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Environ. Res. Lett. 11 (2016)044024 doi:10.1088/1748-9326/11/4/044024
LETTER
Ground-level climate at a peatland wind farm in Scotland is affected
by wind turbine operation
Alona Armstrong
1,2
, Ralph R Burton
3
, Susan E Lee
3,5
, Stephen Mobbs
3
, Nicholas Ostle
2,4
, Victoria Smith
3
,
Susan Waldron
1
and Jeanette Whitaker
4
1
School of Geographical and Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK
2
Lancaster Environment Centre and Energy Lancaster, Lancaster University, Lancaster, LA1 4YQ, UK
3
National Centre for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
4
Centre for Ecology and Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, LA1 4AP, UK
5
Now at School of Civil Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
E-mail: a.armstrong@lancaster.ac.uk
Keywords: wind energy, carbon cycling, microclimate, atmospheric boundary layer
Supplementary material for this article is available online
Abstract
The global drive to produce low-carbon energy has resulted in an unprecedented deployment of
onshore wind turbines, representing a significant land use change for wind energy generation with
uncertain consequences for local climatic conditions and the regulation of ecosystem processes. Here,
we present high-resolution data from a wind farm collected during operational and idle periods that
shows the wind farm affected several measures of ground-level climate. Specifically, we discovered
that operational wind turbines raised air temperature by 0.18 °C and absolute humidity (AH)by
0.03 g m
−3
during the night, and increased the variability in air, surface and soil temperature
throughout the diurnal cycle. Further, the microclimatic influence of turbines on air temperature and
AH decreased logarithmically with distance from the nearest turbine. These effects on ground-level
microclimate, including soil temperature, have uncertain implications for biogeochemical processes
and ecosystem carbon cycling, including soil carbon stocks. Consequently, understanding needs to be
improved to determine the overall carbon balance of wind energy.
Introduction
Globally the installed electricity generating capacity of
wind turbines has increased from 48 to 370 GW over
the last decade (2004–2014)(GWEC 2015)and is
predicted to increase more than any other renewable
energy source by 2035 (IEA 2012). Deployment of this
magnitude will result in wind farms covering
293 333 km
2
by 2035 (Denholm et al 2009, IEA 2012).
Effects of this land use change on human populations
and wildlife, including avian and bat communities,
have received some consideration (Knopper and
Ollson 2011, Pearce-Higgins et al 2012, Lovich and
Ennen 2013, Northrup and Wittemyer 2013). How-
ever, there is a paucity of data on the effects of wind
farms on soil and ground-level climates (Baidya Roy
and Traiteur 2010, Rajewski et al 2013, Smith
et al 2013, Rajewski et al 2014), limiting our ability to
determine effects on biogeochemical processes that
regulate plant-soil carbon cycling (Armstrong
et al 2014). This, in turn, could have implications for
the true carbon balance of this renewable energy
technology.
Wind farms have been postulated to affect climatic
conditions from local to global scale through mod-
ification of the vertical distribution of energy and
moisture within the atmosphere and exchange
between the land surface and atmosphere (Baidya Roy
et al 2004). Previous studies have modelled the effects
of turbines (Baidya Roy et al 2004, Keith et al 2004,
Wang and Prinn 2010, Baidya Roy 2011, Fiedler and
Bukovsky 2011, Lu and Porté-Agel 2015), measured
air temperature differences upwind and downwind of
turbines (Baidya Roy and Traiteur 2010, Rajewski
et al 2013, Smith et al 2013, Rajewski et al 2014), and
used satellite data to examine temperature effects over
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a 10 000 km
2
area of North America (Zhou et al 2012).
More recently, in addition to effects on heat fluxes,
one row of turbines was reported to have increased
carbon dioxide (CO
2
)release from the land during the
night and uptake during the day and thus potentially
impacting soil carbon stocks (Rajewski et al 2014).
However, extrapolating results from a single row of
turbines is problematic given nonlinear interactions
between multiple wakes (Rajewski et al 2014). To fully
resolve the implications of microclimatic influences
on biogeochemical processes in hosting ecosystems,
high-resolution spatially explicit field data are needed
when turbines are operational and idle. Moreover,
these studies need to establish if wind turbine-induced
differences are measureable beyond that of natural
variation (Rajewski et al 2014).
