This study investigates the dominant factors contributing to the spatial (regional and aridity-level dependent) and temporal (interannual) variability of dust emission, drawing upon Earth System Model (ESM) results for the present day provided to CMIP6. Similar prior works (not cited in the manuscript) have previously reached comparable conclusions. So the work lacks originality. A key distinction is that previous studies relied on observations, allowing for absolute comparisons. This study, however, does not utilize global long-term observations of dust properties, such as satellite-based Dust Optical Depth (DOD). Consequently, its findings are limited to a model inter-comparison, without the ability to assess potential strengths or weaknesses.

I would suggest to the authors to build upon the work of Zender and Kwon (2005) and Kim et al. (2017), perhaps focusing on long-term variability, given that MODIS offers over 20 years of daily global DOD data. Surface concentration at Barbados has been observed daily since 1965, providing 60 years of data—ample for studying inter-annual variability. Prospero and Lamb (2003) demonstrated that hydroclimate factors control dust long-term variability. By examining the ESM results that best reproduce the interannual variability observed at Barbados and/or in satellite data, we could discern the strengths and weaknesses of their controlling factor(s).

However, when analyzing interannual variations, certain factors influencing these variations should also be considered. These include land-use, which significantly contributes to dust emission (Ginoux et al., 2012; Stanelle et al., 2014), and fires (Yu and Ginoux, 2022; Wagner and Schepanski, 2025).

Some fundamental information regarding the ESMs, crucial for understanding their differences in dust emission, is either incorrect or inadequately explained. Furthermore, the analysis exhibits a bias towards CESM, with minimal or no discussion of other models.

The citations for MPI-ESM-1.2 and GFDL-ESM4 are erroneous. Tegen et al. (2019) described ECHAM6.3-HAM2.3 with constant roughness and vegetation mask. Is Mauritsen et al. (2019) not the correct reference for MPI-ESM-1.2? MPI-ESM-1.2 utilizes MAC-v1 prescribed aerosol distribution (Kinne et al., 2013). For GFDL-ESM4, it would be appropriate to refer to Shevliakova et al. (2024) instead of Evans et al. (2016). The authors are advised to consult these references or contact the lead authors to ensure an accurate description of their models.

Table 1 lacks critical information, such as LAI. Is it calculated online? Is it static or dynamic? Does it incorporate land-use? Is brown vegetation included? For 10-meter wind-speed derived from the first model level, the robustness of the derivation diminishes with increasing altitude of this level. Horizontal resolution is paramount for all fields. How can models be compared without knowledge of their spatial resolution?

Given these significant issues, I cannot recommend the publication of the present manuscript.

References:

Evans, S., P. Ginoux, S. Malyshev, and E. Shevliakova (2016). Climate-vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11,823–11,830, doi:10.1002/2016GL071016.

Ginoux, P., J. M. Prospero, T. E. Gill, N. C. Hsu, and M. Zhao (2012), Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50, RG3005, doi:10.1029/2012RG000388.

Kim, D., Chin, M., Remer, L. A., Diehl, T., Bian, H., Yu, H., ... and Stockwell, W. R. (2017). Role of surface wind and vegetation cover in multi-decadal variations of dust emission in the Sahara and Sahel. Atmos. Environm., 148, 282-296.

Kinne, S., D. O'Donnel, P. Stier, S. Kloster, K. Zhang, H. Schmidt, S. Rast, M. Giorgetta, T. F. Eck, and B. Stevens (2013). MAC-v1: A new global aerosol climatology for climate studies, J. Adv. Model. Earth Syst., 5, 704–740, doi:10.1002/jame.20035.

Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., et al. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO₂. Journal of Advances in Modeling Earth Systems, 11, 998–1038. https://doi.org/10.1029/2018MS001400

Prospero, J. M., and Lamb, P. J. (2003). African droughts and dust transport to the Caribbean: Climate change implications. Science, 302(5647), 1024-1027.

Stanelle, T., I. Bey, T. Raddatz, C. Reick, and I. Tegen (2014), Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing, J. Geophys. Res. Atmos., 119, 13,526–13,546, doi:10.1002/2014JD022062.

Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., Stier, P., Watson-Parris, D., Stanelle, T., Schmidt, H., Rast, S., Kokkola, H., Schultz, M., Schroeder, S., Daskalakis, N., Barthel, S., Heinold, B., and Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation, Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019.

Wagner, R., and Schepanski, K. (2025). Quantifying fire-driven dust emissions using a global aerosol model. Journal of Advances in Modeling Earth Systems, 17, e2024MS004466. https://doi.org/10.1029/2024MS004466

Yu, Y., and Ginoux, P. (2022). Enhanced dust emission following large wildfires due to vegetation disturbance. Nature Geoscience, 15(11), 878-884.

Zender, C. S., and E. Y. Kwon (2005). Regional contrasts in dust emission responses to climate, J. Geophys. Res., 110, D13201, doi:10.1029/2004JD005501.