Robustness and uncertainties in global multivariate wind-wave climate projections

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Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.

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Fig. 1: Hierarchical clustering of annual \({\bar{\boldsymbol{H}}}_{\boldsymbol{s}}\) for the present-day climate (1979–2004).
Fig. 2: Simulated wave climatological mean fields for the present day (1979–2004) and projected changes in climatological wave values for the period 2081–2100 under RCP4.5 and RCP8.5.
Fig. 3: Robust projected changes in offshore \({\bar{\boldsymbol{H}}}_{\mathbf{s}}\),\({\bar{\boldsymbol{T}}}_{\mathbf{m}}\) and \({\bar{\boldsymbol{\theta }}}_{\boldsymbol{m}}\) for 2080–2100 (under RCP8.5) in the vicinity of the world’s coastlines.
Fig. 4: Hierarchical clustering of projected relative changes in annual \({\bar{\boldsymbol{H}}}_{\boldsymbol{s}}\) (2081–2100 relative to 1979–2004).
Fig. 5: Relative contribution of different sources of uncertainty to projected future changes in the mean of annual/seasonal \({\bar{\boldsymbol{H}}}_{\boldsymbol{s}}\).

Data availability

The data that support the findings of this study are available from the corresponding author on request, or via the COWCLIP data access portal at


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This study represents Task 3 of the second phase of COWCLIP (, an international collaborative working group endorsed by the Joint Technical Commission for Oceanography and Marine Meteorology, a partnership between the World Meteorological Organization) and the Intergovernmental Oceanographic Commission of UNESCO. We acknowledge the different climate-modelling groups, the Program for Climate Model Diagnosis and Intercomparison and the World Climate Research Program’s Working Group on Coupled Modelling. We acknowledge ECMWF for availability of ERAI data, and Australia’s Integrated Marine Observing System for altimeter wind/wave data, used for model validation. J.M., M.H. and C.T. acknowledge the support of the Australian Government National Environmental Science Program Earth Systems and Climate Change Hub. B.T. and M.W. acknowledge the support of the Regional and Global Climate Modeling Program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract No. DE-AC02–05CH11231, and the National Energy Research Supercomputing Center of the LBNL. I.Y. acknowledges ongoing support from the Australian Research Council through grant No. DP160100738, and to the Integrated Marine Observing System. N.M., T.S., A.B. and B.K. acknowledge the support of the TOUGOU Program by MEXT, Japan, JSPS-Kakenhi Program. L.E. acknowledges the support of the US Geological Survey Coastal and Marine Hazards/Resources Program. Ø.B. and O.A. acknowledge the support of the Research Council of Norway through the ExWaMar project through grant No. 256466. We thank all contributors to the COWCLIP project, including C. Appendini (National Autonomous University of Mexico, Mexico), F. Ardhuin (Ifremer, France), N. Groll (Helmholtz-Zentrum Geesthacht Zentrum, Germany), S. Gallagher (Met Éireann, Ireland), S. Gulev (Moscow State University, Russia) and W. Perrie (Bedford Institute of Oceanography, Canada).

Author information

All authors (except C.T., N.C., M.W., B.T. and F.A.) had input into experimental design via workshops. J.M. led the analysis of ensemble, algorithm development for data analysis and writing of the manuscript. M.H. co-led and conceived the experiment, supervised analysis, provided CSIRO ensemble data and co-wrote the manuscript. X.L.W. co-led and conceived the experiment, developed community codes, provided ECCC ensemble data and contributed to analysis and writing of the manuscript. N.C. supervised analysis and contributed to writing the manuscript. C.T. provided CSIRO ensemble data, coordinated data and contributed to writing the manuscript. I.Y. provided satellite data and contributed to analysis and writing the manuscript. A.S. provided IHE ensemble data and contributed to analysis and writing the manuscript. N.M. and T.S. provided KU ensemble data and contributed to writing the manuscript. L.E. provided USGS ensemble data and contributed to writing the manuscript. O.A. and Ø.B. contributed ERAI statistics. M.D., A.B. and J. Staneva contributed IHE ensemble data. L.M. contributed Joint Research Centre ensemble data and developed community codes. M.C.-P. contributed ECCC ensemble data and contributed to writing the manuscript. P.C. and M.M. contributed IHC ensemble data and contributed to writing the manuscript. B.T. and M.W. contributed LBNL ensemble data and contributed to writing the manuscript. L.B. and J.W. contributed NOC ensemble data. A.W. and B.K. had input via workshops. J. Stopa contributed to analysis and writing the manuscript. F.A. assisted with figure development.

Correspondence to Joao Morim.

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Peer review information: Nature Climate Change thanks Gonéri Le Cozannet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Tables 1–4, Figs. 1–29 and references.

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