Will climate change influence extreme wind speeds?
S.C. Pryor1,2, R.J. Barthelmie1,2, N.E. Claussen2, N.M. Nielsen2, E. Kjellström3 and M.
Drews4
1
College of Arts and Sciences, Indiana University, Bloomington, IN 47405 USA
Email: spryor@indiana.edu. Tel: 1-812-855-5155. Fax: 1-812-855-1661
2
Risø DTU National Laboratory for Sustainable Energy, Roskilde, Denmark
3
Rossby Centre, SMHI, Norrköping, Sweden
4
Danish Meteorological Institute, Copenhagen, Denmark
1. Introduction and objectives
There are two principal ways in which global climate change may impact the wind energy industry:

By changing the geographic distribution and/or the inter- and intra- annual variability of the
wind resource (Sailor et al. 2000, Breslow & Sailor 2002, Pryor et al. 2005a, Pryor et al.
2005b, Pryor et al. 2005d, c, 2006, Sailor et al. 2008).

By altering the external conditions (i.e. environmental context, operation and maintenance
and/or design of wind developments), including the magnitude of wind speed extremes (Pryor
et al. 2005a, Claussen et al. 2007).
Here we focus on the second issue. Specifically, our objective is to develop tools that can be applied to
quantify potential changes in extreme wind speeds under a variety of climate change scenarios, to
assess the associated uncertainties, and demonstrate the results of application of those techniques to
northern Europe.
Although extreme wind speeds are of interest in a number of contexts (including building design
(Cook 1986) and transportation safety (Payer & Kuchenhoff 2004)), the current research is being
conducted in the context of wind energy applications. Given our focus on wind energy we conform to
the Wind Turbine design criteria, which define the suitable class of wind turbines based on the 50-year
return period wind speed (U50yr). We determine extreme winds based on the Gumbel distribution:
UT 
 1   T 
ln ln 
 
   T  1 
(1)
Where: UT wind speed for a given return period (T), and  and  are the distribution parameters.
Before discussing the tools and methods used to address this question, and evaluating the results of the
analyses focused on determining the likelihood of changes in the 50-year return period wind speed
under global climate change scenarios, it is worth recalling the challenges that addressing this research
question presents. The 50-year return period wind speed is defined as the wind speed that on average
will be observed once in a 50-year period. Specific to the wind turbine design criteria, this wind speed
should be sustained for a 10-minute period. Developing adequate tools to address such questions
represents a significant challenge to climate scientists. Such rare (or extreme) events represent a
stringent challenge of model physics and parameterizations, and require application of sophisticated
statistical techniques appropriate to both describe extreme events and detect changes therein. The
results of any such analyses are almost certainly going to be associated with larger uncertainties than
characterize descriptions of the central tendency or other lower moments of the probability
distributions of geophysical parameters.
2. Methods
Because wind speeds vary on spatial scales far below those that characterize global climate models
(also known as coupled Atmosphere-Ocean general Circulation Models (AOGCMs)) (Pryor et al.
2006), two methodological (downscaling) tools are applied to generate higher resolution realizations
of the surface winds and herein to calculate the extreme wind speeds:
1. Dynamically downscaled time series of grid-cell averaged wind speeds are used from two
applications of Regional Climate Models (RCMs):
a)
Simulations conducted using the Rossby Centre Regional Coupled Atmosphere-Ocean
(RCAO) model. These RCM simulations used in this research were conducted for the
PRUDENCE project (Räisänen et al. 2004) and were undertaken at SMHI using the
Rossby Centre coupled RCM (RCAO) using lateral boundary conditions from two
General Circulation Models (one coupled AOGCM - ECHAM4/OPYC3 and one
atmosphere-only GCM - HadAM3H) and two emission scenarios (SRES A2 and B2).
Previous analyses of these wind simulations demonstrated reasonable and realistic
features as documented in reanalysis data products during the control period (1961-1990)
(Pryor et al. 2005a).
b)
Simulations conducted using the HIRHAM RCM (Deque et al. 2007, May 2008) and
lateral boundary conditions from ECHAM5/MPI-OM for the A1B SRES. These
simulations are being undertaken within the ENSEMBLES project at the Climate Center
of the Danish Meteorological Institute.
Note that for the RCAO simulations the wind speed time series are instantaneous output of the model
reported at size hour time intervals. For the HIRHAM simulations the wind speeds are the average of
six instantaneous values reported within each hour. These differences in discretization has
implications for the absolute magnitude of the return period wind speeds (Larsen & Mann 2006), but
will likely not greatly influence the relative change in extreme wind speeds that is the focus of the
current research.
Simulated time series of annual maximum wind speeds are used to compute U50yr using the method of
moments to determine  and :
2b1  U
  U max 
b1 
(2)
ln 2

max


1 n i  1 max
Ui

n i 1 n  1
(3)
(4)
The uncertainty on UT is given by:
2
 1  1.14kT  1.10kT
 (U T ) 

