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Agricultural fires affecting grass-, crop- and shrublands represent a major, mainly anthropogenically driven disturbance of many ecosystems. In addition to emissions of carbonaceous aerosol, they were found to inject also mineral dust particles into the atmosphere. The fires can significantly modulate the near-surface wind patterns so that conditions suitable for dust emission occur. However, the exact emission mechanism has not been investigated so far, but is inevitable for the understanding of its impacts on the Earth system. Here, we test two dust emission parameterizations representing saltation bombardment (SALT) and direct aerodynamic dust entrainment by (convective) turbulence (convective turbulent dust emission, CTDE) in the context of fire-modulated wind patterns using large-eddy simulation with an idealized setup to represent typical agricultural fire settings. Favorable aerodynamic preconditions for the initialization of both emission processes are found, however, with sometimes significant differences in dust emission flux depending on specific wind and fire properties. The strong fire-induced modulations of the instantaneous momentum flux suggest that CTDE can be a very potent emission process in the fire vicinity. Nevertheless, fire impacts on the friction velocity can be significant too, so that dust emission through SALT is facilitated as well. Ultimately, the specific aerodynamic conditions within pyro-convectively modulated wind patterns require the development of a parameterization that can describe these unique fire-related dust emissions and their influencing factors properly. This will finally allow for considering fire-induced dust emissions in aerosol-atmosphere models and an investigation of its atmospheric impacts such as on the radiation budget. ; R. Wagner and K. Schepanski thank the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for funding the project SCHE 1678/5–1. M. Klose has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 789630 and from the Helmholtz Association's Initiative and Networking Fund (grant agreement no. VH-NG-1533). The authors acknowledge the Center for Information Services and High performance Computing (ZIH) of the Technische Universität Dresden (TU Dresden) for providing computing capacity. The authors are grateful to the three anonymous reviewers for their valuable comments and suggestions that helped to improved the quality of the paper. ; Peer Reviewed ; Postprint (published version)

Crusted surfaces can be major sources of mineral dust emission. Quantitative understanding of dust emission from crusted surfaces is limited, because (1) theories on dust emission are not well tested for such surfaces; and (2) modelling is hampered by a lack of input data sufficient to describe the surface conditions. Combining detailed field measurements with physics-based numerical modelling, we present new insights into dust emission from crusted surfaces. Our measurements confirm that crust erodibility and dust-emission intensity can increase or decrease after previous erosion events. To support interpretation of the measurements and to test the applicability of a state-of-the-art parameterisation to simulate dust emission from crusted surfaces, we apply the dust emission scheme of Shao (2004). Saltation flux, which is input to the scheme, is approximated using the parameterisation of Kawamura (1964) and a scaling factor obtained from observations. Limitations of this approach are discussed. Our results show that the dust emission scheme is suitable to estimate dust emission from crusted surfaces if accurate input data and parameters describing the soil-surface condition are provided. The parameters were optimized for each dust event to achieve a best estimate. The variation of the resulting parameter values confirms the observed variability of dust-emission efficiency between the events and provides further evidence that it was caused by variations in crust erodibility. Our study demonstrates that available physics-based dust-emission parameterisations are able to simulate dust emissions under complicated conditions, but also that refined information on the soil-surface conditions are needed as input to the schemes. ; This study was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) grant KL 2932/1-1 awarded as a postdoctoral research fellowship to MK. TEG and RSVP acknowledge support from NASA grant NNX16AH13G. 15 NPW acknowledges support through funding from the Department of Interior, Bureau of Land Management. We thank Ralph Lorenz for providing pressure loggers and the Davis anemometer used on Site F. We also thank Sharalyn Peterson, Justin Van Zee, and Bradley Cooper for field and lab assistance. LPI point data were recorded using DIMA (https://jornada.nmsu.edu/monit-assess/dima). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The USDA is an equal opportunity provider and employer. We thank two anonymous reviewers for their positive and helpful comments. ; Peer Reviewed ; Postprint (author's final draft)

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" ; Postprint (published version)

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" ; Postprint (published version)

