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. Even though desert dust is the most abundant aerosol bymass in Earth's atmosphere, atmospheric models struggle to accuratelyrepresent its spatial and temporal distribution. These model errors arepartially caused by fundamental difficulties in simulating dust emission incoarse-resolution models and in accurately representing dust microphysicalproperties. Here we mitigate these problems by developing a new methodologythat yields an improved representation of the global dust cycle. We presentan analytical framework that uses inverse modeling to integrate an ensembleof global model simulations with observational constraints on the dust sizedistribution, extinction efficiency, and regional dust aerosol opticaldepth. We then compare the inverse model results against independentmeasurements of dust surface concentration and deposition flux and find thaterrors are reduced by approximately a factor of 2 relative to currentmodel simulations of the Northern Hemisphere dust cycle. The inverse modelresults show smaller improvements in the less dusty Southern Hemisphere,most likely because both the model simulations and the observationalconstraints used in the inverse model are less accurate. On a global basis,we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than mostmodels account for. This larger PM20 dust flux is needed to matchobservational constraints showing a large atmospheric loading of coarsedust. We obtain gridded datasets of dust emission, vertically integratedloading, dust aerosol optical depth, (surface) concentration, and wet anddry deposition fluxes that are resolved by season and particle size. As ourresults indicate that this dataset is more accurate than current modelsimulations and the MERRA-2 dust reanalysis product, it can be used toimprove quantifications of dust impacts on the Earth system.

Improved representation of the global dust cycle using observational constraints on dust properties and abundance