Article

Climatic Impacts of Wind Power
Lee M. Miller, David W. Keithlmiller@seas.harvard.edu (L.M.M.)
david_keith@harvard.edu (D.W.K.)

HIGHLIGHTSWind power reduces emissions
while causing climatic impacts
such as warmer temperatures
Warming effect strongest at night
when temperatures increase withheightNighttime warming effectobserved at 28 operational US
wind farms
Wind’s warming can exceed
avoided warming from reduced
emissions for a century

Wind beats fossil, but wind power does cause non-negligible climatic impacts. This
study advances work on wind power’s climate impacts by: (1) providing amechanistic explanation for wind turbines’ climate impacts by comparing
numerical simulations with observations, (2) filling a current gap between smalland very-large-scale wind power simulation studies, (3) making the firstquantitative comparison between wind power’s climatic impacts and benefits, and(4) using the same framework to make a quantitative comparison with solar power.

Miller & Keith, Joule 2, 1–15
December 19, 2018 ª 2018 Elsevier Inc.
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ArticleClimatic Impacts of Wind Power
Lee M. Miller1,3,* and David W. Keith1,2,*

SUMMARY

Context & Scale

We find that generating today’s US electricity demand (0.5 TWe) with wind power would warm Continental US surface temperatures by 0.24 C. Warming
arises, in part, from turbines redistributing heat by mixing the boundary layer.
Modeled diurnal and seasonal temperature differences are roughly consistent
with recent observations of warming at wind farms, reflecting a coherent mechanistic understanding for how wind turbines alter climate. The warming effectis: small compared with projections of 21st century warming, approximately
equivalent to the reduced warming achieved by decarbonizing global electricity
generation, and large compared with the reduced warming achieved by decarbonizing US electricity with wind. For the same generation rate, the climatic
impacts from solar photovoltaic systems are about ten times smaller thanwind systems. Wind’s overall environmental impacts are surely less than fossil
energy. Yet, as the energy system is decarbonized, decisions between windand solar should be informed by estimates of their climate impacts.

Wind power can impact theclimate by altering theatmospheric boundary layer, withat least 40 papers and 10
observational studies now linking
wind power to climatic impacts.
We make the first comparison
between the climatic impacts oflarge-scale wind power and sitescale observations, finding
agreement that warming fromwind turbines is largest at night.
Wind power’s climatic impacts willcontinue to expand as more are
installed.

INTRODUCTION
To extract energy, all renewables must alter natural energy fluxes, so climate impacts
are unavoidable, but the magnitude and character of climate impact varies widely.
Wind turbines generate electricity by extracting kinetic energy, which slows winds
and modifies the exchange of heat, moisture, and momentum between thesurface and the atmosphere. Observations show that wind turbines alter local
climate,1–10 and models show local- to global-scale climate changes from thelarge-scale extraction of wind power.11–15 Previous studies have assessed climate
impacts of hydropower,16 biofuels,17 and solar photovoltaic systems (PVs).18 Rapid
expansion of renewable energy generation is a cornerstone of efforts to limit climatechange by decarbonizing the world’s energy system. In addition to climate benefits,
wind and solar power also reduce emissions of criteria pollutants (NOx, SOx, andPM2.5) and toxic pollutants such as mercury that cause significant public healthimpacts.19,20 The climate impacts of wind and solar are small compared with theimpacts of the fossil fuels they displace, but they are not necessarily negligible.
Improved understanding of the environmental trade-offs between renewables
would inform choices between low-carbon energy sources. With growth of windand solar PVs far outstripping other renewables,21 we combine direct observationsof onshore wind power’s impacts with a continental-scale model, and compare it toprior estimates of PVs’ impacts to assess the relative climate impacts of wind andsolar energy per unit energy generation.

Do these impacts matter? How dothese impacts compare to theclimate benefits of reducing
emissions? We offer policyrelevant comparisons: wind’s
climatic impacts are about 10
times larger than solar
photovoltaic systems per unit
energy generated. We explore
the temporal trade-off between
wind’s climatic impacts and theclimate benefits it brings byreducing emissions as it displaces
fossil fuels. Quantitative
comparisons between low-carbon
energy sources should inform
energy choices in the transition toa carbon-free energy system.

