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
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
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