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Weaknesses in Dust Emission Modelling Hidden by Tuning to Dust in the Atmosphere
  • +15
  • Adrian Chappell,
  • Nicholas Webb,
  • Mark Hennen,
  • Charles Sutton Zender,
  • Philippe Ciais,
  • Kerstin Schepanski,
  • Brandon L Edwards,
  • Nancy Parker Ziegler,
  • Yves Balkanski,
  • Daniel Tong,
  • John F Leys,
  • Stephan Heidenreich,
  • Robert Hynes,
  • David Fuchs,
  • Zhenzhong Zeng,
  • Matthew C. Baddock,
  • Jeff Lee,
  • Tarek Kandakji
Adrian Chappell
Cardiff University

Corresponding Author:chappella2@cardiff.ac.uk

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Nicholas Webb
USDA-ARS Jornada Experimental Range
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Mark Hennen
Cardiff University
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Charles Sutton Zender
University of California, Irvine
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Philippe Ciais
Laboratory for Climate Sciences and the Environment (LSCE)
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Kerstin Schepanski
Free University of Berlin
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Brandon L Edwards
New Mexico State University
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Nancy Parker Ziegler
US Army ERDC Cold Regions Research and Engineering Laboratory
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Yves Balkanski
IPSL/LSCE (Laboratoire des Sciences du Climat et de l'Environnement)
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Daniel Tong
George Mason University
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John F Leys
Australian National University
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Stephan Heidenreich
New South Wales government
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Robert Hynes
New South Wales government
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David Fuchs
University of New South Wales
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Zhenzhong Zeng
Southern University of Science and Technology
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Matthew C. Baddock
Loughborough University
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Jeff Lee
Texas Tech University
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Tarek Kandakji
Yale Uni
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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 traditional dust emission models (TEMs) assume that the Earth’s land surface is devoid of vegetation, adjust dust emission using a vegetation cover complement, and calibrate the magnitude of modelled emissions to atmospheric dust. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface normalised shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. Existing datasets of satellite observed dust emission from point sources (DPS) and dust optical depth (DOD) show little spatial relation and DOD frequency exceeds DPS frequency by up to two orders of magnitude. Relative to DPS frequency, both dust emission models showed strong relations, but over-estimate dust emission frequency, suitable for calibration to observed dust emission. Our results show that TEMs over-estimate large dust emission over vast vegetated areas and produce considerable false change in dust emission, relative to the AEM. It is difficult to avoid the conclusion, raised by other literature, that calibrating dust cycle models to atmospheric dust has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without masks or vegetation cover. Considerable potential exists for Earth System Models driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.