Two assumptions commonly applied in convection schemes—the diagnostic and quasi-equilibrium assumptions—imply that convective activity (e.g., convective precipitation) is controlled only by the large-scale (macrostate) environment at the time. In contrast, numerical experiments indicate a “memory” or dependence of convection also on its own previous activity whereby subgrid-scale (microstate) structures boost but are also boosted by convection. In this study we investigated this memory by comparing single-column model behavior in two idealized tests previously executed by a cloud-resolving model (CRM). Conventional convection schemes that employ the diagnostic assumption fail to reproduce the CRM behavior. The memory-capable org and LMDZ cold pool schemes partially capture the behavior, but fail to fully exhibit the strong reinforcing feedbacks implied by the CRM. Analysis of this failure suggests that it is because the CRM supports a linear (or superlinear) dependence of the subgrid structure growth rate on the precipitation rate, while the org scheme assumes a sublinear dependence. Among varying versions of the org scheme, the growth rate of the org variable representing subgrid structure is strongly associated with memory strength. These results demonstrate the importance of parameterizing convective memory, and the ability of idealized tests to reveal shortcomings of convection schemes and constrain model structural assumptions.

Properly predicting the rapid transition from shallow to precipitating atmospheric convection within a diurnal cycle over land is of great importance for both weather prediction and climate projections. In this work, we consider that a cumulus cloud is formed due to the transport of water mass by multiple updrafts during its life-time. Cumulus clouds then locally create favorable conditions for the subsequent convective updrafts to reach higher altitudes, leading to deeper precipitating convection. This mechanism is amplified by the cold pools formed by the evaporation of precipitation in the sub-cloud layer. Based on this conceptual view of cloud-cloud interactions which goes beyond the one cloud equals one-plume picture, it is argued that precipitating clouds may act as predators that prey on the total cloud population, such that the rapid shallow-to-deep transition can be modeled as a simple predator-prey system. This conceptual model is validated by comparing solutions of the Lotka-Volterra system of equations to results obtained using a high-resolution large-eddy simulation model. Moreover, we argue that the complete diurnal cycle of deep convection can be seen as a predator-prey system with varying food supply for the prey. Finally, we suggest that the present model can be applied to weather and climate models, which may lead to improved representations of the transition from shallow to precipitating continental convection.

El Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central-eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, by objectively optimizing the RO equations fit to observations, we develop an improved recharge index. (1) Te-variability is regressed out, building h_ind statistically-independent from Te. Capturing the pure recharge, h_ind reconciles usual indices. (2) The optimum is equatorial plus southwestern Pacific h_ind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using h_ind_eq+sw, the RO becomes more consistent with observations. h_ind_eq+sw is more relevant for ENSO operational diagnostics.