Renewable Energy xxx (2012) 1e8
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Renewable Energy
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Present and future offshore wind power potential in northern Europe based
on downscaled global climate runs with adjusted SST and sea ice cover
Idar Barstad a, *, Asgeir Sorteberg b, c, Michel dos-Santos Mesquita c, d
a
Uni Computing, Uni Research, Allegt 55, Bergen, Norway
Geophysical Institute, University of Bergen, Bergen, Norway
c
Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
d
Uni Bjerknes Centre, Bergen, Norway
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 28 February 2011
Accepted 5 February 2012
Available online xxx
Coupled global climate models coarse results have been downscaled to produce future wind power
maps for northern Europe. The downscaling method utilizes a global, stretched atmospheric numerical
model with sea-surface temperature (SST) as the main forcing. The model has horizontal grid spacing
equivalent to about 30 km in the area of interest. As the climate models have often problems with the
sea ice cover and storm tracks in vicinity of the sea ice, an alternative SST approach has been used. The
SST signal from climate model runs under the A1B scenario has been added to the Era40 reanalysis data
set, and used as lower boundary forcing. A 30-year control period (1972e2001) is compared to a future
period (2020e2049) of equal length. Four realisations of the future period constitute the ensemble,
which the future wind power potential is estimated from.
The results show that a weak reduction of wind power production is expected in the future period. The
reduction of the power potential is in the range from 2 to 6% in most areas. The spread in the model
ensemble is large and consequently the reduction becomes relative small. Regional pockets of increased
potential appear in vicinity of high terrain. These results are regarded as uncertain as a little shift in
storm tracks will lead to very different mountain shadow effects and alter the picture drastically.
Ó 2012 Elsevier Ltd. All rights reserved.
Keywords:
Future wind resource
Downscaling of climate model results
IPCC-AR4
1. Introduction
High ambitions for renewable energy and scarcity of suitable
onshore areas for wind energy in Europe encourage large offshore
wind farm installations. The plans for offshore wind parks for the
next 10e15 years are formidable [1]. The depreciation time for such
large parks can be comparable to the rapid man-made climate
changes, which have already made a foot print on the Earth [2]. The
infrastructure of such large offshore installation is typically more
expensive than onshore counterparts, relying on somewhat even
longer depreciation time - at least in a socio-economical context.
Coupled global climate models (CGCMs) are the only viable tools
for addressing future changes on a time scale of decades. Natural
variability along with man-made climate forcing determines the
future state. CGCMs project changes in climate convincingly if used
in an appropriate manner (e.g [3].). Nevertheless, models are not
perfect due to coarse resolution and shortage in the physical and
numerical treatment. Unsystematic errors in models and natural
* Corresponding author.
E-mail address: idar.barstad@uni.no (I. Barstad).
variability leading to divergence among model results are not
necessary a hindrance to reliable projections. Ensemble means
have proven to be more accurate than individual models in
reproducing the instrumental observational period (e.g [4].). This
gives hope for the separation of the climate signal from that of noise
providing enough ensemble members are used. Even systematic
errors can, to some extent, be dealt with by combining models of
different design. However, the use of model results in a relative
sense (future estimate as a fraction of the present) is probably
among the better method to reduce systematic errors. Not yet
mentioned, the different emission scenarios introduce additional
uncertainties to a future projection, requiring additional model
realisations of a future state.
Downscaling of CGCMs is typically motivated by the desire of
more details for some future time period. Dynamical downscaling
is perhaps the most promising method for such refinement ([5]). In
extra-tropical areas, the weather systems are highly advective of
nature, and limited area numerical models with small model
domains, are strongly influenced by the lateral boundaries and
their placement. Models with larger model domain are first of all
influenced by the lower boundary, i.e., properties of sea-surface
temperature (SST) and sea ice.
0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.renene.2012.02.008
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
2
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
Many CGCMs misrepresent the polar ice sheet grossly. This
connects to poor SST representation at high latitudes (e.g. [6],). To
the degree storm tracks are sensitive to details in SSTs [7], poor SSTs
representation feeds back to the wind-driven ocean currents,
which in turn controls the SST. It is not clear which factor
contributes the most to the misrepresentation, but in summary, an
exorbitant zonal flow behaviour in the extra-tropics is typically
found in CGCMs, (e.g. [8]).
