Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5
Abstract
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 the 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. Results show that for the 22 parameters perturbed in the cloud ensemble, the six 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. The 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 GLM is able to explain moremore »
- Authors:
-
- Pacific Northwest National LaboratoryRichland Washington USA
- Pacific Northwest National LaboratoryRichland Washington USA, College of Atmospheric Sciences, Lanzhou UniversityLanzhou China
- Lawrence Livermore National LaboratoryLivermore California USA
- National Center for Atmospheric ResearchBoulder Colorado USA
- Sandia National LaboratoriesAlbuquerque New Mexico USA
- Institute for Climate and Global Change Research &, School of Atmospheric Science, Nanjing UniversityNanjing China, Collaborative Innovation Center of Climate Change, Jiangsu Province China
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1295964
- Alternate Identifier(s):
- OSTI ID: 1214066; OSTI ID: 1214703; OSTI ID: 1295965; OSTI ID: 1763941
- Report Number(s):
- PNNL-SA-103688; LLNL-JRNL-818817
Journal ID: ISSN 1942-2466
- Grant/Contract Number:
- AC05-76RL01830; AC04-94AL85000; AC52-07NA27344
- Resource Type:
- Published Article
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Additional Journal Information:
- Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 7 Journal Issue: 2; Journal ID: ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; precipitation; CAM5; parameter space; cloud physics
Citation Formats
Qian, Yun, Yan, Huiping, Hou, Zhangshuan, Johannesson, Gardar, Klein, Stephen, Lucas, Donald, Neale, Richard, Rasch, Philip, Swiler, Laura, Tannahill, John, Wang, Hailong, Wang, Minghuai, and Zhao, Chun. Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5. United States: N. p., 2015.
Web. doi:10.1002/2014MS000354.
Qian, Yun, Yan, Huiping, Hou, Zhangshuan, Johannesson, Gardar, Klein, Stephen, Lucas, Donald, Neale, Richard, Rasch, Philip, Swiler, Laura, Tannahill, John, Wang, Hailong, Wang, Minghuai, & Zhao, Chun. Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5. United States. https://doi.org/10.1002/2014MS000354
Qian, Yun, Yan, Huiping, Hou, Zhangshuan, Johannesson, Gardar, Klein, Stephen, Lucas, Donald, Neale, Richard, Rasch, Philip, Swiler, Laura, Tannahill, John, Wang, Hailong, Wang, Minghuai, and Zhao, Chun. Fri .
"Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5". United States. https://doi.org/10.1002/2014MS000354.
@article{osti_1295964,
title = {Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5},
author = {Qian, Yun and Yan, Huiping and Hou, Zhangshuan and Johannesson, Gardar and Klein, Stephen and Lucas, Donald and Neale, Richard and Rasch, Philip and Swiler, Laura and Tannahill, John and Wang, Hailong and Wang, Minghuai and Zhao, Chun},
abstractNote = {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 the 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. Results show that for the 22 parameters perturbed in the cloud ensemble, the six 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. The 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 GLM 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 the mid-latitude continental regions, but very small in tropical continental regions.},
doi = {10.1002/2014MS000354},
journal = {Journal of Advances in Modeling Earth Systems},
number = 2,
volume = 7,
place = {United States},
year = {Fri Apr 10 00:00:00 EDT 2015},
month = {Fri Apr 10 00:00:00 EDT 2015}
}
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