Download Wind Climatology, Climate Change, and Wind Energy

Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind Climatology, Climate Change, and Wind Energy
Scott Greene1*, Mark Morrissey2 and Sara E. Johnson3
1
Department of Geography and Oklahoma Wind Power Initiative, University of Oklahoma
School of Meteorology, University of Oklahoma
3
University of Indiana
2
Abstract
This article presents an overview of research in wind climatology and wind energy. Specifically,
patterns and trends in wind speed are examined. This article looks at both past variability and
trends and projected future patterns of the wind resource in various locations across the globe. A
review of the historical and current research and results in each one of these areas is presented. In
addition, results from research into one of these areas (e.g., analysis of wind speed patterns) are
presented as an example within the overall broader review to illustrate current and potential future
avenues of investigation. In the past, there has been disconnect between the research being conducted on wind speed climatology and the efforts of the wind industry to understand changes in
the resource, and this article represents an attempt to summarize the current status of the research,
and also suggest possible avenues of future research.
Introduction
‘The renewable energy era has begun’. This quote (Sims 2004) summarizes a detailed
analysis and comparison of the costs of renewable energy systems with fossil fuel-derived
energy services (see also Islas et al. 2004; Sims et al. 2003). The importance of renewable
energy for sustainable development, energy security, diversification of energy supply, and
environmental preservation has also been increasingly recognized. The wind industry has
been growing at a tremendous rate over the past decade, with installed capacity worldwide increasing over 1000% (American Wind Energy Association, 2010). There is widespread recognition at the national and international levels of the importance of renewable
energy technologies. Governments around the world have ambitious plans to help facilitate this expansion, including, for example, the report from the US Department of
Energy or US DoE outlining a plan for the country to receive 20% of its electricity from
wind energy by 2030 (US DoE, 2008). However, there has not been a concomitant
increase in the awareness of the impacts that any spatial and temporal trends in the
resource (e.g., in the wind speeds themselves) may have on long-term production, use,
and implementation of renewable energy and renewable energy policy. Thus, it is the
purpose of this article to provide an overview and summary of research on wind climate,
wind variability, and wind energy and to examine their potential impacts on this expanding
field of interest. This is designed as a complement to Barthelmie (2007) who provided an
overview of trends and status of the wind industry. Thus, rather than reproduce an analysis
of the industry status, this article will examine the key themes in the relationship between
wind climate research and wind energy. As will be described in more detail next, the
main topics under examination are: (i) examination of changes in patterns and trends in
wind speed and wind power density (WPD); and (ii) potential implications of climate
change on the wind resource of the future.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Wind climatology and wind energy
1593
Past and Present Wind Trends
Programs such as the DoE 20% vision (US DoE, 2008) and wind farm developers typically assume a stationary pattern in the wind resource to determine anything from development of policy to the economics of a wind farm. However, climate is variable, and
therefore analysis of any changes in the wind speeds over time needs to be performed, as
any changes can have substantial impact on wind farm energy production (Palutikof
1987). Thus, any trends, or non-stationarities, in the resource need to be investigated,
and any such spatial and temporal changes to the wind resource and potential electricity
output could then quantified. For example, there is often a wide disparity between the
wind energy generated in different months (Archer and Jacobson 2003; Baker et al.
1990). Thus, the overall yearly wind energy may be almost exclusively generated in just a
few months during the year. Any changes in the local seasonal wind speeds at regional or
local scales, as well as on annual or seasonal time-frames can have a significant impact and
needs to be examined. This could then be, for example, input into an economic analysis
to determine the financial impacts of the variability.
There has been a range of studies investigating wind speed variability. Many have
focused on large-scale circulation and seasonal patterns, e.g., wind patterns associated with
the positive and negative phases of the El Nino ⁄ Southern Oscillation Index (Enloe et al.
2003), or Pacific ⁄ North American Oscillation (Klink 2007) or with mesoscale systems like
the Low-Level Jet (Greene et al. 2009; Lundquist and Mirocha 2008; Storm et al. 2008);
or with small local systems where wind flow patterns may result from urban development
(Schwartz and Elliott 2006). However, this review will focus on those articles that specifically examine long-term changes in patterns or trends in wind speeds (or WPD). Because
long-term, consistently geographically spaced wind speed records do not exist at many
locations, some of the current research has examined the model output from the North
American Regional Reanalysis (NARR; Mesinger 2006), the European Center for Medium-Range Weather Forecasts reanalysis data (ECMWF; Uppala et al. 2005), or other
similar modeling efforts.
