Abstract
Atmospheric pressure varies within a wide range of scales and thus a multi-scale description of its variability is particularly appealing. In this study, a scale-by-scale analysis of the global sea-level pressure field is carried out from reanalysis data. Wavelet-based analysis of variance is applied in order to describe the variability of the pressure field in terms of patterns representing the contribution of each scale to the overall variance. Signals at the seasonal scales account for the largest fraction of sea-level pressure variance (typically more than 60%) except in the Southern Ocean, in the Equatorial Pacific and in the North Atlantic. In the Southern Ocean and over the North Atlantic, high-frequency signals contribute to a considerable fraction (30-50%) of the overall variance in sea-level pressure. In the Equatorial Pacific, large-scale variability, associated with ENSO, contributes up to 40% of the total variance.

Abstract
Long-term sea-level variability in Chesapeake Bay is examined from long tide gauge records in order to assess the influence of climate factors on sea-level changes in this complex estuarine system. A time series decomposition method based on autoregression is applied to extract flexible seasonal and low-frequency components from the tide gauge records, allowing to analyse long-term sea-level variability not only by estimating linear trends from the records, but also by examining fluctuations in seasonal and long-term patterns. Long-term sea-level variability in Chesapeake Bay shows considerable decadal variability. At the annual scale, variability is mainly determined by atmospheric factors, specifically atmospheric pressure and zonal wind, but no systematic trends are found in the amplitude of the annual cycle. On longer time scales, precipitation rate, a proxy for river discharge, is the main factor influencing decadal sea-level variability. Linear trends in relative sea-level heights range from 2.66 +/- 0.075 mm/year (at Baltimore) to 4.40 +/- 0.086 mm/year (at Hampton Roads) for the 1955-2007 period. Due to the gentle slope of most of the bay margin, a sea-level increase of this magnitude poses a significant threat in terms of wetland loss and consequent environmental impacts.

Abstract
This study addresses the impact of satellite altimetry data processing on sea level studies at regional scale, with emphasis on the influence of various geophysical corrections and satellite orbit on the structure of the derived interannual signal and sea level trend. The work focuses on the analysis of TOPEX data for a period of over twelve years, for three regions in the North Atlantic: Tropical (0 degrees <= phi <= 25 degrees), Sub- Tropical (25 degrees <= phi <= 50 degrees) and Sub-Arctic (50 degrees <= phi <= 65 degrees). For this analysis corrected sea level anomalies with respect to a mean sea surface model have been derived from the GDR-Ms provided by AVISO by applying various state-of-the-art models for the geophysical corrections. Results show that sea level trend determined from TOPEX altimetry is dependent on the adopted models for the major geophysical corrections. The main effects come from the sea state bias (SSB), and from the application or not of the inverse barometer (IB) correction. After an appropriate modelling of the TOPEX A/B bias, the two analysed SSB models induce small variations in sea level trend, from 0.0 to 0.2 mm/yr, with a small latitude dependence. The difference in sea level trend determined by a non IB-corrected series and an IB-corrected one has a strong regional dependence with large differences in the shape of the interannual signals and in the derived linear trends. The use of two different drift models for the TOPEX Microwave Radiometer (TMR) has a small but non negligible effect on the North Atlantic sea level trend of about 0.1 mm/yr. The interannual signals of sea level time series derived with the NASA and the CNES orbits respectively, show a small departure in the middle of the series, which has no impact on the derived sea level trend. These results strike the need for a continuous improvement in the modelling of the various effects that influence the altimeter measurement.

Abstract
Quantile regression is applied for characterizing long-term sea-level variability in the Baltic Sea from long tide gauge records. The approach allows to quantify not only variability in the mean but also in extreme heights and thus provides a more complete description of regional sea-level variability. In the Baltic, slopes in minima are similar to the classical mean-based ordinary least squares slope, but maxima exhibit larger trends, particularly at the northernmost stations, in the Gulf of Bothnia, likely associated with changes in north Atlantic atmospheric circulation and particularly regional wind patterns. Citation: Barbosa, S. M. ( 2008), Quantile trends in Baltic sea level, Geophys. Res. Lett., 35, L22704, doi: 10.1029/2008GL035182.

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.