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Effects of climate change and land use change on the near surface wind characteristics in Belgium, using two downscaling approaches.
As the climate system is changing and fossil resources are diminishing, wind has emerged as an important source of renewable energy. However the amount of energy in the wind is sensitive to changes in wind speed. Projections of future large-scale circulations over Europe indicate potential changes, but it is still unknown if/how these changes might affect the wind energy availability over Europe. Estimations of changes in wind energy availability are important in order to assist the development of wind power industries. Projections of future climate and large-scale circulation systems are commonly performed using globalclimate models (GCMs). However to study the effect of a change in large-scale circulation systems on the wind power availability, the large-scale information of the GCMs needs to be downscaled to higher resolutions.
The overall objective of this dissertation is to investigate the impact of climate change on the future wind power potential over Europe, using an ensemble of statistically downscaled CMIP5 Earth System models (ESMs). For this purpose, the dissertation outlines four major parts: i) the development of a statistical downscaling method, ii) the evaluation of the method, iii) the evaluation of its driving GCMs (the ESMs) and iv) the application of the method.
In the first part, the approach to downscale the large-scale information to the small-scale wind speed climatology at hub-height is developed. This is donefor a specific site in the Netherlands (Cabauw) using long-term wind speed observations from a measurement mast, reanalysis data from ERA-Interim and
large-scale model data from ECHAM5. The method statistically relates the large-scale data (the predictors) to the small-scale hub-height wind speed climate via a regression transfer function. In contrast tocommon downscaling methods, this dissertation develops a method based on the parameters of the probability density functions (PDFs), for different stability conditions separately and includes a variable evaluation prior to the selection of the predictors. The regression results indicatethat the seasonal and diurnal conditions of the atmospheric boundary layer are important in defining which large-scale variables are best in predicting the small-scale wind climatology. During wintertime the large-scale dynamics typically dominate the near-surface wind speeds, hence ECHAM5 is skilful in representing the hub-height wind speeds and little improvement can be brought by the statistical downscaling. On the other hand, during summer, ECHAM5 is not skilful in representing the hub-height wind speed PDF due to the rather local character of the summertime winds.However, the regression analysis shows that during convective summer day conditions the observed hub-height wind speed is strongly linked to the wind speed at higher, skilfully represented levels. The summer-day hub-height wind speed PDF can therefore skilfully be predicted by the wind speedPDF parameters at 500m as the only predictors. However this is not the case during summer nights. During these very stable conditions, the boundary layer is much more shallow and the regression analysis indicates that the addition of information on the temperature gradient between the ABL and above substantially improves the simulation of the observed hub-height wind speed PDFs.
A second part evaluates whether the statistical models, which are developed in Cabauw, can be used at other locations in Europe. The comparison of the downscaled winds with observed near-surface winds over Europe shows that the spatial extent of the regions in which the downscaling models are capable in representing observed hub-height winds, depends on the diurnal, the seasonal and the local conditions (like the orography and the presence of regional wind systems). Depending on the season and time of the day, regions in Europe are defined for which the downscaling model can be skilfully applied.
Before the downscaling method is applied on an ensemble of ESMs, the ESMs are evaluated on their representation of the predictors (part 3). Predictors of statistical downscaling models are commonly derived from upper-atmospheric fields, because these variables are likely to be better represented by the
large-scale models. Since the regression analysis on ECHAM5 (step 1) indicates that the near-surface fields provide the best predictors, the ESMs are evaluated on their representation of wind and temperature PDFs in the lower 1.5km of the atmosphere, using ERA-Interim as the reference. This
height-dependent evaluation approach of theESMs is extra relevant since, compared to former generation GCMs, the resolution of the ESMs is high and their representation of the land-atmosphere interaction processes are described in greater detail. The resultsshow that the ESMss wind speed and temperature variables of the lower 1.5km are suitable to drive statistical downscaling models over most of Europe. However some small-scale and large-scale biases are present. Above coastal bays and capes, small-scale biases in the ESMs result in unskilful wind speed PDFs up to 600m. Orography might affects wind speeds throughout the lowest 1.5km of the atmosphere. This is mostly the case during summer and daytime conditions. During winter, the small-scale biases propagate less high. With exception
of the biases at the small scale, the surface wind speed PDFs north of 45°N are well represented by all the ESMs. South of 45°N, winds are affected by a large-scale bias originating from errors in the representationof the large-scale circulation, especially during winter. The large-scale wind bias is suggested to be related with a largely exaggerated latitudinal pressure gradient, leading to the too strong westerlies in the Northern Hemisphere mid-latitudes. On theother hand, the representation of the temperature PDF by the ESMs is slightly less affected by biases acting at the small scale. However aAparajita" lang="EN-US"> large-scale temperature PDF bias, related to too cold temperatures, is present over the North Atlantic Ocean and the east of Europe exhibits temperatures thatare too high in summer. Most indentified large-scale biases are independent from height and therefore also adopted by downscaling models which
are based on upper-atmospheric fields, underlining the importance of model evaluation before downscaling.
