- Email: [email protected]
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 44 (2012) 398e405
Contents lists available at SciVerse ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
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 29 February 2012
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: [email protected] (I. Barstad). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2012.02.008
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.
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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,
399
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.).
400
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
401
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
402
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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 Pmax PFij Pmax PRij P4 P Pmax PFij j¼1 P P Pmax ðn mÞ þ PRij PFij ¼ P P n$Pmax 4j ¼ 1 PFij P
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
403
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”
404
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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. References [1] Kruse H, et al. Strategic research agenda: market deployment strategy from 2008 to 2030. Brussels: TPWind; July 2008.
405
[2] Kattsov VM, Sporyshev PV. Timing of global warming in IPCC AR4 AOGCM simulations. Geophysical Research Letters 2006;33(L23707). doi:10.1029/ 2006GL027476. [3] Otterå OH, Bentsen M, Drange H, Suo L. External forcing as a metronome for Atlantic multidecadal variability. Nature Geoscience; 2010. doi:10.1038/ NGEO955. [4] Gleckler PJ, Taylor KE, Doutriaux C. Performance metrics for climate models. Journal of Geophysical Research 2008;113(D06104):20. doi:10.1029/ 2007JD008972. [5] Rummukainen M. State-of-the-art with regional climate models. Wiley Interdisciplinary Reviews: Climate Change 2010;1:82e96. doi:10.1002/ wcc.8. [6] Weatherly JW, Briegleb BP, Large WG, Maslanik JA. Sea ice and polar climate in the NCAR CSM. Journal of Climate 1998;11:1472e86. [7] Wollings T, Hoskins B, Blackburn M, Hassel D, Hodges K. Storm track sensitivity to sea surface temperature resolution in a regional atmosphere model. Climate Dynamics; 2010. doi:10.1007/s00382-009-0554-3. [8] Déqué M, Piedelievre JP. High-resolution climate simulation over Europe. Climate Dynamics 1995;11:321e39. [9] Déqué M, Dreveton C, Braun A, Cariolle D. The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling. Climate Dynamics 1994;10:249e66. [10] Uppala SM, Kållberg PW, Simmons AJ, Andrae U, da Costa Bechtold V, et al. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 2005;131:2961e3012. doi:10.1256/qj.04.176. [11] Barstad I, Sorteberg A, Flatøy F, Déqué M. Precipitation, temperature and wind in Norway - dynamical downscaling of ERA40. Climate Dynamics; 2008. doi:10.1007/s00382-008-0476-5. [12] Børresen JA. Wind Atlas for the north sea and the Norwegian sea. Oslo: Norwegian University Press and Norwegian Meteorological Institute; 1987. p. 184. [13] Simmons A, Uppala S, Dee D, Kobayashi S. ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter No.110; 2007:25e35. [14] Barstad I, Grønås S. Southwesterly flows over southern Norway e mesoscale sensitivity to large-scale wind direction and speed. Tellus 2005;57A:136e52. [15] Ren D. Effects of global warming on wind energy availability. Journal of Renewable and Sustainable Energy 2010;2:052301. [16] Mesquita MdS, Kvamstø NG, Sorteberg A, Atkinson DE. Climatological properties of summer time extra-tropical storm tracks in the northern hemisphere. Tellus A 2008;60:557e69. [17] Bader J, Mesquita MdS, Hodges KI, Miles M, Østerhus S, Keenlyside NA. Review on northern hemisphere sea ice, storminess and teleconnection patterns: observations and projected changes. Atmospheric Research; 2011. doi:10.1016/j.atmosres.2011.04.007. [18] Lim E-P, Simmonds I. Effect of tropospheric temperature change on the zonal mean circulation and SH winter extratropical cyclones. Climate Dynamics; 2008. doi:10.1007/s00382-008-0444-0. [19] Sorteberg A, Kvamstø NG. The effect of internal variability on anthropogenic climate projections. Tellus A 2006;58:565e74.
