Moist convective processes in the trades are important not only because of their role in mediating the transfer of latent heat from the ocean into the atmosphere, but also because of the direct radiative effect of the clouds on both the radiative budget at the top of the atmosphere and the surface energy budget. In recent years the earlier diagnostic studies have been supplemented by theoretical studies and numerical experiments which further show that large-scale circulations are sensitive to the representation of the trade wind regimes and their associated low-level clouds (e.g., Tiedtke et al. The low-level trade wind regime has been recognized as a structural component of the general circulation for at least half a century ( von Ficker 1936 Riehl et al. Future observational studies of this regime would be of most benefit if they could provide robust cloud statistics as a function of mean environmental conditions. The simulations indicate that future theoretical research needs to focus on interface rules, whereby the cloud layer is coupled to the subcloud layer below and the free atmosphere above. The development of the stratiform cloud layer is not, however, captured by the mass-flux models. In accord with previous studies, mass-flux models well represent the dynamics of the cloud layer, while mixing-length models well represent the subcloud layer. ![]() Lastly the simulations are used to help evaluate simple models of trade wind boundary layers. The simulations help illustrate the highly variable (in both height and thickness) nature of the transition layer, and we speculate that this variability helps regulate convection. In accord with observations, the simulations predict that this layer is most identifiable in terms of moisture variances and gradients. The simulations also provide new insight into the dynamics of the transition layer at cloud base. Part of this sensitivity is attributed to a physically realistic positive radiative feedback, whereby a propensity toward higher cloud fractions in any given simulation is amplified by longwave radiative cooling. These sensitivities persist even among simulations on relatively refined grid meshes. Chief among these are differences between numerical algorithms. ![]() Although many elements of the turbulent structure (including the wind profiles, the evolution of cloud-base height, the statistics of the subcloud layer, and the nature of mixing in the lower and middle parts of the cloud layer) are robustly predicted, the representation of the stratiform cloud amount by the different simulations is remarkably sensitive to a number of factors. The principal differences are the extent to which the cloud layer is quasi-steady in the current simulations, evidence of weak countergradient momentum transport within the cloud layer, and the development and influence of an incipient stratiform cloud layer at the top of the cloud layer. In many respects the dynamics are similar to those found in many previous simulations of trade cumuli capped by weaker inversions. The simulations help illustrate the turbulent dynamics of trade cumuli in such a regime. These simulations are supplemented by many further sensitivity studies, including some with very refined grid meshes. The basis of the intercomparison is 10 simulations by 7 groups. ![]() The fifth intercomparison of the Global Water and Energy Experiment Cloud System Studies Working Group 1 is used as a vehicle for better understanding the dynamics of trade wind cumuli capped by a strong inversion. (d) In the plot of σ 2 w the integrations from the KNMI and INM groups are not included for reasons discussed in the text (c) The two calculations (UKMO-B and DHARMA) with forward-in-time (and upwinded) representations of momentum advection are not included in the ensemble statistics, but the local maxima produced by each are indicated separately by the filled circle and circle–dot, respectively. (b) The WVU computations were not included due to a diagnostic problem in calculating σ q s in the lowest 500 m. Higher-order statistics: (a) total horizontal velocity variances (and estimated SGS contribution) (b) saturation mixing-ratio variances (c) total-water mixing-ratio variances (d) vertical velocity variances (and estimated SGS contribution), also shown with short horizontal lines are the mixed layer scaling estimates (e) skewness of the vertical velocity (thin line in subcloud layer denotes expected value for dry convective PBL) and (f) terms in the TKE budget, where D, B, and S refer to dissipation, buoyancy, and shear production of TKE, respectively.
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