Statistical downscaling of monthly mean air temperature to the beginning of flowering of Galanthus nivalis L. in Northern Germany

Abstract

 We have examined the relationship between phenological data and concurrent large-scale meterological data. As phenological data we have chosen the beginning of the flowering of Galanthus nivalis L. (flowering date) in Northern Germany, and as large-scale meteorological data we use monthly mean near-surface air temperatures for January, February and March. By means of canonical correlation analysis (CCA), a strong linear correlation between both sets of variables is identified. Twenty years of observed data are used to build the statistical model. To validate the derived relationship, the flowering date is downscaled from air temperature observations of an independent period. The statistical model is found to reproduce the observed flowering dates well, both in terms of variability as well as amplitude. Air temperature data from a general circulation model of climate change are used to estimate the flowering date in the case of increasing atmospheric carbon dioxide concentration. We found that at a time of doubled CO₂ concentration (expected by about 2035) G. nivalis L. in Northern Germany will flower ∼2 weeks and at the time of tripled CO₂ concentration (expected by about 2085) ∼4 weeks earlier than presently.