Articles | Volume 12, issue 5
Geosci. Model Dev., 12, 1885–1907, 2019
https://doi.org/10.5194/gmd-12-1885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 1885–1907, 2019
https://doi.org/10.5194/gmd-12-1885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
14 May 2019
Model description paper
| 14 May 2019
HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module
Marc Guevara et al.
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Marc Guevara, Hervé Petetin, Oriol Jorba, Hugo A. C. Denier van der Gon, Jeroen Kuenen, Ingrid Super, Jukka-Pekka Jalkanen, Elisa Majamäki, Lasse Johansson, Vincent-Henri Peuch, and Carlos Pérez García-Pando
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-31, https://doi.org/10.5194/essd-2022-31, 2022
Revised manuscript accepted for ESSD
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To control the spread of the COVID-19 disease, European governments implemented mobility restriction measures that resulted in an unprecedented drop of anthropogenic emissions. This work presents a dataset of emission adjustment factors that allows quantifying changes in 2020 European primary emissions per country and pollutant sector at the daily scale. The resulting dataset can be used as input in modelling studies aiming at quantifying the impact of COVID-19 on air quality levels.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-390, https://doi.org/10.5194/gmd-2021-390, 2021
Preprint under review for GMD
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, responsible for large uncertainties in air quality modelling. The first step of this screening consists in keeping only emission contributions that are relevant enough. In a second step, the method identifies the largest diffferences that are evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European scale inventory over 150 cities in Europe.
Hervé Petetin, Dene Bowdalo, Pierre-Antoine Bretonnière, Marc Guevara, Oriol Jorba, Jan Mateu Armengol, Margarida Samso Cabre, Kim Serradell, Albert Soret, and Carlos Pérez Garcia-Pando
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-864, https://doi.org/10.5194/acp-2021-864, 2021
Preprint under review for ACP
Short summary
Short summary
This study investigates to which extent ozone forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS) can be improved using surface observations and state-of-the-art statistical methods. Through a case study over the Iberian Peninsula in 2018–2019, it unambiguously demonstrates the value of these methods for improving the raw CAMS O3 forecasts, while highlighting at the same time the complexity of improving the detection of highest O3 concentrations.
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
Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021, https://doi.org/10.5194/acp-21-7373-2021, 2021
Short summary
Short summary
This study provides a comprehensive assessment of air quality changes across the main European urban areas induced by the COVID-19 lockdown using satellite observations, surface site measurements, and the forecasting system from the Copernicus Atmospheric Monitoring Service (CAMS). We demonstrate the importance of accounting for weather and seasonal variability when calculating such estimates.
Marc Guevara, Oriol Jorba, Carles Tena, Hugo Denier van der Gon, Jeroen Kuenen, Nellie Elguindi, Sabine Darras, Claire Granier, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 13, 367–404, https://doi.org/10.5194/essd-13-367-2021, https://doi.org/10.5194/essd-13-367-2021, 2021
Short summary
Short summary
The temporal variability of atmospheric emissions is linked to changes in activity patterns, emission processes and meteorology. Accounting for the change in temporal emission characteristics is a key aspect for modelling the trends of air pollutants. This work presents a dataset of global and European emission temporal profiles to be used for air quality modelling purposes. The profiles were constructed considering the influences of local sociodemographic factors and climatological conditions.
Marc Guevara, Oriol Jorba, Albert Soret, Hervé Petetin, Dene Bowdalo, Kim Serradell, Carles Tena, Hugo Denier van der Gon, Jeroen Kuenen, Vincent-Henri Peuch, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 21, 773–797, https://doi.org/10.5194/acp-21-773-2021, https://doi.org/10.5194/acp-21-773-2021, 2021
Short summary
Short summary
Most European countries have imposed lockdowns to combat the spread of the COVID-19 pandemic. Such a socioeconomic disruption has resulted in a sudden drop of atmospheric emissions and air pollution levels. This study quantifies the daily reductions in national emissions and associated levels of nitrogen dioxide (NO2) due to the COVID-19 lockdowns in Europe, by making use of multiple open-access measured activity data as well as artificial intelligence and modelling techniques.
Hervé Petetin, Dene Bowdalo, Albert Soret, Marc Guevara, Oriol Jorba, Kim Serradell, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, https://doi.org/10.5194/acp-20-11119-2020, 2020
Short summary
Short summary
To control the spread of the COVID-19 coronavirus, the Spanish Government recently implemented a strict lockdown of the population, which strongly reduced the levels of nitrogen dioxide (NO2), one of the most critical air pollutants in Spain. This study quantifies the contribution of the lockdown on these reduced NO2 levels in Spain, taking the confounding effect of meteorology on artificial intelligence techniques into account.
Marc Guevara, Carles Tena, Manuel Porquet, Oriol Jorba, and Carlos Pérez García-Pando
Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020, https://doi.org/10.5194/gmd-13-873-2020, 2020
Short summary
Short summary
Emission inventories are a key input to numerical systems that simulate air quality. In this paper, we present an open-source tool intended for the computation of high-resolution anthropogenic emissions for air quality modelling. Emissions are estimated using detailed methods that combine local activity and emission factors along with meteorological data. Specific results are presented for Spain. Nevertheless, the model is designed so that it can be applicable to any European country or region.
Jaime Benavides, Michelle Snyder, Marc Guevara, Albert Soret, Carlos Pérez García-Pando, Fulvio Amato, Xavier Querol, and Oriol Jorba
Geosci. Model Dev., 12, 2811–2835, https://doi.org/10.5194/gmd-12-2811-2019, https://doi.org/10.5194/gmd-12-2811-2019, 2019
Short summary
Short summary
The 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 street level. We describe CALIOPE-Urban, a system coupling an operational mesoscale air quality forecast system with an urban roadway dispersion model over Barcelona city (Spain). The methodology may be replicated for other cities in the future.
María Teresa Pay, Gotzon Gangoiti, Marc Guevara, Sergey Napelenok, Xavier Querol, Oriol Jorba, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 19, 5467–5494, https://doi.org/10.5194/acp-19-5467-2019, https://doi.org/10.5194/acp-19-5467-2019, 2019
Short summary
Short summary
The poor diagnostic of the O3 issue over southwestern Europe prevents authorities from implementing effective mitigation plans. This work is a pioneer in identifying that imported O3 is the largest input to the ground-level O3 concentration in the Iberian Peninsula, which is largely explained by vertical mixing. This study also proves that anthropogenic emissions control the severe O3 peaks during stagnant conditions. Ad hoc local actions should complement national/European strategies.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Stelios Myriokefalitakis, Elisa Bergas-Massó, María Gonçalves-Ageitos, Carlos Pérez García-Pando, Twan van Noije, Philippe Le Sager, Akinori Ito, Eleni Athanasopoulou, Athanasios Nenes, Maria Kanakidou, Maarten C. Krol, and Evangelos Gerasopoulos
Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, https://doi.org/10.5194/gmd-15-3079-2022, 2022
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We here describe the implementation of atmospheric multiphase processes in the EC-Earth Earth system model. We provide global budgets of oxalate, sulfate, and iron-containing aerosols, along with an analysis of the links among atmospheric composition, aqueous-phase processes, and aerosol dissolution, supported by comparison to observations. This work is a first step towards an interactive calculation of the deposition of bioavailable atmospheric iron coupled to the model’s ocean component.
Antonis Gkikas, Emmanouil Proestakis, Vassilis Amiridis, Stelios Kazadzis, Enza Di Tomaso, Eleni Marinou, Nikos Hatzianastassiou, Jasper F. Kok, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 22, 3553–3578, https://doi.org/10.5194/acp-22-3553-2022, https://doi.org/10.5194/acp-22-3553-2022, 2022
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We present a comprehensive climatological analysis of dust optical depth (DOD) relying on the MIDAS dataset. MIDAS provides columnar mid-visible (550 nm) DOD at fine spatial resolution (0.1° × 0.1°) over a 15-year period (2003–2017). In the current study, the analysis is performed at various spatial (from regional to global) and temporal (from months to years) scales. More specifically, focus is given to specific regions hosting the major dust sources as well as downwind areas of the planet.
Marc Guevara, Hervé Petetin, Oriol Jorba, Hugo A. C. Denier van der Gon, Jeroen Kuenen, Ingrid Super, Jukka-Pekka Jalkanen, Elisa Majamäki, Lasse Johansson, Vincent-Henri Peuch, and Carlos Pérez García-Pando
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-31, https://doi.org/10.5194/essd-2022-31, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
To control the spread of the COVID-19 disease, European governments implemented mobility restriction measures that resulted in an unprecedented drop of anthropogenic emissions. This work presents a dataset of emission adjustment factors that allows quantifying changes in 2020 European primary emissions per country and pollutant sector at the daily scale. The resulting dataset can be used as input in modelling studies aiming at quantifying the impact of COVID-19 on air quality levels.
Jerónimo Escribano, Enza Di Tomaso, Oriol Jorba, Martina Klose, Maria Gonçalves Ageitos, Francesca Macchia, Vassilis Amiridis, Holger Baars, Eleni Marinou, Emmanouil Proestakis, Claudia Urbanneck, Dietrich Althausen, Johannes Bühl, Rodanthi-Elisavet Mamouri, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 22, 535–560, https://doi.org/10.5194/acp-22-535-2022, https://doi.org/10.5194/acp-22-535-2022, 2022
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We explore the benefits and consistency in adding lidar dust observations in a dust optical depth assimilation. We show that adding lidar data to a dust optical depth assimilation has valuable benefits and the dust analysis improves. We discuss the impact of the narrow satellite footprint of the lidar dust observations on the assimilation.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-390, https://doi.org/10.5194/gmd-2021-390, 2021
Preprint under review for GMD
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, responsible for large uncertainties in air quality modelling. The first step of this screening consists in keeping only emission contributions that are relevant enough. In a second step, the method identifies the largest diffferences that are evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European scale inventory over 150 cities in Europe.
