Bayesian analysis of climate change impacts in phenology

Authors: Dose, Volker¹; Menzel, Annette²

Source: Global Change Biology, Volume 10, Number 2, February 2004 , pp. 259-272(14)

Buy & download fulltext article:

Price: $48.00 plus tax (Refund Policy)

Abstract:

The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.

Keywords: Bayesian theory; climate change; phenology; trend

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1529-8817.2003.00731.x

Affiliations: 1: Centre for Interdisciplinary Plasma Science, Max-Planck-Institut für Plasmaphysik, EURATOM Association, Boltzmannstrasse 2, D-85748 Garching bei München, Germany, 2: Department of Ecology, Center of Life Science, TU Munich, Am Hochanger 13, D-85354 Freising, Germany

Publication date: 2004-02-01