Abstract
Long (>30 years) monthly records of relative sea-level heights from tide gauges in the Baltic sea are analyzed. Time series clustering based on forecast densities is applied in order to describe regional sea-level variability in the Baltic Sea in terms of future relative heights. The tide gauge records are clustered on the basis of forecasts at 3-month and 6-month horizons. For the 3-month horizon, the results of the cluster analysis show a fairly spatial coherency in terms of grouping together locations from the same sub-basin, with the northern records in the Bothnian Sea and Gulf of Finland clustering together, followed by the tide gauges in the Baltic Proper and lastly the southernmost stations in the western Baltic. For the 6-month horizon, the results show a higher degree of homogeneity between different locations, but a clear separation between the stations at the Baltic entrance and the tide gauges inside the Baltic basin. Moreover, when considering detrended records, reflecting mainly the seasonal cycle, the clustering results are more homogeneous and suggest a distinct response of coastal sea-level in spring and in summer.

Abstract
Climate change is expected to involve not only changes in the mean of climate parameters, but also in the characteristics of the corresponding seasonal cycle. However, the discrimination from an observational record of long-term changes in the mean and low-frequency variations in the seasonal pattern is a challenging task, requiring the application of specific statistical methods. In this work, a time series decomposition method based on autoregression is applied in order to obtain a flexible description of seasonal variability from European temperature records. The method is based on the dynamic linear model representation for an autoregressive process and is particularly useful for isolating time-varying cycles in climate time series, allowing to retrieve fluctuations in the amplitude and phase of the periodic components and to assess their statistical significance. This approach is utilised in the analysis of long time series of daily mean temperature from the ECA (European Climate Assessment) project. Seasonality in Europe's air temperature is characterised by an annual cycle with a stable phase but considerable inter-annual and inter-decadal variability. In particular, the annual amplitude was highest in the 1940's and exhibits a distinct minimum around 1975, coincident with the climatic regime shift of the mid-1970's.

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

Abstract
Indoor radon activities were measured during a period of 6 months, as well as several physical environmental variables (temperature, pressure, humidity and rainfall). The location was a small room at an administrative building of the University of Coimbra, usually undisturbed by human activities and situated over bedrock of low-uranium Triassic red sandstones. A low average activity of radon was observed (36 Bq m(-3)), however showing a very well marked daily periodicity (10 +/- 5 Bq m(-3)), with maximum values occurring more frequently between 9 and 10 a.m. Daily variations are shown to have no relation with earth tides, and their amplitudes exhibit a significant correlation with outdoor temperature; no dependence on barometric pressure was found. Rainfall disturbs the observed daily radon cycles through a strong reduction of their amplitude, but has no effect on the long-term variability of the gas concentration.

Abstract
This study was carried out to improve diagnosis and prediction of geomaterials status from coastal protection structures. A methodology is proposed for the characterization of rock materials and management of the structures. The characterization was organized in different phases: i) visual inspection; ii) field techniques to study geologic-geotechnical features; iii) in situ measurement of geomechanical parameters; and iv) development of GIS-based mapping and assessment of the block materials. The results obtained allowed to define a geotechnical zonation for the structure armor layer, according to the type of geomaterial source, weathering/degradation grade and geomechanical rebound values. The GIS project developed, combined an applied cartography of the groins' superficial layer with the results of the field techniques. The interactive base included the pilot case presented in this work, from Espinho coastal area (NW Portugal, Iberian Peninsula), which comprises four different sectors. The results of this work, support the need to apply geomechanic concepts to coastal structures, since they allow the evaluation of deterioration levels and improve the planning of intervention works. This methodology contributes to ameliorate the efficiency of monitoring and maintenance, in an economically beneficial mode. © ISRM International Symposium on Rock Mechanics, SINOROCK 2009.