Refinement, Validation and Application of Cloud-Radiation Parameterization in a GCM

摘要

The research performed under this award was conducted along 3 related fronts: (1) Refinement and assessment of parameterizations of sub-grid scale radiative transport in GCMs. (2) Diagnostic studies that use ARM observations of clouds and convection in an effort to understand the effects of moist convection on its environment, including how convection influences clouds and radiation. This aspect focuses on developing and testing methodologies designed to use ARM data more effectively for use in atmospheric models, both at the cloud resolving model scale and the global climate model scale. (3) Use (1) and (2) in combination with both models and observations of varying complexity to study key radiation feedback Our work toward these objectives thus involved three corresponding efforts. First, novel diagnostic techniques were developed and applied to ARM observations to understand and characterize the effects of moist convection on the dynamical and thermodynamical environment in which it occurs. Second, an in house GCM radiative transfer algorithm (BUGSrad) was employed along with an optimal estimation cloud retrieval algorithm to evaluate the ability to reproduce cloudy-sky radiative flux observations. Assessments using a range of GCMs with various moist convective parameterizations to evaluate the fidelity with which the parameterizations reproduce key observable features of the environment were also started in the final year of this award. The third study area involved the study of cloud radiation feedbacks and we examined these in both cloud resolving and global climate models.