Climatic impacts due to wind power extraction were first studied using generalcirculation models (GCMs). These studies found statistically significant climatic
impacts within the wind farm, as well as long-distance teleconnections, with impacts
outside the wind farm sometimes as large in magnitude as impacts inside the wind
farm.11–13,22 Note that such impacts are unlike greenhouse gas (GHG)-driven warming, as in some cases wind power’s climatic impacts might counteract such GHG

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warming—at least four studies have found that mid-latitude wind power extraction
can cool the Arctic.11,12,23,24 However, these studies often used idealized or unrealistic distributions of turbines installed at unrealistic scales. Model simulations ofgeometrically simple, isolated wind farms at smaller scales of 3,000–300,000 km2(10- to 1,000 times larger than today’s wind farms) in windy locations foundsubstantial reductions in wind speed and changes in atmospheric boundary layer(ABL) thickness, as well as differences in temperature,11,13,14,24 precipitation,14,25
and vertical atmospheric exchange.15,26
We want to assess wind power’s climate impacts per unit of energy generation, yetwind’s climatic impacts depend on local meteorology and on non-local climate
teleconnections. These twin dependencies mean that wind power’s impacts arestrongly dependent on the amount and location of wind power extraction, frustrating the development of a simple impact metric.
As a step toward an improved policy-relevant understanding, we explore theclimatic impacts of generating 0.46 TWe of wind-derived electricity over the Continental US. This scale fills a gap between the smaller isolated wind farms and globalscale GCM. We model a uniform turbine density within the windiest one-third of
the Continental US, and vary the density parametrically.
Our 0.46 TWe benchmark scenario is 18 times the 2016 US wind power generation
rate.21 We intend it as a plausible scale of wind power generation if wind power plays
a major role in decarbonizing the energy system in the latter half of this century. For
perspective, the benchmark’s electricity generation rate is only 14% of current US
primary energy consumption,25 about the same as US electricity consumption,27
and about 2.4 times larger than the projected 2050 US wind power generation
rate of the Central Study in the Department of Energy’s (DOE) recent Wind Vision.28
Finally, it is less than one-sixth the technical wind power potential over about thesame windy areas of the US as estimated by the DOE.28,29
Modeling Framework
We use the WRF v3.3.1 high-resolution regional model30 with a domain that encompasses the Continental US, forced by boundary conditions from the North American
Regional Reanalysis.31 The wind farm region is more than 500 km from the model
boundaries, and encompasses only 13% of the domain (shown in Figure 1A).
The model configuration used dynamic soil moisture and 31 vertical levels with
3 levels intersecting the turbine’s rotor and 8 levels representing the lowermost kilometer. The model is run for a full year after a 1-month spin-up using horizontal resolutions of 10 and 30 km. The wind turbine parametrization was originally released
with WRF v3.3,32 and represents wind turbines as both a momentum sink and turbulent kinetic energy (TKE) source. We updated the wind turbine parameterization to
make use of the thrust, power, and TKE coefficients from a Vestas V112 3 MW.
This treatment of wind power is very similar to previous modeling studies.14,15,24
The advantage of the regional model is that we can use a horizontal and vertical resolution substantially higher than previous global modeling studies,11–13,22,23,26,33,34
allowing better representation of the interactions of the wind turbines with the ABL.
The disadvantage of using prescribed boundary conditions is that our simulations
will underestimate the global-scale climatic response to wind power extraction
compared with a global model with equivalent resolution, which would allow the
global atmosphere to react to the increased surface drag over the US and would
reveal climate teleconnections.

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1School of Engineering and Applied Sciences,

Harvard University, Cambridge, MA 02139, USA
2Harvard Kennedy School, Cambridge, MA

02138, USA
3Lead Contact*Correspondence:lmiller@seas.harvard.edu (L.M.M.),
david_keith@harvard.edu (D.W.K.)
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Figure 1. Temperature Response to Benchmark Wind Power Deployment (0.5 MW km 2)(A–C) Maps are 3-year mean of perturbed minus 3-year mean of control for 2-m air temperatures, showing (A) entire period, (B) daytime, and (C)nighttime. The wind farm region is outlined in black, and, for reference, presently operational wind farms are shown as open circles in (A).

We tested horizontal resolution dependence by comparing the 10- and 30-km
simulations with a turbine density of 3.0 MW km 2 with the respective 2012 controls.
Differences in the annual average 2-m air temperature were small, as shown in
Figure S1. The following results use a 30-km resolution (about one-ninth of thecomputational expense) and 2012, 2013, and 2014 simulation periods to reducethe influence of interannual variability. We use four turbine densities (0.5, 1.0, 1.5,
and 3.0 MW km 2) within the wind farm region to explore how increased wind power
extraction rates alter the climatic impacts.

