Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2373-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2373-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2022
Research article |
| 02 Dec 2022
| 02 Dec 2022
Current and future wind energy resources in the North Sea according to CMIP6
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Oscar García-Santiago
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Alfredo Peña
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
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Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
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Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
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We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
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Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
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This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Pedro Santos, Alfredo Peña, and Jakob Mann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-960, https://doi.org/10.5194/acp-2020-960, 2020
Preprint withdrawn
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We show that the vector of vertical flux of horizontal momentum and the vector of the mean vertical gradient of horizontal velocity are not aligned, based on Doppler wind lidar observations up to 500 m, both offshore and onshore. We illustrate that a mesoscale model output matches the observed mean wind speed and momentum fluxes well, but that this model output as well as idealized large-eddy simulations have deviations with the observations when looking at the turning of the wind.
Davide Conti, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 5, 1129–1154, https://doi.org/10.5194/wes-5-1129-2020, https://doi.org/10.5194/wes-5-1129-2020, 2020
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We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. The uncertainty of aeroelastic load predictions is quantified against wind turbine on-board sensor data. This work demonstrates the applicability of nacelle-mounted lidar measurements to extend load and power validations under wake conditions and highlights the main challenges.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
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Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Maarten Paul van der Laan, Mark Kelly, Rogier Floors, and Alfredo Peña
Wind Energ. Sci., 5, 355–374, https://doi.org/10.5194/wes-5-355-2020, https://doi.org/10.5194/wes-5-355-2020, 2020
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The design of wind turbines and wind farms can be improved by increasing the accuracy of the inflow models representing the atmospheric boundary layer (ABL). In this work we employ numerical simulations of the idealized ABL, which can represent the mean effects of Coriolis and buoyancy forces and surface roughness. We find a new model-based similarity that provides a better understanding of the idealized ABL. In addition, we extend the model to include effects of convective buoyancy forces.
Alfredo Peña, Ebba Dellwik, and Jakob Mann
Atmos. Meas. Tech., 12, 237–252, https://doi.org/10.5194/amt-12-237-2019, https://doi.org/10.5194/amt-12-237-2019, 2019
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We propose a method to assess the accuracy of turbulence measurements by sonic anemometers. The idea is to compute the ratio of the vertical to along-wind velocity spectrum within the inertial subrange. We found that the Metek USA-1 and the Campbell CSAT3 sonic anemometers do not show the expected theoretical ratio. A wind-tunnel-based correction recovers the expected ratio for the USA-1. A correction for the CSAT3 does not, illustrating that this sonic anemometer suffers from flow distortion.
Robert Menke, Nikola Vasiljević, Kurt S. Hansen, Andrea N. Hahmann, and Jakob Mann
Wind Energ. Sci., 3, 681–691, https://doi.org/10.5194/wes-3-681-2018, https://doi.org/10.5194/wes-3-681-2018, 2018
Short summary
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This study investigates the behaviour of wind turbine wakes in complex terrain. Using six scanning lidars, we measured the wake of a single turbine at the Perdigão site in Portugal in 2015. Our findings show that wake propagation is highly dependent on the atmospheric stability, which is mostly ignored in flow simulation used for wind farm layout design. The wake is lifted up during unstable atmospheric conditions and follows the terrain downwards during stable conditions.
Laura Valldecabres, Alfredo Peña, Michael Courtney, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 3, 313–327, https://doi.org/10.5194/wes-3-313-2018, https://doi.org/10.5194/wes-3-313-2018, 2018
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Jakob Mann, Alfredo Peña, Niels Troldborg, and Søren J. Andersen
Wind Energ. Sci., 3, 293–300, https://doi.org/10.5194/wes-3-293-2018, https://doi.org/10.5194/wes-3-293-2018, 2018
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Turbulence is usually assumed to be unmodified by the stagnation occurring in front of a wind turbine rotor. All manufacturers assume this in their dynamic load calculations. If this assumption is not true it might bias the load calculations and the turbines might not be designed optimally. We investigate the assumption with a Doppler lidar measuring forward from the top of the nacelle and find small but systematic changes in the approaching turbulence that depend on the power curve.
