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Fernando J. Méndez, Melisa Menéndez, Alberto Luceño, and Inigo J. Losada

Abstract

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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William V. Sweet, Melisa Menendez, Ayesha Genz, Jayantha Obeysekera, Joseph Park, and John J. Marra
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Gil Lemos, Alvaro Semedo, Mikhail Dobrynin, Melisa Menendez, and Pedro M. A. Miranda

Abstract

A quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height H S, mean wave period T m, and mean wave direction (MWD) biases, in comparison with the ERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. Seven CMIP5 EC-EARTH runs (single forcing) were used to force seven wave model (WAM) realizations (single model), following the RCP8.5 scenario (single scenario). The biases for the 1979–2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the H S, T m (and wave energy flux P w), and MWD future climate projections for the 2081–2100 period. The bias-corrected projected changes show increases in the annual mean H S (14%), T m (6.5%), and P w (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes are more striking during local summer, up to 120%, for P w. After bias correction, the magnitude of the H S, T m, and P w original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes.

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