Abstract
The basic statistical features of radon time series from continuous radon monitoring at the Elat granite, Israel are analysed. A similar analysis is carried out for ancillary and possibly related geophysical parameters for the Elat area. The results show that air temperature, precipitable water and longwave radiation time series exhibit constant variance over the analyzed period, while radon time series, atmospheric pressure, short-wave radiation and total electron content exhibit heteroscedasticity. Furthermore, for radon and shortwave radiation the variability is associated with the overall mean level, while for atmospheric pressure such an association is not present. The analyzed radon time series not only are non-stationary but also nonlinear, reflecting the complex dynamics of radon emanation and transport in natural subsurface systems.

Abstract
The detection of a trend in a time series and the evaluation of its magnitude and statistical significance is an important task in geophysical research. This importance is amplified in climate change contexts, since trends are often used to characterize long-term climate variability and to quantify the magnitude and the statistical significance of changes in climate time series, both at global and local scales. Recent studies have demonstrated that the stochastic behavior of a time series can change the statistical significance of a trend, especially if the time series exhibits long-range dependence. The present study examines the trends in time series of daily average temperature recorded in 26 stations in the Tuscany region (Italy). In this study a new framework for trend detection is proposed. First two parametric statistical tests, the Phillips-Perron test and the Kwiatkowski-Phillips-Schmidt-Shin test, are applied in order to test for trend stationary and difference stationary behavior in the temperature time series. Then long-range dependence is assessed using different approaches, including wavelet analysis, heuristic methods and by fitting fractionally integrated autoregressive moving average models. The trend detection results are further compared with the results obtained using nonparametric trend detection methods: Mann-Kendall, Cox-Stuart and Spearman's rho tests. This study confirms an increase in uncertainty when pronounced stochastic behaviors are present in the data. Nevertheless, for approximately one third of the analyzed records, the stochastic behavior itself cannot explain the long-term features of the time series, and a deterministic positive trend is the most likely explanation.

Abstract
High time resolution monitoring of radon (= (222) Rn) in three boreholes, 4, 10 and 53 m deep, along a 0.6 km transect is carried out in massive granite in southern Israel. Three components of variation occur in the measured signal (MS) -seasonal radon (SR -periodic), multiday (MD), and daily radon (DR -periodic). Temporal variation of the components suggests an association between the overall level of the long-term variation and the amplitude of the daily variation. The daily mean level of radon and the daily standard deviation vary periodically throughout the year. Time offsets occur among time series of the MS and were investigated also for the MD and DR components, using consecutive 20-day intervals spanning + 900 days. The resulting time series show that systematic time offsets occur, whereby the radon signal always occurs first at the easternmost site. The MD shows a gradually varying lag of 0 -12 h, and the DR a stable 1 -3 h lag. Spectral analysis shows that diurnal (24-h) and semidiurnal (12-h) periodic components characterize the DR. The amplitudes of these components exhibit regular temporal variation having a seasonal pattern. The ratios of co-occurring amplitudes of these components define a linear pattern indicating a fundamental statistical property in the frequency domain of the radon time series. The results indicate that unrecognized dynamic processes are driving the radon signal in the subsurface regime of the pluton, suggesting new prospects for radon behavior in the frame of interacting geodynamic (tectonic?) and Earth-Sun system related processes.

Abstract
In this paper, long (>40 years) hourly tide gauge records from the North Atlantic are analyzed. A new time series clustering approach which combines Bayesian methodology, extreme value theory, and classification techniques is adopted for the analysis of the regional variability of sea-level extremes. The tide gauge 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 higher latitude stations for which the return values are largest and the remaining locations. This distinction reflects in the U.S. east coast the transition between the Scottian shelf and Gulf of Maine area and the mid-Atlantic Bight area. For the stations at lower latitudes the results show a grouping based on return levels that is not a function of geographical proximity but reflects local effects in extreme sea levels associated with the specific location of each tide gauge.

Abstract
Radon is a naturally occurring radioactive noble gas generated within mineral grains of uranium bearing rocks by alpha decay from radium. The Amram tunnel (A. Bloch Geophysical Observatory) is a particularly suitable location for the investigation of radon variability. Located in the arid environment of the Arava desert, near Elat, the 170 m tunnel that constitutes the observatory enables radon monitoring in a desert environment and under fairly stable environmental conditions. The analysis of the temporal variability of continuous measurements of radon and environmental parameters at the Amram tunnel over a period of several years shows a complex temporal pattern characterized by non-stationary and multiscale features. Radon concentrations exhibit multiyear variability in the form of a increasing trend of similar to 1000 Bq m(-3) yr(-1) in the mean and much larger trends up to similar to 2500 Bq m(-3) yr(-1) in the maximum radon levels. Radon concentrations also display strong seasonal patterns, with maxima in summer and minima in winter, ranging from 2.5 kBq m(-3) in winter to 35 kBq m(-3) in summer. Intraseasonal variability is characterized by very large radon anomalies, with sharp increases of more than 20 kBq m(-3) relative to the base level, that occur in spring and summer and last for several days. Daily periodic variability with maxima around midnight appears also in spring and summer, being absent in the cold months. Radon variability at seasonal, intraseasonal and daily timescales is associated with the air temperature outside the tunnel, specifically the temperature gradient between the external environment and the more stable environment inside the tunnel where the measurements are performed.

Abstract
Long-term variability in global sea surface temperature (SST) is often quantified by the slope from a linear regression fit. Attention is then focused on assessing the statistical significance of the derived slope parameter, but the adequacy of the linear model itself, and the inherent assumption of a deterministic linear trend, is seldom tested. Here, a parametric statistical test is applied to test the hypothesis of a linear deterministic trend in global sea surface temperature. The results show that a linear slope is not adequate for describing the long-term variability of sea surface temperature over most of the earth's surface. This does not mean that sea surface temperature is not increasing, rather that the increase should not be characterized by the slope from a linear fit. Therefore, describing the long-term variability of sea surface temperature by implicitly assuming a deterministic linear trend can give misleading results, particularly in terms of uncertainty, since the actual increase could be considerably larger than the one predicted by a deterministic linear model.