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
Status: this preprint was under review for the journal GMD but the revision was not accepted.

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

Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji

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, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-337', Paul Ginoux, 08 Dec 2021
    • AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
  • RC1: 'Comment on gmd-2021-337', Anonymous Referee #1, 17 Dec 2021
    • AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
  • RC2: 'Comment on gmd-2021-337', Anonymous Referee #2, 14 Feb 2022
    • AC3: 'Reply on RC2', Adrian Chappell, 18 Feb 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-337', Paul Ginoux, 08 Dec 2021
    • AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
  • RC1: 'Comment on gmd-2021-337', Anonymous Referee #1, 17 Dec 2021
    • AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
  • RC2: 'Comment on gmd-2021-337', Anonymous Referee #2, 14 Feb 2022
    • AC3: 'Reply on RC2', Adrian Chappell, 18 Feb 2022
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji

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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.