Understanding and forecasting polar stratospheric variability with statistical models.

Blume, Christian and Matthes, Katja (2012) Understanding and forecasting polar stratospheric variability with statistical models. Open Access Atmospheric Chemistry and Physics, 12 (13). pp. 5691-5701. DOI 10.5194/acp-12-5691-2012.

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The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA); a cluster method based on finite elements (FEM-VARX); a neural network, namely the multi-layer perceptron (MLP); and support vector regression (SVR). These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.

Document Type: Article
Keywords: north-polar stratospheric vortex
Research affiliation: OceanRep > GEOMAR > FB1 Ocean Circulation and Climate Dynamics > FB1-ME Maritime Meteorology
Refereed: Yes
Open Access Journal?: Yes
DOI etc.: 10.5194/acp-12-5691-2012
ISSN: 1680-7316
Projects: Future Ocean
Date Deposited: 02 Aug 2012 11:58
Last Modified: 28 Jun 2019 09:17
URI: http://oceanrep.geomar.de/id/eprint/14969

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