the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Apr 2025
The role of dust mineral composition in atmospheric radiation and pollution in North China: new insights from EMIT and two-way coupled modeling
Abstract. Mineral dust is a major atmospheric aerosol influencing Earth’s energy balance through aerosol-radiation (ARI) and aerosol-cloud interactions (ACI). While homogeneous dust effects have been studied, the impact of mineralogical composition on regional meteorology and air quality remains underexplored, limiting accurate forecasting of dust storm impacts, especially in dust belt regions. In this study, we used a two-way coupled WRF-CHIMERE model with three mineralogical dust atlases (Nickovic et al. (2012) (N2012), Journet et al. (2014) (J2014), and a new dataset, Li et al. (2024) (L2024), from the Earth Surface Mineral Dust Source Investigation (EMIT)) to evaluate ARI effects during the March 2021 dust storm in North China. Results showed significant spatial variations in radiative forcing due to mineralogical differences. Bulk dust (without considering mineralogy) caused an average shortwave radiative forcing of −5.72 W/m², while mineral-specific forcings increased this by up to +0.10 W/m². Integrating EMIT data reduced PM10 biases by over 15 % in high-concentration regions and improved ozone predictions, with localized changes of −2.46 to +3.52 µg/m³. Hematite’s strong absorption and quartz’s reflective properties were key in altering radiative and air quality outcomes. Compared to scenarios of bulk dust, the consideration of ARI effects of mineralogical compositions can increase PM10 concentration by up to 1189.48 µg/m³ in dust source regions. Future research perspectives on the utilization of high-resolution EMIT data in two-way coupled meteorology and air quality models for investigating the ACI effects of mineralogical dust on cloud microphysics are proposed.
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RC1: 'Comment on egusphere-2025-611', Anonymous Referee #1, 26 Aug 2025
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General comments:
The manuscript titled "The role of dust mineral composition in atmospheric radiation and pollution in North China: new insights from EMIT and two-way coupled modeling" presents a novel and comprehensive investigation of mineral dust impacts using multiple dust atlases and a two-way coupled WRF-CHIMERE model. The integration of EMIT satellite-derived data is particularly innovative and demonstrates significant potential for improving model accuracy in historical dust storm simulations and future forecasting works. Overall, the paper is clearly written and methodologically sound. However, if the following comments are thoroughly addressed within this review process I would suggest publishing this paper in ACP.
Major comments:
While the paper demonstrates the benefit of using EMIT data in methodology, it would be helpful to provide a quantitative assessment of uncertainties introduced by the interpolation and assumptions in EMIT data processing (e.g., feldspar/quartz filling).
The manuscript often mentions ACI (aerosol-cloud interaction), yet the modeling focuses on ARI only. Please clarify this distinction earlier in the Introduction and reduce any ambiguity about what has or has not been included.
The SSR and PM10 comparisons are robust, but more details on the performance metrics (bias, RMSE, etc.) across multiple sites and time periods would strengthen the validation claims.
The influence of mineralogy on PM10 and O3 is clearly demonstrated, but more discussion of the physical mechanisms (e.g., specific reactions, photolysis suppression) would help interpret the observed changes.
The results show that quartz and feldspar dominate dust mass, while hematite dominates radiative effects. This contrast deserves more discussion in both the Results and Conclusion sections.
The model bias discussion (Section 3.1) is helpful but could be deepened by exploring possible reasons for the underestimation of PM10 at high dust sites.
Minor comments:
Line 137: Please specify how missing EMIT data (quartz/feldspar) are estimated — a numeric assumption or spatial filling?
Line 187–198: The bias in SSR is discussed, but no mention is made of possible causes (e.g., aerosol loading or model radiation scheme limitations).
Line 194: The overestimation of SSR and WS10 could be more quantitatively discussed. Is this bias consistent with other dust studies in this region?
Line 213–214: “minimizing the negative biases in T2” — perhaps “reducing the magnitude of negative biases” is clearer.
Line 250: “Positive O3 biases increased” is unclear — do you mean O3 concentrations were overestimated?
Line 305: “−900 W m−2” seems unusually large for surface shortwave cooling. Please double-check this value.
Line 584: Suggest shortening this part of the conclusion and moving satellite technical details into Data/Methods.
Figure 1: Please include a scale bar and clear region names to help interpret mineral distributions.
Figure 2: Consider including error bars or confidence intervals for observed values, “Statatiscal metrices” → should be “Statistical metrics” in its caption.
Figure quality could be improved — e.g., Figures 2 and 7 would benefit from enhanced color contrast and labeled axes for clarity.
Reference format is mostly consistent, but some recent references (e.g., Panta et al., 2023) are missing DOIs.
Citation: https://doi.org/10.5194/egusphere-2025-611-RC1
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