Atmospheric convection as an unstable predator-prey process with memory

Heuristic models and observational analyses of atmospheric convection often assume that convective activity, for example rain rate, approaches some given value for any given large-scale (“macrostate”) environmental conditions such as static stability and humidity. We present novel convection-resolving simulations in which the convective activity evolves in a fixed equilibrium mean state (“macrostate”). In this case convective activity is unstable, diverging quasi-exponentially away from equilibrium either to extreme or zero rain rate. Thus almost any rain rate can coexist with an equilibrium profile: the model rain rate also depends on convective history. We then present a two-variable, predator-prey model motivated by this behavior, wherein small-scale (“microstate”) variability is produced by, but also promotes convective precipitation, while macrostate properties such as CAPE promote, but are consumed by convective precipitation. In this model, convection is influenced as much by its own history (via persistent microstate variability) as by its current environment. This model reproduces the simulated instability found above and could account for several lag relationships in simulated and observed convection, including its afternoon maximum over land and the well-known “quasi-equilibrium” balance at synoptic time scales between the forcing and response of key variables. These results point to a strong role for convective memory and suggests that basic strategies for observing, modeling and parameterizing convective processes should pay closer attention to persistent variability on scales smaller than that of the grid box.

Two-fluid single-column modelling of Rayleigh-Bénard convection as a step towards multi-fluid modelling of atmospheric convection

Multi-fluid models have recently been proposed as an approach to improving the representation of convection in weather and climate models. This is an attractive framework as it is fundamentally dynamical, removing some of the assumptions of mass-flux convection schemes which are invalid at current model resolutions. However, it is still not understood how best to close the multi-fluid equations for atmospheric convection. In this paper we develop a simple two-fluid, single-column model with one rising and one falling fluid. No further modelling of sub-filter variability is included. We then apply this model to Rayleigh-Bénard convection, showing that, with minimal closures, the correct scaling of the heat flux (Nu) is predicted over six orders of magnitude of buoyancy forcing (Ra). This suggests that even a very simple two-fluid model can accurately capture the dominant coherent overturning structures of convection.

Coupled Ocean–Atmosphere Covariances in Global Ensemble Simulations: Impact of an Eddy-Resolving Ocean

Patterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean–atmosphere forecasts. This provides a unique perspective into atmosphere–ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 h of the coupled forecast between the ocean surface temperature and atmospheric variables, and between the ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: 1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean–atmosphere exchanges; 2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and 3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12° vs 1° and 1/4°) and vertical (1- vs 10-m depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the tropical Indian Ocean, and the warm pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.

Les travaux de Jean-François Geleyn sur la convection

Jean-François Geleyn s'est intéressé durant des décennies à la prévision de la convection atmosphérique, et particulièrement à la façon de la paramétriser dans les modèles de prévision numérique du temps. Il s'y est intéressé tant au niveau des concepts que des équations et de l'algorithmique permettant de résoudre celles-ci de façon stable et efficace. Aux qualités scientifiques de Jean-François Geleyn s'ajoutait un caractère enthousiaste et communicatif, de sorte qu'il a embarqué dans son aventure des générations d'étudiants et de développeurs. Jean-François Geleyn has been interested for decades in forecasting atmospheric convection, and particularly in how to parameterize it in numerical weather prediction models. He has been interested in the concepts, equations and algorithms to solve them in a stable and efficient way. He was also enthusiastic and communicative, so that generations of students and developers have embarked on his adventure.