Climate change implications for wind power resources in the Northwest United States

ARTICLE IN PRESS

Renewable Energy 33 (2008) 2393–2406 www.elsevier.com/locate/renene

Climate change implications for wind power resources in the Northwest United States David J. Sailor, Michael Smith, Melissa Hart Portland State University, P.O. Box 751-ME, Portland, OR 97207, USA Received 12 September 2007; accepted 10 January 2008 Available online 3 March 2008

Abstract Using statistically downscaled output from four general circulation models (GCMs), we have investigated scenarios of climate change impacts on wind power generation potential in a five-state region within the Northwest United States (Idaho, Montana, Oregon, Washington, and Wyoming). All GCM simulations were extracted from the standardized set of runs created for the Intergovernmental Panel on Climate Change (IPCC). Analysis of model runs for the 20th century (20c3m) simulations revealed that the direct output of wind statistics from these models is of relatively poor quality compared with observations at airport weather stations within each state. When the GCM output was statistically downscaled, the resulting estimates of current climate wind statistics are substantially better. Furthermore, in looking at the GCM wind statistics for two IPCC future climate scenarios from the Special Report on Emissions Scenarios (SRES A1B and A2), there was significant disagreement in the direct model output from the four GCMs. When statistical downscaling was applied to the future climate simulations, a more coherent story unfolded related to the likely impact of climate change on the region’s wind power resource. Specifically, the results suggest that summertime wind speeds in the Northwest may decrease by 5–10%, while wintertime wind speeds may decrease by relatively little, or possibly increase slightly. When these wind statistics are projected to typical turbine hub heights and nominal wind turbine power curves are applied, the impact of the climate change scenarios on wind power may be as high as a 40% reduction in summertime generation potential. r 2008 Elsevier Ltd. All rights reserved. Keywords: Climate change; Renewable energy; Wind power; Statistical downscaling

1. Introduction Just as with the other aspects of climate, wind statistics are subjected to natural variability on a wide range of time scales. Decadal and multi-decadal variability in wind speed statistics currently introduces an element of risk into the decision process for siting new wind power generation facilities. The consensus within the atmospheric science community is that the global climate is already being impacted by emissions from fossil fuel combustion and will continue to be impacted for decades or even centuries regardless of development patterns and government decisions [1]. Global climate change will affect the statistics (max, min, mean, and variance) of all meteorological variables. Corresponding author. Tel.: +1 503 725 4265; fax: +1 503 725 8255.

E-mail address: [email protected] (D.J. Sailor). 0960-1481/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2008.01.007

In the past decade, much attention in the climate research community has been focused on developing scenarios of climate change and assessing the potential impacts of these scenarios on society. The bulk of this work, however, has focused on changes in temperature and precipitation fields. Accordingly, studies of climate change impacts have typically focused on agricultural and water resources. In recent years, a growing number of studies have looked at potential impacts on renewable energy resources, and on wind power, in particular. Harrison and Wallace [2] investigated the vulnerability of marine wave and wind energy resources to climate changes. Using a simple sensitivity study as a proxy for climate change, Harrison explored both how reductions in the wind/wave resource would impact generation potential, and also how stormier climates may pose survivability issues for offshore generation equipment. Using general circulation model (GCM) output from the Hadley model,