The large uncertainty in the mineral dust direct radiative effect (DRE) hinders projections of future climate change due to anthropogenic activity. Resolving modeled dust mineral speciation allows for spatially and temporally varying refractive indices consistent with dust aerosol composition. Here, for the first time, we quantify the range in dust DRE at the top of the atmosphere (TOA) due to current uncertainties in the surface soil mineralogical content using a dust mineral-resolving climate model. We propagate observed uncertainties in soil mineral abundances from two soil mineralogy atlases along with the optical properties of each mineral into the DRE and compare the resultant range with other sources of uncertainty across six climate models. The shortwave DRE responds region-specifically to the dust burden depending on the mineral speciation and underlying shortwave surface albedo: positively when the regionally averaged annual surface albedo is larger than 0.28 and negatively otherwise. Among all minerals examined, the shortwave TOA DRE and single scattering albedo at the 0.44–0.63 µm band are most sensitive to the fractional contribution of iron oxides to the total dust composition. The global net (shortwave plus longwave) TOA DRE is estimated to be within −0.23 to +0.35 W m−2. Approximately 97 % of this range relates to uncertainty in the soil abundance of iron oxides. Representing iron oxide with solely hematite optical properties leads to an overestimation of shortwave DRE by +0.10 W m−2 at the TOA, as goethite is not as absorbing as hematite in the shortwave spectrum range. Our study highlights the importance of iron oxides to the shortwave DRE: they have a disproportionally large impact on climate considering their small atmospheric mineral mass fractional burden (∼2 %). An improved description of iron oxides, such as those planned in the Earth Surface Mineral Dust Source Investigation (EMIT), is thus essential for more accurate estimates of the dust DRE. ; A portion of this work was funded by the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission. Longlei Li, Natalie M. Mahowald, and Douglas S. Hamilton were supported by the Atkinson Centre for a Sustainable Future. Jasper F. Kok received support from NSF grant 1552519. Martina Klose received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 789630 (DUST.ES). Carlos Pérez García-Pando and Maria Gonçalves Ageitos received support from the European Research Council (grant no. 773051, FRAGMENT), EU H2020 project FORCES (grant no. 821205), the AXA Research Fund, the Spanish Ministry of Science, Innovation and Universities (RYC-2015-18690 and NUTRIENT: CGL2017- 88911-R), and PRACE and RES for awarding access to MareNostrum at the Barcelona Supercomputing Center to run MONARCH. Ron L. Miller received for support from the NASA Modeling, Analysis and Prediction Program (NNG14HH42I). ; Peer Reviewed ; Postprint (published version)

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types. ; This work received funding from the European Union's Horizon 2020 research and innovation programme (Marie Skłodowska-Curie (grant no. 754433)), the European Research Council (FRAGMENT (grant no. 773051)), and the AXA Research Fund. We were also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN), as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R, along with PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT2020-1-0007 projects. Martina Klose received funding from the European Union's Horizon 2020 research and innovation programme (Marie Skłodowska-Curie (grant no. 789630)). Martina Klose was also supported by the Helmholtz Association's Initiative and Networking Fund (grant no. VH-NG-1533). Vassilis Amiridis and Eleni Marinou were supported by ERC Consolidator Grant 2016 D-TECT: "Does dust TriboElectrification affect our ClimaTe?" (grant no. 725698). Eleni Marinou was supported by a DLR VO-R young investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Emmanouil Proestakis was supported by the project PANhellenic infrastructure for Atmospheric Composition and climatE change (grant no. MIS5021516), which is implemented under the Action Reinforcement of the Research and Innovation Infrastructure, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (grant no. NSRF2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). This research was supported by the German–Israeli Foundation for Scientific Research and Development (GIF, grant no. I1262-401.10/2014), the European Union's Framework Programme for Research and Innovation, Horizon 2020 (ACTRIS-2, grant no. 654109), and the former European Commission Seventh Framework Programme FP7/2007–2013 (ACTRIS (grant no. 262254) and BACCHUS (grant no. 603445)). ; Peer Reviewed ; Objectius de Desenvolupament Sostenible::13 - Acció per al Clima ; Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l'educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l'adaptació a aquest, la reducció dels efectes i l'alerta primerenca ; Postprint (published version)

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20¿µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000¿Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about -0.90 and -0.63¿W¿m-2 and at the top of the atmosphere between -0.20 and -0.28¿W¿m-2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations. ; This research has been supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 789630. Martina Klose has received funding through the Helmholtz Association's Initiative and Networking Fund (grant agreement no. VH-NG-1533). Jeronimo Escribano and Matthew L. Dawson have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements no. 754433 (Jeronimo Escribano) and no. 747048 (Matthew Dawson). BSC co-authors acknowledge funding from the following: the European Research Council (FRAGMENT (grant no. 773051)); the AXA Research Fund; the Spanish Ministry of Science, Innovation and Universities (grant no. CGL2017- 88911-R); the EU H2020 project FORCES (grant no. 821205); the CMUG-CCI3-TECHPROP contract, an activity carried out under a program of and funded by the European Space Agency (ESA); and the DustClim project, which is part of ERA4CS, an ERA-NET initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR), with cofunding by the European Union (grant no. 690462). Ron L. Miller is funded by the NASA Modeling, Analysis and Prediction Program (NNG14HH42I). Jasper F. Kok acknowledges support from the National Science Foundation (NSF) under grant nos. 1552519 and 1856389 and the Army Research Office (grant no. W911NF-20- 2-0150). Yue Huang has received funding from the Columbia University Earth Institute Postdoctoral Research Fellowship and from NASA (grant no. 80NSSC19K1346) awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. ; Peer Reviewed ; Postprint (published version)

One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1° latitude × 0.1° longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 μm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. ; We acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union's Horizon 2020 research and innovation programme (Grant n. 690462).