Fourth Study Conference on BALTEX Scala Cinema Gudhjem

o Temporal autocorrelation of daily average wind speeds
at coincident grid cells (Figure 1).
o Comparisons of wind speed probability distributions
for individual grid cells and for all common grid cells.
On average, 10 m data from the HadCM3 correctly captures
the spatial pattern of mean wind speeds but HadCM3
simulated wind speeds are typically lower in absolute
magnitude particularly relative to the ECMWF data in
Norway, and over the interior of the Baltic Sea (Figure 3).
NCEP/NCAR
ECMWF
HadCM3
U10 (m/s)
1 to 2.5
2.5 to 4
4 to 5.5
5.5 to 7
7 to 9
Figure 3. Mean daily mean 10 m wind speed from
HadCM3 and the two reanalysis data sets for the period
1990-2001.
Pearson correlation coefficients for daily average wind
speeds at the common grid cells (Figure 1) show the spatial
decay of associations from all three data sets is (i) largest for
the northern grids and (ii) stronger for changing latitude than
longitude. It may be notable that both reanalysis data sets
exhibit small negative correlations between easterly and
westerly grid cells that may be related to storm tracking, but
this feature is not observed in HadCM3. HadCM3 exhibits
higher persistence of wind speeds particularly in the west of
the region at lags beyond one day, which may indicate
HadCM3 underestimates the variance of wind speeds.
ECDFs for daily average wind speeds for 1990-2001
indicate HadCM3 shows relatively good correspondence to
the reanalysis data for the lower percentiles (low wind
speeds), but underestimates the higher percentiles. This
analysis thus offers supporting evidence that HadCM3
underestimates the variability of wind speeds, and most
specifically the upper tail of the wind speed distribution.
Further work is required to clarify whether the discrepancies
between the HadCM3 and reanalysis data are due to the
differing spatial resolution of the models and data archives
or a dynamical cause (e.g. tracking or intensity of synoptic
scale phenomena). As a first analysis of the importance of
the spatial grid resolution, ECDFs for data from all common
grid cells were computed. The results indicate the
correspondence of wind speeds improves with scale, but
over the domain as a whole HadCM3 appears to
underestimate wind speeds across the higher percentiles of
the probability distribution indicating a potential systematic
bias in HadCM3 that may reflect spatial scale or a weakness
in simulation of pressure gradients.
5. Prognostic wind indices based on HadCM3
There is some evidence of an offset in mean absolute wind
speeds between HadCM3 and those in the reanalysis data set
- 132 -
for 1990-2001. However, the spatial variability of wind
speeds and some degree of the variability around the mean
at individual grid cells do seem to be reproduced by
HadCM3. Thus it is deemed reasonable to use the wind
index to calculate prognostic wind energy estimates using
(1) and the daily wind speeds from HadCM3 to provide a
first assessment of likely changes in wind energy
availability (Table 1). There is evidence of a weak
downward trend in the wind index in grid E during C21st
but a tendency towards increased wind indices in grid cell
L which may imply more northerly tracking of synoptic
systems possibly in response to a change in the NAO. The
mean wind index for grid cell E for the C21st does not
differ substantially from the mean value of 92-94 % for
the wind indices for 1958-2001 calculated from the
reanalysis data sets. The inference is that HadCM3, like
the reanalysis data sets, indicates the 1990-2001 period
exhibited atypically high wind speeds over Denmark in
both a historical and prognostic context. It is further
inferred that the coming decades will exhibit a wind
energy climatology for Denmark which is very similar to
that which characterized the latter half of the C20th, but
that the high wind speeds of the 1990s will likely not be
sustained in the C21st. The NE of the Baltic shows
differing temporal variability in both the historical and
prognostic records. Figure 2 indicates the later portion of
the 1980s and the 1990s were also atypically windy in this
region of the Baltic but the preliminary prognostic wind
index for grid cell L implies the C21st will be
characterized by a larger average wind energy resource to
the 1990-2001 and 1958-2001 periods. Further details and
caveats will be given in the presentation.
Table 1.Annual wind indices from 1958-2001 (reanalysis
data) and 1990-2099 (HadCM3) for grid cells E, I and L.
Normalization period Mean and (standard deviation)
1990-2001
wind index
Grid cell→ E I L
NCEP/NCAR: 1958-01 93 (11) 94 (12) 92 (13)
ECMWF: 1958-01 92 (12) 94 (12) 94 (13)
HadCM3: 1990-2099 93 (12) 99 (11) 103 (11)
6. Acknowledgments
Financial support was provided by 'Impacts of Climate
Change on Renewable Energy Sources and their Role in
the Energy System: 2003-2006' project funded by Nordic
Energy Research (Nordic Council of Ministers) and the
'Storpark' project funded by the Danish PSO-F&U
program (PSO 101991(FU2104)). JTS also acknowledges
a Dissertation Year Research Fellowship from IU.
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