Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification
[1];
[2];
[2];
[3];
[4];
- Princeton Environmental Inst., NJ (United States); Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
- Univ. of Washington, Seattle, WA (United States). Dept. of Atmospheric Sciences; Joint Inst. for the Study of the Atmosphere and Ocean, Seattle, WA (United States)
- Geophysical Fluid Dynamics Lab., Princeton, NJ (United States); Program in Atmospheric and Oceanic Sciences, Princeton, NJ (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.
- Research Organization:
- Univ. of Washington, Seattle, WA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Princeton Environmental Inst., NJ (United States); Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); Princeton Univ. (United States)
- Grant/Contract Number:
- AC52-07NA27344; SC0002472
- OSTI ID:
- 1424109
- Report Number(s):
- LLNL-JRNL-731041
- Journal Information:
- Journal of Geophysical Research: Atmospheres, Vol. 122, Issue 23; ISSN 2169-897X
- Publisher:
- American Geophysical UnionCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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