Contents lists available at SciVerse ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
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 29 February 2012
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: [email protected] (I. Barstad). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2012.02.008
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.
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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,
399
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.).
400
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
401
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
402
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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 Pmax PFij Pmax PRij P4 P Pmax PFij j¼1 P P Pmax ðn mÞ þ PRij PFij ¼ P P n$Pmax 4j ¼ 1 PFij P
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
403
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”
404
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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
I. Barstad et al. / Renewable Energy 44 (2012) 398e405
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. References [1] Kruse H, et al. Strategic research agenda: market deployment strategy from 2008 to 2030. Brussels: TPWind; July 2008.
405
[2] Kattsov VM, Sporyshev PV. Timing of global warming in IPCC AR4 AOGCM simulations. Geophysical Research Letters 2006;33(L23707). doi:10.1029/ 2006GL027476. [3] Otterå OH, Bentsen M, Drange H, Suo L. External forcing as a metronome for Atlantic multidecadal variability. Nature Geoscience; 2010. doi:10.1038/ NGEO955. [4] Gleckler PJ, Taylor KE, Doutriaux C. Performance metrics for climate models. Journal of Geophysical Research 2008;113(D06104):20. doi:10.1029/ 2007JD008972. [5] Rummukainen M. State-of-the-art with regional climate models. Wiley Interdisciplinary Reviews: Climate Change 2010;1:82e96. doi:10.1002/ wcc.8. [6] Weatherly JW, Briegleb BP, Large WG, Maslanik JA. Sea ice and polar climate in the NCAR CSM. Journal of Climate 1998;11:1472e86. [7] Wollings T, Hoskins B, Blackburn M, Hassel D, Hodges K. Storm track sensitivity to sea surface temperature resolution in a regional atmosphere model. Climate Dynamics; 2010. doi:10.1007/s00382-009-0554-3. [8] Déqué M, Piedelievre JP. High-resolution climate simulation over Europe. Climate Dynamics 1995;11:321e39. [9] Déqué M, Dreveton C, Braun A, Cariolle D. The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling. Climate Dynamics 1994;10:249e66. [10] Uppala SM, Kållberg PW, Simmons AJ, Andrae U, da Costa Bechtold V, et al. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 2005;131:2961e3012. doi:10.1256/qj.04.176. [11] Barstad I, Sorteberg A, Flatøy F, Déqué M. Precipitation, temperature and wind in Norway - dynamical downscaling of ERA40. Climate Dynamics; 2008. doi:10.1007/s00382-008-0476-5. [12] Børresen JA. Wind Atlas for the north sea and the Norwegian sea. Oslo: Norwegian University Press and Norwegian Meteorological Institute; 1987. p. 184. [13] Simmons A, Uppala S, Dee D, Kobayashi S. ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter No.110; 2007:25e35. [14] Barstad I, Grønås S. Southwesterly flows over southern Norway e mesoscale sensitivity to large-scale wind direction and speed. Tellus 2005;57A:136e52. [15] Ren D. Effects of global warming on wind energy availability. Journal of Renewable and Sustainable Energy 2010;2:052301. [16] Mesquita MdS, Kvamstø NG, Sorteberg A, Atkinson DE. Climatological properties of summer time extra-tropical storm tracks in the northern hemisphere. Tellus A 2008;60:557e69. [17] Bader J, Mesquita MdS, Hodges KI, Miles M, Østerhus S, Keenlyside NA. Review on northern hemisphere sea ice, storminess and teleconnection patterns: observations and projected changes. Atmospheric Research; 2011. doi:10.1016/j.atmosres.2011.04.007. [18] Lim E-P, Simmonds I. Effect of tropospheric temperature change on the zonal mean circulation and SH winter extratropical cyclones. Climate Dynamics; 2008. doi:10.1007/s00382-008-0444-0. [19] Sorteberg A, Kvamstø NG. The effect of internal variability on anthropogenic climate projections. Tellus A 2006;58:565e74.