Ocean surface winds generate wind-waves across the ocean surface, which play an important role in the climate system, modulating the interactions between the atmosphere and the oceans. Wind-waves are also a key environmental variable for coastal and offshore engineering, and influence many coastal dynamics processes, navigation planning, and marine renewable energy sources. With over 300 million people living in low-lying coastal areas, it is essential to understand wave climate to properly address environmental and societal wave-driven impacts.

Global warming might change the atmospheric circulation, affecting ocean surface winds, and, in turn, wind-waves. In 2007, the IPCC Fourth Assessment Report highlighted a low confidence in climate change effects on wind-waves. As a result, this research area has been getting increased attention over the last decade, with a growing number of studies producing several global and regional wave datasets. In 2019, a collaborative international effort in the framework of the  Coordinated Ocean Wave CLimate Project (COWCLIP) resulted in the development and analysis of the first global ensemble of standardized ocean wave climate projections, which was published in Nature Scientific Data in 2020. This analysis showed a robust future wave climate change along half of the worlds’ coastlines but highlighted large uncertainties dominated by global climate models, followed by wave modeling methods and emission scenarios [1]. However, this dataset did not include the uncertainty derived from the internal climate variability.

Climate varies naturally from one year to the next, and over different time scales. Internal (or natural) climate variability refers to the variation in climate parameters caused by nonhuman forces, which can mask or enhance human-induced changes. Climate simulations represent only one possible realization of the climate. Internal climate variability cannot be properly estimated from single climate realizations, in particular if they cover a relatively short period of time (e.g. a few decades), and assessment of trends or extremes that do not account for such variability can be flawed. For example, we might confound natural variability with climate change signals, or underestimate rare, but hazardous, extremes that cannot be properly sampled in just one climate realization.

The internal climate variability of the historical wave climate is also poorly known. Satellite-based global wave observations have been available since the 1980’s, and longer observation records (e.g. buoys) are extremely spatially limited. Most historical wave climate assessments rely on contemporary wave reanalysis or hindcasts (that combine model simulations with assimilated observations). These usually cover a few decades and therefore cannot reproduce well the internal climate variability. A few longer, even century-scale, wave reanalysis/hindcast have been recently developed but the change in type and quantity of ingested observations over time compromises their temporal consistency [3].

The internal climate variability can be investigated with a Single Model Initial-condition Large Ensemble (SMILE), which is a set of simulations starting from different initial conditions but produced with a single climate model and identical external forcing [4]. Over the last decade, SMILEs have been increasingly generated and used in climate science to study the role of the natural climate variability across different variables. However, existing SMILES do not provide information about wind-waves.

To fill in this gap, in this study we developed the d4PDF-WaveHs dataset, the first SMILE-based ensemble of global significant wave height (Hs) simulations, that provides 100 realizations of Hs over 1951-2010 (which is equivalent to 6,000 years of data). Hs is a key parameter used in many coastal, naval and offshore engineering applications to characterize the wind-wave climate. d4PDF-WaveHs was produced with an advanced statistical model [5] and using d4PDF’s historical simulations of sea level pressure (SLP) [6]. d4PDF-WaveHs provides valuable data that can be used to advance our understanding of the internal wave climate variability. For example, in a  recent analysis of this dataset [7], we showed the possible risk of failing to detect the statistically significant positive trend over some parts of the Southern Ocean. We also illustrated how the minimum ensemble size necessary to estimate robust trends depends on the region and variable of interest, but we recommended to consider at least 10 realizations.

References:

[1] Morim, J., Hemer, M., Wang, X.L. et al (2019) Robustness and uncertainties in global multivariate wind-wave climate projections. Nature Climate Change, 9, 711-718.

[2] Morim J., Trenham, C., Hemer, M. et al (2020) A global ensemble of ocean wave climate projections from CMIP5-driven models. Nature Scientific Data, 7, 105.

[3] Meucci, A., Young, I.R., Aarnes, O.J. et al (2020) Comparison of wind speed and wave height trends from twentieth-century models and satellite altimeters. Journal of Climate, 33, 611-624.

[4] Maher, N., Milinski, S. and Ludwig, R (2021). Large ensemble climate models simulations: introduction, overview, and future prospects for utilizing multiple types of large ensembles. Earth System Dynamics, 12, 401-418.

[5] Wang, X.L., Feng, Y. and Swail, V.R. (2014) Changes in global ocean wave heights as projected using multimodel CMIP5 simulations. Geophysical Research Letters, 41, 1026-1034.

[6] Ishii, M. and Mori, M. (2020) d4PDF: Large-ensemble and high-resolution climate simulations for global warming risk assessment. Progress in Earth and Planetary Sciences, 7.

[7] Casas-Prat, M., Wang, X.L., Mori, N. et al (2022) Effects of internal climate variability on historical ocean wave height trend assessment. Frontiers in Marine Science, 9.