The sensitivity of tree growth to air mass variability and the Pacific Decadal Oscillation in coastal Alabama

• Published in: Climate Dynamics
• Main Authors: Kondrashov, D., Shen, J., Berk, R., D'Andrea, F., Ghil, M.
• Other Authors: Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2007
• Subjects: Dendroclimatology; Spatial synoptic classification; Air mass; Alabama; Pacific Decadal Oscillation; [SDU]Sciences of the Universe [physics]; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://hal.science/hal-04110175; https://doi.org/10.1007/s00382-007-0293-2

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.