Michaël Sicard, Oriol Jorba, Jiang Ji Ho, Rebeca Izquierdo, Concepción De Linares, Marta Alarcón, Adolfo Comerón, and Jordina Belmonte
Atmos. Chem. Phys., 21, 17807–17832, https://doi.org/10.5194/acp-21-17807-2021, https://doi.org/10.5194/acp-21-17807-2021, 2021
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This paper investigates the mechanisms involved in the dispersion, structure, and mixing in the vertical column of atmospheric pollen, using observations of pollen concentration obtained at the ground and its stratification in the atmosphere measured by a lidar (laser radar), as well as an atmospheric transport model and a simplified pollen module developed especially for this study. The largest pollen concentration difference between the ground and the layers above is observed during nighttime.
Hervé Petetin, Dene Bowdalo, Pierre-Antoine Bretonnière, Marc Guevara, Oriol Jorba, Jan Mateu Armengol, Margarida Samso Cabre, Kim Serradell, Albert Soret, and Carlos Pérez Garcia-Pando
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-864, https://doi.org/10.5194/acp-2021-864, 2021
Preprint under review for ACP
Short summary
Short summary
This study investigates to which extent ozone forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS) can be improved using surface observations and state-of-the-art statistical methods. Through a case study over the Iberian Peninsula in 2018–2019, it unambiguously demonstrates the value of these methods for improving the raw CAMS O3 forecasts, while highlighting at the same time the complexity of improving the detection of highest O3 concentrations.
Enza Di Tomaso, Jerónimo Escribano, Sara Basart, Paul Ginoux, Francesca Macchia, Francesca Barnaba, Francesco Benincasa, Pierre-Antoine Bretonnière, Arnau Buñuel, Miguel Castrillo, Emilio Cuevas, Paola Formenti, María Gonçalves, Oriol Jorba, Martina Klose, Lucia Mona, Gilbert Montané, Michail Mytilinaios, Vincenzo Obiso, Miriam Olid, Nick Schutgens, Athanasios Votsis, Ernest Werner, and Carlos Pérez García-Pando
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-358, https://doi.org/10.5194/essd-2021-358, 2021
Revised manuscript accepted for ESSD
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MONARCH reanalysis of desert dust aerosols extends the existing observational-based information for mineral dust monitoring by providing 3-hourly upper air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality/health and climate service applications.
Martina Klose, Oriol Jorba, María Gonçalves Ageitos, Jeronimo Escribano, Matthew L. Dawson, Vincenzo Obiso, Enza Di Tomaso, Sara Basart, Gilbert Montané Pinto, Francesca Macchia, Paul Ginoux, Juan Guerschman, Catherine Prigent, Yue Huang, Jasper F. Kok, Ron L. Miller, and Carlos Pérez García-Pando
Geosci. Model Dev., 14, 6403–6444, https://doi.org/10.5194/gmd-14-6403-2021, https://doi.org/10.5194/gmd-14-6403-2021, 2021
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Mineral soil dust is a major atmospheric airborne particle type. We present and evaluate MONARCH, a model used for regional and global dust-weather prediction. An important feature of the model is that it allows different approximations to represent dust, ranging from more simplified to more complex treatments. Using these different treatments, MONARCH can help us better understand impacts of dust in the Earth system, such as its interactions with radiation.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
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
Atmos. Chem. Phys., 21, 8127–8167, https://doi.org/10.5194/acp-21-8127-2021, https://doi.org/10.5194/acp-21-8127-2021, 2021
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Desert dust interacts with virtually every component of the Earth system, including the climate system. We develop a new methodology to represent the global dust cycle that integrates observational constraints on the properties and abundance of desert dust with global atmospheric model simulations. We show that the resulting representation of the global dust cycle is more accurate than what can be obtained from a large number of current climate global atmospheric models.
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
Atmos. Chem. Phys., 21, 8169–8193, https://doi.org/10.5194/acp-21-8169-2021, https://doi.org/10.5194/acp-21-8169-2021, 2021
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The many impacts of dust on the Earth system depend on dust mineralogy, which varies between dust source regions. We constrain the contribution of the world’s main dust source regions by integrating dust observations with global model simulations. We find that Asian dust contributes more and that North African dust contributes less than models account for. We obtain a dataset of each source region’s contribution to the dust cycle that can be used to constrain dust impacts on the Earth system.
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
Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021, https://doi.org/10.5194/acp-21-7373-2021, 2021
Short summary
Short summary
This study provides a comprehensive assessment of air quality changes across the main European urban areas induced by the COVID-19 lockdown using satellite observations, surface site measurements, and the forecasting system from the Copernicus Atmospheric Monitoring Service (CAMS). We demonstrate the importance of accounting for weather and seasonal variability when calculating such estimates.
Longlei Li, Natalie M. Mahowald, Ron L. Miller, Carlos Pérez García-Pando, Martina Klose, Douglas S. Hamilton, Maria Gonçalves Ageitos, Paul Ginoux, Yves Balkanski, Robert O. Green, Olga Kalashnikova, Jasper F. Kok, Vincenzo Obiso, David Paynter, and David R. Thompson
Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, https://doi.org/10.5194/acp-21-3973-2021, 2021
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For the first time, this study quantifies the range of the dust direct radiative effect due to uncertainty in the soil mineral abundance using all currently available information. We show that the majority of the estimated direct radiative effect range is due to uncertainty in the simulated mass fractions of iron oxides and thus their soil abundance, which is independent of the model employed. We therefore prove the necessity of considering mineralogy for understanding dust–climate interactions.
Marc Guevara, Oriol Jorba, Carles Tena, Hugo Denier van der Gon, Jeroen Kuenen, Nellie Elguindi, Sabine Darras, Claire Granier, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 13, 367–404, https://doi.org/10.5194/essd-13-367-2021, https://doi.org/10.5194/essd-13-367-2021, 2021
Short summary
Short summary
The temporal variability of atmospheric emissions is linked to changes in activity patterns, emission processes and meteorology. Accounting for the change in temporal emission characteristics is a key aspect for modelling the trends of air pollutants. This work presents a dataset of global and European emission temporal profiles to be used for air quality modelling purposes. The profiles were constructed considering the influences of local sociodemographic factors and climatological conditions.
Marc Guevara, Oriol Jorba, Albert Soret, Hervé Petetin, Dene Bowdalo, Kim Serradell, Carles Tena, Hugo Denier van der Gon, Jeroen Kuenen, Vincent-Henri Peuch, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 21, 773–797, https://doi.org/10.5194/acp-21-773-2021, https://doi.org/10.5194/acp-21-773-2021, 2021
Short summary
Short summary
Most European countries have imposed lockdowns to combat the spread of the COVID-19 pandemic. Such a socioeconomic disruption has resulted in a sudden drop of atmospheric emissions and air pollution levels. This study quantifies the daily reductions in national emissions and associated levels of nitrogen dioxide (NO2) due to the COVID-19 lockdowns in Europe, by making use of multiple open-access measured activity data as well as artificial intelligence and modelling techniques.
Antonis Gkikas, Emmanouil Proestakis, Vassilis Amiridis, Stelios Kazadzis, Enza Di Tomaso, Alexandra Tsekeri, Eleni Marinou, Nikos Hatzianastassiou, and Carlos Pérez García-Pando
Atmos. Meas. Tech., 14, 309–334, https://doi.org/10.5194/amt-14-309-2021, https://doi.org/10.5194/amt-14-309-2021, 2021
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We present the development of the MIDAS (ModIs Dust AeroSol) data set, providing daily dust optical depth (DOD; 550 nm) at a global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003–2017). It has been developed via the synergy of MODIS-Aqua and MERRA-2 data, while CALIOP and AERONET retrievals are used for its assessment. MIDAS upgrades existing dust observational capabilities, and it is suitable for dust climatological studies, model evaluation, and data assimilation.
Hervé Petetin, Dene Bowdalo, Albert Soret, Marc Guevara, Oriol Jorba, Kim Serradell, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, https://doi.org/10.5194/acp-20-11119-2020, 2020
Short summary
Short summary
To control the spread of the COVID-19 coronavirus, the Spanish Government recently implemented a strict lockdown of the population, which strongly reduced the levels of nitrogen dioxide (NO2), one of the most critical air pollutants in Spain. This study quantifies the contribution of the lockdown on these reduced NO2 levels in Spain, taking the confounding effect of meteorology on artificial intelligence techniques into account.
Marc Guevara, Carles Tena, Manuel Porquet, Oriol Jorba, and Carlos Pérez García-Pando
Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020, https://doi.org/10.5194/gmd-13-873-2020, 2020
Short summary
Short summary
Emission inventories are a key input to numerical systems that simulate air quality. In this paper, we present an open-source tool intended for the computation of high-resolution anthropogenic emissions for air quality modelling. Emissions are estimated using detailed methods that combine local activity and emission factors along with meteorological data. Specific results are presented for Spain. Nevertheless, the model is designed so that it can be applicable to any European country or region.
Bing Pu, Paul Ginoux, Huan Guo, N. Christina Hsu, John Kimball, Beatrice Marticorena, Sergey Malyshev, Vaishali Naik, Norman T. O'Neill, Carlos Pérez García-Pando, Juliette Paireau, Joseph M. Prospero, Elena Shevliakova, and Ming Zhao
Atmos. Chem. Phys., 20, 55–81, https://doi.org/10.5194/acp-20-55-2020, https://doi.org/10.5194/acp-20-55-2020, 2020
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Dust emission initiates when surface wind velocities exceed a threshold depending on soil and surface characteristics and varying spatially and temporally. Climate models widely use wind erosion thresholds. The climatological monthly global distribution of the wind erosion threshold, Vthreshold, is retrieved using satellite and reanalysis products and improves the simulation of dust frequency, magnitude, and the seasonal cycle in the Geophysical Fluid Dynamics Laboratory land–atmosphere model.