Alfredo Peña, Kurt Schaldemose Hansen, Søren Ott, and Maarten Paul van der Laan
Wind Energ. Sci., 3, 191–202, https://doi.org/10.5194/wes-3-191-2018, https://doi.org/10.5194/wes-3-191-2018, 2018
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We analyze the wake of the Anholt offshore wind farm in Denmark by intercomparing models and measurements. We also look at the effect of the land on the wind farm by intercomparing mesoscale winds and measurements. Annual energy production and capacity factor estimates are performed using different approaches. Lastly, the uncertainty of the wake models is determined by bootstrapping the data; we find that the wake models generally underestimate the wake losses.
Bjarke T. Olsen, Andrea N. Hahmann, Anna Maria Sempreviva, Jake Badger, and Hans E. Jørgensen
Wind Energ. Sci., 2, 211–228, https://doi.org/10.5194/wes-2-211-2017, https://doi.org/10.5194/wes-2-211-2017, 2017
Short summary
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Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented. The study shows that model errors are larger and agreement between models smaller at inland sites and near the surface.
Alfredo Peña, Jakob Mann, and Nikolay Dimitrov
Wind Energ. Sci., 2, 133–152, https://doi.org/10.5194/wes-2-133-2017, https://doi.org/10.5194/wes-2-133-2017, 2017
Short summary
Short summary
Nacelle lidars are nowadays extensively used to scan the turbine inflow. Thus, it is important to characterize turbulence from their measurements. We present two methods to perform turbulence estimation and demonstrate them using two types of lidars. With one method we can estimate the along-wind unfiltered variance accurately. With the other we can estimate the filtered radial velocity variance accurately and velocity-tensor parameters under neutral and high wind-speed conditions.
Alfredo Peña, Andreas Bechmann, Davide Conti, and Nikolas Angelou
Wind Energ. Sci., 1, 101–114, https://doi.org/10.5194/wes-1-101-2016, https://doi.org/10.5194/wes-1-101-2016, 2016
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We have developed flow models from different complexities. Unfortunately, high quality and reliable wind observations affected by obstacles are rare and so we have few means to evaluate our models. We have therefore performed a campaign in which we measured the effect of a fence on the atmosphere using laser-based instruments. The effect can still be noticed as far as 11 fence heights. A wake theory seems to predict the obstacle effect when we are looking at distances beyond 6 fence heights.
P. J. H. Volker, J. Badger, A. N. Hahmann, and S. Ott
Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, https://doi.org/10.5194/gmd-8-3715-2015, 2015
Short summary
Short summary
We introduce the Explicit Wake Parametrisation (EWP) for wind farms in mesoscale models that accounts
for the wake expansion within a turbine-containing cell. In the EWP approach, turbulence kinetic energy (TKE) production results from changes in vertical shear. The velocity recovery compares well to mast data downstream of the offshore wind farm Horns Rev I. The vertical structure of the TKE and the velocity profile are qualitatively similar to that simulated with large eddy simulations.
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Brief communication: How does complex terrain change the power curve of a wind turbine?
The wide range of factors contributing to wind resource assessment accuracy in complex terrain
High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning
Impact of the wind field at the complex-terrain site Perdigão on the surface pressure fluctuations of a wind turbine
Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions
Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
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This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
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We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
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This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
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Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
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Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
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In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
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Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
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Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
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The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
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Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Dan Houck, Nathaniel de Velder, David Maniaci, and Brent Houchens
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-130, https://doi.org/10.5194/wes-2023-130, 2023
Revised manuscript accepted for WES
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Experiments offer incredible value to science but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
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Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
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This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
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Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
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This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
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The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
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The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
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Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
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A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
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Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
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Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
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We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
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The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
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Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
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Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
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Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
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This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
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In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
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A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Yulong Ma, Cristina L. Archer, and Ahmadreza Vasel-Be-Hagh
Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, https://doi.org/10.5194/wes-7-2407-2022, 2022
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Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Turið Poulsen, Bárður A. Niclasen, Gregor Giebel, and Hans Georg Beyer
Wind Energ. Sci., 7, 2335–2350, https://doi.org/10.5194/wes-7-2335-2022, https://doi.org/10.5194/wes-7-2335-2022, 2022
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Wind power is cheap and environmentally friendly, but it has a disadvantage: it is a variable power source. Because wind is not blowing everywhere simultaneously, optimal placement of wind farms can reduce the fluctuations.
