We present a dataset of daily resolution climatic time series that has been compiled for the European Climate Assessment (ECA). As of December 2001, this ECA dataset comprises 199 series of minimum, maximum and/or daily mean temperature and 195 series of daily precipitation amount observed at meteorological stations in Europe and the Middle East. Almost all series cover the standard normal period 1961-90, and about 50% extends back to at least 1925. Part of the dataset (90%) is made available for climate research on CDROM and through the Internet (at http://www.knmi.nl/samenw/eca).A comparison of the ECA dataset with existing gridded datasets, having monthly resolution, shows that correlation coefficients between ECA stations and nearest land grid boxes between 1946 and 1999 are higher than 0.8 for 93% of the temperature series and for 51% of the precipitation series. The overall trends in the ECA dataset are of comparable magnitude to those in the gridded datasets.The potential of the ECA dataset for climate studies is demonstrated in two examples. In the first example, it is shown that the winter (October-March) warming in Europe in the 1976-99 period is accompanied by a positive trend in the number of warm-spell days at most stations, but not by a negative trend in the number of cold-spell days. Instead, the number of cold-spell days increases over Europe. In the second example, it is shown for winter precipitation between 1946 and 1999 that positive trends in the mean amount per wet day prevail in areas that are getting drier and wetter.Because of its daily resolution, the ECA dataset enables a variety of empirical climate studies, including detailed analyses of changes in the occurrence of extremes in relation to changes in mean temperature and total precipitation.

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The present study aims to investigate the possible influence of solar/geomagnetic forcing on climate variables, such as the drought index, Danube discharge and large-scale atmospheric indices. Our analysis was performed separately for each season for two time periods, 1901–2000 and 1948–2000. The relationship between terrestrial variables and external indices was established based on the application of (1) information theory elements, namely, synergy, redundancy, total correlation, transfer entropy and (2) wavelet coherence analysis. Bandpass filtering has also been applied. The most significant signature of the solar/geomagnetic forcing in the climate variables was obtained for the data smoothed by the bandpass filter. According to our results, significant solar/geomagnetic forcing appears in the terrestrial variables with a delay of 2–3 years.

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In this study extreme droughts and extremely wet periods in the Danube upper and middle basin (DUMB) have been highlighted with specific indices. The most widely used indices have been considered to estimate both the dry and wet phenomena severity and the frequency or spatio‐temporal extension. The climatic condition of 15 meteorological stations situated in the Danube basin has been evaluated using four indices: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z‐index (ZIND). The four indices have been analysed separately for each of the four seasons between 1901 and 2000. First the internal structure of the time series of the four indices has been analysed separately. Then the overall temporal characteristic has been analysed by means of the principal component of the Multivariate Empirical Orthogonal Functions decomposition of the four indices (PC1‐MEOF). For the discharge in the Danube lower basin, station Orsova has been chosen, representing an integrator of the discharges from the DUMB. A very close connection has been found between Palmer indices and Danube discharge in all seasons (with correlation coefficients greater then 0.80) excepting the spring season. A classification in five classes of both the four indices separately and the PC1‐MEOF has been achieved in order to highlight extreme events. The impact of phenomena quantified by Palmer indices in DUMB upon discharges in Danube lower basin is evident. It was demonstrated in this study that the Greenland‐Balkan Oscillation (GBO) influences the south‐east European hydro‐climatic regime more than the North Atlantic Oscillation (NAO) Index. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract. Of the internal factors, we tested the predictors from the fields of 12 precipitation, temperature, pressure and geopotential at 500hPa. From the external factors, we 13 considered the indices of solar/geomagnetic activity. Our analysis was achieved separately for 14 each season, for two time periods 1901-2000 and 1948-2000. 15 We applied developments in empirical orthogonal functions (EOFs), cross 16 correlations, power spectra, filters, composite maps. In analysis of the correlative results, we 17 took into account, the serial correlation of time series. 18For the atmospheric variables simultaneously, the most significant results (confidence 19 levels of 95%) are related to the predictors, considering the difference between standardized 20 temperatures and precipitation (TPP), except for winter season, when the best predictors are 21 the first principal component (PC1) of the precipitation field and the Greenland-Balkan-22Oscillation index (GBOI). The GBOI is better predictor for precipitation, in comparison with 23North Atlantic Oscillation index (NAOI) for the middle and lower Danube basin. 24The significant results, with the confidence level more than 95%, were obtained for 25 the PC1-precipitation and TPP during winter/spring, which can be considered good predictors 26 for spring/summer discharge in the Danube lower basin. 27

This study addresses the causal links between external factors and the main hydro-climatic variables by using a chain of methods to unravel the complexity of the direct sun–climate link. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on terrestrial variables. This is why the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Additionally, by other methods (e.g., neural networks) we first tested the existent non-linear links of solar–terrestrial influences on the hydro-climate system. The results related to the solar impact on terrestrial phenomena are promising, which is discriminant in the space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact, which makes it easier to consider solar activity in climate models by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and have a coherence with the Solar Flux only for some lags of the Solar Flux (in advance).