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1980–2010 Variability in U.K. Surface Wind Climate 1172 JOURNAL OF CLIMATE VOLUME 26 1980–2010 Variability in U.K. Surface Wind Climate NICK EARL AND STEVE DORLING School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom RICHARD HEWSTON Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii ROLAND VON GLASOW School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom (Manuscript received 6 December 2011, in final form 29 August 2012) ABSTRACT The climate of the northeast Atlantic region comprises substantial decadal variability in storminess. It also exhibits strong inter- and intra-annual variability in extreme high and low wind speed episodes. Here the authors quantify and discuss causes of the variability seen in the U.K. wind climate over the recent period 1980–2010. Variations in U.K. hourly mean (HM) wind speeds, in daily maximum gust speeds and in asso- ciated wind direction measurements, made at standard 10-m height and recorded across a network of 40 stations, are considered. The Weibull distribution is shown to generally provide a good fit to the hourly wind data, albeit with the shape parameter k spatially varying from 1.4 to 2.1, highlighting that the commonly assumed k 5 2 Rayleigh distribution is not universal. It is found that the 10th and 50th percentile HM wind speeds have declined significantly over this specific period, while still incorporating a peak in the early 1990s. The authors’ analyses place the particularly ‘‘low wind’’ year of 2010 into longer-term context and their findings are compared with other recent international studies. Wind variability is also quantified and discussed in terms of variations in the exceedance of key wind speed thresholds of relevance to the insurance and wind energy industries. Associated interannual variability in energy density and potential wind power output of the order of 620% around the mean is revealed. While 40% of network average winds are in the southwest quadrant, 51% of energy in the wind is associated with this sector. The findings are discussed in the context of current existing challenges to improve predictability in the Euro-Atlantic sector over all time scales. 1. Introduction coastline). Seasons dominated by blocking or cyclonic weather types, especially winter, can strongly skew the Located in one of the most common regions for at- magnitude of annual insured losses (Munich Re 2002), mospheric blocking, while also situated toward the end as well as have profound effects on the variability of point of a major midlatitude storm track, the United wind power generated by the expanding U.K. wind en- Kingdom has one of the most variable wind climates and ergy sector (Sinden 2007). northwest Europe is a challenging region for prediction The cold European winter of 2009/10 and the extreme on all time scales (Barriopedro et al. 2006, 2008; Dacre cold of December 2010 have prompted much discussion and Gray 2009; Woollings 2010). Regional wind climate about long-term climate variations and their possible variability in the United Kingdom is large, governed by impacts. However, Cattiaux et al. (2010) show that the latitude (proximity to storm track), altitude, and type of cold European surface temperature anomaly of up to fetch (the United Kingdom has an exceptionally long 68C for winter 2009/10 was in fact not as great as might have been expected given the associated record-breaking North Atlantic Oscillation (NAO) and blocking fre- Corresponding author address: Nick Earl, School of Environ- mental Sciences, University of East Anglia, Norwich, Norwich quency indices. These authors concluded that the event Research Park, Norwich NR4 7TJ, United Kingdom. was a cold extreme that was not in any way inconsistent E-mail: [email protected] with an otherwise generally warming climate. Focusing DOI: 10.1175/JCLI-D-12-00026.1 Ó 2013 American Meteorological Society Unauthenticated | Downloaded 10/04/21 09:19 PM UTC 15 FEBRUARY 2013 E A R L E T A L . 1173 on predictability at the monthly, seasonal, and decadal storms, the relative vulnerability of the affected areas, time scale, many forcing agents are thought to modulate whether trees are in leaf or not, and the relative dryness European climate, such as sea surface temperatures, or wetness of the ground at the time of windstorm pas- stratospheric circulation, and solar variability (Rodwell sage (Hewston and Dorling 2011). et al. 1999; Lockwood et al. 2010, 2011; Woollings et al. Wang et al. (2009) demonstrated that storminess in 2010). Regional responses also arise from the dynamical the North Atlantic–European region, based on atmo- reaction of the climate system to this forcing (Woollings spheric sea level pressure gradients, undergoes sub- 2010; Jung et al. 2011) and internal atmospheric dy- stantial decadal and longer time scale fluctuations and namics can be an important source of low-frequency that these changes have a seasonality and regionality to atmospheric interannual variability. Solar activity in them. In particular, these authors showed that winter 2009/10 fell to values unknown since the start of the storminess reached an unprecedented