This article is published in Environmetrics as a special issue on TIES 2008: Quantitative Methods for Environmental Sustainability, edited by Sylvia R. Esterby, University of British Columbia Okanagan, Canada.
Special Issue Paper
Different ways to compute temperature return levels in the climate change context†
Article first published online: 13 JUL 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Special Issue: TIES 2008: Quantitative methods for environmental sustainability
Volume 21, Issue 7-8, pages 698–718, November - December 2010
How to Cite
Parey, S., Hoang, T. T. H. and Dacunha-Castelle, D. (2010), Different ways to compute temperature return levels in the climate change context. Environmetrics, 21: 698–718. doi: 10.1002/env.1060
- Issue published online: 23 DEC 2010
- Article first published online: 13 JUL 2010
- Manuscript Accepted: 7 MAY 2010
- Manuscript Received: 30 OCT 2008
- climate change;
- extreme value theory;
- return level
The climate change context has raised new problems in the computation of temperature return levels (RLs) in using the statistical extreme value theory. This arises since it is not yet possible to accept the hypothesis that the series of maxima or of high level values are stationary, without at least verifying the assumption. Thus, in this paper, different approaches are tested and compared to derive high order RLs in the nonstationary context. These RLs are computed by extrapolating identified trends, and a bootstrap method is used to estimate confidence intervals. The identification of trends can be made either in the parameters of the extreme value distributions or in the mean and variance of the whole series. Then, a methodology is proposed to test if the trends in extremes can be explained by the trends in mean and variance of the whole dataset. If this is the case, the future extremes can be derived from the stationary extremes of the centered and normalized variable and the changes in mean and variance of the whole dataset. The RL can then be estimated as nonstationary or as stationary for fixed future periods. The work is done for both extreme value methods: block maxima and peak over threshold, and will be illustrated with the example of a long observation time series for daily maximum temperature in France. Copyright © 2010 John Wiley & Sons, Ltd.