Much of the uncertainty in climate model projections stems from limited understanding of cloud and precipitation processes and the parameterization of these processes in global climate models (GCMs). Results from high-resolution models, such as large-eddy simulation (LES) models, can serve as benchmarks for developing GCM parameterizations. However, before LES can be considered as a benchmark, LES solutions should be evaluated against observational constraints to ensure that they accurately represent observed physical processes.
Scientists supported by the Department of Energy’s Atmospheric System Research (ASR) program conducted a study to determine whether the new scanning radars at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site provide useful model constraints for documenting the time-evolving structure of clouds, precipitation, and associated processes. The site recently complemented its single-column measurements with new scanning radars that better suit the sampling of LES-scale domains. Since large-scale atmospheric circulations are much larger than an LES model domain, LES models must be “forced” by providing atmospheric conditions at their boundaries. An additional emphasis in the study was to evaluate the sensitivity of cloud and precipitation properties to differences in the spatial scale and temporal details of the large-scale forcing datasets.
This study focused on a case of shallow cumulus transitioning to precipitating cumulus congestus. Unlike typical idealized LES cases, this case exhibited substantial synoptic-scale variability and strong, height-dependent forcing. The model captured, at least in a general sense, the transition from shallow cumulus to a multilayer cloud system that included deeper cumulus congestus. These results were encouraging, given the substantial synoptic variability and highly idealized modeling framework. Results indicated that measurements obtained from scanning radar such as cloud-top height distributions better highlighted the differences across the ensemble of simulations, compared to metrics obtained from vertically profiling instruments. Multidimensional measures of cloud and precipitation geometry from scanning radar systems (e.g., precipitation onset, precipitation area, and cloud-top probability distribution functions) demonstrated several key advantages for the simulation driven with the time-varying forcing. Linking persistent biases in simulation results to differences in the scale of the forcing or bulk measures of forcing terms was difficult, suggesting that bulk representations of forcing quantities are insufficient in understanding cloud system evolution.
Reference: Mechem, D. B., S. E. Giangrande, C. S. Wittman, P. Borque, T. Toto, and P. Kollias. 2015. "Insights from Modeling and Observational Evaluation of a Precipitating Continental Cumulus Event Observed During the Midlatitude Continental Convective Clouds Experiment Field Campaign," Journal of Geophysical Research - Atmospheres 120(5), 1980-95. DOI: 10.1002/2014JD022255. (Reference link)
Contact: Sally McFarlane, SC-23.1, (301) 903-0943, Ashley Williamson, SC-23.1, (301) 903-3120
SC-23.1 Climate and Environmental Sciences Division, BER
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