maximum in the twentieth century and Lockwood et al. (2010), linking early 1990s in the North Sea and showed a steady in- this to the occurrence of recent cold European winter crease in the northeastern part of the North Atlantic– months, estimate an 8% chance that the decline, which European region, significantly correlated with variability began around 1985, could continue to Maunder mini- in the NAO index. The link to the NAO is found in all mum levels within 50 years, from the previous grand seasons except autumn. As the NAO swings from one solar maximum. On the other hand, European Centre phase to the other, large changes to windstorm intensity for Medium-Range Weather Forecasts (ECMWF) ex- and track and to mean wind speed and direction are periments (Jung et al. 2011), testing the sensitivity to observed over the Atlantic (Hurrell et al. 2003). Both reduced ultraviolet radiation of the onset of the cold Atkinson et al. (2006), analyzing the period 1990–2005, 2009/10 European winter, show that the unusually low and Boccard (2009), for 1979–2007, showed that the solar activity contributed little, if any, to the observed NAO is a good approximation for synoptic weather type NAO anomaly. Much research is ongoing to improve indices such as Grosswetterlagen (Hess and Brezowsky our predictive capability in Europe. 1952; James 2007) and the Jenkinson–Collison weather In Europe, windstorms remain the most economically type classification (Jenkinson and Collison 1977; Jones significant weather peril when averaging over multiple et al. 1993) and for wind indices in Northern Europe years. The winter storms of the early 1990s had some over the respective periods. A decrease in post-1990 dramatic effects on the United Kingdom, the winter of northern European windiness is clearly revealed in these 1989/90 being one of the most damaging on record, ex- studies. By considering the longer-term Grosswetterla- emplified by windstorm Daria on 25 January (McCallum gen and Jenkinson variability through the twentieth 1990). The storm tracked across a large swath of En- century, these authors concluded that care is needed in gland and Wales, causing widespread damage amount- selecting the most appropriate long-term period on ing to £1.9 billion (equivalent to £3.2 billion in 2010 which to base wind energy investment decisions and that values) of U.K. insured losses (Munich Re 2002). A access to reliable and longer-term wind speed mea- second storm, Vivian, buffeted the United Kingdom surements is highly desirable. Recent industry discus- between 26 and 28 February 1990 and contributed to sion of the low-wind year of 2010 requires further U.K. weather-related property losses that year reaching supporting analysis and discussion of the wider context. their highest mark on record. In the winter of 1991/92 As greater reliance on wind power for the United the New Year’s Day Storm affected northern Scotland Kingdom’s electricity generation needs increases, so and (far more severely) Norway (Gronas 1995), pro- will the magnitude of risk due to exposure of the per- ducing stronger U.K. surface winds than Daria and formance of the turbines to climate change (Harrison Vivian, though causing less U.K. damage because of et al. 2008). reduced vulnerability to insurance losses in the affected Both the wind energy and insurance industries are regions. Meanwhile, winter storm Xynthia in February sensitive to wind speed distributions. The Weibull dis- 2010 caused insured losses totaling almost $3 billion in tribution function has become widely used in meteorol- Germany, France, and Spain, representing the world’s ogy to estimate how observed wind speeds tend to vary third most costly catastrophe of that year (Swiss Re around their mean at sites where only a long-term av- 2011), more costly than any 2010 North Atlantic hurri- erage is known. Originally used to describe the size cane. Indeed total European windstorm damage is distribution of particles, the Weibull distribution has considerable, equivalent to that of worldwide hurricanes numerous applications, including in general insurance to when averaged over longer time scales (Malmquist model reinsurance claim sizes (Kremer 1998). The use 1999). Total annual losses attributed to windstorms de- and importance of the Weibull distribution has grown pend, for example, on the precise track and intensities of immensely in the wind power industry and has been used Unauthenticated | Downloaded 10/04/21 09:19 PM UTC 1174 JOURNAL OF CLIMATE VOLUME 26 to help site many thousands of wind turbines (Petersen The results presented in this paper include analysis et al. 1998; see section 2c). and discussion of wind speed threshold exceedance Numerous authors have also been considering the frequencies, the proportion of time that the hourly possible impact of climate change over the twenty-first winds or daily gust speeds are above a set of specific century on the wind climate of northwest Europe, in the speeds, at individual sites and on average across the context of the decadal variability seen over the last network of 40 (39) hourly wind speed (gust speed) sites.
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