The primary application area of this proposal relates to characterising global sea state extremes, their temporal evolution and potential long-term impacts. While various studies have examined the extremes of ocean wave height both locally and globally, on a range of temporal scales and using a range of source data, more recently, independent groups, including European Space Agency, Climate Change Initiative for Sea State
, have produced updated quality controlled and calibrated data sets based on the most currently available satellite observations. However, recent comparative analysis of several global sea state observational gridded products has revealed considerable disagreement in temporal and geographic characteristics of (mean) wave climate with indication that similar, if not more dramatic, discrepancies exist for more extreme conditions. Appropriate statistical methods, including extreme value theory incorporating modeling of complex spatio-temporal structure, will be applied to robustly evaluate temporal variability in extreme sea states from diverse data sets. The development of statistical models is crucial since the underlying causes behind discrepancies remain unclear, as do the potential implications for projected impacts in relevant regions. Different oceans exhibit very different behaviour and statistical descriptions need to be sensitive and flexible in this respect. The proposed statistical approach will be based on graphical models for multivariate extremes and will facilitate spatio-temporal modelling of variables such as significant wave height, wave direction and wind speed, but also combinations of the different variables within one model, with quite general extremal dependence structure. Output from this analysis may be used to direct more detailed study of particular regions (potentially with a view to explore region specific impacts) in order to both identify limitations of the observations and data sets, and, where possible, underlying causes of disagreement. The student will be supported by field-leading research teams in the UK in the areas of applied statistics and extremes, and oceanic and coastal observation and modelling (located in Edinburgh, Liverpool and Southampton), and will learn a range of valuable cross-disciplinary skills, including how to work with large multivariate environmental data sets, and how to develop and apply statistical models for complex environmental information, with application to real-world environmental impacts.