Abstract
Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis show a clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.

Abstract
Radon has continuously been monitored at the Roman spring of Tiberias, Israel since 2000 in the frame of an earthquake research project. However, there was no apparent earthquake related radon anomaly in 5 years of monitoring. Physical mechanisms behind periodic as well as transient radon variations were investigated. The radon signal contained periodic daily and non-periodic multi-day variations as well as seasonal patterns with maxima during winter. Spectral analysis showed diurnal and semidiurnal periodic constituents while tidal effects were absent. In 2003 the long-term average radon concentration dropped by 35%. Coevally, the diurnal and semi-diurnal radon variability considerably decreased. In contrast, the intensity of large-scale signals, corresponding to multi-day radon variability, increased. At this stage the level of the Kinneret Lake is suspected to be the driving force for the radon drop. Until 2003 the lake level hovered around 214 m below sea-level. In spring 2003 the lake level had risen by 4 m. The distance between the monitoring station and the lake shore is about 50 m. The radon concentration inversely followed the lake level with a time delay of about 3 months. Radon measured at a natural hot spring should depend on the flow rate of the hot water rising on the border faults of the pull-apart basin. Increased flow means less time for radon to decay and thus a positive correlation between the flow rate and the radon concentration is expected. Flow velocity is controlled by (i) the pressure at depth, and (ii) the fracture width. Both are affected by the loading forces of the graben filling to which the water column of the lake contributes. Due to the lack of data about the mass flow rates from the spring, a direct link between the flow rate and the radon concentrations cannot be proven. In fact, the hot water discharge seemed to be very stable in time. So, either minor changes of the flow rate affect the radon concentration or another mechanism is needed to explain the observations, e.g., the pressure-dependent gas solubility or the pressure-dependent mixing of different groundwater components. Nevertheless, this does not explain the appearance of long-periodic, intra-seasonal radon signals (with periods in the order of I month) which were practically absent before 2003. Such long-periodic radon signals were not reported till today.

Abstract
Mostly accepted and widely reported radon (Rn(222)) measurements, a tool for earthquake precursor research, is a part of multi-parametric geophysical observation in the Garhwal Lesser Himalaya for earthquake related studies. Radon is being recorded continuously at an interval of 15 min at 10 m depth in a 68 m deep borehole. Three years high resolution 15 min data at 10 m depth shows a complex trend and has a strong seasonal effect along with some diurnal, semi-diurnal and multi-day recurring trends. A well-defined seasonal pattern is prominent with a high emanation in summer and low values in winter accounting for about a 30% decrease in count values in winter when the atmospheric temperature is very low at this station located 1.90 km above mean sea level. Diurnal, semi-diurnal and multi-day trends in this time-series are mainly observed during April-May and October-November. This is the period of spring and autumn when there is a high contrast in day-night atmospheric temperature. Hence the high fluctuation in Rn concentration is mainly caused by the temperature contrast between the air-column inside the borehole and the atmosphere above the earth's surface.

Abstract
A topic of current interest in the analysis of sea-level states is to investigate the occurrence of future rare events which is essential for the prediction of flooding risks, coastal management and in the design of coastal defences and offshore structures. Nowadays, it is widely believed that the frequency of such rare events is increasing as a result of climatic and other changes, although they are hard to predict and their effects are, yet, poorly understood. Recent developments in multivariate statistical techniques for discrimination, clustering and dimension reduction for time series, have the potential to aid on the construction of new tools and models for forecasting the occurrence and impact of such future rare events. In studies of regional sea-level variability, tidal measurements are often analyzed individually for characterizing sea-level variability at each location. Marginal analysis, however, is in itself insufficient to come with an accurate description of regional sea-level variability. An alternative approach is to consider simultaneously the whole data set of sea-level records from a given region, and characterize regional variability in terms of locations exhibiting similar behavior through clustering techniques. Cluster analysis is a useful approach for characterizing regional variability of locations exhibiting similar behavior in terms of, for example, short-term or long-term predictions of extreme values. In this work, time series clustering is applied to the analysis of long tide gauge records from the Baltic Sea. In order to describe the regional variability of Baltic sea-level, tide gauge measurements are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-years return values. This is relevant for the design of marine systems and coastal structures, which requires a good knowledge of the most severe sea-level conditions that they need to withstand during their lifetime, and also for describing and understanding the variability of extreme sea heights in a climate change context.

Abstract
The analysis of trends in air temperature observations is one of the most common activities in climate change studies. This work examines the changes in daily mean air temperature over Central Europe using quantile regression, which allows the estimation of trends, not only in the mean but in all parts of the data distribution. A bootstrap procedure is applied for assessing uncertainty on the derived slopes and the resulting distributions are summarised via clustering. The results show considerable spatial diversity over the central European region. A distinct behaviour is found for lower (5%) and upper (95%) quantiles, with higher trends around 0.15 degrees C decade(-1) at the 5% quantile and around 0.20 degrees C decade(-1) at the 95% quantile, the largest trends (>0.2 degrees C decade(-1)) occurring in the Alps.

Abstract
Seven soil-gas radon monitoring stations were placed along the active front of a granite quarry in Canas de senhorim, Central Portugal, recording continuously for 81 days. Important differences in the radon concentration were found between stations, with average values comprised between 102 and 2982 Bq m(-3), which can be explained by the local presence of uranium anomalies in the regional late-orogenic Hercynian granite, usually associated with faults. One of the boreholes exhibits large radon anomalies lasting for several days, and two, contrary to the others, show a clear daily periodic behaviour, with minima around 19:00 LT and maxima around 07:00 LT. The different patterns observed in stations placed at such a short distance (< 100 m) has no clear explanation and deserves further investigation. Data analysis shows no evidence of soil-gas radon concentration changes during explosions carried out at the quarry. This is likely to result from the absence of a progressive stress field affecting the rock, as typically occurs before an earthquake.