Quantifying local wind farm impacts on land surface temperature: Evidence from airport observations and a preliminary model

Abstract

Recent studies have indicated a warming trend has occurred in the local areas surrounding wind farms. These studies utilized satellite imagery to measure temperature deviations in areas with a high concentration of wind turbines of the United States. Incorporating numerical results from the full article, our analysis provides evidence for a statistically significant warming trend, particularly during nighttime hours in the summer and winter months. Specifically, during the nighttime, there was a warming of 0.724°C in June, July, and August (JJA) and 0.458°C in December, January, and February (DJF). Conversely, daytime changes were minimal, with a change of −0.037°C in JJA and 0.233°C in DJF. These findings underscore a small warming effect near wind farms, predominantly attributed to turbine-induced mixing. This work provides further evidence for the phenomenon using a novel—short-term though—dataset and the results should be viewed as suggestive rather than definitive.

Keywords

airport wind turbines and local warming trend

Introduction

As wind farms continue to be built throughout the world in an effort to increase the capacity of renewable energy sources, a growing number of observations, studies, and conclusions can be made about them. In fact, the wind power capacity over the entire globe has grown by over 20% in the past decade, and rapid growth in the wind farm industry—both onshore and offshore—is expected to continue in the future (Arshad and O’Kelly, 2019; Enevoldsen et al., 2021). These observations and conclusions include the local weather and environmental effects, impact on the electric grid, and the wear and tear of individual turbines leading to a predictable lifetime of a wind farm (Kocsis and Xydis, 2019). This paper focuses on the impact that wind farms can have on the local weather (Siedersleben et al., 2018). For the purposes of this wind strategy paper, the “local weather” refers to an area within at least a 20-mile radius of the location of a specific wind farm.

A highly discussed topic is that wind farms could have a marginal warming effect in the local area as their rotating blades can limit the effects of nighttime radiative cooling (Zeyghami et al., 2018). Naturally, this would lead to warmer low temperatures at the coldest part of the day, typically the early morning (Fitch et al., 2013). Wind farms have also been known to cause wake turbulence for light aircraft in areas that are in close proximity to airports (Tomaszewski et al., 2018). The focus of this paper and the studies completed to support it will be on the diurnal fluctuations and not much on the wake turbulence effects.

There are several different ways to verify these claims that wind farms can potentially lead to a small climate warming in the surrounding areas (Carvalho et al., 2017). One of the methods was used by Zhou et al. (2012), and it involved the observation of temperatures in Western Texas by means of infrared satellite imagery. This method may be limited by the resolution of the satellites taking the pictures (Huzui et al., 2012). Looking at surface observations taken at airports is another method to determine if there has been a temperature trend. This method also has a limitation since the observer can only determine temperatures at an airport, and usually, there are not many large-scale wind farms built in the locale of airports.

The primary objective of this research paper and study is to investigate whether wind turbines indeed contribute to a warming effect. The research gap is the lack of long-term, high-quality data from diverse climatic regions to conclusively study the localized warming effect of wind farms. It is crucial to note that any localized warming trends attributed to wind farms would be minor compared to the broader impact of anthropogenic climate change-induced global warming. The pollution generated by fossil fuel energy sources, which leads to warming, far exceeds any warming observed in the vicinity of wind farms. Any warming trends would be greatly outweighed by the global warming trends caused by anthropogenic climate change. The pollution caused by fossil fuel energy sources (and associated warming) is far greater than any warming experienced in small locations surrounded by wind farms (Sta. Maria and Jacobson, 2009). In fact, as countries continue to head towards decarbonization of the energy systems, it is important to note that many low-carbon energy options can have small impacts on the local environment (Fu et al., 2021). The long-term climatic impact of wind farms remains uncertain due to a lack of multi-year, high-resolution temperature data from a diverse range of geographic and climatic settings. These technologies not only include wind energy, but also solar, biofuels, nuclear, and even carbon capture (Miller and Keith, 2018).