To assess whether there were operational wind
turbine-induced changes to the microclimate at a ter-
restrial wind farm of a magnitude to impact ecosystem
carbon processes we measured absolute humidity
(AH), and air, surface and soil temperature (T
A
,T
SU
and T
SO
, respectively)during a meteorologically ‘nor-
mal’period (±1 standard deviation from a long-term
mean, see SI for more details). We compared data col-
lected during periods when the wind farm was opera-
tional and idle at sites downwind and not downwind
of wind turbines. Given the expected trend for stable
boundary layers at night (warm air above cold air)and
neutral (well-mixed)or unstable (cold air above warm
air)boundary layers during the day, we also examined
diurnal variation.
Materials and methods
Study area
This research was undertaken at Black Law Wind
Farm, Scotland (55°46′01″N03°44′20″W, elevation
250–320 m), which comprises 54 turbines within
18.6 km
2
. The turbine blade hub heights are approxi-
mately 70 m, the rotor diameter 82 m and the total
capacity is 124 MW. The wind farm was operational
(hereafter referred to as ON)from 24th May 2012 to
7th June 2012, idle (hereafter OFF)from 12th June
2012 to 25th July 2012 and operational (ON)from
28th July to 15th November 2012. The switching on
and off of the turbines was a phased operation lasting
several days; data from this period were excluded from
analysis. Given the commercial sensitivity of the data
and the de-powering of the entire site, no wind data at
hub height was available from which to calculate the
capacity factor. However, the average capacity factor
(i.e. the ratio of actual output compared to the
maximum potential output)in the UK in 2012 was
26.2% (DECC 2014).
The vegetation across the wind farm comprised
coniferous plantation (tree height up to approximately
20 m)and blanket bog, with a small amount of acid
and improved grassland. However, most monitoring
was undertaken in the blanket bog, with a limited
number of sensors in grassland areas. The topography
of the site was relatively flat, with most of the site lying
between 260 and 320 m and with the topography vary-
ing predominantly in the north–south orientation
(figure 1).
Figure 1. Location of monitoring equipment at Black Law Wind Farm, Scotland (55°46′01″N03°44′20″W). Turbines are denoted
by black triangles, the clusters of T
SU
and T
SO
sensors denoted by red stars (A–D, 9 sensors at both the surface and subsurface at each
location), the T
A
and RH sensors by blue circles and the height contours by grey lines. Wind direction was monitored at site B and the
inset wind rose depicts wind direction during the study period. The colours denote the wind speeds (ms
−1
)and the percentage rings
indicate the relative frequency of occurrence. Wind directions are plotted according to where the wind originates. Map generated
using ArcGIS 10.
2
Environ. Res. Lett. 11 (2016)044024
Data collection
Our field data collection framework was designed to
capture the impacts on ecosystem processes within a
wind farm, in terms of the scale, density and resolu-
tion, and did not undertake an energy balance
approach. Air temperature (T
A
)and relative humidity
(RH)were measured every second and five-minute
averages recorded at 2 m above the land surface
(HOBO U23-002, Onset, USA; see SI). Sensors were
deployed in a grid at 101 locations across a 2.6 by
1.4 km area of Black Law Wind Farm (figure 1). The
elevation of the sampling locations varied from 277 to
305 m (no elevation corrections were required; see SI
for details). Sensor resolution was 0.02 °Cat25°C for
temperature and 0.03% for RH. Surface and soil
(−5cm)temperatures (T
SU
and T
SO
respectively)were
recorded every 30 min (HOBO Pendants, Onset, USA)
at 36 locations, clustered at four sites (figure 1). Sensor
resolution was 0.14 °Cat25°C. At the same sites soil
moisture (−10 cm)was measured every minute and
averaged every 30 min (using a site-specific calibra-
tion)(CS625 water content reflectometers and CR200
loggers, Campbell Scientific Limited, UK). Sensor
resolution was 0.1% volumetric water content. Wind
direction was measured with a 2D sonic anemometer
(Gill Instruments, UK)at site B, 2 m above the land
surface every 10 s and averaged over 10 min intervals
(figure 1). Wind speed resolution was 0.01 m s
−1
and
direction was 1°. All sensors were checked and
calibrated prior to deployment. The sampling strategy
was chosen to allow an adequate representation of the
response of microclimate to wind turbine operation at
a resolution appropriate for ecosystem processes (see
SI). To ensure the period of field measurement was
‘normal’we examined wind speed and stability and
mixing ratios using data from nearby Met Office
stations (see SI for full details).
