Suchergebnisse

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates. ; The research leading to these results has received funding from the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. We acknowledge support from the Ministerio de Ciencia, Innovación y Universidades (MICINN), as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R; the AXA Research Fund; and the 620 European Research Council (grant no. 773051, FRAGMENT). We also acknowledge PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT-2020-1-0007 projects. This project has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. Carlos Pérez García-Pando also acknowledges the support received through the Ramón y Cajal programme (grant no. RYC-2015-18690) of the MICINN. Modelling and satellite data were produced by the Copernicus Atmosphere Monitoring Service. We thank the three anonymous reviewers for their helpful comments that improved this paper. ; Peer Reviewed ; "Article signat per 27 autors/es: Jérôme Barré, Hervé Petetin, Augustin Colette, Marc Guevara, Vincent-Henri Peuch, Laurence Rouil, Richard Engelen, Antje Inness, Johannes Flemming, Carlos Pérez García-Pando, Dene Bowdalo, Frederik Meleux, Camilla Geels, Jesper H. Christensen, Michael Gauss, Anna Benedictow, Svetlana Tsyro, Elmar Friese, Joanna Struzewska, Jacek W. Kaminski, John Douros, Renske Timmermans, Lennart Robertson, Mario Adani, Oriol Jorba, Mathieu Joly, and Rostislav Kouznetsov" ; Postprint (published version)

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe. ; This research had free and open access to all data sources. The work described in this paper has received funding from European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf the European Union through commercial contract Ref. CAMS_95p. Several CAMS Regional Models of the CAMS_50 Service contributed to the present work (CHIMERE, LOTOS-EUROS, MINNI, MOCAGE, MONARCH, SILAM) under CAMS_71 coordination. CAMS_COP066 service provided the lockdown emissions information. O.J. and M.G. thankfully acknowledge the computer resources at Marenostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2020-1-0007). SILAM model runs was also funded by Finnish Academy GLORIA project (No310372). The study was supported by the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). ; Peer Reviewed ; "Article signat per 18 autors/es: Rochelle Schneider, Pierre Masselot, Ana M. Vicedo-Cabrera, Francesco Sera, Marta Blangiardo, Chiara Forlani, John Douros, Oriol Jorba, Mario Adani, Rostislav Kouznetsov, Florian Couvidat, Joaquim Arteta, Blandine Raux, Marc Guevara, Augustin Colette, Jérôme Barré, Vincent-Henri Peuch & Antonio Gasparrini " ; Postprint (published version)

The spread of the new coronavirus SARS-CoV-2 that causes COVID-19 forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on 14 March 2020. Over the following days and weeks, strong reductions in nitrogen dioxide (NO2) pollution were reported in many regions of Spain. A substantial part of these reductions was obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown's impact upon the observed pollution levels. Our study uses machine-learning (ML) models fed by meteorological data along with other time features to estimate the "business-as-usual" NO2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO2 reductions induced by the lockdown measures by comparing the estimated business-as-usual values with the observed NO2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations, with an overall bias, root mean square error and correlation of +4 %, 29 % and 0.86, respectively. During the period of study, from the enforcement of the state of alarm in Spain on 14 March to 23 April, we found the lockdown measures to be responsible for a 50 % reduction in NO2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity with respect to mobility restrictions. As expected, the meteorology-normalized change in NO2 was found to be stronger during phase II (the most stringent phase) and phase III of the lockdown than during phase I. In the largest agglomerations, where both urban background and traffic stations were available, a stronger meteorology-normalized NO2 change is highlighted at traffic stations ...

he NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona. ; Jaime Benavides' PhD work is funded by grant BES-2014-070637 from the FPI programme by the Spanish Ministry of the Economy and Competitiveness. Jaime Benavides developed part of this work as a research visitor at the Institute for the Environment at UNC funded by mobility grant EEBB-I-17-12296 from the same ministry. IDAEA-CSIC acknowledges the Barcelona City Council for their support to the experimental campaign. Carlos Pérez García-Pando acknowledges the long-term support from the AXA Chair in Sand and Dust Storms (AXA Research Fund), as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. This research has been supported by the Spanish Ministry of the Economy and Competitiveness (grant nos. CGL2013-46736-R, CGL2016-75725-R and RTI2018-099894-BI00), as well as the Catalan Government (grant no. RIS3CAT-COM15-1-0011-04). ; Peer Reviewed ; Postprint (published version)