Jaime Benavides, Michelle Snyder, Marc Guevara, Albert Soret, Carlos Pérez García-Pando, Fulvio Amato, Xavier Querol, and Oriol Jorba
Geosci. Model Dev., 12, 2811–2835, https://doi.org/10.5194/gmd-12-2811-2019, https://doi.org/10.5194/gmd-12-2811-2019, 2019
Short summary
Short summary
The 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 street level. We describe CALIOPE-Urban, a system coupling an operational mesoscale air quality forecast system with an urban roadway dispersion model over Barcelona city (Spain). The methodology may be replicated for other cities in the future.
María Teresa Pay, Gotzon Gangoiti, Marc Guevara, Sergey Napelenok, Xavier Querol, Oriol Jorba, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 19, 5467–5494, https://doi.org/10.5194/acp-19-5467-2019, https://doi.org/10.5194/acp-19-5467-2019, 2019
Short summary
Short summary
The poor diagnostic of the O3 issue over southwestern Europe prevents authorities from implementing effective mitigation plans. This work is a pioneer in identifying that imported O3 is the largest input to the ground-level O3 concentration in the Iberian Peninsula, which is largely explained by vertical mixing. This study also proves that anthropogenic emissions control the severe O3 peaks during stagnant conditions. Ad hoc local actions should complement national/European strategies.
Rachel A. Scanza, Douglas S. Hamilton, Carlos Perez Garcia-Pando, Clifton Buck, Alex Baker, and Natalie M. Mahowald
Atmos. Chem. Phys., 18, 14175–14196, https://doi.org/10.5194/acp-18-14175-2018, https://doi.org/10.5194/acp-18-14175-2018, 2018
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Soluble iron input to remote oceans from dust and combustion aerosols may significantly impact the ability of the ocean to remove carbon dioxide from the atmosphere. In this paper, the processing of insoluble iron during atmospheric transport is simulated using parameterizations that can be implemented in most Earth system models. Our mechanism reasonably matches observations and is computationally efficient, enabling the study of trends and climate impacts due to the Fe–C cycle.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
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Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Marta G. Vivanco, Mark R. Theobald, Héctor García-Gómez, Juan Luis Garrido, Marje Prank, Wenche Aas, Mario Adani, Ummugulsum Alyuz, Camilla Andersson, Roberto Bellasio, Bertrand Bessagnet, Roberto Bianconi, Johannes Bieser, Jørgen Brandt, Gino Briganti, Andrea Cappelletti, Gabriele Curci, Jesper H. Christensen, Augustin Colette, Florian Couvidat, Cornelis Cuvelier, Massimo D'Isidoro, Johannes Flemming, Andrea Fraser, Camilla Geels, Kaj M. Hansen, Christian Hogrefe, Ulas Im, Oriol Jorba, Nutthida Kitwiroon, Astrid Manders, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Luca Pozzoli, Efisio Solazzo, Svetlana Tsyro, Alper Unal, Peter Wind, and Stefano Galmarini
Atmos. Chem. Phys., 18, 10199–10218, https://doi.org/10.5194/acp-18-10199-2018, https://doi.org/10.5194/acp-18-10199-2018, 2018
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European wet and dry atmospheric deposition of N and S estimated by 14 air quality models was found to vary substantially. An ensemble of models meeting acceptability criteria was used to estimate the exceedances of the critical loads for N in habitats within the Natura 2000 network, as well as their lower and upper limits. Scenarios with 20 % emission reductions in different regions of the world showed that European emissions are responsible for most of the N and S deposition in Europe.
Antonis Gkikas, Vincenzo Obiso, Carlos Pérez García-Pando, Oriol Jorba, Nikos Hatzianastassiou, Lluis Vendrell, Sara Basart, Stavros Solomos, Santiago Gassó, and José Maria Baldasano
Atmos. Chem. Phys., 18, 8757–8787, https://doi.org/10.5194/acp-18-8757-2018, https://doi.org/10.5194/acp-18-8757-2018, 2018
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The present study investigates the direct radiative effects (DREs), induced during 20 intense Mediterranean desert dust outbreaks, based on regional short-term numerical simulations of the NMMB-MONARCH model: more specifically, (i) the DREs and their associated impacts on temperature and surface sensible and latent heat fluxes, (ii) the feedbacks on dust AOD and dust emissions, and (iii) the possible improvements in short-term forecasts (up to 84 h) of temperature and radiation.
Albert Ansmann, Franziska Rittmeister, Ronny Engelmann, Sara Basart, Oriol Jorba, Christos Spyrou, Samuel Remy, Annett Skupin, Holger Baars, Patric Seifert, Fabian Senf, and Thomas Kanitz
Atmos. Chem. Phys., 17, 14987–15006, https://doi.org/10.5194/acp-17-14987-2017, https://doi.org/10.5194/acp-17-14987-2017, 2017
Alejandro Marti, Arnau Folch, Oriol Jorba, and Zavisa Janjic
Atmos. Chem. Phys., 17, 4005–4030, https://doi.org/10.5194/acp-17-4005-2017, https://doi.org/10.5194/acp-17-4005-2017, 2017
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We describe and evaluate NMMB-MONARCH-ASH, a novel online multi-scale meteorological and transport model developed at the BSC-CNS capable of forecasting the dispersal and deposition of volcanic ash. The forecast skills of the model have been validated and they improve on those from traditional operational offline (decoupled) models. The results support the use of online coupled models to aid civil aviation and emergency management during a crisis such as the 2010 eruption of Eyjafjallajökull.
Enza Di Tomaso, Nick A. J. Schutgens, Oriol Jorba, and Carlos Pérez García-Pando
Geosci. Model Dev., 10, 1107–1129, https://doi.org/10.5194/gmd-10-1107-2017, https://doi.org/10.5194/gmd-10-1107-2017, 2017
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A data assimilation capability has been built for a chemical weather prediction system, with a focus on mineral dust. Before this work, dust was produced uniquely from model estimated emissions. As emissions are recognized as a major factor limiting the accuracy of dust modelling, satellite observations have been used to improve the description of the atmospheric dust load, with a significant impact on dust forecast from assimilating observations particularly relevant for dust applications.
Alba Badia, Oriol Jorba, Apostolos Voulgarakis, Donald Dabdub, Carlos Pérez García-Pando, Andreas Hilboll, María Gonçalves, and Zavisa Janjic
Geosci. Model Dev., 10, 609–638, https://doi.org/10.5194/gmd-10-609-2017, https://doi.org/10.5194/gmd-10-609-2017, 2017
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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), an online chemical weather prediction system conceived for both the regional and global scales. We provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations, ozonesondes, aircraft data and satellite observations.
Ioannis Kioutsioukis, Ulas Im, Efisio Solazzo, Roberto Bianconi, Alba Badia, Alessandra Balzarini, Rocío Baró, Roberto Bellasio, Dominik Brunner, Charles Chemel, Gabriele Curci, Hugo Denier van der Gon, Johannes Flemming, Renate Forkel, Lea Giordano, Pedro Jiménez-Guerrero, Marcus Hirtl, Oriol Jorba, Astrid Manders-Groot, Lucy Neal, Juan L. Pérez, Guidio Pirovano, Roberto San Jose, Nicholas Savage, Wolfram Schroder, Ranjeet S. Sokhi, Dimiter Syrakov, Paolo Tuccella, Johannes Werhahn, Ralf Wolke, Christian Hogrefe, and Stefano Galmarini
Atmos. Chem. Phys., 16, 15629–15652, https://doi.org/10.5194/acp-16-15629-2016, https://doi.org/10.5194/acp-16-15629-2016, 2016
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Four ensemble methods are applied to two annual AQMEII datasets and their performance is compared for O3, NO2 and PM10. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill at each station over the single models and the ensemble mean. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way.
Antonis Gkikas, Sara Basart, Nikos Hatzianastassiou, Eleni Marinou, Vassilis Amiridis, Stelios Kazadzis, Jorge Pey, Xavier Querol, Oriol Jorba, Santiago Gassó, and José Maria Baldasano
Atmos. Chem. Phys., 16, 8609–8642, https://doi.org/10.5194/acp-16-8609-2016, https://doi.org/10.5194/acp-16-8609-2016, 2016
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This study presents the 3-D structures of intense Mediterranean desert dust outbreaks, over the period Mar 2000–Feb 2013. The desert dust (DD) episodes are identified through an objective and dynamic algorithm, which utilizes satellite retrievals (MODIS, TOMS and OMI) as inputs. The performance of the satellite algorithm is evaluated vs. AERONET and PM10 data. The geometrical characteristics of the identified DD episodes are analyzed using the collocated CALIOP profiles as a complementary tool.
N. Huneeus, S. Basart, S. Fiedler, J.-J. Morcrette, A. Benedetti, J. Mulcahy, E. Terradellas, C. Pérez García-Pando, G. Pejanovic, S. Nickovic, P. Arsenovic, M. Schulz, E. Cuevas, J. M. Baldasano, J. Pey, S. Remy, and B. Cvetkovic
Atmos. Chem. Phys., 16, 4967–4986, https://doi.org/10.5194/acp-16-4967-2016, https://doi.org/10.5194/acp-16-4967-2016, 2016
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Five dust models are evaluated regarding their performance in predicting an intense Saharan dust outbreak affecting western and northern Europe (NE). Models predict the onset and evolution of the event for all analysed lead times. On average, differences among the models are larger than differences in lead times for each model. The models tend to underestimate the long-range transport towards NE. This is partly due to difficulties in simulating the vertical dust distribution and horizontal wind.
