Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-2051-2020
https://doi.org/10.5194/gmd-13-2051-2020
Model description paper
 | 
24 Apr 2020
Model description paper |  | 24 Apr 2020

Modelling the mineralogical composition and solubility of mineral dust in the Mediterranean area with CHIMERE 2017r4

Laurent Menut, Guillaume Siour, Bertrand Bessagnet, Florian Couvidat, Emilie Journet, Yves Balkanski, and Karine Desboeufs

Related authors

Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023,https://doi.org/10.5194/gmd-16-4265-2023, 2023
Short summary
A new process-based and scale-aware desert dust emission scheme for global climate models – Part I: Description and evaluation against inverse modeling emissions
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023,https://doi.org/10.5194/acp-23-6487-2023, 2023
Short summary
An improved version of the Piecewise Parabolic Method advection scheme: description and performance assessment in a bidimensional testcase with stiff chemistry in toyCTM v1.0
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-78,https://doi.org/10.5194/gmd-2023-78, 2023
Preprint under review for GMD
Short summary
Impact of the Guinea Coast upwelling on atmospheric dynamics, precipitation and pollutant transport over Southern West Africa
Gaëlle de Coëtlogon, Adrien Deroubaix, Cyrille Flamant, Laurent Menut, and Marco Gaetani
EGUsphere, https://doi.org/10.5194/egusphere-2023-681,https://doi.org/10.5194/egusphere-2023-681, 2023
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023,https://doi.org/10.5194/gmd-16-1119-2023, 2023
Short summary

Related subject area

Atmospheric sciences
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023,https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023,https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023,https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023,https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023,https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary

Cited articles

Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distribution in source areas, J. Geophys. Res., 106, 18075–18084, 2001. a, b
Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007. a
Bedidi, A. and Cervelle, B.: Light scattering by spherical particles with hematite and goethitelike optical properties: Effect of water impregnation, J. Geophys. Res.-Sol. Ea., 98, 11941–11952, https://doi.org/10.1029/93JB00188, 1993. a
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. a
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. a
Download
Short summary
Modelling of mineral dust is often done using one single mean species. In this study, differentiated mineral species with their chemical composition are implemented in the CHIMERE regional chemistry-transport model by using global databases. Simulations are carried out to quantify the realism and gain of such mineralogy.