Bayesian Hierarchical Modeling of the Ocean Windiness

Chapter
Part of the Ocean Engineering & Oceanography book series (OEO, volume 2)

Abstract

Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular pertaining to long-term trends in the wave climate. In this chapter, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over essentially the same area in the North Atlantic Ocean is investigated. When the results from the model for North Atlantic windiness are compared to the results for significant wave height over the same area, it is interesting to observe that, whereas, an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

Keywords

Wind Speed Significant Wave Height Wave Climate Wind Speed Data Spatial Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bader, J., Mesquita, M.D.S., Hodges, K.I., Keenlyside, N., Østerhus, S., Miles, M.: A review on Northern Hemisphere sea-ice, storminess and the North Atlantic oscillation: observations and projected changes. Atmos. Res. 101, 809–834 (2011)CrossRefGoogle Scholar
  2. 2.
    Beauchamp, J.J., Olson, J.S.: Corrections for bias in regression estimates after logarithmic transformation. Ecology 54, 1403–1407 (1973)CrossRefGoogle Scholar
  3. 3.
    Bender, F.A.M., Ramanathan, V., Tselioudis, G.: Changes in extratropical storm track cloudiness 1983–2008: observational support for a poleward shift. Clim. Dyn. 38, 2037–2053 (2012)CrossRefGoogle Scholar
  4. 4.
    Collins, M., Chandler, R.E., Cox, P.M., Huthnance, J.M., Rougier, J.: Quantifying future climate change. Nat. Clim. Change 2, 403–409 (2012)CrossRefGoogle Scholar
  5. 5.
    Ferguson, R.: River loads underestimated by rating curves. Water Resour. Res. 22, 74–76 (1986)CrossRefGoogle Scholar
  6. 6.
    Gastineau, G., Soden, B.J.: Model projected changes of extreme wind events in response to global warming. Geophys. Res. Lett. 36(L10810), 1–5 (2009)Google Scholar
  7. 7.
    Stephenson, D.B., Collins, M., Rougier, J.C., Chandler, R.E.: Statistical problems in the probabilistic prediction of climate change. Environmetrics 23, 364–372 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Stow, C.A., Reckhow, K.H., Qian, S.S.: A Bayesian approach to retransformation bias in transformed regression. Ecology 87, 1472–1477 (2006)CrossRefGoogle Scholar
  9. 9.
    Talley, L.D., Pickard, G.L., Emery, W.J., Swift, J.H.: Descriptive Physical Oceanography an introduction, 6th edn. Elsevier, Boston (2011)Google Scholar
  10. 10.
    Tebaldi, C., Knutti, R.: The use of the multi-model ensemble in probabilistic climate projections. Philos. Trans. R. Soc. A 365, 2053–2075 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., Da Costa Bechtold, V., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Van de Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., McNally, A.P., Mahfouf, J.F., Morcrette, J.J., Rayner, M.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Vitebro, P., Woolen, J.: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc. 131, 2961–3012 (2005)Google Scholar
  12. 12.
    Vanem, E., Breivik, O.N.: Bayesian hierarchical modelling of North Atlantic windiness. Nat. Hazard Earth Syst. Sci. 13, 545–557 (2013)CrossRefGoogle Scholar
  13. 13.
    Vanem, E., Huseby, A.B., Natvig, B.: A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic. Stoch. Env. Res. Risk Assess. 26, 609–632 (2012)CrossRefGoogle Scholar
  14. 14.
    Vanem, E., Huseby, A.B., Natvig, B.: Modeling ocean wave climate with a Bayesian hierarchical space-time model and a log-transform of the data. Ocean Dyn. 62, 355–375 (2012)CrossRefGoogle Scholar
  15. 15.
    Vanem, E., Huseby, A.B., Natvig, B.: A stochastic model in space and time for monthly maximum significant wave height. In: Abrahamsen, P., Haugen, R., Kolbjørnsen, O. (eds.) Geostatistics Oslo 2012, pp. 505–517. Springer, Heidelberg (2012)Google Scholar
  16. 16.
    Vanem, E., Huseby, A.B., Natvig, B.: Bayesian hierarchical spatio-temporal modelling of trends and future projections in the ocean wave climate with a CO\(_2\) regression component. Environ. Ecol. Stat. (in press) (2013)Google Scholar
  17. 17.
    Wallcraft, A.J., Kara, A.B., Barron, C.N., Metzger, E.J., Pauley, R.L., Bourassa, M.A.: Comparison of monthly mean 10 m wind speeds from satellites and NWP products over the global ocean. J. Geophys. Res. 114(D16109), 1–14 (2009)Google Scholar
  18. 18.
    Yin, J.H.: A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett. 32(L18701), 1–4 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Mathematics DepartmentUniversity of OsloOsloNorway

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