This is explored for a small isolated area. Combining wind farms reduces wind power fluctuations for timescales up to 1–2 d. By optimally placing four wind farms, the hourly fluctuations are reduced by 15 %. These wind farms are located distant from each other.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
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This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Xiaodong Zhang and Anand Natarajan
Wind Energ. Sci., 7, 2135–2148, https://doi.org/10.5194/wes-7-2135-2022, https://doi.org/10.5194/wes-7-2135-2022, 2022
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Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed using the Gaussian mixture model and has been shown to agree well with 15 years of measurements. The environmental contour with a 50-year return period (extreme turbulence) is estimated. The results from the model could be taken as inputs for structural reliability analysis and uncertainty quantification of wind turbine design loads.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sebastian Brune and Jan D. Keller
Wind Energ. Sci., 7, 1905–1918, https://doi.org/10.5194/wes-7-1905-2022, https://doi.org/10.5194/wes-7-1905-2022, 2022
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A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.
Rieska Mawarni Putri, Etienne Cheynet, Charlotte Obhrai, and Jasna Bogunovic Jakobsen
Wind Energ. Sci., 7, 1693–1710, https://doi.org/10.5194/wes-7-1693-2022, https://doi.org/10.5194/wes-7-1693-2022, 2022
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As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in the marine atmospheric boundary layer (MABL) is becoming crucial to help improve offshore wind turbine design and reliability. The present study discusses the wind characteristics at the first offshore wind farm, Vindeby, and compares them with the wind measurements at the FINO1 platform. Consistent wind characteristics are found between Vindeby measurements and the FINO1 measurements.
Pradip Zamre and Thorsten Lutz
Wind Energ. Sci., 7, 1661–1677, https://doi.org/10.5194/wes-7-1661-2022, https://doi.org/10.5194/wes-7-1661-2022, 2022
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To get more insight into the influence of the urban-terrain flow on the performance of the rooftop-mounted two-bladed Darrieus vertical-axis wind turbine, scale resolving simulations are performed for a generic wind turbine in realistic terrain under turbulent conditions. It is found that the turbulence and skewed nature of the flow near rooftop locations have a positive impact on the performance of the wind turbine.
Eduardo Weide Luiz and Stephanie Fiedler
Wind Energ. Sci., 7, 1575–1591, https://doi.org/10.5194/wes-7-1575-2022, https://doi.org/10.5194/wes-7-1575-2022, 2022
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This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
Patrick Letzgus, Giorgia Guma, and Thorsten Lutz
Wind Energ. Sci., 7, 1551–1573, https://doi.org/10.5194/wes-7-1551-2022, https://doi.org/10.5194/wes-7-1551-2022, 2022
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The research article presents the results of a study of highly resolved numerical simulations of a wind energy test site in complex terrain that is currently under construction in the Swabian Alps in southern Germany. The numerical results emphasised the importance of considering orography, vegetation, and thermal stratification in numerical simulations to resolve the wind field decently. In this way, the effects on loads, power, and wake of the wind turbine can also be predicted well.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
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This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
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In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Louis de Montera, Henrick Berger, Romain Husson, Pascal Appelghem, Laurent Guerlou, and Mauricio Fragoso
Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, https://doi.org/10.5194/wes-7-1441-2022, 2022
Short summary
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A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
Florian Wenz, Judith Langner, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 7, 1321–1340, https://doi.org/10.5194/wes-7-1321-2022, https://doi.org/10.5194/wes-7-1321-2022, 2022
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To get a better understanding of the influence of the terrain flow on the unsteady pressure distributions on the wind turbine surface, a fully resolved turbine was simulated in the complex terrain of Perdigão, Portugal. It was found that the pressure fluctuations at the tower caused by vortex shedding are significantly hampered by the terrain flow, while the pressure fluctuations caused by the blade–tower interaction are hardly changed.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 7, 1289–1304, https://doi.org/10.5194/wes-7-1289-2022, https://doi.org/10.5194/wes-7-1289-2022, 2022
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Based on the IEC standards, a limited number of simulations is sufficient to calculate the extreme and fatigue loads on a wind turbine. However, this means inaccuracy in the output statistics. This paper aims to build a surrogate model on blade element momentum aerodynamic model simulation output employing non-intrusive polynomial chaos expansion. The surrogate model is then used in a large number of Monte Carlo simulations to provide an accurate statistical estimate of the loads.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
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Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Cited articles
Alonso Díaz, Y., Bezanilla, A., Roque, A., Centella, A., Borrajero, I.,
and Martinez, Y.: Wind resource assessment of Cuba in future climate
scenarios, Wind Eng., 43, 311–326, https://doi.org/10.1177/0309524X18780399, 2019. a
Badger, M., Peña, A., Hahmann, A. N., Mouche, A. A., and Hasager, C. B.:
Extrapolating satellite winds to turbine operating heights, J. Appl.