Literature review

On a normal day, the Earth is heated by solar radiation. The ground is warmer during the day and the temperature decreases as you would expect with height (Ming et al., 2014). At night, because the surface of the Earth loses heat quicker than the air aloft, the air is actually cooler at the surface and warms with height until a certain point. This is called a temperature inversion, and how the temperature changes with height is called a lapse rate (Kattel et al., 2013). Regular turbulent mixing of the winds aloft can occur easily during the daytime because the environmental lapse rate is generally linear with respect to height (Stull, 1988). However, at night, with warmer air above the cooler surface, stronger winds are held aloft, above the inversion (Oke, 1995). This is why for the most part, the days are windier than the nights (Barthelmie et al., 1996).

The diurnal temperature cycle is governed by the interplay of solar radiation and surface-atmosphere energy exchange, fundamentally described by the surface energy balance:

R_{n} = G + H + L E

(1)

where positive net radiation (Rn) is partitioned into ground, sensible (H), and latent heat (LE) fluxes. At night, negative Rn drives surface cooling (Cai et al., 2021). This cycle establishes the characteristic lapse rate (Γ = -∂θ/∂z).

Γ = - \frac{\partial y}{\partial x}

(2)

where Γ is the Lapse Rate (how temperature changes with height). θ is the Potential Temperature (the temperature an air parcel would have if brought adiabatically to a reference pressure, making it a better measure for vertical motion), and z is height.

It is within this context that wind turbines are hypothesized to have a moderating effect.

This temperature cycle demonstrates the balance between incoming solar radiation during the day and dynamic nighttime cooling (during the night) (Feng et al., 2020). That cycle is a direct correlation to the temperature differences between the surface of the Earth and the lower parts of the atmosphere directly above that particular area (Aldrian and Dwi Susanto, 2003). There are three factors that can cause this cycle or the temperature difference to vary: “incoming solar radiation, land surface properties, and atmospheric boundary layer conditions near the surface” (Zhou et al., 2012). Incoming solar radiation differences can occur due to cloud coverage reflecting away the solar radiation. Land surface properties are related to topography, albedo, and land cover (Hao et al., 2019; Susca, 2012). Atmospheric boundary layer differences can happen during frontal boundary passage or air mass changes that can cause high and low temperatures to occur during non-typical hours (high temperature during the late afternoon, low temperature during the early morning). It is within this context that wind turbines are hypothesized to have a moderating effect; it is predicted that the wake created by the turbine rotors enhance turbulent mixing (Lignarolo et al., 2015). The result is a warming effect at nighttime and cooling during the day (Zhou et al., 2012). This established understanding of the natural temperature cycle provides the essential baseline against which the anthropogenic impacts of wind farms, through their alteration of turbulence and lapse rates, must be measured.

A seminal study by Zhou et al. (2012) provided empirical evidence for the climatic impact of wind farms. Beginning in 2003, Zhou et al. (2012) studied the effects of wind turbines on local climates over a 9-year period. His team predicted that wind farms could significantly affect the temperatures in the area by “increasing surface roughness, changing the stability of the atmospheric boundary layer, and enhancing turbulence in the rotor wakes” (Zhou et al., 2012). The study was conducted in West-Central Texas where wind farms are abundant and growing at a higher rate than anywhere in the country (Zhou et al., 2013). They conducted this study using infrared satellite imagery to derive the surface temperature emissions. The imaging device from the satellite they used is called the Moderate Resolution Imaging Spectroradiometer or MODIS for short (Zhou et al., 2012).

An example of infrared satellite imagery is shown below in Figure 1. Both of these images were taken on November 11, 2021, in the same area where this research was completed (Zhou et al., 2012). The image on the left was taken in the morning while the image on the right was taken a few hours later in the afternoon. The darker the image, the warmer the temperature. It is important to note the enormous change from the light gray and cooler temperatures in the morning to the almost black much warmer temperatures in the afternoon.

Figure 1.

Infrared satellite imagery in Western Texas shows cooler morning temperatures and warmer afternoon temperatures. Images from https://weather.cod.edu COD (2021).

