the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 May 2025
Uncertainty in Offshore Wind Power Forecasts: A Regional Climate Modeling Approach for the North Sea
Abstract. With the transition towards green energies gaining momentum, the expansion of wind farm areas and associated technologies is growing faster. The North Seas Energy Cooperation group has set an ambitious target to increase the offshore wind-generated power capacity from 26 GW in 2022 to 300 GW by 2050 in the geographical areas of the North Seas. With this goal, an extensive offshore infrastructure is planned to be deployed in the region. Studies have been carried out to assess the power production of such future development. However, the uncertainty of such assessments has not been fully addressed. Wake effects have been identified as the primary source of power losses. They are often studied within individual wind farms or small clusters, but the dynamics of large wind farm clusters at a regional scale are only beginning to be explored. In this study, we address uncertainties of power output derived from projected wind farm areas at the North Sea in scenarios that encompass different turbine setups and atmospheric conditions. To achieve this, we used COSMO6.0-CLM, the newest version of the regional climate model COSMO-CLM, and further improved the existing wind farm module to extend the model's capability to design more flexible and realistic scenarios. This allows us to quantify impacts from different factors that contribute to power output uncertainties. Our results show that wake dynamics resulting from different turbine density distributions can account for up to 5 % of the variability of the generated power, while wind regimes at different hub heights contribute an additional 2 %. Approximately 6 % of the variability is attributed to discrepancies in atmospheric circulation states inherent to the reanalysis datasets used to force the simulations. The total uncertainty in power output accounts for 13 %. In a scenario with an installed capacity of 150 GW the total power output would range from 58 to 74 GW, corresponding to an uncertainty of 20 GW. Since economic and environmental studies rely on such scenarios, it is crucial to consider these uncertainties.
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CC1: 'Comment on wes-2025-64', Jan-Peter Schulz, 16 May 2025
Dear authors,
thanks for the very interesting manuscript. I have two comments:
1. Beside the references you mentioned for a model description of COSMO6.0, the usual (or default) references additionally include:
- sub-grid scale orography (SSO) (Lott and Miller, 1997; Schulz, 2008)
- TERRA (Schrodin and Heise, 2001; Schulz et al., 2016; Schulz and Vogel, 2020)
Maybe you could also say a word whether the updated parameterizations were only available in the latest model version COSMO6.0, or they were really switched on and used. The model performance depends very much on this.
2. Maybe you could discuss a bit if you want to transfer this work and continue it to COSMO's successor model ICON.
Refererences:
Schulz, J.-P., 2008: Introducing sub-grid scale orographic effects in the COSMO model, COSMO Newsletter, 9, 29–36. (Available at http://www.cosmo-model.org/)
Schulz, J.-P., G. Vogel, C. Becker, S. Kothe, U. Rummel and B. Ahrens, 2016: Evaluation of the ground heat flux simulated by a multi-layer land surface scheme using high-quality observations at grass land and bare soil, Meteor. Z., 25, 607–620.
Schulz, J.-P. and G. Vogel, 2020: Improving the processes in the land surface scheme TERRA: Bare soil evaporation and skin temperature, Atmosphere, 11, 513.
Best regards
Disclaimer: this community comment is written by an individual and does not necessarily reflect the opinion of their employer.Citation: https://doi.org/10.5194/wes-2025-64-CC1 - RC1: 'Comment on wes-2025-64', Anonymous Referee #1, 29 May 2025
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RC2: 'Comment on wes-2025-64', Anonymous Referee #2, 08 Jun 2025
This is an interesting study on regional climate modelling of wind power in the North Sea. I do however have an issue with the paper, in particularly with the claim that it provides an uncertainty assessment of offshore wind farm power forecasts, which, in my opinion, it does not deliver. In this paper, the uncertainty assessment essentially boils down to evaluating the impact of different driving data for the regional climate model—namely ERA5 and ERA-Interim—and secondly, to comparing the implementation of 3.6 MW and 15 MW turbines at the same capacity density.
In my view, this does not constitute an uncertainty assessment. Firstly, I do not consider the sensitivity of different turbine types at the same capacity density to be part of the uncertainty. A wind farm developer typically knows which type of turbine they intend to use. And even if there is uncertainty, then the 3.6 MW turbine is much to small to consider for future parks.
Secondly, the driving data are not the only source of uncertainty. More important sources of uncertainty include: 1) Uncertainty in the background wind fields without wind farms, which can be much broader than the sensitivity to different reanalysis datasets – regional climate models also have biases. 2) The wake effects within and between wind farms, which are subject to uncertainties, even if the turbine type and capacity density is known, the wakes are uncertain. 3) Deviations of actual turbine power curves from the industry-specified ones. And 4) Downtime of the turbines due to curtailment and maintenance.