Rather than to directly downscale the CGCMs with its shortcoming in SST, it is tempting to try alternative approaches e.g.,
adjust sea ice and SSTs to more realistic fields. Thus, in this paper
we remove the systematic errors (bias) in the SSTs by retrieving the
climate signal in SST due to the greenhouse gas forcing (e.g.
increased CO2-concentration) and add it on top of today’s observed
climate. The inter-annual variability does not change, but the sea
ice extent will retreat due to global warming. SSTs will have high
level of details and a more meridional structure.
The overall goal of this paper is to identify a trend (if any) in the
future wind energy potential for northern Europe. This will be
addressed by dynamical downscaling CGCM data based on four
simulations of future states and comparing power production
potential in the future to a simulated control period.
This paper is organized as follows: Chapter 2 provides information on the downscaling model, the method on how SSTs are
constructed and adjustments that have been done to the model
results in order to say something about future wind energy
potential in northern Europe. Chapter 3 presents the results of the
simulations in the view of the power potential. An explanation of
the future change is assigned to reasons in changes of storm tracks.
Chapter 4 discusses different related aspects. Chapter 5 is left for
conclusions.
2. Data and method
The Arpege/IFS model version 4.4 [9] is set-up with a T159L60c3
resolution. This means there are 60 levels going from the surface
(lowest model level at 10 m) to 0.1 hPa with 10 levels below
1000 m. This is similar to the Era40 reanalysis set-up [10]. The
modification in this approach is a stretching of the grid - from
a global average spatial resolution of about 80 km to approximately
25 km resolution in the focus area, northern Europe. This is the
same grid as applied in [11]. Fig. 1 shows the isolines of the horizontal grid spacing. The underlying terrain is interpolated from the
USGS database, and the SST data is interpolated from the Era40 data
set. As the sea ice extent is dominated by advective processes and
does not follow the freezing point of sea water, a threshold SST of
273.16 K for detection was chosen. In this way, a realistic ice edge
was achieved.
Five time-slice simulations will be presented in this paper. The
control run covers the period 1960e2001, whereas the future runs
span from 2020 to 2060. We focus on 30-years periods: 1972e2001
for the control run, and 2020e2049 for the four future runs (see
Table 1 for details). In [11], a second simulation of the control period
was presented. Their run used long-wave spectral nudging from
Era40. This simulation result is not discussed in this paper.
2.1. The approach on adjusted SSTs
For the present day simulations, monthly observed mean SST
and sea ice concentrations data from Era40 are used.
For the future monthly SSTs, we applied a new and slightly more
complicated approach. In order to remove the biases found in the
coupled model runs and thus avoid running a control run for each
scenario, drift-corrected anomalies from a chosen coupled model
ðDSSTÞ was added to the trend-corrected observed SSTs ðSSTcorr Þ,
Fig. 1. The model grid. Red isolines indicate 25 (inner), 30, 35, 40, 50, 70, 90 and
100 km grid distance. (For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of this article.)
SSTdownsc ¼ SSTcorr þ DSST
(1)
and this SSTdownsc was used in the future time-slice runs presented
herein. By doing so, the variability in the observations is added to
the climate signal.
The trend-corrected observed SSTs was calculated as:
SSTcorr ¼ SST vSSTobs $ tobs tobs
vt
(2)
where SST is the observed monthly SST for a corresponding month
vSSTobs
in the 1961e1990 period,
is the trend in the observed SST
vt
for the 1960e1991 period and ðtobs tobs Þ is the time deviation
from the mean time for the control period (1975).
The drift-corrected anomalies from the chosen coupled model
were calculated as:
DSST ¼ SST20c3m SSTscen vSSTcntrl $ tscen t20c3m
vt
(3)
where SST20c3m is the monthly mean SST from the coupled model
for the period 1961e1990 using observed changes in greenhouse
gases, SSTscen is the monthly mean using a 30-year running average
SST from the coupled model using the SRES A1B scenario for
vSSTcntrl
is the drift in the coupled models
greenhouse gases.
vt
control scenario (the simulation with constant radiative forcing)
and ðtscen t20c3m Þ is the time from the scenario year and month in
question to the mean of the control period for that month. The SRES
A1B scenario was chosen for this investigation. It has a moderate
global temperature increase towards year 2100, between 2 and
4 C.
As we use 30-year running averages to make the climate change
signal, we are assuming that the inter-annual variability in SST will
remain the same in the future as it is observed today (e.g., no
change in the El Niño Southern Oscillation cycles, etc.).
