|Title||Evaluating convective parameterization closures using cloud-resolving model simulation of tropical deep convection|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Suhas E, Zhang GJ|
|Journal||Journal of Geophysical Research-Atmospheres|
|Type of Article||Article|
|Keywords||climate simulations; cumulus parameterization; ensemble; equilibrium; large-scale circulations; mass; moist convection; scheme; sensitivity; warm pool|
Closure is an important component of a mass flux-based convective parameterization scheme, and it determines the amount of convection with the aid of a large-scale variable (closure variable) that is sensitive to convection. In this study, we have evaluated and quantified the relationship between commonly used closure variables and convection for a range of global climate model (GCM) horizontal resolutions, taking convective precipitation and mass flux at 600 hPa as measures for deep convection. We have used cloud-resolving model simulation data to create domain averages representing GCM horizontal resolutions of 128km, 64 km, 32 km, 16 km, 8 km, and 4km. Lead-lag correlation analysis shows that except moisture convergence and turbulent kinetic energy, none of the other closure variables evaluated in this study show any relationship with convection for the six subdomain sizes. It is found that the correlation between moisture convergence and convective precipitation is largest when moisture convergence leads convection. This correlation weakens as the subdomain size decreases to 8km or smaller. Although convective precipitation and mass flux increase with moisture convergence at a given subdomain size, as the subdomain size increases, the rate at which they increase becomes smaller. This suggests that moisture convergence-based closure should scale down the predicted mass flux for a given moisture convergence as GCM resolution increases.