More recently, a global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts 11 highlighted the discrepancies among modern wave products of historical data. Studies based on a single (or reduced sample of) realizations of the climate system might underestimate extreme events or confound trends with internal climate variability 9, 10. However, the internal climate variability was not properly sampled as most combinations of forcing and climate/wave models considered just one realization of the climate system. Most of these efforts were consolidated with COWCLIP2.0 8, the first coherent, community-driven multi-method ensemble of historical and future global wave simulations, which included dominant sources of uncertainty, namely forcing uncertainty, and wave and climate model uncertainty. To fill in this gap, a growing number of studies have been developed over the last decade, producing several global and regional wave datasets. This relates to the fact that most climate models provide no information about waves therefore, the availability of wave simulations is relatively limited. The IPCC (2013) 7 highlighted a low knowledge confidence of wave climatology in comparison with many other climate variables. Over 300 million people live in low-lying coastal areas 6, and detailed knowledge of wave climate is essential to address the environmental and societal wave-driven impacts properly. They are also a key environmental variable for coastal and offshore engineering 2, and affect many coastal dynamics processes 3, navigation planning 4, and are a potential source of renewable energy 5.
Ocean wind-waves, hereafter called waves, are an important element of the climate system, modulating the interactions between the atmosphere and the oceans 1. This dataset may be of interest to a variety of researchers, engineers and stakeholders in the fields of climate science, oceanography, coastal management, offshore engineering, and energy resource development. Overall, this is crucial to properly assess wave-driven impacts, such as extreme sea levels on low-lying populated coastal areas. It also provides a better sampling of extreme events. d4PDF-WaveHs provides unique data to understand better the poorly known role of internal climate variability in ocean wave climate, which can be used to estimate better trend signals. Technical comparison of model skill against modern reanalysis and other historical wave datasets was undertaken at global and regional scales. d4PDF-WaveHs provides 100 realizations of H s for the period 1951–2010 (hence 6,000 years of data) on a 1° × 1° lat.-long. It was produced using an advanced statistical model with predictors derived from Japan’s d4PDF ensemble of historical simulations of sea level pressure. The d4PDF-WaveHs dataset represents the first single model initial-condition large ensemble of historical significant ocean wave height ( H s) at a global scale.
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