Dear authors, 

thanks for the very interesting manuscript. I have two comments: 

Q1. 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. 

 

References: 

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. 



Reply to CC1-Q1: 

 

Thank you for your comments. We agree that the TERRA scheme and the land–atmosphere interaction processes are fundamental components of the COSMO model and represent an essential part of its general description. In our original approach, we chose to highlight only those model schemes most relevant to our experiments, in order to maintain focus and clarity. This decision was motivated by the fact that our atmospheric domain primarily targets the North Sea region, where roughly 60% of the area is covered by water, and our analysis is limited to model performance over open-water areas. Nevertheless, we acknowledge that a more comprehensive model overview should also include a reference to the TERRA scheme and the corresponding literature. 

 

In section 3.1, Schulz (2008) will be added to the SSO literature and TERRA scheme will be included as follows: 



The land–atmosphere interaction is represented by the TERRA multi-layer soil–vegetation–atmosphere transfer scheme, which explicitly resolves heat and moisture transport within soil, snow, and canopy layers. TERRA ensures closure of the surface energy and water balance by simulating soil temperature and moisture dynamics, evapotranspiration, snowpack evolution, and the surface fluxes of heat, moisture, and momentum. (chrodin and Heise, 2001; Schulz et al., 2016; Schulz and Vogel, 2020).

 

Regarding the updated parametrization: The reimplementation of Fitch et al. (2012) in COSMO 6.0 was developed specifically for this research and therefore cannot be included in the standard COSMO distribution, since COSMO 6.0 was the last official release. Instead, the reimplementation is made openly available as a separate module, published as a patch for COSMO 6.0 in the Zenodo repository (Elizalde, 2023). After applying the patch, the parametrization can be activated through a dedicated switch within the standard model configuration settings. 

 

In section 3.2, the first paragraph will also include the description:

The reimplementation, along with the new updates, is provided as a separate module, publicly available as a patch for the COSMO6.0-clm code in the Zenodo repository (Elizalde, 2023), and it can be activated through a new dedicated switch within the standard model configuration settings.  

In ‘Code availability’ Section, the text will be modified:  

 

In ‘Code availability’ Section, the text will be modified:  

 

The reimplementation of the wind farm parameterization of Fitch et al. (2012), along with the new updates, is provided as a separate module, publicly available as a patch for COSMO 6.0 in the Zenodo repository (Elizalde, 2023). 



References: 

Elizalde, A. (2023). Wind farm parametrization for COSMO6.0-clm. Zenodo. https://doi.org/10.5281/zenodo.10069391 

Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I.: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model, Monthly Weather Review, 140, 3017–3038, https://doi.org/10.1175/mwr-d-11-00352.1, 2012 

Q2. Maybe you could discuss a bit if you want to transfer this work and continue it to COSMO's successor model ICON. 

Reply to CC1-Q2: 

To the best of our knowledge, DWD will proceed with the implementation of Fitch et al. (2012) scheme in ICON. 

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. 

Comment: 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. 

 

Reply: We sincerely thank the reviewer for their detailed and constructive comments, which have helped us improve the manuscript. We now address their remarks point by point below: 

 

It is important to note that for long-term wind farm planning, often spanning decades, the exact turbine types are typically not finalized at the early stages of development. While developers may have a general range of turbine capacities in mind, specific models, rotor diameters, and hub heights are subject to technological advancements, supply chain availability, and economic considerations at the time of construction. Additionally, when new wind farms are installed within existing clusters, the exact performance of both the new and neighbouring farms—in terms of atmospheric circulation, wake interactions, and power generation—cannot be precisely predicted in advance (Finserås et al. 2024). Therefore, contrary to the reviewer’s comment, uncertainty in turbine types represents a genuine component of the overall power generation uncertainty and should not be considered solely a sensitivity study. 

 

We are fully aware that the use of a 3.6 MW turbine is not realistic for future offshore wind farms; however, the two rated powers considered in the scenarios (3.6 MW and 15 MW) were intentionally chosen to represent, in purely theoretical scenarios, the extreme ends of the turbine size spectrum. This approach allows us to quantify the maximum range of uncertainty associated with turbine type and layout, rather than to suggest that a 3.6 MW turbine would actually be deployed. By examining these bounds, our analysis captures the potential variability in power generation due to turbine characteristics, providing an estimate of uncertainty that is relevant for long-term planning and assessment of wind farm clusters. 

 

Comment: 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. 

