A Mechanistic Model of Multidecadal Climate Variability
This thesis addresses the problem of multidecadal climate variability by constructing and analyzing the output of a mechanistic model for the Northern Hemisphere’s multidecadal climate variability. The theoretical backbone of our modeling procedure is the so-called “stadium-wave” concept, in which i...
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UWM Digital Commons
2015
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| Online Access: | https://dc.uwm.edu/etd/830 https://dc.uwm.edu/context/etd/article/1835/viewcontent/Plamondon_uwm_0263m_11079.pdf |
| Summary: | This thesis addresses the problem of multidecadal climate variability by constructing and analyzing the output of a mechanistic model for the Northern Hemisphere’s multidecadal climate variability. The theoretical backbone of our modeling procedure is the so-called “stadium-wave” concept, in which interactions between regional climate subsystems are thought to result in a phase-space propagation of multidecadal climate anomalies across the hemispheric and global scales. The current generation of comprehensive climate models do not appear to support the “stadium wave,” which may indicate that either the models lack the requisite physics, or that the “stadium wave” itself is an artifact of statistical analyses used to identify it. This research aims to construct a process model that captures realistic multidecadal teleconnections between well known climatic indices, namely the North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and El Nino/Southern Oscillation (ENSO) indices. The model is shown to predict some major components of the observed temporal structure within this climate-index network, in particular the maximum positive correlation between AMO and PDO at a +15 year lag, maximum anti-correlation between AMO and NAO at a lag of -10 years, and a peak positive correlation between the latter two indices at a +20 year lag, as well as the maximum anti-correlation between NAO and PDO at a lag of around +30 years. Future work will include exploring the model’s parametric dependencies and physical feedback mechanisms leading to this behavior. |
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