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Ocean Dynamics

, Volume 62, Issue 3, pp 355–375 | Cite as

Modelling ocean wave climate with a Bayesian hierarchical space–time model and a log-transform of the data

  • Erik Vanem
  • Arne Bang Huseby
  • Bent Natvig
Article

Abstract

Long-term trends in the ocean wave climate because of global warming are of major concern to many stakeholders within the maritime industries, and there is a need to take severe sea state conditions into account in design of marine structures and in marine operations. Various stochastic models of significant wave height are reported in the literature, but most are based on point measurements without exploiting the flexible framework of Bayesian hierarchical space–time models. This framework allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space–time model with a log-transform for significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined, and the results from applying the model to data of different temporal resolutions will be discussed. Different model alternatives have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are in reasonable agreement and also agree fairly well with previous studies. The log-transform was included in order to account for observed heteroscedasticity in the data, and results are compared to previous results where a similar model was employed without a log-transform. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms with relevant meteorological data, will be presented.

Keywords

Ocean wave climate Significant wave height Stochastic modelling Spatiotemporal modelling Long-term trends Effects of climate change 

Notes

Acknowledgements

The authors want to express their thanks to Dr. Andreas Sterl at KNMI for kindly providing the data used in this analysis and for clarifying some issues discovered when investigating the data. Thanks also to one of the reviewers for valuable suggestions on how to improve the paper, most notably with regards to bias correction and the semi-annual component. The simulations for the six-hourly data, which were very computational intensive and time-consuming, were performed on the Titan Cluster, owned by the University of Oslo and the Norwegian metacenter for High Performance Computing (NOTUR) and operated by the Research Computing Services group at USIT, the University of Oslo IT-department.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of OsloOsloNorway

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