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Spectral analysis of climate data

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Abstract

The complexity of climate variability on all time scales requires the use of several refined tools to unravel its primary dynamics from observations. Indeed, ideas from the theory of dynamical systems have provided new ways of interpreting the information contained in climatic time series.

We review the properties of several modern time series analysis methods. Those methods belong to four main classes: Fourier techniques (Blackman-Tukey and Multi-Taper), Maximum Entropy technique, Singular-spectrum techniques and wavelet analysis. Their respective advantages and limitations are illustrated by numerical experiments on synthetic time series. As climate data can be irregularly spaced in time, we also compare three interpolating methods on those time series. Those tests are aimed at showing the pitfalls of the blind use of mathematical or statistical techniques on climate data.

We apply those methods to ‘real” climatic data from temperature variations over the last century, and the Vostok ice core deuterium record over the last glacial cycle. Then we show how interpretations on the dynamics of climate can be derived on those time scales.

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Authors and Affiliations

  1. Laboratoire de Modélisation du Climat et de l'Environnement DSM, CE Saclay, l Orme des Merisiers, F-91191, Gif-sur-Yvette, France

    P. Yiou

  2. Unité de Biométrie et Analyse des données, Université catholique de Louvain, Place Croix du Sud 2 bte 16, B-1348, Louvain-la-Neuve, Belgium

    E. Baert

  3. Institut d'Astronomie e de Géophysique G. Lemaître, Université catholique de Louvain, 2 Chemin du Cyclotron, B-1348, Louvain-la-Neuve, Belgium

    M. F. Loutre

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  3. M. F. Loutre
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Yiou, P., Baert, E. & Loutre, M.F. Spectral analysis of climate data. Surv Geophys 17, 619–663 (1996). https://doi.org/10.1007/BF01931784

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