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In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This m...
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Intensive management in European forests in the 20th century resulted in the substitution of the old-growth natural forests with plantations on vast territories. The spatial structure and tree species composition of the planted forests are simplified making the forests vulnerable to diseases and disturbances. The old-growth natural forests that rem...
Citations
The article presents a recent update of a comprehensive dataset of long‐term series of precipitation data from instrumental observations in the Greater Alpine Region (GAR), that is, the region of Europe including the Alpine mountain range and their nearer surroundings (4°–19° E in longitude and 43°–49° N in latitude). A comparison to different national homogenized datasets is also presented. Results show that in the national homogenized datasets more breaks have been detected due to higher station density. They also demonstrate the necessity of constant exchange with data providers. The resulting trends in all datasets are mainly weak and only a minority of them is statistically significant. In most cases the similarity of statistical index numbers are promising, with, for example, small RMSE between the presented new HISTALP homogenization and the time series of the national homogenized datasets. Nevertheless, for some stations higher differences occur and break signals are not what would be expected due to possible causes in the station history. The differences between the national and the HISTALP new homogenization—due to, for example, different methods used, different points in time when the homogenization took place, different options of data handling (combination of station data, gap filling routines, …) and different reference stations—illustrate the inherent uncertainty unavoidably associated to homogenization and point out the need of careful communication and use of the data. On the other hand, the results highlight the advantage of consistently homogenized datasets, versus the risks associated with mixing results from different homogenizations.
