NDLI: Predicting weather regime transitions in Northern Hemisphere datasets

Content Provider	Springer Nature Link
Author	Kondrashov, D. Shen, J. Berk, R. D’Andrea, F. Ghil, M.
Copyright Year	2007
Abstract	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.
Starting Page	535
Ending Page	551
Page Count	17
File Format	PDF
ISSN	09307575
Journal	Climate Dynamics
Volume Number	29
Issue Number	5
e-ISSN	14320894
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-07-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Oceanography Meteorology/Climatology Geophysics/Geodesy
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science

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