There has been research that has examined methods to reconstruct long-term wind
records. These approaches have focused on the techniques themselves, rather than the
analysis of any long-term trends. For example, Palutikof et al. (1992) developed an
approach to examine such long-term variability in the United Kingdom using a technique
that reconstructed wind speeds. They used a data set consisting of gridded mean sea level
pressure from 1881 to the present. Their effort was not aimed at analyzing trends in the
data, but rather to illustrate how to best reproduce or model long-term wind measurements, and the impact of inter-annual variability. For example, they showed that the
annual mean varied from about 5 to 7 m ⁄ s, which would correspond to an almost 50%
change in electrical output. Similarly, Dukes and Palutikof (1995) were able to reconstruct a series of hourly speeds and gusts for locations in the British Isles to determine the
impact of extreme wind speed events. Using a combined one-step Markov chain model
and a modified form of Monte Carlo analysis, they were able to extrapolate a synthesized
series to determine the return period of extreme events on the order of hundreds to
thousands of years. The aim of their article was to develop an approach to analysis longterm wind variability, rather than actually examine the variability itself. They conclude
that, ‘results from the Markov model may be more realistic’ than a traditional extreme
event analysis. The development and estimation of the wind power resource requires an
accurate fit of the probability density function of the wind speeds and WPD (Manwell
et al. 2002; Petersen et al. 1997). It has been shown that improved and, more specifically,
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1594 Wind climatology and wind energy
direct, fits to the WPD probability density function can dramatically improve the overall
understanding of the wind resource (Morrissey et al. 2010). As the WPD is a function of
the cube of the velocity of the wind, it is critical to have the power curve fit most
accurately at the high end of the wind speed distribution. Thus, methods like the one
outlined in Dukes and Palutikof (1995), and the ability to reproduce or generate estimates
of the return time of high-speed ⁄ power events are important to the improved understanding of the overall wind resource.
As discussed before, there have been articles that have examined patterns or trends in
the wind resource in several different geographical locations. Klink (1999, 2002) noted a
decrease in the annual mean daily wind speeds across the northern US plains. However,
results (Klink 1999) also suggested a more complex pattern than a simple decrease, as
there appeared to be increased maximum monthly wind speeds, in addition to an overall
reduction. Keimig and Bradley (2002) showed that for a majority of stations in their analysis
across western Canada and Alaska there has been a decrease in wind speeds. Pryor
et al. (2005a) evaluated wind speeds and wind energy output over a 30-year period
(1961–1990) across northern Europe. This article used the Weibull distribution parameter
to model the wind probability density function (PDF). They note an increase in wind
speeds over northern Europe (see also Pryor and Barthelmie 2003). In a similar study, also
using a Weibull distribution Garcia-Bustamante et al. (2008) examined monthly wind
energy trends over a 5-year period in Spain. They noted no specific trend in the data,
although such a short time period does not lend itself to long-term trend analysis. However, they did point out that that energy calculations using a Weibull fit results in an
underestimation of total wind energy.
Using reanalysis data, Najac et al. (2009), examined 10-m wind speed patterns in
France, using data from 1971 to 2002. They note a decrease in wind speed, especially in
the Mediterranean area, and attempted to link this to changes in weather types. For
example, they found that for Southeast France in the cold season, there was a decrease in
the mean 10-m wind speed and in the number of windy days, and this could be linked
to changes in synoptic-scale disturbances. Specifically, they theorize that the synoptic
patterns over the Atlantic and Mediterranean Sea are changing, and that this has resulted
in the noted changes in the speed and duration of wind patterns.
Many other studies have also pointed out a stilling of the wind in different locations
across the globe. For example, Pirazzoli and Tomasin (1999, 2003) reported decreasing
winds, especially easterly winds, in the Mediterranean and Adriatic Areas. A decrease in
winds has been reported elsewhere by Tuller (2004), who noted a decrease in mean
annual and winter wind speeds in British Columbia. This study also reported an increase
in the percentage of calm wind days and a decrease in the number of high wind speed
observations. McVicar et al. (2008) reported a decrease in over 88% of the stations over
land in Australia for the period 1975–2006. Over the continent, the averaged trend for
1975–2006 was computed to be )0.009 m ⁄ s ⁄ a. A more recent study (McVicar et al.
2010) compared the patterns of temporal wind speed changes at different mountain locations (China and Switzerland). For these locations, the noted a decrease in winds, and
also a strong seasonal component, with the trends showing a strong topographical component as well, as the patterns ranged, from approximately )0.025 m ⁄ s per kilometer per
year in winter to approximately )0.005 m ⁄ s per kilometer per year in winter. As they
report, wind speeds ‘has declined more rapidly at higher than lower elevations’. In a similar study over China, Jiang et al. (2010) analyzed wind speed changes in China over
1956–2004. They noted a broad decrease in annual wind speed, and also in days of
strong wind and in maximum wind. They also found small increases in some locations,
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
1595
including the Tibetan Plateau and Guangxi. A study of wind patterns from the Czech
Republic (Brazdil 2008) identified a statistically significant reduction in mean wind
speeds in all months, seasons, and annual values over the time period of 1961–2005.
In one of the few studies to examine wind speeds closer to hub heights, Schwartz and
Elliot (2005) analyzed tall towers for selected stations in the central plains. While they did
not report any specific wind trends, they did highlight areas of potential future research
that might be important to the wind climatology research community. That included, for
example, results that indicate that for some locations (e.g., Kansas), the strength of the
Low-Level Jet seems to have the largest impact on determining wind resources (see also
Greene et al. 2009). In a similar analysis of tall tower data, Klink (2007) examined spatial
coherence between average monthly wind speeds at 70-m turbine heights and large-scale
atmospheric circulation patterns at sites around Minnesota. Although no statistically significant long-term trends were reported, Klink found that a relationship between wind
speed and large scale indices (e.g., PNA, ENSO) could be drawn and then applied to
climate change models in to assess future patterns for wind variability at a given location.
