Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections

Tobin, Isabelle; Vautard, Robert; Balog, Irena; Bréon, François‐Marie; Jerez, Sónia; Ruti, Paolo; Thais, F.; Vrac, Mathieu; Yiou, Pascal

doi:10.1007/s10584-014-1291-0

Cited by 193 publications

(152 citation statements)

References 26 publications

Supporting

Mentioning

137

Contrasting

Unclassified

Order By: Relevance

“…Therefore, a rapid increase in wind power has been predicted in many countries and regions Lu et al 2009;Pryor and Barthelmie 2010;Greene et al 2012;Tobin et al 2015Tobin et al , 2016. Changes in the SWS can influence the distribution of wind energy.…”

Section: Introductionmentioning

confidence: 99%

Changes in terrestrial near-surface wind speed and their possible causes: an overview

et al. 2017

View full text Add to dashboard Cite

drag forces are caused by changes in the external and internal friction in the atmosphere. Changes in surface friction are mainly caused by changes in the surface roughness due to land use and cover change (LUCC), including urbanization, and changes in internal friction are mainly induced by changes in the boundary layer characteristics. Future studies should compare the spatio-temporal differences in SWS between high and low altitudes and quantify the effects of different factors on the SWS. Additionally, in-depth analysis of extreme SWS events and prediction of future mean and extreme SWS values at global and regional scales are also necessary. Abstract Changes in terrestrial near-surface wind speed (SWS) are induced by a combination of anthropogenic activities and natural climate changes. Thus, the study of the long-term changes of SWS and their causes is very important for recognizing the effects of these processes. Although the slowdown in SWS has been analyzed in previous studies, to the best of knowledge, no overall comparison or detailed examination of this research has been performed. Similarly, the causes of the decreases in SWS and the best directions of future research have not been discussed in depth. Therefore, we analyzed a series of studies reporting SWS trends spanning the last 30 years from around the world. The changes in SWS differ among different regions. The most significant decreases have occurred in Central Asia and North America, with mean linear trends of − 0.11 m s −1 decade −1 ; the second most significant decreases have occurred in Europe, East Asia, and South Asia, with mean linear trends of − 0.08 m s −1 decade −1 ; and the weakest decrease has occurred in Australia. Although the SWS in Africa has decreased, this region lacks long-term observational data. Therefore, the uncertainties in the long-term SWS trend are higher in this region than in other regions. The changes in SWS, caused by a mixture of global-, regional-, and localscale factors, are mainly due to changes in driving forces and drag forces. The changes in the driving forces are caused by changes in atmospheric circulation, and the changes in the Keywords

show abstract

Section: Introductionmentioning

confidence: 99%

Changes in terrestrial near-surface wind speed and their possible causes: an overview

et al. 2017

View full text Add to dashboard Cite

show abstract

“…First, the hourly 10 m wind speeds are extrapolated to the average turbine hub height using a vertical wind profile, which is the standard procedure in wind energy applications from the 'large-scale' perspective (e.g. Hueging et al, 2013;Tobin et al, 2014). Here, the power law is used to extrapolate the 10 m wind speeds to a height of 80 m (v80; Reyers et al, 2015).…”

Section: Statistical-dynamical Downscaling Methodologymentioning

confidence: 99%

“…In recent years, several studies investigated the impact of climate change on wind speeds and wind energy production over Europe on the regional scale for the middle and end of the 21st century (e.g. Barstad et al, 2012;Pryor et al, 2012;Hueging et al, 2013;Tobin et al, 2014;Reyers et al, 2016). These studies used different global and regional climate models (GCMs and RCMs) with different emission scenarios and downscaling techniques, and focused on different parts of Europe.…”

Section: Introductionmentioning

confidence: 99%

“…These differences seem to result not only from different initial conditions and model parameterisations but also from downscaling technique (e.g. Pryor et al, 2005Pryor et al, , 2012Tobin et al, 2014;Reyers et al, 2016). However, both potential long-term trends and future changes for wind speed and wind energy potentials are quite small compared to temperature trends (IPCC, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Moemken¹,

Reyers²,

Buldmann

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

A B S T R A C T Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip ('Mittelfristige Klimaprognosen') decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

show abstract

“…Ensembles give an indication of the possible spread of a model over time and contain exact traces of the different inputs, making them highly valuable for domain experts [26]. They are commonly used in weather prediction [8,16], climate change [20,38] and hurricane prediction [12,35].…”

Section: Introductionmentioning

confidence: 99%

Multi-Granular Trend Detection for Time-Series Analysis

Van

Staals

Löffler

et al. 2017

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in onedimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored. Permanent repository link

show abstract

Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections

Cited by 193 publications

References 26 publications

Changes in terrestrial near-surface wind speed and their possible causes: an overview

Changes in terrestrial near-surface wind speed and their possible causes: an overview

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Multi-Granular Trend Detection for Time-Series Analysis

Contact Info

Product

Resources

About