Climate Dyn., sub judice. Revised version
A statistical learning method called random forests is applied to the prediction of tran-sitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low-frequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present st...
| 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=10.1.1.605.7832 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_reply_final.pdf |
| Summary: | A statistical learning method called random forests is applied to the prediction of tran-sitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low-frequency 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 com-ponents that were statistically significant in earlier work are robust; they are the Pacific– North America (PNA) regime, its approximate reverse (the reverse PNA, 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 PNA → BNAO, PNA → RNA and BNAO → PNA. The break of a regime and subsequent onset of an-other 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 or-thogonal 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 |
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