Finally, more recently, Pryor et al. (2009) have examined wind speed trends over the
contiguous USA. They note an ‘overwhelming dominance of trends toward declining
values’ for the median values and for annual wind speeds. This fits with many of the articles in different locations across the globe, as discussed before. Pryor et al. (2009), do,
however, point out a wide range in the spatial patterns of these values, and note that the
patterns are not uniformly consistent between the different models and reanalysis data that
they used. They also state that,
Given the importance of the wind energy industry to meeting Federal and State mandates for
increased use of renewable energy supplies and the impact of changing wind regimes on a variety of other industries and physical processes, further research on wind climate variability and
evolution is required.
Figure 1 summarizes the existing refereed research into geographical location of wind
trends. This figure summarizes all of the articles discussed before, and shows the approximate locations of the different studies, as well as the trends identified in each location.
As some of the articles cover a wide area, the geographic indicators on the map are
meant as approximate, rather than specific, locations to show the general patterns. Areas
on the map left blank indicate those areas where large-scale, detailed wind trend and
pattern analysis have not been performed. The most striking feature is that, as noted in
the previous discussion, there is an overwhelming preponderance of decreasing values.
The explanation for the trends varies spatially, as local geography and topography can
exercise a significant impact on the local patterns. Some of the explanation centers on
changes in synoptic-scale weather patterns (e.g., Najac et al. 2009). As Jiang et al.
(2010) noted, one main reason for the declining trends is that ‘under the background of
global warming, the contrasts of the sea level pressure, and near-surface temperature … have become significantly smaller’. This is also found in other areas, where
large-scale decreases in the pressure gradient are at least partially responsible for the
decreasing wind speeds. The widespread stilling reported across much of the globe
above could have noticeable impacts on the wind industry if the patterns continue into
the future. Specifically, such reductions in wind speed and the associated wind energy
could result in significantly longer times for the companies to recoup their initial costs
associated with instillation and development of the wind farm. It should also be noted
that this figure shows there is room for significant additional analysis in many locations
across the globe.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1596 Wind climatology and wind energy
Changes in WPD and Potential Electrical Output over Oklahoma
Wind speed and potential electrical production are not the same, as the WPD is a function of the cube of the wind speed. While many of the studies described above may be
useful in assessing overall trends in wind speed, it is the WPD that is of most interest to
utilities and policy makers. The WPD is required for the estimation of power potential
from wind turbines. WPD (see Li and Li 2005), can be expressed as:
Z 1
1 3
Watts
WPD ¼
;
ð1Þ
qw f ðwÞ dw
m2
0 2
where w is the wind speed, q is the air density, and f(w) is the wind speed probability
density function.
The turbine power output depends on the power curve of the specific turbine used in
an individual wind farm. Thus, if one truly wants to examine the impact of climatic variability and change on electricity, one should examine trends in estimated power production. As an illustration of this, data from the NARR was analyzed to illustrate how
trends in estimated power production can be produced (data obtained from: http://
wwwt.emc.ncep.noaa.gov/mmb/rreanl). The NARR data have a temporal resolution of
3 h and a spatial resolution of 32 km. The WPD was computed from NARR wind, temperature, pressure, and specific humidity data over Oklahoma and regions of adjacent
states for the years 1979–2008. Thus, using the previous equation, WPD was calculated
explicitly (e.g., using the summation of all the available 3-hourly observations, rather than
an approximation of the WPD PDF). Power curves for three turbines (GE 1.5 MW,
Vestas V90 2.0MW, and Enercon E70 2.3MW) were modeled using natural cubic
splines, which were fit to publicly available plots and tables of turbine outputs (see, e.g.,
http://www.gepower.com/prod_serv/products/wind_turbines/en/15mw/index.htm as an
illustration of the power curves used). Using NARR 30-m wind speeds and applying a
Fig. 1. Summary of reported past and present wind speed trends.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
1597
power-law approximation to hub height (e.g., 80 m for the GE 1.5 turbine), and also
adjusting for air density and turbine hub-heights, these power curves were used to simulate total energy production of each turbine type for each year of the study period by grid
point. Trends in simulated annual energy production (megawatt-hours per decade) at
each grid point were then computed for each turbine type using least squares regression.
Figure 2a–c illustrates the results of this analysis. Although the patterns are generally the
same (e.g., a general overall increase over the 30-year period; higher values in the west,
and lower values in the southeast), there are noticeable differences in the magnitude of
the trends. This is due in part to the differences in the power curves for each of the turbines. This is the type of information that would be most beneficial to policymakers and
private stakeholders. Thus, an analysis of wind speeds only would show the same increase
for each type of turbine, but for information that is more relevant to a developer or utility operator, the specific turbine power curves should be included in any analysis. If
researchers wish to make their findings more relevant to the wind industry, inclusion of
patterns associated with the specific wind power output from different turbines should be
included in any future studies of changes in wind patterns.