Data processing
Sunrise and sunset are the meteorologically relevant
temporal controls on boundary layer development
(Stull 1998). Consequently, ‘day’was classified as one
hour after sunrise to one hour before sunset, as defined
by the National Oceanic and Atmospheric Adminis-
tration algorithm (ESRL 2014), and ‘night’as one hour
after sunset to one hour before sunrise (transition
periods were excluded). As the Sun rose and set at
significantly different times during the period of
measurement, fraction of day was calculated, with
sunrise as 0.0 (or 1.0), sunset as 0.5, and time linearly
scaled between. The fraction of the day data were then
categorised into 24 pseudo-hourly bins.
AH was derived from RH by first calculating the
saturated water vapour pressure, then calculating the
vapour pressure, and then finally the AH based upon
temperature and vapour pressure (see SI for more
information).
Departures were used in the analysis of the T
A
,AH
and RH data to remove diurnal and seasonal signals
(the greatest controls)from the data. Essentially, the
site-wide instantaneous mean was calculated for all
measurement locations, and subtracted from each
individual measurement, and then averaged over the
ON and OFF periods and for downwind and not
downwind groups where appropriate. In numerical
form, the departures were derived by considering the
vector of Ntemperature measurement locations at
time t:
=¼=
=¼=
̲() [ () () ()]
()()
Tt Tt T t T t t t
tt tt
M
,,., for,
,,
i. e. times in the sample set 1
N
M
12 1
2
then, calculating the site-wide scalar mean temper-
ature at time t=t
j
å
=
=
() () ()Tt Tt.2
jNi
N
ijAV
1
1
The vector of departures from the mean at time
t
j
¢= - - ¼
-
̲()[()()()()
() ()]
()
Tt Tt TtTt Tt
Tt T t
,,,
3
jj jj j
Nj j
1AV2AV
AV
allowing the time-averaged mean departure vector to
be constructed:
åå
å
¢= ¢¢
¼
¢
==
=
̲
¯() ()
() ()
⎡
⎣
⎢
⎢
⎤
⎦
⎥
⎥
TTtTt
Tt
,,,
.4
Mj
M
jMj
M
j
Mj
M
Nj
1
1
1
1
1
2
1
1
This vector is the one used in the paper, and when
departures are mentioned they refer to this vector, or
elements of it.
Variation in T
A
,T
SU
,T
SO
, and AH was assessed by
calculating the coefficient of variation (c
v
)across the
site for each measurement interval, and averaging in
pseudo-hourly bins (based on fraction of the day)for
ON and OFF periods (figure 3). Given the c
v
of a popu-
lation is defined as the standard deviation divided by
the mean of the population (c
v
=σ/μ), the tempera-
tures were first converted to degrees Kelvin to ensure
meaningless values of c
v
were not generated from
negative or zero values of μ. The c
v
was calculated
using the same steps as above for T
A
, AH and RH
departures: the site-wide c
v
was calculated and the
time-averaged c
v
vector constructed. In numerical
form, at time t=t
j
å
=
-
()
(() ())
() ()ct Tt T t
Tt ,5
jN
N
ij j
j
v
1
1AV 2
AV
where the temperature vector Tand site-wide average
T
AV
are defined above (equations (1)and (2)).
Analogous formulae apply to the other fields consid-
ered (AH and surface and soil temperatures). Note that
the c
v
was subsequently binned, according to time of
3
Environ. Res. Lett. 11 (2016)044024
day, giving a measure of spatial and temporal
variability.