 

Reply: We acknowledge that additional sources of uncertainty exist and agree that further research on their impacts would be highly valuable. We address the comments point by point below: 

 1) To our knowledge, few studies have specifically compared wind speeds over the North Sea using different regional climate models (RCMs) driven by the same reanalysis boundary conditions, as relevant to the reviewer’s comment. Within the Euro-CORDEX framework, Moemken et al. (2018) compared the RCA and COSMO RCMs, both forced with ERA-Interim. Their results (their Figure 1) indicate that the North Sea wind speed fields are very similar, with differences of less than 1 m s^-1. In contrast, background wind fields from RCMs driven by different boundary conditions derived from future climate projections exhibit substantially larger uncertainty, primarily due to differences in the driving conditions. Ganske et al. (2016) reported that the 50th percentile range of wind speed among RCMs over the period 1961–2099 is approximately 4 m s^-1. While it is feasible to estimate power output directly from existing wind datasets, this is not the focus of the present study. Instead, our objective is to investigate the dynamical interactions of wake effects within wind farm clusters by explicitly calculating wakes at each model timestep, thereby capturing their continuous evolution throughout the simulation and assessing the resulting impacts on power generation. 

2) Part of our investigation is precisely to investigate wake effects within and between wind farms as stated in our abstract (line 6): “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 driving conditions.” Specifically, Subsection 4.5 (renamed ‘Uncertainty in Wind Farm Power Production’) details our findings on this topic. 

3) & 4) We acknowledge that the lack of real turbine power and thrust coefficients, as well as turbine downtime and maintenance, contribute to total power uncertainty. However, these data are typically proprietary and not publicly available due to company economical strategies, making it currently infeasible to include them in our assessment. 

Comment: 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. 

 

Reply: We agree that ERA5 and ERA-Interim do not represent the full range of uncertainty for model driving conditions.  We included a discussion to this respect at line 56 to read: 

 

“With respect to boundary conditions, Hahmann et al. (2022) compared wind speed estimates from four reanalysis products (NEWA, ERA5, MERRA2, and 20CRv3) with mast observations from several offshore platforms in the North Sea (FINO1, FINO2, FINO3, IJmuiden, and Ekofisk) over the period from 1980 to 2014. Their analysis showed that, when evaluated in relation to the observational data (see their Fig. 2), the wind climates produced by the different reanalyses were “nearly identical,” with mean values ranging from 10.0 to 10.2 m s^-1. To investigate for these small uncertainties associated with the choice of boundary conditions and their impact on power production, our scenarios were driven using two different reanalysis datasets (ERA-Interim and ERA5).  

Comment: 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. 

 

Reply: We respectfully disagree with the reviewer’s assessment that our study constitutes only a sensitivity analysis rather than an uncertainty assessment. In our view, the sources we investigate indeed represent fundamental uncertainties. Specifically, it is uncertain which turbine types will be installed in the future (a decade or more), how they will be configured, and what capacity densities will be employed. These design choices inherently affect wake dynamics, which themselves constitute an important source of uncertainty. In addition, future wind speed conditions over the North Sea are subject to considerable uncertainty arising primarily from the unpredictable development, tracks, and intensities of low-pressure systems, as well as the complex atmospheric processes influencing them. As a concrete example from our study, the reanalysis datasets employed do not agree on the magnitude of the average wind speed over the North Sea. This average wind speed is therefore itself uncertain, as demonstrated in our comparison with satellite observations, which also may contain errors and uncertanities (see Subsection 4.1 Wind speed validation).  

 

We are not examining changes from single input parameters (except in our new Subsection 4.6). We are quantifying the range of possible outcomes given unknown or unknowable future conditions. Taken together, these factors demonstrate that our work goes beyond sensitivity testing and addresses genuine uncertainties in future wind power projections. 

Comment: A second important point for improvement is the presentation of key numbers in the paper: 

Comment: 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%. 

 

Reply: Addressed in the reply below. 

Comment: 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. 

 

Reply: Addressed in the reply below. 

Comment: 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. 

 

Reply: Addressed in the reply below. 

Comment: 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. 

 

Reply: We acknowledge the points previously raised. Some of our calculated percentages, and consequently their interpretations, were unfortunately mixed up. We have revised Subsection 4.5 and the corresponding text throughout the manuscript to provide a corrected explanation. In particular, the calculation of percentages attributable to driving conditions and turbine types has been reinterpreted. A concise summary of the changes is provided below: 

 

Our analysis indicates that the combined uncertainty due to driving conditions and the difference of turbine types amounts to approximately 15 GW, corresponding to 10% of the total installed capacity (150 GW). Of this, the contribution from driving conditions accounts for about 4 GW (2.5% w.r.t. 150 GW), whereas the difference of turbine types has a larger influence, contributing roughly 11 GW (7.5%). Further breaking down this last contribution by separating the effects of turbine size and wake interactions, we find that, when neglecting wake effects, the difference in turbine size accounts for approximately 8 GW (5.3%), while the remaining 3 GW (2.2%) is attributed to difference of wake effects depending on the turbine size. 

 

Minor comments: 

Comment: 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. 

 

Reply: Indeed, similar to spectral nudging, domain size—particularly smaller domains—can influence how closely the model follows observed values. In our case, we do not use spectral nudging; however, the domain is relatively small for the kind of atmospheric model used, so that synoptic-scale low-pressure systems, imposed through the boundary conditions, still exert a strong influence on the interior of the domain. We agree that using RMSE as a metric is challenging for model evaluation, yet despite this, our results demonstrate good temporal agreement with FINO1 observations (Table 4). In addition to RMSE, we also employed other statistical metrics to further assess model performance. 

