气候变化领域集成服务门户(CCPortal): Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

CCPortal

DOI	10.1029/2019MS001730
	Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework
	Stanford M.W.; Morrison H.; Varble A.; Berner J.; Wu W.; McFarquhar G.; Milbrandt J.
发表日期	2019
ISSN	19422466
起始页码	3362
结束页码	3389
卷号	11 期号:11
英文摘要	Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called “natural variability” impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aDb) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cDd) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field. ©2019. The Authors.
英文关键词	cloud radiative forcing; ice microphysics; mesoscale convective systems; model-observation comparison; parameterization development; stochastic physics
语种	英语
scopus关键词	Air navigation; Atmospheric radiation; Clouds; Natural convection; Rain; Small satellites; Stochastic models; Stochastic systems; Storms; Weather forecasting; Cloud radiative forcing; Empirical relationships; Functional properties; Lateral boundary conditions; Mesoscale Convective System; Microphysics; Potential temperature; Weather research and forecasting models; Ice; boundary condition; cirrus; cloud microphysics; cloud radiative forcing; convection; model validation; optical depth; parameterization; potential temperature; stochasticity
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156840
作者单位	Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, United States; National Center for Atmospheric Research, Boulder, CO, United States; Pacific Northwest National Laboratory, Richland, WA, United States; Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK, United States; School of Meteorology, University of Oklahoma, Norman, OK, United States; Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
推荐引用方式 GB/T 7714	Stanford M.W.,Morrison H.,Varble A.,et al. Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework[J],2019,11(11).
APA	Stanford M.W..,Morrison H..,Varble A..,Berner J..,Wu W..,...&Milbrandt J..(2019).Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework.Journal of Advances in Modeling Earth Systems,11(11).
MLA	Stanford M.W.,et al."Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework".Journal of Advances in Modeling Earth Systems 11.11(2019).