Abstract. We explore the changes in wind energy resources in Northern Europe using output from historical to mid-twenty-first century simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. This study improves upon many assumptions made in the past: (1) we interpolate the winds to hub height; (2) we use a large ensemble of CMIP6 models; and (3) we consider the possible wake effects on the energy production of a large wind farm. The wind climatologies in the CMIP6 models show good correspondence with measurements and reanalysis data. Our results show that annual mean wind speed and wind resources in Northern Europe are not particularly affected by climate change in 2031–2050 relative to 1995–2014, according to a sub-set of 16 models in the CMIP6 collection. However, climate change could significantly alter the seasonal distribution of these resources. Most models agree on reductions in the future wind in summer in a band that extends from the British Isles to the Baltic Sea and on increases in winter in the Southern Baltic Sea. The energy production calculations show that summer energy production in a planned large wind farm cluster in the North Sea could be reduced by a median of 6.9 % during 2031–2050 when taking into account the wind farm wakes (that account for -0.7 %) and the changes in air density (that account for -0.9 %). Extrapolating 10-meter wind speeds to turbine height using the power law with a constant shear exponent is often a poor approximation. It can exaggerate the future changes in wind resources and ignore possible surface roughness and stability changes.

Abstract. We explore the changes in wind energy resources in northern Europe using output from historical to mid-21st century CMIP6 simulations and the high-emission SSP5-8.5 scenario. This study improves upon many assumptions made in the past. First, we interpolate the winds to hub height using model-level raw data; second, we use a large ensemble of CMIP6 models; third, we consider the possible wake effects on the annual energy production of a large wind farm cluster proposed for the North Sea. The common practice of extrapolating 10 m wind speeds to turbine height using the power law with a constant shear exponent is often a poor approximation of the actual turbine-height wind speed. This approximation can exaggerate the future changes in wind resources and ignore possible surface roughness and atmospheric stability changes. The evaluation of the wind climatologies in the CMIP6 models over the North Sea for the historical period shows good correspondence with measurements from tall masts and three reanalysis data points for 16 of the 18 models. Some of the models run at relatively high spatial resolution are as good as the reanalyses at representing the wind climate in this region. Our results show that annual mean wind speed and wind resources in northern Europe are not particularly affected by climate change in 2031–2050 relative to 1995–2014, according to a subset of 16 models in the CMIP6 collection. However, the seasonal distribution of these resources is significantly altered. Most models agree on reductions in the future wind in summer in a band that extends from the British Isles to the Baltic Sea and on increases in winter in the southern Baltic Sea. The energy production calculations show that summer energy production in a planned large wind farm cluster in the North Sea could be reduced by a median of 6.9 % during 2031–2050 when taking into account the wind farm wakes (that accounts for −0.7 %) and the changes in air density (that account for −0.9 %).

Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Wind-farm parametrizations in numerical weather prediction models continue to be developed and used to study the impact of wind farms on both the local climate and other wind farms. Therefore, it is important to evaluate their accuracy in simulating the wind field. Here, we evaluate the Fitch scheme, which is the in-built wind-farm parametrization of the Weather Research and Forecasting (WRF) model by simulating a small wind farm through idealized simulations in the WRF model. We simulate the wind-farm impact on the atmosphere by using two planetary boundary layer schemes, one of these designed for the terra incognita, a range of scales finer than used for traditional mesoscale simulations, yet still too coarse for large-eddy simulation (LES). For both schemes, we use two horizontal grid spacings, 960 and 240 m, which are typical within the terra incognita. We compare these simulations using the Fitch wind farm parametrization with wakes modelled at high-resolution using the LES capability of the WRF model combined with an actuator disk wind turbine model designed for LES. From this preliminary assessment, we find that the simulations at the coarsest resolution are in better agreement with spatially-averaged outputs of the LES compared to that of the medium-range resolution simulations with LES, which are spatially averaged to the medium size spacing. The medium-range resolution simulations show the greatest velocity differences between the simulations without and with wakes.

