Suchergebnisse

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species. ; This work was funded by the SEV-2011- 00067 grant of the Severo Ochoa Program awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish Ministry of Economy and Competitiveness, and the ACTRIS Research Infrastructure Project of the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654169. The authors thank all the Principal Investigators and their staff for establishing and maintaining the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3 product, and the MODIS and OMI mission scientists and associated NASA personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They also thank Francesco Benincasa for his technical support. Carlos Pérez García-Pando acknowledges long-term support from the 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. Comments from two anonymous reviewers are gratefully acknowledged. ; Peer Reviewed ; Postprint (published version)

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species. ; This work was funded by the SEV-2011- 00067 grant of the Severo Ochoa Program awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish Ministry of Economy and Competitiveness, and the ACTRIS Research Infrastructure Project of the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654169. The authors thank all the Principal Investigators and their staff for establishing and maintaining the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3 product, and the MODIS and OMI mission scientists and associated NASA personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They also thank Francesco Benincasa for his technical support. Carlos Pérez García-Pando acknowledges long-term support from the 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. Comments from two anonymous reviewers are gratefully acknowledged. ; Peer Reviewed ; Postprint (published version)

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)

The spread of the new coronavirus (COVID-19) forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on March 14 th 2020. Over the following days and weeks, strong reductions of nitrogen dioxide (NO 2 ) pollution were reported in many regions of Spain. A substantial part of these reductions is obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown 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 NO 2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO 2 reductions induced by the lockdown measures by comparing the business-as-usual with the actually observed NO 2 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. During the period of study, going from the enforcement of the state of alarm in Spain on March 14 th to April 23 rd , we found the lockdown measures to be responsible for a 50 % reduction of NO 2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity in the mobility restrictions. As expected the meteorology-normalized change of NO 2 was found to be stronger during the phases II (the most stringent one) and III than during phase I. In the largest agglomerations where both urban background and traffic stations were available, a stronger meteorology-normalized NO 2 change is highlighted at traffic stations compared to urban background ones. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking into account meteorological variability for accurately assessing the impact of the lockdown on NO 2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as the ones presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.

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 presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error – RMSE – below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This may be attributed to an overestimation of CO concentration over the Southern Hemisphere leading to an excessive production of O3 or to the lack of specific chemistry (e.g., halogen chemistry, aerosol chemistry). Overall, O3 correlations range between 0.6 and 0.8 for daily mean values. The overall performance of the NMMB-MONARCH is comparable to that of other state-of-the-art global chemistry models. ; The authors wish to thank WOUDC, GAW, EMEP, WDCGG, CASTNET-EPA, NADP and EANET for the provision of measurement stations. The authors acknowledge for the strong support of the European Commission, Airbus, and the Airlines (Lufthansa, Austrian, Air France) who carry free of charge the MOZAIC equipment and perform the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Meteo-France, and Forschungszentrum (FZJ, Julich, Germany). The MOZAIC database is supported by ETHER (CNES and INSU-CNRS). Also, thanks go to the free use of the MOPITTCO data obtained from the NASA Langley Research Center Atmospheric Science Data Center. SCIAMACHY radiances have been provided by ESA. We also thank Beatriz Monge-Sanz for providing the COPCAT coefficients. This work is funded by grants CGL2013-46736-R, Supercomputación and e-ciencia Project (CSD2007-0050) from the Consolider-Ingenio 2010 program of the Spanish Ministry of Economy and Competitiveness. Further support was provided by the SEV-2011-00067 grant of the Severo Ochoa Program, awarded by the Spanish Government. Andreas Hilboll received funding from the Earth System Science Research School (ESSReS), an initiative of the Helmholtz Association of German research centres (HGF) at the Alfred Wegener Institute for Polar and Marine Research. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2015-1-0007). Comments from two anonymous reviewers are gratefully acknowledge. ; Peer Reviewed ; Postprint (published version)

This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error – RMSE – below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This may be attributed to an overestimation of CO concentration over the Southern Hemisphere leading to an excessive production of O3 or to the lack of specific chemistry (e.g., halogen chemistry, aerosol chemistry). Overall, O3 correlations range between 0.6 and 0.8 for daily mean values. The overall performance of the NMMB-MONARCH is comparable to that of other state-of-the-art global chemistry models. ; The authors wish to thank WOUDC, GAW, EMEP, WDCGG, CASTNET-EPA, NADP and EANET for the provision of measurement stations. The authors acknowledge for the strong support of the European Commission, Airbus, and the Airlines (Lufthansa, Austrian, Air France) who carry free of charge the MOZAIC equipment and perform the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Meteo-France, and Forschungszentrum (FZJ, Julich, Germany). The MOZAIC database is supported by ETHER (CNES and INSU-CNRS). Also, thanks go to the free use of the MOPITTCO data obtained from the NASA Langley Research Center Atmospheric Science Data Center. SCIAMACHY radiances have been provided by ESA. We also thank Beatriz Monge-Sanz for providing the COPCAT coefficients. This work is funded by grants CGL2013-46736-R, Supercomputación and e-ciencia Project (CSD2007-0050) from the Consolider-Ingenio 2010 program of the Spanish Ministry of Economy and Competitiveness. Further support was provided by the SEV-2011-00067 grant of the Severo Ochoa Program, awarded by the Spanish Government. Andreas Hilboll received funding from the Earth System Science Research School (ESSReS), an initiative of the Helmholtz Association of German research centres (HGF) at the Alfred Wegener Institute for Polar and Marine Research. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2015-1-0007). Comments from two anonymous reviewers are gratefully acknowledge. ; Peer Reviewed ; Postprint (published version)