Wind Energy and Climate Change
Changes in the global climate have been reported over the past several decades, including, for example, some of the wind speed changes described before. A changing climate
can alter large-scale atmospheric dynamics that in turn will impact the radiation balance
and wind patterns. Recent findings from the Intergovernmental Panel on Climate
Change or IPCC and others have shown that greenhouse gas-induced climate change is
likely to significantly alter climate patterns in the future (IPCC, 2007). For example,
climate change is predicted to affect synoptic and regional weather patterns, which would
cause associated changes in wind speed and variability. Therefore, there is a need to
examine climate change scenarios to determine potential changes in wind speed, and thus
wind power. Wind power facilities typically operate on the scale of decades, so understanding any potential vulnerabilities related to climate variability is critical for siting such
facilities.
An important aspect of using global climate model (GCM) data for these types of wind
energy-related studies is appropriate downscaling. Downscaling, or translation across scales,
refers to a set of techniques that relate local- and regional-scale climate variables to the larger-scale atmospheric forcing. The downscaling approach was developed specifically to
address present needs in global environmental change research, and the need for more
detailed temporal and spatial information from climate models (Hewitson and Crane 1996).
Dynamic downscaling utilizes a numerical model to reduce a large-scale climate model to a
finer resolution. Statistical downscaling applies a transfer function that is created from an
association between the scales, or it may be based on a relationship between GCM output
of current conditions and surface observations. Statistical downscaling may be done fairly
efficiently, provided there is a sufficient amount of data for a specific area. Dynamic downscaling does not depend on availability of data for a region, but does require a strong relationship between predictor and predictand (Pryor et al. 2005b,c). Many approaches to
statistical downscaling have been applied to GCM output (e.g., Bernardin et al. 2009; Cayan et al. 2008; Hayhoe et al. 2008; Sailor et al. 1999; Vrac et al. 2007).
There have been different approaches specifically designed to examine downscaling
from GCM output to study wind patters. One approach, taken by Bernardin et al.
(2009), is to apply a Lagrangian stochastic model developed by Pope (1994). This
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1598 Wind climatology and wind energy
approach can result in improved characterization of various statistical moments of the
wind. Bernardin et al. (2009) determined that this type of downscaling can be effective in
modeling wind variability. Similarly, Michelangeli et al. (2009) used a probabilistic
approach to examine and downscale wind intensity anomalies for France. However, as
illustrated by De Rooy and Kok (2004), there can be errors in the development of
downscaled estimates if the resolution on the model output and actual data do not match.
The difference in statistics and error characteristics in different model sizes needs to be
accounted for in any downscaling exercise. Such errors can be removed through careful
(a)
100°W
95°W
35°N
35°N
MW-h per decade
250 – 200
100°W
200 – 150
150 – 100
95°W
100 – 50
50 – 0
0 – – 50
E70 1.5 MW Wind Turbine
(b)
100°W
95°W
35°N
35°N
MW-h per decade
200 – 150
100°W
150 – 100
95°W
100 – 50
50 – 0
0 – – 50
GE 1.5 MW Wind Turbine
Fig. 2. Trends in estimated wind energy production over Oklahoma (1979–2008). (a) Energy production trend for
Enercon E70 turbine; (b) energy production trend for GE 1.5 turbine;
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
(c)
100°W
1599
95°W
35°N
35°N
MW-h per decade
300 – 250
250 – 200
100°W
200 – 150
95°W
150 – 100
100 – 50
50 – 0
0 – – 50
V90- 2.0 MW Wind Turbine
Fig. 2. (Continued) (c) energy production trend for Vestas V90 turbine.
statistical adjustments to the GCM output using locally available data, thus future GCM
data can provide useful information as to the projections of a future climate change on
wind speeds and wind characteristics. An overview of the research examining the GCMestimated impacts of climate change on wind speeds is discussed next, and then summarized visually in Figure 3.
Sailor et al. (2008) examined impacts of climate change on wind speed and WPD in the
northwest United States. Their results showed that summertime wind speeds in the northwest may decrease by 5–10% over the next 50–100 years, while wintertime wind speeds
will show little or no change, or perhaps a slight increase. Recall that the WPD is a
function of the cube of wind speed, so that when these changes were projected to typical
turbine hub heights, the impact of climate change on wind power may be as high as a
40% decrease. Using the Hadley Centre general circulation model, Segal et al. (2001)
found that most of the USA, especially in the north-central USA and the western mountainous region is projected to show a decrease in daily WPD. The wind power availability decrease varied by 0–30%. In some locations in the southern and northwestern USA,
a slight increase is projected. Other locations, such as northern Texas and western Oklahoma, show either no pattern, or mixed results. Winter months had a moderate increase
in the northeast USA, and also for most months across the south central plains. In a similar study, Breslow and Sailor (2002) examined the impacts of climate change on wind
speeds and power across the continental USA. They used GCM output from the Canaª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1600 Wind climatology and wind energy
Fig. 3. Summary of estimated future wind speed trends.
dian Climate Center (CCC) and the Hadley Center to determine variability in seasonal
mean wind magnitude. The models were generally consistent in predicting that the USA
will see reduced wind speeds of 1.0–3.2% in the next 50 years, and 1.4–4.5% over the
next 100 years. For summer and fall months the CCC model suggested a 1 m ⁄ s or 15%
decrease in wind speeds by the end of the century. These changes, however, do show
some seasonal variability, as spring is projected to show a slight increase while the wind
speeds in fall and summer are projected to decrease. Breslow and Sailor (2002), however,
suggest that there is a great amount of uncertainty regarding these future estimates. Segal
et al. 2001 also note that these efforts should be considered, in their words, an ‘exploration’ because of the large uncertainties in the GCM model estimates.
