Suchergebnisse

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 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)