Impacts of climate change on seasonal extreme waves in the Northwest Atlantic using a Spatial Neural Gas clustering method

Abstract

Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities, such as coastal management, design of inshore and offshore structures, marine transport, coastal recreational activities, fisheries, etc. This study investigates the efficiency of a state-of-the-art spatial neutral gas clustering method in the classification of wind/wave data and the evaluation of extreme values of significant wave heights (Hs), mean wave direction (MWD) and mean wave periods (T0) for two 39-year time periods; from 1979 to 2017 for the present climate, and from 2060 to 2098, for a future climate change scenario in the Northwest Atlantic. These data were constructed by application of a numerical model, WAVEWATCHIII™ (hereafter, WW3), to simulate the wave climate for the study area for both present and future climates. Data from the model was extracted for the wave climate, in terms of the wave parameters, specifically Hs, MWD and T0, which were analyzed and compared for winter and summer seasons, for present and future climates. In order to estimate extreme values in the study area, a Natural Gas (hereafter, NG) clustering method was applied, separate clusters were identified, and corresponding centroid points were determined. To analyze data at each centroid point, time series of wave parameters were extracted, and using standard stochastic models, such as Gumbel, exponential and Weibull distribution functions, the extreme values for 50 and 100-year return periods were estimated. Thus, the impacts of climate change on wave regimes and extreme values can be specified.