Objective extraction and analysis of statistical features of Dansgaard–Oeschger events

The strongest mode of centennial to millennial climate variability in the paleoclimatic record is represented by Dansgaard–Oeschger (DO) cycles. Despite decades of research, their dynamics and physical mechanisms remain poorly understood. Valuable insights can be obtained by studying high-resolution...

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Bibliographic Details
Published in:Climate of the past 2019-09, Vol.15 (5), p.1771-1792
Main Authors: Lohmann, Johannes, Ditlevsen, Peter D
Format: Article
Language:English
Subjects:
Carbon dioxide
Climate
Climate change
Climate cycles
Climate effects
Climate models
Climate variability
Cooling
Cooling rate
Cycles
Empirical analysis
Feature extraction
Ice cores
Noise levels
Proxy
Statistical analysis
Statistical methods
Statistical models
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Summary:The strongest mode of centennial to millennial climate variability in the paleoclimatic record is represented by Dansgaard–Oeschger (DO) cycles. Despite decades of research, their dynamics and physical mechanisms remain poorly understood. Valuable insights can be obtained by studying high-resolution Greenland ice core proxies, such as the NGRIP δ18O record. However, conventional statistical analysis is complicated by the high noise level, the cause of which is partly due to glaciological effects unrelated to climate and which is furthermore changing over time. We remove the high-frequency noise and extract the most robust features of the DO cycles, such as rapid warming and interstadial cooling rates, by fitting a consistent piecewise linear model to Greenland ice core records. With statistical hypothesis tests we aim to obtain an empirical understanding of what controls the amplitudes and durations of the DO cycles. To this end, we investigate distributions and correlations between different features, as well as modulations in time by external climate factors, such as CO2 and insolation. Our analysis suggests different mechanisms underlying warming and cooling transitions due to contrasting distributions and external influences of the stadial and interstadial durations, as well as the fact that the interstadial durations can be predicted to some degree by linear cooling rates already shortly after interstadial onset.
ISSN:1814-9332
1814-9324
1814-9332
DOI:10.5194/cp-15-1771-2019