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Vena¨la¨inen et al. [3] found that wind power potential throughout Finland might increase by 2–10% under conditions of climate change. Investigations of wind power potential under climate change over Northern Europe have been undertaken using both dynamical [4] and empirical [5,6] downscaling techniques. In the work by Pryor et al., empirical downscaling of five GCMs for 46 stations over Northern Europe showed a slight decrease in mean wind speeds, 90th percentile wind speeds and wind density under a 2080–2100 climate projection [6]. Using dynamical downscaling of the ECHAM (ECHAM4/ OPYC3 AOGCM) and Hadley (HadAM3 H atmosphere only GCM) models, Pryor et al. [4] found that annual wind power potential over Northern Europe under the Intergovernmental Panel on Climate Change (IPCC) A2 and B2 scenarios was highly dependent on the boundary conditions used in regional climate modeling (RCM) simulations. Using ECHAM simulations, annual wind power potential over Northern Europe may increase slightly; however, the magnitude of the difference between the current climate and future climate scenarios was similar to the difference between current climate RCM simulations and NCEP/NCAR reanalysis data. When boundary conditions from the Hadley model were used, there were slight decreases or no change in wind speed and energy density between the current and future climate. Using dynamical downscaling of the Hadley Centre GCM (using a 1% per year increase in effective greenhouse gas emission after 1990) with the regional climate model RegCM2 and a horizontal resolution of 52 km over the continental US, Segal et al. found a decrease in daily average wind power availability in the range 0–30% over most of the US by the year 2050. However, an increase in wind power, peaking at 30%, was found in limited areas in the southern and northwestern US [7]. In earlier work, Sailor et al. explored climate change implications for wind power in California and Texas using neural network-based downscaling [8]. In a subsequent analysis involving two GCMs, the Canadian Climate Center model and the Hadley model from the UK Meteorological Office, Breslow and Sailor [9] found a 1–4.5% reduction in wind speeds associated with climate change over a 100-year period. This study did not downscale the model output and noted that the two models yielded qualitatively similar projections initially, but began to diverge further into the output analysis period, leading to growing uncertainty for long-term projections. 1.1. Recent trends in wind power With generation resource areas including Tehachapi, Altamont, and Palm Springs, California has long been one of the world’s leaders in wind power generation with a capacity in excess of 2361 MW. In 2006, the state of Texas surpassed California in generating capacity with 2768 MW of installed capacity [10]. There are, however, several regions within the US where major wind power projects are

either in the planning, being constructed, or recently have become operational [11]. The Pacific Northwest is one such region, with significant wind power potential and activity in the states of Oregon, Washington, and Montana. In fact, the third largest installed wind farm in the US is located along the Oregon/Washington state border [10]. An early study of wind power potential in the Pacific Northwest [12] identified the coastal zones of Oregon and Washington, the Columbia River Gorge and most of the higher elevations, as having significant wind power potential, especially during the winter months when a persistent storm track moves over the area. In 2003, 13% of total wind power generation in the US came from the Northwest (NW) [13]. The present study focuses its analysis on wind power resources in the NW (Idaho, Montana, Oregon, Washington, and Wyoming). As the data necessary for validating climate model surface predictions are available at a small number of sites—typically weather stations at major airports—we have chosen to focus this analysis on a single weather station within each state in the Pacific Northwest. While there is significant variability in weather within each state due to variations in land cover and topography, we feel that changes in wind statistics at an airport site are qualitatively representative of how wind statistics might change in the surrounding countryside. Nevertheless, the city-specific results presented here ought to be taken in the aggregate with respect to their qualitative implications for the Northwest region as a whole. 2. Climate change models and scenarios GCMs of the atmosphere were first developed in the late 1950s and early 1960s to study the interaction between the atmosphere and the environment and improve weather forecasting [14]. Although the initial modeling centers were in the US, the GCM community now includes large modeling teams in a number of countries (e.g., Australia, Canada, Germany, Japan, UK, US). While it is generally agreed that GCMs are the best tool for assessing the likely climate response to increasing carbon dioxide levels, their output suffers in both resolution and accuracy. The highest resolution models typically have grid cell sizes of 2–41 latitude and longitude [8], although recent developments are producing model runs at 11 resolution. The IPCC has established a suite of simulation experiments to explore past and present climate as well as various scenarios of climate change [15]. From the Special Report on Emissions Scenarios (SRES), four story lines for future world development (A1, A2, B1, and B2) were developed. The A1 scenario family represents a future world of rapid population and economic growth peaking in mid-century (at a population of 8.7 billion) and declining thereafter (to a population of 7.1 billion by 2100). The A2 scenario family describes a heterogeneous world with respect both to changes in population growth patterns and changes in economic growth and technological progress. In this scenario, world population reaches 11.3 billion by 2050

