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We applied coarse spectral analysis to more than 2 decades of daily near-surface water temperature (WT) measurements from Muggelsee, a shallow polymictic lake in Germany, to systematically characterize patterns in WT variability from daily to yearly temporal scales. Comparison of WT with local air temperature indicates that the WT variability patterns are likely attributable to both meteorological forcing and internal lake dynamics. We identified seasonal patterns of WT variability and showed that WT variability increases with increasing Schmidt stability, decreasing Lake number and decreasing ice cover duration, and is higher near the shore than in open water. We introduced the slope of WT spectra as an indicator for the degree of lake mixing to help explain the identified temporal and spatial scales of WT variability. The explanatory power of this indicator in other lakes with different mixing regimes remains to be established.
Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Nino-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an 'uncertainty-aware' framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.