Meteorol. Clim., 55, 975–991, https://doi.org/10.1175/JAMC-D-15-0197.1, 2016. a
Barthelmie, R. J. and Jensen, L. E.: Evaluation of wind farm efficiency and
wind turbine wakes at the Nysted offshore wind farm, Wind Energy, 13,
573–586, https://doi.org/10.1002/we.408, 2010. a
Beiter, P., Cooperman, A., Lantz, E., Stehly, T., Shields, M., Wiser, R.,
Telsnig, T., Kitzing, L., Berkhout, V., and Kikuchi, Y.: Wind power costs
driven by innovation and experience with further reductions on the horizon,
Wiley Interdiscip. Rev. Energy Environ., 10, e398, https://doi.org/10.1002/wene.398,
2021. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C.,
Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur,
G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G.,
Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L.,
Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y.,
Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A.,
Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J.,
Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the
IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010,
https://doi.org/10.1029/2019MS002010, 2020. a
Carvalho, D., Rocha, A., Gomez-Gesteira, M., and Santos, C. S.: Potential
impacts of climate change on European wind energy resource under the CMIP5
future climate projections, Renew. Energ., 101, 29–40,
https://doi.org/10.1016/j.renene.2016.08.036, 2017. a
Carvalho, D., Rocha, A., Costoya, X., DeCastro, M., and Gómez-Gesteira,
M.: Wind energy resource over Europe under CMIP6 future climate projections:
What changes from CMIP5 to CMIP6, J. Renew. Sust. Energ., 151, 111594,
https://doi.org/10.1016/j.rser.2021.111594, 2021. a
Chang, T.-J., Chen, C.-L., Tu, Y.-L., Yeh, H.-T., and Wu, Y.-T.: Evaluation of
the climate change impact on wind resources in Taiwan Strait, Energy
Convers. Manag., 95, 435–445, https://doi.org/10.1016/j.enconman.2015.02.033, 2015. a
Chen, L.: Impacts of climate change on wind resources over North America based
on NA-CORDEX, Renew. Energ., 153, 1428–1438,
https://doi.org/10.1016/j.renene.2020.02.090, 2020. a, b
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S.,
Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.:
Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled
Model, J. Adv. Model. Earth Sy., 11, 185–209,
https://doi.org/10.1029/2018MS001369, 2019. a
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F.,
Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell,
G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S.,
Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid
Federation: An open infrastructure for access to distributed geospatial
data, Futur. Gener. Comput. Syst., 36, 400–417,
https://doi.org/10.1016/j.future.2013.07.002,
2014. a
COWI: Vindressource, layouts og energiproduktion For Bornholm I + II,
Nordsøen II + III Og Området Vest For Nordsøen II + III, Tech.
rep., Danish Energy Agency,
https://ens.dk/sites/ens.dk/files/Vindenergi/2-3_vindressource_layouts_og_energiproduktion.pdf (last access: 13 November 2022),
2020. a, b, c, d
Cronin, J., Anandarajah, G., and Dessens, O.: Climate change impacts on the
energy system: a review of trends and gaps, Climatic Change, 151, 79–93,
https://doi.org/10.1007/s10584-018-2265-4, 2018. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer,
C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E.,
Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch,
P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Sy., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020. a
Devis, A., Van Lipzig, N. P. M., and Demuzere, M.: Should future wind speed
changes be taken into account in wind farm development?, Environ. Res.
