A new process-based and scale-respecting desert dust emission scheme for global climate models – Part I: description and evaluation against inverse modeling emissions
Abstract. Desert dust accounts for most of the atmosphere’s aerosol burden by mass and produces numerous important impacts on the Earth system. However, current global climate models (GCMs) and land surface models (LSMs) struggle to accurately represent key dust emission processes, in part because of inadequate representations of soil particle sizes that affect the dust emission threshold, surface roughness elements that absorb wind momentum, and boundary-layer characteristics that control wind fluctuations. Furthermore, because dust emission is driven by small-scale (~1 km or smaller) processes, simulating the global cycle of desert dust in GCMs with coarse horizontal resolutions (~100 km) presents a fundamental challenge. This representation problem is exacerbated by dust emission fluxes scaling nonlinearly with wind speed above a threshold wind speed that is sensitive to land surface characteristics. Here, we address these fundamental problems underlying the simulation of dust emissions in GCMs and LSMs by developing improved descriptions of (1) the effect of soil texture on the dust emission threshold, (2) the effects of non-erodible roughness elements (both rocks and green vegetation) on the surface wind stress, and (3) the effects of boundary-layer turbulence on driving intermittent dust emissions. We then use the resulting revised dust emission parameterization to simulate global dust emissions in a standalone model forced by reanalysis meteorology and land surface fields. We further propose (4) a simple methodology to scale up high-resolution gridded dust emissions to the coarse resolution of GCMs. The resulting dust emission simulation shows substantially improved agreement against regional dust emissions observationally constrained by inverse modeling. We thus find that our revised dust emission parameterization can substantially improve dust emission simulations in GCMs and LSMs.
Danny M. Leung et al.
Status: final response (author comments only)
- RC1: 'Comment on acp-2022-719', Anonymous Referee #1, 02 Dec 2022
- RC2: 'Comment on acp-2022-719', Anonymous Referee #2, 05 Dec 2022
- AC1: 'Responses to Reviewers on “A new process-based and scale-respecting desert dust emission scheme for global climate models – Part I: description and evaluation against inverse modeling emissions” by Danny M. Leung et al. (MS No: acp-2022-719)', Danny Leung, 07 Feb 2023
Danny M. Leung et al.
Danny M. Leung et al.
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The paper titled “A new process-based and scale-respecting desert dust emission scheme for global climate models – Part I: description and evaluation against inverse modeling emissions” correctly identifies the known problems and challenges in current dust schemes used in GCM/RCM including the effect of threshold friction velocity u*t, wind stress partitioning, boundary layer turbulence, and grid-size-dependency. I appreciate the author’s effort in improving dust emission schemes. The proposed changes are reasonable and have some physical basis. The emission estimates are validated as well. The paper is overall well written. However, there are a few concerns that should be addressed before the paper is accepted.
The resulting emissions are validated using DUSTCOMM data which is reasonable. The author mentions that they plan to implement the code in CESM later, in which case, it should be possible to conduct more robust validation of the results, for example, using DOD data, dust concentrations, and deposition. However, I still think that additional validation of the proposed changes is necessary to justify the added changes.
The proposed changes are numerous, not to mention that the paper is too long. An ideal, effective dust model should be simple in description and should use commonly available input datasets. Such a complex treatment of parameterizations must be justified appropriately. A step-wise validation and analysis of each added term would justify the complexity of the model. In this context, I suggest addressing the following four major points:
Title: scale-aware or scale-invariant instead of scale-respecting?
The title seems self-contradictory to the content because the paper itself compares the emissions at different resolutions which are very different (lines 165-195). So in what sense is the model scale-respecting? And why limit it only to GCMs, it should be applicable to RCMs as well. Perhaps a better title could be ‘Towards a scale-invariant process-based dust emission scheme for climate models ……
Line 43: I understand that you scaled up your high-resolution data so that it can be compared to GCM outputs. However, I don’t see why scaling up high-resolution gridded dust emissions to the coarse resolution of GCMs would be so important so as to develop such an additional methodology, which also diverts the focus of the paper. The world is moving towards a high-resolution era and actually, the opposite would be more beneficial – to convert GCM outputs to high-resolution emissions.
Line 51-52, this may not be entirely true, for example, Osipov et al. (2022) show that anthropogenic aerosols contribute more than 90% of PM:
Line 55: Literature on dust-climate interaction should be expanded to include the most recent developments: e.g.,
113-116: I am not sure about it because I think the main challenge is to represent small-scale roughness elements of grounds and rocks, not vegetation. The vegetation roughness effect is already taken into account in calculating friction velocity in most models in terms of displacement height and ground surface roughness, although crudely. So we need to be careful not double-accounting the effect of roughness.
122-123: that is true for GCMs but not for RCMs (e.g. WRF) which typically use model time steps in seconds.
125: Are you talking about wind gusts that could be better represented in a sub-grid scale? Turbulence and subgrid variability of winds are two things. Wind gusts are probably better represented with the increase in resolution. Turbulence is usually understood differently in relation to large convective cells so it could be confusing.
290-291: since Bit is constant u*it is proportional to u*ft0, I don’t understand how it is going to change the simulated spatiotemporal distribution of dust as mentioned. u*ft also includes soil moisture term fm in addition to u*ft0 (which is only a function of Dp and air density) so u*ft will show more variability, isn’t it so?
314-316: This is an interesting formulation but I am concerned that roughness could be double-accounted because roughness is already used in calculating u* (law of the wall) in most GCM/RCM.
328: delete comma
384-385: Perhaps Chappel and Webb (2016) consider both since it uses satellite albedo, which has been recently implemented by Legrand et al. (2022).
Chappel and Webb (2016): https://gmd.copernicus.org/preprints/gmd-2022-157/
Legrand et al. (2022): https://www.sciencedirect.com/science/article/pii/S1875963716300957?via%3Dihub
Eq 14: Using a constant Dp in arid regions is fine but it does not make much sense to use a variable Dp in vegetated areas which are not the dominant dust sources anyway. The question is: is adding such complexity worth that will likely not have any significant effect on the results of dust emission? In the vegetated areas, roughness/drag force will dominantly govern threshold friction velocity and the Dp will not likely have a remarkable effect on the resulting dust emission. Another concern is that fm already depends upon clay content so using clay content again in this formulation of Dp may double-account the effect of clay/silt content on dust emission.
Eq 15, again there is a possibility that zos is double-accounted as u* already uses this term.
Eq 7, as far as I remember the equation is dp/30, not 2*dp/30, e.g., see https://hal.archives-ouvertes.fr/hal-00677875/document
Page 26 Line 38: You mentioned earlier that the K14 scheme is increasingly used in GCM/RCMs. The description of this model was already published and validated. Could you explain why this model still gives such a high estimate of dust emission flux (29,300 Tg/year)?
313-315 Was Z03 also conducted at the same spatial resolution?
313-315: Since this scheme uses an additional representation of drag partition, which is not in the Z03 scheme, wouldn't it make more sense to compare the results with another dust scheme that also contains a drag partition scheme, for example, Marticorena and Bergametti 1995 dust scheme which has been discussed in the paper:
Section 5: The limitation section is too detailed, which somehow undermines the value of the study itself. This section could be shortened to highlight only the key limitations associated with estimates of dust emission fluxes.