Refining simulated mineral dust composition through modified size distributions: dual validation with mineral-specific and elemental observations

Gómez Maqueo Anaya, Sofía; Aryasree, Sudharaj; Kandler, Konrad; dos Santos Souza, Eduardo José; Fomba, Khanneh Wadinga; Althausen, Dietrich; Kezoudi, Maria; Faust, Matthias; Heinold, Bernd; Tegen, Ina; Haarig, Moritz; Baars, Holger; Schepanski, Kerstin

doi:10.5194/egusphere-2026-23

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https://doi.org/10.5194/egusphere-2026-23

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15 Jan 2026

| 15 Jan 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Refining simulated mineral dust composition through modified size distributions: dual validation with mineral-specific and elemental observations

Sofía Gómez Maqueo Anaya, Sudharaj Aryasree, Konrad Kandler, Eduardo José dos Santos Souza, Khanneh Wadinga Fomba, Dietrich Althausen, Maria Kezoudi, Matthias Faust, Bernd Heinold, Ina Tegen, Moritz Haarig, Holger Baars, and Kerstin Schepanski

Abstract. The JATAC2022 campaign in Cape Verde provided a unique opportunity to collect mineral dust aerosols from multiple Saharan source regions and characterize their composition. Mineral dust aerosols comprise a complex assemblage of minerals with distinct physico-chemical properties, leading to differentiated climatic impacts through interactions with radiation, cloud microphysics, and atmospheric chemistry. A crucial physical property governing these interactions is the particle size distribution (PSD), which strongly influences aerosol optical properties, transport, and deposition. Although contemporary atmospheric models have begun integrating mineralogical data into their dust aerosol representations, implementation faces complications due to variations in dust emission parameterizations, making some models more compatible with existing soil mineralogical databases than others.

This work addresses the challenges encountered when incorporating mineralogical information into the COSMO5.05-MUSCAT atmospheric model, which employs a dust emission scheme based on Marticorena and Bergametti (1995). We present an improved approach that refines the translation of mineralogical soil PSDs into emitted aerosol PSDs. The revised implementation is evaluated using historical Saharan dust measurements and new mineralogical observations from the JATAC2022 and DUSTRISK2022 campaigns. Model performance is assessed using a dual validation framework considering both mineral-resolved and elemental composition. The elemental validation approach provides complementary constraints that expose discrepancies in internal mixing assumptions and reveal limitations invisible to mineral-only comparisons. Results indicate that the proposed modification substantially improves representation of phyllosilicates, quartz, and feldspar, while biases in iron, calcium, and magnesium highlight fundamental challenges in representing the heterogeneous internal structure of natural dust particles.

Received: 03 Jan 2026 – Discussion started: 15 Jan 2026

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'Comment on egusphere-2026-23', Anonymous Referee #1, 26 Feb 2026 reply
Dust aerosols are key players in the Earth system, exerting a variety of impacts that are many times shaped by their mineralogical composition. This manuscript presents advances in the characterization of the dust mineralogy from North African sources and approaches one fundamental problem that dust models face when trying to incorporate minerals in their formulation: the distribution of minerals across aerosol particle sizes. The core of the study focuses on the methodological improvements included in the COSMO-MUSCAT model to improve the emitted minerals’ Particle Size Distribution (PSD) and the evaluation of a set of simulations against observations. The authors also report new observational data of mineral and elemental composition from two different and recent campaigns, which are of value for increasing our understanding of the dust mineralogy and its regional variations. The improvements included in the model as well as the observational data merit publication and are useful for the scientific community to advance in this field.
However, in my view, some aspects of the observational campaigns and the model evaluation methodology have to be clarified. Also, the manuscript's current presentation is quite dense and the structure makes it difficult for the reader to clearly identify the primary findings. I would recommend the authors to split the work in two different articles: one devoted to the observational campaigns and the other related to the modelling work and its evaluation. Alternatively, a synthesis of these two parts is recommended, moving to supplementary materials the non-essential information.
Please, see my specific comments below.
Specific comments:
The authors report information on two different experimental campaigns: DUSTRISK2022 and JATAC2022. The protocols, instruments and characteristics of the measurement methods are explained in some detail, however the actual measurements, their representativity and uncertainty ranges are not reported or clearly discussed in the main article. These are relevant details to assess their strengths and weaknesses as a target for model evaluation. Some of the details reported in sections 4.2 and 4.3 (e.g., Table 3) could be moved to supplementary materials, while more information on the results of the measurement campaigns and their uncertainties could be added in the main paper. For instance, the JATAC2022 campaign reports close to 0 % of illite and smectite in fine particle sizes in Cape Verde. Is this expected? How is this reconciled with dusts close to sources with illite contents up to 40%? On the other hand, the estimate of iron oxides content from JATAC2022 relies on information from other experimental studies (DiBiagio et al., 2019), how does this impact their reliability?
The description of the model evaluation procedure overrelies on a workflow schematic (Figure 3). I would recommend to expand the description and justify the different steps taken in the main text.
As mentioned in the general comments, some aspects of the paper organization could be improved for clarity. For instance, in the current structure:
Section 2 is not connected to the narrative of the paper. This should be either contextualized, moved or entirely removed.