In other locations across the globe, Najac et al. (2009) used linear regression downscaling in their examination of winds in France for projected model output out to the end of
the twenty-first century. Their results noted projected lower wind speeds in winter. They
also examined extreme wind days, and noted a projected decrease near the Mediterranean
Sea and a slight increase in northwestern France. They also noted decreases at most
stations during the summer months toward the end of the century. A slight increase,
however, is possible for the winds over western Europe (Pryor et al. 2005b,c). Beniston
et al. (2007) examined how patterns in wind storms, along with a range of other meteorological variables, will change between present (1961–1990) and future (2071–2100).
This analysis used present data and diagnostics combined with regional climate model
simulations. Results showed that for winter storms extreme wind speeds increased, except
for some locations near the Alps. Directional patterns also are projected to shifts, and
these extreme winds are estimated to become more north-westerly.
In a combined analysis of past and future wind speed trends, Walter (2006) produced a
1 · 1 KM resolution data of monthly wind speeds over Germany from 1951 to 2001.
This useful dataset was produced, using a so-called ‘relative altitude’ scheme, interpolated
using a simple Inverse Distance Weighting approach and retransformed to the actual
topography. Over the entire period, Walter finds patterns similar to those observed elsewhere, of a stilling, or reduction in long-term wind speeds, with a linear trend of
)0.05 m ⁄ s. Walter (2006) also used regional climate models (REMO, MM5, and CLM)
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
1601
to develop projections of wind speeds for the future. In this case, he produced estimates
for the time period 2070–2099. Each model showed significant changes in wind speed.
This increase was most pronounced over the Baltic Sea and the North Sea. Seasonally,
the pattern is for an increase over winter and a decrease during summer. For the Baltic
region, the change is up to an increase of 1.0 m ⁄ s in mean wind speeds. Over other locations, such as portions of the Mediterranean are projected to have a similar magnitude
decrease for the 2070–2099 period. In one of the only articles to examine the potential
changes in the off-shore wind resource, Harrison et al. 2008 looked at the projections for
the off-shore areas of the United Kingdom using series of regional climate scenarios.
They note a complex pattern and seasonally varying pattern, with, in general, increases
for winder and decreases in summer.
More recently, Pryor and Barthelmie (2010) performed a meta-analysis of future projections of wind speeds over northern Europe. Their analysis suggests that, ‘natural variability exceeds the climate change signal in the wind energy resource and extreme wind
speeds’. Their conclusion for northern Europe is that by the end of the twenty-first century there will be a small change in wind speeds, and slight increase in higher and
extreme wind speed events. Thus, as noted by this discussion, there is a wide estimation
in the impacts of climate change, illustrating that there is much uncertainty in these
future projections. This is in part due to the reliance of the model output on different
input parameters (e.g., Pryor et al. 2005b,c), and also the different downscaling
approaches. It is clear from this analysis that additional work on estimating future patterns
of trends in wind speeds and WPD is necessary.
The results of the studies analyzing projected changes in wind speeds for the future are
summarized in Figure 3. Several of the studies listed before note a seasonal pattern in the
projected trends, where one seasonal might show a positive trend and another a negative
trend. This is reflected in the map by a ‘ ⁄ ’, where the value before the slash is summer
and the value after the slash is winter. So, a reading of ‘±’ would mean a projected positive trend in summer and a negative one in winter. As can been seen by Figure 3, the
projections are for the stilling patterns of the past few decades to continue, although there
is much more uncertainty about the future projections, and there are several locations
which are projected to show a complicated pattern, or slight increases.
Summary and Conclusions
This article has provided an overview of the linkages between research into wind climatology and wind energy. The focus has been on two areas of importance in this topic: (i)
examination of trends in wind speed and WPD for past and present; and (ii) potential
implications of climate change on the wind resource of the future.
The initial area of discussion focused on the analysis of trends in the wind resource. As
wind farm siting typically assumes a stationary field, any non-stationarities in the wind
resource can potentially have dramatic impacts on the economic viability of any wind
farm. A range of research has been performed at locations worldwide examining wind
speed and climatic variability. A large percentage of these articles have noticed a decrease
(or ‘stilling’) of winds in many different geographical locations. This suggests, as discussed
in some of the literature, that there are larger, synoptic-scale explanations for these trends.
However, much of the research examining the trends has failed to include a discussion of
the statistical significance or provide error bars in the discussion, so the exact explanation
of statistical relevance of the trends is hard to assess. In addition, most of these studies are
based upon winds at 10 m (which is where most the available data is located). However,
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1602 Wind climatology and wind energy
changes in electrical output will be noticed at hub height, which can be up to 100 m or
higher. Thus, in addition to improved statistical rigor, additional research using actual
data, or derived products such as the NARR or ERA reanalysis needs to be performed at
higher heights to see if the patterns identified near the surface can also been seen at
higher elevations.