RESULTS AND DISCUSSION
Figure 1 shows the climate impacts of the benchmark scenario (0.5 MW km 2). The
wind farm region experiences warmer average temperatures (Figure 1A), with about
twice the warming effect at night compared with during the day (Figures 1B and 1C).
Warming was generally stronger nearer to the center of the wind farm region, but

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perhaps because teleconnections are suppressed by the forced boundary conditions. The climate response is concentrated in the wind farm region, but there areregions well outside the wind farm region also experiencing a climate response.
The clearest example here is along the East Coast during the daytime, where
average daytime temperatures are 0.1 C–0.5 C cooler (Figure 1B).
To separate the local direct boundary layer impacts from the mesoscale climatechanges, we ran a diagnostic simulation with a 250 3 250-km ‘‘hole’’ near the centerof the wind farm region, finding that the ‘‘hole’’ experienced about half the warmingof the original ‘‘no hole’’ benchmark scenario during 2014 (Table S1 and Figure S2).
This suggests that about half the warming effect is attributed to localized changes inatmospheric mixing, with the other half attributed to mesoscale changes, but thisrequires further study.
Changes in precipitation are small and show no clear spatial correlation (Figure S3).
The warming is greatest in an N-S corridor near the center of the wind turbine array,perhaps because of an interaction between wind turbines and the nocturnal lowlevel jet (LLJ). The LLJ is a fast nocturnal low-altitude wind (>12 m s 1 at 0.5 km) common in the US Midwest, which occurs when the atmosphere decouples from surface
friction, resulting in a steep vertical temperature gradient35—meteorological conditions that might be sensitive to perturbations by wind turbines. We quantified thepresence of the LLJ in our control simulation but did not find a strong spatial
correlation between the probability of LLJ occurrence and the nighttime warming(Figure S4). To explore mechanisms, we examine the vertical temperature gradient,atmospheric dissipation, and wind speed (Figure S5), and then explore therelationship between warming and these variables using scatterplots (Figure S6).
We find some consistency between the dissipation rate of the control and thewarming effect of wind turbines, but the correlation is weak.
Figure 2 explores the relationship between changes in vertical temperature
gradient, atmospheric dissipation, and the simulated warming. Wind turbines
reduce vertical gradients by mixing. During the day, vertical temperature gradientsnear the surface are small due to solar-driven convection and are only slightly
reduced by the turbines. Gradients are larger at night, particularly during summer,
and the gradient reduction caused by turbine-induced mixing is larger. The largest
warming occurs when the reduction in gradient is strongest and the proportional
increase in TKE is largest.
Warming and power generation saturate with increasing turbine density (Figure 3).
The temperature saturation is sharper, so the ratio of temperature change per unit
energy generation decreases with increasing turbine density. This suggests thatwind’s climate impacts per unit energy generation may be somewhat larger for lower
values of total wind power production.
Power generation appears to approach the wind power generation limit at turbine
densities somewhat above the maximum (3.0 MW km 2) we explored. A capacity
density of 1.5 MWi km 2 roughly matches that of US wind farms installed in2016,36 and that simulation’s power density of 0.46 We m 2 is very close to the0.50 We m 2 observed for US wind farms during 2016.36 The highest turbine density
yields an areal (surface) power density of 0.70 We m 2, consistent with some previous
studies,15,22,24,26,33 but half the 1.4 We m 2 assumed possible by 2050 from thesame 3.0 MW km 2 turbine density into windy regions by the DOE.28 While we
did not compute a maximum wind power generation rate here, extrapolation of

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Figure 2. Monthly Day-Night Climate Response to the Benchmark Scenario(A and B) Average monthly day and night values over the wind farm region for (A) vertical temperature gradient between the lowest two model levels(0–56 and 56–129 m) for the control and benchmark scenario (0.5 MWi km 2 ), and (B) differences between the benchmark scenario and control in 2-m
air temperature (solid blue boxes) and turbulent kinetic energy (TKE) in the lowest model level (transparent boxes). In both, the vertical line extent shows
the standard 1.5,interquartile range, and the box represents the 25th, 50th, and 75th percentiles.

