Changes in extreme wind speeds in NW Europe simulated by generalized linear models |
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| Authors: | Z Yan S Bate R E Chandler V Isham H Wheater |
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| Institution: | (1) RCE-TEA, Institute of Atmospheric Physics, Beijing, China;(2) Laboratory for Climate Studies, China Meteorological Administration, Beijing, China;(3) Department of Statistical Science, University College London, London, United Kingdom;(4) Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, London, United Kingdom |
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| Abstract: | 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. |
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