Time series modeling and central European temperature impact assessment of phenological records over the last 250 years

Abstract

[1] Long-term spring and autumn phenological observations from Switzerland and Burgundy (eastern France) as well as long-term Swiss monthly and seasonal temperature measurements offer a unique possibility to evaluate plant phenological variability and temperature impacts over the last 250 years. We compare Pearson correlation coefficients and linear moving window trends of two different lengths with a Bayesian correlation and model comparison approach. The latter is applied to calculate model probabilities, change-point probabilities, functional descriptions, and rates of change of three selected models with increasing complexity and temperature weights of single months. Both approaches, the moving window trends as well as the Bayesian analysis, detect major changes in long-term phenological and temperature time series at the end of the 20th century. Especially for summer temperatures since the 1980s, Bayesian model-averaged trends reveal a warming rate that increased from an almost zero rate of change to an unprecedented rate of change of 0.08°C/a in 2006. After 1900, temperature series of all seasons show positive model-averaged trends. In response to this temperature increase, the onset of phenology advanced significantly. We assess the linear dependence of phenological variability by a linear Pearson correlation approach. In addition we apply the Bayesian correlation to account for nonlinearities within the time series. Grape harvest dates show the highest Bayesian correlations with June temperatures of the current year. Spring phenological phases are influenced by May temperatures of the current year and summer temperatures of the preceding growing season. For future work we suggest testing increasingly complex time series models such as multiple change-point models.Citation: Schleip, C., T. Rutishauser, J. Luterbacher, and A. Menzel (2008), Time series modeling and central European temperature impact assessment of phenological records over the last 250 years,

Temperature sensitivity of Swiss and British plant phenology from 1753 to 1958

“…As a consequence, the Bayesian model comparison and especially the change-point model should account for the possibility of identifying major nonlinear changes. Schleip et al (2008b) found the highest probability for a change-point in 3 long-term phenological records from France and Switzerland at the end of the 20th century. Analysing the Swiss PPSI for the period 1753 to 2006, a dominating change-point was found in 1984 with a 2% probability (Fig.…”

“…triangular functions weighting each year by its respective change-point probability (see Schleip et al 2008b for details). Using the Bayesian approach we studied: (1) the probabilities of Models I to III, (2) years with maximum change-point probability in the change-point model and (3) the rates of change of the constant and linear model (Dose & Menzel 2004, Schleip et al 2008b).…”

“…In addition to the detection of shifts (Pettitt's test) we used a Bayesian model comparison analysis to detect possible change-points, where 2 distinct trends meet, as opposed to a constant or linear trend evolution of the time series (analogous to Schleip et al 2008b, based on Dose & Menzel 2004). In this approach, Model I (constant model) represents the hypothesis of no change at all with an associated zero rate of change.…”

“…In addition to Pearson correlation analysis between the phenological series and monthly mean temperatures, we used a Bayesian correlation approach for the analysis of the coherence of temperature and phenological time series , Schleip et al 2008b and references therein). We related the temporal distribution of the change-point of phenological and temperature series to each other and tested the hypothesis whether 2 time series evolve independently or exhibit coherence.…”

Seasonal climate impacts on the grape harvest date in Burgundy (France)

“…Besides the spring and summer temperature dependence, another interpretation of the GHD was given by Souriau and Yiou (2001), who found a relationship to the North Atlantic Oscillation (NAO). A different approach in analysing the GHD was performed by Schleip et al (2008), who used the Bayesian analysis to examine temperature impacts on the GHD. Thereby, the June temperature was found to be most important for the GHD.…”