| 04 Feb 2026
Dynamic drag partitioning in GEOS-Chem (v. 14.2.3) eliminatessource function and tuning, revealing equifinality of atmosphericdust observations
Abstract. Large surface roughness elements (typically vegetation) extract momentum from near-surface winds partitioning it between the vegetation canopy and the unsheltered soil surface, altering the spatial distribution and intensity of dust emission. Data for global drag partition varying over space and time are limited and classical dust models often adopt simplified assumptions or neglect the effect of roughness entirely, relying instead on empirical dust source function to reproduce observed atmospheric dust. Such approaches ignore the three-dimensional spatiotemporal sheltering provided by vegetation and the soil roughness which limits the physical fidelity and predictive capability of dust models. Here we implement the global sheltering parameterization based on surface albedo data in the dust emission scheme of a global atmospheric chemical transport model and evaluate its impact on simulated global dust emissions. We compare simulations from this sheltering parameterization with default settings from classical dust modelling and validate our results against atmospheric dust observations following the widely accepted approach. This is the first study to evaluate the performance of the albedo-based sheltering parameterization globally. The approach eliminates the need for an empirical source function to constrain sources and avoids the need to tune dust emission models. It produces atmospheric dust loadings consistent with atmospheric dust observations despite substantially altering the frequency and spatial pattern of dust emissions. Evidently, different patterns of dust emission frequency can produce similar atmospheric dust loadings, revealing that previous studies intent on reproducing general patterns of large-scale atmospheric dust burden have overlooked the equifinality of dust emission frequency. Accurate dust emission magnitude and frequency remain hampered by entrainment threshold fixed over space, static over time with endless sediment supply, consistent with all other models.
This study applies an albedo-based drag partitioning approach to GEOS-Chem (v. 14.2.3) for simulating dust emission globally. The authors compare the simulated spatial and seasonal distribution of dust emission from the current albedo-based drag partitioning approach with those obtained from two existing dust emission schemes that used (1) an observation-based dust source function that masks out non-erodible area, and (2) a simplified vegetation roughness drag partitioning approach, and further validate against atmospheric dust observations and dust deposition observations. The comparison and validation lead to the conclusion that the albedo-based drag partitioning approach eliminates an empirical source function to constrain dust sources and avoids the need to tune dust emission models.
I generally appreciate the equifinality issue within dust modeling as raised by the authors, and the albedo-based drag partitioning approach appears as an encouraging alternative to the classic dust source function approach. However, I have several concerns about the present manuscript regarding (1) the applicability this new approach being overly stated, and (2) the deviates between the current and previous approaches being insufficiently explored. Given the uncertain outcome of my proposed additional work, I recommend rejection of the current manuscript and encourage resubmission.
Major concerns:
A main purpose of the current study seems to eliminate the use of dust source function because, according to the authors, (1) the source function is empirical and not physical, (2) it is static over time. While I agree with these two critiques about the present usage of dust source function, the present albedo-based drag partitioning approach has its own limitations by nature. First, I would not call the present approach as “purely physical”, because (1) the aerodynamical shadow of roughness elements is empirically linked with surface albedo, and (2) the formulation of the present approach, e.g. equation (8), is semi-empirical.
Second, because of the semi-empirical nature of equation (8), it is hard to directly use it for predicting dust emission under climate change because to do so will require a radiative transfer module in the land model to calculate the surface albedo that accounts for the shadow of vegetation. In contrast, a more physics-based drag partitioning approach, e.g. as used in Leung et al. (2024), is more directly applicable in a climate or Earth system model than the present albedo-based drag partitioning approach.
If the authors refine the applicability of the current albedo-based drag partitioning approach to chemical transport models that deal with air quality problems during the satellite era when albedo observation is available, I believe it is fair to say that the albedo-based drag partitioning is an encouraging approach for capturing the episodic and dynamical nature of dust emission. Therefore, I suggest to rephrase the entire manuscript to clearly state the applicability of the present approach. Yet, the relative performance of different dust emission modeling approaches should be further evaluated, which I will cover in the next section of my review.
（1） Which dust source function do you actually use in Exp 1? What is it based off? While the authors state it is the default GOCART setting and cite the Ginoux et al (2001) paper, they also mention that it is “based on the geographical distribution of atmospheric dust”. But indeed the dust source function used in the GOCART default setting was not from atmospheric dust, but from surface topography and has a physical foundation in terms of sediment cumulation (Ginoux et al. 2001). I believe the default DEAD model use the same or similar idea for generating dust emission model (Zender et al. 2003). Please check the dust source function used in your Exp 1 and clarify its empirical or physical foundation.
（2）In Exp2, z0 is obtained at what temporal scale? I see a subscript m in equation (4) but not on line 123. Are they the same thing? Leung et al. (2024) used similar, simplified vegetation drag partitioning formulation, but aerodynamical roughness length above canopy is updated with dynamical vegetation. They show good performance of the dust emission scheme when compare the day-to-day variation in DAOD with multiple sources of observation. So I wonder if the vegetation drag partitioning in your Exp2 reflects the dynamical nature of vegetation, or is it set static? If it is currently set static, could you follow the approach of Leung et al. (2024) but use satellite observation to derive a dynamical vegetation drag partitioning and compare it with your albedo-based drag partitioning approach? Do you still get the conclusion that Exp2 overly flatten the seasonal cycle of dust emission.
（3）If my understanding is correct, a key strength of the albedo-based approach is that the albedo field updates daily, which reflects the dynamical nature of soil erodibility. However, the current comparison on the simulated dust fields between the three experiments, as well as the simulated versus observed dust fields, mostly focus on the climatological scale. Could you show some extreme cases that are successfully captured by your albedo-based drag partitioning approach but missed by the other two schemes? Can you also show the statistics for all three schemes regarding their performance on extreme dust outbreak events?
（4）When you do comparison between model and observation, are all the observational datasets restricted to the year 2016? At least for dust deposition from Albani et al. (2014), it is unlikely that the observational data covers year 2016. I think the temporal match is necessary for model-observation comparison, especially in terms of the extreme events as I proposed in my previous comment.
Minor suggestions
Reference
Leung, D. M., Kok, J. F., Li, L., Mahowald, N. M., Lawrence, D. M., Tilmes, S., ... & Pérez García-Pando, C. (2024). A new process-based and scale-aware desert dust emission scheme for global climate models–Part II: Evaluation in the Community Earth System Model version 2 (CESM2). Atmospheric Chemistry and Physics, 24(4), 2287-2318.
Bullard, J. E., et al. (2016), High-latitude dust in the Earth system, Rev. Geophys., 54, 447–485, doi:10.1002/2016RG000518.