The last part performs the statistical downscaling method using only the skilful ESM fields as possible predictors and focusing only at the regions where the downscaling has shown to be skilful. Hub-height
windspeeds are downscaled for three periods: present-day (1989-2000), near future (2020-2049) and end of the century period (2070-2099). The hub-height wind speed PDF parameters are converted into power for a sample turbine and the change in power relative to the present-day climate is analyzed in a Bayesian ensemble approach. This probabilistic approach weights the participation of the ESMs in the ensemble on their bias and convergence. The analysis exhibits the importance of the PDF based approach. It shows that in a climate in which both PDF parameters increase (resulting in a wider and more symmetric PDF) the true power output will increase to a relatively lesser extent than expected from the change in mean wind speed. This is for example the case in Western-Europe during wintertime, where the expected change in large-scale westerly wind speeds might lead to an (insignificant) increase of power output of about 5%. The opposite situation is true for the Mediterranean region, where the decreasing PDF parameters (resulting in a more narrow and skewed PDF) have a large effect on the power output. In these southern regions, power outputs are expected to decrease (significantly) by magnitudes up to 16%. When power change estimations would be calculated from the change in the mean of the wind (as it
is common practice), the expected decrease inthe Mediterranean wind power output would be overestimated by almost 20% of the change. These inaccuracies in the estimations of the change in power output resulting from neglecting the changes in the PDF (its skewness and width) of the wind, are in the order of magnitude relevant for future wind power yield estimations. Not only the changes in the PDF of the wind speed, but also the form of the Cp-curve has shown to affect thechange in power output, although this is not significant.
Finally a comparison of the changes in power output projections with andwithout the statistically downscaling step, indicates no substantially differences. Although the present-day summertime hub-height wind speed PDF is clearly improved by the downscaling, the future change of is not affected by the
downscaling practice. This implies that at least for wind power, and possible other applications, the direct output of the current generation of the ESMs do not necessarily need downscaling. </></></></>
The overall objective of this dissertation is to investigate the impact of climate change on the future wind power potential over Europe, using an ensemble of statistically downscaled CMIP5 Earth System models (ESMs). For this purpose, the dissertation outlines four major parts: i) the development of a statistical downscaling method, ii) the evaluation of the method, iii) the evaluation of its driving GCMs (the ESMs) and iv) the application of the method.
In the first part, the approach to downscale the large-scale information to the small-scale wind speed climatology at hub-height is developed. This is donefor a specific site in the Netherlands (Cabauw) using long-term wind speed observations from a measurement mast, reanalysis data from ERA-Interim and
large-scale model data from ECHAM5. The method statistically relates the large-scale data (the predictors) to the small-scale hub-height wind speed climate via a regression transfer function. In contrast tocommon downscaling methods, this dissertation develops a method based on the parameters of the probability density functions (PDFs), for different stability conditions separately and includes a variable evaluation prior to the selection of the predictors. The regression results indicatethat the seasonal and diurnal conditions of the atmospheric boundary layer are important in defining which large-scale variables are best in predicting the small-scale wind climatology. During wintertime the large-scale dynamics typically dominate the near-surface wind speeds, hence ECHAM5 is skilful in representing the hub-height wind speeds and little improvement can be brought by the statistical downscaling. On the other hand, during summer, ECHAM5 is not skilful in representing the hub-height wind speed PDF due to the rather local character of the summertime winds.However, the regression analysis shows that during convective summer day conditions the observed hub-height wind speed is strongly linked to the wind speed at higher, skilfully represented levels. The summer-day hub-height wind speed PDF can therefore skilfully be predicted by the wind speedPDF parameters at 500m as the only predictors. However this is not the case during summer nights. During these very stable conditions, the boundary layer is much more shallow and the regression analysis indicates that the addition of information on the temperature gradient between the ABL and above substantially improves the simulation of the observed hub-height wind speed PDFs.
