Climate and Dynamics
Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at Atmospheric Radiation Measurement sites
Article first published online: 26 APR 2005
Copyright 2005 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 110, Issue D15, 16 August 2005
How to Cite
2005), Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at Atmospheric Radiation Measurement sites, J. Geophys. Res., 110, D15S15, doi:10.1029/2004JD005042., , , , , , , , and (
- Issue published online: 26 APR 2005
- Article first published online: 26 APR 2005
- Manuscript Accepted: 16 DEC 2004
- Manuscript Revised: 5 NOV 2004
- Manuscript Received: 19 MAY 2004
 The Community Atmospheric Model 2 (CAM2) is run as a short-term (1–5 days) forecast model initialized with reanalysis data. The intent is to reveal model deficiencies before complex interactions obscure the root error sources. Integrations are carried out for three Atmospheric Radiation Measurement (ARM) Program intensive operational periods (IOPs): June/July 1997, April 1997, and March 2000. The ARM data are used to validate the model in detail for the Southern Great Plains (SGP) site for all the periods and in the tropical west Pacific for the March 2000 period. The model errors establish themselves quickly, and within 3 days the model has evolved into a state distinctly different from the ARM observations. The summer forecasts evince a systematic error in convective rainfall. This error manifests itself in the temperature and moisture profiles after a single diurnal cycle. The same error characteristics are seen in the March 2000 tropical west Pacific forecasts. The model performs well in the spring cases at the SGP. Most of the error is manifested during rainy periods. The ARM cloud radar comparison to the model reveals cloud errors which are consistent with the relative humidity profile errors. The cloud errors are similar to those seen in climatological integrations, but the state variable errors are different. Thus there is the possibility that the some basic parameterization errors are obscured in the climatological integrations. The approach described here will facilitate parameterization experimentation, diagnoses, and validation. One way of reducing cloud feedback uncertainty is to make the physical processes behave in the most realistic manner possible. Paradoxically, perhaps the best way to reduce uncertainty in cloud feedback mechanisms is to evaluate the model processes with realistic forcing before such feedbacks have any significant affect.