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[1] Recent evidence suggests long-term changes in the intensity and frequency of extreme wave climate around the globe. These changes may be attributable to global warming as well as to the natural climate variability. A statistical model to estimate long-term trends in the frequency and intensity of severe storm waves is presented in this paper. The model is based on a time-dependent version of the Peak Over Threshold model and is applied to the Washington NOAA buoy (46005) significant wave height data set. The model allows consideration of the annual cycle, trends, and relationship to atmosphereocean-related indices. For the particular data set analyzed the inclusion of seasonal variability substantially improves the correlation between the model and the data. Also, significant correlations with the Pacific-North America pattern, as well as long-term trend, are detected. Results show that the model is appropriate for a rigorous analysis of long-term trends and variability of extreme waves and for providing time-dependent quantiles and confidence intervals.

A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales-such as seasonality, interdecadal variability, and secular trends-in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

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[1] A time-dependent generalized extreme value (GEV) model for monthly significant wave height maxima from satellite databases is used to model the seasonal and interannual variability of the extreme wave climate throughout southern Europe. In order to avoid a misleading use of the maxima time series, the classical extreme value model has been modified to cope with nonhomogeneous monthly observations. Seasonality is represented using intraannual harmonic functions in the model, while interannual variability is modeled including North Atlantic and Mediterranean regional scale sea level pressure predictors, such as the North Atlantic Oscillation (NAO), the east Atlantic (EA), or the east Atlantic/western Russian (EA/WR) patterns. The results quantify the strong spatial variability detected in the seasonal location and scale GEV parameters. In general, prominent zonal (west-east) and meridional (north-south) gradients of these location and scale parameters reveal the predominance of low-pressure centers located in the NAO region (e.g., a gradient of 4 m for the location parameter and 1.5 units for the scale parameter between north-south is shown in the month of September). The model also quantifies the influence of regional climate patterns on extreme wave climate. Results show a great influence of NAO and EA on the Atlantic basin (e.g., every unit of the monthly NAO index explains 25 cm of the extreme wave height in the Gulf of Biscay and the EA index explains 20 cm) while the negative phases of EA/WR contribute greatly to the western Mediterranean basin.