Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-214
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper
06 Sep 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).
Lagrangian condensation microphysics with Twomey CCN activation
Wojciech W. Grabowski1,2, Piotr Dziekan2, and Hanna Pawlowska2 1National Center for Atmospheric Research, Boulder, CO, USA
2Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
Abstract. We report the development of a novel Lagrangian microphysics methodology for improved simulations of warm ice-free clouds. The approach applies the traditional Eulerian method for the momentum and continuous thermodynamic fields, the temperature and water vapor mixing ratio, and uses Lagrangian super-droplets to represent condensed phase such as cloud droplets and drizzle/rain drops. In all other applications of the Lagrangian warm-rain microphysics, the super-droplets outside clouds represent un-activated cloud condensation nuclei (CCN) that become activated upon entering a cloud and can further grow through diffusional and collisional processes. The original methodology allows studying in detail not only effects of CCN on cloud microphysics and dynamics, but also CCN processing by a cloud. However, when cloud processing is not of interest, a simpler and computationally more efficient approach can be used with super-droplets forming only when CCN is activated and no super-droplet existing outside a cloud. This is possible by applying the Twomey activation scheme where the local supersaturation dictates the concentration of cloud droplets that need to be present inside a cloudy volume, as typically used in Eulerian bin microphysics schemes. Since a cloud volume is a small fraction of the computational domain volume, the Twomey super-droplets provide significant computational advantage when compared to the original super-droplet methodology. Additional advantage comes from significantly longer time steps that can be used when modeling of CCN deliquescence is avoided. Moreover, other formulation of the droplet activation can be applied in case of low vertical resolution of the host model, for instance, linking the concentration of activated cloud droplets to the local updraft speed.

This paper discusses the development and testing of the Twomey super-droplet methodology focusing on the activation and diffusional growth. Details of the activation implementation, transport of SDs in the physical space, and the coupling between super-droplets and the Eulerian temperature and water vapor field are discussed in detail. Some of these are relevant to the original super-droplet methodology as well and to the ice phase modeling using the Lagrangian approach. As a computational example, the scheme is applied to an idealized moist thermal rising in a stratified environment, with the original super-droplet methodology providing benchmark to which the new scheme is compared.


Citation: Grabowski, W. W., Dziekan, P., and Pawlowska, H.: Lagrangian condensation microphysics with Twomey CCN activation, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-214, in review, 2017.
Wojciech W. Grabowski et al.
Wojciech W. Grabowski et al.
Wojciech W. Grabowski et al.

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Short summary
This paper introduces a novel approach to simulate ice-free clouds. The key process is formation and transport of cloud droplets that are represented through Lagrangian particles referred to as super-droplets with each super-droplet representing a multitude of natural cloud droplets. The essential component of the scheme that makes it different and more efficient from previous approaches is the presence of super-droplets only within a cloud.
This paper introduces a novel approach to simulate ice-free clouds. The key process is formation...
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