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

Authors: CHAPPELL, ADRIAN - Cardiff University; WEBB, NICHOLAS - New Mexico State University; HENNEN, MARK - Cardiff University; ZENDER, CHARLES - University Of California Irvine; CIAIS, PHILIPPE - Laboratoire Des Sciences Du Climat Et De L'Environnement (LSCE); SCHEPANSKI, KERSTIN - Freie University; EDWARDS, BRANDON - New Mexico State University; ZEIGLER, NANCY - Environmental Laboratory, Us Army Engineer Research And Development Center, Waterways Experiment St; JONES, SANDRA - Environmental Laboratory, Us Army Engineer Research And Development Center, Waterways Experiment St; BALKANSKI, YVES - Laboratoire Des Sciences Du Climat Et De L'Environnement (LSCE); TONG, DANIEL - George Mason University; LEYS, JOHN - Australian National University; HEIDENREICH, STEPHAN - Nsw Office Of Environment And Heritage; HYNES, ROBERT - Nsw Office Of Environment And Heritage; FUCHS, DAVID - Nsw Office Of Environment And Heritage; ZENG, ZHENZHONG - University Of Science And Technology Of China; ECKSTROM, MARIE - Cardiff University; BADDOCK, MATTHEW - Loughborough University; LEE, JEFFREY - Texas Tech University; KANDAKJI, TAREK - Yale University

Submitted to: Geoscientific Model Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/2/2021
Publication Date: 11/4/2021
Citation: Chappell, A., Webb, N., Hennen, M., Zender, C., Ciais, P., Schepanski, K., Edwards, B., Zeigler, N., Jones, S., Balkanski, Y., Tong, D., Leys, J., Heidenreich, S., Hynes, R., Fuchs, D., Zeng, Z., Eckstrom, M., Baddock, M., Lee, J., Kandakji, T. 2021. Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere. Geoscientific Model Development. Article gmd-2021-337. https://doi.org/10.5194/gmd-2021-337.
DOI: https://doi.org/10.5194/gmd-2021-337

Interpretive Summary: Dust emissions from drylands and agricultural landscapes affect climate but models used to assess dust effects are highly uncertain. This is because dust emission models are currently calibrated to represent dust measured in the atmophere, rather than measurements of dust emission in eroding landscapes. This study uses satellite observations of dust emissions at point sources around the world to calibrate a new dust emission model that represents variability in vegetation across space and through time. The new model is compared with a traditionally formulated dust model that is calibrated to measurements of dust in the atmosphere. The model comparison reveals how large uncertainties about effects of vegetation on dust emission have been hidden in traditional dust models, and how those uncertainties can be addressed using a dynamic, satellite remote sensing approach to dust modeling and model calibration.

Technical 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 traditional dust emission models (TEM) assume that the Earth’s land surface is devoid of vegetation, then adjust the dust emission using a vegetation cover complement, and finally calibrate the magnitude of simulated emissions to dust in the atmosphere. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compared the TEM and AEM dust emissions with estimates of dust in the atmosphere using dust optical depth frequency (DOD) and satellite observed dust emission from point sources (DPS). We show that during the same period, the DOD frequency exceeds by two orders of magnitude DPS frequency (RMSEDOD=151 days). Also relative to DPS frequency, both models over-estimated dust emission frequency but by only one order of magnitude (RMSETEM=27 days; RMSEAEM=20 days) and showed strong relations with DPS frequency, suitable for calibrating models to observed dust emission. Theoretically, the TEMs are incomplete in their formulation, which despite the pragmatic adjustment using the vegetation cover complement, 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 TEMs produce considerable false change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness, our results suggest that its crude representation in the TEMs has caused large, previously unknown, uncertainty in Earth System Models (ESMs). It is difficult to avoid our conclusion, also raised by others, 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 before calibration. The major advantage for ESMs, is that the AEM can be driven by intrinsic prognostic albedo to represent the fidelity of drag partition physics and reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.