Global Ocean Extreme Wave Heights from Spatial Ensemble Data

Abstract

A novel approach to estimation of extreme value ocean significant wave height is investigated, in which data from adjacent regions are pooled to form a spatial ensemble. The equivalent duration of this ensemble region is the sum of the durations of the data pooled to form the ensemble. To create such a spatial ensemble, data from regions to be pooled must be independent and identically distributed. ERA-Interim reanalysis data are used to investigate the requirement of independent and identically distributed data on a global basis. As a result, typical spatial ensembles are defined for a number of regions of the world and the 100-yr return period significant wave height is calculated for these regions. It is shown that the method can result in a reduction in the confidence interval for such extreme value estimates of between 30% and 60%. The method is demonstrated both with ERA-Interim data and altimeter data.

Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

“…Figure 2 shows the resulting colour-filled contours of non-seasonal H 100 s for 1 January 1979 (Figure 2a) and 1 January 2019 (Figure 2b). The results are generally consistent with previous analyses using conventional EVA approaches obtained from both satellite and model datasets [12][13][14]21]. The results show the largest values of H 100 s in the North Atlantic and North Pacific with these values displaced towards the western boundaries of both of these oceanic basins.…”

“…Hence, for the present application, we adopted a GPD approach for all global locations, as described by Mentaschi et al [15] [see their Equations ( 19)-( 21) and ( 28)-( 30)]. To ensure the data are independent, we required storm-peaks in the PoT analysis to be separated by a minimum of 48 h [12][13][14].…”

“…As a result, a range of alternative approaches has been used in an attempt to address the issues and obtain statistically significant results from shorter time series. These approaches include: fitting the extreme value probability distribution to all the data rather than just extremes (the Initial Distribution Method) [11] and "pooling" data from either an ensemble of model results [12,13] or over adjacent spatial regions [14]. A further approach is to relax the requirement that the time series is stationary and undertake a non-stationary extreme value analysis [15].…”

Spatial dependence of extreme seas in the North East Atlantic from satellite altimeter measurements

“…Satellite radar altimeters have been shown to be capable of measuring wind speed and significant wave height to high accuracy (Young et al, 2017; Ribal & Young, 2019). For Earth observation, the combined data record from multiple satellites now spans more than 30 years and has been shown to provide a high‐quality data source for global climatology (Young & Donelan, 2018) and extreme value analysis (Takbash et al, 2019; Takbash & Young, 2019). A unique characteristic of altimeter remote sensing is that it provides near‐instantaneous measurements over spatial domains (along track) of significant wave height.…”

Modelling cross-shore shoreline change on multiple timescales and their interactions

“…In addition to these temporal limitations, spatial restrictions exist. Most of the research on shoreline change over the last decades has been conducted at storm-dominated mid-latitudes, obscuring understanding to various mechanisms that play a role at different latitudes (e.g Takbash & Young, 2019). In the mid-latitudes, winter storms or the seasonality in the storms dominate the wave regime.…”