J. P. Perlwitz, C. Pérez García-Pando, and R. L. Miller
Atmos. Chem. Phys., 15, 11593–11627, https://doi.org/10.5194/acp-15-11593-2015, https://doi.org/10.5194/acp-15-11593-2015, 2015
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The mineral composition of dust aerosols is modeled. The mineral composition is derived by extending a method that provides the composition of wet-sieved soil to account for differences between the mineral fractions of the wet-sieved soil and the resulting aerosol concentration. An empirical constraint upon the relative emission of clay and silt is applied that further differentiates soil and aerosol mineral composition. A method is proposed for mixing minerals with iron oxide impurities.
J. P. Perlwitz, C. Pérez García-Pando, and R. L. Miller
Atmos. Chem. Phys., 15, 11629–11652, https://doi.org/10.5194/acp-15-11629-2015, https://doi.org/10.5194/acp-15-11629-2015, 2015
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A global compilation from nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols. Dust emission based on wet-sieved soil is assumed for the first method. The second method reconstructs the aggregates and size distribution of the emitted dust aerosols. Only the second method is able to reproduce observed phyllosilicate fractions in the silt size range and reduces quartz overestimation. Substantial uncertainties remain.
W. R. Sessions, J. S. Reid, A. Benedetti, P. R. Colarco, A. da Silva, S. Lu, T. Sekiyama, T. Y. Tanaka, J. M. Baldasano, S. Basart, M. E. Brooks, T. F. Eck, M. Iredell, J. A. Hansen, O. C. Jorba, H.-M. H. Juang, P. Lynch, J.-J. Morcrette, S. Moorthi, J. Mulcahy, Y. Pradhan, M. Razinger, C. B. Sampson, J. Wang, and D. L. Westphal
Atmos. Chem. Phys., 15, 335–362, https://doi.org/10.5194/acp-15-335-2015, https://doi.org/10.5194/acp-15-335-2015, 2015
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A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
M. Spada, O. Jorba, C. Pérez García-Pando, Z. Janjic, and J. M. Baldasano
Atmos. Chem. Phys., 13, 11735–11755, https://doi.org/10.5194/acp-13-11735-2013, https://doi.org/10.5194/acp-13-11735-2013, 2013
Related subject area
Atmospheric sciences
Description and evaluation of the community aerosol dynamics model MAFOR v2.0
Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0
Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application
Earth system modeling of mercury using CESM2 – Part 1: Atmospheric model CAM6-Chem/Hg v1.0
Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)
On the application and grid-size sensitivity of the urban dispersion model CAIRDIO v2.0 under real city weather conditions
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs)
An ensemble-based statistical methodology to detect differences in weather and climate model executables
Multiphase processes in the EC-Earth model and their relevance to the atmospheric oxalate, sulfate, and iron cycles
Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1
Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS
Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5
Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs)
Bedymo: a combined quasi-geostrophic and primitive equation model in σ coordinates
Simulation of organics in the atmosphere: evaluation of EMACv2.54 with the Mainz Organic Mechanism (MOM) coupled to the ORACLE (v1.0) submodel
An update on the 4D-LETKF data assimilation system for the whole neutral atmosphere
Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning
A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models
Multiple same-level and telescoping nesting in GFDL's dynamical core
Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme
Representing low-intensity fire sensible heat output in a mesoscale atmospheric model with a canopy submodel: a case study with ARPS-CANOPY (version 5.2.12)
A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1
Deep-learning spatial principles from deterministic chemical transport models for chemical reanalysis: an application in China for PM2.5
Model development in practice: a comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40
A parameterization of long-continuing-current (LCC) lightning in the lightning submodel LNOX (version 3.0) of the Modular Earth Submodel System (MESSy, version 2.54)
Air Control Toolbox (ACT_v1.0): a flexible surrogate model to explore mitigation scenarios in air quality forecasts
The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
The Flexible Modelling Framework for the Met Office Unified Model (Flex-UM, using UM 12.0 release)
Integration-based extraction and visualization of jet stream cores
Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0
Evaluating the assimilation of S5P/TROPOMI near real-time SO2 columns and layer height data into the CAMS integrated forecasting system (CY47R1), based on a case study of the 2019 Raikoke eruption
A comparative analysis for deep learning model (hyDL-CO v1.0) and Kalman Filter to predict CO in China
Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4
Improvement of stomatal resistance and photosynthesis mechanism of Noah-MP-WDDM (v1.42) in simulation of NO2 dry deposition velocity in forests
Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets
The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at km-scale
A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO2 data using a length scale computed from airborne lidar measurements
Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model
An aerosol classification scheme for global simulations using the K-means machine learning method
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the WRF (v4.1.3) model during the ICE-POP 2018 field campaign
Parameterization of the collision–coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach
Inline coupling of simple and complex chemistry modules within the global weather forecast model FIM (FIM-Chem v1)
Downscaling of air pollutants in Europe using uEMEP_v6
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Matthias Karl, Liisa Pirjola, Tiia Grönholm, Mona Kurppa, Srinivasan Anand, Xiaole Zhang, Andreas Held, Rolf Sander, Miikka Dal Maso, David Topping, Shuai Jiang, Leena Kangas, and Jaakko Kukkonen
Geosci. Model Dev., 15, 3969–4026, https://doi.org/10.5194/gmd-15-3969-2022, https://doi.org/10.5194/gmd-15-3969-2022, 2022
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The community aerosol dynamics model MAFOR includes several advanced features: coupling with an up-to-date chemistry mechanism for volatile organic compounds, a revised Brownian coagulation kernel that takes into account the fractal geometry of soot particles, a multitude of nucleation parameterizations, size-resolved partitioning of semi-volatile inorganics, and a hybrid method for the formation of secondary organic aerosols within the framework of condensation and evaporation.
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022, https://doi.org/10.5194/gmd-15-3845-2022, 2022
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Mercury is one of the most toxic pollutants in the environment, and wet deposition is a major process for atmospheric mercury to enter, causing ecological and human health risks. High-mercury wet deposition in the southeastern US has been a problem for many years. Here we employed a newly developed high-resolution WRF-GC model with the capability to simulate mercury to study this problem. We conclude that deep convection caused enhanced mercury wet deposition in the southeastern US.
Jeong-Beom Lee, Jae-Bum Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee
Geosci. Model Dev., 15, 3797–3813, https://doi.org/10.5194/gmd-15-3797-2022, https://doi.org/10.5194/gmd-15-3797-2022, 2022
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The predication of PM2.5 has been carried out using a numerical air quality model in South Korea. Despite recent progress of numerical air quality models, accurate prediction of PM2.5 is still challenging. In this study, we developed a data-based model using a deep neural network (DNN) to overcome the limitations of numerical air quality models. The results showed that the DNN model outperformed the CMAQ when it was trained by using observation and forecasting data from the numerical models.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Haibo Wang, Ting Yang, Zifa Wang, Jianjun Li, Wenxuan Chai, Guigang Tang, Lei Kong, and Xueshun Chen
Geosci. Model Dev., 15, 3555–3585, https://doi.org/10.5194/gmd-15-3555-2022, https://doi.org/10.5194/gmd-15-3555-2022, 2022
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In this paper, we develop an online data coupled assimilation system (NAQPMS-PDAF) with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating 1 month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.
Peng Zhang and Yanxu Zhang
Geosci. Model Dev., 15, 3587–3601, https://doi.org/10.5194/gmd-15-3587-2022, https://doi.org/10.5194/gmd-15-3587-2022, 2022
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Mercury is a global pollutant that can be transported over long distance through the atmosphere. We develop a new online global model for atmospheric mercury. The model reproduces the observed global atmospheric mercury concentrations and deposition distributions by simulating the emissions, transport, and physicochemical processes of atmospheric mercury. And we find that the seasonal variations of atmospheric Hg are the result of multiple processes and have obvious regional characteristics.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 15, 3417–3431, https://doi.org/10.5194/gmd-15-3417-2022, https://doi.org/10.5194/gmd-15-3417-2022, 2022
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Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
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Numerical models are an important tool to assess the air quality in cities,
as they can provide near-continouos data in time and space. In this paper,
air pollution for an entire city is simulated at a high spatial resolution of 40 m.
At this spatial scale, the effects of buildings on the atmosphere,
like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
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NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
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In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
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Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Stelios Myriokefalitakis, Elisa Bergas-Massó, María Gonçalves-Ageitos, Carlos Pérez García-Pando, Twan van Noije, Philippe Le Sager, Akinori Ito, Eleni Athanasopoulou, Athanasios Nenes, Maria Kanakidou, Maarten C. Krol, and Evangelos Gerasopoulos
Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, https://doi.org/10.5194/gmd-15-3079-2022, 2022
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We here describe the implementation of atmospheric multiphase processes in the EC-Earth Earth system model. We provide global budgets of oxalate, sulfate, and iron-containing aerosols, along with an analysis of the links among atmospheric composition, aqueous-phase processes, and aerosol dissolution, supported by comparison to observations. This work is a first step towards an interactive calculation of the deposition of bioavailable atmospheric iron coupled to the model’s ocean component.
Santos J. González-Rojí, Martina Messmer, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 15, 2859–2879, https://doi.org/10.5194/gmd-15-2859-2022, https://doi.org/10.5194/gmd-15-2859-2022, 2022
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Different configurations of physics parameterizations of a regional climate model are tested over southern Peru at fine resolution. The most challenging regions compared to observational data are the slopes of the Andes. Model configurations for Europe and East Africa are not perfectly suitable for southern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell–Freitas cumulus parameterization provides the most accurate results over Madre de Dios.
Angel Navarro Trastoy, Sebastian Strasser, Lauri Tuppi, Maksym Vasiuta, Markku Poutanen, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev., 15, 2763–2771, https://doi.org/10.5194/gmd-15-2763-2022, https://doi.org/10.5194/gmd-15-2763-2022, 2022
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Production of satellite products relies on information from different centers. By coupling a weather model and an orbit determination solver we eliminate the dependence on one of the centers. The coupling has proven to be possible in the first stage, where no formatting has been applied to any of the models involved. This opens a window for further development and improvement to a coupling that has proven to be as good as the predecessor model.
