Identifying trends in the ocean wave climate by time series analyses of significant wave heightdata

Abstract Reliable return period estimates of sea state parameters such as the significant wave height are of great importance in marine structural design and ocean engineering. Hence, time series of significant wave height have been extensively studied in recent years. However, with the possibility of an ongoing change in the global climate, this might influence the ocean wave climate as well and it would be of great interest to analyze long time series to see if any long-term trends can be detected. In this paper, long time series of significant wave height stemming from the ERA-40 reanalysis project containing six-hourly data over a period of more than 44 years are investigated with the purpose of identifying long term trends. Different time series analysis methods are employed, i.e. seasonal ARIMA, multiple linear regression, the Theil–Sen estimator and generalized additive models, and the results are discussed. These results are then compared to previous studies; in particular results are compared to a recent study where a spatio-temporal stochastic model was applied to the same ERA-40 data. However, in the current analysis, the spatial dimension has been reduced and time series of the spatial minima, spatial mean and spatial maxima respectively have been analyzed for temporal trends.

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