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

Preprints

15 Jan 2026

| 15 Jan 2026

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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Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-23', Anonymous Referee #1, 26 Feb 2026
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?
Citation: https://doi.org/10.5194/egusphere-2026-23-RC1
RC2: 'Comment on egusphere-2026-23', Anonymous Referee #2, 03 Mar 2026

Review of "Refining simulated mineral dust composition through modified size distributions: dual validation with mineral-specific and elemental observations", by Gómez Maqueo Anaya et al.
GENERAL COMMENTS:
This manuscript presents a scheme for obtaining mineral dust aerosol particle size distribution at emission (discriminated by mineral species) from given mineral-specific precursor surface sediment particle size distributions in the COSMO5.05-MUSCAT atmospheric model, and validates this scheme against aerosol observations of size-resolved mineralogy and elemental concentrations.
I congratulate the authors for a very in-depth analysis that clarifies many of the difficulties in successfully representing size-segregated dust aerosol mineralogy in models. The problem is very nicely and precisely described in the introductory section. I particularly liked the dual validation against both aerosol mineralogy and elemental concentration observations, and the insights this allowed on the difficulties in representing iron in dust aerosols.
You will see I have quite a number of recommendations for improving the manuscript. While many, none of my observations are substantial, with maybe one exception (see my recommendation below to eliminate MONARC vs. COSMO-MUSCAT comparison). Also, in order to speak to the community of dust researchers that do not work on models, but rather on experimental techniques, I would really like the authors to consider sharing their opinions in the conclusions section on surface sediment sampling and sieving methods to best serve dust modelers (see my comment below).
SPECIFIC COMMENTS:
(lines 1-2) I suggest eliminating the first sentence of the abstract, as it describes one of multiple observational campaigns used to validate the modeling work, and instead the next phrase introduces the topic more generally. The JATAC 2022 campaign is mentioned later in the abstract, so by eliminating the first sentence of the abstract a reference to the JATAC campaign is not lost.
(line 28) Replace "oxide bearing minerals" by "oxide-bearing minerals".
(line 48) The silt-sand grain size boundary is usually set at 62.5 mm (e.g., Blott and Pye, doi:1111/j.1365-3091.2012.01335.x). Why is 50 mm used here? Is that the definition used in one of the the soil mineral atlas? Please, specify.
(line 72) Specify the type of measurements of this North African compilation.
(lines 74-75) The phrase "JATAC offers mineral- and elemental-specific measurements" is confusing. Measurements or what? Do you mean measurements of elemental concentrations and of mineralogical composition? Are these size-resolved? Please, clarify.
(line 103) By "anthropogenic aerosols" do you mean biomass burning aerosols from man-made fires? Please, specify, as "anthropogenic aerosols" could also be understood as aerosols emitted from industrial activities.
(line 144) For clarity, specify the range of possible values of f_eff. Is it a scalar from 0 to 1? Also, please clarify if U_ts^* > U_t^* or if U_ts^* < U_t^*.
(lines 146-148) How is U_ts^* derived from COSMO's 10-m wind speed? Can you provide an equation or at least expand a bit the explanation in words?
(line 155) You used "D_p" for particle diameter previously, so I would replace "D_pi" by "D_p" here.
(line 157) Replace "Because threshold" for "Because the threshold".
(line 158) Add "mass" in "the soil particle mass size distribution (PSD)".
(lines 206-207) What is the thickness of the uppermost ERA5-Land soil layer? This is an important parameter to mention here.
(lines 216-217) This sentence on phosphorous seems arbitrary here, as this is not relevant for the topic of this manuscript. Phosphorous is not described in terms of results, neither are the measurements including phosphorous. I recommend eliminating this sentence.
(lines 222) Here it is the first time that it is mentioned what minerals are part of the "phyllosilicates". However, the term "phyllosilicates" appears twice before (including in the abstract). Instead of clarifying it here, do it in the abstract and upon first appearance in the main text.
