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| Titel |
Nonlinear multivariable analysis of SOI and local precipitation and temperature |
| VerfasserIn |
Y.-H. Jin, A. Kawamura, K. Jinno, R. Berndtsson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 12, no. 1 ; Nr. 12, no. 1 (2005-01-21), S.67-74 |
| Datensatznummer |
250010380
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| Publikation (Nr.) |
 copernicus.org/npg-12-67-2005.pdf |
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| Zusammenfassung |
| Global climate variability affects important local hydro-meteorological
variables like precipitation and temperature. The Southern Oscillation (SO)
is an easily quantifiable major driving force that gives impact on regional
and local climate. The relationships between SO and local climate variation
are, however, characterized by strongly nonlinear processes. Due to this,
teleconnections between global-scale hydro-meteorological variables and
local climate are not well understood. In this paper, we suggest to study
these processes in terms of nonlinear dynamics. Consequently, the nonlinear
dynamic relationship between the Southern Oscillation Index (SOI),
precipitation, and temperature in Fukuoka, Japan, is investigated using a
nonlinear multivariable approach. This approach is based on the joint
variation of these variables in the phase space. The joint phase-space
variation of SOI, precipitation, and temperature is studied with the primary
objective to obtain a better understanding of the dynamical evolution of
local hydro-meteorological variables affected by global atmospheric-oceanic
phenomena. The results from the analyses display rather clear low-order
phase space trajectories when treating the time series individually.
However, when plotting phase space trajectories for several time series
jointly, complicated higher-order nonlinear relationships emerge between the
variables. Consequently, simple data-driven prediction techniques utilizing
phase-space characteristics of individual time series may prove successful.
On the other hand, since either the time series are too short and/or the
phase-space properties are too complex when analysing several variables
jointly, it may be difficult to use multivariable statistical prediction
techniques for the present investigated variables. In any case, it is
essential to further pursue studies regarding links between the SOI and
observed local climatic and other geophysical variables even if these links
are not fully understood in physical terms. |
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