caa a22 4500 605474818 CHVBK 20210128100349.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00382-014-2244-z doi (NATIONALLICENCE)springer-10.1007/s00382-014-2244-z Non-random correlation structures and dimensionality reduction in multivariate climate data [Elektronische Daten] [Martin Vejmelka, Lucie Pokorná, Jaroslav Hlinka, David Hartman, Nikola Jajcay, Milan Paluš] 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. Springer-Verlag Berlin Heidelberg, 2014 Climate dynamics nationallicence Sea level pressure nationallicence Surface air temperature nationallicence Principal component analysis nationallicence Varimax nationallicence Complex networks nationallicence Modes of variability nationallicence Vejmelka Martin Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Pokorná Lucie Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Hlinka Jaroslav Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Hartman David Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Jajcay Nikola Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Paluš Milan Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut Climate Dynamics Springer Berlin Heidelberg 44/9-10(2015-05-01), 2663-2682 0930-7575 44:9-10<2663 2015 44 382 https://doi.org/10.1007/s00382-014-2244-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00382-014-2244-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vejmelka Martin Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 700 1- Pokorná Lucie Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 700 1- Hlinka Jaroslav Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 700 1- Hartman David Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 700 1- Jajcay Nikola Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 700 1- Paluš Milan Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 182 07, Prague 8, Czech Republic aut NATIONALLICENCE 773 0- Climate Dynamics Springer Berlin Heidelberg 44/9-10(2015-05-01), 2663-2682 0930-7575 44:9-10<2663 2015 44 382