To examine the magnitude of effect of the turbines
on T
A
, RH and AH, only data when the wind direction
at 2 m was from between 220°and 240°were con-
sidered. This directional sector was chosen as it was
aligned with the main axis of the wind farm (figure 1),
thus any cumulative effect of the turning turbines was
likely to be greatest, and it was approximately the
dominant wind direction. For the data to be included
the wind had to be originating from between 220°and
240°at the measurement time and the previous
30 min, and also satisfy the day–night criteria for the
entire 30 min period. This allowed the development of
any underlying signal to be captured, while still pro-
viding a significant amount of data. Departures for all
the data that fit these criteria were calculated for each
sampling location, (see above), thus allowing the rela-
tive T
A
, RH and AH of sites classified as downwind and
not downwind of turbines to be compared. Further, as
no hub height wind data was available, to take into
account the potential effects of directional shear
(between surface and hub height), we repeated the
analysis for 200°–220°, 220°–240°and 280°–300°
wind direction sectors. Unfortunately, given the lay-
out of the wind farm, all measurement sites were clas-
sified as downwind in the 240°–260°and 260°–280°
direction sectors. Given the complexity of wake
dynamics, especially the interaction of multiple wakes,
it was not appropriate to make assumptions regarding
wake expansion and movement. Thus our analysis is
prudent: measurement locations categorised as down-
wind (not downwind)may have been not downwind
(downwind)of turbines and consequently the differ-
ences in T
A
, RH and AH we quantified may be smaller
than actual.
Statistics
Logarithmic best-fit lines were used to characterise the
relationship between temperature and AH departure
and distance to the nearest turbine (figure 2). Differ-
ences in c
v
between ON and OFF periods for each
pseudo-hour (figure 3)and temperature departures
between downwind and not downwind groups were
tested (table 1)using a paired t-test with unequal
variances using Stata13 (StataCorp, Texas).
Figure 2. Turbine proximity influences observed effects of wind farm operation on T
A
and AH. The effect of distance (x)from the
nearest turbine on the temperature and AH departure during the night (a)and day (b). Blue dots represent the T
A
departure difference
for ON–OFF periods and red triangles the AH departure. The dotted blue lines and solid red lines represent the logarithmic
approximation of the T
A
and AH departures respectively. Black dotted lines represent the zero departure and 200 m distance baselines.
To convert the xaxis into multiples of rotor diameters, divide by 82 m.
4
Environ. Res. Lett. 11 (2016)044024
Results and discussion
Effects of wind turbine proximity on T
A
and AH
To determine the integrated effect (i.e. irrespective of
wind direction and speed)of the whole wind farm
across the measurement period, we analysed the mean
day and night-time differences in T
A
and AH depar-
ture for each site from the site-wide mean between ON
and OFF periods, using all data (i.e. data were not
categorised as downwind or not downwind and there-
fore wake dynamics do not need to be considered).To
assess the spatial extent of effects we related the
departures to distance from the nearest turbine.
During the night, air closer to a wind turbine was
warmer and more moist, with T
A
departures reaching
0.25 °C and AH departures 0.1 g m
−3
(figure 2(a)).
Night-time warming and moistening has been
attributed to downward mixing of warmer moister air
by turbines during stable conditions (Baidya
Roy 2011). The potential occurrence of this at Black
Law is supported by analysis of midnight soundings
from the two nearest upper-air stations: this reveals
that during the most stable conditions the lapse rate is
positive for both temperature and moisture (see SI)
(Baidya Roy 2011). The larger effects closer to wind
turbines could be the composite effect of that wind
turbine and others upwind. This suggests that the
interactions between multiple wakes may enhance
effects found in single row turbine studies (Rajewski
et al 2014). While positive departures are evident up to
200 m (2.4 rotor diameters)away (figure 2(a)), this
does not suggest that effects of downward mixing were
only evident within 200 m (2.4 rotor diameters)of a
turbine. Compared to the site-wide mean, it was
Figure 3. Diurnal variations in T
A
,T
SU
and T
SO
and AH differ during wind farm operational and idle periods. Data points represent
the average c
v
±standard error (SE)of the T
A
(a),T
SU
(b),T
SO
(c)and AH (d)during the ON (blue dashed lines)and OFF (solid red
lines)periods. Fraction of the day was calculated based on sunrise and sunset times (see methods), with 0 representing sunrise and 0.5
sunset and 0.04 approximately 1 h, with sunset to sunrise shaded grey.
