Preprints
https://doi.org/10.5194/gmd-2021-423
https://doi.org/10.5194/gmd-2021-423
Submitted as: methods for assessment of models
10 Jan 2022
Submitted as: methods for assessment of models | 10 Jan 2022
Status: a revised version of this preprint is currently under review for the journal GMD.

Evaluating dust emission model performance using dichotomous satellite observations of dust emission

Mark Hennen1, Adrian Chappell1, Nicholas Webb2, Kerstin Schepanski3, Matthew Baddock4, Frank Eckardt5, Tarek Kandakji6, Jeff Lee7, Mohamad Nobakht8, and Johanna von Holdt5 Mark Hennen et al.
  • 1School of Earth and Environmental Science, Cardiff University, Cardiff, UK
  • 2USDA-ARS Jornada Experimental Range, Las Cruces, NM, 88003, USA
  • 3Institute of Meteorology, Freie Universität Berlin, Germany
  • 4Geography and Environment, Loughborough University, Loughborough, UK
  • 5Department of Environmental and Geographical Science, University of Cape Town, Rondebosch 7701, South Africa
  • 6Centre for Earth Observation, Yale University, USA
  • 7Texas Tech University, Texas, USA
  • 8Telespazio UK Ltd, Capability Green, Luton LU1 3LU, Bedfordshire, UK

Abstract. Measurements of dust in the atmosphere have long been used to calibrate dust emission models. However, there is growing recognition that atmospheric dust confounds the magnitude and frequency of emission from dust sources and hides potential weaknesses in dust emission model formulation. In the satellite era, dichotomous (presence = 1 or absence = 0) observations of dust emission point sources (DPS) provide a valuable inventory of regional dust emission. We used these DPS data to develop an open and transparent framework to routinely evaluate dust emission model (development) performance using coincidence of simulated and observed dust emission (or lack of emission). To illustrate the utility of this framework, we evaluated the recently developed albedo-based dust emission model (AEM) which included the traditional entrainment threshold (u*ts) at the grain scale, fixed over space and static over time, with sediment supply infinite everywhere. For comparison with the dichotomous DPS data, we reduced the AEM simulations to its frequency of occurrence in which soil surface wind friction velocity (us*) exceeds the u*ts, P(us* > u*ts). We used a global collation of nine DPS datasets from established studies to describe the spatio-temporal variation of dust emission frequency. A total of 37,352 unique DPS locations were aggregated into 1,945 1° grid boxes to harmonise data across the studies which identified a total of 59,688 dust emissions. The DPS data alone revealed that dust emission does not usually recur at the same location, are rare (1.8 %) even in North Africa and the Middle East, indicative of extreme, large wind speed events. The AEM over-estimated the occurrence of dust emission by between 1 and 2 orders of magnitude. More diagnostically, the AEM simulations coincided with dichotomous observations ~71 % of the time but simulated dust emission ~27 % of the time when no dust emission was observed. Our analysis indicates that u*ts was typically too small, needed to vary over space and time, and at the grain-scale u*ts is incompatible with the us* scale (MODIS 500 m). During observed dust emission, us* was too small because wind speeds were too small and/or the wind speed scale (ERA5; 11 km) is incompatible with the us* scale. The absence of any limit to sediment supply caused the AEM to simulate dust emission whenever P (us* > u*ts), producing many false positives when and where wind speeds were frequently large. Dust emission model scaling needs to be reconciled and new parameterisations are required for u*ts and to restrict sediment supply varying over space and time. Whilst u*ts remains poorly constrained and unrealistic assumptions persist about sediment supply and availability, the DPS data provide a basis for the calibration of dust emission models for operational use. As dust emission models develop, these DPS data provide a consistent, reproducible, and valid framework for their routine evaluation and potential model optimisation. This work emphasises the growing recognition that dust emission models should not be evaluated against atmospheric dust.

Mark Hennen et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-423', Anonymous Referee #1, 16 Feb 2022
    • AC2: 'Reply on RC1', Adrian Chappell, 24 Jun 2022
  • AC1: 'Comment on gmd-2021-423', Mark Hennen, 07 Mar 2022
  • RC2: 'Comment on gmd-2021-423', Anonymous Referee #2, 19 Jun 2022
    • AC3: 'Reply on RC2', Adrian Chappell, 24 Jun 2022

Mark Hennen et al.

Mark Hennen et al.

Viewed

Total article views: 722 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
580 121 21 722 5 6
  • HTML: 580
  • PDF: 121
  • XML: 21
  • Total: 722
  • BibTeX: 5
  • EndNote: 6
Views and downloads (calculated since 10 Jan 2022)
Cumulative views and downloads (calculated since 10 Jan 2022)

Viewed (geographical distribution)

Total article views: 738 (including HTML, PDF, and XML) Thereof 738 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jun 2022
Download
Short summary
We use 90,000 dust point source observations (DPS), identified in satellite imagery across 9 global dryland environments to develop a novel dust emission model performance assessment. We evaluate the albedo-based dust emission model (AEM), which agrees with dust emission observations, or lack of emission 71 % of the time. Modelled dust occurs 27 % of the time with no observation, caused mostly by the incorrect assumption of infinite sediment supply and lack of dynamic dust entrainment thresholds.