| 04 Mar 2026
Vegetation effects redistribute dust globally
Abstract. Dust aerosols play a pivotal role in climate, ecosystems, and human health, yet global dust emission estimates in current Earth System Models (ESMs) remain highly uncertain due to over-simplified surface parameterizations and inconsistent particle size representations. Vegetation effects on dust emissions are rarely explicitly accounted for in the models, limiting physical realism and land–atmosphere coupling. This study bridges this gap by utilizing the dynamic vegetation cover derived from the land surface model ORCHIDEE, and accounting for its effects on the dust emission scheme from the IPSL coupled model. The influence of including the very large dust particles (diameter greater than 100 μm) is also studied using two representations: a single-mode dominated by fine micrometre-sized particles, and a multi-mode representation comprising four size modes covering a range exceeding 100 μm. Incorporating vegetation reduces the global dust emissions by 23 %, primarily over semi-arid regions, and shifts the spatial dominance toward sparsely vegetated deserts, such as North Africa and East Asia. Including vegetation also leads to an improvement in model agreement with observations by reducing mean biases by approximately 50 %–80 % across various dust metrics, notably mitigating overestimations in dust aerosol optical depth (DAOD) over north-western India and in dust deposition over Antarctica. Furthermore, different particle size representations indicate that accurate reproduction of DAOD depends on the adequate representation of fine particles. Overall, this ESM-consistent framework, achieved by explicitly integrating vegetation effects and comprehensive particle size distributions, provides a pathway for future coupled land–atmosphere simulations under climate change.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
The manuscript by Xu and coauthors presents new model developments within the IPSL Earth System Model framework, aimed at including vegetation dependence on desert dust emissions. Simulated fields are validated against observational datasets. The topic is of interest for the aerosol and climate modelling communities. Both the manuscript and the underlying scientific work are well crafted. This work is suitable publication after minor revisions, from my perspective.
Specific comments
67-68. Perhaps not many “original” studies such as, e.g. Marticorena and Bergametti (1995), but many “CMIP-class” models already include this effect, e.g. UKESM (Woodward et al., 2001: https://doi.org/10.1029/2000JD900795 ), CESM (Zender et al., 2003: https://doi.org/10.1029/2002JD002775 ), MPI (Stanelle et al., 2014: https://doi.org/10.1002/2014JD022062D ) among others.
155. Why only January? What about the Southern Hemisphere, for instance?
156. How is a “potential dust source area” defined?
157. Which variable is a “surface wetness proxy”?
191. Considering that fr >=1 by definition, there are three possible cases for the application of equation 4.4, from my understanding:
(1) u < ut < ut*fr , leading to no dust emissions
(2) u > ut*fr > ut , leading to dust emissions from both “pure” bare soil and bare soil between vegetation patches
(3) ut < u < ut*fr , resulting in positive emissions from “pure” bare soil but apparently negative emissions from bare soil between vegetation patches, which would pose some problems. Please clarify this aspect.
16 (Supplement). Regridded how? Via bilinear interpolation?
252. Do you mean that deep convection follows the “Standard Physics” while turbulent mixing parameterizations follows the New Physics” scheme?
296. Was this calculation done online or rather calculated a posteriori based on monthly output?
329. Are those scaling factors applied to dust emissions? If so, in the case of the 4-mode configuration you would imply changing the overall size distribution at emission. Please comment on that.
332. How was the overall calibration process carried out in order to determine the optimal scaling coefficients (considering all three variables)?
341. These processes apply to dust atmospheric dispersion in general, not specifically to PM10. This sentence seems out of place here.
374. I do not understand this distinction, since I could not find any subsequent budget segregating e.g. global land vs oceanic dust deposition.
491. It is not clear at which point those scaling factors were applied, i.e. online at the stage of dust emissions, or rather offline, on the monthly history files?