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Non-Random Correlation Structures and Dimensionality Reduction in Multivariate Climate Data
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SYSNO ASEP 0435055 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Non-Random Correlation Structures and Dimensionality Reduction in Multivariate Climate Data Author(s) Vejmelka, Martin (UIVT-O) SAI, RID, ORCID
Pokorná, Lucie (UIVT-O)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCID
Hartman, David (UIVT-O) RID, SAI, ORCID
Jajcay, Nikola (UIVT-O) RID, ORCID, SAI
Paluš, Milan (UIVT-O) RID, SAI, ORCIDSource Title Climate Dynamics. - : Springer - ISSN 0930-7575
Roč. 44, 9-10 (2015), s. 2663-2682Number of pages 20 s. Language eng - English Country US - United States Keywords climate dynamics ; sea level pressure ; surface air temperature ; principal component analysis ; Varimax Complex networks ; modes of variability Subject RIV BB - Applied Statistics, Operational Research R&D Projects GCP103/11/J068 GA ČR - Czech Science Foundation (CSF) GA13-17187S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000351459800019 EID SCOPUS 84939888879 DOI https://doi.org/10.1007/s00382-014-2244-z Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2015
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