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| Titel |
Evaluating Holocene Climate Variability using EOF and Time Series Analysis on 80 Records with Global Distribution: Common Forcing or Regional Noise? |
| VerfasserIn |
S. A. Marcott, V. Ersek, N. G. Pisias, P. U. Clark |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250019409
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| Zusammenfassung |
| During the last million years, climate variability on multi-millennial timescales (104 – 105 yr)
has been dominantly driven by variations in the Earth’s orbit around the Sun (Hays et al.
1976). At millennial timescales (103 yr) a 7000 year oscillation appears to exist during the
last glacial period that is punctuated by abrupt climate changes occurring every 2000 – 3000
years (Bond et al., 1993; Clark et al., 2007). At both timescales, the climate changes are
globally distributed and at the millennial scale the changes can occur in less than a decade.
The climate of the last 11,000 years, however, is more stable, and a common mode of
climate variability among various records appears to be lacking. Nonetheless, it
has been proposed that a ~1500 year cycle may persist in some records, albeit
with small amplitudes (Bond et al., 2001; Debret et al., 2007). This notion has
spurred much debate as to whether a dominant mode of variability actually exists in
the Holocene and what may be driving the climate system to oscillate around this
period. To address these issues, we have evaluated a globally distributed dataset of
80 paleoclimate records using empirical orthogonal function (EOF), spectral, and
wavelet analysis. We find two dominant modes of climate variability that appear to be
associated with long term insolation change and an unresolved Northern Hemisphere
forcing. After high-pass filtering of frequencies above 3000 years and performing
multiple Monte Carlo simulations, we further find that that a robust ~1000 year
climate oscillation appears to exist when evaluated with spectral and wavelet analysis. |
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