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and continues to grow to 15.1 billion in 2100. In contrast, the B1 scenario family describes more of a convergent world with emphasis on economic, social, and environmental sustainability. It is an optimistic scenario with global carbon dioxide emissions peaking around 2050 and then declining slightly through 2100. Population growth in this family of scenarios is the same as in the A1 family. The B2 scenario family represents a world with an emphasis on local solutions to economic, social, and environmental issues. The rates of population growth and emissions under this scenario continue to increase to 2100, but at a lower rate than in the A2 family. Of these scenarios, A1B represents the lower middle range of growth in CO2 emissions. The A2 scenario family represents the higher range of growth in emissions. These two scenario families were, therefore, chosen to represent a reasonable range of possible forcing for a future climate. The IPCC data intercomparison project at Lawrence Livermore National Laboratory (www-pcmdi.llnl.gov) include model output for the 20th century simulation for 21 different models (including multiple variations of several models). Output from the SRES A1B and SRES A2 scenarios are available for a subset of 16 of these models. We selected four of these models for analysis in this study. These were ECHAM5/MPI-OM, GFDL-CM2.1, GISS-ER, and MRI-CGCM2.3.2. These models represent a range of physical parameterization options and a range of grid scale resolution. The Goddard Institute for Space Studies’ GISS-ER model [16] is similar to the GISS-EH model except that it contains the ocean model of Russell et al. [17]. Of the models used in this study, the GISS model has the coarsest horizontal atmospheric resolution at 4.01 latitude and 5.01 longitude. The MRI-CGCM2.3.2 model, developed at the Meteorological Research Institute of the Japan Meteorological Agency, has an atmospheric resolution of approximately 2.81 (T42 grid). The GFDL-CM2.1 model, developed at the Geophysical Fluid Dynamics Laboratory has an atmospheric resolution of 2.51 longitude and 2.01 latitude [18]. The Max Planck Institute for Meteorology’s ECHAM5/MPI-OM model was the highest resolution model tested with an atmospheric grid resolution of approximately 1.91 (T63 grid) [19]. Details of physics parameterizations in each model can be found in the model documentation as cited above. For the purposes of this study, climate model output was extracted for the surface grid cells containing five major NW airport weather stations. These stations (and their three-letter airport identifiers) are Boise Idaho (BOI), Casper Wyoming (CPR), Great Falls Montana (GTF), Portland Oregon (PDX) and Yakima Washington (YKM). The relative location of each station within the various GCM grid structures is shown in Fig. 1. As is evident from this figure, some stations are very near the edge of a grid cell, for example, Great Falls in the MRI model. In each case, however, a single grid cell from each GCM was identified as containing each station, and the correspond-

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ing GCM model output extracted from one GCM cell was used in modeling the conditions at that weather station. In the case of the GISS model, the grid resolution is so coarse that both PDX and BOI fall within the same GISS grid cell. 3. GCM downscaling GCMs are generally good in replicating large-scale circulation features of the current climate [20]. However, the GCM surface predictions are not accurate on regional scales. Thus, caution should be taken when applying them to climate change impact analyses. The limitations of GCMs have long been recognized and several approaches have been suggested to remedy the problem. Downscaling is a technique that bridges the gap of GCM prediction skills over different scales. Giorgi and Mearns (1991) provided an early review of the various efforts for simulating climate change at regional and finer spatial scales. The three categories of approaches discussed in their review were empirical, semi-empirical, and nested modeling approaches. The semi-empirical (statistical downscaling) approaches use GCM output to represent large-scale forcing and develop empirical statistical relationships to account for mesoscale and local phenomena. Nested modeling, on the other hand, uses a higher resolution dynamical climate model to account for mesoscale forcing. Each approach has been successfully applied in a wide range of studies. The nested modeling (dynamical downscaling) approach is attractive for wind resource studies as it produces a somewhat continuous (grided) representation of wind statistics over the entire region of study. There is also evidence in the literature of dynamic downscaling being successfully applied for wind resource studies (e.g., the work of Pryor et al.). The reliance of dynamic downscaling on boundary conditions driven by GCM output that is known to be suspected near the surface, however, introduces some uncertainty in the reliability of the resulting regional scenarios. Furthermore, the relative computational cost of simulating a large number of scenarios driven by multiple GCMs makes dynamic downscaling less attractive in the present type of study. As a result, the statistical downscaling approach was selected for the present study due to its relative ease of application combined with its high degree of accuracy. 3.1. Statistical downscaling overview Statistical downscaling is a two-step procedure. First, it uses statistical techniques to relate large-scale climate parameters to local surface variables such as temperature, precipitation, or wind speed. Then GCM output for a future climate scenario is substituted into the statistical models to generate spatially refined scenarios of future climate. These models are sometimes called transfer functions since, once established, they can transfer information

