Articles | Volume 11, issue 3
https://doi.org/10.5194/gmd-11-989-2018
https://doi.org/10.5194/gmd-11-989-2018
Development and technical paper
 | 
16 Mar 2018
Development and technical paper |  | 16 Mar 2018

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

Klaus Klingmüller, Swen Metzger, Mohamed Abdelkader, Vlassis A. Karydis, Georgiy L. Stenchikov, Andrea Pozzer, and Jos Lelieveld

Related authors

Global atmospheric hydrogen chemistry and long-term source-sink budget simulation with the EMAC v2.55 model
Nic Surawski, Benedikt Steil, Christoph Brühl, Sergey Gromov, Klaus Klingmüller, Anna Martin, Andrea Pozzer, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2025-1559,https://doi.org/10.5194/egusphere-2025-1559, 2025
Short summary
Impact of mineral dust on the global nitrate aerosol direct and indirect radiative effect
Alexandros Milousis, Klaus Klingmüller, Alexandra P. Tsimpidi, Jasper F. Kok, Maria Kanakidou, Athanasios Nenes, and Vlassis A. Karydis
Atmos. Chem. Phys., 25, 1333–1351, https://doi.org/10.5194/acp-25-1333-2025,https://doi.org/10.5194/acp-25-1333-2025, 2025
Short summary
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024,https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023,https://doi.org/10.5194/gmd-16-3013-2023, 2023
Short summary
Climate-model-informed deep learning of global soil moisture distribution
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 14, 4429–4441, https://doi.org/10.5194/gmd-14-4429-2021,https://doi.org/10.5194/gmd-14-4429-2021, 2021
Short summary

Related subject area

Climate and Earth system modeling
Correction of sea surface biases in the NEMO ocean general circulation model using neural networks
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev., 18, 4789–4804, https://doi.org/10.5194/gmd-18-4789-2025,https://doi.org/10.5194/gmd-18-4789-2025, 2025
Short summary
Representing lateral groundwater flow from land to river in Earth system models
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025,https://doi.org/10.5194/gmd-18-4601-2025, 2025
Short summary
FINAM is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025,https://doi.org/10.5194/gmd-18-4483-2025, 2025
Short summary
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025,https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025,https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary

Cited articles

Abdelkader, M., Metzger, S., Mamouri, R. E., Astitha, M., Barrie, L., Levin, Z., and Lelieveld, J.: Dust–air pollution dynamics over the eastern Mediterranean, Atmos. Chem. Phys., 15, 9173–9189, https://doi.org/10.5194/acp-15-9173-2015, 2015. a, b, c
Abdelkader, M., Metzger, S., Steil, B., Klingmüller, K., Tost, H., Pozzer, A., Stenchikov, G., Barrie, L., and Lelieveld, J.: Sensitivity of transatlantic dust transport to chemical aging and related atmospheric processes, Atmos. Chem. Phys., 17, 3799–3821, https://doi.org/10.5194/acp-17-3799-2017, 2017. a, b
AERONET: available at: http://aeronet.gsfc.nasa.gov, last access: 31 August 2016. a
Albani, S., Mahowald, N. M., Perry, A. T., Scanza, R. A., Zender, C. S., Heavens, N. G., Maggi, V., Kok, J. F., and Otto-Bliesner, B. L.: Improved dust representation in the Community Atmosphere Model, J. Adv. Model. Earth Sy., 6, 541–570, https://doi.org/10.1002/2013MS000279, 2014. a, b
Allen, C. J. T., Washington, R., and Engelstaedter, S.: Dust emission and transport mechanisms in the central Sahara: Fennec ground-based observations from Bordj Badji Mokhtar, June 2011, J. Geophys. Res.-Atmos., 118, 6212–6232, https://doi.org/10.1002/jgrd.50534, 2013. a
Download
Short summary
More than 1 billion tons of mineral dust particles are raised into the atmosphere every year, which has a significant impact on climate, society and ecosystems. The location, time and amount of dust emissions depend on surface and wind conditions. In the atmospheric chemistry–climate model EMAC, we have updated the relevant surface data and equations. Our validation shows that the updates substantially improve the agreement of model results and observations.
Share