Figure 3 suggests that it is about 2 TWe, significantly less than the 3.7 TWe of technical potential estimated by the DOE28,29 over less land area. Clearly, interactions ofwind turbines with climate must be considered in estimates of technical wind power
potential.
Interpretation
The climatic impacts of wind power may be unexpected, as wind turbines only redistribute heat within the atmosphere, and the 1.0 W m 2 of heating resulting fromkinetic energy dissipation in the lower atmosphere is only about 0.6% of the diurnallyaveraged radiative flux. But wind’s climatic impacts are not caused by additional
heating from the increased dissipation of kinetic energy. Impacts arise because
turbine-atmosphere interactions alter surface-atmosphere fluxes, inducing climatic
impacts that may be much larger than the direct impact of the dissipation alone.
As wind turbines extract kinetic energy from the atmospheric flow and slow windspeeds, the vertical gradient in wind speed steepens, and downward entrainment
increases.15 These interactions increase the mixing between air from above andair near the surface. The strength of these interactions depends on the meteorology
and, in particular, the diurnal cycle of the ABL.
During the daytime, solar-driven convection mixes the atmosphere to heights of
1–3 km.35

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A

B

C

D

Figure 3. Variation in Mean Response to Changes in Installed Capacity Density(A–D) The shared x axis is the installed electrical generation capacity per unit area. All values areaverages over the wind farm region. (A) Eighty-four-meter hub-height wind speed, (B) capacity

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Figure 3. Continued
factor, i.e., the ratio of realized electrical output to generation capacity, (C) power output as a sum
and per unit area, and (D) difference in 2-m air temperature. For each value, three distinct years ofdata (2012–2014 from left to right) are shown as three boxplots (1.5,interquartile range, with 25th,
50th, and 75th percentiles). Colors help group identical installed capacity densities. The 3-year
mean is shown using white points and connecting solid lines. Dashed lines illustrate the expected
results if climate did not respond to the deployment of wind turbines.

Wind turbines operating during the daytime are enveloped within this alreadywell-mixed air, so climatic impacts such as daytime temperature differences are
generally quite small. At night, radiative cooling results in more stable surface
conditions, with about 100–300 m of stable air separating the influence of surface
friction from the winds aloft.35 Wind turbines operating at night, with physicalextents of 100–150 m and an influence height at night reaching 500 m or
more,15 can entrain warmer (potential temperature) air from above down into
the previously stable and cooler (potential temperature) air near the surface,warming surface temperatures. In addition to the direct mixing by the turbine
wakes, turbines reduce the wind speed gradient below their rotors and thus
sharpen the gradient aloft. This sharp gradient may then generate additional
turbulence and vertical mixing.
This explanation is broadly consistent with the strong day-night contrast of ourbenchmark scenario (Figures 1B and 1C). Within the wind farm region during theday, most locations experience warmer air temperatures, although 15% oflocations show a daytime cooling effect in July-September. At night during JulySeptember, less than 5% of locations show a cooling effect, and the warming effectat night over all months is much larger than during the daytime. This daytime andnighttime warming effect is also larger with higher turbine densities (Figure S7).
Finally, the temperature perturbation in the benchmark scenario shows a strong correlation to differences in TKE within the lowest model level from 0 to 56 m (Figure 2B),
with these increases in TKE downwind of turbines previously observed in Iowa4 andoffshore Germany,37 and supporting our explanation that the temperature responseis driven by increased vertical mixing (Figure 2).
Observational Evidence of Climatic Impacts
While numerous observational studies have linked wind power to reduced windspeeds and increased turbulence in the turbine wakes,1,4,7,38,39 ten studies havequantified the climatic impacts resulting from these changes (Table 1).
Three ground-based studies have measured differences in surface temperature1,5,7
and evaporation.5 Generally, these ground-based observations show minimal
climatic impacts during the day, but increased temperatures and evaporation rates
at night.
Seven satellite-based studies have quantified surface (skin) temperature differences.
By either comparing time periods before and after turbine deployment, or bycomparing areas upwind, inside, and downwind of turbines, the spatial extent andintensity of warming for 28 operational wind farms in California,40 Illinois,6 Iowa,2and Texas8–10 has been observed. There is substantial consistency between thesesatellite observations despite the diversity of local meteorology and wind farm
deployment scales. Daytime temperature differences were small and slightly warmer
and cooler, while nighttime temperature differences were larger and almost always
warmer (Table 1). Interpretation of the satellite data is frustrated by fixed overpass
times and clouds that sometimes obscure the surface.

