Micrometeorological impacts of offshore wind farms as seen in observations and simulations

The numerical setup and the parameterizations in this study are the same as in Siedersleben et al (2018); the reader is referred to this paper for full details. Numerical simulations are performed with Weather Research and Forecasting model WRF 3.8.1 (Skamarock et al 2008) with three domains having a resolution of 15, 5 and 1.67 km (figure 1), resulting in up to six wind turbines per grid cell. ECMWF analysis data provide the initial and the boundary condition for the simulation. The model uses 50 vertical levels, with a spacing of  ≈35 m at the bottom with the lowest level at 17 m above mean sea level, resulting in one level below rotor height and three levels intersecting with the rotor area (figure 2).

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We use the wind farm parameterization (WFP) of Fitch et al (2012) to simulate the interaction between atmosphere and wind farms. The WFP extracts kinetic energy from the mean flow and acts as a source of turbulence, depending on the thrust and power coefficients of the installed turbines in the model (Fitch et al 2012, Jiménez et al 2015, Lee and Lundquist 2017). Therefore, the WFP interacts with the planetary boundary layer scheme of Nakanishi and Niino (2004) that we use in all three domains.

We focus mainly on wakes generated by a wind farm cluster consisting of the wind farms MeerwindSued∣Ost, Nordsee Ost and Amrumbank West (see close-up, figure 1). The observations conducted on 10 September 2016 were carried out at this wind farm cluster. These three wind farms have two turbine types: SIEMENS SWT 3.6-120 and SENVION 6.2, with 90 and 95 m hub heights and rotor diameters of 120 and 126 m. The thrust and power coefficients of these wind farms are not available to the public. Therefore, we use the coefficients of the wind turbine Siemens SWT 3.6-120-onshore as these are available (http://wind-turbine-models.com/turbines/646-siemens-swt-3.6-120-shore (30 October 2018)). We have shown in Siedersleben et al (2018) that the errors introduced by these uncertainties have only a marginal effect on the wake effect.

Additional simulations include all approved offshore wind farms under construction at the North Sea (i.e. all blue and orange wind farms shown in figure 1). For these simulations we assume the same wind turbine type for simplicity, the SIEMENS SWT 3.6-120. To assess the overall impact of these wind farms at the North Sea, we conducted a second simulation with the WFP switched off. We refer to the simulations without wind farms as no wind farm simulation (NWF) and to the ones using the WFP as wind farm simulation (WF).

As part of the research project Wind Park Far Field (WIPAFF) (Emeis et al 2016), 26 flights were conducted in the far field of large offshore wind farm clusters at the North Sea from September 2016 to October 2017 (Platis et al 2018) (see table 1 for a overview). During eight flights in this project we observed a change in temperature and/or humidity within the wake of a wind farm cluster; one case is difficult to interpret and will be discussed in section 4. We will focus on aircraft measurements conducted on 10 September 2016 from 08:00 to 11:00 UTC and then compare the results against 25 other flights.

On 10 September 2016, the aircraft (Dornier DO–128 of TU Braunschweig) sampled the wind vector, humidity, pressure and temperature at 100 Hz (Platis et al 2018) along the flight path (figure 1). The aircraft flew the first flight leg (identical to the location of cross-section A–B) 5 km downwind of the last turbine at hub height, followed by four further flight legs, located 15, 25, 35 and 45 km downwind of the wind farm cluster—also at hub height. This horizontal flight pattern started at 08:20 UTC and ended at 09:24 UTC (see annotations figure 1).

Besides the measurements at hub height downwind, the aircraft conducted measurements along the vertical cross-section indicated with A, B in figure 1. The aircraft flew at five different heights, starting at 60 m AMSL followed by measurements at 90, 120, 150, and 220 m AMSL along the vertical A–B. With these measurements, we investigate the effect of large offshore wind farms on the stratification of the MBL. The measurements along vertical A–B started at 10:00 UTC and ended at 11:00 UTC.

The aircraft probed the atmosphere 5 km upwind of the wind farm cluster to quantify the upwind conditions at 08:00 UTC (see location in figure 1).

The atmosphere upwind of the wind farms was stably stratified at 08:00 UTC 10 September 2016 (figures 2(a)–(c)). From hub-height at 90 m to the top of the rotor disk at 150 m, an inversion is visible associated with a decreasing water vapor mixing ratio from 11.0 to 8.5 g kg⁻¹. The model simulates the upwind conditions reasonably well, showing a clear inversion starting at hub height and the associated decreasing water vapor concentration. However, the model has a constant cool bias between 0.6 and 1 K. This deviation is mainly caused by an overestimated nocturnal cooling on the land upwind (Siedersleben et al 2018).