Lett., 13, 64012, https://doi.org/10.1088/1748-9326/aabff7, 2018. a, b, c
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b
Emeis, S.: Wind Energy Meteorology: Atmospheric Physics for Wind Power
Generation, second edn., Green Energy and Technology, Springer International Publishing,
Cham, ISBN 978-3-319-72858-2, https://doi.org/10.1007/978-3-319-72859-9, 2018. a
ESGF: esgf-pyclient, ESGF [code],
https://esgf-pyclient.readthedocs.io/en/latest/, last access: 13 November 2022. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016a. a
Eyring, V., Gleckler, P. J., Heinze, C., Stouffer, R. J., Taylor, K. E., Balaji, V., Guilyardi, E., Joussaume, S., Kindermann, S., Lawrence, B. N., Meehl, G. A., Righi, M., and Williams, D. N.: Towards improved and more routine Earth system model evaluation in CMIP, Earth Syst. Dynam., 7, 813–830, https://doi.org/10.5194/esd-7-813-2016, 2016b. a
Fernandez-Granja, J. A., Casanueva, A., Bedia, J., and Fernandez, J.: Improved
atmospheric circulation over Europe by the new generation of CMIP6 earth
system models, Clim. Dynam., 56, 3527–3540, https://doi.org/10.1007/s00382-021-05652-9,
2021. a
Fischereit, J., Brown, R., Larsén, X. G., Badger, J., and Hawkes, G.:
Review of Mesoscale Wind-Farm Parametrizations and Their Applications,
Bound.-Lay Meteorol., 182, 175–224, https://doi.org/10.1007/s10546-021-00652-y, 2022. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G.,
Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R.,
Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition
of the IEA 15-Megawatt Offshore Reference Wind, Tech. Rep., National
Renewable Energy Laboratory,
https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 13 November 2022), 2020. a
Gelaro, R., McCarty, W., Suarez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Da
Silva, A., Gu, W., Kim, G.-K., Koster, R. D., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyke, G., Pawson, S., Putman, W., Reinecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gernaat, D. E., de Boer, H. S., Daioglou, V., Yalew, S. G., Müller, C.,
and van Vuuren, D. P.: Climate change impacts on renewable energy supply,
Nat. Clim. Change, 11, 119–125, https://doi.org/10.1038/s41558-020-00949-9, 2021. a, b
Giorgi, F. and Gutowski, W. J.: Regional Dynamical Downscaling and the CORDEX
Initiative, Annu. Rev. Environ. Resour., 40, 467–490, 2015. a
Global Modeling and Assimilation Office (GMAO): tavg1_2d_slv_Nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/VJAFPLI1CSIV, 2015. a
Göçmen, T., van der Laan, P., Réthoré, P.-E., Diaz,
A. P., Larsen, G. C., and Ott, S.: Wind turbine wake models developed at the
Technical University of Denmark: A review, Renew. Sustain. Energy Rev., 60,
752–769, https://doi.org/10.1016/j.rser.2016.01.113, 2016. a
Gonzalez, P. L., Brayshaw, D. J., and Zappa, G.: The contribution of North
Atlantic atmospheric circulation shifts to future wind speed projections for
wind power over Europe, Clim. Dynam., 53, 4095–4113,
https://doi.org/10.1007/s00382-019-04776-3, 2019. a, b
GWEC: Global Offshore Wind: Annual Market Report 2020, Tech. Rep. February,
Global Wind Energy Council, Bru,
https://gwec.net/wp-content/uploads/2020/12/GWEC-Global-Offshore-Wind-Report-2020.pdf (last access: 13 November 2022),
2020. a
GWEC: Global Wind Report 2021, Tech. Rep., Global Wind Energy Council,
https://gwec.net/wp-content/uploads/2021/03/GWEC-Global-Wind-Report-2021.pdf (last access: 13 November 2022),
2021. a
Hahmann, A. N.: future-wind Initial Release. In Wind Energy Sciences (v0.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7260128, 2022. a
Hahmann, A. N., Vincent, C. L., Peña, A., Lange, J., and Hasager, C. B.:
Wind climate estimation using WRF model output: Method and model
sensitivities over the sea, Int. J. Climatol., 35, 3422–3439, 2015. a
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. a, b
Hahmann, A. N., Sīle, T., Witha, B., Davis, N., Dörenkämper, N., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J, Olsen, B. T., Söderberg, S., Barcons, J, Sastre-Marugán, M., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., Mann, J., and Vasiljevic, N.: New European Wind Atlas: Mesoscale Atlas, Technical University of Denmark [data set], https://doi.org/10.11583/DTU.14414096.v1, 2021. a