Soon-Young Park, Uzzal Kumar Dash, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno, and Chul Han Song
Geosci. Model Dev., 15, 2773–2790, https://doi.org/10.5194/gmd-15-2773-2022, https://doi.org/10.5194/gmd-15-2773-2022, 2022
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An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
Lars Hoffmann, Paul F. Baumeister, Zhongyin Cai, Jan Clemens, Sabine Griessbach, Gebhard Günther, Yi Heng, Mingzhao Liu, Kaveh Haghighi Mood, Olaf Stein, Nicole Thomas, Bärbel Vogel, Xue Wu, and Ling Zou
Geosci. Model Dev., 15, 2731–2762, https://doi.org/10.5194/gmd-15-2731-2022, https://doi.org/10.5194/gmd-15-2731-2022, 2022
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We describe the new version (2.2) of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the free troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16 of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.
Clemens Spensberger, Trond Thorsteinsson, and Thomas Spengler
Geosci. Model Dev., 15, 2711–2729, https://doi.org/10.5194/gmd-15-2711-2022, https://doi.org/10.5194/gmd-15-2711-2022, 2022
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In order to understand the atmosphere, we rely on a hierarchy of models ranging from very simple to very complex. Comparing different steps in this hierarchy usually entails comparing different models. Here we combine two such steps that are commonly used in one modelling framework. This makes comparisons both much easier and much more direct.
Andrea Pozzer, Simon F. Reifenberg, Vinod Kumar, Bruno Franco, Matthias Kohl, Domenico Taraborrelli, Sergey Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev., 15, 2673–2710, https://doi.org/10.5194/gmd-15-2673-2022, https://doi.org/10.5194/gmd-15-2673-2022, 2022
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A newly developed setup of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is evaluated here. A comprehensive organic degradation mechanism is used and coupled with a volatility base model.
The results show that the model reproduces most of the tracers and aerosols satisfactorily but shows discrepancies for oxygenated organic gases. It is also shown that this model configuration can be used for further research in atmospheric chemistry.
Dai Koshin, Kaoru Sato, Masashi Kohma, and Shingo Watanabe
Geosci. Model Dev., 15, 2293–2307, https://doi.org/10.5194/gmd-15-2293-2022, https://doi.org/10.5194/gmd-15-2293-2022, 2022
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The 4D ensemble Kalman filter data assimilation system for the whole neutral atmosphere has been updated. The update includes the introduction of a filter to reduce the generation of spurious waves, change in the order of horizontal diffusion of the forecast model to reproduce more realistic tidal amplitudes, and use of additional satellite observations. As a result, the analysis performance has been greatly improved, even for disturbances with periods of less than 1 d.
Harish Baki, Sandeep Chinta, C Balaji, and Balaji Srinivasan
Geosci. Model Dev., 15, 2133–2155, https://doi.org/10.5194/gmd-15-2133-2022, https://doi.org/10.5194/gmd-15-2133-2022, 2022
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WRF model accuracy relies on numerous aspects, and the model parameters are one of them. By calibrating the model parameters, we can improve the model forecast. However, there exist hundreds of parameters, and calibrating all of them is unimaginably expensive. Thus, there is a need to identify the sensitive parameters that influence the model output variables to reduce the parameter dimensionality. This study addresses the different methods and outcomes of parameter sensitivity analysis.
Jessica Keune, Dominik L. Schumacher, and Diego G. Miralles
Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, https://doi.org/10.5194/gmd-15-1875-2022, 2022
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Air transports moisture and heat, shaping the weather we experience. When and where was this air moistened and warmed by the surface? To address this question, atmospheric models trace the history of air parcels in space and time. However, their uncertainties remain unexplored, which hinders their utility and application. Here, we present a framework that sheds light on these uncertainties. Our approach sets a new standard in the assessment of atmospheric moisture and heat trajectories.
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-442, https://doi.org/10.5194/gmd-2021-442, 2022
Revised manuscript accepted for GMD
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The single nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allow concurrent execution of multiple same level and telescoping multi level nested grids in both global and regional setups.
Soyoung Ha
Geosci. Model Dev., 15, 1769–1788, https://doi.org/10.5194/gmd-15-1769-2022, https://doi.org/10.5194/gmd-15-1769-2022, 2022
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In an effort to improve air quality forecasting, the WRFDA 3D-Var system is newly extended for the assimilation of surface PM2.5 and PM10 using the RACM/MADE-VBS chemistry in the WRF-Chem model. Through a case study during the Korea–United States Air Quality (KORUS-AQ) period, it is demonstrated that the analysis can lead to improving the prediction of surface PM concentrations up to 26 % for 24 h, diminishing most bias errors.
Michael T. Kiefer, Warren E. Heilman, Shiyuan Zhong, Joseph J. Charney, Xindi Bian, Nicholas S. Skowronski, Kenneth L. Clark, Michael R. Gallagher, John L. Hom, and Matthew Patterson
Geosci. Model Dev., 15, 1713–1734, https://doi.org/10.5194/gmd-15-1713-2022, https://doi.org/10.5194/gmd-15-1713-2022, 2022
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We examine methods used to represent wildland fire sensible heat release in atmospheric models. A set of simulations are evaluated using observations from a low-intensity prescribed fire in the New Jersey Pine Barrens. The comparison is motivated by the need for guidance regarding the representation of low-intensity fire sensible heating in atmospheric models. Such fires are prevalent during prescribed fire operations and can impact the health and safety of fire personnel and the public.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen
Geosci. Model Dev., 15, 1677–1687, https://doi.org/10.5194/gmd-15-1677-2022, https://doi.org/10.5194/gmd-15-1677-2022, 2022
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The high computational cost of chemical integration is a long-standing limitation in global atmospheric chemistry models. Here we present an adaptive and efficient algorithm that can reduce the computational time of atmospheric chemistry by 50 % and maintain the error below 2 % for important species, inspired by machine learning clustering techniques and traditional asymptotic analysis ideas.
Baolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu
Geosci. Model Dev., 15, 1583–1594, https://doi.org/10.5194/gmd-15-1583-2022, https://doi.org/10.5194/gmd-15-1583-2022, 2022
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Data fusion is used to estimate spatially completed and smooth reanalysis fields from multiple data sources of observations and model simulations. We developed a well-designed deep-learning model framework to embed spatial correlation principles of atmospheric physics and chemical models. The deep-learning model has very high accuracy to predict reanalysis data fields from isolated observation data points. It is also feasible for operational applications due to computational efficiency.
Wim C. de Rooy, Pier Siebesma, Peter Baas, Geert Lenderink, Stephan R. de Roode, Hylke de Vries, Erik van Meijgaard, Jan Fokke Meirink, Sander Tijm, and Bram van 't Veen
Geosci. Model Dev., 15, 1513–1543, https://doi.org/10.5194/gmd-15-1513-2022, https://doi.org/10.5194/gmd-15-1513-2022, 2022
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This paper describes a comprehensive model update to the boundary layer schemes. Because the involved parameterisations are all built on widely applied frameworks, the here-described modifications are applicable to many NWP and climate models. The model update contains substantial modifications to the cloud, turbulence, and convection schemes and leads to a substantial improvement of several aspects of the model, especially low cloud forecasts.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Patrick Jöckel, and Francisco J. Gordillo-Vázquez
Geosci. Model Dev., 15, 1545–1565, https://doi.org/10.5194/gmd-15-1545-2022, https://doi.org/10.5194/gmd-15-1545-2022, 2022
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This study reports the first parameterization of long-continuing-current lightning in a climate model. Long-continuing-current lightning is proposed to be the main precursor of lightning-ignited wildfires and sprites, a type of transient luminous event taking place in the mesosphere. This parameterization can significantly contribute to improving the implementation of wildfires in climate models.
Augustin Colette, Laurence Rouïl, Frédérik Meleux, Vincent Lemaire, and Blandine Raux
Geosci. Model Dev., 15, 1441–1465, https://doi.org/10.5194/gmd-15-1441-2022, https://doi.org/10.5194/gmd-15-1441-2022, 2022
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We introduce the first toolbox that allows exploration of the benefits of air pollution mitigation scenarios in the every-day air quality forecasts through a web interface. The toolbox relies on the joint use of chemistry-transport models (CTMs) and surrogate modelling techniques.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
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This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-19, https://doi.org/10.5194/gmd-2022-19, 2022
Revised manuscript accepted for GMD
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In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existed models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models especially in those regions with middle and high-intensity rainfall. Considering these regions would cause more threats to human activity, the research in our manuscript is significant to prevent natural disasters caused by heavy rainfall.
Penelope Maher and Paul Earnshaw
Geosci. Model Dev., 15, 1177–1194, https://doi.org/10.5194/gmd-15-1177-2022, https://doi.org/10.5194/gmd-15-1177-2022, 2022
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Climate models do a pretty good job. But they are far from perfect. Fixing these imperfections is really hard because the models are complicated. One way to make progress is to create simpler models: think impressionism rather than realism in the art world. We changed the Met Office model to be intentionally simple and it still does a pretty good job. This will help to identify sources of model imperfections, develop new methods and improve our understanding of how the climate works.
Lukas Bösiger, Michael Sprenger, Maxi Boettcher, Hanna Joos, and Tobias Günther
Geosci. Model Dev., 15, 1079–1096, https://doi.org/10.5194/gmd-15-1079-2022, https://doi.org/10.5194/gmd-15-1079-2022, 2022
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Jet streams are coherent air flows that interact with atmospheric structures such as warm conveyor belts (WCBs) and the tropopause. Individually, these structures have a significant impact on the weather evolution. A first step towards a deeper understanding of the meteorological processes is to extract jet stream core lines, for which we develop a novel feature extraction algorithm. Based on the line geometry, we automatically detect and visualize potential interactions between WCBs and jets.
