FINAL REPORT (DE-FG02-97ER62338): Single-column modeling, GCM parameterizations, and ARM data

Somerville, Richard C. J.

Scripps Institution of Oceanography

关键词: Forecasting;    Implementation;    Testing Atmospheric Radiation Measurement (Arm) Program, Single Column Modeling, Global Climate Models (Gcms), Parameterizations;    Simulation;    Downwelling;   

DOI  :  10.2172/948450
RP-ID  :  DOE/ER/62338-1
RP-ID  :  FG02-97ER62338
RP-ID  :  948450

美国|英语

来源: UNT Digital Library

【 摘 要 】

Our overall goal is the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data at all three ARM sites, and the implementation and testing of these parameterizations in global models. To test recently developed prognostic parameterizations based on detailed cloud microphysics, we have compared SCM (single-column model) output with ARM observations at the SGP, NSA and TWP sites. We focus on the predicted cloud amounts and on a suite of radiative quantities strongly dependent on clouds, such as downwelling surface shortwave radiation. Our results demonstrate the superiority of parameterizations based on comprehensive treatments of cloud microphysics and cloud-radiative interactions. At the SGP and NSA sites, the SCM results simulate the ARM measurements well and are demonstrably more realistic than typical parameterizations found in conventional operational forecasting models. At the TWP site, the model performance depends strongly on details of the scheme, and the results of our diagnostic tests suggest ways to develop improved parameterizations better suited to simulating cloud-radiation interactions in the tropics generally. These advances have made it possible to take the next step and build on this progress, by incorporating our parameterization schemes in state-of-the-art three-dimensional atmospheric models, and diagnosing and evaluating the results using independent data. Because the improved cloud-radiation results have been obtained largely via implementing detailed and physically comprehensive cloud microphysics, we anticipate that improved predictions of hydrologic cycle components, and hence of precipitation, may also be achievable.