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Theoretical and Applied Climatology

, Volume 83, Issue 1–4, pp 121–137 | Cite as

Changes in extreme wind speeds in NW Europe simulated by generalized linear models

  • Z. Yan
  • S. Bate
  • R. E. Chandler
  • V. Isham
  • H. Wheater
Article

Summary

We investigate the capability of generalized linear models (GLMs) to simulate sequences of daily maximum wind speed (DMWS), at a selection of locations in NW Europe. Models involving both the gamma and Weibull distributions have been fitted to the NCEP reanalysis data for the period 1958–1998. In simulations, these models successfully reproduce the observed increasing trends up to 0.3 m/s per decade in coastal or oceanic locations for the wintertime and the decreasing trends down to –0.2 m/s per decade in inland Europe for the summertime. Annually extreme winds exhibit an increasing tendency (with median estimates up to 0.6 m/s per decade) at the studied locations. The gamma model slightly overestimates the upper percentiles of the wind speed distribution, but reproduces trends better than the Weibull model. In both the NCEP data and GLM simulations, local extreme DMWS events (defined in terms of threshold exceedances) have increased dramatically in frequency during winter; decreasing trends are more common in summer. The NCEP data indicate similar trends in the frequencies of large-scale windy events (defined via simultaneous exceedances at 2 or more locations). Overall, these events have increased in number; at the scale of the North Sea basin, their number may have changed from 3–5 days per year during the earlier decades, to 5–7 days per year during later decades based on observational estimates. An increase in the frequency of large-scale extreme winter storms is implied. The GLMs underestimate these large-scale event frequencies, and provide imprecise estimates of the corresponding secular trends. These problems could be rectified by using a better representation of spatial dependence. The present results suggest that GLMs offer a useful tool to study local climate extremes in the context of changing climate distributions; they also provide some pointers towards improving the representation of extremes at a regional scale.

Keywords

Generalize Linear Model Weibull Model NCEP Reanalysis Winter Storm Extreme Wind 
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.

