title: Does flower phenology mirror the slowdown of global warming?
abstract: Although recent global warming trends in air temperature are not as pronounced as those observed only one decade ago, global mean temperature is still at a very high level. Does plant phenology - which is believed to be a suitable indicator of climate change - respond in a similar way, that is, does it still mirror recent temperature variations? We explored in detail long-term flowering onset dates of snowdrop, cherry, and lime tree and relevant spring temperatures at three sites in Germany (1901-2012) using the Bayesian multiple change-point approach. We investigated whether mean spring temperature changes were amplified or slowed down in the past decade and how plant phenology responded to the most recent temperature changes. Incorporating records with different end points (i.e., 2002 and 2012), we compared differences in trends and inferred possible differences caused by extrapolating phenological and meteorological data. The new multiple-change point approach is characterized by an enhanced structure and greater flexibility compared to the one change point model. However, the highest model probabilities for phenological (meteorological) records were still obtained for the one change point (linear) model. Marked warming trends in the recent decade were only revealed for mean temperatures of March to May, here better described with one or two change point models. In the majority of cases analyzed, changes in temperatures were well mirrored by phenological changes. However, temperatures in March to May were linked to less strongly advancing onset dates for lime tree flowering during the period 1901-2012, pointing to the likely influence of photoperiodic constraints or unfulfilled chilling requirements. Due to the slowdown of temperature increase, analyses conducted on records ending in 2002 demonstrated distinct differences when compared with records ending in 2012. Extrapolation of trends could therefore (along with the choice of the statistical method) lead to distinctly different results and most recent data should be integrated in order not to over- or underestimate future phenological changes.
authors.affiliation: Physical Geography/Landscape Ecology and Sustainable Ecosystem Development, Catholic University Eichstätt-Ingolstadt, Ostenstraβe 18 85072, Eichstätt, Germany ; Ecoclimatology, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2 85354, Freising, Germany ; Institute for Advanced Study, Technische Universität München Lichtenbergstraße 2 a, 85748, Garching, Germany.
authors.forename: Susanne
authors.initials: S
authors.lastname: Jochner
authors.affiliation: Ecoclimatology, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2 85354, Freising, Germany ; Institute for Advanced Study, Technische Universität München Lichtenbergstraße 2 a, 85748, Garching, Germany.
authors.forename: Annette
authors.initials: A
authors.lastname: Menzel
keywords: Bayesian statistics; climate change; flowering; multiple change-point model; phenology; trend