Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (.2 km), middle (2-6 km), and high (.6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synoptic patterns, variables such as relative humidity (RH) and vertical pressure velocity (omega) from North American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ~ 40%-50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ~75%-85%. The PDFs of modeled low clouds are similar to those observed; however, for high clouds the PDFs are shifted toward higher values of RH. This results in a negative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCMspecified stratiform parameterization thresholdRHof 60%. Despite many similarities between PDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.
ASJC Scopus subject areas
- Atmospheric Science