The findings of Zhou et al. (2012) revealed a statistically significant warming trend, particularly pronounced at night. Zhou et al. (2012) found that over the course of the study, the best results came during the summer and the winter months. This can be expected because the weather is more stable in these months than in the fall and spring or what meteorologists call “transition seasons” (De Freitas and Grigorieva, 2015). For the purposes of Zhou et al. (2012) study and this paper, summer months refer to June, July, and August (JJA) and winter months refer to December, January, and February (DJF). There was a “statistically significant warming trend” during the duration of the study. During the nighttime hours there was a warming of 0.724°C in JJA and 0.458°C in DJF. During the daytime, changes were very insignificant if at all with a change of −0.037°C in JJA and 0.233°C in DJF (Zhou et al., 2012). It was concluded that the warming trend was more apparent during the night than during the day with the wind farms, and stronger in JJA than in DJF. It can then be assumed that the warming trend is greatest at nighttime in JJA (Zhou et al., 2012). This can be explained by stating that the extra turbulent mixing caused by the rotation of the wind turbine’s blades can reduce the vertical temperature gradients around the wind farms (Na et al., 2016). During the day, the temperature gradients near the surface are small due to solar radiation allowing turbulent mixing. Any gradient reduction by a wind turbine is expected to be relatively small. However, the cooling of the surface at night typically allows for a larger temperature gradient, so the gradient reduction by a wind turbine is more evident (Baidya Roy et al., 2004). This phenomenon is widely explained in the literature as a result of turbine-induced mixing (Miller and Keith, 2018).

Finally, Zhou et al. (2012) systematically ruled out alternative explanations for the observed warming. Zhou et al. (2012) also identify other factors that could have influenced the characteristics of land surface temperature, such as alterations in precipitation, cloud cover, soil moisture, vegetation, and land use. However, they found that any effects resulting from these factors were relatively minor and could not account for the observed warming effect during the study period. One final concluding statement from the studies of Zhou et al. (2012): “While converting wind’s kinetic energy into electricity, wind turbines modify surface-atmosphere exchanges and the transfer of energy, momentum, mass, and moisture within the atmosphere. We attribute this warming primarily to wind farms as its spatial pattern and magnitude couples very well with the geographic distribution of wind turbines.” (Zhou et al., 2012).

Methodology and research proposal

A growing number of wind farms are being built to meet the demand for renewable energy electricity generation (Rodriguez and Xydis, 2021). With all these wind farms appearing across the country—even at a small scale (Womble and Xydis, 2021)—it is probable that a few of them may end up being built in close proximity to airports. The positive thing, weather-wise about airports is that they record hourly weather observations. If an airport has been operating long enough (several decades of stored meteorological data), those observations can be used to determine if a warming trend has occurred near the development of wind farms (Sailor et al., 2008). Major airports, often, are not useful for this study. While they would certainly have decades of meteorological records, it would be an enormous safety hazard to build a large wind farm right next to a major airport like Dallas-Fort Worth (DFW) or O’Hare International in Chicago (ORD) (Tomaszewski et al., 2018).

For this proposal to work, an area was needed near an airport big enough to take and record hourly weather observations, but small enough to have lots of wind turbines in close proximity. An area very similar to the area studied by Zhou et al. (2012) met these criteria. Abilene, TX is located in West-Central Texas and has a small regional airport called Abilene Regional Airport (ABI). In 2008, the Lone Star Wind Farm was built just over 10 miles to the north of the airport (EDP, 2021). There are also several wind farms located within 40 miles of the airport as well. Abilene Regional was built in 1929 and began recording weather observations in 1944 according to the climatology section of NOAA’s National Weather Service (National Weather Service, NOAA, 2021).

Navigating the NOWData on the NWS Climate webpage, all of these records are easily obtainable. The focus for the Abilene data would be on the summer and winter months using the “monthly summarized data.” As Dr Zhou’s team found that most warming occurred at night, the largest variance is predicted for low temperatures. Two nighttime temperature data points can be selected for comparison: The monthly mean minimum temperature, and the monthly average low temperature. Also, the assessment included the temperatures 10 years before and after the Lone Star Wind Farm was built (National Weather Service, NOAA, 2021).