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
3
Table 1
Model simulations: *resolution over Scandinavia. The model has a variable resolution. The nudged (“N”) simulation was not used in this paper, but was a part of the total
produced model data set.
Boundary conditions
Lower boundary conditions
Acronym
Lateral boundary conditions
Era40
Era40
GFDL V2.0 (T42)
ECHAM5 (T42)
HADCM3 (T42)
CCSM3 (T85)
N
F
R1
R2
R3
R4
Spectral nudging
None
None
None
None
None
2.2. The sea ice
The coupled models bias in sea ice was removed by not allowing
the model sea ice extent for 2020e2060 to be larger than the
maximum observed present sea ice extent. Regions where this was
the case were (in some of the models) parts of the Barents Sea and
along east Greenland. In the region where the sea ice was artificially
removed for the scenario run, the change in SST
(SST20c3m SSTscen ) could not be used directly as it would have
given too large changes (in the order of 10e15 C instead of the
more common open ocean changes of 2 C). Instead we picked the
SST20c3m SSTscen value from the closest grid square with open
ocean in the control run. To sum up, this procedure allows the sea
ice cover to have seasonal variation, which gradually retreats as the
temperature increases.
2.3. Reduction of winds over the ocean - from 925 hPa to 100 m
Wind energy production takes place in the lower boundary
layer. For large turbines, the rotor will typically sweep an area
extending from 50 m to 150 m above ground. In this paper, we use
wind at 925 hPa to estimate the wind speed at 100 m height
(wsp100) and subsequently calculate the potential for wind power
production at this level. During the study of Barstad et al. [11], it
became clear that the model performance near the ground over
land was doubtful. So our analysis is limited to sea areas. We take
a simplistic approach using the power law to reduce the wind from
925 hPa to 100 m:
wsp100m ¼ wsp925 ð100=z925 Þ0:12
(4)
where z925 is the height of the 925 hPa level and wsp925 is the wind
speed at this level. This is an approach taken by, among others [12],
and has proven to be useful for wind mapping. As apparent from
(4), there is no correction for stability or other effects. We judge the
accuracy level in other aspects of our study to be poorer than this
assumption.
2.4. BIAS-correction of model wind distribution
In order to say something about the control run performance,
we evaluate the run by use of an independent data set. This data set
is the Era Interim [13] with higher resolution and improved
assimilation techniques compared to Era40. Fig. 2a shows the bias
between the new reanalysis, Era Interim and the control run at the
925 hPa level. Only overlapping years are considered (1989e2001).
The bias is below 1 ms1 in the North Sea which for an average of
8 ms1 wind speed corresponds to 60% in power level. In Fig. 2b, we
show the whole distribution for a point (55N,2E) at the 925 hPa
level.
Period
Emission scenario
Horizontal resolution
No vertical layers
1961e1990
1961e1990
2020e2060
2020e2060
2020e2060
2020e2060
OBS
OBS
SRES
SRES
SRES
SRES
35
35
35
35
35
35
60
60
60
60
60
60
A1B
A1B
A1B
A1B
km*
km*
km*
km*
km*
km*
For the bias not to influence the data more than necessary, we
will apply a bias-correction procedure in our assessment of point
data. It works on percentile of the distribution and it is applied on
the 925 hPa level, before reduction to the 100 m level.
Although we work with relative values (future simulation
divided by the control period run), we apply a power curve to our
wind speed results. This is to minimize the influence of the power
curve. In Fig. 2b, lower section of the panel, the power curve is
shown for various wind speeds. For calm to weak winds, the
turbines are at halt, and at an intermediate wind regime, starting
for 4.5 ms1, the power production rises sharply to the maximum
value at 12.5 ms1. Above, 27.5 ms1, the turbines are shut down to
avoid damages. The shape of the power curve in the intermediate
regime varies a little depending on what turbine is assumed. In this
paper, we use the power curve for the REpower 5 MW turbine
which is one of the biggest available.
