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Abstract. Sedimentary records indicate that atmospheric dust has increased substantially since preindustrial times. However, state-of-the-art global Earth system models (ESMs) are unable to capture this historical increase, posing challenges in assessing the impacts of desert dust on Earth's climate. To address this issue, we construct a globally gridded dust emission dataset (DustCOMMv1) spanning 1841–2000. We do so by combining 19 sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived emission dataset contains interdecadal variability of dust emissions as forced by the deposition flux records, which increased by approximately 50 % from 1851–1870 to 1981–2000. We further provide future dust emission datasets for 2000–2100 by assuming three possible scenarios for how future dust emissions will evolve. We evaluate the historical dust emission dataset and illustrate its effectiveness in enforcing a historical dust increase in ESMs by conducting a long-term (1851–2000) dust cycle simulation with the Community Earth System Model (CESM2). The simulated dust depositions are in reasonable agreement with the long-term increase in most sedimentary dust deposition records and with measured long-term trends in dust concentration at sites in Miami and Barbados. This contrasts with the CESM2 simulations using a process-based dust emission scheme and with simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends in dust deposition, concentration, and optical depth. The DustCOMM emissions thus enable ESMs to account for the historical radiative forcings (RFs), including due to dust direct interactions with radiation (direct RF). Our CESM2 simulations estimate a 1981–2000 minus 1851–1870 direct RF of −0.10 W m−2 by dust aerosols up to 10 µm in diameter (PM10) at the top of atmosphere (TOA). This global dust emission dataset thus enables models to more accurately account for historical aerosol forcings, thereby improving climate change projections such as those in the Intergovernmental Panel on Climate Change (IPCC) assessment reports. 

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes. 

Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass andproduces numerous important impacts on the Earth system. However, currentglobal climate models (GCMs) and land-surface models (LSMs) struggle toaccurately represent key dust emission processes, in part because ofinadequate representations of soil particle sizes that affect the dustemission threshold, surface roughness elements that absorb wind momentum,and boundary-layer characteristics that control wind fluctuations.Furthermore, because dust emission is driven by small-scale (∼ 1 km or smaller) processes, simulating the global cycle of desert dust inGCMs with coarse horizontal resolutions (∼ 100 km) presents afundamental challenge. This representation problem is exacerbated by dustemission fluxes scaling nonlinearly with wind speed above a threshold windspeed that is sensitive to land-surface characteristics. Here, we addressthese fundamental problems underlying the simulation of dust emissions inGCMs and LSMs by developing improved descriptions of (1) the effect of soiltexture on the dust emission threshold, (2) the effects of nonerodibleroughness elements (both rocks and green vegetation) on the surface windstress, and (3) the effects of boundary-layer turbulence on drivingintermittent dust emissions. We then use the resulting revised dust emissionparameterization to simulate global dust emissions in a standalone modelforced by reanalysis meteorology and land-surface fields. We further propose(4) a simple methodology to rescale lower-resolution dust emissionsimulations to match the spatial variability of higher-resolution emissionsimulations in GCMs. The resulting dust emission simulation showssubstantially improved agreement against regional dust emissionsobservationally constrained by inverse modeling. We thus find that ourrevised dust emission parameterization can substantially improve dustemission simulations in GCMs and LSMs. 

Abstract. 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.