Finally, a review of research examining the potential implications of climate change on
the wind energy resource was undertaken. The majority of these results show a projected
slight decrease in wind speeds in the future, which would result in serious implications
for wind farm siting. In the United States, particularly for the wintertime, this is theorized to be associated with a poleward shift of the mean thermal gradient as the earth
warms and results in a northward shift of the associated storm track patterns. There also
appears to be a strong regional and seasonal pattern. Thus, expanded analysis of this relationship, focusing on location-specific studies of the effects of climate change would be
valuable for industry to improve their wind farm siting efforts.
As described before, the wind industry has been growing at a phenomenal rate, and,
with policy efforts like the US DoE 20% plan, is projected to expand and continue to
play an ever-increasing role in electrical production around the world. The geographical
and climatological research community can help with this transition to a sustainable
energy future via an improved understanding of the variables and research questions of
interest to industry, policymakers, and other stakeholders, and also by addressing some of
the research deficiencies identified and discussed in this article.
Acknowledgement
The authors like to recognize the Oklahoma Economic Development Generating Excellence program, the US Department of Energy, and the Oklahoma state energy office for
providing funding for this project. They also like to thank Nikki Acton, Matt Chatelain,
and Ethan Cook for their help with the data processing and mapping.
Short Biographies
Dr. Scott Greene is currently Professor of Geography and Director of the Environmental
Verification and Analysis Center and the Oklahoma Wind Power Initiative at the University of Oklahoma. Dr. Greene’s current research includes a variety of applied climatological research focused on the broad theme of climate diagnostics and analysis of climatic
and environmental hazards. Specific examples include examination of the impacts of
extreme climatic events (e.g., drought, heat waves) upon agriculture and human health
and the development of statistical methods to study variability in meteorological patterns
across Oklahoma and the tropical Pacific. Dr. Greene also has extensive expertise in analysis
of statistical models for use in validation of applied meteorological modeling. In his
capacity as director of EVAC and OWPI, Dr. Greene has received numerous research
grants, and is responsible for overseeing the activities of the center, interacting with a
variety of local and state officials, and for coordinating outreach and educational activities.
For the last decade, Dr. Greene has also worked with, and is currently director or, the
Oklahoma Wind Power Initiative (OWPI), a 10-year old, award-winning collaboration
between the University of Oklahoma and Oklahoma State University. In addition to
publishing over 40 papers on renewable energy, applied climatology and statistical methods and teaching a variety of courses, Dr. Greene has also previously served as a technical
consultant to a wide range of public and private interests. Dr. Greene has also provided
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
1603
his consulting expertise to a range of public and privates groups to help support the
development of wind energy in Oklahoma. Finally, Dr. Greene has received a number of
prestigious awards, including a NASA Global Change Fellowship, and the Tromp Scientific Award, which is awarded by the International Society of Biometeorology every
3 years for outstanding research in biometeorology, as well as different awards for outstanding teaching and research from the University of Oklahoma. Dr. Greene holds
degrees in Applied mathematics (B.A.; University of California, Berkeley), Geography
(M.A., University of Hawaii), and Climatology (Ph.D., University of Delaware).
Mark Morrissey is a professor in the School of Meteorology at the University of
Oklahoma. His research interests included wind power assessment and the application
of random function theory to meteorological and climate problems, especially in the
analysis of tropical meteorology and climate. He received his doctorate in Meteorology
in 1986 from the University of Hawaii. Notable accomplishments include the establishment of the Environmental Verification and Analysis Center (EVAC). He is the author
or 48 refereed publications and was the first Pacific Island Global Climate Observing
Systems (PI-GCOS) Coordinator, headquartered in Apia Samoa from 2004 through
2005.
Sara Johnson is currently a Geography graduate student at the University of Indiana.
Recently, she completed a B.S. in Geography at the University of Oklahoma. While an
undergraduate, Sara worked with the Oklahoma Wind Power Initiative where she
researched past wind trends and future wind power potential for Oklahoma. Other
research interests include climate change, applied climatology and natural hazards.
Note
* Correspondence address: Scott Greene, Department of Geography and Oklahoma Wind Power Initiative, University of Oklahoma, 100 E. Boyd St., Suite 684, Norman, OK 73019, USA. E-mail: [email protected].
References
American Wind Energy Association. (2010). 2009 Annual Market Report. [Online.] Retrieved on 15 June 2010
from: http://www.awea.org/reports/Annual_Market_Report_Press.pdf.
Archer, C. L. and Jacobson, M. (2003). Spatial and temporal distributions of U.S. winds and wind power at 80 m
derived from measurements. Journal of Geophysical Research 108 (D9), pp. 4289–4309.
Baker, R. W., Walker, S. and Wade, J. (1990). Annual and seasonal variations in mean wind speed and wind
turbine energy production. Solar Energy 45 (5), pp. 285–289.
Barthelmie, R. (2007). Wind energy: status and trends. Geography Compass 1 (3), pp. 275–301.
