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Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust 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 North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system. This research has been supported by the National Science Foundation (NSF) (grant nos. 1552519 and 1856389) and the Army Research Office (cooperative agreement number W911NF-20-2-0150). This research was further support by the University of California President's Postdoctoral Fellowship awarded to Adeyemi A. Adebiyi and the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 708119 awarded to Samuel Albani and no. 789630 awarded to Martina Klose. Ramiro Checa-Garcia received funding from the European Union's Horizon 2020 research and innovation under grant 641816 (CRESCENDO). Akinori Ito received support from JSPS KAKENHI grant number 20H04329 and Integrated Research Program for Advancing Climate Models (TOUGOU) grant number JPMXD0717935715 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Peter R. Colarco and Adriana Rocha-Lima were supported by the NASA Atmospheric Composition: Modeling and Analysis Program (Richard Eckman, program manager) and the NASA Center for Climate Simulation (NCCS) for computational resources. Yue Huang was supported by NASA grant 80NSSC19K1346 awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. Ron L. Miller and Vincenzo Obiso received support from the NASA Modeling, Analysis and Prediction Program (NNG14HH42I) along with the NASA EMIT project and the Earth Venture Instrument program, with computational resources from the NASA Center for Climate Simulation (NCCS). Samuel Albani received funding from MIUR (Progetto Dipartimenti di Eccellenza 2018-2022). Carlos Pérez García-Pando received support from the European Research Council (grant no. 773051, FRAGMENT), the EU H2020 project FORCES (grant no. 821205), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (RYC-2015-18690 and CGL2017-88911-R). Longlei Li received support from the NASA EMIT project and the Earth Venture – Instrument program (grant no. E678605). Yves Balkanski and Ramiro Checa-Garcia received funding from the PolEASIA ANR project under allocation ANR-15-CE04-0005. Peer Reviewed "Article signat per 18 autors/es: Jasper F. Kok, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Mian Chin, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Longlei Li, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Carlos Pérez García-Pando, Adriana Rocha-Lima, and Jessica S. Wan"

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 2 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 a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, 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 datasets 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 dataset 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. This research has been supported by the National Science Foundation (NSF) (grant nos. 1552519 and 1856389) and the Army Research Office (cooperative agreement number W911NF-20-2-0150). This research was further supported by the University of California President's Postdoctoral Fellowship awarded to Adeyemi A. Adebiyi and the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 708119 awarded to Samuel Albani and no. 789630 awarded to Martina Klose. Ramiro Checa-Garcia received funding from the European Union Horizon 2020 research and innovation grant 641816 (CRESCENDO). Akinori Ito received support from JSPS KAKENHI grant number 20H04329 and Integrated Research Program for Advancing Climate Models (TOUGOU) grant number JPMXD0717935715 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Peter R. Colarco and Adriana Rocha-Lima were supported by the NASA Atmospheric Composition: Modeling and Analysis Program (Richard Eckman, program manager) and the NASA Center for Climate Simulation (NCCS) for computational resources. Yue Huang was supported by NASA grant 80NSSC19K1346 awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. Ron L. Miller and Vincenzo Obiso received support from the NASA Modeling, Analysis and Prediction Program (NNG14HH42I) along with the NASA EMIT project and the Earth Venture Instrument program with computational resources from the NASA Center for Climate Simulation (NCCS). Samuel Albani received funding from MIUR (Progetto Dipartimenti di Eccellenza 2018-2022). Carlos Pérez García-Pando received support from the European Research Council (grant no. 773051, FRAGMENT), the EU H2020 project FORCES (grant no. 821205), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (RYC-2015-18690 and CGL2017-88911-R). Longlei Li received support from the NASA EMIT project and the Earth Venture – Instrument program (grant no. E678605). Yves Balkanski and Ramiro Checa-Garcia received funding from the PolEASIA ANR project under allocation ANR-15-CE04-0005. Peer Reviewed "Article signat per 20 autors/es: Jasper F. Kok, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Mian Chin, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Danny M. Leung, Longlei Li, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Carlos Pérez García-Pando, Adriana Rocha-Lima, Jessica S. Wan, and Chloe A. Whicker"