DOI: 10.1007/s00382-013-1780-2
Scopus记录号: 2-s2.0-84897573038
论文题名: Non-linear dependence and teleconnections in climate data: Sources, relevance, nonstationarity
作者: Hlinka J. ; Hartman D. ; Vejmelka M. ; Novotná D. ; Paluš M.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2014
卷: 42, 期: 2017-07-08 起始页码: 1873
结束页码: 1886
语种: 英语
英文关键词: Climate networks
; Mutual information
; Nonlinearity
; Nonstationarity
; Seasonality in variance
; Teleconnections
英文摘要: Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation. The use of linear Pearson's correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patterns, including signal nonstationarities. In detail we study the consequences of the choice of a general nonlinear dependence measure, namely mutual information, focusing on its relevance and potential alterations in the discovered dependence structure. We document the method by applying it to monthly mean temperature data from the NCEP/NCAR reanalysis dataset as well as the ERA dataset. We have been able to identify main sources of observed non-linearity in inter-node couplings. Detailed analysis suggested an important role of several sources of nonstationarity within the climate data. The quantitative role of genuine nonlinear coupling at monthly scale has proven to be almost negligible, providing quantitative support for the use of linear methods for monthly temperature data. © 2013 Springer-Verlag Berlin Heidelberg. 资助项目: GACR, Czech Science Foundation
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54382
Appears in Collections: 过去全球变化的重建
There are no files associated with this item.
作者单位: Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic; Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Prague 8, Czech Republic
Recommended Citation:
Hlinka J.,Hartman D.,Vejmelka M.,et al. Non-linear dependence and teleconnections in climate data: Sources, relevance, nonstationarity[J]. Climate Dynamics,2014-01-01,42(2017-07-08)