Articles | Volume 12, issue 12
https://doi.org/10.5194/gmd-12-5177-2019
https://doi.org/10.5194/gmd-12-5177-2019
Development and technical paper
 | 
11 Dec 2019
Development and technical paper |  | 11 Dec 2019

Explicit aerosol–cloud interactions in the Dutch Atmospheric Large-Eddy Simulation model DALES4.1-M7

Marco de Bruine, Maarten Krol, Jordi Vilà-Guerau de Arellano, and Thomas Röckmann

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Cited articles

Aan de Brugh, J. M. J., Ouwersloot, H. G., Vilà-Guerau de Arellano, J., and Krol, M. C.: A large-eddy simulation of the phase transition of ammonium nitrate in a convective boundary layer, J. Geophys. Res.-Atmos., 118, 826–836, https://doi.org/10.1002/jgrd.50161, 2013. a
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C. A.: The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?, J. Geophys. Res.-Atmos., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a
Arabas, S., Jaruga, A., Pawlowska, H., and Grabowski, W. W.: libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++, Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015, 2015. a
Barbaro, E., Vilà-Guerau de Arellano, J., Krol, M. C., and Holtslag, A. A. M.: Impacts of Aerosol Shortwave Radiation Absorption on the Dynamics of an Idealized Convective Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 148, 31–49, https://doi.org/10.1007/s10546-013-9800-7, 2013. a
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Short summary
An aerosol scheme with multiple aerosol species is introduced in the Dutch Atmospheric Large-Eddy Simulation model (DALES) and focused to simulate the feedback of aerosol–cloud interaction (ACI) on the aerosol population. Cloud aerosol processing is found to be sensitive to the numerical method, while removal by precipitation is more stable. How ACI increases or decreases the mean aerosol size depends on the balance between the evaporation of clouds/rain and ultimate removal by precipitation.