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Table 1. Overview of Observational Studies Linking Air Temperature Differences to Wind Farms
Reference

SAT or GND

PeriodState

Notes: Climatic Impacts within or Very near to the Operational Wind Farm

Baidya Roy andTraiteur,1 2010

GND

53 daysCA

summer; 1 C increase in 5-m air temperature downwind at night through the earlymorning; slight cooling effect during the day

Walsh-Thomas
et al.40 2012

SAT

CA

2 C warmer skin temperatures extending to about 2 km downwind, with visible
temperature differences to 12 km downwind

Zhou et al.9 2012

SAT

9 years

TX

JJA night = +0.72 C, DJF night = +0.46 C; JJA day = 0.04 C; DJF day = +0.23 C;
warming is spatially consistent with the arrangement of wind turbines

Zhou et al.10 2013

SAT

6 years

TX

QA1 values: DJF night = +0.22 C, MAM night = +0.29 C, JJA night = +0.35 C,
SON night = 0.40 C, DJF day = +0.11 C, MAM day = 0.11 C, JJA day = +0.17 C,
SON day = 0.04 C

Zhou et al.10 2013

SAT

2 years

TX

QA1 values: DJF night = 0.01 C, MAM night = +0.42 C, JJA night = +0.67 C,
SON night = 0.47 C, DJF day = +0.14 C, MAM day = 0.42 C, JJA day = +1.52 C,
SON day = +0.12 C

Xia et al.8 2016

SAT

7 years

TX

DJF night = +0.26 C, MAM night = +0.40 C, JJA night = +0.42 C, SON
night = +0.27 C, Annual night = +0.31 C, DJF day = +0.18 C, MAM day =JJA day = 0.26 C, SON day = 0.02 C, Annual day = 0.09 C

Harris et al.2 2014

SAT

11 yearsIA

MAM night = +0.07 C, JJA night = +0.17 C, SON night = +0.15 C

Rajewski et al. 2013

GND

122 daysIAalong the edge of a large wind farm directly downwind of 13 turbines; generallycooler temperatures (0.07 C) with daytime periods that were 0.75 C cooler andnighttime periods that were 1.0–1.5 C warmer

Rajewski et al.5 2014

GND

122 daysIAalong the edge of a large wind farm downwind of 13 turbines co-located with cornand soybeans; night-sensible heat flux and CO2 respiration increase 1.5–2 times
and wind speeds decrease by 25%–50%; daytime H2O and CO2 fluxes increase
5-fold 3–5 diameters downwind

Slawsky et al.6 2015

SAT

11 years

IL

DJF night = +0.39 C, MAM night = +0.27 C, JJA night = +0.18 C, SON = +0.26 C;
Annual = +0.26 C

Smith et al.7 2013

GND

47 daysconfidential

Spring; nighttime warming of 1.9 C downwind of a 300 turbine wind farm4–

0.25 C,

SAT, satellite-based observations; GND, ground-based observations. Note that measurements identified as the same state were completed over the samewind farms.

Although our benchmark scenario is very different in scale and turbine placementcompared with operational wind power, it is nevertheless instructive to compare
our simulation with observations. We compare results at a single Texas location(100.2 W, 32.3 N) where one of the world’s largest clusters of operational windturbines (200 km2, consisting of open space and patchy turbine densities of
3.8–4.7 MW km 2)41 has been linked to differences in surface temperature in 3of the observational studies in Table 1. Weighting the observations by the numberof observed-years, the Texas location is 0.01 C warmer during the day and 0.29 C
warmer at night (data in Table S2). Our benchmark scenario with a uniform turbine
density of 0.5 MW km 2 at this location is 0.33 C warmer during the day and0.66 C warmer at night. To explore the quantitative correlation between theseasonal and diurnal response, we take the 8 seasonal day and night values as
independent pairs (Table S2), and find that the observations and the simulations
are strongly correlated (Figure 4). This agreement provides strong evidence thatthe physical mechanisms being modified by the deployment of wind turbines arebeing captured by our model. This mechanism could be tested more directly iftemperature observations upwind and downwind of a large turbine array wereavailable at a high temporal resolution (<3 hr).
Limitations of Model Framework
Climate response is partly related to the choice and placement of wind turbine(s).
We modeled a specific 3.0-MW turbine, but future deployment may shift to windturbines with taller hub heights and larger rotor diameters. We also assumed

