Articles | Volume 15, issue 12
https://doi.org/10.5194/acp-15-6879-2015
https://doi.org/10.5194/acp-15-6879-2015
Research article
 | 
24 Jun 2015
Research article |  | 24 Jun 2015

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

A. Kuchar, P. Sacha, J. Miksovsky, and P. Pisoft

Abstract. This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979–2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer–Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

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Short summary
We have studied the solar cycle manifestation in the latest generation of the reanalysed data sets by the means of both linear and nonlinear attribution analyses. The study is supplemented by the discussion of the dynamical implications.
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