general overview:

The manuscript (egusphere-2024-950) entitled “Global Spatial Variation in the PM2.5 to AOD Relationship Strongly Influenced by Aerosol Composition” presented an attractive aspect of the relation between PM2.5 and AOD. Under the consideration of such relationships, as the manuscript title itself declared, PM2.5 composition is quite an important point. However, the current presentation severely lacked the description and validation of them. Without the clarification of these points, I do not go through to further discussion points. Please address the following comments before the possible publication from the journal of ACP.

critical concerns:

From Section 2.4, we can follow the description of PM2.5 components. However, for example, “A 50% reduction of the surface nitrate concentration is applied to account for the long persisting bias in surface nitrate simulated by GEOS-Chem” was quite an unusual treatment considering the modeling results. In addition, “We artificially increase simulated AOD by 0.04 globally to address a poorly understood systematic bias.” also seems to be a trick. Despite such unusual post-analysis for PM2.5 components, we can only find the modeling evaluation for η, PM2.5, and AOD. It is desired to present the modeling evaluation for PM2.5 components such as SNA, black carbon, organic matter, sea salt, and dust. Without the detailed information for them, the result and discussion based on Fig. 3 cannot be understood.

specific comments:

• Line 14 and Line 46: Under the context of the abstract, the meaning of η is not clear. In addition, the meaning of η isnot clear even in the main text. How about defining η with a mathematical formula? Although a clear definition could be followed from the authors’ previous studies, this manuscript should be standalone.
• Line 74: Taking into consideration the year-to-year variation of dust, why the year 2019 was focused in this study? Were there severe dust events? The motivation for the selection of a target year will be helpful for readers.
• Line 132-139: The target year is 2019 in this study but did these emission datasets correspond to the year 2019? Or, was there a difference between the simulation year and the emission year? This point should be clarified, and if the latter, how about their impacts on modeling reproducibility?
• Line 171-172: The sentence “P represents population density in each grid box” can be moved into Line 167-168 because these definitions used P.
• Line 169-177: The order of i, j, k, and S are confusing in the definitions of F and R. Why the order was different between them? If there is no specific reason, these orders should be unified through variables.

technical corrections:

• Line 255: Correct “coorelation” in this Fig. 2.

Based on analysis of 2019 modeled PM2.5 and AOD, the manuscript claims that global spatial variation in the PM2.5 to AOD relationship is strongly influenced by aerosol composition. The study finds that the relationship is affected by aerosol composition and vertical profile. Although this seems to be an important thing, the manuscript has drawbacks in supporting the conclusions, which must be addressed before publication.

1. Why the ratio of a modeled PM2.5 to AOD is important? Model simulation of PM2.5 is totally based on emissions, meteorological and the modeling algorithm, there is no necessary connection with AOD.
2. GEOS-Chem settings are not detailed enough. What is the resolution? How are the emissions processed? How is the model performance against observations of chemical composition? We have no idea if the model results are good enough for following analysis with only validation of PM total mass.
3. The study does not use all available observations. With very sparse observations sites in Africa, South American, it is quite challenging to obtain solid conclusions as claimed.
4. The importance of chemical composition or vertical profile is simply depending on a sensitivity test to a global uniformed value. There is no logic here. It can’t be claimed by just Figure 5 with a sensitivity test and global averages. I believe you can find other things important if you do a sensitivity test.
5. Vertical profile also influences the results a lot (8.4 compared to 12.3 of chemical composition), I think it is worth noting in the title.