PNNL

The Science

Shallow clouds play an important role in regulating energy and water transport in the lower atmosphere. But these shallow clouds are too small in size to be resolved at the resolutions of traditional global climate models. Global climate models rely on parameterizations, which are simple embedded models, to represent these clouds. This study found that the key assumptions used in a group of popular shallow cloud parameterizations are flawed. Discrepancies in representing the moisture variability near cloud tops and bases lead to biases in the modeled cloud properties. Increasing model resolution diminishes the impact of these discrepancies on the simulated clouds.

The Impact

The results revealed the complexities of moisture and temperature variability within the shallow cloud layer. They pinpointed a challenge currently used parameterizations have in representing this variability in shallow clouds. This study also suggests these problems will persist in next-generation climate models that run at higher resolutions, although the biases will be less severe. For current-generation models running at lower resolutions, these parameterizations need modification to improve their performance.

Summary

Researchers analyzed a large-eddy simulation (LES) of a continental shallow-to-deep convection transition event observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems field campaign over the Atmospheric Radiation Measurement Southern Great Plains user facility. The results showed that the vertical structure of moisture and temperature variability in the shallow cloud layer is more complicated than what most shallow convection parameterizations can capture. Most parameterizations use assumptions that can only represent the variability associated with the active, mature cloud eddies that dominate the middle of the cloud layer. They fail to adequately capture the variability associated with the overshooting or decaying eddies that exist near the cloud base and top. Offline calculations of cloud properties using the assumed probability density functions used in the parameterizations and input from the LES shows biases in the simulated shallow cloud properties that persist near the cloud base, but diminish near the cloud top with increasing horizontal resolution (from 100 km to 2 km).

PNNL Contact

Jerome Fast, Pacific Northwest National Laboratory, Jerome.Fast@pnnl.gov 

Funding

This research is supported by the Atmospheric Science Research program as part of the U.S. Department of Energy’s Biological and Environmental Research program. It was also supported by the National Key Project operated by the Ministry of Science and Technology of China, the Jiangsu Government Scholarship for Overseas Studies, and the Open Project jointly managed by the Key Laboratory of Meteorological Disaster in Ministry of Education and the Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters at Nanjing University of Information Science and Technology. Computing resources were provided by Environmental Molecular Sciences Laboratory.