Versatile Modelling of Extreme Surges in Connection with Large-Scale Circulation Drivers

Abstract

In this article, we investigate the dependence of extreme surges on the North Atlantic weather regime variability across different timescales using the North Atlantic Oscillation (NAO) and Scandinavian blocking (SCAND) indices. The analysis was done using time series of surges along the North French Coast, covering long time periods (43 to 172 years of data). Time series that exhibited gaps were filled using linear interpolation to allow spectral analyses to be conducted. First, a continuous wavelet analysis on monthly maxima surges in the North French Coast was conducted to identify the multi-timescale variability. Second, a wavelet coherence analysis and maximum overlap discrete wavelet transform (MODWT) were used to study the timescale-dependent relationships between maxima surges and NAO or SCAND. Finally, NAO and SCAND were tested as physical covariates for a nonstationary generalized extreme value (GEV) distribution to fit monthly maxima surge series. Specific low-frequency variabilities characterizing these indices (extracted using MODWT) were also used as covariates to determine whether such specific variabilities would allow for even better GEV fitting. The results reveal common multi-annual timescales of variability between monthly maxima surge time series along the North French coasts: ~2–3 years, ~5–7 years, and ~12–17 years. These modes of variability were found to be mainly induced by the NAO and the SCAND. We identified a greater influence of the NAO on the monthly maxima surges of the westernmost stations (Brest, Cherbourg, Le Havre), while the SCAND showed a greater influence on the northernmost station (Dunkirk). This shows that the physical climate effects at multi-annual scales are manifested differently between the Atlantic/English Channel and the North Sea regions influenced by NAO and SCAND, respectively. Finally, the introduction of these two climate indices was found to clearly enhance GEV models as well as a few timescales of these indices.