The research into the human health impacts from air pollution has advanced substantially over the last decade; in Europe ∼500 000 premature deaths are attributed annually to air pollution exposure [1], with adverse health effects beginning at much lower concentrations than previously understood [2]. The latest epidemiological evidence is now reflected in the new 2021 World Health Organization (WHO) Air Quality Guidelines (AQGs$^{\textrm{v2021}}$) [3], which represent a major revision over the previous ones set in 2005 (AQGs$^{\textrm{v2005}}$) [4]. Ten new AQGs have been set for the major health-damaging air pollutants, across different averaging times (annual$^{\textrm{(AN)}}$, peak season$^{\textrm{(PS)}}$, 24 h$^{\textrm{(24H)}}$, daily maximum 8 h average$^{\textrm{(8H)}})$, namely: particulate matter (PM$_{2.5}^{\textrm{AN, 24H}}$ and PM$_{10}^{\textrm{AN, 24H}})$, ozone (O$_3^{\textrm{PS, 8H}})$, nitrogen dioxide (NO$_2^{\textrm{AN, 24H}})$, sulphur dioxide (SO$_2^{\textrm{24H}})$ and carbon monoxide (CO$^{\textrm{24H}})$. The AQGs$^{\textrm{v2021}}$ are significantly more stringent, except for a relaxation in SO$_2^{\textrm{24H}}$, and O$_3^{\textrm{8H} }$ staying the same (supplementary table 1 (available online at stacks.iop.org/ERL/17/021002/mmedia)). This article quantifies the increase in non-compliance across all European measurement stations with AQGs$^{\textrm{v2021}}$ compared with AQGs$^{\textrm{v2005}}$, during business-as-usual (BAU) conditions (section 2); evaluates the impact that emission reductions during COVID-19 lockdowns had on non-compliance (section 3); and finally discusses the implications of our findings for any new European Union (EU) standards (section 4). ; This research has received funding from the Agencia Estatal de Investigacion (AEI) (MITIGATE, PID2020-116324RA-I00/AEI/10.13039/501100011033), European Research Council (#773051, FRAGMENT), and the AXA Research Fund. We also acknowledge Red Temática ACTRIS España (CGL2017-90884-REDT), H2020 ACTRIS IMP (#871115), and PRACE and RES for awarding access to Marenostrum4 Supercomputer based at the Barcelona Supercomputing Center. ; Peer reviewed

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)

Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in the tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation. ; Antonis Gkikas acknowledges support from the European Union's Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie Actions (grant no. 749461; DUST-GLASS). We would like to thank the principal investigators maintaining the AERONET sites used in the present work. We thank the NASA CALIPSO team and NASA/LaRC/ASDC for making the CALIPSO products available, which have been used to build the LIVAS products, and ESA, who funded the LIVAS project (contract no. 4000104106/11/NL/FF/fk). We are grateful to the AERIS/ICARE Data and Services Center for providing access to the CALIPSO data and their computational center (http://www.icare.univ-lille1.fr/, last access: 8 August 2019). Vassilis Amiridis acknowledges support from the European Research Council (grant no. 725698; D-TECT). Eleni Marinou was funded by a DLR VO-Ryoung investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Carlos Pérez García-Pando acknowledges support from the European Research Council (grant no. 773051; FRAGMENT), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (grant nos. RYC-2015-18690 and CGL2017-88911-R). The authors acknowledge support from the DustClim project as part of ERA4CS, an ERA-NET project 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). PRACE (Partnership for Advanced Computing in Europe) is acknowledged for awarding access to the MareNostrum Supercomputer in the Barcelona Supercomputing Center. We acknowledge support of this work by the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) project (grant no. MIS 5021516), which is implemented under the Horizon 2020 Action of "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme Competitiveness, Entrepreneurship, and Innovation (NSRF 2014–2020) and cofinanced by Greece and the European Union (under the European Regional Development Fund). NOA members acknowledge support from the Stavros Niarchos Foundation (SNF). The authors would like to thank Andrew Mark Sayer for his valuable and constructive comments. The authors would like also to thank Thanasis Georgiou for developing the ftp server on which the MIDAS data set is stored. We would like to thank the two anonymous reviewers, who were very helpful and who provided constructive comments that improved the paper. ; Peer Reviewed ; Postprint (published version)