General comments

This paper reports the effect of three different inventories on aerosol optical depth and radiative forcing simulated with one model. Two of the inventories are different versions of CEDS. Both magnitude and trend of aerosol optical depth are compared with MODIS.

I find the contribution of this paper to scientific understanding to be rather modest. The use of models to understand how anthropogenic emissions affect the atmosphere’s aerosol content and climate is of course a worthwhile pursuit. But an interesting work would include careful diagnosis of the causes of difference, or their implications for radiative forcing and climate response in different regions. This work doesn’t provide that. Authors argue that the later version of CEDS was not included in AR6 and therefore it is worthwhile to analyze implications of the new inventory. That may be true, but the paper simply reports averages and shows spatial distributions. It doesn’t provide much understanding of how or why the new inventory is different or whether it is more or less suitable to represent anthropogenic influence.

Specific comments

Emission inventories do not affect actual aerosol influence (as is suggested in the title), rather simulated influence. The purpose of a model is to attempt to reflect the real evolution. Certainly if one changes any flux (emissions) or any input then it changes the simulation, but this shouldn’t be a surprise if one’s model is working properly. Perhaps some understanding could be gained by exploring whether the model outputs (AOD, RFari, RFaci) scale with the inventory changes. This sort of analysis is hinted at, e.g. in lines 207-208, but for a helpful contribution to the community, much more analysis would be presented.

Emissions are a component of the physical system that are affected by processes. This paper compares different compilations of or assumptions about those processes. But ascribing these differences to the compilation label itself, eg ‘CEDS’, ‘ECLIPSE’ is overly simplistic. What assumptions have the inventory developers made that cause these differences?

Aerosol trends are discussed in lines 234-249 and 342-364. This sort of analysis could aid in identifying, explaining and quantifying differences in the input (emissions) and response (AOD, RFari). But the analysis presented here is rather broad (‘weaker in magnitude’, ‘consistent with…’) What are the implications for RFari and RFaci, since a global average is given for these measures relative to 1990?

The presentation also ascribes some masking of trends to interannual variability, especially among natural (sea-salt) or biomass burning emissions. This is a well-known issue in comparing model results with observations. It would seem that model evaluators should have some set of best practices about how to account for this effect after many years of such studies, such as using running means. Otherwise, the persistent inability to draw conclusions will render all such studies only marginally useful. Do authors have thoughts on this?  

It may be worthwhile to define how well one needs to know the forcing since emissions and other inputs are always uncertain. Then one would have more confidence in stating a ‘strong effect’ as is done in the title.

The paper acknowledges analysis by other researchers on the same topic, e.g. Lund et al 2018, Mortier et al 2020, Quaas et al 2022. However, other than broadly comparing findings, this work doesn’t indicate what new insights it has offered – what has been done here that wasn’t done before, and if another future paper is done with similar approach, what questions should it attempt to answer? There seems to be a limited review of prior work, especially considering that Asian emission inventories have been evaluated against observations, and those emissions are also stated to play an important role in this work.

Technical comment

I found the paper well written and I did not note technical corrections to make it clearer or more accurate.

Reviewer comment in atached pdf document