Ralph Rayner Marine Information Alliance

1. Assessing the impact of long term trends in extreme sea levels on offshore and coastal installations Ralph Rayner Marine Information Alliance Sea level Workshop – Paris 2006

2. Introduction • Lessons learnt from recent events • Chance, nature and human influence • Risk and uncertainty in design • Some conclusions

4. Sea level Workshop – Paris 2006

6. These events have highlighted the devastating impact extreme sea levels can have on coastlines and coastal communities

7. As well as causing tragic loss of life and damage to property Hurricanes Rita and Katrina highlighted the potential inadequacy of present design criteria for offshore and coastal installations • For the specific case of hurricane extremes • For the more general case of long-term trends in any environmental parameters that impact safe design

8. Offshore and coastal structures cannot be designed to be absolutely safe against all environmental impacts • They are designed such that the risk of failure is acceptably low • Design is a compromise between cost and risk of structural failure

12. 114 shallow water fixed platforms destroyed • 52 sustained major structural damage • Even recent designs damaged or destroyed

13. Economic impact • Cost of replacement/repair (being assessed, $ billions) • Loss of production ($8-10 billion for GoM) • Cost of making structures safer in the future

14. Raised serious questions about whether climate can be considered ‘stationary’ in the context of design and… • If not, how should long term change be factored into the design process?

15. Has ignited a heated and sometimes acrimonious debate about causality

16. Chance, nature or human influence? • Were the recent ‘extreme’ hurricane events unusual? • Use as an example of the general problems of attributing causality when dealing with long term trends • Database inaccuracy? • Natural long-term climate variability? • Global warming? • Chance?

17. Database inaccuracy? • Reliability of historical observations • Changes in observation/measurement techniques • In the GoM hurricane example bias in pre 1950 data (based on extrapolation of coastal observations prior to this date) a major factor in under design (Cooper and Steer, 2006)

18. Nature? • Problem of understanding natural variability • Historical records short • Paleo records uncertain or do not exist • Understanding of processes imperfect • Numerical/mathematical prediction tools imperfect especially when dealing with extreme events

19. For the GoM hurricane example Goldenberg et al., Science, 2001. Sutton and Hodson, Science, 2005.

20. Chance? • Unique combination of rare but not impossible events

22. Global warming? • Overwhelming evidence of anthropogenic change but…. • Large uncertainties in magnitude of long term trends, eg trends in global mean sea level, 9-88cm by end of century (IPCC, 2001) • Large uncertainties in potential impacts • What rate to factor into design?

23. Strong correlation between hurricane power and mean sea surface temperature (Emanuel, 2005) • Not sufficient to explain 2005 hurricane intensity

24. The challenge • Uncertainties in causality of extreme events and the magnitude of long term trends poses a significant challenge for those concerned with design of offshore and coastal structures • How to incorporate these long term trends into the design process?

26. Risk and uncertainty in design • Engineers seek to design structures so that they will survive the most extreme events they are likely to encounter during their design life • The greater the economic, safety or environmental impact of design failure the more conservative the approach eg • Offshore structures designed for 1 in 100 year event • Coastal nuclear power stations designed for a 1 in 10,000 year (or more) event

27. Risk and uncertainty in design • In almost all cases the determination of design environmental extremes assumes that climate is ‘stationary’ and that extreme events are randomly distributed over the design return period • Long term trends (ie more than seasonal) are not considered

28. Derivation of extremes • Based on analysis of time histories of observations and/or analysis of hindcast model data • Approximate the distribution of available data to idealised probability distributions eg Weibull, Fisher Tippett, Gumbel

29. Derivation of extremes • Consider the joint probability of occurrence of different parameters (if known eg relationship between extreme surge elevation and extreme tide)

30. Conclusions • Make an ‘arbitrary’ allowance for long term trends (eg 5mm per year increase in msl for a coastal LNG facility) • Design to permit future changes as required • Determine the economic case for over design versus risk of failure • Reduce uncertainty in knowledge of long term trends

31. Conclusions • Importance of maintaining high quality time history data • Better understanding of any ‘bias’ in past data or data from proxies • Ensure sufficient investment in improving knowledge • Engineers will work with a single extreme value in the final design • Need to work at reducing the error in determining the extreme for a given environmental parameter and… • Understanding the economic implications at designing for the upper bound of projected long term trends

32. Conducting economic assessments • Advocating better support of long term observations and measurements • Advocating research into better understanding and predicting long term trends in the marine environment www.infomarine.org

33. ‘A pessimist complains about the wind, an optimist expects the wind, a realist adjusts the sails’ William Ward Thanks for listening Sea level Workshop – Paris 2006