Abstract
The European Union has set ambitious CO
2
reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly ...the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms
−1
depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.
The European Union has set the ambitious goal of becoming climate neutral by 2050, which has stimulated renewable energy production and accelerated the deployment of offshore wind energy in the North ...Sea. Here, a high-resolution regional climate model was used to investigate the impact on the sea surface climate of large-scale offshore wind farms that are proposed for the North Sea. The results show a significant reduction in the air-sea heat fluxes and a local, annual mean net cooling of the lower atmosphere in the wind farm areas down to more than 2.0 Wm
, due to a decrease in 10 m wind speed and turbulent kinetic energy and an increase in low-level clouds. Mean surface winds decreased by approximately 1 ms
downstream of wind farms. Furthermore, an increase of approximately 5% in mean precipitation was found over the wind farm areas. At a seasonal timescale, these differences are higher during winter and autumn than in other seasons. Although the offshore wind farms reduce the heat transport from the ocean to the atmosphere in the region of large wind farms, the atmospheric layers below the hub height show an increase in temperature, which is on the order of up to 10% of the climate change signal at the end of the century, but it is much smaller than the interannual climate variability. In contrast, wind speed changes are larger than projected mean wind speed changes due to climate change. Our results suggest that the impacts of large clustered offshore wind farms should be considered in climate change impact studies. Moreover, the identified offshore windfarm impacts on the sea surface climate and the introduced spatial pattern in atmospheric conditions, in particular the modeled wind speed changes, suggest potential impacts on local ocean dynamics and the structure of the marine ecosystem. This should be considered in future scenarios for the North Sea marine environment and taken into account as a structuring influence in the offshore environment.
The EU aims for carbon neutrality by 2050, focusing on offshore wind energy. Investments in North Sea wind farms, with optimal wind resources, play a crucial role. We employed a high-resolution ...regional climate model, which incorporates a wind farm parametrization, to investigate and address potential mitigating impacts of large wind farms on power generation and air-sea fluxes. Specifically, we examined the effects of replacing 5 MW turbines with larger 15 MW turbines while maintaining total capacity. Our study found that substituting 15 MW turbines increases the capacity factor by 2-3%, enhancing efficiency. However, these turbines exhibit a slightly smaller impact on 10 m wind speed (1.2-1.5%) and near-surface kinetic energy (0.1-0.2%), leading to reduced effects on sea surface heat fluxes compared to 5 MW turbines. This was confirmed by a stronger reduction in net heat flux of about 0.6-1.3% in simulations with 5 MW compared to 15 MW wind turbines. Air-sea fluxes influence ocean dynamics and marine ecosystems; therefore, minimizing these impacts is crucial. Overall, deploying 15 MW turbines in offshore wind farms may offer advantages for ocean dynamics and marine ecosystems, supporting the EU's carbon-neutral objectives.
•The first study on wind energy at hub height with high-resolution over the BYS.•Climatology, variability, and extreme climate of winds were investigated.•Wind energy features for water areas and ...potential wind farm sites are given.
China has set ambitious goals for the development of offshore wind energy to meet the increasing energy demand of coastal provinces. Many studies have assessed the potential offshore wind energy in Chinese territorial waters. However, few studies have focused on the climatology, variability, and extreme climate of wind speeds and wind power, especially at hub height in this area. This type of study is important for selecting promising sites for offshore wind farms. In the present study, a 35-year (1979–2013) high-resolution (7km) wind hindcast over the Bohai Sea and the Yellow Sea (BYS) at 100-m height was constructed using the regional climate model COSMO-CLM (CCLM) driven by the ERA-Interim reanalysis dataset. The quality of wind speeds reconstructed by CCLM was assessed by a comparison with observation data at several stations. After verification, the climatology, variability, and extreme climate of winds over the BYS were spatially and temporally investigated. The results show that the 35-year mean wind speed is mostly between 7.0 and 7.5m/s; in the coastal areas of the BYS, the mean is less than 7.0m/s, and in the remote offshore areas, the mean is greater than 7.5m/s. The daily mean wind speed is stronger (weaker) in winter (summer) half year, with stronger (weaker) spatial variability. Wind power density is mainly 300–500W/m2. The interannual variability of annual mean wind speed and the wind power are in the range of 0.1–0.3m/s and 10–40W/m2, respectively. Decadal variances of the mean wind speed and the wind power are roughly within ±2% and ±5%, respectively, with a stronger variability along the southwestern coasts of the Yellow Sea. The distribution patterns of extreme winds (i.e., 5, 10, 30, and 50-year return values) are generally similar, with strength increasing from the northwest to the southeast. The wind energy characteristics for water areas and potential wind farm sites are summarized.
