Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2397-2017
https://doi.org/10.5194/gmd-10-2397-2017
Model description paper
 | 
28 Jun 2017
Model description paper |  | 28 Jun 2017

CHIMERE-2017: from urban to hemispheric chemistry-transport modeling

Sylvain Mailler, Laurent Menut, Dmitry Khvorostyanov, Myrto Valari, Florian Couvidat, Guillaume Siour, Solène Turquety, Régis Briant, Paolo Tuccella, Bertrand Bessagnet, Augustin Colette, Laurent Létinois, Kostantinos Markakis, and Frédérik Meleux

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Cited articles

Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas, J. Geophys. Res.-Atmos., 106, 18075–18084, https://doi.org/10.1029/2000JD900339, 2001.
Arakawa, A. and Lamb, V. R.: Computational Design of the Basic Dynamical Processes of the {UCLA} General Circulation Model, in: General Circulation Models of the Atmosphere, edited by: Chang, J., Methods in Computational Physics: Advances in Research and Applications, Elsevier, Vol. 17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977.
Aumont, B., Szopa, S., and Madronich, S.: Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach, Atmos. Chem. Phys., 5, 2497–2517, https://doi.org/10.5194/acp-5-2497-2005, 2005.
Beegum, S., Gherboudj, I., Chaouch, N., Couvidat, F., Menut, L., and Ghedira, H.: Simulating Aerosols over Arabian Peninsula with CHIMERE: Sensitivity to soil, surface parameters and anthropogenic emission inventories, Atmos. Environ., 128, 185–197, https://doi.org/10.1016/j.atmosenv.2016.01.010, 2016.
Bessagnet, B., Menut, L., Aymoz, G., Chepfer, H., and Vautard, R.: Modelling dust emissions and transport within Europe: the Ukraine March 2007 event, J. Geophys. Res., 113, D15202, https://doi.org/10.1029/2007JD009541, 2008.
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Short summary
CHIMERE is a chemistry-transport model initially designed for box-modelling of the regional atmospheric composition. In the recent years, CHIMERE has been extended to be able to model atmospheric composition at all scales from urban to hemispheric scale, which implied major changes on the coordinate systems as well as on physical processes. This study describes how and why these changes have been brought to the model, largely increasing the range of its possible use.
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