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Abstract

In this study two complementary approaches have been combined to estimate the reliability of the data-driven seasonal predictability of the meteorological summer mean temperature (TJJA) over Europe. The developed model is based on linear regressions and uses early season predictors to estimate the target value TJJA. We found for the Potsdam (Germany) climate station that the monthly standard deviations (σ) from January to April and the temperature mean (m) in April are good predictors to describe TJJA after 1990. However, before 1990 the model failed. The core region where this model works is the north-eastern part of Central Europe. We also analyzed long-term trends of monthly Hess/Brezowsky weather types as possible causes of the dynamical changes. In spring, a significant increase of the occurrences for two opposite weather patterns was found: Zonal Ridge across Central Europe (BM) and Trough over Central Europe (TRM). Both currently make up about 30% of the total alternating weather systems over Europe. Other weather types are predominantly decreasing or their trends are not significant. Thus, the predictability may be attributed to these two weather types where the difference between the two Z500 composite patterns is large. This also applies to the north-eastern part of Central Europe. Finally, the detected enhanced seasonal predictability over Europe is alarming, because severe side effects may occur. One of these are more frequent climate extremes in summer half-year.