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

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.