A note on the use of drag partition in aeolian transport models

Webb, Nicholas P.; Chappell, Adrian; LeGrand, Sandra L.; Ziegler, Nancy P.; Edwards, Brandon L.

doi:10.1016/j.aeolia.2019.100560

Cited by 26 publications

(39 citation statements)

References 19 publications

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“…However, areal applications of these schemes rely on estimates of z 0 that include unexplained variability (uncertainty) in field measurements, or approximations based on the lateral cover of roughness ( λ ) which underestimate surface sheltering (Chappell & Webb, 2016) and inadequately represents spatial heterogeneity in the momentum partition (Chappell & Webb, 2016; Webb, Okin, & Brown, 2014). Uncertainties in shear stress partitioning propagate through models and contribute to the inaccuracy of current dust models (e.g., LeGrand et al, 2019; Shao et al, 2011; Webb et al, 2020). Approaches that resolve the spatial distribution of u s * have been shown to be more accurate than models based on z 0 and λ (Li et al, 2013; Okin, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…This is because of the fundamental feedback between roughness and wind speed represented by aerodynamic roughness: As wind speed increases, the relative influence of the (vertical) roughness is reduced. Large spatial variability in surface aerodynamic properties and aeolian sediment transport necessitate more sophisticated measurement approaches that can support field research and improve the accuracy of areal wind erosion and dust emission models (Webb et al, 2019(Webb et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

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Scale Invariance of Albedo‐Based Wind Friction Velocity

Ziegler

Webb²,

Chappell

et al. 2020

JGR Atmospheres

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*) derived from traditional methods (wind speed profiles) with those derived from the albedo model at two separate scales using bare soil patch (via net radiometers) and landscape (via Moderate Resolution Imaging Spectroradiometer [MODIS] 500 m) data sets. Results show that profile‐derived estimates of u* are highly variable in anisotropic surface roughness due to changes in wind direction and fetch. Wind speed profiles poorly estimate soil surface (bed) wind friction velocities necessary for aeolian sediment transport research and modeling. Albedo‐based estimates of u* at both scales have small variability because the estimate is integrated over a defined, fixed area and resolves the partition of wind momentum between roughness elements and the soil surface. We demonstrate that the wind tunnel‐based calibration of albedo for predicting wind friction velocities at the soil surface (us*) is applicable across scales. The albedo‐based approach enables consistent and reliable drag partition correction across scales for model and field estimates of us* necessary for wind erosion and dust emission modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scale Invariance of Albedo‐Based Wind Friction Velocity

Ziegler

Webb²,

Chappell

et al. 2020

JGR Atmospheres

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“…in combination with the drag partition from Marticorena and Bergametti (1995) and with s bare,R = (1 − η snow ) × (1 − η br ) in combination with the drag partition parameterization from Raupach et al (1993). The area covered by vegetation is already accounted for in the latter, which determines the fraction of shear stress acting on the uncovered surface (Raupach et al, 1993;Webb et al, 2020). Alternatively, the bare soil fraction can be applied to the saltation flux.…”

Section: Surface Roughness Drag Partition and Covermentioning

confidence: 99%

Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0

Klose

Jorba

Gonçalves³

et al. 2021

Geosci. Model Dev.

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Abstract. We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20 µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000 Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about −0.90 and −0.63 W m−2 and at the top of the atmosphere between −0.20 and −0.28 W m−2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.

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“…The 𝑢 𝑠 * is required for sediment flux equations where 𝑢 𝑠 * ≠ 𝑢 * and Q (Eq. 1) is modified (Darmenova et al, 2009) in the TEMs: Instead of estimating directly 𝑢 𝑠 * , the 𝑢 * 𝑡𝑠 is divided by R for the model implementation to account for the drag partition and to make use of 𝑢 * (Webb et al, 2020). Following this approach, this form (Eq.…”

Section: )mentioning

confidence: 99%

Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere

Chappell

Webb

Hennen

et al. 2021

Preprint

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Abstract. Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days; RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large (> 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.

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A note on the use of drag partition in aeolian transport models

Cited by 26 publications

References 19 publications

Scale Invariance of Albedo‐Based Wind Friction Velocity

Scale Invariance of Albedo‐Based Wind Friction Velocity

Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0

Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere

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