Abstract
Long term mean sea level in the Chagos Archipelago has been relatively stable over the similar to 20 year length of the available instrumental records. Tide-gauge data from Diego Garcia (1988-2000, and 2003-2011) show no statistically significant long-term rise, whilst the rates of rise obtained from the satellite altimeter record for 1993-2011 span the range of 0.16-4.56 mm yr(-1) in the surrounding sea areas (70-74 degrees E and 4-9 degrees S) and are also consistent with a zero rate except in the far south of the region. The dominant feature is one of considerable inter-annual variability in mean sea level of up to similar to 10 cm, such that the very weak seasonal pattern of highest and lowest sea level in February and May respectively, is absent or reversed in some years. The Indian Ocean Dipole appears to exert an important influence on mean sea level in the area, with positive and negative dipole mode indices preceding periods of elevated or lowered sea levels respectively. The Chagos also lie outside the Indian Ocean cyclone belt and experience relatively low wind speeds, and there is no evidence of changes in the wind or wave environment in the past 20 years. Although in an area of seismic activity, there is no record of island subsidence, indeed on Diego Garcia minor crustal uplift of 0.63 +/- 0.28 SE mm yr(-1) has occurred between 1996 and 2009. Collectively, these results suggest that this has been a relatively stable physical environment, and that these low-lying coral islands should continue to be able to support human habitation, as they have done for much of the last 200 years. Nonetheless, future sea-level rise and its effect on the Chagos remains an important issue for further studies such as those of the Intergovernmental Panel on Climate Change.

Abstract
In this paper, 10-years of ozone (O-3) hourly concentrations collected over the period 2000-2009 in the Iberian Peninsula (IP) are analyzed using records from 11 background sites. All the selected monitoring stations present an acquisition efficiency above 85%. The changes in surface ozone over the Iberian Peninsula are examined by means of quantile regression, which allows to analyse the trends not only in the mean but in the overall data distribution. In addition, the ozone hourly concentrations records are clustered on the basis of their resulting distributions. The analysis showed that high altitude stations (>900 m) have higher background O-3 concentrations (similar to 80 mu g m(-3)). The same magnitude of background O-3 concentrations is found in stations near the Mediterranean Sea. On the other hand, the rural stations near the Atlantic coast present lower background values (similar to 50-60 mu g m(-3)) than those of Mediterranean influence. The two sub-urban stations exhibit the lowest background concentrations (similar to 45 mu g m(-3)). The results of the quantile regression show a very distinct behaviour of the data distribution, the slopes for a fixed quantile are not the same over IP, reflecting the spatial dependence of O-3 trends. Hence the rate of temporal change is not the same for all parts of the data distribution, as implicitly assumed in ordinary regression. The lower quantile (percentile 5) presents higher rates of change than the middle (percentile 50) and the upper quantile (percentile 95). The clustering procedure reveals what has been already detected in the quantile regression. The station with highest rates of decrease on the O-3 concentrations (easternmost station of IP) is isolated and then other clusters are formed among the moderately positive/negative O-3 trends around the IP. The clustering procedure highlighted that the largest trends are found for the lower ozone O-3 values, with largest negative trend at the easternmost station of IP, and also in northern and mainland stations, and an opposite behaviour, with positive O-3 trends, is observed at the Atlantic coast stations.

Abstract
Satellite altimetry provides continuous and spatially regular measurements of the height of the sea surface. Sea level responds to density changes of the water, to mass changes, due to addition or reduction of water mass, and to changes in the atmosphere above it. The present study examines the influence of atmospheric effects on sea-level variability in the North-East Atlantic. The association between the height of the sea surface and the North Atlantic Oscillation (NAO) is investigated by considering different sets of altimetry measurements for which the atmospheric effects have been handled differently. Altimetry data not corrected for atmospheric effects are strongly anti-correlated with the state of the NAO, reflecting the hydrostatic response of sea-level to the NAO pressure dipole. The application of an atmospheric correction to satellite altimetry observations in the NE Atlantic decreases variability of the height time series by more than 70% and reduces the amplitude of the seasonal cycle by similar to 5 cm. Altimetry data for which atmospheric effects are removed via an inverse barometer correction show a non-negligible correlation with the NAO index at some locations suggesting further indirect non-hydrostatic influences of the state of the NAO on sea level variability.

Abstract
Mean sea level is a variable of considerable interest in meteorological and oceanographic studies, particularly long-term sea level variation and its relation to climate changes. This study concerns the analysis of monthly mean sea level data from tide gauge stations in the Northeast Atlantic with long and continuous records. Much research effort on mean sea level studies has been focused on identifying long-term linear trends, usually estimated through least-squares fitting of a deterministic function. Here, we estimate nonparametric and robust trends using lowess, a robust smoothing procedure based on locally weighted regression. This approach is more flexible than a linear trend to describe the deterministic part of the variation in tide gauge records, which has a complex structure. A common trend pattern of reduced sea levels around 1975 is found in all the analysed records and interpreted as the result of hydrological and atmospheric forcing associated with drought conditions at the tide gauge sites. This feature is overlooked by a linear regression model. Moreover, nonlinear deterministic behaviour in the time series, such as the one identified, introduces a bias in linear trends determined from short and noisy records.

Abstract
Spatial and temporal sea level variability in the North Atlantic is investigated from Topex/Poseidon (T/P) altimetry data. Time series of sea level anomalies on a regular 5 degrees grid are analysed. Non-linear denoising through thresholding in the wavelet transform domain is carried out for each series in order to remove noise while preserving non-smooth features. Principal Component Analysis (PCA) is used to obtain a spatio-temporal description of the sea level field, To avoid modal mixing and improve interpretation of the principal modes, PCA is implemented separately for seasonal and trend components of the sea level field obtained from a wavelet-based multiresolution analysis. The leading pattern of the seasonal field reflects the dominance of a stable annual cycle over the study area and the change in the seasonal regime approaching the equator with contribution of the semi-annual cycle and phase-shift in the annual cycle in the tropical Atlantic. The leading pattern of the trend field is a broad spatial pattern associated with North Atlantic Oscillation (NAO), reflecting the influence of atmospheric conditions on interannual sea level variability.

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.