Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4287-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4287-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
16 Sep 2020
Model evaluation paper |
| 16 Sep 2020
| 16 Sep 2020
Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Johannes Hendricks
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Mattia Righi
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Bernd Heinold
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Ina Tegen
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Silke Groß
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Daniel Sauer
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Ludwig-Maximilians-Universität München, Meteorologisches Institut, Munich, Germany
Adrian Walser
University of Vienna, Faculty of Physics, Aerosol Physics and Environmental Physics, Vienna, Austria
Ludwig-Maximilians-Universität München, Meteorologisches Institut, Munich, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Bernadett Weinzierl
University of Vienna, Faculty of Physics, Aerosol Physics and Environmental Physics, Vienna, Austria
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Cited
11 citations as recorded by crossref.
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- Satellite remote sensing and deep learning for aerosols prediction N. Mirkov et al. 10.5937/vojtehg71-40391
- Global health burden of ambient PM2.5 and the contribution of anthropogenic black carbon and organic aerosols S. Chowdhury et al. 10.1016/j.envint.2021.107020
- Improving the forecast of fine dust emission and transmission from cattle barns: a comprehensive data package and analysis E. Mostafa et al. 10.1186/s12302-024-00845-5
- Exploring the uncertainties in the aviation soot–cirrus effect M. Righi et al. 10.5194/acp-21-17267-2021
- Black carbon aerosol reductions during COVID-19 confinement quantified by aircraft measurements over Europe O. Krüger et al. 10.5194/acp-22-8683-2022
- African biomass burning affects aerosol cycling over the Amazon B. Holanda et al. 10.1038/s43247-023-00795-5
- Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study A. Bier et al. 10.5194/acp-24-2319-2024
- Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC C. Beer et al. 10.5194/acp-24-3217-2024
- A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with MADE3 in EMAC C. Beer et al. 10.5194/acp-22-15887-2022
- Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations J. Li et al. 10.5194/acp-24-12727-2024
11 citations as recorded by crossref.
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- Satellite remote sensing and deep learning for aerosols prediction N. Mirkov et al. 10.5937/vojtehg71-40391
- Global health burden of ambient PM2.5 and the contribution of anthropogenic black carbon and organic aerosols S. Chowdhury et al. 10.1016/j.envint.2021.107020
- Improving the forecast of fine dust emission and transmission from cattle barns: a comprehensive data package and analysis E. Mostafa et al. 10.1186/s12302-024-00845-5
- Exploring the uncertainties in the aviation soot–cirrus effect M. Righi et al. 10.5194/acp-21-17267-2021
- Black carbon aerosol reductions during COVID-19 confinement quantified by aircraft measurements over Europe O. Krüger et al. 10.5194/acp-22-8683-2022
- African biomass burning affects aerosol cycling over the Amazon B. Holanda et al. 10.1038/s43247-023-00795-5
- Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study A. Bier et al. 10.5194/acp-24-2319-2024
- Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC C. Beer et al. 10.5194/acp-24-3217-2024
- A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with MADE3 in EMAC C. Beer et al. 10.5194/acp-22-15887-2022
- Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations J. Li et al. 10.5194/acp-24-12727-2024
Latest update: 13 Dec 2024
Short summary
Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions have often been represented in global models by prescribed monthly-mean emission fields representative of a specific year. We now apply an online calculation of wind-driven dust emissions. This results in an improved agreement with observations, due to a better representation of the highly variable dust emissions. Increasing the model resolution led to an additional performance gain.
Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions...