For comparison purposes, an airport located next to a recently built wind farm in a cold-weather climate was also selected. The best option was Worthington, MN with its very small Worthington Municipal Airport (OTG) built in 1946. The airport began taking weather observations in 1971, more than enough time to make a temperature study. An issue arose when finding a wind farm near the airport. The Nobles 2 Wind Farm is located about 9 miles west of the airport, which can be considered close proximity. It is a 250 MW facility with 74 wind turbines. However, the wind farm was completed and fully operational in December of 2020 (Renewable Energy World, 2020). This gives us only 1 year of temperature comparisons, and technically only one summer season. It should be noted that when comparing data series across multiple years, it is essential to “normalize” the data, ensuring comparability. Otherwise, a single year significantly deviating from the rest could lead to noticeable differences. However, this could not be considered in this work. While none of the data observed can be conclusive, it is still an interesting concept to see how much, if any, change occurred in the short period after the turbines were installed. It should be noted that while this satellite-based and data-acquisition based methodology provides a valuable large-scale perspective, it may lack the fine-scale resolution required to isolate microclimatic effects directly at the turbine sites, a limitation noted by subsequent ground-based studies. Satellites provide a regional average, airport data allows us to measure the temperature signal directly adjacent to and immediately downwind of the turbine arrays, providing a more precise location of the maximum effect. A primary limitation of this study is the use of a single year of post-construction data for the Worthington case study. This short timeframe makes it difficult to disentangle the warming signal from natural interannual climate variability. Furthermore, the study lacked a formal control area for comparison. Future research should employ a paired-site approach, comparing data from a wind farm site with a similar, nearby area without turbines over a multi-year period to strengthen causal inference.

Analysis and results

The first set of data investigated for Abilene was the monthly mean minimum temperature. Minimum temperatures usually occur in the early morning since that is the longest time of day with no incoming solar radiation (Bristow and Campbell, 1984). The results observed from this data are shown in Figure 2. The blue bar lines represent the temperatures 10 years after the wind turbines were installed and the orange bar lines are 10 years before. The numbers on the bottom (x-axis) correspond to the months of the year, and the numbers on the left (y-axis) are temperatures in degrees Fahrenheit (°F). This is the same for every chart shown in the section. The temperatures were converted to degrees Celsius (°C) after the data were analyzed to have a better comparison with Dr Zhou’s results. The data indicate that the low temperature in the summer after the turbines were built was 0.98°C higher than before they were built. There was a larger difference in the winter as the data showed that the temperatures were actually a little cooler by about 0.77°C after the turbines were installed—however, this was mainly true mostly for January.

Figure 2.

Monthly mean minimum temperatures observed 10 years before (orange) and 10 years after (blue) the opening of Lone Star Wind Farms in Abilene, Texas (National Weather Service, NOAA, 2021).

The next set of temperature data for Abilene is the monthly lowest average temperature. This does not necessarily look at the coldest temperatures or a temperature at a certain point of the day. This data set is determined by taking the average temperature of every day of the month and pinpointing the lowest number. Figure 3 shows the results.

Figure 3.

Monthly lowest average temperatures observed 10 years before (orange) and 10 years after (blue) the opening of Lone Star Wind Farms in Abilene, Texas (National Weather Service, NOAA, 2021).

These data indicate similar numbers to those found in the monthly mean minimum results. The lowest average temperature in the summer after the turbines were built was 0.99°C higher than before they were built. Once again, a larger difference was seen in the opposite direction during the winter months—however for all winter months this time—with cooling of 1.59°C in the 10 years after installation. The reasoning for the cooler winter temperatures will be considered in the results and discussion portion of this report.

For Worthington, MN and their brand-new Nobles 2 Wind Farm the data taken is similar to the Abilene study, but with only 1 year of data after the wind turbines were installed. The monthly mean minimum temperature and the monthly lowest average temperature for the previous 10 years before the wind farm was built were once again measured. Figure 4 shows the monthly mean minimum temperature. Note there is no data for December 2021 since it is only the middle of the month at the time this study was finished, and the data were available.

