Abstract
Isolating long-term trend in sea surface temperature (SST) from El Nino southern oscillation (ENSO) variability is fundamental for climate studies. In the present study, trend-empirical orthogonal function (EOF) analysis, a robust space-time method for extracting trend patterns, is applied to isolate low-frequency variability from time series of SST anomalies for the 1982-2006 period. The first derived trend pattern reflects a systematic decrease in SST during the 25-year period in the equatorial Pacific and an increase in most of the global ocean. The second trend pattern reflects mainly ENSO variability in the Pacific Ocean. The examination of the contribution of these low-frequency modes to the globally averaged SST fluctuations indicates that they are able to account for most (>90%) of the variability observed in global mean SST. Trend-EOFs perform better than conventional EOFs when the interest is on low-frequency rather than on maximum variance patterns, particularly for short time series such as the ones resulting from satellite retrievals. Copyright (C) 2009 Royal Meteorological Society

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
The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.

Abstract
The quantification of the long-term variability of relative sea-level is a fundamental problem in geodesy. In the present study, quantile regression is applied for characterising the long-term variability in relative sea-level at the Gedser and Hornbaek tide gauges, in the North Sea-Baltic Sea transition zone. Quantile regression allows to quantify not only the rate of change in mean sea-level but also in extreme heights, providing a more complete description of long-term variability. At Gedser the lowest relative heights are increasing at a rate approximately 40% higher than the mean rate, while at Hornbaek the relative sea-level slopes are stable across most of the quantiles. A 30-year running window analysis shows that the linear trends display considerable decadal variability over the twentieth century for both stations.