A second part evaluates whether the statistical models, which are developed in Cabauw, can be used at other locations in Europe. The comparison of the downscaled winds with observed near-surface winds over Europe shows that the spatial extent of the regions in which the downscaling models are capable in representing observed hub-height winds, depends on the diurnal, the seasonal and the local conditions (like the orography and the presence of regional wind systems). Depending on the season and time of the day, regions in Europe are defined for which the downscaling model can be skilfully applied.
Before the downscaling method is applied on an ensemble of ESMs, the ESMs are evaluated on their representation of the predictors (part 3). Predictors of statistical downscaling models are commonly derived from upper-atmospheric fields, because these variables are likely to be better represented by the
large-scale models. Since the regression analysis on ECHAM5 (step 1) indicates that the near-surface fields provide the best predictors, the ESMs are evaluated on their representation of wind and temperature PDFs in the lower 1.5km of the atmosphere, using ERA-Interim as the reference. This
height-dependent evaluation approach of theESMs is extra relevant since, compared to former generation GCMs, the resolution of the ESMs is high and their representation of the land-atmosphere interaction processes are described in greater detail. The resultsshow that the ESMss wind speed and temperature variables of the lower 1.5km are suitable to drive statistical downscaling models over most of Europe. However some small-scale and large-scale biases are present. Above coastal bays and capes, small-scale biases in the ESMs result in unskilful wind speed PDFs up to 600m. Orography might affects wind speeds throughout the lowest 1.5km of the atmosphere. This is mostly the case during summer and daytime conditions. During winter, the small-scale biases propagate less high. With exception
of the biases at the small scale, the surface wind speed PDFs north of 45°N are well represented by all the ESMs. South of 45°N, winds are affected by a large-scale bias originating from errors in the representationof the large-scale circulation, especially during winter. The large-scale wind bias is suggested to be related with a largely exaggerated latitudinal pressure gradient, leading to the too strong westerlies in the Northern Hemisphere mid-latitudes. On theother hand, the representation of the temperature PDF by the ESMs is slightly less affected by biases acting at the small scale. However aAparajita" lang="EN-US"> large-scale temperature PDF bias, related to too cold temperatures, is present over the North Atlantic Ocean and the east of Europe exhibits temperatures thatare too high in summer. Most indentified large-scale biases are independent from height and therefore also adopted by downscaling models which
are based on upper-atmospheric fields, underlining the importance of model evaluation before downscaling.
The last part performs the statistical downscaling method using only the skilful ESM fields as possible predictors and focusing only at the regions where the downscaling has shown to be skilful. Hub-height
windspeeds are downscaled for three periods: present-day (1989-2000), near future (2020-2049) and end of the century period (2070-2099). The hub-height wind speed PDF parameters are converted into power for a sample turbine and the change in power relative to the present-day climate is analyzed in a Bayesian ensemble approach. This probabilistic approach weights the participation of the ESMs in the ensemble on their bias and convergence. The analysis exhibits the importance of the PDF based approach. It shows that in a climate in which both PDF parameters increase (resulting in a wider and more symmetric PDF) the true power output will increase to a relatively lesser extent than expected from the change in mean wind speed. This is for example the case in Western-Europe during wintertime, where the expected change in large-scale westerly wind speeds might lead to an (insignificant) increase of power output of about 5%. The opposite situation is true for the Mediterranean region, where the decreasing PDF parameters (resulting in a more narrow and skewed PDF) have a large effect on the power output. In these southern regions, power outputs are expected to decrease (significantly) by magnitudes up to 16%. When power change estimations would be calculated from the change in the mean of the wind (as it
is common practice), the expected decrease inthe Mediterranean wind power output would be overestimated by almost 20% of the change. These inaccuracies in the estimations of the change in power output resulting from neglecting the changes in the PDF (its skewness and width) of the wind, are in the order of magnitude relevant for future wind power yield estimations. Not only the changes in the PDF of the wind speed, but also the form of the Cp-curve has shown to affect thechange in power output, although this is not significant.
Finally a comparison of the changes in power output projections with andwithout the statistically downscaling step, indicates no substantially differences. Although the present-day summertime hub-height wind speed PDF is clearly improved by the downscaling, the future change of is not affected by the
downscaling practice. This implies that at least for wind power, and possible other applications, the direct output of the current generation of the ESMs do not necessarily need downscaling. </></></></>
Date:1 Jan 2010 → 22 Sep 2014
Keywords:Dynamical downscaling, COSMO-CLM, Windspeed, GCM ensemble, Statistical downscaling, Gust, High resolution, Landuse change
Disciplines:Geomatic engineering, Physical geography and environmental geoscience, Atmospheric sciences, Atmospheric sciences, challenges and pollution, Geology
Project type:PhD project