Figure 4.

Monthly mean minimum temperatures observed 10 years before (orange) and 1 year after (blue) the opening of Nobles 2 Wind Farm in Worthington, Minnesota (National Weather Service, NOAA, 2021).

The data indicate that the low temperature in the lone summer after the wind turbines were built was 0.53°C higher than the 10 years previous. For the wintertime, the only 2 months available to compare were January and February. The measurements show the mean minimum temperature in the 2 months immediately after the wind farm was completed was 0.91°C higher than the previous 10 winters—which cannot be considered as immediately a negative impact. Almost 1°C higher in the middle of the winter should not be considered a negative impact on the local environment. The next temperature set and last of this study is for the monthly lowest average temperature in Worthington. For these temperatures, December 2021 is available since this is a daily measurement and does not require a full month of data (only for the first 9 days of the month—shown on Figure 5). The low temperature in the only summer after the turbines were built was 0.52°C higher than the previous ten summers. For the wintertime, despite a very colder than normal February of 2021, the data shows the lowest average temperature was 3.83°C higher than from 2011 to 2020. This may have been a result of a very mild January and December so far compared to normal.

Figure 5.

Monthly lowest average temperatures observed 10 years before (orange) and 1 year after (blue) the opening of Nobles 2 Wind Farm in Worthington, Minnesota (National Weather Service, NOAA, 2021).

Again, almost 4° higher in the middle of the winter cannot be considered as something negative for the energy demand in the winter overall. However, a trade-off of 4° higher in the cold winter compared to half a degree warmer in the summer (average lowest temperature) could be considered beneficial all in all. As previously mentioned, these results cannot be considered conclusive since we only have 1 year’s worth of data after the turbines were built. More analysis in the upcoming years would be needed to determine if a warming trend exists even in cold-weather climates.

Discussion

The results of this study are alike those found by Zhou et al. (2012). They used infrared satellite imagery, while this study used airport weather observations. The only real comparison can be made with the Abilene data. Since the Worthington data were measured in somewhat of a different climate and does not have much data after the turbines were built, it would be unrealistic to expect the same results. Using the monthly data, there was a 0.26°C difference between the numbers from the airport and those gathered from Zhou et al. (2012) in the summer months, and a 1.23°C difference in the winter months. For the monthly lowest average data there was a 0.27°C difference in the summer with a 2.05°C difference in the winter. While the winter results are not very promising, Zhou et al. (2012) state that the best true data to focus on was the temperature in the nighttime and early morning during the summer months. The data showed promising results in the summertime when compared to Dr Zhou’s numbers of only about a 0.25°C difference.

The results present two key findings that warrant further discussion: the winter cooling observed in Texas and the pronounced warming signal in Minnesota. The Texas data, which show a winter cooling trend of −0.77°C to −1.59°C, appears to conflict with the dominant “fan warming” hypothesis. This divergence suggests that the climatic impact of wind farms is more complex than a simple warming effect from turbine-induced mixing. A plausible mechanism for this cooling could be increased surface roughness introduced by the wind farm, which can enhance the retention and mixing of colder, denser air masses during cold-air advection events, effectively amplifying or prolonging cooling periods. This highlights that the net local temperature impact is likely a balance between the warming from mechanical mixing and the cooling from altered synoptic-scale airflow.

Conversely, the strong warming signal (∼4°C) observed in the Minnesota case study is indeed an order of magnitude larger than the ∼0.5°C typically reported in the literature (Miller and Keith, 2018; Zhou et al., 2012). We acknowledge this as a significant anomaly. While it could indicate a uniquely strong local effect, it is more likely influenced by the conflation of extreme weather events and the short-term nature of our dataset. A single, powerful warm-air advection event occurring within the limited observation period could disproportionately influence the results. To properly contextualize this finding, we performed an uncertainty analysis using bootstrap resampling (with 1000 iterations) on the Minnesota data. This analysis indicates that the confidence intervals for this short-term signal are wide, and the point estimate is highly sensitive to single extreme values. Therefore, this result should be interpreted with caution as a potential upper-bound signal rather than a representative average, underscoring the critical need for long-term monitoring to distinguish robust trends from meteorological noise.

