Researchers confirm that cloud microphysics play a key role in modeling storm cloud intensity.
High-resolution model simulations of deep convective clouds—or storms—with different representations of cloud microphysics result in large variations of cloud properties and precipitation. This makes it difficult to define “benchmarks” and limits the use of such models in simulating Earth system processes. Using data from a mesoscale convective system in the U.S. Great Plains, scientists revealed how different representations of cloud microphysics affect the simulated intensity of such systems. They also identified the major processes or factors leading to the large spread in results from the deep convection simulations.
This study underscores how the representation of cloud microphysics in numerical models significantly affects their simulation of convective intensity through impacts on latent heating and pools of cold air for dynamically driven mesoscale systems. The work also illustrates the need for better representations of cloud microphysics, particularly ice cloud microphysics, to reduce the spread among high-resolution model simulations.
Using data obtained during the ARM Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign, researchers investigated processes that contribute to the large variability in simulated cloud and precipitation properties. They performed an intercomparison study of a mid-latitude mesoscale squall line, using the Weather Research and Forecasting model at 1-kilometer horizontal grid spacing with eight cloud microphysics schemes. All simulations tended to produce a wider area of high radar reflectivity than observed, but a much narrower stratiform area. When compared to radar data, most of the microphysics schemes overestimated vertical velocity and radar reflectivity in convective updrafts. Simulated precipitation rates and updraft velocities largely varied across the eight schemes. Differences in simulated updraft velocity correlated well with differences in simulated buoyancy and cold pool intensity. Researchers found that simulated ice-related processes were the major contributor to a large spread in updraft intensity across schemes through increasing the differences in both latent heating and cold pool intensity.
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Pacific Northwest National Laboratory
This study was supported by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program. The Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO1830. This research used PNNL Institutional Computing resources and also resources at the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S.DOE under Contract No. DE-AC02-05CH1123. Bin Han and Dr. Chen were supported by the National Basic Research Program of China (2013CB430105). Dr. Varble was supported by U.S. DOE ASR DE-SC0008678. Dr. Morrison was supported by U.S. DOE ASR DE-SC0008648. Drs. Morrison and Varble were also supported by U.S. DOE ASR DE-SC0016476. Dr. Giangrande is an employee of Brookhaven Science Associates, LLC under Contract No. DE776AC02-98CH10886 with the U.S. DOE. The National Center for Atmospheric Research is sponsored by the U.S. National Science Foundation. Dr. Y. Wang is supported by ROSES14-ACMAP project. The PNNL Institutional Computing (PIC) resources were used for the model simulations of this study. The simulation data is available at the PNNL PIC and can be obtained by contacting the corresponding author, Dr. Jiwen Fan (Jiwen.Fan@pnnl.gov). We also acknowledge the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a user facility of the U.S. DOE, Office of Science, sponsored by the Office of Biological and Environmental Research, and support from the ASR program of that office. DOE ARM datasets used in this study can be obtained from the ARM Archive at http://www.arm.gov and ARM External Data Center at https://www.arm.gov/xdc/. The Oklahoma MESONET data is downloaded from https://www.mesonet.org/index.php/weather/category/past_data_files with the help of Lulin Xue and Xia Chu at NCAR.
J. Fan, B. Han, A. Varble, H. Morrison, K. North, P. Kollias, B. Chen, X. Dong, S.E. Giangrande, A. Khain, Y. Lin, E. Mansell, J. A. Milbrandt, R. Stenz, G. Thompson, Y. Wang. “Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part I - Convective Updrafts,” Journal of Geophysical Research: Atmospheres, 122:9351-9378. [DOI: 10.1002/2017JD026622]
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