Anelastic Convective Entities (ACEs): An intermediate-complexity process model for convection

Over continental plains, precipitation tends to peak in the late afternoon or during nighttime. Yet the accurate simulation of the land precipitation diurnal cycle in GCMs has been a long-standing challenge. Conventional understanding attributes this to the representation of convective inhibition (CIN), i.e., nighttime surface cooling or cold pool expansion tends to yield a stable layer with large CIN. However, CIN arises from traditional parcel consideration—measuring the inhibition of an infinitesimal parcel. Here, we argue that the CIN layer is less effective in inhibiting convection than previously thought for convective entities of typical horizontal cloud size of a few kilometers. A time-dependent process model for anelastic convective entities (ACE) is formulated to consistently include dynamic entrainment/detrainment as well as a representation of nonhydrostatic perturbation pressure. Spatially nonlocal effects mediated by the pressure field imply that horizontal feature size becomes a factor in the vertical conditional-instability problem. ACE simulations using nighttime GoAmazon soundings with strong surface inversion demonstrate that the vertically nonlocal pressure response and its interaction with the surface boundary condition make the CIN layer ineffective for convective features of substantial horizontal size. This implies that a much smaller vertical velocity perturbation (or more generally, nonlocal buoyancy forcing from neighboring disturbances) can tunnel through the CIN layer. Within the convective column, buoyancy of different signs offset each other via nonlocal interaction over vertical scales comparable to the typical horizontal scale. A related implication is that the vertical acceleration due to deep-convective buoyancy tends to extend above the level of neutral buoyancy (LNB). This leads to cloud top much higher than the LNB, exhibiting the convective cold-top feature previously noted in observations. The ACE model provides an alternative representation of moist convection (suitable for a simplified version of superparameterization). Example of a multi-ACE system is provided to demonstrate the adjustment processes associated with deep convection—including anvil formation and higher-mode gravity wave response. Results here point to revision for convective parameterizations under existing paradigm, specifically concerning (1) CIN/TKE closure for triggering convection; (2) estimated cloud-top height and cold-top feature; and (3) relation linking updraft mass flux and conditional instability, all of which are under ongoing investigation.