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Titel |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
VerfasserIn |
J. I. Deza, C. Masoller, M. Barreiro |
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 ; 21, no. 3 ; Nr. 21, no. 3 (2014-05-26), S.617-631 |
Datensatznummer |
250120917
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Publikation (Nr.) |
copernicus.org/npg-21-617-2014.pdf |
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Zusammenfassung |
The fact that the climate on the earth is a highly complex dynamical system
is well-known. In the last few decades great deal of effort has been focused
on understanding how climate phenomena in one geographical region affects the
climate of other regions. Complex networks are a powerful framework for
identifying climate interdependencies. To further exploit the knowledge of
the links uncovered via the network analysis (for, e.g., improvements in
prediction), a good understanding of the physical mechanisms underlying these
links is required. Here we focus on understanding the role of atmospheric
variability, and construct climate networks representing internal and forced
variability using the output of an ensemble of AGCM runs. A main strength of
our work is that we construct the networks using MIOP (mutual information
computed from ordinal patterns), which allows the separation of
intraseasonal, intra-annual and interannual timescales. This gives further
insight to the analysis of climatological data. The connectivity of these
networks allows us to assess the influence of two main indices, NINO3.4 –
one of the indices used to describe ENSO (El Niño–Southern oscillation)
– and of the North Atlantic Oscillation (NAO), by calculating the networks
from time series where these indices were linearly removed. A main result of
our analysis is that the connectivity of the forced variability network is
heavily affected by "El Niño": removing the NINO3.4 index yields a
general loss of connectivity; even teleconnections between regions far away
from the equatorial Pacific Ocean are lost, suggesting that these regions are
not directly linked, but rather, are indirectly interconnected via
El Niño, particularly at interannual timescales. On the contrary, on the
internal variability network – independent of sea surface temperature (SST)
forcing – the links connecting the Labrador Sea with the rest of the world
are found to be significantly affected by NAO, with a maximum at intra-annual
timescales. While the strongest non-local links found are those forced by the
ocean, the presence of teleconnections due to internal atmospheric
variability is also shown. |
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