Time clustering of wave storms in the Mediterranean Sea

Abstract

Abstract. In this contribution we identify storm time clustering in the Mediterranean Sea through a comprehensive analysis of the Allan factor. This parameter is evaluated from a long time series of wave height provided by oceanographic buoy measurements and hindcast reanalysis of the whole basin, spanning the period 1979-2014 and characterized by a horizontal resolution of about 0.1 • in longitude and latitude and a temporal sampling of 1 h (Mentaschi et al., 2015). The nature of the processes highlighted by the AF and the spatial distribution of the parameter are both investigated. Results reveal that the Allan factor follows different curves at two distinct timescales. The range of timescales between 12 h to 50 days is characterized by a departure from the Poisson distribution. For timescales above 50 days, a cyclic Poisson process is identified. The spatial distribution of the Allan factor reveals that the clustering at smaller timescales is present to the north-west of the Mediterranean, while seasonality is observed across the whole basin. This analysis is believed to be important for assessing the local increased flood and coastal erosion risks due to storm clustering.

Uncertainties in Shoreline Projections to 2100 at Truc Vert Beach (France): Role of Sea‐Level Rise and Equilibrium Model Assumptions

“…We also observe that the interannual patterns of shoreline evolution are clearly correlated to those of winter wave energy. These behaviors underline the critical role of high/low energy winters interannual cycles, as well as storms sequencing, in wave‐driven shoreline response, in line with previous studies (Besio et al., 2017; Dissanayake et al., 2015; Dodet et al., 2019). In addition, the temporal variability of wave climate (e.g., seasonal distribution of storm events) has been observed to affect the frequency (or “mode”) of shoreline response (Ibaceta et al., 2020; Splinter et al., 2017).…”

“…However, these studies do not explicitly resolve wave-driven shoreline change, and it is advocated that new methods have to be developed to predict the impacts of SLR on the coast (Cooper et al, 2020). Short-and long-term variability in wave energy, as well as the chronology of storm events, can strongly affect future shoreline patterns (Besio et al, 2017;Cagigal et al, 2020;Coco et al, 2014;Dissanayake et al, 2015;Vitousek et al, 2021). Recently, Cagigal et al (2020) developed and used a stochastic climate-based wave emulator to generate ensembles of wave time series at several beaches, and addressed shoreline response to different wave chronologies.…”

Large Scale Tests on Foreshore Evolution During Storm Sequences and the Performance of a Nearly Vertical Structure

“…Note that sequences of storms separated by intervals that are shorter than recovery time of the beaches are often referred to as clusters. However, this term implies that storms inter-arrival times do not follow the Poisson distribution as found for the Mediterranean Sea by Besio et al (2017). Here the response of the beach is studied regardless of the statistical property of the forcing, hence only the general term sequence will be used throughout the paper for this phenomenon.…”

BetaBit: A fast generator of autocorrelated binary processes for geophysical research

“…However, most 33 of the existing methods have serious restrictions on the 34 class of autocorrelation functions that can be effectively 35 modeled [12][13][14][15]. 36 Serinaldi and Lombardo [16] showed that classical spec- 37 tral techniques can effectively be used if one focuses on 38 the parent continuous process of beta distributed transi-39 tion probabilities rather than on the target binary process. 40 This change of paradigm yields a simulation procedure ef- 41 fectively embedding a spectrum-based iterative amplitude 42 p-1 adjusted Fourier transform (IAAFT) method [17][18][19] devised for continuous processes, and allows the simulation 44 of binary processes with prescribed dependence structure 45 and surrogate data reproducing the empirical ACF of the 46 observed sequences.…”