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| Titel |
A unified nonlinear stochastic time series analysis for climate science |
| VerfasserIn |
Woosok Moon, John Wettlaufer |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250154090
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| Publikation (Nr.) |
 EGU/EGU2017-19142.pdf |
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| Zusammenfassung |
| Earth’s orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate
variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher
frequency processes. We can interpret this as a competition between the orbitally
enforced monthly stability and the fluctuations/noise induced by weather. Here
we introduce a new time-series method that determines these contributions from
monthly-averaged data. We find that the spatio-temporal distribution of the monthly
stability and the magnitude of the noise reveal key fingerprints of several important
climate phenomena, including the evolution of the Arctic sea ice cover, the El Niño
Southern Oscillation (ENSO), the Atlantic Niño and the Indian Dipole Mode. In
analogy with the classical destabilising influence of the ice-albedo feedback on
summertime sea ice, we find that during some period of the season a destabilising process
operates in all of these climate phenomena. The interaction between the destabilisation
and the accumulation of noise, which we term the memory effect, underlies phase
locking to the seasonal cycle and the statistical nature of seasonal predictability. |
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