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271
Climate change impacts on extreme wind speeds
S.C. Pryor 1,2 , R.J. Barthelmie 1,2 , N.E. Claussen 2 , N.M. Nielsen 2 , E. Kjellström 3 and M. Drews 4
1 Indiana University, Bloomington, IN 47405 USA spryor@indiana.edu
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
Our objective is to quantify potential changes in extreme
wind speeds across northern Europe under a variety of
climate change scenarios. For wind energy applications 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 (U 50yr ). We determine extreme
winds based on the Gumbel distribution so:
−1
⎡ ⎛ T ⎞⎤
(1)
U T = ln⎢ln⎜
⎟⎥
+ β
α ⎣ ⎝ T −1⎠⎦
U T wind speed for a given return period (T), and α and β are
the distribution parameters.
2. Methods
Two downscaling tools are applied to AOGCM output to
generate higher resolution realizations of surface winds from
which we 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 RCAO lateral boundary conditions from
two General Circulation Models (ECHAM4/OPYC3 and
HadAM3H) and two emission scenarios (SRES A2 and B2)
(Pryor et al. 2005). (b) Simulations conducted using the
HIRHAM RCM and lateral boundary conditions from
ECHAM5/MPI-OM for the A1B SRES. Simulated time
series of annual maximum wind speeds are used to compute
U 50yr using the method of moments to determine α and β:
ln 2
(2)
α =
max
2b1
−U
γ
β = max
(3)
U −
α
1 n i −1
(4)
max
b1
= ∑ Ui
n i=
1n
− 1
The uncertainty on U T is given by:
2
(5)
π 1+
1.14kT
+ 1.10kT
σ ( UT
) = α 6n
Where n is the sample size, the frequency factor (k T ) is:
6 ⎛ ⎡
⎞
⎜
⎛ T ⎞⎤
(6)
k T = − ln − ⎟
⎢ln⎜
⎟⎥
γ
π ⎝ ⎣ ⎝ T −1⎠⎦
⎠
Where γ = Euler’s constant (0.577216)
Assuming a Gaussian distribution of U T , then 95% of all
realizations will lie with ±1.96σ of the mean, and thus σ 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, GFDL-CM2.0, 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 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:
⎡ k ⎤
(7)
⎛ U ⎞
P(
U ) = 1−
exp⎢−
⎜ ⎟ ⎥
⎢
⎣
⎝ A ⎠ ⎥
⎦
This approach is advantageous in the current context
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 U 50yr using eq.
(1) and:
⎛
( )
⎟ ⎞
=
k ⎜
− n
1
(8)
1
α ln( ) k
A ⎝ ⎠
1/
β = A( ln( n)
) k
(9)
Where n is the number of independent observations
For the 8 AOGCMs presented and the 43 stations, the
range of downscaled U 50yr for 1961-1990 lie within ±4%
of the mean U 50yr 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. Estimates 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.
3. Dynamically downscaled extreme wind
speeds
RCAO simulations for the end of the C21st conducted
using boundary conditions from HadAM3 imply little
change in U 50yr (Table 1). Estimated U 50yr 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 U 50yr in 1961-1990). This is
also the case for simulations conducted using lateral
boundary conditions from ECHAM4/OPYC3, although in
those simulations U 50yr for the future period under either
SRES show increases particularly in the southwest of the
study domain (Figure 1). The choice of SRES appears to
have little influence on the d(U 50yr ) in either set of runs.
Table 1. Fraction of grid cells (in %) that exhibit a
significant increase, decrease or no change for the 4 future
simulations (2071-2100) relative to 1961-1990.
Declines No change Increases
ECHAM4: A2 0.1 73.2 26.7
ECHAM4: B2 0.1 72.9 27.0
HadAM3: A2 6.0 90.1 3.9
HadAM3: B2 1.8 95.8 2.4