6n
(5)
Where n is the sample size, the frequency factor (kT) is:
kT  

6    T 
 ln ln 




    T  1  
Where γ = Euler’s constant (0.577216)
(6)
Assuming a Gaussian distribution of UT, then 95% of all realizations will lie with 1.96 of the mean,
and thus  can be used to provide 95% confidence intervals on the estimates of extreme winds with
any return period.
2. Empirical downscaling is used to develop site specific estimates of extreme wind speeds using
output from eight AOGCMs; BCCR-BCM2.0, CGCM3.1, CNRM-CM3, ECHAM5/MPI-OM, GFDLCM2.0, GISS-ModelE20/Russell, IPSL-CM4, and MRI-CGCM2.3.2. AOGCM output for two
historical periods (1982-2000 and 1961-1990) are taken from climate simulations of the twentieth
century. AOGCM output for 2046-2065 and 2081-2100 are from simulations conducted using the A2
SRES. Wind speed observations (1982-2000) at 10-m for 43 stations used to condition the transfer
functions are as in our previous research (Pryor et al. 2006). This empirical downscaling technique
develops a probability distribution of wind speeds during a specific time window rather than a time
series of wind speeds. Thus the downscaled parameters are the scale (A) and shape (k) of the Weibull
distribution:
 U k 
P(U )  1  exp    
  A  
(7)
This approach is advantageous because it avoids a focus on mean conditions, underestimation of
variance, and difficulties associated with reproducing the time structure of wind speeds. Downscaled
Weibull A and k for each time period, AOGCM and station are used to compute U50yr using eq. (1)
and:
1
k
   ln( n) 1 k 
A

