Testing the performance of state-of-the-art dust emission schemes using DO4Models field data
Geoscientific Model Development
School of Geography and the Environment, University of Oxford, Oxford, United Kingdom; University of Cape Town, Environmental and Geographical Science, Cape Town, South Africa; Department of Geography, University of Sheffield, Sheffield, United Kingdom; Laboratoire de Météorologie Dynamique, Ecole Polytechnique, Palaiseau, France
Within the framework of the Dust Observations for Models (DO4Models) project, the performance of three commonly used dust emission schemes is investigated in this paper using a box model environment. We constrain the model with field data (surface and dust particle properties as well as meteorological parameters) obtained from a dry lake bed with a crusted surface in Botswana during a 3 month period in 2011. Our box model results suggest that all schemes fail to reproduce the observed horizontal dust flux. They overestimate the magnitude of the flux by several orders of magnitude. The discrepancy is much smaller for the vertical dust emission flux, albeit still overestimated by up to an order of magnitude. The key parameter for this mismatch is the surface crusting which limits the availability of erosive material, even at higher wind speeds. The second-most important parameter is the soil size distribution. Direct dust entrainment was inferred to be important for several dust events, which explains the smaller gap between modelled and measured vertical dust fluxes. We conclude that both features, crusted surfaces and direct entrainment, need to be incorporated into dust emission schemes in order to represent the entire spectra of source processes. We also conclude that soil moisture exerts a key control on the threshold shear velocity and hence the emission threshold of dust in the model. In the field, the state of the crust is the controlling mechanism for dust emission. Although the crust is related to the soil moisture content to some extent, we are not as yet able to deduce a robust correlation between state of crust and soil moisture. © Author(s) 2015.
atmospheric pollution; data set; dust; emission; magnitude; meteorology; numerical model; parameterization; performance assessment; pollutant source; size distribution; soil moisture; Botswana
NE/H021841/1, NERC, Natural Environment Research Council