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Conference Poster Year : 2016

MAD-VenLA: a microphysical modal representation of clouds for the IPSL Venus GCM

Sabrina Guilbon
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Anni Määttänen
Franck Montmessin
Aurélien Stolzenbach
Slimane Bekki


Venus is enshrouded by 20km-thick clouds, which are composed of sulfuric acid-water solution droplets. Clouds play a crucial role on the climate of the planet. Our goal is to study the formation and evolution of Venusian clouds with microphysical models. The goal of this work is to develop the first full 3D microphysical model of Venus coupled with the IPSL Venus GCM and the photochemical model included (Lebonnois et al. 2010, Stolzenbach et al. 2016). Two particle size distribution representations are generally used in cloud modeling: sectional and modal. The term 'sectional' means that the continuous particle size distribution is divided into a discrete set of size intervals called bins. In the modal approach, the particle size distribution is approximated by a continuous parametric function, typically a log-normal, and prognostic variables are distribution or distribution-integrated parameters (Seigneur et al. 1986, Burgalat et al. 2014). These two representations need to be compared to choose the optimal trade-off between precision and computational efficiency. At high radius resolution, sectional models are computationally too demanding to be integrated in GCMs. That is why, in other GCMs, such as the IPSL Titan GCM, the modal scheme is used (Burgalat et al. 2014). The Venus Liquid Aerosol cloud model (VenLA) and the Modal Dynamics of Venusian Liquid Aerosol cloud model (MAD-VenLA) are respectively the sectional and the modal model discussed here and used for defining the microphysical cloud module to be integrated in the IPSL Venus GCM. We will compare the two models with the key microphysical processes in 0D setting: homogeneous and heterogeneous nucleation, condensation/evaporation and coagulation. Then, MAD-VenLA will be coupled with the IPSL VGCM. The first results of the complete VGCM with microphysics coupled with chemistry will be presented.
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insu-01373895 , version 1 (29-09-2016)


  • HAL Id : insu-01373895 , version 1


Sabrina Guilbon, Anni Määttänen, Jeremie Burgalat, Franck Montmessin, Aurélien Stolzenbach, et al.. MAD-VenLA: a microphysical modal representation of clouds for the IPSL Venus GCM. DPS 48 / EPSC 11 (Division for Planetary Sciences and the European Planetary Science Congress), Oct 2016, Pasadena, United States. ⟨insu-01373895⟩
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