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Fig. 1. Location of the five airport weather stations within the grid structures of the four GCMs used in the present study—GISS, MPI, GFDL, and MRI.

contained in large-scale GCM variables to predictions of local near-surface climate. Statistical methods used in most downscaling studies generally fall into two categories. The first category treats the data pool as one unit and develops transfer functions for it. This approach includes principle component analysis (PCA)/canonical correlation analysis (CCA) [21,22], regression analysis [23], and artificial neural network (ANN) function approximation [24]. The second category classifies the data pool into several classes, or weather patterns (WPs) as a first step. Then, predictor–predictand relationships for each class are established. Cluster analysis [25,26], classification and regression trees (CART) [27,28], and radial-based function (RBF) neural networks [29] belong to this WP-based approach. The analog technique used by Zorita et al. [28] and Cubasch et al. [30] is somewhat similar to clustering analysis, although it does not explicitly define WPs/clusters. The transfer function approach can be viewed as a special case of the classification approach when there is only one WP. Theoretically, the statistical

techniques used in the first category also apply to the second category for establishing the predictor–predictand relationships within each WP. There are two distinct ways to classify WPs: a selforganizing (unsupervised) classification that includes clustering analysis and some ANNs, and a guided (supervised) classification scheme such as CART. Currently, most WPbased downscaling studies use clustering analysis. Clustering analysis finds cluster structure inside a data pool. Resulting clusters consist of points separated by small distances, relative to the distances between clusters. The distances are calculated by using only the predictor variables. Due to the difficulties in rescaling, different types of large-scale variables to calculate distances selforganizing classification usually uses only one or two types of large-scale variables such as sea level pressure or geopotential height. CART is a rule-based classification technique pioneered by Breiman et al. [31]. There are two components in CART: the classification tree and the regression tree (also

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called tree-structured regression or TSR). The primary difference between the classification tree and the regression tree is that the former seeks to find a set of rules that classify the data into pre-defined groups while in the latter approach, there are no pre-defined groups. In regression trees, the groups are generated automatically during the classification process. In other words, the predictand in the classification tree method is categorical while in the regression tree approach, it is continuous. TSR was selected as the modeling tool in this study because of its distinctive features: any number and any kind (categorical or continuous) of predictors can be used; it can handle categorical predictors such as month, season, or Julian Day; and it is relatively simple to implement. The algorithm described in the original Breiman book is not very complicated. It consists of only a few elements—a splitting rule, a stopping rule, and an assigning rule. Being a component of CART, TSR shares the same strengths with respect to accuracy. According to Steinberg [32], when automatic CART analyses are compared with stepwise logistic regressions or discriminant analyses, CART typically performs about 10–15% better on the training sample. When automatic CART analyses are compared with the best parametric models of sophisticated teams of statisticians, CART is still competitive. TSR models are also easy to interpret and can be simply displayed using a number of descriptive rules. 3.2. Data for downscaling The TSR-based statistical downscaling approach requires the selection of appropriate predictor variables. As the data used for this study were extracted from IPCC data archived at the intercomparison project at Lawrence Livermore National Laboratory, the predictor variable selection was limited to variables that were readily available for all models and scenarios of interest. We selected the following six GCM output variables for use in our statistical downscaling: zonal, meridonal, and total wind speeds, maximum and minimum air temperatures, and sea level pressure. In all cases, we limited TSR model development such that each model had no more than four rules and each rule represented a coverage of at least 10% of the data. Airport weather station data for the period 1964–2000 were obtained from the National Climatic Data Center’s Surface Airways database. These data include hourly values of air temperature, humidity, wind speed, and wind direction for more than 300 weather stations across the US. 4. Current climate results To assess the performance of the GCMs with respect to replicating the current wind statistics at the five airport weather stations, we first extracted observed daily average wind speed data for each airport for the period of 1964–2000. During this period, approximately 20% of

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Table 1 Wind speed averages (m/s) observed monthly (10 m height) for five Northwest weather stations Month

Boise

Casper

Great Falls

Portland

Yakima

January February March April May June July August September October November December