Philipp Franke, Anne Caroline Lange, and Hendrik Elbern
Geosci. Model Dev., 15, 1037–1060, https://doi.org/10.5194/gmd-15-1037-2022, https://doi.org/10.5194/gmd-15-1037-2022, 2022
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The paper proposes an ensemble-based analysis framework (ESIAS-chem) for time- and altitude-resolved volcanic ash emission fluxes and their uncertainty. The core of the algorithm is an ensemble Nelder–Mead optimization algorithm accompanied by a particle filter update. The performed notional experiments demonstrate the high accuracy of ESIAS-chem in analyzing the vertically resolved volcanic ash in the atmosphere. Further, the system is in general able to estimate the emission fluxes properly.
Antje Inness, Melanie Ades, Dimitris Balis, Dmitry Efremenko, Johannes Flemming, Pascal Hedelt, Maria-Elissavet Koukouli, Diego Loyola, and Roberto Ribas
Geosci. Model Dev., 15, 971–994, https://doi.org/10.5194/gmd-15-971-2022, https://doi.org/10.5194/gmd-15-971-2022, 2022
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This paper describes the way that the Copernicus Atmosphere Monitoring Service (CAMS) produces forecasts of volcanic SO2. These forecasts are provided routinely every day. They are created by blending SO2 data from satellite instruments (TROPOMI and GOME-2) with the CAMS model. We show that the quality of the CAMS SO2 forecasts can be improved if additional information about the height of volcanic plumes is provided in the satellite data.
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-420, https://doi.org/10.5194/gmd-2021-420, 2022
Revised manuscript accepted for GMD
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We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The performance of DL model is better than a Kalman Filter (KF) system in the training period (2005–2018). Furthermore, DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over E. China.
Ronny Badeke, Volker Matthias, Matthias Karl, and David Grawe
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-376, https://doi.org/10.5194/gmd-2021-376, 2022
Revised manuscript accepted for GMD
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For air quality modeling studies it is very important to distribute pollutants correctly into the model system. This has not yet been done for shipping pollution in a high detail. We studied the effect of different vertical distributions of shipping pollutants on the urban air quality and derived advanced formulas for it. These formulas take weather conditions and ship-specific parameters like the exhaust gas temperature into account.
Ming Chang, Jiachen Cao, Qi Zhang, Weihua Chen, Guotong Wu, Liping Wu, Weiwen Wang, and Xuemei Wang
Geosci. Model Dev., 15, 787–801, https://doi.org/10.5194/gmd-15-787-2022, https://doi.org/10.5194/gmd-15-787-2022, 2022
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Despite the importance of nitrogen deposition, its simulation is still insufficiently represented in current atmospheric chemistry models. In this study, the improvement of the canopy stomatal resistance mechanism and the nitrogen-limiting schemes in Noah-MP-WDDM v1.42 give new options for simulating nitrogen dry deposition velocity. This study finds that the combined BN-23 mechanism agrees better with the observed NO2 dry deposition velocity, with the mean bias reduced by 50.1 %.
Jinfang Yin, Xudong Liang, Hong Wang, and Haile Xue
Geosci. Model Dev., 15, 771–786, https://doi.org/10.5194/gmd-15-771-2022, https://doi.org/10.5194/gmd-15-771-2022, 2022
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An ensemble (EN) approach was designed to improve autoconversion (ATC) from cloud water to rainwater in cloud microphysics schemes. One unique feature of the EN approach is that the ATC rate is a mean value based on the calculations from several widely used ATC schemes. The ensemble approach proposed herein appears to help improve the representation of cloud and precipitation processes in weather and climate models.
Julian F. Quinting and Christian M. Grams
Geosci. Model Dev., 15, 715–730, https://doi.org/10.5194/gmd-15-715-2022, https://doi.org/10.5194/gmd-15-715-2022, 2022
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Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial-intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt (WCB) – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.
Julian F. Quinting, Christian M. Grams, Annika Oertel, and Moritz Pickl
Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, https://doi.org/10.5194/gmd-15-731-2022, 2022
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This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.
Juan Manuel Castillo, Huw Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-7, https://doi.org/10.5194/gmd-2022-7, 2022
Revised manuscript accepted for GMD
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A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air-sea coupling is included.
David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler
Geosci. Model Dev., 15, 649–668, https://doi.org/10.5194/gmd-15-649-2022, https://doi.org/10.5194/gmd-15-649-2022, 2022
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The OCO-2 satellite measures many closely spaced column-averaged CO2 values around its orbit. To give these data proper weight in flux inversions, their error correlations must be accounted for. Here we lay out a 1-D error model with correlations that die out exponentially along-track to do so. A correlation length scale of ∼20 km is derived from column CO2 measurements from an airborne lidar flown underneath OCO-2 for use in this model. The model's performance is compared to previous ones.
Matthias Göbel, Stefano Serafin, and Mathias W. Rotach
Geosci. Model Dev., 15, 669–681, https://doi.org/10.5194/gmd-15-669-2022, https://doi.org/10.5194/gmd-15-669-2022, 2022
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We present WRFlux, an open-source software that allows numerically consistent, time-averaged budget evaluation of prognostic variables for the numerical weather prediction model WRF as well as the transformation of the budget equations from the terrain-following grid of the model to the Cartesian coordinate system. We demonstrate the performance and a possible application of WRFlux and illustrate the detrimental effects of approximations that are inconsistent with the model numerics.
Jingmin Li, Johannes Hendricks, Mattia Righi, and Christof G. Beer
Geosci. Model Dev., 15, 509–533, https://doi.org/10.5194/gmd-15-509-2022, https://doi.org/10.5194/gmd-15-509-2022, 2022
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The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-417, https://doi.org/10.5194/gmd-2021-417, 2022
Revised manuscript accepted for GMD
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This study evaluates the performance of the four microphysics parameterizations, WDM6, WDM7, Thompson, and Morrison, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events, classified into three categories (cold-low, warm-low, and air-sea interaction), depending on the synoptic characteristics, are selected. The evaluation is conducted focusing on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Camilo Fernando Rodríguez Genó and Léster Alfonso
Geosci. Model Dev., 15, 493–507, https://doi.org/10.5194/gmd-15-493-2022, https://doi.org/10.5194/gmd-15-493-2022, 2022
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The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.
Li Zhang, Georg A. Grell, Stuart A. McKeen, Ravan Ahmadov, Karl D. Froyd, and Daniel Murphy
Geosci. Model Dev., 15, 467–491, https://doi.org/10.5194/gmd-15-467-2022, https://doi.org/10.5194/gmd-15-467-2022, 2022
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Applying the chemistry package from WRF-Chem into the Flow-following finite-volume Icosahedra Model, we essentially make it possible to explore the importance of different levels of complexity in gas and aerosol chemistry, as well as in physics parameterizations, for the interaction processes in global modeling systems. The model performance validated by the Atmospheric Tomography Mission aircraft measurements in summer 2016 shows good performance in capturing the aerosol and gas-phase tracers.
Qing Mu, Bruce Rolstad Denby, Eivind Grøtting Wærsted, and Hilde Fagerli
Geosci. Model Dev., 15, 449–465, https://doi.org/10.5194/gmd-15-449-2022, https://doi.org/10.5194/gmd-15-449-2022, 2022
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Our study has achieved air quality modelling down to 100 m for all of Europe. This solves the current problem that street-level air quality modelling is usually limited to individual cities. With publicly available downscaling proxy data, even regions without their own high-resolution proxy data can obtain air quality maps at 100 m. The work is of significance for air quality mitigation strategies and human health exposure studies.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-2, https://doi.org/10.5194/gmd-2022-2, 2022
Revised manuscript accepted for GMD
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Ozone is a toxic greenhouse gas with high spatial variability. We present a machine learning-based ozone mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Cited articles
Ackermann, I. J., Hass, H., Memmsheimer, M., Ebel, A., Binkowski, F. S., and
Shankar, U.: Modal aerosol dynamics model for Europe: development and first
applications, Atmos. Environ., 32, 2981–2999,
https://doi.org/10.1016/S1352-2310(98)00006-5, 1998.
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C.,
Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H.,
Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam,
R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L.,
Wong, D. C., and Young, J. O.: Description and evaluation of the Community
Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model
Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017.
Badia, A., Jorba, O., Voulgarakis, A., Dabdub, D., Pérez
García-Pando, C., Hilboll, A., Gonçalves, M., and Janjic, Z.:
Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe
CHemistry model (NMMB-MONARCH) version 1.0: gas-phase chemistry at global
scale, Geosci. Model Dev., 10, 609–638,
https://doi.org/10.5194/gmd-10-609-2017, 2017.
Baldasano, J. M., Guereca, L. P., Lopez, E., Gasso, S., and Jimenez-Guerrero,
P.: Development of a high-resolution (1 km × 1 km, 1 h) emission
model for Spain: The high-elective resolution modelling emission system
(HERMES), Atmos. Environ. 42, 7215–7233, https://doi.org/10.1016/j.atmosenv.2008.07.026,
2008.
Baldasano, J. M., Goncalves, M., Soret, A., and Jimenez-Guerrero, P.: Air
pollution impacts of speed limitation measures in large cities: The need for
improving traffic data in a metropolitan Area, Atmos. Environ., 44,
2997–3006, https://doi.org/10.1016/j.atmosenv.2010.05.013, 2010.
Bieser, J., Aulinger, A., Matthias, V., Quante, M., and Denier van der Gon,
H.: Vertical emission profiles for Europe based on plume rise calculations.,
Environ. Pollut., 159, 2935–2946, https://doi.org/10.1016/j.envpol.2011.04.030, 2011.
