A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission

Mark Hennen; Adrian Chappell; Nicholas P Webb; Kerstin Schepanski; Matthew C Baddock; Frank D Eckardt; Tarek Kandakji; Jeffrey A Lee; Mohamad Nobakht; Johanna von Holdt

doi:10.1016/j.scitotenv.2023.169237

A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission

Sci Total Environ. 2024 Feb 20:912:169237. doi: 10.1016/j.scitotenv.2023.169237. Epub 2023 Dec 13.

Authors

Affiliations

¹ School of Earth and Environmental Science, Cardiff University, Cardiff, UK; Open Cosmos Ltd. Electron Building, Fermi Avenue, Harwell Campus, Didcot, OX11 0QR, England, UK.
² School of Earth and Environmental Science, Cardiff University, Cardiff, UK. Electronic address: chappella2@cardiff.ac.uk.
³ USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003, USA.
⁴ Institute of Meteorology, Freie Universität Berlin, Germany.
⁵ Geography and Environment, Loughborough University, Loughborough, UK.
⁶ Department of Environmental and Geographical Science, University of Cape Town, Rondebosch 7701, South Africa.
⁷ Centre for Earth Observation, Yale University, USA.
⁸ Texas Tech University, TX, USA.
⁹ Telespazio UK Ltd, Capability Green, Luton LU1 3LU, Bedfordshire, UK.

PMID: 38101644
DOI: 10.1016/j.scitotenv.2023.169237

Abstract

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (u_s∗) and common, poorly constrained grain-scale entrainment threshold (u_∗ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(u_s∗>u_∗tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y^-1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u_∗ts needed restricted sediment supply and compatibility with areal u_s∗. Failure to predict dust emission during observed events, was due to u_s∗ being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with u_s∗ across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(u_s∗>u_∗tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u_∗ts is required to restrict sediment supply over space and time.