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., Thépaut, J. N.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M.,
Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
IEA: World Energy Outlook 2019, Tech. Rep., International Energy Agency,
https://www.iea.org/reports/world-energy-outlook-2019 (last access: 13 November 2022), 2019. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A.,
Connors, S., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L.,
Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T.,
Waterfield, T., Yelek ci, O., Yu, R., and Zhou, B., Cambridge University
Press, Cambridge, United Kingdom, and New York, USA, in press, https://doi.org/10.1017/9781009157896, 2021. a, b
IPCC: Summary for Policymakers, in: Global Warming of 1.5 ∘C: IPCC Special Report on Impacts of Global Warming of 1.5 ∘C above Pre-industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty, Cambridge University Press, Cambridge, 1–24, https://doi.org/10.1017/9781009157940.001, 2022. a, b, c
Jerez, S., Tobin, I., Turco, M., Jiménez-Guerrero, P., Vautard, R., and
Montávez, J.: Future changes, or lack thereof, in the temporal
variability of the combined wind-plus-solar power production in Europe,
Renew. Energ., 139, 251–260, https://doi.org/10.1016/j.renene.2019.02.060, 2019. a
Karnauskas, K. B., Lundquist, J. K., and Zhang, L.: Southward shift of the
global wind energy resource under high carbon dioxide emissions, Nat.
Geosci., 11, 38–43, https://doi.org/10.1038/s41561-017-0029-9, 2018. a, b, c
Kawai, H., Yukimoto, S., Koshiro, T., Oshima, N., Tanaka, T., Yoshimura, H., and Nagasawa, R.: Significant improvement of cloud representation in the global climate model MRI-ESM2, Geosci. Model Dev., 12, 2875–2897, https://doi.org/10.5194/gmd-12-2875-2019, 2019. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann,
S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la
Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S.,
Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner,
K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E.
M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K.,
Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S.,
Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H.,
Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens,
B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H.,
Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M
Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing
CO2, J. Adv. Model. Earth Sy., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019. a
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.:
Intercomparison makes for a better climate model, Eos, Trans. Am. Geophys.
Union, 78, 445–446, https://doi.org/10.1029/97EO00276, 1997. a
Moemken, J., Reyers, M., Feldmann, H., and Pinto, J. G.: Future Changes of
Wind Speed and Wind Energy Potentials in EURO-CORDEX Ensemble Simulations,
J. Geophys. Res., 123, 6373–6389, https://doi.org/10.1029/2018JD028473, 2018. a
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M.,
Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine,
T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E.,
Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the
Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J. Adv. Model Earth
Sy., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018. a
National Oceanic and Atmospheric Administration (NOAA): The Twentieth Century Reanalysis Project V3, National Oceanic and Atmospheric Administration Climate Program Office and NOAA Physical Sciences Laboratory, Boulder, Colorado, USA [data set], https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html (last access: 24 November 2022), 2019. a
Nygaard, N. G.: Wakes in very large wind farms and the effect of neighbouring
wind farms, J. Phys. Conf. Ser., 524, 012162,
https://doi.org/10.1088/1742-6596/524/1/012162, 2014. a, b
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Oudar, T., Cattiaux, J., and Douville, H.: Drivers of the Northern
Extratropical Eddy‐Driven Jet Change in CMIP5 and CMIP6 Models, Geophys.