The BIAS-correction of the wind distribution is done by first
mapping the cumulative wind distribution from the control run
ðFcntrl ðxÞÞ onto the Era Interim cumulative distribution ðFEI ðxÞÞ. The
bias-corrected modelled wind speed x’cntrl; k on day k can be
calculated as,
1
x’cntrl; k ¼ FEI
Fcntrl xcntrl; k
(5)
where xcntrl is the original model value for day k of the control run.
To apply the same procedure on the scenario data, we first have to
remove the climate change signal. The climate change signal for
a certain day i in the scenario is found by using the ratio between
the value where the cumulative distribution functions of the
control and scenario values have the same value.
fi xscen; i ¼
xscen; i
1 F
Fcntrl
scen xscen; i
(6)
where xscen is the original model value for day i of the scenario run.
We then do the distribution mapping (5) and finally multiply back
the climate change signal to get the bias-corrected scenario value
for day i ðx’scen; i Þ,
1
x’scen; i ¼ Fcntl
Fscen xscen; i $fi xscen; i :
(7)
The correction is designed to retain the relative differences
between the control and scenario simulation (for example if the
mean change is 10% and 20% for the extremes in the original data it
will be the same in the bias-corrected data even if the absolute
values have changed). This approximation is exact if the Era Interim
and modelled data exactly follows a chosen distribution. Here we
have used a Weibull distribution as a fit to both the Era Interim and
model data.
In Fig. 2c, we present the uncorrected and bias-corrected
distribution at a point in the proximity of (55N,2E), located on
our grid (Station 2783). We see that the adjustment is similar as
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
4
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
Fig. 2. a. The bias in wind speed (ms1) at the 925 hPa level for the Era Interim and the control run. It is valid for the overlapping period (1989e2001). Only sea points are evaluated.
Negative values indicate too weak winds in the downscaling run. b. Distribution of 925 hPa wind speed for Era Interim (solid) and control run (dotted) at (55N, 2E) for the years
(1989e2001). The power curve used is shown in shade. c. Distribution of wind speed at 100 m for the original uncorrected (dotted) and bias-corrected (solid) time series valid for
(1972e2001) for station 2783. Median and mean are indicated in the plot in ms1. The shading indicates the power curve applied to the data.
before: The wind speeds in the nonlinear area of the power curve
(4.5e12.5 ms1) are corrected towards stronger winds.
3. Results of wind power potential
The annual power potential has a high economical impact for
wind parks. The annual power production varies with the wind
speed, and from that by the wind speed climatology. The standard
procedure to determine the climatology of winds has been to look
at 30-year averages (WMO-sanctioned norm), typically the period
1961e1990. In this work, we choose to update the 30-year period to
a more recent one, encompassing the later part of our data set,
1972e2001. Preferably, a more updated 30-year period would be
beneficial, but the ERA40’s last complete year is 2001. Consequently, the last decade with relative strong anthropogenic climate
impact is omitted. Tests shifting the reference period to
(1961e1990) show negligible differences in our results.
Fig. 3 shows the averaged annual wind speed at 100 m for the
period 1972e2001 and the averaged annual full-load hours from
the control run. The figure shows a clear gradient from the midAtlantic toward the European main land. To the west of Great
Britain, the gradient points westward, whereas in the North Sea, it
points north-westward.
In Fig. 4, we show the future power (2020e2049) potential as
a fraction of the control period (1972e2001). We find a reduction in
most waters, except in the Baltic Sea. Outside the map, there is
a clear reduction in the Mediterranean, but an increase southwest
of the Iberian Peninsula. An increase in the power potential around
high terrain is most likely to wind enhancement because of
regional scales mountains, e.g., [14]. A small shift in storm tracks
may change the picture completely in the areas. Findings on
regional scales should, thus, be interpreted with a high degree of
caution. We have further tested the sensitivity of the chosen control
period by using the period 1961e1990, but find very little change.
Less than 0.02% change in Fig. 4 was found.
