Probing into responses of moist convection to large-scale temperature perturbations using a Lagrangian Particle Dispersion Model

How moist convection responds to small large-scale temperature and moisture perturbations has a strong influence on the behavior of moist convecting atmospheres. While such responses, sometimes called linear response functions, have been constructed for convection represented by a cloud resolving model (CRM), the processes by which convection produces such responses, and how such processes depend on the setting, require further clarification.

In particular, it was found recently that convective responses to perturbations strongly depend on the degree of convective organization. For unorganized convection, effects of temperature and moisture anomalies in the free troposphere on convection are broadly consistent with a parcel view of moist convection: a warm anomaly forms a buoyancy barrier that eliminates some of the convective updraft parcels, leading to cooling at and above the perturbed layer, this barrier also preferentially eliminates barely or slightly buoyant cloudy parcels that leaves the perturbed cloudy layer with higher total water content and increased vertical velocity. Whereas a moist anomaly reduces the degree of entrainment drying experienced by the parcels and allow them to reach higher, leading to warming at and above the perturbed layer. On the other hand, when convection is highly organized (eg. squall line), the general behavior is more consistent with a layer mode of convective overturning, where a warm anomaly in the lower troposphere (above the cloud base) leads to warming of the entire free troposphere and cooling of the sub-cloud layer, while a warm anomaly in the middle and upper troposphere has the opposite effect.

This study analyzes the mechanisms of convective response for both shallow and deep convection, its dependence on degree of organization and the effects of mean environmental states using a Lagrangian Particle Dispersion Model (LPDM) embedded CRM. Better understanding of the underlying dynamics can potentially lead to better cumulus parameterization in large-scale circulation models.