Sections 4.1, 4.2 and 4.3 mix the methodology used to obtain or gather the observations with the actual processing and methods used to compare to the model outputs.

Section 5.1 (“North Africa”) includes a comparison of the emitted PSD of a specific soil type with results from another model which could be moved to supplementary material.

My general recommendation would be to:
Clearly distinguish between observational data gathering (e.g., from previous studies or observational campaigns) and the methods to assess the model itself.

Include a section where model configuration and setup is clearly defined. Adding a table with the simulations conducted, their differences and an id to track them throughout the article could help.

Define the methodology followed for model evaluation in detail, including how the temporal and spatial dimensions are matched between model and observations.

The results are organized at present by campaign or observational dataset, which makes some of the aspects of the discussion of the improvements repetitive (e.g., improvements in the representation of PM10 PM2.5 Si in DUSTRISK vs JATAC, same for quartz). Maybe organizing them by "area" (sources vs. transport) or target variable (elemental composition vs mineral) could help streamline the discussion of results.

Technical comments:
Section headers could be more descriptive of its contents (e.g. “North Africa” shows results of mineral evaluation, “Cape Verde - JATAC 2022” refers to the results of the evaluation with this specific campaign data…).

L1: The focus put in the abstract on the JATAC campaigns seems disproportionate after reading the article.

L47-48: keep consistent size definitions for clay and silt throughout the manuscript.

L49-51: while the statement is true, the quartz overestimation is only one of the many uncertainties related to size distribution of minerals in soils.

L64-79: These lines now first state model evaluation against mineralogy, then discusses the two PSD approaches, then describes the datasets for evaluation again, mentions the seasonal variability analysis and again the results of the evaluation. Please, summarize and reorganize for clarity.

L183-184: There is no previous definition of the model configuration or simulation domain. Please, clarify or reorganize the text (the simulation setup is explained next in Sect 3.1.2).

L187-189: Rewrite this sentence to make it clearer.

Section 3.1.2 - How long are the simulations? Which is the output frequency? A graphical representation of the domain, even if in a supplementary file, would help the reader. It could be used also to represent the location of the observational datasets used as a reference for evaluation.

L223-224: Please, clarify the last sentence. Why is the Journet et al. 2014 article cited here?

L225-233: It is difficult to understand the justification of the use of a specific SMA for this work based on the purpose of a previous study. Given that this work aims at assessing the impact of different methods to define the emitted PSD I would say this justification is unnecessary.

L241: Figure 2 is mentioned before Figure 1. Revise.

Figure 2. The assumption made for aerosol particles larger than 50 um has to be explained somewhere

L355: which is the assumption for gypsum and feldspar?

L363: if aggregates disintegrate, why do they contribute to larger size classes?

L392: Perlwitz is a global dataset … explain which sites are selected.

Figure 4: Figure 4 and comparison with MONARCH can be moved to supplementary materials. There should be some clarification regarding the last bin of COSMO, which has unknown composition from 50 to 80 um.

Figure 5 caption - remove the modified from note. Include the size range. What are the uncertainty bars? Are all measurements bulk?

Figures 5 to 12: Use a consistent set of evaluation metrics across the paper and explain how these are calculated in the methods section. Is the correlation spatial, temporal? Please, use the same range in the figures when possible to allow “by-eye” comparison. For instance in Figure 8.

L560: what do the uncertainties represent?

L573: what do these size limitations refer to?

L623: the Fe content of phyllosilicates different from illite, and other minerals (e.g., feldspar) is not considered in this study, why? Please, discuss the impact of these assumptions on the Fe evaluation results. (Related also to line 804 in the conclusions).

Figure 8: Which minerals are included in the clay measurements? Are they exclusively illite, kaolinite, smectite? How is the mineral composition matched between model and observations?

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Citation: https://doi.org/10.5194/egusphere-2026-23-RC1

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Short summary

During the JATAC 2022 campaign in Cape Verde, Saharan dust aerosols were collected and analyzed for mineral composition. Mineralogy is crucial for dust–radiation and dust–cloud interactions. We improve dust representation in an atmospheric model by refining the translation of soil into aerosol particle size distributions. Validation with mineral and elemental measurements shows improved representation of some minerals and reveals biases missed by mineral-only comparisons.


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