Climate Dyn., sub judice. Revised version
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric lowfrequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present stud...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7287 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf |
| Summary: | A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric lowfrequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present study. A mixture model finds that the three Gaussian components that were statistically significant in earlier work are robust; they are the Pacific– North American (P NA) regime, its approximate reverse (the reverse P NA, or RNA), and the blocked phase of the North Atlantic Oscillation (BNAO). The most significant and robust transitions in the Markov chain generated by these regimes are P NA → BNAO, P NA → RNA and BNAO → P NA. The break of a regime and subsequent onset of another one is forecast for these three transitions. Taking the relative costs of false positives and false negatives into account, the random-forests method shows useful forecasting skill. The calculations are carried out in the phase space spanned by a few leading empirical orthogonal functions of dataset variability. Plots of estimated response functions to a given predictor confirm the crucial influence of the exit angle on a preferred transition path. This result points to the dynamic origin of the transitions. |
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