Lastly, it is not at all clear whether ERA5 and ERA-Interim represent the full range of uncertainty found in state-of-the-art reanalysis products since there are others such as COSMO-REA, NCEP and MERRA. The winds in other re-analyses products over the region of interest should be discussed at the minimum.
Therefore, I believe the paper needs to be reworked and the scope should be changed. The most important point for improvement is that the paper should not claim to provide a uncertainty assessment, because that is not what it actually does, it is rather a sensitivity analysis to driving re-analysis data and the effect of using different (spatially homogeneous and non homogeneous) turbine types at the same capacity density.
A second important point for improvement is the presentation of key numbers in the paper:
1) The paper states that increasing the turbine nominal power from 3.6 MW to 15 MW leads to a 5% increase in power production when wakes are not accounted for. In the conclusion, this is presented as part of the uncertainty attributed to turbine type. However, treating this full range as uncertainty implies we have no information about which turbines will be installed—which is not the case. Thus, this is not a true uncertainty range but rather a sensitivity analysis (see above) and should be presented in that way. In the abstract, it is written that wind regimes at different hub heights contribute an additional 2%. Apart from being confusing, I believe the authors may have mixed up the numbers, and it should actually be 5%.
2) The paper identifies a 2% stronger reduction in power output due to wake effects when comparing 3.6 MW and 15 MW turbines. This is however not correct – it should be either 2%pt or 14% (the latter being (15.5-13.5) / (0.5 * (15.5 + 13.5))) In the conclusion, this is summarized as approximately 2% being derived from wake intensities caused by the turbine density distribution. This sentence is very unclear, and the earlier formulation would be much better to include in the conclusions. In the abstract, it is written: “Our results show that wake dynamics resulting from different turbine density distributions can account for up to 5% of the variability in generated power.” However, it is not different turbine density distributions, but rather the change from 3.6 MW to 15 MW turbines while keeping the capacity density constant. This should be written more clearly. Additionally, I believe the authors have mixed up the numbers — should be 2%pt, not 5%pt.
In both the abstract and the conclusion, it is stated that the total uncertainty in power output is approximately 13%. In a wind farm scenario with an installed capacity of 150 GW, this results in a power output range from 58 to 74 GW, corresponding to an uncertainty of 20 GW. I assume the authors mean that 20 GW is 13% of the total installed capacity of 150 GW, and this should be made explicit. It is not 13% of the actual power production.
Lastly, for the reasons mentioned above, this number of 13% of the installed capacity does not represents the total uncertainty in power output. Many important factors are not included, and there is already knowledge about which turbines will be installed, so this range of turbine types is not a real uncertainty. I cannot recommend acceptation of the paper when it is presented as a total uncertainty of north sea wind farm power production.
Minor comments:
Regarding the evaluation method, I am not a strong proponent of using RMSE and R² for regional climate model evaluation, assuming that spectral nudging is not used to maintain the timing of events. A small displacement—known as the double penalty—in the position or timing of a weather event can lead to large RMSE and low R², even if the model performs reasonably well. Regional climate models should aim to get the statistics right, rather than the exact wind speeds at the right time and moment.
Figure 6: Please change this to show the wind speeds for a certain wind direction interval to get a much more statistically robust signal. The authors have that information available from the long term simulation.
There appears to be a displacement of a weather system, with high and low wind speed deficits occurring close to each other—likely from a single event. Please analyze the individual differences between the model runs for different timestamps to determine whether this displacement is due to model error or a real signal caused by the wind farm. If it is a displacement, it should be removed from the sample; if it is a real signal, the authors should explain it.
Line 321: It is interesting that turbines with a hub height of 150 metres are exposed to more stable atmospheric conditions than turbines with a 90-metre hub height. Explain why. Moreover, discuss that the rotor area spans a large vertical range, so not only the stability at hub height is relevant.
Line 335: Another important factor is that newer turbines have different power curves, typically with larger regime II. Please assess and quantify the impact of three factors: (1) the smaller wake effects for the 15 MW turbines, (2) the higher positioning of the 15 MW turbines, and (3) the differences in power curves.
Lines 339–355 and beyond: When comparing results to other studies, add the capacity density, which differs between studies. Furthermore, the discussion refers to percentage points. A reduction in capacity factor from 49% to 42% is a reduction of 7 percentage points, not 7%. If it were expressed as a percentage, it would correspond to a 15% reduction in annual energy production or capacity factor. This distinction should be made clear. The paragraph below should also refer to percentage points, not percentages. Check the entire manuscript.
Citation: https://doi.org/10.5194/wes-2025-64-RC2
Data sets
Wind farm scenarios for the North Sea using COSMO6.0-clm Alberto Elizalde et al. https://www.wdc-climate.de/ui/entry?acronym=cD4_wfns
Model code and software
Wind farm parametrization for COSMO6.0-clm. Alberto Elizalde https://doi.org/10.5281/zenodo.10069391
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