Preprints
https://doi.org/10.5194/gmd-2021-337
https://doi.org/10.5194/gmd-2021-337

Submitted as: development and technical paper 04 Nov 2021

Submitted as: development and technical paper | 04 Nov 2021

Review status: this preprint is currently under review for the journal GMD.

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

Adrian Chappell1, Nicholas Webb2, Mark Hennen1, Charles Zender3, Philippe Ciais4,5, Kerstin Schepanski6, Brandon Edwards2, Nancy Ziegler7, Sandra Jones7, Yves Balkanski4, Daniel Tong8, John Leys9,10, Stephan Heidenreich9, Robert Hynes9, David Fuchs9, Zhenzhong Zeng11, Marie Ekström1, Matthew Baddock12, Jeffrey Lee13, and Tarek Kandakji14 Adrian Chappell et al.
  • 1School of Earth and Environmental Sciences, Cardiff University, Cardiff CF10 3XQ, UK
  • 2USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003, USA
  • 3Department of Earth System Science, University of California, Irvine, USA
  • 4Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UPSACLAY, Gif-sur-Yvette, France
  • 5Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, 20 Konstantinou Kavafi Street, 2121 Nicosia, Cyprus
  • 6Institute of Meteorology, Freie Universität Berlin, Germany
  • 7US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory (CRREL), 72 Lyme Rd, Hanover, NH 03755-1290, USA
  • 8Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030 USA
  • 9Department of Planning, Industry and Environment, NSW, Australia
  • 10The Fenner School of Environment and Society, Australian National University, Australia
  • 11School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen 518055, China
  • 12Geography and Environment, Loughborough University, Loughborough, UK
  • 13Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA
  • 14Yale Center for Earth Observation, Yale University, New Haven, CT 06520, USA

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.

Adrian Chappell et al.

Status: open (until 30 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Adrian Chappell et al.

Adrian Chappell et al.

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Short summary
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. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.