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

Abstract

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.

Editorial: Atmospheric dust: How it affects climate, environment and life on Earth?

“…Unfortunately, there has been no solid progress in terms of improving dust emission fluxes given the large diversity of natural as well as anthropogenic dust sources. A few notable attempts have emerged recently in this regard (e.g., Chappell et al, 2021;Kok et al, 2021;Leung et al, 2022) but reasonable representation of dust fluxes remains a big challenge in climate models.…”

A new process-based and scale-respecting desert dust emission scheme for global climate models – Part I: description and evaluation against inverse modeling emissions

“…For instance, modeled emissions were found to increase by ~29 % from a 1° × 1° to 0.25° × 0.25° resolution (Ridley et al, 2013;Feng et al, 2022). As a consequence, GCMs and ESMs often need to tune emissions separately for different grid resolutions to match observational dust budgets (Ginoux et al, 2001;Zender et al, 2003a;Albani et al, 2014;Kok et al, 2014b;Chappell et al, 2021). This issue occurs 150 because current GCM grid sizes of ~1° or 100 km cannot resolve the spatial scales of ~1 m to ~1 km over which soil properties and wind speeds change (Ridley et al, 2013).…”