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Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes. 

Abstract. Even though desert dust is the most abundant aerosol bymass in Earth's atmosphere, the relative contributions of the world's majorsource regions to the global dust cycle remain poorly constrained. Thisproblem hinders accounting for the potentially large impact of regionaldifferences in dust properties on clouds, the Earth's energy balance, andterrestrial and marine biogeochemical cycles. Here, we constrain thecontribution of each of the world's main dust source regions to the globaldust cycle. We use an analytical framework that integrates an ensemble ofglobal aerosol model simulations with observationally informed constraintson the dust size distribution, extinction efficiency, and regional dustaerosol optical depth (DAOD). We obtain a dataset that constrains therelative contribution of nine major source regions to size-resolveddust emission, atmospheric loading, DAOD, concentration, and depositionflux. We find that the 22–29 Tg (1 standard error range) global loading ofdust with a geometric diameter up to 20 µm is partitioned as follows:North African source regions contribute ∼ 50 % (11–15 Tg),Asian source regions contribute ∼ 40 % (8–13 Tg), and NorthAmerican and Southern Hemisphere regions contribute ∼ 10 %(1.8–3.2 Tg). These results suggest that current models on averageoverestimate the contribution of North African sources to atmospheric dustloading at ∼ 65 %, while underestimating the contribution ofAsian dust at ∼ 30 %. Our results further show that eachsource region's dust loading peaks in local spring and summer, which ispartially driven by increased dust lifetime in those seasons. We alsoquantify the dust deposition flux to the Amazon rainforest to be∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred fromsatellite data by previous work that likely overestimated dust deposition byunderestimating the dust mass extinction efficiency. The data obtained inthis paper can be used to obtain improved constraints on dust impacts onclouds, climate, biogeochemical cycles, and other parts of the Earth system. 

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.