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Figure 4. Comparison of Observations and Simulations for the Texas Location (Table 1)
We compare day and night response over four seasons. Observations are surface (skin)temperature differences. Simulation is differences in 2-m air temperatures between the benchmark
scenario (0.5 MW km 2 ) and control. Note that while correlation over eight points is high, thesimulated response is larger, likely due to the much larger perturbed area and the difference
between skin and 2 m air temperature.

that turbines were evenly spaced over the wind farm region, but real turbine
deployment is patchier, potentially also altering turbine-atmosphere-surface
interactions.
The model’s boundary conditions are prescribed and do not respond to changes
caused by wind turbines. Yet prior work has established that non-local climate
responses to wind power may be significant,12 suggesting that simulating ourbenchmark scenario with a global model (no boundary conditions restoring results
to climatology) would allow possible climatic impacts outside the US to beassessed. Removal of the boundary conditions might also increase the warmingin the wind farm region. The 3-year simulation period was also completed in
1-year blocks, so we do not simulate the response of longer-term climate dynamics
influenced by variables such as soil moisture. Finally, model resolution influenced
the estimated climatic impacts. Simulations with a 10-km horizontal resolution andthe highest turbine density of 3.0 MW km 2 caused 18% less warming than the
30-km simulation (+0.80 C and +0.98 C). Simulations using a global model withan unequally spaced grid with high-resolution over the US could resolve someof these uncertainties.
Comparing Climatic Impacts to Climatic Benefits
Environmental impacts of energy technologies are often compared per unit energy
production.42 Because a central benefit of low-carbon energies like wind and solar
is reduced climate change, dimensionless climate-to-climatic comparisons between
the climate impacts and climate benefits of reduced emissions are relevant for
public policy.
Climate impacts will, of course, depend on a range of climate variables that would
need to be examined in a comprehensive impact assessment. In this analysis wenevertheless use 2-m air temperature as a single metric of climate change given(1) that there are important direct impacts of temperature, (2) that temperature

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change is strongly correlated with other important climate variables, and (3) that useof temperature as a proxy for other impacts is commonplace in climate impacts
assessments. Limitations and caveats of our analysis are addressed in the following
sub-section.
When wind (or solar) power replace fossil energy, they cut CO2 emissions, reducing
GHG-driven global climate change, while at the same time causing climatic impacts
as described above and elsewhere.1–15,22–26,34,40,43–45 The climatic impacts differ in(at least) two important dimensions. First, the direct climatic impact of wind power isimmediate but would disappear if the turbines were removed, while the climatic
benefits of reducing emissions grows with the cumulative reduction in emissions
and persists for millennia. Second, the direct climatic impacts of wind power are predominantly local to the wind farm region, while the benefits of reduced emissions areglobal. We revisit and elaborate these differences in a systematic list of caveats at
the end of this subsection.
As a step toward a climate-impact to climate-benefit comparison for wind, we
compare warming over the US. We begin by assuming that US wind power generation increases linearly from the current level to 0.46 TWe in 2080 and isconstant thereafter. We estimate the associated warming by scaling our benchmark
scenario’s temperature differences linearly with wind power generation. The
amount of avoided emissions—and thus the climate benefit—depends on theemissions intensity of the electricity that wind displaces. We bracket uncertainties
in the time evolution of the carbon-intensity of US electric power generation in
the absence of wind power by using two pathways. One pathway assumes a static
emissions intensity at the 2016 value (0.44 kgCO2 kWh 1), while the second pathway’s emissions intensity decreases linearly to zero at 2100, which is roughly
consistent with the GCAM model46 that meets the IPCC RCP4.5 scenario. The
two emissions pathways are then reduced by the (zero emission) wind power
generation rate at that time (Figure 5C). The first pathway likely exaggerates
wind power’s emission reductions, while the second reflects reduced climatebenefit for wind in a transition to a zero-carbon grid that might be powered bysolar or nuclear.
It is implausible that the US would make deep emissions cuts while the rest of theworld continues with business-as-usual, so we include a third pathway, which functions just like the first pathway, except that the global (rather than just US) electricity
emissions intensity declines to zero (Figure S8)
We estimate wind’s reduction in global warming by applying the two US and one global
emission pathways to an emissions-to-climate impulse response function.47 We convert
these global results to a US warming estimate using the 1.34:1 ratio of US-to-global
warming from IPCC RCP4.5 and RCP8.5 ensemble means (Figure S9,48).
The benchmark scenario’s warming of 0.24 C over the Continental US and 0.54 C
over the wind farm region are small-to-large depending on the baseline. Climatic impacts are small if compared with US temperature projections— historical andongoing global emissions are projected to cause the Continental US to be 0.24 C
warmer than today by the year 2030 (Figure S8). Assuming emissions cuts are implemented globally, then the climatic impacts of wind power affecting the US in 2100
are approximately equivalent to the avoided warming from reduced global emissions (green region of Figure 5D). Climatic impacts are large if the US is the only
country reducing emissions over this century (blue and gray shaded regions of10