(line 241) Figure 1 should be Figure 2, and viceversa, based on the first mention of each figure in the text.
(lines 251-253 and caption of Figure 1) Where does the measurement of the total mass PSD come from? It doesn't come from GMINER, right, as GMINER only compiles PSD's within the clay-silt size ranges? Is it instead coming from a separately collected soil sample? If so, add info on the sample. Please, clarify in the text and in the caption.
(caption to Figure 1) Replace "Gómez Maqueo Anaya, Sofía (2025)" by "Gómez Maqueo Anaya et al. (2025)".
(lines 275-276) This phrase is confusing. Do you mean that Ds corresponds to what parameter specifically of the wet-sieved soil PSD? It is confusing because the wet-sieved soil PSD is a curve that describes how the mass is distributed across particle sizes, while Ds is a scalar. Please, be more explicit on how Ds relates to the wet-sieved soil PSD.
(line 314) What does this M represent? Mathematically, it looks like k is a natural number that goes from 1 to the number of minerals. I would either describe this, or directly eliminate the use of M here (just leave "the mass fraction of each mineral k in...").
(Figure 2) I highly recommend to add the percentage of each fraction of the pie charts, for more direct visual identification of these fractions.
(equation 9 and 12) I recommend replacing "k e M" by "k = 1" in the bottom of the sum symbol, and "8" at the top of the sum symbol.
(line 324) Replace "The combined mineral mass" by "The combined soil mineral mass".
(line 327) Replace "total mineral mass" by "total soil mineral mass".
(line 329) Replace "formulation, mineral" by "formulation, soil mineral".
(line 333) With respect to "in the clay and silt categories": but based on Table 1, the aerosol bins are five rather than just two. Clarify.
(line 341) With respect to "size classes": are these the five bins described in Table 1? Clarify.
(line 345) By "fully dispersed" soil, do you mean the wet-sieved soil? Is a wet-sieved soil fully dispersed? Or by "fully dispersed" soil you mean a conceptual (not achievable methodologically) PSD of a hypothetic fully dispersed soil?
(line 348) Replace "present in the silt-sized range" by "present in the soil in the silt-sized range".
(line 349) Not clear. If what is emitted as aerosol are these clay-sized fragments, they would be emitted as clay-sized aerosols (not silt-sized). Shouldn't it be "fragments of silt-sized particles...". Please, clarify.
(line 386) Change sub-title to "Compilation of North Africa aerosol mineralogical measurements".
(line 388) "in-situ aerosol mineralogical measurements".
(lines 389-391) It is not clear to me why for this first model-observation comparison you choose to select time windows from short- and long-term monitoring programs that specifically fall within the DUSTRISK 2022 campaign period. Why wasn't a different simulation run for the full length of each of the monitoring programs in the compilation? One reason may be that each simulation is too computationally expensive, and so it is preferable to, as you did, run one single simulation with a given simulation time period, and then sub-sample data from the campaigns to coincide with this period. Are the simulations really that expensive to warrant this? If so, why was the time window of the DUSTRISK 2022 campaign selected? Wouldn't it be preferable to choose a period that is as long as possible while still falling within as many of these campaign as possible? In any case, please explain in the text the motivation for these choices in methodology.
(line 394) Does this mean including summer and non-summer months? But weren't winter months filtered for?
(line 396) Cite the Zenodo DOI.
(lines 439-443) What about potential contamination from local dust emissions (from the island)? Can you say something about this possibility? Were local soils sampled and analyzed to evaluate this possibility?
(lines 471-472) Why is 38 mg m^-3 chosen as a threshold? Also, please explain how dust is quantified (at least the small version, citing Souza et al. (2025) for the details).
(lines 538-556 and Figure 4) It is not clear why the MONARCH vs. COSMO-MUSCAT comparison is done. I believe these are different model architectures, and the soil mineralogy compilations used are also different, so it is not clear if a comparison here can provide useful insights (many potential contributions from different processes to model output differences). Also, the authors do not come back to discussing this comparison later in the manuscript. I suggest eliminating this analysis (including eliminating panels a and b from Figure 4).
