Self-aggregation of clouds in conditionally unstable moist convection

Abstract

The behavior of moist Rayleigh-Bénard convection is investigated using a Boussinesq model with a simplified thermodynamics for phase transitions. This idealized configuration makes the problem accessible to high-resolution three-dimensional direct numerical simulations without small-scale parameterizations of the turbulence for extended layers with aspect ratios up to 64. Our study is focused on the frequently observed conditionally unstable environment that is stably stratified for unsaturated air, but is unstable for cloudy air. We find that no sharp threshold for the transition to convective turbulence exists, a situation similar to wall-bounded shear flows. Rather, the transition depends on the amplitude of the initial perturbation of the quiescent equilibrium and on the aspect ratio of the convective domain. In contrast to the classical dry Rayleigh-Bénard case, convection is highly asymmetric with respect to the vertical direction. Moist upwelling air inside turbulent cloud aggregates is surrounded by ambient regions of slowly descending unsaturated air. It is also found that conditionally unstable moist convection is inefficient at transporting energy. Our study suggests that there is an upper bound on the Nusselt number in moist convection that is lower than that of the classical dry case.C onvective processes in the Earth's atmosphere are often associated with condensation of water vapor and the formation of clouds. These involve spatial scales as small as a micron for the activation of cloud water droplets and as large as a few kilometers for a whole cloud, with time scales ranging from milliseconds to hours (1, 2). Convective systems are often themselves embedded within larger structures, such as mesoscale cloud clusters or midlatitude baroclinic eddies. Because of their complex multiscale physics, clouds remain one of the main sources of uncertainty in predictions of future climate change (3, 4). This holds particularly true for low clouds over subtropical and tropical oceans (5), where small changes in cloud behavior can dramatically alter the amount of solar energy absorbed or reflected. Even with stateof-the-art large eddy simulations that can describe the dynamics on mesoscales in atmospheric layers with a side length of up to approximately 10 2 km and grid resolutions of 100 m or less, many of the dynamically relevant scales still remain unresolved and have to be parameterized (6). Our understanding of the formation of clouds, their life cycles, and the resulting turbulent transport is still incomplete. Further progress can be gained by increasing the resolution of the numerical model (7,8) and a refinement of the subgrid scale parameterizations to obtain the most realistic simulations of convection. Equally important for our understanding is the need to disentangle the various phenomena and to identify the most important physical processes that determine the dynamics of clouds in the atmosphere. This is the approach followed in this paper in which we analyze the behavior of convection in an ide...

New perspectives in turbulent Rayleigh-Bénard convection

“…The DNS in the layer with an aspect ratio Γ = 32 is horizontally resolved with 4096 grid points and vertically with 129. The Rayleigh numbers are Ra 10 7 in both cases (see [188,189] for details).…”

“…The number of system parameters is five, a Prandtl number P r, a dry and a moist Rayleigh number, Ra D and Ra M , and two parameters that prescribe the amount of liquid water (or a deficit) at the top and bottom plane. One can run this model in two fundamentally different states of turbulent moist convection: the linearly unstable regime [188] and the conditionally unstable regime [189]. These two regimes start from different equilibria and result in the two main regimes of low cloud formations -stratocumulustype convection in the former and cumulus-type convection for the latter.…”

Minimal models for precipitating turbulent convection

“…FARE is similar to models used for non-precipitating shallow convection in the atmosphere (Cuijpers & Duynkerke 1993;Stevens 2005Stevens , 2007 or moist Rayleigh-Bénard convection Pauluis & Schumacher 2011), which use nearly the same equations except with V T = 0. Here we will show that, despite their relative simplicity, the FARE equations are sufficient to capture the basic features of precipitating organized convection.…”

Expansion of a Holepunch Cloud by a Gravity Wave Front

“…The development of such a buoyancy switch follows an early use by Bjerknes (1938), an integration into a cloud dynamics by Kuo (1961), and a thermodynamic derivation by Bretherton (1987). More recently, the switch idea has been revived for understanding clusters of conditionally unstable (and nonprecipitating) clouds (Pauluis andSchumacher 2010, 2011). For an evolving cloud, the key component in the switch formulation is a dynamical FIG.…”