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Climate change, extreme sea levels & hydrodynamical models

Climate change, extreme sea levels & hydrodynamical models. Introduction. Quantifying the impacts of climate change upon extreme sea levels . Spatial variation in impacts. Scientific tools - Observational data, Process knowledge, Numerical models Statistical tools

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Climate change, extreme sea levels & hydrodynamical models

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  1. Climate change, extreme sea levels & hydrodynamical models

  2. Introduction • Quantifying the impacts of climate change upon extreme sea levels. • Spatial variation in impacts. • Scientific tools - • Observational data, • Process knowledge, • Numerical models • Statistical tools • Extreme value theory, • Spatial statistics, • Nonparametric regression • Multivariate analysis

  3. Hydrodynamical models

  4. North Sea models < 35km NEAC grid < 12km NISE grid V V

  5. Scientific problems Observational climate data • Observational climate data for 1970-1999. • Model output verified against observational sea level data. • Test for evidence for temporal change in extreme values. Numerical climate model • Generate 30-year long stationary sequences of sea level data. • Climate input data generated using ECHAM-4 climate model under two scenarios: current CO2, double current CO2. • Interest is in comparing extremal characteristics of the spatio-temporal fields generated under the two scenarios.

  6. Surge residuals Annual maxima Univariate extreme value modelling for each site Use the GEV (Generalized Extreme Value) distribution Previous findings Changes in 50 year surge residuals (in cm) as a result of a doubling of CO2 levels

  7. Statistical methods for spatial extremes

  8. An example Initial location = 0, Scale = 1, Shape = -0.35, Dependence = 0.7 • Spatial variability • Spatial coherence • Residual spatial dependence Insert a map here: simulated parameter surface

  9. Methods Insert a diagram here

  10. Multivariate extremes BEVL (Bivariate Extreme Value Logistic) distribution General theory • Componentwise maxima • Multivariate extreme value distribution • Modelling of marginal and dependence characteristics • Parameter linking Inference • 2-step likelihood estimation • Independence working assumption • Sandwich variance estimator • Fix margins, transform and estimate  • 1-step likelihood estimation.

  11. A local grid-based method Grid Evaluation points Kernels

  12. Acknowledgements • Supervisors • Janet Heffernan • Jonathan Tawn • Assistance and advice • Johan Segers, Vadim Kuzmin, Alexandra Ramos, • Christopher Ferro, Alec Stephenson, Matthew Killeya • Images • Web sources (list available on request) • Lamb, H. (1991) Historic storms of the North Sea, British Isles and Northwest Europe,CUP, Cambridge.

  13. Apparent independence

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