Table 1. Temperature and absolute humidity differences between sites downwind and not downwind from turbines for three
direction sectors during the night time. Differences are in °C for temperature and g m
−3
for AH, positive values indicate the
downwind locations were warmer and moister, negative values that downwind locations were cooler and drier, ns—not sig-
nificant at p<0.05.
Direction sector T, ON, Night T, OFF, Night AH, ON, Night AH, OFF, Night
pDifference pDifference pDifference pDifference
200–220 <0.01 0.18 ns 0.04 <0.01 0.03 ns 0.00
220–240 <0.01 0.16 ns −0.03 ns 0.00 ns −0.03
280–300 ns −0.06 ns 0.05 ns 0.03 ns 0.04
5
Environ. Res. Lett. 11 (2016)044024
generally warmer and more moist within 200 m (2.4
rotor diameters)of a turbine and cooler and drier
beyond 200 m (2.4 rotor diameters). During the day,
air closer to a wind turbine was cooler, with departures
up to 0.05 °C, but AH was not influenced (figure 2(b)).
This weaker day-time effect could be attributable to a
convectively driven, well mixed boundary layer (Zhou
et al 2013).
The trends between T
A
departure
(ΔT
A
=T
A
[ON]−T
A
[OFF])during both day and
night, and AH departure (ΔAH=AH[ON]−AH
[OFF])at night, relative to distance from the nearest
turbine (x), can be approximated by logarithmic func-
tions: ΔT
A
=0.62−0.12 ln (x),r=0.75 and
ΔAH=0.15−0.03 ln (x),r=0.61 during the night
and ΔT
A
=−0.11+0.02 ln (x),r=0.53 during the
day (figure 2). The daytime ΔAH cannot be approxi-
mated by a logarithmic function (r<0.2). These loga-
rithmic trends demonstrate that the effect of wind
turbines can be quantified and thus potentially repre-
sented in models of Earth surface energy balance. This
would be valuable given the complexity of extrapolat-
ing impacts from studies examining single or a single
row of turbines (Rajewski et al 2014). Currently,
model parametrisations (for example, Fitch et al
(2012)) typically represent the effect of turbines on the
atmosphere by imposing a sink of momentum and a
source of turbulence kinetic energy. Multiple turbines
are represented by integrating over a typical model
grid cell (which may contain more than one turbine).
With all such parametrisations a number of coeffi-
cients are required (such as the fraction of energy con-
verted into turbulent kinetic energy—unknown
a priori—which controls the subsequent mixing).
Thus, the integrated effect we identified could be used
to test such parameterisations of wind farms in num-
erical weather prediction models (Cervarich
et al 2013), where the spatial resolution is greater than
the distance between individual turbines, and the inte-
grated effect of a whole wind farm needs to be
established.
Diurnal effects of wind turbines on spatial
variability in T
A
,T
SU
,T
SO
, and AH
To quantify the spatio-temporal effects of wind
turbine operation we examined the variation in T
A
,
T
SU
,T
SO
, and AH throughout the diurnal cycle
(irrespective of wind direction and thus wake
dynamics do not need to be considered). We used the
coefficient of variation c
v
of each of the measurements
across the whole site for each measurement interval
averaged for both the ON and OFF periods as a
function of time of day. Using the c
v
allows compar-
ison of the ON and OFF periods: reporting this in
degrees (such as would be obtained by the standard
deviation)would be misleading as the temperature is
more variable in summer months compared with
autumn months.
