Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996

• Published in: Global change biology 2001-08, Vol.7 (6), p.657-666
• Main Authors: Menzel, Annette, Estrella, Nicole, Fabian, Peter
• Format: Article
• Language: English
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1046/j.1365-2486.2001.00430.x

Various indications for shifts in plant and animal phenology resulting from climate change have been observed in Europe. This analysis of phenological seasons in Germany of more than four decades (1951-96) has several major advantages: (i) a wide and dense geographical coverage of data from the phenological network of the German Weather Service, (ii) the 16 phenophases analysed cover the whole annual cycle and, moreover, give a direct estimate of the length of the growing season for four deciduous tree species. After intensive data quality checks, two different methods - linear trend analyses and comparison of averages of subintervals - were applied in order to determine shifts in phenological seasons in the last 46 years. Results from both methods were similar and reveal a strong seasonal variation. There are clear advances in the key indicators of earliest and early spring (-0. 18 to -0. 23 d y super(-1)) and notable advances in the succeeding spring phenophases such as leaf unfolding of deciduous trees (-0. 16 to -0. 08 d y super(-1)). However, phenological changes are less strong during autumn (delayed by + 0. 03 to + 0. 10 d y super(-1) on average). In general, the growing season has been lengthened by up to -0. 2 d y super(-1) (mean linear trends) and the mean 1974-96 growing season was up to 5 days longer than in the 1951-73 period. The spatial variability of trends was analysed by statistical means and shown in maps, but these did not reveal any substantial regional differences. Although there is a high spatial variability, trends of phenological phases at single locations are mirrored by subsequent phases, but they are not necessarily identical. Results for changes in the biosphere with such a high resolution with respect to time and space can rarely be obtained by other methods such as analyses of satellite data.