A Boussinesq moist turbulence model

Spyksma, Kyle; Bartello, Peter; Yau, M. K.

doi:10.1080/14685240600577865

Cited by 15 publications

(18 citation statements)

References 31 publications

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“…The different levels of complexity are manifest in different levels of simplification for the buoyancy (67). In the Boussinesq or anelastic moist Rayleigh-Bénard model [183][184][185] the (potential) temperature is substituted by the equivalent (potential) temperature and the additional impact of the liquid water on the buoyancy of the air parcels is included. This results in (68) where ξ = R v /R d −1 with the gas constants for vapor and dry air, R v and R d [183].…”

Section: Moist Convection and Clouds Formationmentioning

confidence: 99%

New perspectives in turbulent Rayleigh-Bénard convection

2012

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Abstract. Recent experimental, numerical and theoretical advances in turbulent Rayleigh-Bénard convection are presented. Particular emphasis is given to the physics and structure of the thermal and velocity boundary layers which play a key role for the better understanding of the turbulent transport of heat and momentum in convection at high and very high Rayleigh numbers. We also discuss important extensions of Rayleigh-Bénard convection such as non-Oberbeck-Boussinesq effects and convection with phase changes.

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Section: Moist Convection and Clouds Formationmentioning

confidence: 99%

New perspectives in turbulent Rayleigh-Bénard convection

2012

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“…For the model described below, we will be considering a "no-liquid water" potential energy that is calculated using Ј , the virtual potential temperature perturbation, defined below in (14). Unfortunately, while the condensation scheme we use [(9)] is very simple, its if/then characteristic makes it impossible to define an energy invariant for the wet model (see section 2.3 of Spyksma et al 2006). Therefore, we have no real equivalent to potential energy and the best we can do is to frame the discussion in terms of a dry PE, defined in (16), even if KE ϩ PE is not conserved.…”

Section: A Ensemble Predictability Formulationmentioning

confidence: 97%

“…We explore this by performing large-ensemble predictability runs of nonprecipitating dry and moist shallow convection with different background conditions using a simple dry/moist Boussinesq model (Spyksma et al 2006). This model does not contain parameterizations, but couples the high resolution over a small domain with a simple condensation scheme to model the moist convection explicitly.…”

Section: Introductionmentioning

confidence: 99%

Predictability in Wet and Dry Convective Turbulence

Spyksma

Bartello

2008

Journal of the Atmospheric Sciences

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There is a growing interest in understanding the role that moisture plays in atmospheric dynamics, particularly in its effect on predictability. Current research indicates that when moisture effects are added to an atmospheric model, the error growth produced by the new moist dynamics reduces the predictability times, especially at the scales of moist convection.The issue of moist convection's effect on predictability is addressed herein. By performing high-resolution large-ensemble runs, it is shown that although nonprecipitating moist convection is less predictable than dry convection resulting from the same forcing, this effect can be explained by the energy injected into the system through the latent heating and cooling arising from the convective motion. This extra energy is spread evenly over most scales of the convective dynamics. When the predictability times are scaled to account for the extra kinetic energy, and the resulting earlier growth of error energy, wet and dry convection have very similar error growth characteristics.Sensitivity tests are performed to ensure that the results from the large ensembles have converged and that they are consistent with either changing resolution, diffusion levels, initial error energy length scales, or forcing amplitude.

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“…Specifically, the minimal model should capture the basic regimes of convective organization, scattered convection versus a squall line, in response to different background wind shears, and the basic realistic features of the squall line should be captured: propagation direction, propagation speed, circulation and tilted cloud structure. It is shown here that these features can be captured by a set of equations that is simpler than those used by comprehensive CRMs; it is, in fact, quite similar to those typically used for non-precipitating moist convection (Kuo 1961;Bretherton 1987;Cuijpers & Duynkerke 1993;Grabowski & Clark 1993;Spyksma, Bartello & Yau 2006;Stevens 2007;Spyksma & Bartello 2008;. Precipitation-cooled downdrafts are a main feature distinguishing deep convection (vertical scales approximately the height of the troposphere, 10 km) from non-precipitating shallow convection (vertical scales of approximately 1 km).…”

Section: Introductionmentioning

confidence: 95%

Minimal models for precipitating turbulent convection

et al. 2013

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Simulations of precipitating convection would typically use a non-Boussinesq dynamical core such as the anelastic equations, and would incorporate water substance in all of its phases: vapour, liquid and ice. Furthermore, the liquid water phase would be separated into cloud water (small droplets suspended in air) and rain water (larger droplets that fall). Depending on environmental conditions, the moist convection may organize itself on multiple length and time scales. Here we investigate the question, what is the minimal representation of water substance and dynamics that still reproduces the basic regimes of turbulent convective organization? The simplified models investigated here use a Boussinesq atmosphere with bulk cloud physics involving equations for water vapour and rain water only. As a first test of the minimal models, we investigate organization or lack thereof on relatively small length scales of approximately 100 km and time scales of a few days. It is demonstrated that the minimal models produce either unorganized ('scattered') or organized convection in appropriate environmental conditions, depending on the environmental wind shear. For the case of organized convection, the models qualitatively capture features of propagating squall lines that are observed in nature and in more comprehensive cloud resolving models, such as tilted rain water profiles, low-altitude cold pools and propagation speed corresponding to the maximum of the horizontally averaged, horizontal velocity.

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A Boussinesq moist turbulence model

Cited by 15 publications

References 31 publications

New perspectives in turbulent Rayleigh-Bénard convection

New perspectives in turbulent Rayleigh-Bénard convection

Predictability in Wet and Dry Convective Turbulence

Minimal models for precipitating turbulent convection

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