DOI: 10.1007/s00382-014-2244-z
Scopus记录号: 2-s2.0-84939888879
论文题名: Non-random correlation structures and dimensionality reduction in multivariate climate data
作者: Vejmelka M. ; Pokorná L. ; Hlinka J. ; Hartman D. ; Jajcay N. ; Paluš M.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2015
卷: 44, 期: 2017-09-10 起始页码: 2663
结束页码: 2682
语种: 英语
英文关键词: Climate dynamics
; Complex networks
; Modes of variability
; Principal component analysis
; Sea level pressure
; Surface air temperature
; Varimax
英文摘要: It is well established that the global climate is a complex phenomenon with dynamics driven by the interaction of a multitude of identifiable but intertwined subsystems. The identification, at some level, of these subsystems is an important step towards understanding climate dynamics. We present a method to determine the number of principal components representing non-random correlation structures in climate data, or components that cannot be generated by a surrogate model of independent stochastic processes replicating the auto-correlation structure of each time series. The purpose of the method is to automatically reduce the dimensionality of large climate datasets into spatially localised components suitable for further interpretation or, for example, for use as nodes in a complex network analysis of large-scale climate dynamics. We apply the method to two 2.5° resolution NCEP/NCAR reanalysis global datasets of monthly means: the sea level pressure (SLP) and the surface air temperature (SAT), and extract 60 components explaining 87 % variance and 68 components explaining 72 % variance, respectively. The obtained components are in agreement with previous results in that they recover many well-known climate modes previously identified using other approaches including regionally constrained principal component analysis. Selected SLP components are discussed in more detail with respect to their correlation with important climate indices and their relationship to other SLP and SAT components. Finally, we consider a subset of the obtained components that have not yet been explicitly identified by other authors but seem plausible in the context of regional climate observations discussed in literature. © 2014, Springer-Verlag Berlin Heidelberg. 资助项目: GACR, Czech Science Foundation
; GACR, Czech Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54217
Appears in Collections: 过去全球变化的重建
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作者单位: Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, Prague 8, Czech Republic; Department of Climatology, Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Boční II 1401, Prague 4, Czech Republic
Recommended Citation:
Vejmelka M.,Pokorná L.,Hlinka J.,et al. Non-random correlation structures and dimensionality reduction in multivariate climate data[J]. Climate Dynamics,2015-01-01,44(2017-09-10)