3.24 3.68 4.07 4.16 3.99 3.83 3.58 3.50 3.46 3.40 3.44 3.43

7.03 6.52 5.93 5.47 4.99 4.74 4.38 4.45 4.74 5.22 6.18 6.94

6.10 5.75 5.36 5.26 4.87 4.59 4.21 4.22 4.66 5.45 5.92 6.35

4.37 4.12 3.58 3.26 3.17 3.27 3.38 3.21 2.97 2.91 3.79 4.26

2.46 2.84 3.44 3.76 3.74 3.71 3.46 3.30 3.22 2.86 2.57 2.32

Annual

3.24

5.55

5.23

3.52

3.14

hourly wind speed data records were missing for the sites. The daily average wind speed data were combined to create corresponding monthly averages (shown in Table 1) for comparison with GCM output. Boise and Yakima experience peak wind speeds in the spring months with monthly averages in April and May of around 4 m/s. In contrast, the stations for Casper, Great Falls, and Portland have peak winds in the winter months with much calmer wind speeds in spring and summer. Of these sites, Casper Wyoming is the most windy with an annual average wind speed greater than 5 m/s. Yakima is the calmest of the stations with annual average wind speeds close to 3 m/s. 4.1. Direct GCM current climate output The direct GCM output was extracted from each GCM by first identifying the GCM grid cells whose center latitude and longitude were closest to the location of each airport weather station. Daily wind speed data were then extracted for each of the GCMs for the current climate (20c3m) simulation for the modeled period of 1961–2000. These data were then aggregated to create monthly wind speed averages. Current climate wind speed output from each of the four GCMs reveals significant limitations in the abilities of these models to replicate observed wind speed characteristics. As illustrated in Fig. 2, the GISS model fairly consistently underpredicts mean wind speeds, sometimes by as much as 50 or 60%. This underestimation is most pronounced in the summer months. The other models show more sitedependent behavior. For example, the MRI model underpredicts wind speeds in Great Falls and Casper by about 20% in the summer and 40% in the winter. The same model, however, overpredicts winds at the other three sites in certain months of the year by as much as 50%. Two statistics are used to compare GCM model output with the corresponding observations. The root mean square error (RMSE) is the square root of the mean squared difference in prediction–observation

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pairings: " RMSE ¼

J X I  2 1 X Pij  Oij IJ j¼1 i¼1

#1=2 .

(1)

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The RMSE can be calculated for each individual site (holding j constant) or aggregated over the suite of five observation sites. RMSE is a good overall measure of model performance. However, since large errors are 80%

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Fig. 2. Comparison of current climate GCM (20c3m) simulations with observations at the five weather station sites. Table 2 Summary statistics for GCM model performance (without downscaling) GFDL

GISS

MPI

Table 3 Summary statistics for downscaled GCM model performance

MRI

GFDL

RMSE/IOA RMSE/IOA RMSE/IOA RMSE/IOA Boise, ID Casper, WY Great Falls, MT Portland, OR Yakima, WA

0.616/0.250 2.732/0.383 2.311/0.359 0.574/0.213 0.699/0.140

1.442/0.224 3.401/0.323 2.653/0.333 0.811/0.507 0.803/0.055

0.482/0.405 2.815/0.363 1.923/0.393 0.663/0.202 0.903/0.315

0.552/0.621 1.983/0.413 1.608/0.461 1.015/0.066 0.909/0.527

Root mean square error (RMSE) of monthly wind speeds (m/s) and index of agreement (IOA).

GISS

MPI

MRI

RMSE/IOA RMSE/IOA RMSE/IOA RMSE/IOA Boise, ID Casper, WY Great Falls, MT Portland, OR Yakima, WA

0.057/0.989 0.072/0.999 0.104/0.994 0.050/0.997 0.097/0.989

0.036/0.996 0.059/0.999 0.109/0.994 0.052/0.997 0.062/0.996

0.077/0.979 0.046/0.999 0.134/0.990 0.036/0.999 0.088/0.991

0.058/0.988 0.061/0.999 0.091/0.996 0.039/0.998 0.028/0.995

Root mean square error (RMSE) of monthly wind speeds (m/s) and index of agreement (IOA).