This paper investigates the mechanisms involved in the dispersion, structure, and mixing in the vertical column of atmospheric pollen. The methodology used employs observations of pollen concentration obtained from Hirst samplers (we will refer to this as surface pollen) and vertical distribution (polarization-sensitive lidar), as well as nested numerical simulations with an atmospheric transport model and a simplified pollen module developed especially for this study. The study focuses on the predominant pollen type, Pinus, of the intense pollination event which occurred in the region of Barcelona, Catalonia, NE Spain, during 27–31 March 2015. First, conversion formulas are expressed to convert lidar-derived total backscatter coefficient and model-derived mass concentration into pollen grains concentration, the magnitude measured at the surface by means of aerobiological methods, and, for the first time ever, a relationship between optical and mass properties of atmospheric pollen through the estimation of the so-called specific extinction cross section is quantified in ambient conditions. Second, the model horizontal representativeness is assessed through a comparison between nested pollen simulations at 9, 3, and 1 km horizontal resolution and observed meteorological and aerobiological variables at seven sites around Catalonia. Finally, hourly observations of surface and column concentration in Barcelona are analyzed with the different numerical simulations at increasing horizontal resolution and varying sedimentation/deposition parameters. We find that the 9 or 3 km simulations are less sensitive to the meteorology errors; hence, they should be preferred for specific forecasting applications. The largest discrepancies between measured surface (Hirst) and column (lidar) concentrations occur during nighttime, where only residual pollen is detected in the column, whereas it is also present at the surface. The main reason is related to the lidar characteristics which have the lowest useful range bin at ∼ 225 m, above the usually very thin nocturnal stable boundary layer. At the hour of the day of maximum insolation, the pollen layer does not extend up to the top of the planetary boundary layer, according to the observations (lidar), probably because of gravity effects; however, the model simulates the pollen plume up to the top of the planetary boundary layer, resulting in an overestimation of the pollen load. Besides the large size and weight of Pinus grains, sedimentation/deposition processes have only a limited impact on the model vertical concentration in contrast to the emission processes. For further modeling research, emphasis is put on the accurate knowledge of plant/tree spatial distribution, density, and type, as well as on the establishment of reliable phenology functions. ; The lidar data analysis has been supported by funding from the H2020 program from the European Union (grant nos. 654109, 778349, and 871115), the Spanish Ministry of Economy, Industry and Competitiveness (grant no. CGL2017-90884- REDT), the Spanish Ministry of Science and Innovation (grant no. PID2019-103886RB-I00), and the Unity of Excellence "María de Maeztu" financed by the Spanish Agencia Estatal de Investigación (grant no. MDM-2016-0600). Modeling activities have been supported by funding from the Ministerio de Ciencia, Innovación y Universidades, as part of the BROWNING project (grant no. RTI2018-099894-BI00) and ACTRIS-España (grant no. CGL2017-90884-REDT). Airborne pollen data sampling and analyzing have been supported by funding from sponsors of the Catalan Aerobiological Network (LETI Pharma, Diputació de Tarragona, Servei Meteorològic de Catalunya, Diputació de Lleida, Sociedad Española de Alergología e Inmunología Clínica (SEAIC), Societat Catalana d'Al·lèrgia i Immunologia Clínica (SCAIC), and J Uriach y Compañía, S.A.), and from the Spanish Ministry of Economy, Industry, and Competitiveness (grant nos. CGL2012- 39523-C02-01, CTM2017-89565-C2-1-P, and CTM2017-89565- C2-2-P). This work is contributing to the ICTA "Unit of Excellence" (MinECo; grant no. MDM2015-0552). ; Peer Reviewed ; Postprint (published version)

