What do cloud-resolving models tell us about critical phenomena in atmospheric precipitation?

Recent work suggests that observations of Tropical precipitation conform to properties associated with critical phenomena of other systems (Peters and Neelin 2006). The precipitation observations are averages over 25-km by 25-km areas and are snapshots in time, and therefore unable to reveal the underlying, smaller-scale physical processes. We are using a 3D cloud-resolving model (CRM) to resolve these processes in space and time, and thereby allow us to investigate the underlying physics in detail. We performed large domain (1000 km by 1000 km) multiday (~10 days) CRM simulations in order to adequately sample the rare large events.

In addition, we are using results from a 4-year global simulation using a climate model based on the multi-scale modeling framework (MMF). Whereas conventional parameterizations are based on statistical theories involving uncertain closure assumptions, MMFs represent cloud processes on their native scales by embedding a 2D CRM with a 4-km horizontal grid size in each climate model grid column.

We have analyzed the model results following the methodology of Peters and Neelin. We used the results to produce rainfall rates conditioned on column water vapor and column temperature over the Tropical oceans. We have also analyzed additional statistical aspects of Tropical convection in the 3D CRM simulations that are related to critical behavior.

We have found that: (1) CRMs are able to reproduce nearly all of the observed statistics of strong convective precipitation over tropical oceans. (2) CRMs and MMFs do not generally reproduce the roll-off of retrieved precipitation rates at large column water vapor values. (3) Analysis of CRM results suggests that many of the observed features are due to the tight coupling between dynamics and moist thermodynamics in convective updrafts.