DOI: 10.1002/2014MS000354
Scopus记录号: 2-s2.0-85027917292
论文题名: Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5
作者: Qian Y ; , Yan H ; , Hou Z ; , Johannesson G ; , Klein S ; , Lucas D ; , Neale R ; , Rasch P ; , Swiler L ; , Tannahill J ; , Wang H ; , Wang M ; , Zhao C
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2015
卷: 7, 期: 2 起始页码: 382
结束页码: 411
语种: 英语
英文关键词: Aerosols
; Clouds
; Monte Carlo methods
; Precipitation (chemical)
; Sensitivity analysis
; Uncertainty analysis
; CAM5
; Community atmosphere model
; Extreme precipitation
; Generalized linear model
; Parametric sensitivity
; Parametric sensitivity analysis
; Precipitation characteristics
; Quasi-monte carlo samplings
; Precipitation (meteorology)
; aerosol
; amplitude
; atmospheric modeling
; cloud
; cloud microphysics
; convection
; diurnal variation
; ensemble forecasting
; precipitation (climatology)
; sensitivity analysis
英文摘要: We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and Quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75-90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions. © 2015. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/76058
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Pacific Northwest National Laboratory, Richland, Washington, United States; College of Atmospheric Sciences, Lanzhou University, Lanzhou, China; Lawrence Livermore National Laboratory, Livermore, California, United States; National Center for Atmospheric Research, Boulder, Colorado, United States; Sandia National Laboratories, Albuquerqueast, New Mexico, United States; Institute for Climate and Global Change Research and School of Atmospheric Science, Nanjing University, Nanjing, China; Collaborative Innovation Center of Climate Change, Jiangsu Province, China
Recommended Citation:
Qian Y,, Yan H,, Hou Z,et al. Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5[J]. Journal of Advances in Modeling Earth Systems,2015-01-01,7(2)