(8)
  Aln( n) 1 / k
(9)
Where n is the number of independent observations
Estimates of U50yr from the future time periods are defined as being different from the historical period
(1961-1990) if they lie beyond the 95% confidence intervals derived based on propagation of the
uncertainty in A and k following uncertainties presented in our prior research (Pryor et al. 2004).
As noted above, the simulations used herein were conducted using three different SRES (IPCC 2000).
The global climate sensitivity of these scenarios is given in Table 1. The empirical downscaling is
conducted for a single SRES – the A2 scenario. The RCAO simulations are conducted for two
scenarios – A2 and B2, and the HIRHAM simulations are conducted for the A1B SRES. The A2
scenario equates to a moderate to high greenhouse gas cumulative emission for 1990 to 2100 as a
result of projected population growth (the global population increases continuously reaching 15 billion
in 2100) and fairly slow introduction of alternative technologies. This scenario results in global carbon
dioxide (CO2) emissions from industry and energy in 2100 that are almost four times the 1900 value
and by 2100 emissions from land use change are close to zero, leading to a global CO2 emission in
2100 of almost 28 GtC yr-1. The SRES B2 scenario is based on a medium population projection (the
global population reaches 10.4 billion in 2100), and development of sustainable technologies which
proceeds faster than in the A2 scenario. This results in a global CO2 emission in 2100 of less than half
the A2 scenario. Thus the B2 SRES is associated with lower global warming projections (Table 1).
The A1B and A2 SRES are somewhat similar, though the A1B SRES exhibits smaller population
growth and more balanced energy portfolio. The A1B SRES exhibits a peak in greenhouse gas
emissions in the middle of the C21st with subsequent declines, while the A2 scenario shows increasing
emissions throughout the C21st. For this reason by the end of C21st greenhouse gas emissions from
the A1B and B2 SRES are broadly similar, while those from the A2 SRES are considerably higher (by
over a factor of 2) (Nakicenovic & Swart 2000). As shown, A1B and A2 are relatively similar in terms
of their net global temperature change for the end of the century, while the B2 scenario results in
lower emissions of greenhouse gases, lower greenhouse gas concentrations and hence lower radiative
forcing and temperature increase. These differences should be kept in mind in interpreting differences
in the extreme wind analysis results.
Table 1. ‘Climate sensitivity’ i.e. Projected global average surface warming at the end of the 21st
century for the different SRES (adapted from the (IPCC 2007)).
SRES
B1
A1B
A2
Difference in global mean temperature: 2080-99 v 1980-99 (ºC)
0.6
2.8
3.4
3. Dynamically downscaled extreme wind speeds
As one example of the extreme wind estimates from the dynamically downscale wind speed time
series, we will focus here on output from the RCAO, and analyses focused on quantifying the relative
importance of the lateral boundary conditions and emission scenario in determining the simulation of
extreme winds, and particularly the change in extreme wind speeds.
RCAO simulations for the end of the C21st conducted using boundary conditions from HadAM3
imply little change in U50yr (Table 2). Note that for a grid cell to be defined as showing a significant
change in U50yr the simulation for the future period must lie beyond the 95% confidence intervals
defined for the historical period (1961-1990). Estimated U50yr for the future period (and both SRES)
generally lie within the 95% confidence intervals derived for the control period simulations (O(4-15%)
of the U50yr in 1961-1990). This is also the case for simulations conducted using lateral boundary
conditions from ECHAM4/OPYC3, although in those simulations U50yr for the future period under
either SRES show increases particularly in the southwest of the study domain (Figure 1).
The choice of emission scenarios (SRES) appears to have little influence on the d(U50yr) in either set of
simulations (Table 2), and certainly the differences with SRES appear small relative to differences in
the simulations conducted with the two AOGCMs.
Table 2. Fraction of grid cells (in %) in the RCAO simulations that exhibit a significant increase,
decrease or no change in U50yr for the 4 future simulations (2071-2100) relative to 1961-1990.
Nesting GCM and SRES
ECHAM4: A2
ECHAM4: B2
HadAM3: A2
HadAM3: B2
Declines
0.1
0.1
6.0
1.8
No change
73.2
72.9
90.1
95.8
Increases
26.7
27.0
3.9
2.4
Figure 1. Magnitude (in %) of statistically significant differences in U50yr computed by grid cell for
2071-2100 v 1961-1990 for simulations nested within (above) ECHAM4/OPYC3 and (below)
HadAM3 for the A2 SRES.
4. Empirically downscaled extreme wind speeds
The 50-year return period wind speed (U50yr) for each observational station were computed from the
downscaled Weibull A and k parameters for the 8 AOGCMs for three time periods; 1961-1990, 20462065 and 2081-2100. For the 8 AOGCMs presented and the 43 stations, the range of downscaled U50yr
for 1961-1990 lie within ±4% of the mean U50yr and 95% lie within ±2%, so the downscaling results
for extreme winds from the Weibull A and k parameters are relatively consistent for the historical
period (Figure 2).
The ensemble mean U50yr for 1961-1990 and the ensemble average change between 1961-1990 v
2081-2100 are shown in Figure 2 and Table 3. In the ensemble average there are a large number of
stations that exhibit no change in U50yr (26 of 43 stations for 2046-2065, and 14 of 43 for 2081-2100
exhibit change of less than 1% relative to 1961-1990), this is also true for the majority of individually
downscaled AOGCMs. However, of the stations that exhibit a change in the 50-year return period
wind speed (|d(U50yr)|) >1% between 1961-1990 and the future period, the majority exhibit increases.
Seventeen of 43 stations (i.e. 40%) exhibit an ensemble average increase in U50yr of 1-5% in 20462065 relative to 1961-1990. Twenty-seven of 43 stations (i.e. 63%) exhibit an ensemble average
increase in U50yr of 1-10% in 2081-2100 relative to 1961-1990. This bias towards increased extreme
wind speeds is also exhibited in results from the majority of individually downscaled AOGCMs.
Consistent with the results from the dynamically downscaled wind speeds, the largest magnitude
changes in extreme wind speeds are focused on the southwest of the study domain. However, for the
majority of stations considered, results from at least one downscaled AOGCM exhibit declining U50yr.
A further key caveat that should be noted is that application of the Weibull method to compute U50yr is
subject to some uncertainty related to the accuracy of the Weibull fit. The 95% confidence intervals
computed for the 1961-1990 period are -12% to +10%. Downscaling of only two AOGCMs exhibit
any single station for which the difference in U50yr in the historical period (1961-1990) and future time
periods (2046-2065 or 2081-2100) exceeds the 95% confidence intervals computed for 1961-1990.
Figure 2. (a) U50yr at 10-m empirically downscaled for 1961-1990 (ms-1), (b) the ensemble average
change (%) in U50yr computed from the 8 AOGCMs for 2081-2100 relative to 1961-1990 (if positive
the future period exhibits a higher U50yr than 1961-1990).
Table 3. Ensemble average change in 50-year return period wind speed (d(U50yr)) from the empirically
downscaled Weibull A and k expressed in terms of the number of stations from which U50yr estimates
show a change of the specified magnitude (in %) between the future period and 1961-1990 (if positive
the future period exhibits a higher U50yr than the historical period).
% change 2046-65 v 1961-90 2081-2100 v 1961-90
-10 to -5
0
0
-5 to 0
6
7
0 to 5
37
33
5 to 10
0
3
5. Concluding remarks
U50yr computed using both dynamically and empirically downscaled wind speeds exhibit some weak
evidence for an increase in extreme wind speeds at sites in northern Europe during the middle and end
of the C21st relative to 1961-1990. The spatial distribution of simulated changes in U50yr between the
RCAO and the empirical downscaling are to some degree consistent – showing maximal changes in
the southwest of the study domain, though the relative changes are of larger magnitude from the
RCAO in simulations conducted with lateral boundaries from ECHAM4/OPYC3. The increase in U50yr
from the ECHAM4/OPYC3 nested runs exceeds 10% for many grid cells, but the majority of grid
cells show no change beyond the 95% confidence intervals for 1961-1990. This tendency towards ‘no
statistically significant change’ is even more marked in HadAM3 nested RCAO simulations.
Uncertainty calculations presented herein illustrate the challenge of estimating climate change impacts
on geophysical extremes, but also provide critical context for interpreting the results of such analyses.
6. Acknowledgements
Financial support was supplied by the National Science Foundation (grants # 0618364 & 0647868),
Nordic Energy Research and the energy sector in the Nordic countries.
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