Borge, R., Lumbreras, J., Pérez, J., de la Paz, D., Vedrenne, M., de
Andrés, J. M., and Rodríguez, M. E.: Emission inventories and
modeling requirements for the development of air quality plans. Application
to Madrid (Spain), Sci. Total Environ., 466–467, 809–819,
https://doi.org/10.1016/j.scitotenv.2013.07.093, 2014
Burling, I. R., Yokelson, R. J., Griffith, D. W. T., Johnson, T. J., Veres,
P., Roberts, J. M., Warneke, C., Urbanski, S. P., Reardon, J., Weise, D. R.,
Hao, W. M., and de Gouw, J.: Laboratory measurements of trace gas emissions
from biomass burning of fuel types from the southeastern and southwestern
United States, Atmos. Chem. Phys., 10, 11115–11130,
https://doi.org/10.5194/acp-10-11115-2010, 2010.
Carn, S. A., Fioletov, V. E., McLinden, C. A., Li, C., and Krotkov, N. A.: A
decade of global volcanic SO2 emissions measured from space, Sci.
Rep.-UK, 7, 44095, https://doi.org/10.1038/srep44095, 2017.
Carter, W. P. L.: Development of a database for chemical mechanism
assignments for volatile organic emissions, J. Air Waste Manage., 65,
1171–1184, https://doi.org/10.1080/10962247.2015.1013646, 2015.
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van
Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and
Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period
1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013,
https://doi.org/10.5194/essd-10-1987-2018, 2018.
Denier van der Gon, H. A. C., Hendriks, C., Kuenen, J., Segers, A., and
Visschedijk, A. J. H.: Description of current temporal emission patterns and
sensitivity of predicted AQ for temporal emission patterns, EU FP7 MACC
deliverable report D_D-EMIS_1.3, 2011.
Ferreira, J., Guevara, M., Baldasano, J. M., Tchepel, O., Schaap, M.,
Miranda, A. I., and Borrego, C.: A comparative analysis of two highly
spatially resolved European atmospheric emission inventories, Atmos. Environ.
75, 43–57, https://doi.org/10.1016/j.atmosenv.2013.03.052, 2013.
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell,
G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC
– 1.0: a preprocessor of trace gas and aerosol emission fields for regional
and global atmospheric chemistry models, Geosci. Model Dev., 4, 419–433,
https://doi.org/10.5194/gmd-4-419-2011, 2011.
Friedrich, R. and Reis, S. (Eds.): Emissions of air pollutants –
measurements, calculations and uncertainties, GENEMIS, EUROTRAC-2 subproject
final report, Springer Verlag, Berlin, Heidelberg, New York, 2004.
Frost, G. J., Middleton, P., Tarrasón, L., Granier, C., Guenther, A., Cardenas, B., Denier van der Gon, H. A. C., Janssens-Maenhout, G., Kaiser, J. W., Keating, T., Klimont, Z., Lamarque, J.-F., Liousse, C., Nickovic, S., Ohara, T., Schultz, M. G., Skiba, U., van Aardenne, J., and Wang, Y.: New Directions: GEIA's 2020 vision for better air emissions information, Atmos. Environ., 81, 710–712, https://doi.org/10.1016/j.atmosenv.2013.08.063, 2013.
Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., Denier van der Gon, H.,
Frost, G., Heil, A., Kaiser, J., Kinne, S., Klimont, Z., Kloster, S.,
Lamarque, J.-F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T.,
Raut, J.-C., Riahi, K., Schultz, M., Smith, S., Thompson, A., Aardenne, J.,
Werf, G., and Vuuren, D.: Evolution of anthropogenic and biomass burning
emissions of air pollutants at global and regional scales during the
1980–2010 period, Climatic Change, 109, 163–190,
https://doi.org/10.1007/s10584-011-0154-1, 2011.
Grell, G., Peckham, S., Schmitz, R., McKeen, S., Frost, G., Skamarock, W. C.,
and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos.
Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T.,
Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols
from Nature version 2.1 (MEGAN2.1): an extended and updated framework for
modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Guevara, M., Martínez, F., Arévalo, G., Gassó, S., and
Baldasano, J. M.: An improved system for modelling Spanish emissions:
HERMESv2.0, Atmos. Environ., 81, 209–221,
https://doi.org/10.1016/j.atmosenv.2013.08.053, 2013.
Guevara, M., Pay, M. T., Martínez, F., Soret, A., Denier van der Gon, H.
A. C., and Baldasano, J. M.: Inter-comparison between HERMESv2.0 and
TNO-MACC-II emission data using the CALIOPE air quality system (Spain),
Atmos. Environ., 98, 134–145, https://doi.org/10.1016/j.atmosenv.2014.08.067, 2014.
Guevara, M., Tena, C., Soret, A., Serradell, K., Guzmán, D., Retama, A.,
Camacho, P., Jaimes-Palomera, M., and Mediavilla, A.: An emission processing
system for air quality modelling in the Mexico City metropolitan area:
Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic
emissions, Sci. Total Environ., 584–585, 882–900,
https://doi.org/10.1016/j.scitotenv.2017.01.135, 2017.
Guevara, M., et al.: HERMESv3, a stand-alone multiscale atmospheric emission
modelling framework – Part 2: bottom-up module, in preparation,
2019.
Hill, C., DeLuca, C., Balaji, V., Suarez, M., and Silva, A. D.: The
Architecture of the Earth System Modeling Framework, Comput. Sci. Eng., 6,
18–28, https://doi.org/10.1109/MCISE.2004.1255817, 2004.
Hodzic, A., Wiedinmyer, C., Salcedo, D., and Jimenez, J. L.: Impact of Trash
Burning on Air Quality in Mexico City, Environ. Sci. Technol., 46,
4950–4957, https://doi.org/10.1021/es203954r, 2012.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G.,
Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T.
C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z.,
Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014)
anthropogenic emissions of reactive gases and aerosols from the Community
Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408,
https://doi.org/10.5194/gmd-11-369-2018, 2018.
Houyoux, M. R., Vukovich, J. M., Coats, C. J., Wheeler, N. J. M., and
Kasibhatla, P. S.: Emission inventory development and processing for the
Seasonal Model for Regional Air Quality (SMRAQ) project, J. Geophys.
Res.-Atmos., 105, 9079–9090, https://doi.org/10.1029/1999JD900975, 2000.
Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C.,
Dore, C., Guizzardi, D., Muntean, M., Schaaf, E., and Friedrich, R.:
Speciation of anthropogenic emissions of non-methane volatile organic
compounds: a global gridded data set for 1970–2012, Atmos. Chem. Phys., 17,
7683–7701, https://doi.org/10.5194/acp-17-7683-2017, 2017.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M.,
Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier
van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi,
B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid
maps for 2008 and 2010 to study hemispheric transport of air pollution,
Atmos. Chem. Phys., 15, 11411–11432,
https://doi.org/10.5194/acp-15-11411-2015, 2015.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones,
L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der
Werf, G. R.: Biomass burning emissions estimated with a global fire
assimilation system based on observed fire radiative power, Biogeosciences,
9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.
Keller, C. A., Long, M. S., Yantosca, R. M., Da Silva, A. M., Pawson, S., and
Jacob, D. J.: HEMCO v1.0: a versatile, ESMF-compliant component for
calculating emissions in atmospheric models, Geosci. Model Dev., 7,
1409–1417, https://doi.org/10.5194/gmd-7-1409-2014, 2014.
Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P.,
Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of
particulate matter including black carbon, Atmos. Chem. Phys., 17,
8681–8723, https://doi.org/10.5194/acp-17-8681-2017, 2017.
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der
Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009)
consistent high-resolution European emission inventory for air quality
modelling, Atmos. Chem. Phys., 14, 10963–10976,
https://doi.org/10.5194/acp-14-10963-2014, 2014.
López-Aparicio, S., Guevara, M., Thunis, P., Cuvelier, K., and Tarrason,
L.: Assessment of discrepancies between bottom-up and regional emission
inventories in Norwegianurban areas, Atmos. Environ., 154, 285–296,
https://doi.org/10.1016/j.atmosenv.2017.02.004, 2017.
Luderer, G., Trentmann, J., Winterrath, T., Textor, C., Herzog, M., Graf, H.
F., and Andreae, M. O.: Modeling of biomass smoke injection into the lower
stratosphere by a large forest fire (Part II): sensitivity studies, Atmos.
Chem. Phys., 6, 5261–5277, https://doi.org/10.5194/acp-6-5261-2006, 2006.
Mailler, S., Khvorostyanov, D., and Menut, L.: Impact of the vertical
emission profiles on background gas-phase pollution simulated from the EMEP
emissions over Europe, Atmos. Chem. Phys., 13, 5987–5998,
https://doi.org/10.5194/acp-13-5987-2013, 2013.
Mues, A., Kuenen, J., Hendriks, C., Manders, A., Segers, A., Scholz, Y.,
Hueglin, C., Builtjes, P., and Schaap, M.: Sensitivity of air pollution
simulations with LOTOS-EUROS to the temporal distribution of anthropogenic
emissions, Atmos. Chem. Phys., 14, 939–955,
https://doi.org/10.5194/acp-14-939-2014, 2014.
Olivier, J. G. J., Bouwman, A. F., van der Maas, C. W. M., Berdowski, J. J.
M., Veldt, C., Bloos, J. P. J., Visschedijk, A. J. H., Zandveld, P. Y. J.,
and Haverlag, J. L.: Description of EDGAR Version 2.0: A set of global
emission inventories of greenhouse gases and ozone-depleting substances for
all anthropogenic and most natural sources on a per country basis and on
Olsen, S. C., Wuebbles, D. J., and Owen, B.: Comparison of global 3-D
aviation emissions datasets, Atmos. Chem. Phys., 13, 429–441,
https://doi.org/10.5194/acp-13-429-2013, 2013.