To accurately calculate the impact of renewables on power production in complex electric power grids, high-resolution and ideally seamless data within the planetary boundary layer are required. ...Therefore, the quality of different regional reanalyses and hindcasts is evaluated with respect to the representation of the planetery boundary layer and related sub-daily processes. On the one hand, high resolution regional reanalysis from the UERRA (UE-SMHI, UE-UKMO) and a similar project (COSMO-REA6) are considered. On the other hand, two hindcasts based on the COSMO-REA6 configuration are included in this study, i.e. a simulation with perfect boundaries and a simulation additionally utilizing spectral nudging. The focus of the evaluation is on measurements at four flux towers that are not part of any assimilation procedure. In this paper, we will show that the model's quality depends on both the complete model system - assimilation method, resolution and physical parameterization - as well as on the performance measure. The daily cycle is best depicted by the hindcasts and even COSMO-REA6 hardly introduces spurious variability. UE-SMHI (3D-Var) suffers from spin-up in particular visible at the elevated levels, whereas the spin-up is damped in UE-UKMO (4D-Var). Investigation of atmospheric stability reveals that diurnal variation of stratification is for the most part well reproduced, but strong deficits were found for all COSMO simulations in reproducing strong stratification and corresponding wind speed gradients. Moreover, an overestimation of superadiabatic lapse rates and corresponding overly weak turbulent mixing is found for UE-UKMO. Furthermore, a combination of ramp statistics and contingency tables is utilized to detect a clear advantage of sophisticated assimilation systems over hindcasts. The evaluation framework presented underpins the importance of ramp statistics and vertical measurement profiles, especially with respect to assessing long-term simulations.
Reproducibility of research results is a fundamental quality criterion in science; thus, computer architecture effects on simulation results must be determined. Here, we investigate whether an ...ensemble of runs of a regional climate model with the same code on different computer platforms generates the same sequences of similar and dissimilar weather streams when noise is seeded using different initial states of the atmosphere. Both ensembles were produced using a regional climate model named COSMO-CLM5.0 model with ERA-Interim forcing. Divergent phase timing was dependent on the dynamic state of the atmosphere and was not affected by noise seeded by changing computers or initial model state variations. Bitwise reproducibility of numerical results is possible with such models only if everything is fixed (i.e., computer, compiler, chosen options, boundary values, and initial conditions) and the order of mathematical operations is unchanged between program runs; otherwise, at best, statistically identical simulation results can be expected.
Ensemble simulations of regional climate exhibit phases with trajectories either staying close, or strongly diverging. These phases are found to be the same in two different ensembles, one with slightly shifted initial dates and another executed on different platforms.
The lockdown of large parts of Chinese economy beginning in late January 2020 lead to significant regional changes of aerosol loads, which suggests a reduction of backscatter and consequently a ...regional warming in the following months. Using local data and a numerical experiment with a limited area model, we have examined how strong this response may have been. The observed (local and reanalysis) observations point to a warming of less than 1.0 K, the simulations to a warming of the order of 0.5 K. These numbers are uncertain, because of large-scale natural variability and an ad-hoc choice of aerosol optical depth anomaly in the simulation. Thus, the result was, in short, that there was actually a weak warming of a few tenth of degrees, while noteworthy changes in circulation or in precipitation were not detected. More specifically, we found that at selected central China stations temperatures were found to be higher than in previous two years. This warming goes with a marked diurnal signal, with a maximum warming in the early afternoon (06 UTC), weakest at night (18 UTC). This may be related to a general warming of large swaths of Asia (including Siberia, which is not related to local aerosol forcing). Indeed, also the stations outside the immediate strong lockdown region are showing warming, albeit a weaker one. Thus, the difference 2020 minus 2019/2018 may overestimate the effect. The ad-hoc series of numerical experiments indicates that the simulated changes are robust and suffer little from internal dynamical variability. In particular, the overall reduction of the aerosol optical depth does not lead to phases of larger intermittent divergence among the model simulations, irrespective of the aerosol load. Instead, the simulations with reduced anthropogenic aerosol load show more a mere locally increased temperature. This may indicate that the aerosol effect is mostly thermodynamic in all local air columns in the region.
The effects of coupling between the atmospheric model of the Consortium for Small-Scale Modelling-Climate Limited-area Modelling (CCLM) and the wind wave model (WAM) on the lower atmosphere within ...the North Sea area are studied. Due to the two-way coupling between the models, the influences of wind waves and the atmosphere on each other can be determined. This two-way coupling between these models is enabled through the introduction of wave-induced drag into CCLM and updated winds into WAM. As a result of wave-induced drag, different atmospheric parameters are either directly or indirectly influenced by the wave conditions. The largest differences between the coupled and reference model simulation are found during storm events as well as in areas of steep gradients in the mean sea level pressure, wind speed or temperature. In the two-way coupled simulation, the position and strength of these gradients vary, compared to the reference simulation, leading to differences that spread throughout the entire planetary boundary layer and outside the coupled model area, thereby influencing the atmosphere over land and ocean, although not coupled to the wave model. Ultimately, the results of both model simulations are assessed against in situ and satellite measurements, with a better general performance of the two-way coupled simulation with respect to the observations.
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