Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4287-2020
https://doi.org/10.5194/gmd-13-4287-2020
Model evaluation paper
 | 
16 Sep 2020
Model evaluation paper |  | 16 Sep 2020

Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54

Christof G. Beer, Johannes Hendricks, Mattia Righi, Bernd Heinold, Ina Tegen, Silke Groß, Daniel Sauer, Adrian Walser, and Bernadett Weinzierl

Related authors

Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations
Jingmin Li, Mattia Righi, Johannes Hendricks, Christof G. Beer, Ulrike Burkhardt, and Anja Schmidt
Atmos. Chem. Phys., 24, 12727–12747, https://doi.org/10.5194/acp-24-12727-2024,https://doi.org/10.5194/acp-24-12727-2024, 2024
Short summary
Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC
Christof G. Beer, Johannes Hendricks, and Mattia Righi
Atmos. Chem. Phys., 24, 3217–3240, https://doi.org/10.5194/acp-24-3217-2024,https://doi.org/10.5194/acp-24-3217-2024, 2024
Short summary
A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with MADE3 in EMAC
Christof G. Beer, Johannes Hendricks, and Mattia Righi
Atmos. Chem. Phys., 22, 15887–15907, https://doi.org/10.5194/acp-22-15887-2022,https://doi.org/10.5194/acp-22-15887-2022, 2022
Short summary
An aerosol classification scheme for global simulations using the K-means machine learning method
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
Short summary
Exploring the uncertainties in the aviation soot–cirrus effect
Mattia Righi, Johannes Hendricks, and Christof Gerhard Beer
Atmos. Chem. Phys., 21, 17267–17289, https://doi.org/10.5194/acp-21-17267-2021,https://doi.org/10.5194/acp-21-17267-2021, 2021
Short summary

Related subject area

Climate and Earth system modeling
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025,https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025,https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024,https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Architectural insights into and training methodology optimization of Pangu-Weather
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024,https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Evaluation of global fire simulations in CMIP6 Earth system models
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024,https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary

Cited articles

Aquila, V., Hendricks, J., Lauer, A., Riemer, N., Vogel, H., Baumgardner, D., Minikin, A., Petzold, A., Schwarz, J. P., Spackman, J. R., Weinzierl, B., Righi, M., and Dall'Amico, M.: MADE-in: a new aerosol microphysics submodel for global simulation of insoluble particles and their mixing state, Geosci. Model Dev., 4, 325–355, https://doi.org/10.5194/gmd-4-325-2011, 2011. a, b, c, d
Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012. a
Balkanski, Y., Schulz, M., Claquin, T., Moulin, C., and Ginoux, P.: Global emissions of mineral aerosol: formulation and validation using satellite imagery, in: Emissions of Atmospheric Trace Compounds, Springer, 18, 239–267, https://doi.org/10.1007/978-1-4020-2167-1_6, 2004. a
Banks, J. R., Brindley, H. E., Stenchikov, G., and Schepanski, K.: Satellite retrievals of dust aerosol over the Red Sea and the Persian Gulf (2005–2015), Atmos. Chem. Phys., 17, 3987–4003, https://doi.org/10.5194/acp-17-3987-2017, 2017. a
Baumgardner, D., Jonsson, H., Dawson, W., O'Connor, D., and Newton, R.: The cloud, aerosol and precipitation spectrometer: a new instrument for cloud investigations, Atmos. Res., 59–60, 251–264, https://doi.org/10.1016/s0169-8095(01)00119-3, 2001. a
Download
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.