Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1007/s00704-015-1690-9 |
A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example | |
Sun, Guodong1; Mu, Mu1,2 | |
通讯作者 | Mu, Mu |
来源期刊 | THEORETICAL AND APPLIED CLIMATOLOGY |
ISSN | 0177-798X |
EISSN | 1434-4483 |
出版年 | 2017 |
卷号 | 128期号:3-4页码:587-601 |
英文摘要 | An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semi-arid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply "target observations" to reduce the uncertainties in model parameters. |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000399702200008 |
WOS关键词 | NONLINEAR OPTIMAL PERTURBATION ; LAND-SURFACE SCHEME ; SPRING PREDICTABILITY BARRIER ; DATA ASSIMILATION ; CLIMATE-CHANGE ; UNCERTAINTY ESTIMATION ; METROPOLIS ALGORITHM ; GLOBAL OPTIMIZATION ; CO2 FERTILIZATION ; PREDICTIONS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
来源机构 | 中国科学院大气物理研究所 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/202667 |
作者单位 | 1.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China; 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
推荐引用方式 GB/T 7714 | Sun, Guodong,Mu, Mu. A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example[J]. 中国科学院大气物理研究所,2017,128(3-4):587-601. |
APA | Sun, Guodong,&Mu, Mu.(2017).A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example.THEORETICAL AND APPLIED CLIMATOLOGY,128(3-4),587-601. |
MLA | Sun, Guodong,et al."A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example".THEORETICAL AND APPLIED CLIMATOLOGY 128.3-4(2017):587-601. |
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