The spatial variations in the T
A
,T
SU
,T
SO
and AH
data were significantly greater during the ON period
compared with the OFF period (p<0.05 for pseudo
10:00 and 11:00 for the T
SO
data and pseudo 07:00 for
the AH data, p<0.01 for all other data and hour
intervals)(figure 3), suggesting turbine operation
increased vertical mixing and turbulence. This effect,
at several levels (i.e. soil, surface, and air at 2 m), has
not been identified in other field studies but has been
reported in modelling studies (Lu and Porté-
Agel 2015). The spatial variance in microclimate is of
crucial importance as ecosystem processes respond to
small-scale variation in climate (Baidya Roy et al 2004,
Baidya Roy and Traiteur 2010, Baidya Roy 2011, Zhou
et al 2012, De Frenne et al 2013, Rajewski et al 2013,
Smith et al 2013, Zhou et al 2013). The differences in
temperature variation between ON and OFF periods
were greatest for T
SU
data (figure 3(b)) and smallest for
T
A
data (figure 3(a)),reflecting relatively well-mixed
air at 2 m and increased variability at the surface aris-
ing from peatland micro-topography and vegetation
shading. AH aside, the spatial variance was greatest for
T
SU
and least for T
A
, during ON and OFF periods
(figure 3). This is attributable to the patchy influence
of vegetation shading and surface water content on the
temperatures of the surface sensors. Further, peatland
temperatures are highly spatially variable, in response
to the peat thermal properties (Kettridge and
Baird 2010), thus explaining the T
SO
spatial variability.
In contrast, although there are differences across the
wind farm, the air max is relatively well mixed.
The difference in T
A
and AH variability between
the ON and OFF periods was greater during night than
day (figures 3(a)and (d)), whereas it was approxi-
mately equal for T
SU
and T
SO
, with smaller differences
during transition periods around sunrise and sunset
(figures 3(b)and (c)). This suggests that night-time T
A
and AH were most sensitive to turbines, due to down-
ward mixing of warm air. Further, the mixing down of
warm air during the ON period appears to have affec-
ted the diurnal trend in T
SO
and AH: the maximum
variability in T
SO
and AH were later in the day during
the OFF period compared with the ON period
(figures 3(c)and (d)). This suggests that the night-time
background gradient of T
SO
and AH are eroded by tur-
bulence earlier in the day during the ON period. No
trends in soil moisture were found (see SI)probably as
a result of the high water table across the wind farm
(Armstrong et al 2015).
Downwind effects of wind turbines on T
A
and AH
Our final analysis examined the magnitude of effect of
wind turbines on T
A
and AH during the night (see SI
for day-time and RH results)when we observed a
strong relationship between T
A
and AH departures
and distance from the turbine (figure 2). Conse-
quently, we excluded all data from one hour before
sunrise to one hour after sunset and filtered for wind
6
Environ. Res. Lett. 11 (2016)044024
directions between 220°and 240°(aligned along the
main axis of the wind farm)(figure 1). Temperature
departures for all the data that fit these criteria were
calculated for each sampling location, thus allowing
the relative T
A
and AH of sites downwind and not
downwind of turbines to be compared. Directional
shear between hub height and the surface, both clock-
wise and anticlockwise, can occur (Walter et al 2009,
Cariou et al 2010, Rhodes and Lundquist 2013), and
consequently we also analysed the data filtered for
wind directions between 200°–220°, 220°–240°and
280°–300°.
We found that temperatures were significantly
warmer in areas downwind of turbines during the ON
period (p<0.01), with an average relative warming of
0.18 °C for the 200°–220°direction sector and 0.16 °C
for the 220°–240°sector (table 1). The AH of air
downwind of the turbines was, on average, 0.03 g m
−3
greater during the ON period (p<0.01)for the 200°–
220°direction sector but there was no significant dif-
ference for the 220°–240°sector (table 1). This sug-
gests that there may have been slight anti-clockwise
directional shear in the boundary layer, although we
cannot rule out stronger slightly clockwise shear as all
sites were downwind of turbines in the 240°–260°and
260°–280°sectors. Although clockwise shear is expec-
ted given the Ekman spiral, anti-clockwise shear has
been observed previously (Walter et al 2009)and could
have been caused by the wind turbines disturbing the
atmospheric boundary layer.