Beniston, M., et al. (2007). Future extreme events in European climate: a exploration of regional climate model
projections. Climatic Change 81, pp. 71–95.
Bernardin, F., et al. (2009). Stochastic downscaling method: application to wind refinement. Stochastic Environmental
Research and Risk Assessment 23, pp. 851–859.
Brazdil, R. (2008). Climate fluctuations in the Czech Republic during the period 1961–2005. International Journal of
Climatology 29, pp. 223–242.
Breslow, P. B. and Sailor, D. (2002). Variability of wind power resources to climate change in the continental
United States. Renewable Energy 27 (4), pp. 585–598.
Cayan, D., et al. (2008). Climate change scenarios for the California region. Climatic Change 87, pp. 21–42.
De Rooy, W. C. and Kok, K. (2004). A combined physical-statistical approach for downscaling of model wind
speed. Weather and Forecasting 19, pp. 485–495.
Dukes, M. D. G. and Palutikof, J. P. (1995). Estimation of extreme wind speeds with very long return periods.
Journal of Applied Meteorology 34 (9), pp. 1950–1961.
Enloe, J., O’Brien, J. and Smith, S. (2003). ENSO impacts on peak wind gusts in the United States. Journal of
Climate 17, pp. 1728–1737.
Garcia-Bustamante, E., et al. (2008). The influence of the Weibull assumption on monthly wind energy estimation.
Wind Energy 11, pp. 483–502.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
1604 Wind climatology and wind energy
Greene, J. S., et al. (2009). Analysis of vertical wind shear in the southern Great Plains and potential impacts on
estimation of wind energy production. International Journal of Global Energy Issues 32 (3), pp. 191–211.
Harrison, G. P., Cradden, L. and Chick, J. (2008). Preliminary assessment of climate change impacts on the UK
onshore wind energy resource. Energy Sources, Part A 30, pp. 1286–1299.
Hayhoe, K., et al. (2008). Emissions pathways, climate change, and impacts on California. Proceeding of the National
Academy of Sciences 101, pp. 12422–12427.
Hewitson, B. C. and Crane, R. (1996). Climate downscaling: techniques and application. Climate Research 7, pp.
85–95.
IPCC. (2007). Climate change (2007). In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.
B., Tignor, M. and Miller, H. L. (eds) The physical science basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY: Cambridge University Press, pp. 750–800.
Islas, J., Manzini, F. and Martinez, M. (2004). CO2 mitigation costs for new renewable energy capacity in the Mexican electricity sector using renewable energies. Solar Energy 76, pp. 499–507.
Jiang, Y., Luo, Y., Zhao, Z. C., et al. (2010). Changes in wind speed over China during 1956–2004. Theoretical
and Applied Climatology 99 (3–4), pp. 421–430.
Keimig, F. T. and Bradley, R. S. (2002). Recent changes in wind chill temperatures at high latitudes in North
America, 2002. Geoophysical Research Letters 29 (8), pp. 1163.1–1163.4; doi: 10.1029/2001GL013228.
Klink, K. (1999). Trends in mean monthly maximum and minimum surface wind speeds in the conterminous United States, 1962 to 1990. Climate Research 13, pp. 193–205.
Klink, K. (2002). Trends in and interannual variability of wind speed distributions in Minnesota. Journal of Climate
15, pp. 3311–3317.
Klink, K. (2007). Atmospheric circulation effects on wind speed variability at turbine height. Journal of Applied Meteorology and Climatology 46, pp. 445–456.
Li, M. and Li, X. (2005). MEP-type distribution function: a better alternative to Weibull function for wind speed
distributions. Renewable Energy 30, pp. 1221–1240.
Lundquist, J. K. and Mirocha, J. (2008). Interaction of nocturnal low-level jets with urban geometries as seen in
joint urban 2003 data. Journal of Applied Meteorology and Climatology 47 (1), pp. 44–58.
Manwell, J. F., McGowan, J. and Rogers, A. (2002). Wind energy explained: theory, design and application. Chichester:
Wiley.
McVicar, T. R., et al. (2008). Wind speed climatology and trends for Australia, 1975–2006: capturing the stilling
phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters 35. L2040.1–
L2040.6; doi: 10.1029/2008GL035627.
McVicar, T. R., et al. (2010). Observational evidence from two mountainous regions that nearsurface wind speeds
are declining more rapidly at higher elevations than lower elevations: 1960–2006. Geophysical Research Letters 37.
L06402.1–L06402.6; doi: 10.1029/2009GL042255.
Mesinger, F. (2006). North American regional reanalysis. Bulletin of the American Meteorological Society 87, pp. 343–
360. doi: 10.1175/BAMS-87-3-343.
Michelangeli, P. A., Vrac, M. and Loukos, H. (2009). Probabilistic downscaling approaches: application to wind
cumulative distribution functions. Geophysical Research Letters 36, pp. 1–6.
Morrissey, M. L., Albers, A., Greene, J. S. and Postawko, S. (2010). An isofactorial change-of-scale model for the
wind speed probability density function. Journal of Atmospheric and Oceanic Technology 27 (2), pp. 257–273.