 

Comment: 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. 

 

Reply: Figure 6 is intended to complement Figure 5 (line 266). Both figures represent snapshots from the day and time of the airborne measurements (10 September 2016 at 09:00 UTC). The measurements were taken under conditions such that the wind direction transported the wake from the southern cluster toward the northern wind farm. The cross-section shown in Figure 6 (marked in red in Figure 5) follows this wind direction and depicts the simulated wind speeds at the moment of the flight. Using data from other dates and wind directions from an interval would not accurately represent the wind speed during the measurements. 

 

Comment: 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. 

 

Reply: The reviewer did not specify which figure they were referring to. Based on the comment, we infer that it concerns Figure 7. The explanation for the apparent displacement is as follows: 

 

Figure 7 shows composites of wind speed deficits based on different atmospheric conditions. Each composite is formed from the average of cases (constituents) filtered according to wind direction and atmospheric stability criteria applied only at the Amrumbank West wind farm—the northernmost farm of the southern cluster within the delimited area marked by the red line—not from the entire wind field. The wind direction criterion, described in line 286, requires that “the wind direction should have a southerly component within ±5° centered at δ = 188°, i.e., in the range 183°–193°". Outside the Amrumbank West wind farm, the wind direction can be any. 

 

Since each composite is constructed from the average of constituents at different time steps and under different atmospheric conditions, the resulting composite does not necessarily depict a physically realistic wind field. However, the averaged pattern of the composite provides an indication of the typical tendencies of the atmospheric state under the selected criteria. 

 

Comment: 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. 

 

Reply: We acknowledge this point, noting that the impact depends not only on hub height but also on rotor diameter. Our intention was to assess the full turbine (hub height and rotor diameter), not hub height in isolation. For brevity, turbines with a 150 m hub height and 240 m rotor diameter were referred to as “taller” turbines, while those with a 90 m hub height and 120 m rotor diameter were referred to as “smaller” turbines; however, only hub height was mentioned, which may have caused confusion. We revisited the manuscript and revised the text where it might have implied that only hub height was considered. In addition, an explanation on variation of stability as height is gain is added. We have revised the text at line 321 to read: 

 

“Turbines with a hub height of 150 m experience a different wind regime, characterized by more frequent stable and very stable conditions compared to turbines with a 90 m hub height. This is due to reduced surface-driven turbulence and persistent temperature gradients at higher altitudes. Near the ground, friction and solar heating generate turbulence and mixing, but these effects weaken with height.”

Comment: 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. 

 

Reply: Newer turbines achieve higher efficiency through technical and mechanical improvements. However, their thrust and power coefficients are not publicly available, preventing access to their detailed power curves. Consequently, performing a direct comparison is currently beyond our reach. Regarding the quantification between our simulated 3.6 MW and 15 MW turbines, Table 6 provides the annual mean power output for simulations with these turbine types, both neglecting and including wake effects. The quantification of such effects is as follows (starting from point 2): 

2) The difference attributed solely to turbine size, with wake effects neglected, is 93.9 GW − 85.9 GW = 8 GW. 

1) The difference due to wakes alone is (73.7 GW − 62.6 GW) − 8 GW = 3.1 GW. 

3) Differences arising from turbine-specific power curves are intrinsic to the simulated power of each turbine and are reported in Table 6. 

Comment: 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. 

Reply: The manuscript has been revisited and revised to update the relevant text regarding the comparison of percentages as the reviewer suggested, correcting them to percentage points, and to include the appropriate capacity densities in the comparison with results from other authors. 

 

 

References 

 

Finserås, Eirik / Herrera Anchustegui, Ignacio / Cheynet, Etienne / Gebhardt, Cristian Guillermo / Reuder, Joachim Gone with the wind? Wind farm-induced wakes and regulatory gaps 2024-01Marine Policy , Vol. 159 Elsevier BV p. 105897 

 

Ganske, Anette / Tinz, Birger / Rosenhagen, Gudrun / Heinrich, Hartmut Interannual and Multidecadal Changes of Wind Speed and Directions over the North Sea from Climate Model Results 2016-09 Meteorologische Zeitschrift , Vol. 25, No. 4 Schweizerbart p. 463-478 

 

Hahmann, Andrea N. / García-Santiago, Oscar / Peña, Alfredo Current and future wind energy resources in the North Sea according to CMIP6 2022-12 Wind Energy Science , Vol. 7, No. 6 Copernicus GmbH p. 2373-2391 

 

Moemken, Julia / Reyers, Mark / Feldmann, Hendrik / Pinto, Joaquim G. Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations 2018-06 Journal of Geophysical Research: Atmospheres , Vol. 123, No. 12 American Geophysical Union (AGU) p. 6373-6389