Open AccessFluctuation of Mass Flux in a Cloud-Resolving Simulation with Interactive Radiation
Author(s) -
Jahanshah Davoudi,
N. A. McFarlane,
Thomas Birner
Publication year - 2010
Publication title -
journal of the atmospheric sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.853
H-Index - 173
eISSN - 1520-0469
pISSN - 0022-4928
DOI - 10.1175/2009jas3215.1
Subject(s) - forcing (mathematics) , convection , statistical physics , physics , mass flux , canonical ensemble , buoyancy , cloud forcing , radiative transfer , radiative forcing , atmospheric sciences , environmental science , computational physics , meteorology , mechanics , mathematics , statistics , monte carlo method , aerosol , quantum mechanics
It was shown by Craig and Cohen that fluctuations of cumulus clouds under homogeneous large-scale forcing satisfy the Gibbs canonical ensemble in a strict radiative–convective equilibrium (RCE). In the limit of random noninteracting convective cells, an analytical expression for the distribution function of total mass flux over a region of given size was derived. The authors examine the consistency of the Gibbs canonical ensemble as a representation for the mass flux fluctuations when the large-scale forcing is time dependent. A cloud-resolving simulation (CRM) with interactive radiation, fixed imposed surface temperature, and diurnally varying solar forcing to mimic the diurnal cycle over the tropical ocean is used. As a necessary condition for the existence of a state of quasi-equilibrium, the time-scale separation between convective processes and forcing is studied. Detailed evaluation of time scales of convective adjustment and memory in a three-month run confirms the hypothesis of time-scale separation. The Craig and Cohen theory, in a varying range of heights between the cloud base up to the level of neutral buoyancy (LNB), is tested. It is shown that, although the theory is capable of reproducing the qualitative features of the variability, systematic deviations are detected. By quantifying the spatial distribution of the clouds, the authors suggest that deviations are associated with clustering effects.