Projected wave climate temporal variability due to climate change

Abstract

This work proposes a new general procedure to stochastically analyze multi-model multivariate wave climate time series projections at different temporal scales. For every projection, it characterizes significant wave height, peak period and mean direction by means of univariate non-stationary distributions capable of capturing cyclic climate behavior over a reference time interval duration. The temporal dependence between the values at a given sea state and previous short-term wave climate is described with a vector autoregressive model (VAR). The multi-model ensemble wave climate characterization is based on a compound distribution of the individual non-stationary distributions and a weighted averaged VAR model. The methodology is applied to bias-adjusted wave climate projections derived using WaveWatch III forced by wind field data from EURO-CORDEX models at a location close to the Mediterranean Spanish coast. Results are compared to hindcast data which shows a clear bi-seasonal behavior. Different temporal references were considered, starting with a 1-year reference period to analyze overall changes in wave climate at scales ranging from days, months and seasons with respect to historic conditions. The results show that the projected wave climate has a very different temporal behavior than hindcast data, delaying and widening/shortening the start and duration of the two main seasons and including shorter term variations. Regarding the energetic content of the sea states, the compound variable highest percentiles of the significant wave height present lower values than the hindcast (&3-10%) during the traditionally more severe period (November-March) but higher values (&10-35%) during the calmer months. The projected peak period presents a similar temporal pattern to the hindcast data, while the mean wave direction shows a significant change from the historical bi-modal behavior towards more likely easterly waves throughout the year. Additionally, a 10-year analysis is done to find larger temporal variabilities such as decadal variations associated with the North Atlantic Oscillation. The observed temporal variability in the yearly seasonal pattern throughout the century is addressed by analysing 20-year rolling windows in all the model projections and in the compound variable. The compound distribution shows significant temporal variabilities throughout the century with the most severe periods and more likely severe waves during summer at the end of the century.

Future changes and seasonal variability of the directional wave spectra in the Mediterranean Sea for the 21st century

“…The results are presented in terms of the prevailing incoming wave directions and systems, highlighting the difference in future behavior of the wind-sea waves comprising waves with periods between 3-6 seconds and swell waves for waves with periods higher than 7 s. Additionally, where possible, the results will be compared to projected changes in integrated wave parameters: significant wave height, H s , peak period, T p , and mean wave direction, θ m presented in the supplementary information and referencing previous studies (e.g. Mentaschi et al 2017, Morim et al 2019, De Leo et al 2021, Lira-Loarca et al 2021a, 2021b. This section is organized as follows.…”

“…Many studies in the recent decades have focused on understanding the projected future changes in mean and extreme values of integrated parameters, mainly significant wave height, and some include information on mean wave period and wave direction Hemer et al (2013), Mentaschi et al (2017), Morim et al (2019), De Leo et al (2021), Lira-Loarca et al (2021a), e.g.. Although there is a general agreement regarding projections of annual and seasonal mean changes in wave climate there is still a limited knowledge regarding changes in temporal variability and in-depth analysis of changes in wave direction and period (Collins et al 2019, Morim et al 2019, Lira-Loarca et al 2021a.…”

A method to characterize climate, Earth or environmental vector random processes

“…Some examples of applications are (i) the study of beach evolution (Payo et al 2004;Baquerizo and Losada 2008;Callaghan et al 2008;Fe ´lix et al 2012;Ranasinghe et al 2012), (ii) the optimal design and management of an oscillating water column system (Jalo ´n et al 2016;Lo ´pez-Ruiz et al 2018), (iii) the planning of maintenance strategies of coastal structures (Lira- Loarca et al 2020), and (iv) the assessment of water quality management strategies in an estuary according to density variations and recovery time (Cobos 2020). It has also been used for the analysis of observed wave climate variability in the preceding century and the expected changes in projections under a climate change scenario (Loarca et al 2021).…”

On the role of wave climate temporal variability in bias correction of wave climate projections

“…Swell and wind-sea waves are an important contributor to ECWLs and crucial for coastal impacts assessment and damage of coastal infrastructure. Despite being the predominant factor in many nearshore processes and coastal impacts, the contribution of waves has often been neglected given the difficulty to predict the large long-term variability in surge and wave conditions and lacking local coastal topography and bathymetry data (Bricheno and Wolf, 2018;Lira-Loarca et al, 2021a;Almar et al, 2021;Nicholls et al, 2021).…”