Abstract
Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.
Highlights
Desert dust produces a wide range of important impacts on the Earth system, including through interactions with radiation, clouds, the cryosphere, biogeochemistry, atmospheric chemistry, and public health (Shao et al, 2011)
We obtain the predictions of the inverse model for the main properties of the global dust cycle, namely dust aerosol optical depth (DAOD), dust emission, dust column loading, dust surface concentration, and dust deposition flux (Sect. 4.2)
We evaluate whether the integration of observational constraints on dust properties and abundance yields an improved representation of the global dust cycle by comparing our results against independent measurements and observations in the Northern Hemisphere (NH) (Sect. 4.3.1) and the Southern Hemisphere (SH) (Sect. 4.3.2)
Summary
Desert dust produces a wide range of important impacts on the Earth system, including through interactions with radiation, clouds, the cryosphere, biogeochemistry, atmospheric chemistry, and public health (Shao et al, 2011). Despite some recent progress, accounting for the effect of sub-grid-scale wind variability on dust emissions remains a substantial challenge that causes the simulated global dust cycle to be sensitive to model resolution (Lunt and Valdes, 2002; Cakmur et al, 2004; Comola et al, 2019), especially at low resolution (Ridley et al, 2013) Another substantial challenge for models is the small-scale variability of vegetation (Raupach et al, 1993; Okin, 2008), surface roughness (Menut et al, 2013), soil texture (Laurent et al, 2008; Martin and Kok, 2019), mineralogy (Perlwitz et al, 2015a), and soil moisture (McKenna Neuman and Nickling, 1989; Fécan et al, 1999). As a result of these fundamental challenges in accurately representing dust emission, most models use both a source function map (Ginoux et al, 2001) and a global dust emission tuning constant to produce a global dust cycle that is in reasonable agreement with measurements (Cakmur et al, 2006; Huneeus et al, 2011; Albani et al, 2014; Wu et al, 2020)
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