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Figure 5. Climate Warming Impacts Compared to Climate Benefits of Reduced Emissions(A) Two US scenarios, static (black) and declining (blue) emissions intensity, I, from US electric
power.
(B) A scenario in which power output, P, from wind or solar power increases to our benchmark
scenario’s 0.46 TWe by 2080.
(C and D) Avoided emissions computed as DE = I3P (C) and the resulting 2-m temperature
differences within the wind farm region (dotted lines) and the Continental US (solid lines) (D). Values
for wind power linearly scaled from our benchmark scenario, while values for solar power are
derived from Nemet. 18 For comparison, the avoided warming of the Continental US from reduced
emissions is shown for the static US scenario (gray) and the declining US scenario (blue). The green
area shows the avoided warming of the Continental US if global electricity emissions were zero by2080. The range of avoided warming for each pathway is estimated from the min and max values
within the emissions-to-climate impulse response function.

Figure 5D). Timescale matters because climatic impacts are immediate, while
climate benefits grow slowly with accumulated emission reductions. The longer
the time horizon, the less important wind power’s impacts are compared with its
benefits (Box 1).

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Please cite this article in press as: Miller and Keith, Climatic Impacts of Wind Power, Joule (2018), https://doi.org/10.1016/j.joule.2018.09.009

Box 1. Limitations of Using these Results to Compare the Climatic Impacts of Wind Power to Climate Change from Long-Lived Greenhouse
Gases
The comparison above suggests that if US electricity demand was met with US-based wind power, the wind farm array would need to operate for morethan a century before the warming effect over the Continental US caused by turbine-atmosphere interactions would be smaller than the reduced
warming effect from lowering emissions. This conclusion is subject to a number of caveats including: Fundamentally different mechanisms cause warmer temperatures from climate change compared with wind power. Increased GHG concentrations
reduce radiative heat losses to space, trapping more heat in the atmosphere and causing warmer surface temperatures. Wind power does not add
more heat to the atmosphere—wind turbines redistribute heat by mixing and alter large-scale flows both which can change climate.
 Our comparison was based solely on surface air temperature differences. Wind turbines and GHGs both alter a host of interrelated climate
variables. The use of surface temperature as the sole proxy for climate impacts may bias the resulting ratio of impacts-to-benefits in either direction.
 Climate impacts of the benchmark scenario will likely be larger and more widespread if we did not use forced boundary conditions, which prevents
any feedbacks from the large-scale circulation.
 Results depend on the wind electricity generation rate, consistent with previous work.11 Our results (Figure 3) suggest the temperature response isroughly linear to the generation rate and power density. To the extent that we see deviations from linearity (Figure S7), climate impacts per unit
generation are larger for lower turbine densities.
 Results depend on the spatial distribution and density of wind turbines. We assumed that the windiest areas would be exploited and that developers would use low turbine densities to maximize per-turbine generation. Based on simulated results with higher turbine densities (Figure 3),
doubling the turbine density over an area half as large as the benchmark scenario might generate almost the same power as the benchmark
scenario, while increasing warming over this smaller region by only about a third.
 Our comparison metric ignores many possible benefits and drawbacks of the climate impacts caused by wind power deployment, including:
11,23,24,45
B Arctic cooling shown in most large-scale wind power modeling studies.
B

B

B
B

Warmer minimum daily temperatures reduce the incidence and severity of frost, and lengthen the growing season. Compared to the control, thegrowing season of the wind farm region was 8 days longer in our benchmark scenario, and 13 days longer with 3.0 MWi km 2.
Some locations experience cooler average temperatures during the summer (Figure 2B), consistent with observations,1,4 and could reduce heatstress.
Warmer minimum daily temperatures have been observed to reduce crop yield.49
Warmer minimum temperatures could influence insect life history in unknown ways.50