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called "superblocks" and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (-13% and -30%, respectively) with estimated NO2 reductions of -36% and -23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed. ; The authors acknowledge CARNET-The Future Mobility Research HUB to allow the usage and work on the BCN-VML network, as well as PTV VISUM for the traffic software license. The author thankfully acknowledges the computer resources at MareNostrum and the technical support pro- vided by Barcelona Supercomputing Center (PRACE, AECT-2020-1-0007, AECT-2021-1-0027). The authors also acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R and the support from the Agencia Estatal de Investigacion (AEI) as part of the VITALISE project (PID2019-108086RA- I00/AEI/10.13039/501100011033). Carlos Pérez García-Pando acknowl- edges the long-term support from the AXA Chair in Sand and Dust Storms (AXA Research Fund), as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. ; 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 offer an overview of the COVID-19 -driven air quality changes across 11 metropolises in Spain with the focus on lessons learned on how continuing abating pollution. Traffic flow decreased by up to 80% during the lockdown and remained relatively low during the full relaxation (June and July). After the lockdown a significant shift from public transport to private vehicles (+21% in Barcelona) persisted due to the pervasive fear that using public transport might increase the risk of SARS-CoV-2 infection, which need to be reverted as soon as possible. NO2 levels fell below 50% of the WHO annual air quality guidelines (WHOAQGs), but those of PM2.5 were reduced less than expected due to the lower contributions from traffic, increased contributions from agricultural and domestic biomass burning, or meteorological conditions favoring high secondary aerosol formation yields. Even during the lockdown, the annual PM2.5 WHOAQG was exceeded in cities within the NE and E regions with high NH3 emissions from farming and agriculture. Decreases in PM10 levels were greater than in PM2.5 due to reduced emissions from road dust, vehicle wear, and construction/demolition. Averaged O3 daily maximum 8-h (8hDM) experienced a generalized decrease in the rural receptor sites in the relaxation (June–July) with -20% reduced mobility. For urban areas O3 8hDM responses were heterogeneous, with increases or decreases depending on the period and location. Thus, after canceling out the effect of meteorology, 5 out of 11 cities experienced O3 decreases during the lockdown, while the remaining 6 either did not experience relevant reductions or increased. During the relaxation period and coinciding with the growing O3 season (June–July), most cities experienced decreases. However, the O3 WHOAQG was still exceeded during the lockdown and full relaxation periods in several cities. For secondary pollutants, such as O3 and PM2.5, further chemical and dispersion modeling along with source apportionment techniques to identify major precursor reduction targets are required to evaluate their abatement potential. ; The present work was supported by the Spanish Ministerio para la Transición Ecológica y Reto Demográfico (17CAES010), the "Agencia Estatal de Investigación" from the Spanish Ministry of Science and Innovation (IDAEA-CSIC is a Centre of Excellence Severo Ochoa CEX2018-000794-S), FEDER funds under the project CAIAC (PID2019- 108990RB-I00), and by the Generalitat de Catalunya (AGAUR 2017 SGR41). We would like to thank the Spanish Meteorological Office (AEMET) for providing meteorological data as well as NASA for providing OMI-NO2 data. BSC co-authors acknowledge the support of the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission, the Ministerio de Ciencia, Innovación y Universidades (MICINN) (RTI2018-099894-BI00, CGL2017-88911-R), the Agencia Estatal de Investigación (PID2019-108086RA-I00/AEI/0.13039/501100011033), the AXA Research Fund, and PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center. H. Petetin also acknowledges the European Union's Horizon 2020 - Research and Innovation Framework Programme under the H2020 Marie Skłodowska-Curie Actions grant agreement H2020-MSCACOFUND-2016-754433. ; Peer Reviewed ; Article signat per 20 autors/es: Xavier Querol (a), Jordi Massagué (a, b), Andrés Alastuey (a), Teresa Moreno (a), Gotzon Gangoiti (c), Enrique Mantilla (d), José Jaime Duéguez (d), Miguel Escudero (e), Eliseo Monfort (f), Carlos Pérez García-Pandog (h), Hervé Petetin (g), Oriol Jorba (g), Víctor Vázquez (i, j), Jesús de la Rosa (k), Alberto Campos (l), Marta Muñóz (l), Silvia Monge (l), María Hervás (l), Rebeca Javato (l), María J. Cornide (l) a- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain b- Department of Mining, Industrial and ICT Engineering, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Manresa 08242, Spain c- Department of Chemical and Environmental Engineering, University of Basque Country, Leioa 48940, Spain d- Centro de Estudios Ambientales del Mediterráneo, CEAM, València 46980, Spain e- Centro Universitario de la Defensa, Academia General Militar, Zaragoza 50090, Spain f- Instituto de Tecnología Cerámica ITC-UJI, Castelló 12006, Spain g- Barcelona Supercomputing Center, BSC-CNS, Barcelona 08034, Spain h- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona 08010, Spain i- Department of Ecology, Faculty of Sciences, University of Málaga, 29071 Málaga, Spain j- Department of Research and Development, Coccosphere Environmental Analysis, 29120 Málaga, Spain k- Department of Geology, University of Huelva, Unidad de Investigación Associada a IDAEA-CSIC, Huelva 21819, Spain l- D.G. Calidad y Evaluación Ambiental del Ministerio de Transición Ecológica y Reto Demográfico, Madrid 28071, Spain ; 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)