3.1. Time series
In Fig. 5, we show the time series for a chosen station in the
North Sea, see location in Fig. 4. Box plots are shown for this and
other coastal points in northern Europe. The locations of these are
given in Table 2 and on Fig. 4. From Fig. 5a, it is discernable that the
future averaged power production is somewhat lower than the 30years of the control period. Assuming that the future members
belong to a Gaussian distribution, the mean is slightly below unity
(the average of the control period), 0:98 0:01. Using a t-test, none
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
5
Fig. 3. a) LEFT: Averaged wind speed reduced to 100 m height for the period (1972e2001) based on the control run. Solid line indicates the 8.5 ms1 isotach. b) RIGHT: Averaged
annual full-load hours (no adjustments). The 3750 h contour is shown in back.
of the future distributions are significantly different (at the 95%
level) from the control. The inter-annual variability for the future
estimates is reduced as a result of the four-member average.
However, the spread of the whole (all 4 members) future distribution show about þ10% uncertainty at 95% level, see indications
on Fig. 5a. In Fig. 5bef, we see that the bias-correction has little
influence when mapping the future potential as a fraction of the
control period. From Table 2, we find that in an absolute sense, the
bias-correction has up to, say, 10% influence on the power
production. From the time series plot and the box plots in Fig. 5, the
reduced variability in the future ensembles is apparent in most
cases. The station at about 70 N seems to have large spread in the
ensemble, reflect a greater natural variability closer to the pole.
We will now take a look at what causes the reduced future
power potential. We group all cases into wind speed categories
reflecting the position on the power curve; the four categories (I, II,
III, IV) equals (<4.5, 4.5e12.5, 12.5e27.5, >27.5 ms1). In Table 3, we
have broken down the change of wind speed within categories
applying different measures: Power production (P) shows the
change in percent of the accumulated potential power production.
For a given category (j), this may be expressed as:
P
PRij PFij
P4 P
PFij
j¼1
P
Pj ¼
(8)
Fig. 4. The fractional change in averaged annual power production for the period
(2020e2049) versus (1972e2001). Unity indicated by black contour also shown over
land for clarity. Above unity means increased future power potential. Latitudes and
longitudes are indicated every 10 . Stations in Table 2 are indicated.
where PRij is the power potential for a category for a given time (i)
in the future time period and PFij the power potential for the same
category for a given time in the control period. In the denominator,
it is also summed all four categories. This measure shows naturally
no change for category I and IV since these they have no production.
To enable address of what happen in these categories, we introduce
a second measure: Power production deficit (PD). For a given
category (j), it is defined as the accumulated power deficit in
reference to full-load and is expressed as:
P
P
Pmax PRij Pmax PFij
P4 P
Pmax PFij
j¼1
P
P
Pmax ðn mÞ þ
PRij PFij
¼ P
P
n$Pmax 4j ¼ 1 PFij
PDj ¼
(9)
This measure may have values for category I and IV as the
number of cases n in control period and m in future period may be
different. Pmax ¼ 5 MW is the maximum value of the power curve
shown in Fig. 2. The measures are given in percent in Table 3.
Summing all the wind speed categories, P and PD have similar
absolute magnitude (right column in Table 3), but without being
identical which follows from (8) and (9). In case (8) or (9) is used to
calculation of an ensemble-overall measure, the subtraction of the
control period term has to be multiplied by the number of members
which is four in our case.
For category I, III and IV, the share number of counts (C) will
determine the change in the potential. In category II, a shift in the
average wind speed value ðuÞ will contribute along with number of
counts.
For example, at station 2783 and the R1 run presented in Table 3,
a small reduction (w3%) in production is predicted for the future
period. The main reason for this comes due to a reduction of counts
in category III. Category I and II have gained counts, but since the
average wind speed in category II is reduced, this will not have
a compensating effect. Category IV has fewer counts which reduced
the deficit, c.f. the PD measure. However, this has too little effect to
counter the reduction in category III.
The overall (R1-4) change for this station indicates a mere 2%
reduction. Similar as for the R1 run, the number of counts in
category III is the main reason. Increased counts in category II will
not compensate as the mean wind speed in this category has
decreased.
