North Sea nearsurface wind climate and its relati

Authors: Kay Sušelj Abha Sood Detlev Heinemann

Publish Date: 2009/05/23

Volume: 99, Issue: 3-4, Pages: 403-419

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Abstract

The North Sea 10m wind speed WS10 climate is compared and related to circulation patterns based on the sea level pressure SLP extracted from three reanalysis and one highresolution model dataset The mean magnitude and the trends of WS10 depend considerably on the selected reanalysis The variability of WS10 among the three reanalysis datasets is highly correlated in the recent period 1980–2000 but less so in the past period 1960–1980 The WS10 over the North Sea is well represented by the relatively low reanalysis resolution when compared to the highresolution WS10 model data partially owing to the high spatial correlation of WS10 Exceptions are observed only at the coastal areas The dominant mode of WS10 explains coherent variability of WS10 over the North Sea and is related to a SLP pattern similar to the North Atlantic oscillation NAO The increase of the magnitude of the dominant WS10 pattern is related to the increase of the magnitude of the NAOlike SLP pattern from 1960s to mid1990s The second dominant WS10 pattern—a dipole in WS10 to the north and south of Great Britain—is related to the differences in SLP between Scandinavia and Iceland The relation between the second WS10 and SLP patterns is more prominent in the recent period The extreme WS10 in the German Bight is related to the low SLP over Scandinavia The extreme WS10 is strongly increasing from the early 1980s to the beginning of 1990s which is not observed in the corresponding SLP time series over ScandinaviaThe climatology of marine surface wind field is of considerable commercial interest eg for harnessing wind energy at favorable offshore sites for the shipping and for the offshore oil and gas industry In view of a strong increase in the industrial exploitation of the North Sea this study addresses some questions related to the climatology of the daily mean wind speed at 10m height WS10 namely 1 whether there are important differences in the mean and extreme WS10 in different lowresolution reanalysis datasets especially related to their longterm trends 2 how well is WS10 represented in the lowresolution global reanalysis dataset horizontal resolution of roughly 100 × 100 km compared to the highresolution simulations horizontal resolution of 3 × 3 km or alternatively are there important processes that remain unresolved at low resolution and are resolved in higherresolution simulations and whether there is a subsequent longterm impact 3 can the relationship between the monthly mean WS10 and the largescale circulation patterns over the European–Atlantic region and their interannual variability in the last few decades be quantified and 4 can the extreme WS10 over the German Bight be related to extreme circulation patterns and do the longterm trends of extreme WS10 and circulation patterns agreeThe WS10 from different reanalysis datasets can be used to study the nearsurface wind climatology and its relation to the circulation patterns The reanalysis data have previously been widely used for climate studies since the data quality is mostly consistent over a number of decades which is achieved by using the same model throughout the reanalysis period Kalnay et al 1996 However with the development of the observation system the quality of reanalysis data has improved over the last two decades The most pronounced change affecting WS10 is probably the assimilation of the satellitemeasured nearsurface wind speeds which started in the late 1970s Kistler et al 2001 This can lead to discontinuities in the WS10 time series and artificial trends It was confirmed that some basic parameters such as the sea level pressure SLP from different reanalysis generally agree well over Europe eg Greatbatch and Rong 2006 which increases confidence in using the reanalysis data The WS10 from reanalysis may be sensitive to the model details such as the selected surface and boundary layer parameterizations Kalnay et al 1996 As far as we are aware there has been no systematic comparison of the WS10 from different reanalysis over northern EuropeMost studies concerned with the wind climatology over northern Europe use lowresolution wind speed or a proxy dataset eg derived from the horizontal pressure gradients For example Pryor and Barthelmie 2003 Pryor et al 2006 and Yan et al 2006 use reanalysis data to infer the climatology of nearsurface mean and extreme winds over northern Europe their interdecadal variability and linear trends Weisse et al 2005 investigated the changes of cyclone intensity and frequency over the North Atlantic and the North Sea from the downscaled National Centers for Environmental Prediction NCEP reanalysis data of around 50km horizontal grid size Studying extreme winds some authors prefer to use proxy data such as the mean SLP or geostrophic wind speed inferred from the SLP gradients arguing that the SLP data are less prone to systematic errors which may lead to artificial trends eg Schmidt and von Storch 1993 Alexandersson et al 2000 Matulla et al 2007 The proxy data derived from SLP are usually able to capture the largescale forcing on the wind field but not the local effects The ability of the lowresolution wind speed datasets or the proxy data to represent realistic surface wind conditions over the North Sea remains unclear The largest differences between low and highresolution wind speed are expected close to the coastline or orographic features where the wind speed may deviate due to strong gradients in forcing and formation of local circulation patterns or in case of strong horizontal gradients eg frontsThe influence of the North Atlantic oscillation NAO which is described by the anomaly of pressure