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Abstract

Aeolian dust has significant impacts on climate,
public health, infrastructure and ecosystems. Assessing dust
concentrations and the impacts is challenging because the
emissions depend on many environmental factors and can
vary greatly with meteorological conditions. We present
a data-driven aeolian dust scheme that combines machine
learning components and physical equations to predict at-
mospheric dust concentrations and quantify the sources. The
numerical scheme was trained to reproduce dust aerosol op-
tical depth retrievals by the Infrared Atmospheric Sounding
Interferometer on board the MetOp-A satellite. The input
parameters included meteorological variables from the fifth-
generation atmospheric reanalysis of the European Centre for
Medium-Range Weather Forecasts. The trained dust scheme
can be applied as an emission submodel to be used in cli-
mate and Earth system models, which is reproducibly de-
rived from observational data so that a priori assumptions
and manual parameter tuning can be largely avoided. We
compared the trained emission submodel to a state-of-the-
art emission parameterisation, showing that it substantially
improves the representation of aeolian dust in the global at-
mospheric chemistry–climate model EMAC.