Cloud Resolving Models as Scaffolding for Cloud Parameterizations in Large-Scale Models

Pincus, R.(a), Klein, S.A.(b), Hannay, C.(a), and Xu, K.-M.(c), NOAA-CIRES Climate Diagnostics Center (a), NOAA Geophysical Fluid Dynamics Laboratory (b), NASA Langley Research Center (c)
Twelfth Atmospheric Radiation Measurement (ARM) Science Team Meeting

The treatment of clouds in large scale models has evolved from fixed to diagnostics to predictive as the importance of cloud feedbacks has become clear. In development now are schemes which account for the resolution-dependent sub-grid scale variability in condensate, which is thought to be a significant factor driving ad hoc model tuning. Parameterizations have their roots in theory, experiment, and observational data. It's very hard, though, to observe the four-dimensional structure of clouds and precipitation in the atmosphere as is needed to assess the sub-grid scale variability. Here we use small-scale, high-resolution (512 km domain, 2 km grid size) cloud models to inform the development of a new statistical cloud scheme. The model results can be applied in three ways: as a testbed for parameterization assumptions, as a source of specific parameter values, and by providing target behavior for the parameterization. We demonstrate these roles using a one-month simulation of summer-time deep convection at the ARM SGP CART site.

Note: This is the poster abstract presented at the meeting; an extended version was not provided by the author(s).