Najac, J., Boe, J. and Terray, L. (2009). A multi-model ensemble approach for assessment of climate change impact
on surface winds in France. Climate Dynamics 32, pp. 615–634.
Palutikof, J. P. (1987). Impacts of spatial and temporal wind speed variability on wind energy output. Journal of
Climate and Applied Meteorology 26, pp. 1124–1133.
Palutikof, J. P., Geo, X. and Halliard, J. A. (1992). Climate variability and UK wind resource. Journal of Wind
Engineering and Industrial Aerodynamics 39 (1:3), pp. 243–249.
Petersen, E. L., et al. (1997). Wind power meteorology. Roskilde, Denmark: Riso National Laboratory, publication #
RISO-I-1206.
Pirazzoli, P. and Tomasin, A. (1999). Recent abatement of easterly winds in the northern Adriatic. International
Journal of Climatology 19, pp. 1205–1219.
Pirazzoli, P. and Tomasin, A. (2003). Recent near-surface wind changes in the central and Mediterranean and
Adriatic areas. International Journal of Climatology 23, pp. 963–973.
Pope, S. (1994). Lagrangian PDF methods for turbulent flows. Annual Review of Fluid Mechanics 26, pp. 23–63.
Pryor, S. C. and Barthelmie, R. J. (2003). Long term trends in near surface flow over the Baltic. International Journal
of Climatology 23, pp. 271–289.
Pryor, S. C. and Barthelmie, R. J. (2010). Climate change impacts on wind energy: a review. Renewable and
Sustainability Energy Reviews 14 (1), pp. 430–437.
Pryor, S. C., Barthelmie, R. J. and Kjellstrom, E. (2005a). Potential climate change impact on wind energy
resources in Northern Europe: analyses using a regional climate model. Climate Dynamics 25, pp. 815–835.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x
Wind climatology and wind energy
1605
Pryor, S. C., Schoof, J. T. and Barthelmie, R. J. (2005b). Empirical downscaling of wind speed probability distributions. Journal of Geophysical Research 110, 12 pp. D19109; doi:10.1029/2005JD005899.
Pryor, S. C., Schoof, J. T. and Barthelmie, R. J. (2005c). Climate changes impacts on wind speeds and wind
energy density in Northern Europe: empirical downscaling of multiple AOGCMs. Climate Research 29, pp. 183–
198.
Pryor, S. C., Barthelmie, R. J., Young, D. T., et al. (2009). Wind speed trends over the contiguous United States.
Journal of Geophysical Research 114, 18 pp. D14105; doi: 10.1029/2088JD011416.
Sailor, D. J., Rosen, J. N., Hu, T. and Li., X. (1999). A neural network approach to local downscaling of GCM
output for assessing wind power implications of climate change. Renewable Energy 24, pp. 235–243.
Sailor, D. J., Smith, M. and Hart, M. (2008). Climate change implications for wind power resources in the northwest United States. Renewable Energy 33, pp. 2393–2406.
Schwartz, M. and Elliot, D. (2005). Towards a wind energy climatology at advanced turbine hub heights, Golden, CO:
National Renewable Energy Lab. NREL ⁄ CP-500-38109.
Schwartz, M. and Elliott, D. (2006). Wind shear characteristics at central plains tall towers, Golden, CO: National
Renewable Energy Lab. NREL ⁄ CP-500-40019.
Segal, M., Pan, Z., Arritt, R. W. and Tackle, E. S. (2001). On the potential change in wind power over the US
due to increases of atmospheric greenhouse gases. Renewable Energy 24, pp. 235–243.
Sims, R. E. H. (2004). Renewable energy: a response to climate change. Solar Energy 76, pp. 9–17.
Sims, R. E. H., Rogner, H. H. and Gregory, K. (2003). Carbon emission and mitigation costs comparisons
between fossil fuel, nuclear, and renewable energy resources for electricity generation. Energy Policy 31, pp.
1315–1326.
Storm, B., et al. (2008). Evaluation of the weather research and forecasting model on forecasting low-level jets:
implications for wind energy. Wind Energy 12, pp. 81–90.
Tuller, S. E. (2004). Measured wind speed trends on the west coast of Canada. International Journal of Climatology 24
(11), pp. 1359–1374.
Uppala, S. M., et al. (2005). The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 131, pp.
2961–3012.
U.S. Department of Energy. (2008). 20% wind energy by 2030: increasing wind energy’s contribution to U.S. electric supply, Golden, CO. DOE ⁄ GO-102008-2567.
Vrac, M., Stein, M. L., Hayhoe, K., et al. (2007). A general method for validating statistical downscaling methods
under future climate change. Geophysical Research Letters 34 (18), pp. L18701.1–L10801.5. doi: 10.1029/
2007GL030295.
Walter, A. (2006). A high resolution reference data set of Germany Win Velocity, 1951–2001 and comparison with
regional climate model results. Meteorologische Zeitschrift 15, pp. 585; doi: 10.1127/0941-2948/2006/0162.
ª 2010 The Authors
Geography Compass ª 2010 Blackwell Publishing Ltd
Geography Compass 4/11 (2010): 1592–1605, 10.1111/j.1749-8198.2010.00396.x