(lines 557-558) Do you mean that a "modified"-approach simulation was conducted for the time period coinciding with the DUSTRISK 2022 campaign? Please, clarify.
(Figure 4) I suggest eliminating panels a and b (see my previous comment). Also, One single legend with minerals is needed. Add y-axis label. The caption needs to specify the location with respect to the source of the aerosol output. Is it right after emission?
(line 562) Please, add a couple more sentences explaining the differences between the two simulations.
(lines 562-563) This is confusing. Do you mean that compared to observations, the modified model predicts higher (lower) mass fractions for illite and kaolinite (quartz)? If this is the case, this is actually true for illite, but not for kaolinite and quartz. Or instead, are you comparing models? If so, why do you write "bulk measurements" (measurements = observations).
(lines 564-565) But where are the simulation output and observations shown for the clay-size fraction? Isn't Figure 5 the "bulk" quantity (i.e., mass fractions integrated with respect to particle size)?
(lines 572-573) What do you mean by size limitations? Be explicit.
(line 576) "Feldspar shows little change in model-observation correlation between the two modeling approaches".
(line 577) "in the "modified" scheme with respect to the original scheme".
(lines 596-597) Cannot point measurements falling within a model grid cell be averaged for better comparisons against model output?
(caption in Figure 6) Eliminate "are" in "The error bars are represent".
(lines 610-615) Prior to the second sentence in this paragraph, I suggest stating that both model approaches cannot represent the variability in observed variability in elemental concentrations. In fact, this is relevant to this full section. This model underrepresentation is quite marked.
(line 611) Replace "silicate" by "silicon".
(lines 613-615) Isn't there a blank measurement of silicon concentration?
(line 617) Call Figure 6 at the end of the first sentence of this paragraph.
(lines 617-618) Where is this shown (what figure)?
(lines 618-619) Where is this shown (what figure)?
(line 621) Call Figure 6 at the end of the first sentence of this paragraph.
(lines 621-623) I would lower the emphasis here, that is, in line 622, I would eliminate "sharply" and replace "reasonable" with "comparatively better".
(line 625) Again, I would replace "adequate" with "comparatively better" to be more objective.
(line 629) Call Figure 6 after "in both schemes".
(line 630) Call Figure 5.
(line 634) "clay-sized particles".
(Figure 6 and Figure 7) Can these figures be combined into a single figure and still be readable? If so, I recommend doing so.
(line 639) Call Figure 7 at the end of the first sentence of this paragraph.
(lines 665) In order to avoid confusion between the use of clay for size and for elements (clay = phyllosilicates), I recommend replacing "clay minerals" and "silt minerals" here for the actual minerals.
(line 672) By how much does the "original" model underestimate phyllosilicates?
(line 677) Replace "underestimates" by "underestimating".
(caption of Figure 9) I recommend not using the term "silt minerals". Rather, specify the minerals.
(lines 704 and 708) Replace "silicone" by "silicon".
(line 736) I do not see that dramatic improvement for BIN01.
(line 739) I do not see that increased underestimation for BIN26.
(line 749) Call Figure 12 after ""original" and "modified" approaches".
(Figure 12) Why aren't the original model values included for Mg for BIN09 and BIN26, and for S for BIN01?
(line 756) "comparison (Figure 12) mirroring".
(line 787) "compilation of aerosol measurements".
(line 794) "Validation with in-situ elemental concentration-based, mineral-like measurements".
(line 818) Why are these two mineral phases particularly ill-represented in SMAs? For gypsum, is it due to its high water solubility and the fact that water sieving is usually used? And for calcite? Can you make a recommendation in this section on how to measure soil PSD? And what about soil sampling methods? (in both cases, for the objective of improving dust representation in models).

Citation: https://doi.org/10.5194/egusphere-2026-23-RC2

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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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