Modelled direct causes of dust emission change (2001–2020) in southwestern USA and implications for management

Hennen, Mark; Chappell, Adrian; Webb, Nicholas P.

doi:10.1016/j.aeolia.2022.100852

Cited by 10 publications

(6 citation statements)

References 41 publications

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“…Using extant DPS (Hennen et al, 2022(Hennen et al, , 2023, our results demonstrate that DOD frequency is limited to areas with highly reflective surfaces e.g., creating a bias over northern areas of the Chihuahuan Desert. The DOD frequency hotspots for the period 2001-2016 were located upwind of the DPS locations.…”

Section: Overcoming Dust Emission Model Tuning To Dodmentioning

confidence: 92%

“…It is timely that a new dust emission point source (DPS) database for southwestern North America has been collated and has been used with the albedo-based dust emission model (AEM) to circumvent the simplifying assumptions about sediment entrainment and sediment supply (Hennen et al, 2022(Hennen et al, , 2023. We follow that established approach and evaluate dust emission modeling against dichotomous DPS observations and also compare those DPS to the frequency of atmospheric DOD.…”

Section: Introductionmentioning

confidence: 99%

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Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Chappell,

Webb,

Hennen

et al. 2023

JGR Atmospheres

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Large‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

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Section: Overcoming Dust Emission Model Tuning To Dodmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Chappell,

Webb,

Hennen

et al. 2023

JGR Atmospheres

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“…These assumptions are unrealistic in dust source regions (Chappell et al, 2007;Chappell et al, 2005Chappell et al, , 2006Gillette et al, 2001;Sekiyama et al, 2023;Vos et al, 2020;Webb and Strong, 2011). In the absence of improved and areal u *ts , we follow recent F developments (Chappell, Web, Hennen, Schepanski, et al, 2023;Chappell, Webb, Hennen, Zender, et al, 2023;Hennen et al, 2022;Hennen et al, 2023) and fit a logarithmic function to F AEM and correct for over-estimation in F cal (Chappell, Web, Hennen, Schepanski, et al, 2023) due to assumptions about model scale, entrainment and sediment supply…”

Section: Dust Emission Modelingmentioning

confidence: 99%

Reducing Resolution Dependency of Dust Emission Modeling Using Albedo‐Based Wind Friction

Chappell,

Hennen,

Schepanski

et al. 2024

Geophysical Research Letters

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Numerical simulations of dust emission processes are essential for dust cycle modeling and dust‐atmosphere interactions. Models have coarse spatial resolutions which, without tackling sub‐grid scale heterogeneity, bias finely resolved dust emission. Soil surface wind friction velocity (us*) drives dust emission non‐linearly with increasing model resolution, due mainly to thresholds of sediment entrainment. Albedo is area‐integrated, scales linearly with resolution, is related to us* and hence represents its sub‐grid scale heterogeneity. Calibrated albedo‐based global dust emission estimates decreased by only 2 Tg y−1 (10.5%) upscaled from 0.5 to 111 km, largely independent of resolution. Without adjusting wind fields, this scaling uncertainty is within recent estimates of global dust emission model uncertainty (±14.9 Tg y−1). This intrinsic scaling capability of the albedo‐based approach offers considerable potential to reduce resolution dependency of dust cycle modeling and improve the representation of local dust emission in Earth system models and operational air quality forecasting.

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“…(2020) demonstrated the scale invariance of this approach at the field (plot) scale. This scale invariance has been assumed to hold over large areas when the albedo‐based approach has been applied to dust emission studies in North America (Hennen et al., 2022, 2023) and globally (Chappell, Webb, Hennen, Schepanski, et al., 2023). Considering the existence of multiple, very different, roughness element types, such as plants and gravel, and different spatial combinations (density variation, non‐homogeneous spatial distribution) in arid and semi‐arid landscapes, it is necessary to further evaluate the applicability of this new approach based on albedo across a representative range of cover types.…”

Section: Introductionmentioning

confidence: 99%

Using Field Measurements Across Land Cover Types to Evaluate Albedo‐Based Wind Friction Velocity and Estimate Sediment Transport

Zhou,

Zhang,

Chappell

et al. 2024

JGR Atmospheres

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The soil surface wind friction velocity (us*) is an essential parameter for predicting sediment transport on rough surfaces. However, this parameter is difficult and time‐consuming to obtain over large areas due to its spatiotemporal heterogeneity. The albedo‐based approach calibrates normalized shadow retrieved from any source of albedo data with laboratory measurements of aerodynamic properties. This enables direct and cross‐scale us* retrieval but has not been evaluated against field measurements for different cover types. We evaluated the approach's performance using wind friction velocity (u*) measurements from ultrasonic anemometers. We retrieved coincident field pyranometer and satellite albedo at 48 sites that were spread over approximately 1,800 km on the Inner Mongolia Plateau, including grassland, artificial shrubland, open shrubland, and gobi land. For all cover types, u* estimates from ultrasonic anemometers were close to the albedo‐based results approach. Our results confirm and extend the findings that the approach works across scales from lab to field measurements and permits large‐area assessments using satellite albedo. We compared the seasonal sediment transport across the region calculated from albedo‐based us* with results from an exemplar traditional transport model (QT) driven by u* with aerodynamic roughness length varying with land cover type and fixed over time. The traditional model could not account for spatiotemporal variation in roughness elements and considerably over‐estimated sediment transport, particularly in partially vegetated and gravel‐covered central and western parts of the Inner Mongolia Plateau. The albedo‐based sediment transport (QA) estimates will enable dynamic monitoring of the interaction between wind and surface roughness to support Earth System models.

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Modelled direct causes of dust emission change (2001–2020) in southwestern USA and implications for management

Cited by 10 publications

References 41 publications

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Reducing Resolution Dependency of Dust Emission Modeling Using Albedo‐Based Wind Friction

Using Field Measurements Across Land Cover Types to Evaluate Albedo‐Based Wind Friction Velocity and Estimate Sediment Transport

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