NPG Nonlinear Processes in Geophysics NPG Nonlin. Processes Geophys. 1607-7946 Copernicus Publications Göttingen, Germany 10.5194/npg-22-65-2015 Fluctuations in a quasi-stationary shallow cumulus cloud ensemble Sakradzija M. 1 2 Seifert A. https://orcid.org/0000-0001-9760-3550 3 Heus T. 1 4 Max Planck Institute for Meteorology, Hamburg, Germany International Max Planck Research School on Earth System Modelling (IMPRS-ESM), Hamburg, Germany Hans-Ertel Centre for Weather Research, Deutscher Wetterdienst, Hamburg, Germany Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany 28 01 2015 22 1 65 85 Copyright: © 2015 M. Sakradzija et al. 2015 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://npg.copernicus.org/articles/22/65/2015/npg-22-65-2015.html The full text article is available as a PDF file from https://npg.copernicus.org/articles/22/65/2015/npg-22-65-2015.pdf

We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES) model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. <br><br> Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the distribution.

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