Res. Lett., 47, e2019GL086695, https://doi.org/10.1029/2019GL086695, 2020. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake (v1.0.10), Zenodo [code], https://doi.org/10.5281/zenodo.2562662, 2019. a
Peña, A. and Hahmann, A. N.: Atmospheric stability and turbulence fluxes
at Horns Rev – an intercomparison of sonic, bulk and WRF model data, Wind
Energy, 15, 717–731, 2012. a
Peña, A. and Rathmann, O.: Atmospheric stability‐dependent infinite
wind‐farm models and the wake‐decay coefficient, Wind Energy, 17,
1269–1285, https://doi.org/10.1002/we.1632, 2014. a, b
Peña, A., Réthoré, P., and Laan, M. P.: On the application
of the Jensen wake model using a turbulence‐dependent wake decay
coefficient: the Sexbierum case, Wind Energy, 19, 763–776,
https://doi.org/10.1002/we.1863, 2016. a, b
Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi,
K.: Climate change impacts on wind power generation, Nat. Rev. Earth
Environ., 1, 627–643, https://doi.org/10.1038/s43017-020-0101-7, 2020. a
Rabin, J., Delon, J., and Gousseau, Y.: Circular Earth Mover's Distance for
the comparison of local features, in: 2008 19th Int. Conf. Pattern
Recognit., Tampa, FL, USA,
8–11 December 2008, IEEE, 1–4, https://doi.org/10.1109/ICPR.2008.4761372, 2008. a
Reyers, M., Moemken, J., and Pinto, J. G.: Future changes of wind energy
potentials over Europe in a large CMIP5 multi-model ensemble, Int. J.
Climatol., 36, 783–796, https://doi.org/10.1002/joc.4382, 2016. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Glob. Environ.
Chang., 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017. a
Sailor, D. J., Smith, M., and Hart, M.: Climate change implications for wind
power resources in the Northwest United States, Renew. Energ., 33,
2393–2406, https://doi.org/10.1016/j.renene.2008.01.007, 2008. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a, b
Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L., Andrejczuk,
M., Andrews, M. B., Andrews, T., Archibald, A. T., de Mora, L., Dyson, H.,
Elkington, M., Ellis, R., Florek, P., Good, P., Gohar, L., Haddad, S.,
Hardiman, S. C., Hogan, E., Iwi, A., Jones, C. D., Johnson, B., Kelley,
D. I., Kettleborough, J., Knight, J. R., Köhler, M. O., Kuhlbrodt, T.,
Liddicoat, S., Linova-Pavlova, I., Mizielinski, M. S., Morgenstern, O.,
Mulcahy, J., Neininger, E., O'Connor, F. M., Petrie, R., Ridley, J., Rioual,
J.-C., Roberts, M., Robertson, E., Rumbold, S., Seddon, J., Shepherd, H.,
Shim, S., Stephens, A., Teixiera, J. C., Tang, Y., Williams, J., Wiltshire,
A., and Griffiths, P. T.: Implementation of U.K. Earth System Models for
CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001946,
https://doi.org/10.1029/2019MS001946, 2020. a, b, c
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese,
B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J.,
Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D.,
Cornes, R., Cram, T. A., Crouthamel, R., Domínguez‐Castro, F.,
Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan,
A., Kubota, H., Blancq, F. L., Lee, T., Lorrey, A., Luterbacher, J., Maugeri,
M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C.,
Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A., Wang,
X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: Towards a more
reliable historical reanalysis: Improvements for version 3 of the Twentieth
Century Reanalysis system, Q. J. Roy. Meteor. Soc., 145, 2876–2908,
https://doi.org/10.1002/qj.3598, 2019. a
Solbrekke, I. M., Kvamstø, N. G., and Sorteberg, A.: Mitigation of offshore wind power intermittency by interconnection of production sites, Wind Energ. Sci., 5, 1663–1678, https://doi.org/10.5194/wes-5-1663-2020, 2020. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin,
J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S.,
Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O.,
Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig,
R., Salas-y Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R.,
Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of
CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in
Present-Day and Future Climate, J. Adv. Model. Earth Sy., 11, 4182–4227,
https://doi.org/10.1029/2019MS001791, 2019. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Tilo, Z., A., C. M., M., L. R., Andrew, L., W., B. R., Martin, D., Lauren, S.,
Ying-Ping, W., and Jhan, S.: The Australian Earth System Model:
ACCESS-ESM1.5, J. of Southern Hemisphere Earth Systems Science, 70,
193–214, https://doi.org/10.1071/ES19035, 2020. a
Tobin, I., Jerez, S., Vautard, R., Thais, F. F., Van Meijgaard, E., Prein,
A., Déqué, M., Kotlarski, S., Maule, C. F., Nikulin, G.,
Noël, T., Teichmann, C., Gobiet, A., Thais, F. F., Meijgaard, E. V.,
Prein, A., Kotlarski, S., Maule, C. F., Nikulin, G., Noël, T., and
Teichmann, C.: Climate change impacts on the power generation potential of a
European mid-century wind farms scenario, Environ. Res. Lett., 11, 034013,
https://doi.org/10.1088/1748-9326/11/3/034013, 2016.
a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat,
P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I.,
Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville,
H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G.,
Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and
Waldman, R.: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1, J. Adv.
Model. Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683,
2019. a
Volker, P. J. H., Hahmann, A. N., Badger, J., and Jørgensen, H. E.:
Prospects for generating electricity by large onshore and offshore wind
farms, Environ. Res. Lett., 12, 034022, https://doi.org/10.1088/1748-9326/aa5d86,
2017. a
Wang, S., Yang, H., Pham, Q. B., Khoi, D. N., and Nhi, P. T. T.: An ensemble
framework to investigate wind energy sustainability considering climate
change impacts, Sustainability, 12, 876, https://doi.org/10.3390/su12030876, 2020. a
Wiser, R., Rand, J., Seel, J., Beiter, P., Baker, E., Lantz, E., and Gilman,
P.: Expert elicitation survey predicts 37 % to 49 % declines in wind
energy costs by 2050, Nat. Energy, 6, 555–565,
https://doi.org/10.1038/s41560-021-00810-z, 2021. a
Yalew, S. G., van Vliet, M. T., Gernaat, D. E., Ludwig, F., Miara, A., Park,
C., Byers, E., De Cian, E., Piontek, F., Iyer, G., Mouratiadou, I., Glynn,
J., Hejazi, M., Dessens, O., Rochedo, P., Pietzcker, R., Schaeffer, R.,
Fujimori, S., Dasgupta, S., Mima, S., da Silva, S. R., Chaturvedi, V.,
Vautard, R., and van Vuuren, D. P.: Impacts of climate change on energy
systems in global and regional scenarios, Nat. Energy, 5,
794–802,
https://doi.org/10.1038/s41560-020-0664-z, 2020. a
Yang, Y., Wang, B., and Cao, J. e. a.: Improved historical simulation by
enhancing moist physical parameterizations in the climate system model
NESM3.0, Clim. Dynam., 54, 3819–3840, https://doi.org/10.1007/s00382-020-05209-2, 2020. a
Zappa, G., Hoskins, B. J., and Shepherd, T. G.: Improving climate change
detection through optimal seasonal averaging: The case of the North Atlantic
jet and European precipitation, J. Climate, 28, 6381–6397,
https://doi.org/10.1175/JCLI-D-14-00823.1, 2015. a
Zheng, C.-W., Li, X.-Y., Luo, X., Chen, X., Qian, Y.-H., Zhang, Z.-H., Gao,
Z.-S., Du, Z.-B., Gao, Y.-B., and Chen, Y.-G.: Projection of future global
offshore wind energy resources using CMIP data, Atmos. Ocean, 57,
134–148, https://doi.org/10.1080/07055900.2019.1624497, 2019. a
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
We explore the changes in wind energy resources in northern Europe using output from simulations...
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