Using Long-Term Data from ARM to Evaluate Precipitation in Climate Models
Precipitation is one of the most poorly simulated physical processes in general circulation models (GCMs). One difficulty with modeling precipitation is that precipitation is affected by a variety of complex processes that need to ben parameterized in large-scale models. The single-column model (SCM), which isolates a single-grid column from a global model, is a useful and effective tool to study the parameterization schemes in GCMs. However, most SCM intercomparison studies with Atmospheric Radiation Measurement (ARM) data focused on special cases or week-to-month-long periods. To make a statistically meaningful comparison and evaluation on modeled precipitation, three-year-long SCM simulations of seven GCMs participating in the Brookhaven National Laboratory (BNL) led “FASTER” project at the ARM Southern Great Plains (SGP) site have been completed. The results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant differences and deficiencies such as problems in frequency-intensity trade-off during cold seasons, too much rain during the day rather than at night, and differences in how various models partition rain between convective and stratiform clouds. Further analysis reveals distinct meteorological backgrounds for model precipitation underestimation and overestimation, offering clues to why the models are deficient.
The different SCM performances and associations with large-scale forcing and thermodynamic factors shed useful insights on cloud and convection parameterizations and will guide future model development.
Song, H., W. Lin, Y. Lin, A. B. Wolf, R. Neggers, L. J. Donner, A. D. Del Genio, and Y. Liu. 2013.
“Evaluation of Precipitation Simulated by Seven SCMs Against the ARM Observation at the SGP Site,” Journal of
Climate, DOI: 10.1175/JCLI-D-12-00263.1.