Cloud Microphysical Factors Affecting Simulations of Deep Convection During the Presummer Rainy Season in Southern China. Issue 18 (19th September 2018)

• Record Type: Journal Article
• Authors: Furtado, Kalli; Field, Paul R.; Luo, Yali; Liu, Xi; Guo, Zhun; Zhou, Tianjun; Shipway, Benjamin J.; Hill, Adrian A.; Wilkinson, Jonathan M.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 18(2018)
• Page Start: 10, 477
• Page End: 10, 505
• Publication Date: 2018-09-19
• Subjects: clouds -- microphysics -- monsoons -- China -- modeling -- observations; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2017JD028192
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The sensitivity of subtropical deep convection to the parameterization of cloud microphysics is elucidated through high‐resolution modeling of extreme presummer rainfall over southern China. An ensemble of physics configuration experiments is used to identify several drivers of model errors in comparison to radar observations from the South China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of cloud, precipitation, and radiation from satellites in the A‐train constellation. The benefits of increasing the number of prognostic variables in the microphysics scheme is assessed, relative to the effects of the parameterization of cloud microphysical properties and cloud fraction diagnosis. By matching individual parameterizations between the microphysical configurations, it is shown that a small subset of the parameterization changes can reproduce most of the dependence of model performance on physics configuration. In particular, biases that are due to the low‐level clouds and rain are strongly influenced by cloud fraction diagnosis and raindrop size distribution, whereas variations in the effects of high clouds are strongly influenced by differences in the parameterization of ice crystal sedimentation. Hence, for the case studied here, these parameterizations give more insight into the causes of variability in model performance than does the number of model prognostics per se. Key Points: Biases in model clouds and rain arise from the partitioning of water between hydrometeor species A small subset of parameterization changes can reproduce the variations between microphysics schemes The number of prognostic variables has less effect than the choice of subgrid‐scale closures