Systematic Metastable Atmospheric Regime Identification in an AGCM
- 1 July 2009
- journal article
- Published by American Meteorological Society in Journal of the Atmospheric Sciences
- Vol. 66 (7) , 1997-2012
- https://doi.org/10.1175/2009jas2939.1
Abstract
In this study the authors apply a recently developed clustering method for the systematic identification of metastable atmospheric regimes in high-dimensional datasets generated by atmospheric models. The novelty of this approach is that it decomposes the phase space in, possibly, overlapping clusters and simultaneously estimates the most likely switching sequence among the clusters. The parameters of the clustering and switching are estimated by a finite element approach. The switching among the clusters can be described by a Markov transition matrix. Possible metastable regime behavior is assessed by inspecting the eigenspectrum of the associated transition probability matrix. The recently introduced metastable data-analysis method is applied to high-dimensional datasets produced by a barotropic model and a comprehensive atmospheric general circulation model (GCM). Significant and dynamically relevant metastable regimes are successfully identified in both models. The metastable regimes in the b... Abstract In this study the authors apply a recently developed clustering method for the systematic identification of metastable atmospheric regimes in high-dimensional datasets generated by atmospheric models. The novelty of this approach is that it decomposes the phase space in, possibly, overlapping clusters and simultaneously estimates the most likely switching sequence among the clusters. The parameters of the clustering and switching are estimated by a finite element approach. The switching among the clusters can be described by a Markov transition matrix. Possible metastable regime behavior is assessed by inspecting the eigenspectrum of the associated transition probability matrix. The recently introduced metastable data-analysis method is applied to high-dimensional datasets produced by a barotropic model and a comprehensive atmospheric general circulation model (GCM). Significant and dynamically relevant metastable regimes are successfully identified in both models. The metastable regimes in the b...Keywords
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