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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 2
Earth Syst. Dynam., 7, 517-523, 2016
https://doi.org/10.5194/esd-7-517-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Earth Syst. Dynam., 7, 517-523, 2016
https://doi.org/10.5194/esd-7-517-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Jun 2016

Research article | 20 Jun 2016

A wavelet-based approach to detect climate change on the coherent and turbulent component of the atmospheric circulation

Davide Faranda and Dimitri Defrance Davide Faranda and Dimitri Defrance
  • LSCE-IPSL, CEA Saclay l'Orme des Merisiers, CNRS UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France

Abstract. The modifications of atmospheric circulation induced by anthropogenic effects are difficult to capture because wind fields feature a complex spectrum where the signal of large-scale coherent structures (planetary, baroclinic waves and other long-term oscillations) is mixed up with turbulence. Our purpose is to study the effects of climate changes on these two components separately by applying a wavelet analysis to the 700hPa wind fields obtained in climate simulations for different forcing scenarios. We study the coherent component of the signal via a correlation analysis to detect the persistence of large-scale or long-lasting structures, whereas we use the theory of autoregressive moving-average stochastic processes to measure the spectral complexity of the turbulent component. Under strong anthropogenic forcing, we detect a significant climate change signal. The analysis suggests that coherent structures will play a dominant role in future climate, whereas turbulent spectra will approach a classical Kolmogorov behaviour.

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We introduce a general technique to detect a climate change signal in the coherent and turbulent components of the atmospheric circulation. Our analysis suggests that the coherent components (atmospheric waves, long-term oscillations) will experience the greatest changes in future climate, proportionally to the greenhouse gas emission scenario considered.
We introduce a general technique to detect a climate change signal in the coherent and turbulent...
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