The use of PD vs P may seem problematic. If we continue to look
at the overall P and PD for station 2783, we find that the counts at I
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
6
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
Fig. 5. a) Time series for future annual power production (averaged among members showed in gray) for a point in the North Sea (54.73N, 1.85E) as a fraction of the 30-year control
period (1972e2001). Unity (broken line) reflects 30-year average for the control period. Thick, straight lines show the tendencies, for the respective time series. The 5% and 95%
levels for a Gaussian fitted distribution are indicated. b) as in a) but shown as a box plot. The future period is (2020e2049). The bias-corrected distributions are placed to the left,
and the uncorrected to the right. In the box plot, outliers are individually marked. The box is set at 25%ilee75%ile where the median is marked in the centre. cef) as in b) but for
different sites.
Table 2
Longitude and latitude of grid point given particular consideration. Mean production for bias-corrected and uncorrected values.
Point
Lat/lon-value
Station number (grid indices)
Mean production corr e uncorr ¼ sum (kW)
1
2
3
4
5
6
7
8
69.98/13.68
64.25/6.81
60.17/4.08
59.12/2.99
56.88/1.12
54.73/1.85
55.96/6.90
51.36/4.98
1373
870
1442
1431
2104
2783
2315
3531
1886e1807
2027e1872
2193e2006
2481e2289
2362e2158
2242e2066
2443e2267
2172e2008
¼
¼
¼
¼
¼
¼
¼
¼
79
155
187
192
204
176
176
164
Remarks
“North of Scotland”
“Offshore Stavanger”
“Dogger bank”
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
7
Table 3
In the right side column, per mille change in production potential deficit (PD), percentage change in production (P) and percentage change in counts (C) are indicated for wind
speed bins (<4.5 4.5e12.5 12.5e27.5 > 27.5 and Total) ms1 expected for the future (2020e2049). u denotes the average wind speed in category II, control run/scenario run.
Station
Exp
(ms1)
<4.5
4.5e12.5
2783
R1
PD (%)
P (%)
C (%)
u (ms1)
PD (%)
P
C
u (ms1)
PD (%)
P
C
u (ms1)
PD (%)
P
C
Avg_u
PD (%)
P (%)
C (%)
u (ms1)
0.4
0
98
8:03/7:97
1.6
0
93
8:03/8:03
0.5
0
98
8:03/7:97
1.0
0
105
8:03/7:96
0.4
0
98
8:0/7:98ð0:05Þ
2.9
0.5
103
0
3.6
89
0.9
0.5
101
0
0.3
101
2.3
0.2
102
0
1.9
94
0.1
0
66
1.7
2.1
2.0
0.8
101
0
2.8
92
0.1
0
55
2.9
3.6
2.0
0
102
0
2.0
94
0.1
0
71
1.5
2
R2
R3
R4
Overall
and IV have been reduced. This will necessarily lead to an increased
production, as these categories give no production. The PD measure
reflects this. Looking only at the P, it seems like the mentioned
counts has compensated for the reduced mean wind speed in II and
may be to avoid larger reduction in III. However, PD indicates that
there is no change in III.
4. Discussions
The recent paper by Ren [15] emphasizes the effects global
warming may have on the harvesting of wind for energy production. The study used data from eight global coupled oceanatmospheric climate models and a power law relationship
between global warming and usable wind energy. According to the
author, the wind energy resource may shrink in the future as
climate warms. The study shows the seriousness of this subject
matter, as highlighted by the author “.the earlier we switch to
clean energy, and thereby decrease the global climate warming
trend, the more cost-effective will be the harnessing of wind
energy”.
In our study, a different assessment method and higher resolution data was used compared to that of Ren [15]. However, our
downscaling of data from four CGCMs show results similar to that
of the aforementioned study: a slight decrease of wind power
output in most regions analysed. There are a few exceptions, as for
example for the mid North Atlantic ocean and the a small region on
the west coast of Norway e but even in these regions, the increase
represents a short percentage of today’s energy output.
The mid North Atlantic region represents the core of Atlantic
extra-tropical storms (see for example [16],). The slight increase of
wind power production in that region could represent a shift from
one wind power regime to another or a latitudinal shift of the storm
track, as suggested in several studies (see [17]), hence increasing
the wind energy potential in regions where wind speeds were
previously lower.
