript for its further improvement.

Major comments:
Dust belt (in the title and main text): Generally speaking, the dust belt should consist of a larger region. In addition to the region shown in the study, it can also include western North Africa and adjacent oceans as well as northern China and Mongolia located in East Asia. I think the study region is misinterpreted as dust belt.

MIDAS dust AOD: It is used as a reference to evaluate the model performance. Although it is a great dataset which largely enhances our understating, I think it should be noted that it is subject to large uncertainty. It is partly based on MERRA-2 which uses the GOCART dust emission scheme. I think this is one reason why WRF-Chem with GOCART scheme resembles MIDAS DOD (dust optical depth). For AFWA, soil erodibility factor is also used as GOCART, which can partly explain the similarity between GOCART and AFWA.

In fact, in MERRA-2, biases in DOD would lead to the biases in the ratio of DOD to total AOD. This would further lead to the biases in the estimation of DOD in MIDAS, which combines MODIS AOD with MERRA-2 ratio of DOD to total AOD (according to Equation in Line 223). If MERRA-2 underestimates the dust concentrations (especially the peak dust concentrations during strong dust events as some previous studies have shown), MIDAS DOD would be underestimated too. Therefore, compared to MIDAS DOD, WRF-Chem with UoC scheme shows significant overestimation, but this is not necessarily true. Indeed, compared to AERONET observations, WRF-Chem with UoC scheme shows underestimation, which is inconsistent with overestimation when compared to MIDAS DOD. Given the uncertainty in MERRA-2, it is not appropriate to use MIDAS DOD to have a quantitative conclusion. If the authors want to use MIDAS DOD to derive a quantitative conclusion, I think it should be combined with AERONET AOD to generate a new dataset for a consistent conclusion.
EMEP: some stations which are located quite far from the source regions. Although the authors have developed some methods to rule out the stations in the build-up region, it might be still possible to be affected by transported particles from anthropogenic sources nearby. In fact, for the selected stations, the coarse particle concentrations at some moments are quite low (below 1-5 μg/m³). I suspect that some of these particles are not dust particles. I understand it is challenging to detect dust events (especially weak dust events), but it might be more reliable to use a high threshold.

Soil moisture and surface wind: These two variables are evaluated in a broad region, with no specific focus on the dust emission regions. I do not think the general analysis shown in Section 4.2 could be applied to dust emission regions.

Line 115: 0.1 to 10 μm in diameter: I think it is in radius.

Specific comments:
Line 5: year-long: please also mention the year here.
Lines 30-31: within the atmospheric model: deleted?
Line 34: models: not clear.
Line 40: for the GOCART aerosol model: deleted?
Line 42: to model: changed to “to simulate”.
Line 63: in both cases: not clear.
Line 67: the dust scheme: which scheme?
Line 70: land surface: changed to “land surface model”?
Line 73: that captures: changed to “that is affected by”?
Line 76: The sentence is too long.
Line 78: in the broader spatio-temporal domain affected by the dust belt: not clear.
Lines 111-112: based on topographic source identification: Moved to after "Ginoux et al. (2001)"?
Line 123: dust particles: changed to “soil particles”.
Line 133: mesoscale features: What it means is not clear to me.
Line 139: m is the mass: m is the mass of saltating particle.
Line 146: Shao (2004): changed to “(Shao, 2004)”.
Line 148: deleted “, (Shao, 2004)”.
Line 151: where σ_p represents the mass ratio between free dust particles and aggregated dust: I do not think so. Probably not use σ_p here, and directly use p_m(d_i)/p_f(d_i) as above.
Line 153: The grammar is not correct.
Line 163: Niu et al. (2011): changed to “(Niu et al., 2011)”.
Lines 166-167: The grammar is not correct.
Line 169: which consists of.
Lines 176-177: It is not clear to me. I think only soil moisture at the top surface layer affects dust emission.
Line 178: Sec. 4.1.2: Sec. 3.1?
Line 179: fiels?
Lines 184-188: It is not clear to me.
Lines 214-215: AOD 550 nm MODerate resolution Imaging Spectroradiometer
215 (MODIS) satellite products?
Lines 235-238: Use the unit of cm as the previous paragraph?
Lines 241-242: What do the differences mean here (difference between different layers)? Please clarify.
Line 256: Table 2: It appears before Table 1.
Line 274: pockets?
Figure 2: What does SF mean?
Figure 2: middle: It is better to use modeled dust AOD minus a reference data.
Figure 4: Table 1: Why are only one value of MBE, MAE, or NME shown in Table 1?
Figure 5: The range of soil moisture is too large for dust emission region.
Line 332: East Asia: changed to “Central Asia”.
Lines 396-398: It is not clear.
Section 6: Conclusions: It is a bit too long. Please shorten it.
Line 469: the coarse dust consistency of surface concentrations: not clear.

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Citation: https://doi.org/10.5194/egusphere-2026-1900-RC1

Semontee Deb, Elena Louca, Angelos Violaris, Pantelis Kiriakidis, Yannis Proestos, Jonilda Kushta, Antonis Gkikas, Andrea Pozzer, and Theodoros Christoudias

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Short summary

This study presents an extensive model evaluation of WRF-Chem (v4.7.1) dust simulations over the dust belt. We test three dust emission schemes and two land surface models using different measurements and reanalysis datasets. Our results identify effective model configurations and propose empirical scaling factors that improve dust representation and may be applicable more broadly.


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