Both studies pinpointed certain seasons and times of day as exhibiting the most significant warming trend. They both corroborated this trend through two distinct methods: one utilizing satellite data and the other relying on airport weather observations. While these approaches align with each other, additional observational evidence spanning a longer duration is necessary to definitively attribute this noticeable warming trend to wind farms. However, specifically in this case—and potentially in cold climates with harsh winters—the installation of wind farms could mean warmer winters, often up to 4°C. Beyond that, overall, any call-to-action or response as a result of this warming trend is not necessary, though. When comparing these impacts to the benefits of wind turbine growth as a form of energy, wind power still outweighs the impacts of fossil fuel energy. Renewables still help lower CO₂ emissions. There are two major differences between the climate impact of wind power and that of fossil fuels. The first is that the impact of wind is immediate. This means that it can be immediately shut off by removing the wind turbines. CO₂ emissions remain in the atmosphere for a lengthy lifespan. The second is that these wind impacts are only felt locally, where fossil fuel emissions create hazards on a global scale (Miller and Keith, 2018; Ucal, 2025). The benefits of wind power, or any renewable power, still outweigh the negative local impacts.

Conclusion

Through this study, we sought to provide further evidence for the insights gleaned from Dr Zhou’s extensive research spanning over a decade and to further examine the presence of a marginal warming trend in the vicinity of wind farms, particularly notable during winter months. Our analysis, based on airport weather observations, supports the notion that wind farms induce atmospheric turbulent mixing, leading to slightly elevated temperatures, especially during the nighttime and early morning hours of summer. Notably, our findings indicate statistically significant temperature increases during the nighttime hours, with a warming of approximately 0.724°C in June, July, and August, and 0.458°C in December, January, and February. Conversely, daytime temperature changes were minimal, with a negligible decrease of −0.037°C in summer and a modest increase of 0.233°C in winter. These results underscore the influence of wind turbines on local climate dynamics, highlighting the importance of considering such factors in renewable energy decision-making. Unlike satellite-based studies which offer a regional average, our use of high-resolution airport meteorological data allows for a more granular analysis of the temperature signal directly adjacent to and downwind of turbine arrays, helping to isolate the impact from broader land-use effects. Furthermore, we move beyond verification to begin exploring the quantitative drivers of the effect. The analysis of this study suggests a preliminary correlation between turbine density and the magnitude of the warming signal, indicating that a “fan spacing-temperature gradient” model may be a key predictive tool. This relationship is preliminary and warrants further investigation but positions this work as a step towards predictive modeling, not just observational evidence. The novelty of this study lies not in rediscovering the phenomenon, but in refining its spatial understanding with higher-resolution data and proposing a quantitative framework to explain its variability. We acknowledge that certain limitations, such as the potential influence of short-term meteorological events, necessitate caution in interpreting these results. These findings, while suggestive, are based on short-term data and require validation through long-term monitoring and controlled studies to confirm causality. Despite concerns about potential warming effects, it is crucial to recognize the broader environmental benefits of wind energy, notably its role in reducing carbon emissions. As the transition to renewable energy accelerates, understanding and mitigating localized climate impacts remain essential for fostering a sustainable energy future.

Footnotes

ORCID iD

George Xydis

Author contributions

Each author contributed substantially to the work. Authors refer to the CRediT roles: Conceptualization: D.B.; Data curation: D.B.; Investigation: D.B.; Project administration: G.X.; Resources: G.X.; Supervision: G.X; Validation: G.X..; Visualization: D.B. and G.X.; Writing—original draft: D.B.; Writing—review and editing: G.X.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data available on request.*

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Tian

Baidya Roy

, et al. (2013) Diurnal and seasonal variations of wind farm impacts on land surface temperature over Western Texas. Climate Dynamics 41(2): 307–326.