Behind the wind farms, warmer air was observed and simulated at hub height within the wake even 45 km downwind of the last turbine (figures 3(a) and (c)). According to the simulations, potential temperature in the wake is 0.4 K warmer than the air upwind. This effect is more pronounced in the observations. At hub height in the upwind climb flight, a potential temperature of 291.2 K was measured (figure 2) compared to maximal 291.8 K within the wake, indicating a warming of up to 0.6 K at hub height. Additionally, the observations show a stronger horizontal difference between wake and no-wake region downwind of the wind farm cluster. At the eastern flank a potential temperature gradient of 0.8 K was observed, in contrast to a difference of 0.4 K in the simulations.

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The model has a cold bias of ≈0.6 K compared to the observations. However, the model is stably-stratified above hub height, corresponding to the observations (more details in Siedersleben et al (2018)). As we want to investigate the impact of wind farms on the MBL and not the bias of the simulations, we add 0.6 K to all shown potential temperature figures in this study.

The potential temperature wake was associated with a mixed layer, as seen in the cross-section 5 km downwind of the wind farm cluster (figures 4(a) and (c)). However, both cross-sections from observation and simulation indicate that warmer air was mixed downward. The mixed layer extends up to 120 m AMSL in the observations (figure 4(a)); in the simulation this neutral layer is only 100 m thick.

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Mixing warmer air downward corresponds to an enhanced sensible heat flux downward (figure 4(e)). In the observations, the atmosphere was stably stratified, consequently, we expect a sensible heat flux towards the surface (i.e. a negative sensible heat flux). Figure 4(e) shows the difference in sensible heat flux between a WF and a NWF simulation along the cross-section C–D. The blue contours indicate that the wind farms caused a greater downward heat flux above and within the farm, hence, explaining the warming below ≈180 m AMSL (figure 4(e)). The simulations show cooling aloft right above the farm area and warming within the farms but starting half-way through the farm area and extending much farther downwind than the cooler area figure (4(f)).

Within the wake of the wind farm cluster the air is dryer than in the ambient air outside of the wake (figures 3(b) and (d)). Similar to the wake in the potential temperature, the dryer air is still visible 45 km downwind of the wind farm cluster. Within the wake region the air has a minimum water vapor mixing ratio of 9.8 g kg⁻¹ compared to maximal values of 11.8 g kg⁻¹ outside of the wake. Corresponding to the observations, the model simulates dryer air within the wake region with values around 9.8 g kg⁻¹. However, the simulations suggest lower water vapor mixing ratios to the west of the wake. The observations show values up to 11.5 g kg⁻¹ whereas the model predicts values in the order of 11 g kg⁻¹ (figure 3(b)).

Associated with the neutrally-stratified layer, dryer air is evident in the vertical cross-section A–B (figures 4(b) and (d)) 5 km downwind of the wind farm cluster. Similar to the potential temperature, it is most likely that the dryer air originated from the dryer layer aloft above 150 m AMSL. This height corresponds to the upper limit of the rotor area, emphasizing that air stemming from above the rotor area is mixed downwards. The mixing of air above the rotor area seems to be be more pronounced in the model than in the observation. Within the upper rotor area, the model simulates a water vapor concentration of 9.5 g kg⁻¹, whereby the observations show concentrations in the order of 10.2 g kg⁻¹, indicating that dry air was entrained into too low elevations, due to enhanced vertical mixing into the farms as described in (e.g. Abkar and Porté-Agel 2015, Pan and Archer 2018).

Given the results from the case study of the 10 September 2016, one could draw the conclusion that stable conditions are a sufficient constraint to observe a warming and drying at hub height downwind of large offshore wind farms However, this assumption does not hold when analyzing the remaining 25 cases. For example, in the afternoon of the 10 September 2016, the aircraft flew a similar pattern as shown in figure 3, but did not observe any change in temperature and humidity although the atmosphere was stably stratified at hub height (figure 5(p)). The vertical profiles taken in the morning and in the afternoon differ mainly in terms of wind speed. In the afternoon the wind speed at hub height decreased from 7 m s⁻¹ to values below 6 m s⁻¹ compared to the measurements in the morning, suggesting that the wind speed has to be above a certain threshold to generate enough turbulence to mix the air and induce a warming or drying. Applying these two constraints—stable conditions and wind speeds over 6 m s⁻¹ at hub height to all 26 cases, eleven cases fulfill both criteria. Indeed, in eight of the eleven cases we observe a change in temperature (figure 6). In two of the remaining cases we cannot state for certain that a temperature change did not occur. In the first case (figure 6(f)) we have a strong background gradient in potential temperature hindering the observation of a change in temperature. In the second case ((j), not shown) we have only measurements along the wake and, hence, can not measure any difference between wake and none wake air. In the third case ((k), not shown) we did not observe an impact on temperature, despite the fact that the wind speed was above our defined threshold of 6 m s⁻¹ and the atmosphere was stably stratified at rotor height (figure 5(k)). However, the measurements were conducted downwind of Godewind (see location in figure 1), a wind farm with fewer wind turbines than the wind farm cluster around Amrumbank West. Consequently, higher wind speeds are necessary to achieve the same amount of mixing. Therefore, we suggest that higher wind speeds would have been necessary to observe a change of temperature at hub height in this case. This assumption is underscored by the observation conducted on the 11 April 2017 (case (c)), where we measured a warming at Godewind with wind speeds of over 10 m s⁻¹ at hub height.