Analysis of the data shows that the relative increase
in saturated water vapour pressure had more effect on
AH than the combined increase in temperature, and
lowering of RH (see SI), consistent with turbine-
induced mixing of warmer and more moist air down-
wards (Baidya Roy et al 2004). During the OFF period,
temperature and AH departures were variable with no
statistically significant difference between downwind
and not downwind sites (p>0. 05), further demon-
strating the variation during the ON period was due to
wind turbine operation (table 1).
The impacts of temperature change on ecosystem
carbon cycling
Terrestrial carbon cycling is highly variable, both
spatially and temporally, and influenced by many
biotic and abiotic conditions and their interactions, as
demonstrated at this site (Armstrong et al 2015). Given
this, and the relatively small variation in microclimate
between ON and OFF periods compared to spatial and
temporal variability, it was not possible to measure the
impact of wind farm operation on carbon cycling.
However, peatlands are highly temperature sensitive
environments, and consequently these observed
small-scale changes in temperature could have signifi-
cant implications for peatland carbon stocks. For
example, during the growing season, a 0.88 °C warm-
ing was found to increase rates of ecosystem
respiration in a peatland in northern England by 20%
and decrease net CO
2
uptake by 11% (Ward
et al 2013). In contrast, during the non-growing season
when CO
2
fluxes were much lower, a 0.72 °C warming
increased ecosystem respiration rates by 44% and CO
2
uptake by 7% (Ward et al 2013). Therefore, as we
found night time warming was most influenced,
decomposition processes may be accelerated and thus
soil carbon losses observed. However, effects on
respiration could also be offset by plant physiological
responses to warming (Peng et al 2013). Our results
also indicated that wind farm operation had greater
effects on spatial and diurnal variability in T
SU
and T
SO
than T
A
.T
SU
and T
SO
are recognised as stronger
regulators of plant-soil carbon dynamics (Graae
et al 2012, De Frenne et al 2013); consequently, the
effects on the net carbon balance of the hosting
ecosystem may be stronger than inferred from T
A
(Baidya Roy and Traiteur 2010, Rajewski et al 2013,
Smith et al 2013).
Conclusion
This research provides the first field evidence that
operational wind turbines can have a measureable
effect on soil and soil surface temperature, and
demonstrates the effect of multiple turbine wakes on
air temperature and humidity. When the turbines
were operational we found greater variability in
temperature (soil, soil surface and air)and AH
throughout the diurnal cycle with T
A
and AH increas-
ing at night. Whilst the effects were statistically
significant, the observed differences were small com-
pared with spatial variation recorded across the site.
Importantly, we also demonstrate that the effects on
both T
A
and AH can be described by a logarithmic
function of distance from nearest turbine, a generic
approach showing for the first time how the integrated
effect of a wind farm may be estimated.
This research demonstrates that effects of wind
turbines on ground-level microclimate could have
implications for biogeochemical processes and ecosys-
tem carbon cycling. Consequently, improved mea-
surements and modelling approaches are needed to
determine the true carbon balance of wind energy that
includes the effects of altered ground-level
microclimates.
Acknowledgments
This research was supported by the UK Natural
Environment Research Council (NE/H01036X/1,
NE/H010351/1, NE/H010335/1). AA acknowledges
financial support from an Energy Lancaster fellowship
during which she undertook data analysis and manu-
script preparation. We thank Scottish Power Renew-
ables and the land owners for allowing site access. We
thank Martin Coleman, Ross Herbert, Hemanth
7
Environ. Res. Lett. 11 (2016)044024
Pasumarthi, Salvatore Peppe, Harriet Richardson,
Kenny Roberts, Fraser Russell, Gavin Thompson,
Bethan White and Scott Wylie for assistance in the field,
and Barbara Brooks, James Groves, Salvatore Peppe
and FelicityPerry for assistance calibrating the loggers.
The authors would like to thank the anonymous
reviewers whose comments and suggestions have
greatly improved this paper.
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