A global ensemble of ocean wave climate projections from CMIP5-driven models

Abstract

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21 st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (H S), mean wave period (T m), and mean wave direction (θ m) computed over time-slices 1979-2004 and 2081-2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative windwave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research

“…Overall, the above analyses indicate that both the simulated spatial distributions and seasonal variations in FIO-ESM v2.0 are broadly consistent with the ERA5 data, including not only the monthly but also the 3-hourly significant wave height data. However, the simulated wave parameters still suffer several biases, especially in the North Atlantic, North Pacific, and tropical Pacific oceans, with an approximately 10% relative difference from the ERA5 data, which is similar to other ocean wave data from COWCLIP 28 .…”

“…However, ocean waves are not included in most of the state-of-the-art global climate models, which is the key tool to assess and provide future projections of climate systems. Therefore, as the growing demand to understand the response of the global wave climate to increasing greenhouse gas concentrations, especially through the Coordinated Ocean Wave Climate Project (COWCLIP) 1 , several studies on future ocean wave climate research have provided ocean wave information by using the output of global and regional climate models to force the standalone ocean surface wave model 15 – 28 .…”

Global Ocean Wind‐Wave Model Hindcasts Forced by Different Reanalyzes: A Comparative Assessment

“…Previous studies (Gulev & Grigorieva, 2006; Shimura et al., 2013) demonstrated that the response of wind waves to the changes in atmospheric circulation is quite complex since it is associated both with the changes in the local conditions and the variations in the characteristics of cyclonic activity. This provides another interesting paradigm for further comparative assessments of wind wave model hindcasts with an obvious perspective for the analysis of multimodel projections of wind waves for the 21st century (Morim et al., 2020).…”

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

“…While numerical modelling provides a 'key-hole' to observe the explicit interactions among defined sets of variables, sensitivity analysis provides a way to understand the role of input variables uncertainties in shoreline predictions. Here, we use the framework proposed by D'Anna et al (2020), who used a variance-based Global Sensitivity Analysis (GSA) (Saltelli et al, 2008;Sobol', 2001) to investigate the relative contributions of the uncertainties affecting input variables to the uncertainties of modelled shoreline predictions, and their evolution in time. The method consists in propagating the input uncertainties through the model obtaining a probabilistic estimate of the shoreline projections, and performing a GSA which decomposes the variance of model results into several contributions, each one associated with an input variable.…”