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The height of the inversion (which is partially driven by SST) determines whether the wind farms warm or cool the atmosphere at hub height in stable conditions. In the cases shown in figures 6(a)–(e) a warming is observed at hub height whereby in figures 6(g)–(i) a cooling of the atmosphere was measured. In the warming cases, a pronounced inversion occurred above hub height accompanied by a less stable layer below, indicating that the enhanced negative heat fluxes in the wakes were stronger above than below the hub height. As a result, mixing of dry and warm air from above hub height dominates and causes an overall warming at hub height, as schematically indicated in figure 7(a). In contrast, in figures 6(g)–(i) cold SSTs were accompanied by inversions (figures 5(g)–(i)) with their strongest gradients below rotor height, emphasizing that the enhanced negative heat fluxes in the wakes were stronger below than above the hub height, thus causing a net cooling at hub height (figure 7(b)).

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We suggest that these inversions that cause a cooling can be less than 30 m thick. For example, in figure 6(i) we observe a cooling of up to 0.6 K although there is a constant lapse rate within most of the rotor area. However, the SST at FINO1 (see location in figure 1) was 284 K compared to a potential temperature of ≈294 K at 30 m AMSL, indicating that a shallow cold layer close to the ocean surface caused an inversion through the lower portion of the rotor area and below, thus cooling due to the enhanced mixing of this cool air within and partly below the rotor area caused the observed cooling.

The observed warming or cooling is decoupled from the drying downwind. For example, we see a warming in figures 6(a)–(e) and cooling in figure 6(i) but in all measurements we see dryer air downwind, meaning that the moisture flux is decoupled from the heat flux—a result in agreement with the findings of Foreman et al (2017). However, in nine of the cases that fulfill the criteria for a potential change in temperature at hub height we observed six times a drying and only one time a humidification at hub height. In the remaining two cases we could not measure any change in humidity (case (g), (h)), whereby in case (g) the aircraft was flying at 200m AMSL—a height too high to detect any impact on the humidity (figure 4(b)).

We have shown that a single wind farm cluster can cause a warming of up to 0.6 K and a drying of ≈0.5 g kg⁻¹ at hub height in the MBL according to the observations. Consequently, the overall effect of all wind farms that are operational, approved or under construction is, hence, of interest. To answer this question, we conducted a simulation for 10 September 2016 including all planned and existing wind farms as they are shown in figure 1 (i.e. orange and blue wind farms). For this case study we have measurements along the cross section A–B (figure 3) and could show that the model simulates the vertical and horizontal impact on temperature and humidity, increasing the confidence in the future scenario. Additionally, we had more warming than cooling cases.

The difference between the WF and NWF simulations (figure 8) suggests a similar warming response (within ± 0.1 K) to the case of 10 September 2016 in the presence of more wind farms. For example, downwind of the large wind farm cluster around Riffgrund, a wide area with a warming of up to 0.5 K is found (figure 8), while downwind of the large westernmost cluster the warming is less than 0.3 K. However, not only the size of a wind farm seems to determine the degree of warming. The wind farm Riffgat with only 30 wind turbines causes also a warming of up to 0.5 K. The simulation suggests a stronger warm air advection aloft at Riffgat and, hence, the inversion at Riffgat is even more pronounced and that in turn allows an even more enhanced downward mixing of warm air. In contrast, the warming at Riffgat is associated with a cooling on the eastern flank of the wake.

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While these temperature and moisture changes are novel and may seem consequential when considering the effects of wind farms on local microclimates, it is important to recognize that these are limited effects. The observed local warming and cooling of ±0.6 K are small compared to the warming that is caused globally by land cover change (LCC) and land management change (LMC). According to Luyssaert et al (2014) the warming caused by LCC and LMC is in the order of 1.7 K within the planetary boundary layer. Out of the 26 flights that occurred over a year, an impact on temperature and humidity only occurred when a strong stable stratification existed at turbine hub height or below rotor height. In other cases, without inversions or with inversions located well above turbine rotor height, the enhanced mixing caused by wind farms would not have such an effect.