Paugam, R., Wooster, M., Atherton, J., Freitas, S. R., Schultz, M. G., and
Kaiser, J. W.: Development and optimization of a wildfire plume rise model
based on remote sensing data inputs – Part 2, Atmos. Chem. Phys. Discuss.,
15, 9815–9895, https://doi.org/10.5194/acpd-15-9815-2015, 2015.
Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., and Henze,
D. K.: Ammonia emissions in the United States, European Union, and China
derived by high-resolution inversion of ammonium wet deposition data:
interpretation with a new agricultural emissions inventory (MASAGE_NH3),
J. Geophys. Res.-Atmos., 119, 4343–4364, https://doi.org/10.1002/2013JD021130, 2014.
Pérez, C., Haustein, K., Janjic, Z., Jorba, O., Huneeus, N., Baldasano,
J. M., Black, T., Basart, S., Nickovic, S., Miller, R. L., Perlwitz, J. P.,
Schulz, M., and Thomson, M.: Atmospheric dust modeling from meso to global
scales with the online NMMB/BSC-Dust model – Part 1: Model description,
annual simulations and evaluation, Atmos. Chem. Phys., 11, 13001–13027,
https://doi.org/10.5194/acp-11-13001-2011, 2011.
Pernigotti, D., Belis, C. A., and Spanò, L.: SPECIEUROPE: The European
data base for PM source profiles, Atmos. Pollut. Res., 7, 307–314,
https://doi.org/10.1016/j.apr.2015.10.007, 2016.
Pouliot, G., Denier van der Gon, H. A. D., Kuenen, J., Zhang, J., Moran, M.
D., and Makar, P. A.: Analysis of the emission inventories and model-ready
emission datasets of Europe and North America for phase 2 of the AQMEII
project, J. Atmos. Environ., 115, 345–360,
https://doi.org/10.1016/j.atmosenv.2014.10.061, 2015.
Rappenglueck, B., Lubertino,G., Alvarez, S., Golovko, J., Czader, B., and
Ackermann, L.: Radical precursors and related species from traffic as
observed and modeled at an urban highway junction, J. Air Waste Manage., 63,
1270–1286, https://doi.org/10.1080/10962247.2013.822438, 2013.
Reff, A., Bhave, P. V., Simon, H., Pace, T. G., Pouliot, G. A., Mobley, J.
D., and Houyoux, M.: Emissions inventory of PM2.5 trace elements across
the United States, Environ. Sci. Technol., 43, 5790–5796,
https://doi.org/10.1021/es802930x, 2009.
Roselle, S. J., Appel, K. W., Foley, K. M., Bhave, P. V., Pleim, J. E.,
Mathur, R., Otte, T. L., Gilliam, R. C., Sarwar, G., Nolte, C. G., Gililand,
A. B., Pinder, R. W., and Swall, J. L.: Incremental testing of updates to the
Community Multiscale Air Quality (CMAQ) modeling system version 4.7, the 7th
Annual CMAS conference, 6–8 October 2008, Chapel Hill, NC, USA, 2008.
Saikawa, E., Kim, H., Zhong, M., Avramov, A., Zhao, Y., Janssens-Maenhout,
G., Kurokawa, J.-I., Klimont, Z., Wagner, F., Naik, V., Horowitz, L. W., and
Zhang, Q.: Comparison of emissions inventories of anthropogenic air
pollutants and greenhouse gases in China, Atmos. Chem. Phys., 17, 6393–6421,
https://doi.org/10.5194/acp-17-6393-2017, 2017.
Schultz, M. G., Backman, L., Balkanski, Y., Bjoerndalsaeter, S., Brand, R.,
Burrows, J. P., Dalsoeren, S., de Vasconcelos, M., Grodtmann, B.,
Hauglustaine, D. A., Heil, A., Hoelzemann, J. J., Isaksen, I. S. A., Kaurola,
J., Knorr, W., Ladstaetter-Weißenmayer, A., Mota, B., Oom, D., Pacyna,
J., Panasiuk, D., Pereira, J. M. C., Pulles, T., Pyle, J., Rast, S.,
Richter,A., Savage, N., Schnadt, C., Schulz, M., Spessa, A., Staehelin, J.,
Sundet, J. K., Szopa, S., Thonicke, K., van het Bolscher, M., van Noije, T.,
van Velthoven, P., Vik, A. F., and Wittrock, F.: Reanalysis of the
Tropospheric chemical composition over the past 40 years (RETRO) – a
long-term global modeling study of tropospheric chemistry, Final report,
edited by: Schultz, M. G., Reports on Earch System Science, 48/2007, Max
Planck Institute for Meteorology, Hamburg, Germany, available at:
https://www.mpimet.mpg.de/fileadmin/publikationen/Reports/WEB_BzE_48.pdf
(last access: July 2018), 2007.
Simon, H., Beck, L., Bhave, P. V., Divita, F., Hsu, Y., Luecken, D., Mobley,
J. D., Pouliot, G. A., Reff, A., Sarwar, G., and Strum, M.: The development
and uses of EPA's SPECIATE database, Atmos. Pollut. Res., 1, 196–206, 2010.
Skjøth, C. A., Geels, C., Berge, H., Gyldenkærne, S., Fagerli, H.,
Ellermann, T., Frohn, L. M., Christensen, J., Hansen, K. M., Hansen, K., and
Hertel, O.: Spatial and temporal variations in ammonia emissions – a freely
accessible model code for Europe, Atmos. Chem. Phys., 11, 5221–5236,
https://doi.org/10.5194/acp-11-5221-2011, 2011.
Snyder, M. G., Venkatram, A., Heist, D. K., Perry, S. G., Petersen, W. B.,
and Isakov, V.: RLINE: A line source dispersion model for near-surface
releases, Atmos. Environ., 77, 748–756, https://doi.org/10.1016/j.atmosenv.2013.05.074,
2013.
Sofiev, M., Vankevich, R., Ermakova, T., and Hakkarainen, J.: Global mapping
of maximum emission heights and resulting vertical profiles of wildfire
emissions, Atmos. Chem. Phys., 13, 7039–7052,
https://doi.org/10.5194/acp-13-7039-2013, 2013.
Soret, A., Guevara, M., and Baldasano, J. M.: The potential impacts of
electric vehicles on air quality in the urban areas of Barcelona and Madrid
(Spain), Atmos. Environ., 99, 51–63, https://doi.org/10.1016/j.atmosenv.2014.09.048,
2014.
Stockwell, W. R., Middleton, P., Chang, J. S., Tang, X.: The 2nd generation
regional acid deposition model chemical mechanism for regional air-quality
modeling, J. Geophys. Res.-Atmos., 95, 16343–16367,
https://doi.org/10.1029/JD095iD10p16343, 1990.
Timmermans, R. M. A., Denier van der Gon, H. A. C., Kuenen, J. J. P., Segers,
A. J., Honoré, C., Perrussel, O., Builtjes, P. J. H., and Schaap, M.:
Quantification of the urban air pollution increment and its dependency on the
use of down-scaled and bottom-up city emission inventories, Urban Climate, 6,
44–62, https://doi.org/10.1016/j.uclim.2013.10.004, 2013.
Trombetti, M., Thunis, P., Bessagnet, B., Clappier, A., Couvidat, F.,
Guevara, M., Kuenen, J., and López-Aparicio, S.: Spatial inter-comparison
of Top-down emission inventories in European urban areas, Atmos. Environ.,
173, 142–156, https://doi.org/10.1016/j.atmosenv.2017.10.032, 2018.
Tuccella, P., Curci, G., Visconti, G., Bessagnet, B., Menut, L., and Park, R.
J.: Modeling of gas and aerosol with WRF/Chem over Europe: Evaluation and
sensitivity study, J. Geophys. Res., 117, D03303, https://doi.org/10.1029/2011JD016302,
2012.
Veira, A., Kloster, S., Schutgens, N. A. J., and Kaiser, J. W.: Fire emission
heights in the climate system – Part 2: Impact on transport, black carbon
concentrations and radiation, Atmos. Chem. Phys., 15, 7173–7193,
https://doi.org/10.5194/acp-15-7173-2015, 2015.
Warner, J. X., Dickerson, R. R., Wei, Z., Strow, L. L., Wang, Y., and Liang,
Q.: Increased atmospheric ammonia over the world's major agricultural areas
detected from space, Geophys. Res. Lett., 44, 2875–2884,
https://doi.org/10.1002/2016GL072305, 2017.
Whitten, G. Z., Heo, G., Kimura, Y., McDonald-Buller, E., Allen, D. T.,
Carter, W. P. L., and Yarwood, G.: A new condensed toluene mechanism for
Carbon Bond: CB05-TU, Atmos. Environ., 44, 5346–5355,
https://doi.org/10.1016/j.atmosenv.2009.12.029, 2010.
Wiedinmyer, C., Yokelson, R. J., and Gullett, B. K.: Global emissions of
trace gases, particulate matter, and hazardous air pollutants from open
burning of domestic waste, Environ. Sci. Technol., 48, 9523–9530,
https://doi.org/10.1021/es502250z, 2014.
Zhang, L., Chen, Y., Zhao, Y., Henze, D. K., Zhu, L., Song, Y., Paulot, F.,
Liu, X., Pan, Y., Lin, Y., and Huang, B.: Agricultural ammonia emissions in
China: reconciling bottom-up and top-down estimates, Atmos. Chem. Phys., 18,
339–355, https://doi.org/10.5194/acp-18-339-2018, 2018.
Short summary
Atmospheric emission inventories, which describe the amounts of pollutants released into the air by different sources and for specific regions, are an essential input to numerical models that estimate air quality. This work presents the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), an open-source modelling framework that allows adapting existing global and regional emission inventories to the input requirements of air quality models in a flexible and transparent way.
Atmospheric emission inventories, which describe the amounts of pollutants released into the air...
