The sensitivity of tree growth to air mass variability and the Pacific Decadal Oscillation in coastal Alabama
International audience A statistical learning method called random forests is applied to the prediction of transitions 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...
Published in: | Climate Dynamics |
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Main Authors: | , , , , |
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2007
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Subjects: | |
Online Access: | https://hal.science/hal-04110175 https://doi.org/10.1007/s00382-007-0293-2 |
Summary: | International audience A statistical learning method called random forests is applied to the prediction of transitions 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 components that were statistically significant in earlier work are robust; they are the Pacific-North American (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 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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