The increase of wind energy on a small region located on the
west coat of Norway may be caused by the complex terrain in that
region. Turbines depend on winds within the boundary layer and
surface topography add some degree of complexity [15]. A shift in
storm track location may make the wind accelerate in complexityterrain areas, thus increasing the potential for wind energy.
12.5e27.5
>27.5
Total
0.1
0
62
2.4
3.1
0
0
100
0.7
0.8
As sea ice melts under a global warming scenario, the northsouth gradient of temperature decreases. This will also affect the
static stability of the atmosphere, by decreasing it in the lower
atmosphere in mid-to-high latitudes [18]. Both the north-south
gradient of temperature and the increased static stability will
affect the number of storm tracks as well as their intensity e and
hence decreasing the available wind. This is in accordance with the
slight reductions found in our study as well as that of [15], even
though the latter was applied to China, as the author pointed out:
“.it is generic to GCMs that higher temperatures may lead to
a weaker atmospheric circulation over, not just China, but also
higher latitude regions”.
When interpreting the climate signal in these simulations, one
has to bear in mind that the percentage of deviation is not large. For
instance, natural variability increases with latitude [19], masking
the climate signal, and consequently, the results further to the
north should be used with carefulness.
5. Conclusions
A global, stretched atmosphere model (Arpege/IFS) has been
employed to simulate the atmospheric state for a control period of
30-years (1972e2001; control period). Observed SST similar the
one used in the Era40 reanalysis has been used as boundary
condition. The stretched grid had a grid mesh of about 30 km in the
Nordic waters. The wind speed from the simulation valid at the
925 hPa level was evaluated against a new reanalysis data set, Era
Interim. The control run had a bias of less than 1 ms1. For individual station points, this was mitigated by applying a biascorrection algorithm. The bias-correction showed an adjustment of
the power potential of up to about 10%.
Due to unrealistic SSTs from the CGCMs, an adjustment procedure prepared the SST forcing for the future model simulation.
Briefly described, the procedure put the relative SST changes in the
CGCM onto the Era40 SST. As a result of warming in the CGCMs, the
future sea ice cover retreats from today’s extent.
Using the same model set-up as for the control period, a fourmember ensemble for future scenarios (SRES - A1B scenario) of
wind was produced. Subsequently, the future wind power
production potential was estimated at the 100 m level. For most
offshore coastal areas, a weak reduction was found in the power
potential and a reduction of the potential of about 0e5% can be
Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008
8
I. Barstad et al. / Renewable Energy xxx (2012) 1e8
expected for the future period (2020e2049). For most areas, the
mean wind speed reduction was of the order of 1%. The reduction in
potential came mainly through a shift of wind speeds from the fullload area on the power curve (12.5e27.5 ms1) down to weaker
winds. The reduction of the unproductive very strong wind cases
(>27.5 ms1) and weak winds (<4.5 ms1) were too few to counter
the effect. When using a relative measure (future potential divided
on control potential), it turns out that the bias-correction was
redundant as systematic errors in the model cancelled out. With
the above results, we have to keep in mind that the spread of the
model realisation is large, and that the reduction in reference to this
is small.
The main results from this work may be summaries as follows:
It is expected a weak reduction (0e5% in power potential) in
future wind power potential over most of northern Europe
during the next 30e40 years. The spread between model runs
is large.
In some regions, a weak increase in the potential may be
encountered. This is highly uncertain, not only because of the
large spread in the model ensemble, but also because a small
shift in storm tracks will alter the picture completely.
New IPCC runs are under way, and these should also be evaluated in similar fashion as have been done herein. Perhaps more
models than used here should be included for the assessment,
particularly when addressing sites farther to the north having
larger natural variability.
Acknowledgement
The study has been funded by the RENERGI-program in the
Norwegian Research Council, channelled through NORCOWE
consortium.
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Please cite this article in press as: Barstad I, et al., Present and future offshore wind power potential in northern Europe based on downscaled
global climate runs with adjusted SST and sea ice cover, Renewable Energy (2012), doi:10.1016/j.renene.2012.02.008