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References

  1. Aitkin M, Clayton DG (1980) The fitting of exponential, Weibull and extreme value distributions to complex censored survival data using GLIM. Appl Statist (29): 156–163Google Scholar
  2. Bate SM (2004) Generalized linear models for large dependent data sets. Ph.D. thesis, Department of Statistical Science, University College LondonGoogle Scholar
  3. Chandler RE (2002) GLIMCLIM: Generalised linear modelling for daily climate time series (software and user guide). Research Report No. 227, Department of Statistical Science, University College London. http://www. ucl.ac.uk/Stats/research/abs02.html#227Google Scholar
  4. Chandler RE (2005) On the use of generalized linear models for interpreting climate variability. Environmetrics.(in press)Google Scholar
  5. Chandler RE, Wheater HS (2002) Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland. Water Resour Res 38(10): 1192, doi: 10.1029/2001WR000906Google Scholar
  6. Coe, R, Stern, RD 1982Fitting models to daily rainfall data.J Appl Meteor2110241031CrossRefGoogle Scholar
  7. Coles S (2001) An introduction to the statistical modelling of extreme values. London: SpringerGoogle Scholar
  8. Conradsen, K, Nielsen, LB, Prahm, LP 1984Review of Weibull statistics for estimation of wind speed distributions.J Climate Appl Meteor2311731183CrossRefGoogle Scholar
  9. Cox DR, Hinkley DV (1974) Theoretical statistics. London: Chapman and HallGoogle Scholar
  10. Dobson AJ (2001) An introduction to generalized linear models, 2nd edn. Boca Raton: Chapman and Hall/CRCGoogle Scholar
  11. Folland CK, Karl TR, Christy JR, Clarke RA, Gruza GV, Jouzel J, Mann ME, Oerlemans J, Salinger MJ, Wang S-W (2001) Observed climate variability and change. In: Houghton et al (eds) Climate change 2001. The Scientific Basis. Cambridge: Cambridge University PressGoogle Scholar
  12. Frich, P, Alexander, LV, Della-Marta, P, Gleason, B, Haylock, M, Klein Tank, A, Peterson, T 2002Observed coherent changes in climatic extremes during the 2nd half of the 20th century.Climate Research19193212Google Scholar
  13. Hurrell, JW 1995Decadal trends in the North Atlantic Oscillation – regional temperatures and precipitation.Science269676679Google Scholar
  14. IPCC (2001) Third Assessment Report – Climate Change 2001: The Scientific Basis. Cambridge: Cambridge University PressGoogle Scholar
  15. Jones, PD, Horton, EB, Folland, CK, Hulme, M, Parker, DE, Basnett, TA 1999The use of indices to identify changes in climatic extremes.Climatic Change42131149CrossRefGoogle Scholar
  16. Kalnay, EM,  et al. 1996The NCEP/NCAR 40-Year Reanalysis Project.Bull Amer Meteor Soc77437471CrossRefGoogle Scholar
  17. Karl TR et al (1999) Weather and climate extremes. Boston: Kluwer Academic PublishersGoogle Scholar
  18. Katz, RW, Brown, BG 1992Extreme events in a changing climate are more important than averages.Climatic Change21289302CrossRefGoogle Scholar
  19. McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. London: Chapman and HallGoogle Scholar
  20. Monahan JF (2001) Numerical methods of statistics. Cambridge: Cambridge University PressGoogle Scholar
  21. Nelder, JA, Wedderburn, RWM 1972Generalized linear models.J Roy Stat Soc Series A135370384Google Scholar
  22. Stern, RD, Coe, R 1984A model fitting analysis of daily rainfall data.J Roy Stat Soc Series A147134Google Scholar
  23. Tuller, SE, Brett, AC 1984The characteristics of wind velocity that favour the fitting of a Weibull distribution in wind speed analysis.J Climate Appl Meteor23124134CrossRefGoogle Scholar
  24. Verkaik JW (2000a) Documentatie windmetingen in Nederland (documentation on wind speed measurements in the Netherlands). Technical Report, Koninklijk Nederlands Meteorologisch Institut (http://www.knmi.nl/samenw/hydra/documents/docum0.htm). In DutchGoogle Scholar
  25. Verkaik, JW 2000bEvaluation of two gustiness models for exposure correction calculation.J Appl Meteor3916131626CrossRefGoogle Scholar
  26. Yan, Z, Jones, PD, Davies, TD, Moberg, A, Bergstrom, H, Camuffo, D, Cocheo, C, Maugeri, M, Demaree, G, Verhoeve, T, Thoen, E, Barriendos, M, Rodriguez, R, Martin-Vide, J, Yang, C 2002Trends of extreme temperatures in Europe and China based on daily observations.Climatic Change53355392Google Scholar
  27. Yan, Z, Bate, S, Chandler, RE, Isham, V, Wheater, H 2002aAn analysis of daily maximum wind speed in northwestern Europe using generalized linear models.J Climate1520732088CrossRefGoogle Scholar
  28. Yan, Z, Jones, PD, Moberg, A, Bergstrom, H, Davies, TD, Yang, C 2001Recent trends in weather and seasonal cycles, an analysis of daily data from Europe and China.J Geophys Res10651235138CrossRefGoogle Scholar
  29. Yan Z, Yang C (2000) Climate extreme changes in China during 1950–1997. Climate and Environmental Research 5(3): 267–272 (in Chinese)Google Scholar
  30. Zwiers, FW, Kharin, VV 1998Changes in extremes of climate simulated by CCC GCM2 under CO2 doubling.J Clim1122002222CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Z. Yan
    • 1
    • 2
  • S. Bate
    • 3
  • R. E. Chandler
    • 3
  • V. Isham
    • 3
  • H. Wheater
    • 4
  1. 1.RCE-TEA, Institute of Atmospheric PhysicsBeijingChina
  2. 2.Laboratory for Climate Studies, China Meteorological AdministrationBeijingChina
  3. 3.Department of Statistical ScienceUniversity College LondonLondonUnited Kingdom
  4. 4.Department of Civil and Environmental EngineeringImperial College of Science, Technology and MedicineLondonUnited Kingdom

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