Cloud‐Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties
Williams, Christopher R.
Giangrande, Scott E.
Milbrandt, Jason A.
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationHan, B., Fan, J., Varble, A., Morrison, H., Williams, C. R., Chen, B., et al. ( 2019). Cloud‐resolving model intercomparison of an MC3E squall line case: Part II. Stratiform precipitation properties. Journal of Geophysical Research: Atmospheres, 124, 1090– 1117. https://doi.org/10.1029/2018JD029596
Rights©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License.
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AbstractIn this second part of a cloud microphysics scheme intercomparison study, we focus on biases and variabilities of stratiform precipitation properties for a midlatitude squall line event simulated with a cloud-resolving model implemented with eight cloud microphysics schemes. Most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. All schemes underestimate the frequency of moderate stratiform rain rates (2-6mm/hr), which may result from low-biased ice number and mass concentrations for 0.2-2-mm diameter particles in the stratiform ice region. Most simulations overestimate ice water content (IWC) at altitudes above 7km for temperatures colder than -20 degrees C but produce a decrease of IWC approaching the melting level, which is opposite to the trend shown by in situ observations. This leads to general underestimations of stratiform IWC below 5-km altitude and rainwater content above 1-km altitude for a given rain rate. Stratiform precipitation area positively correlates with the convective condensate detrainment flux but is modulated by hydrometeor type, size, and fall speed. Stratiform precipitation area also changes by up to 17%-25% through alterations of the lateral boundary condition forcing frequency. Stratiform precipitation, rain rate, and area across the simulations vary by a factor of 1.5. This large variability is primarily a result of variability in the stratiform downward ice mass flux, which is highly correlated with convective condensate horizontal detrainment strength. The variability of simulated local microphysical processes in the stratiform region plays a secondary role in explaining variability in simulated stratiform rainfall properties. Plain Language Summary This is a unique model intercomparison study about different microphysics parametrizations commonly used, with the purposes of examining model biases and variability as well as identifying major factors/processes leading to bias and variability. The study simulated a well-observed squall line MCS from MC3E field campaign, and focused on the stratiform precipitation, following on our part 1 study focusing on convective part. We employed a more constrained approach compared with past intercomparison studies to better identify processes contributing to the differences. Another unique part is our comprehensive model evaluation, that is, we identify stratiform columns and evaluate vertical evolution of cloud properties including size distribution. We find that most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. Moderate stratiform rain rates are underestimated, mainly due to incorrect vertical evolution of ice particles. Stratiform precipitation properties across the simulations vary by a factor of 1.5, primarily a result of variability in detrained condensate amount. In addition, we find that stratiform precipitation area correlates well with detrainment amount and is modulated by the detrained hydrometeor properties. So convective microphysics plays a key role in determining stratiform properties.
Note6 month embargo; published online: 2 January 2019
VersionFinal published version
SponsorsU.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program; U.S. Department of Energy (DOE) Climate Model Development and Validation (CMDV) program; DOE [DE-AC06-76RLO1830]; Office of Science of the U.S. DOE [DE-AC02-05CH1123]; National Basic Research Program of China [2013CB430105]; National Natural Science Foundation of China [41575130, 41775132]; U.S. DOE ASR grant [DE-SC0008678, DE-SC0008648, DE-SC0016476]; DOE CMDV project at University of Arizona [DE-SC0017015]; U.S. DOE [DE-AC02-98CH10886]; Israel Science Foundation [2027/17]; U.S. National Science Foundation; [DE-SC008811]