gradient between northwestern usually Iceland and southwestern North Atlantic usually Azores on the wind speed over northern Europe is well documented eg Marshall et al 2001 Hurrell and van Loon 1997 Trigo et al 2002 The NAO index NAOI is well correlated with the wind speed over northern Europe especially in the winter season where the largescale circulation has a more dominant influence on the weather In the summer season the surface heating produces disturbances weakening the largescale circulation An increase of the NAOI from the 1960s to the mid1990s was observed It is known that the increase in NAOI leads to higher wind speeds over northern Europe eg Hurrell and van Loon 1997 Here we investigate whether a regional spatial pattern of SLP exists which has a more dominant influence on the WS10 variability over northern Europe than the NAO It is also not clear if there are other patterns which are able to describe an additional fraction of WS10 variability For example Barnston and Livezey 1987 have observed that the second dominant mode of the North Atlantic pressure variability is the East Atlantic EA pattern which can explain a considerable part of the wind speed variability in northern Europe Kaas et al 1996 found that a pressure structure similar to the NAO is related to the wind speed in northern Europe although the northern center of their pattern is more elongated and shifted eastwards Rogers 1997 identified a pattern similar to the one from Kaas et al 1996 and observed that it was related to the position of the storm tracks Another alternative is to relate the surface wind speeds to other largescale fields such as the surface temperature The study by Deser and Blackmon 1993 suggests that the Sea Surface Temperature SST is well correlated with the wind speed However they found that the relationship is local and suggest that the surface wind influences the SST by mixing the upper ocean watersHighresolution simulations can resolve a number of physical processes such as the land–sea breeze in the coastal areas which is additional information not obtained by Statistical Downscaling of lowresolution reanalysis data The climate signature of these processes may be vital for some applications such as for wind energy resource studies The longterm fluctuations and trends extracted from the reanalysis can be combined with the detailed regional information from highresolution simulations This information is vital for risk management resource assessment and longterm planning to compensate for the inherent fluctuations of the climate system in the development of renewable energy resources and by other stakeholders Here we use 3year data from a mesoscale model Weather Research and Forecasting WRF simulation with 3km resolution to determine the significance of key regional physical processesAs opposed to the wellstudied variability of the mean wind speed over northern Europe the variability of extreme winds over the past few decades is less known On one hand with an increase in the NAOI and the mean wind speed from the 1960s to the mid1990s the extreme wind speeds are expected to rise On the other hand there are studies that show a trend in shifting of the North Atlantic storm track northwards in the past decades which causes a trend of decreasing storms in the southern part of northern Europe Schneidereit et al 2005 Wang et al 2006 The extreme winds can be characterized by different criteria such as the occurrence of wind speeds exceeding a threshold eg Weisse et al 2005 or wind speeds at certain high percentiles such as for example the 90 percentile Pryor and Barthelmie 2003 Matulla et al 2007 With the first approach the number of storm events exceeding a predefined threshold level is investigated while with the second approach the intensity of storms is studied Different and sometimes contradictory results regarding the trends of the extreme winds or storms have been presented Based on the SLP measurements Schmidt and von Storch 1993 found no increase and very little interannual variability of the high wind speed percentiles over the last century in the German Bight From the SLP analysis Matulla et al 2007 concluded that the extreme wind speeds in the northeastern Atlantic region were high in the late nineteenth and the beginning of the twentieth century lower in the middle of the twentieth century and then at the end of the twentieth century reach a similar level as at the beginning of the twentieth century Their analysis is focused on the wind speed north and east of the North Sea Pryor and Barthelmie 2003 observed an increase in the high wind speed in the last few decades over the Baltic Sea and parts of the North Sea derived from the NCEP/National Center for Atmospheric Research NCR reanalysis An increase in the extreme wind speeds over the Baltic is related to an increase in the frequency of the western types of “Grosswetterlagen” GWL in the last quarter compared to the middle of the twentieth century The extreme weather conditions are usually not well linked to the largescale circulation patterns such as the NAO since the NAO explains well the variability on monthly to interdecadal timescales while the extreme values by definition occur only for short periods Therefore some authors link the extreme values to different circulation patterns Yiou and Nogaj 2004 Panja and Selten 2007The paper is structured as follows The data are described in “Section 2” In “Section 3” the WS10s over the North Sea from different reanalysis datasets are compared to each other and to the highresolution WS10 The relation of the mean WS10 to the circulation patterns and their time evolution and the relation of the extreme WS10 to the extreme circulation patterns are then derived Finally the discussions and conclusions are presented in “Section 4”

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