 The comparison depends on area-weighting. We used equal weighting but one could consider weighting by, for example, population or
agricultural production.
 The comparison depends very strongly on the time horizon. We examined the century timescale consistent with Global Warming Potentials, butthere is no single right answer for time discounting.51,52
 Finally, results depend on the comparison of US and global-scale impacts and benefits: our model framework prevents global-scale analyses, but,assuming a substantial fraction of the warming effect occurred where US wind turbines were operating, global area-weighted benefits would offset
the climatic impacts sooner than if impacts and benefits were quantified over just the US (as done here).

Implications for Energy System Decarbonization
Wind beats fossil fuels under any reasonable measure of long-term environmental
impacts per unit of energy generated. Assessing the environmental impacts ofwind power is relevant because, like all energy sources, wind power causes climatic
impacts. As society decarbonizes energy systems to limit climate change, policy
makers will confront trade-offs between various low-carbon energy technologies
such as wind, solar, biofuels, nuclear, and fossil fuels with carbon capture. Eachtechnology benefits the global climate by reducing carbon emissions, but eachalso causes local environmental impacts.
Our analysis allows a simple comparison of wind power’s climate benefits andimpacts at the continental scale. As wind and solar are rapidly growing sources of
low-carbon electricity, we compare the climate benefit-to-impact ratio of wind andsolar power.
The climate impacts of solar PVs arise from changes in solar absorption (albedo). A
prior study estimated that radiative forcing per unit generation increased at 0.9
mWm 2/TWe, in a scenario in which module efficiency reaches 28% in 2100 withinstallations over 20% rooftops, 40% grasslands, and 40% deserts.18 Assumingthat the climatic impact is localized to the deployment area and using a climate

12

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sensitivity of 0.8K/Wm 2,53 generating 0.46 TWe of solar PVs would warm the Continental US by 0.024 C. This warming effect is 10-times smaller than wind’s (0.24 C,
Figure 5D) for the same energy generation rate. This contrast is linked to differencesin power density and thus to the areal footprint per unit energy—US solar farms presently generate about 5.4 We m 2, while US wind farms generate about 0.5 We m 2.36
We speculate that solar PVs’ climatic impacts might be reduced by choosing low
albedo sites to reduce impacts or by altering the spectral reflectivity of panels.
Reducing wind’s climatic impacts may be more difficult, but might be altered byincreasing the height of the turbine rotor above the surface distance to reduce
interactions between the turbulent wake and the ground, or switching the turbines
on or off depending on meteorological conditions.
In agreement with observations and prior model-based analyses, US wind power will
likely cause non-negligible climate impacts. While these impacts differ from theclimate impacts of GHGs in many important respects, they should not be neglected.
Wind’s climate impacts are large compared with solar PVs. Similar studies areneeded for offshore wind power, for other countries, and for other renewable
technologies. There is no simple answer regarding the best renewable technology,
but choices between renewable energy sources should be informed by systematic
analysis of their generation potential and their environmental impacts.

SUPPLEMENTAL INFORMATION
Supplemental Information includes nine figures and two tables and can be foundwith this article online at https://doi.org/10.1016/j.joule.2018.09.009.

ACKNOWLEDGMENTS
We thank A. Karmalkar for providing ensemble mean surface temperature data for
the IPCC RCP4.5 and RCP8.5 projections,48 S. Leroy and N. Brunsell for technicalassistance, and J. Holdren, S. Pacala, and three anonymous reviewers for constructive comments on the manuscript. This work was supported by the Fund for Innovative Climate and Energy Research.

AUTHOR CONTRIBUTIONS
Conceptualization, L.M.M. and D.W.K.; Methodology, L.M.M.; Investigation, L.M.M.
and D.W.K.; Writing – Original Draft, L.M.M. and D.W.K.; Writing – Review & Editing,
L.M.M. and D.W.K.; Visualization, L.M.M. and D.W.K.; Funding Acquisition, D.W.K.

DECLARATION OF INTERESTS
D.W.K. is an employee, shareholder, and executive board member at Carbon Engineering (Squamish, BC). Carbon Engineering is developing renewable electricity tofuels projects and is developing procurement contracts for wind and solar power.
Received: May 4, 2018
Revised: August 16, 2018
Accepted: September 13, 2018
Published: October 4, 2018

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