<p>In 2020, the North Sea already had 19.8 GW or 79% of the European offshore wind installations. The size and number of wind farms in this region are expected to increase substantially to reach climate mitigation targets, with forecasts of offshore wind commitments across Europe adding up to 111 GW of offshore wind by 2030. However, governments base their climate mitigation plans on past historical wind resources data. Still, there is a probable threat that these will change in the future due to climate change during the lifetime of a wind farm. </p><p>The study of future changes in wind resources is not a new subject. A systematic literature search with the keywords "Wind Resources" and "Climate Change" returned over 80 peer-reviewed articles that assessed future wind resources at the global, regional and local scale. Most of these studies used the 10-m wind speed output from the climate or regional model to directly estimate a wind turbine's power production, using the power law and sometimes an idealised power curve. As far as we know, only two studies explored the possible implications of changes in wind direction. </p><p>In this presentation, we explore the implications of the various assumptions. We use the example of the North Sea and Northern Europe and the CMIP6 climate model archive to demonstrate that some assumptions can exaggerate future wind resource changes. We also consider the consequences of the changes in boundary layer stability, wind direction and vegetation changes to the future wind resources in Northern Europe. </p>

Regions with high wind energy potential require a comprehensive understanding of possible climate change impacts on wind resources. We investigate the performance of two reanalyses (ERA5 and MERRA2) and a regional climate model (RegCM4) on simulating atmospheric circulations over Central America, Mexico, and the Caribbean (CORDEX-CAM) and projected low-level wind changes in the region. RegCM4 historical wind patterns in Mexico are evaluated using seven tall inland masts and the two reanalyses. The evaluation indicates that MERRA2 is more accurate than ERA5 in representing observed winds, and RegCM4 captures present-day wind climate patterns with overestimations (> 2 m/s) over complex terrain. The mean recurrence of weather patterns based on self-organising maps in the historical period identifies high near-surface winds close to Mexico's major wind farm areas. Large pressure gradients between semi-permanent pressure systems and orographic features drive most of these patterns. We use the high temporal and spatial resolution RegCM4's data to obtain near-future (2021--2050) wind changes under the highest emission scenario. These changes are assessed using mean differences and recurrence pattern analysis from the RegCM4 downscaled simulations of three global climate models relative to the historical period (1981--2010). The mean RegCM4 ensemble of wind projections across most of the CORDEX-CAM domain over land shows no substantial changes in regional circulation patterns nor in mean wind speeds (< 2%) at 100 m in the near future, except for Colombia and Venezuela in summer and autumn (< 10% wind speed increases). In the southern Caribbean, summer offshore winds may increase (< 10%).

&lt;p&gt;The European Union has set ambitious greenhouse gas reduction targets, stimulating renewable energy production and accelerating the deployment of offshore wind energy in northern European waters, mainly the North Sea. We investigate if the set targets are achievable given the wind climate of the North Sea and efficiency loss resulting from large-scale extraction of kinetic energy.&lt;/p&gt;&lt;p&gt;We utilise the wind climate of the North Sea estimated from ERA5 and the New European Wind Atlas (NEWA) to evaluate the offshore energy potential of this region. We consider three scenarios of wind turbine technologies: wind farms in operation today, existing plus wind farms in the construction and planning stage, and all wind farms, which include all possible areas where offshore wind farms could be built in the future, which are determined from current exclusions zones in the North Sea. We estimate the annual energy production and capacity factors per country for the various scenarios under free-stream conditions, considering wind farm wakes from engineering models and the loss of efficiency of huge wind farms. We study the sensitivity of the energy potential to the source of wind climate (ERA5 versus NEWA), whether the data is bias-corrected or not, and the method used to apply the wake losses to the wind farm considered. We also evaluate the possible year-to-year variability of these estimates.&lt;/p&gt;