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weighted heavily (due to squaring), large errors at one site may produce a large aggregated RMSE even though the errors may be small and acceptable elsewhere. The index of agreement (IOA) is another useful statistic for evaluating GCM model performance. This metric condenses the differences between model estimates and observations for all sites and for a given time period (monthly here) into one statistical quantity. Following the approach of Willmott et al. [33], the IOA is the ratio of the total RMSE to the sum of two differences—between each prediction and the observed mean, and each observation and the observed mean: 2 3 6 IOA ¼ 1  4P

IJ  RMSE2    7 P   i 5 . J I  i P  M  M þ O     0 0 j j¼1 i¼1 j

10%

(2)

Viewed from another perspective, the IOA is a measure of the match between the departure of each prediction from the observed mean and the departure of each observation from the observed mean. Thus, the correspondence between predicted and observed values across the domain at a given time may be quantified in a single metric and displayed as a time series. The IOA has a theoretical range of 0–1, the latter score suggesting perfect agreement. Table 2 presents the summary statistics regarding performance of the raw GCM output for the current climate (20c3m simulations) relative to the observed wind statistics at the five sites. For most sites and models, the IOA between observations and direct GCM output is less than 0.5 with the average being 0.33. The average RMSE before downscaling is also relatively large at 1.5 m/s. As a general rule, the sites 10%

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Fig. 3. Comparison of downscaled current climate GCM output with observations at the five weather station sites.

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that were the most inland demonstrated the weakest agreement between direct GCM output and observation. Both Casper Wyoming and the Great Falls Montana sites had average RMSE greater than 2.0 m/s. 4.2. Downscaled GCM current climate output The TSR method discussed earlier was used with the historical 20th century wind speed observational data set and the direct 20c3m model output from the GCMs. As noted earlier, the TSR models in this study were developed (trained) using the following six GCM output variables: zonal, meridonal, and total wind speeds; maximum and minimum air temperatures; and sea level pressure. In all cases, the model construction options were

40%

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set to require a minimum rule coverage of 10% of the data, and a maximum number of four rules. Table 3 presents annual summary statistics for the downscaled GCM model output. For all sites and models, the IOA between observations and GCM output was significantly improved through downscaling. The IOA average increased from 0.33 before downscaling to more than 0.99 after downscaling. Likewise, downscaling significantly improved the average RMSE, reducing it by more than a factor of 10, from 1.5 to 0.1 m/s. Fig. 3 illustrates the comparison of current climate observations with GCM downscaled wind speeds for the four GCMs and five locations. In comparison with Fig. 2, the average errors are significantly smaller. Much of the bias evident in Fig. 2 has been removed and the errors

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Fig. 4. Model-predicted changes in wind speeds based on direct output from the four GCMs for the SRES A1B scenario.

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exhibited in Fig. 3 show a more random character with a relatively even distribution of positive and negative wind speed errors.

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were then added to the observed current climate wind speed averages to create downscaled scenarios of future wind speed. This approach minimizes errors associated with internal GCM model bias.

5. Future climate results 5.1. Direct GCM future climate output Having established the accuracy of the statistically downscaled GCM wind speeds, we applied the same downscaling models to the GCM output from the SRES scenarios A1B and A2. This approach assumes stationarity in the downscaling relationships. In other words, it is assumed that relationships developed for the current climate GCM output are applicable to the future climate GCM output. The downscaled monthly wind speed results for the A1B and A2 scenarios were subtracted from the downscaled monthly wind speed results for the 20c3m (current climate) scenario in order to generate estimates of wind speed change. These estimates of wind speed change

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Prior to investigating the results from the downscaling models applied to the future climate scenarios, it is instructive to briefly consider the implications from the raw model output for wind speeds in the grid cells corresponding to each weather station. Just as the direct GCM model output for the current climate differed significantly from one model to another, the GCM model output for future climate scenarios (A1B and A2) showed similar variability. These results are illustrated in Fig. 4 for the SRES A1B scenario. Results from the A2 scenario showed comparable variability and are not presented here.

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Fig. 5. Future changes (2050–present) in monthly wind speed averages at the five weather station sites using statistically downscaled results from the SRES A1B Scenario.

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As is evident from Fig. 4, the four GCMs investigated offer no consensus with respect to monthly changes in wind speeds under climate change scenarios. While it is reasonable to expect the climate change impact on wind speeds to have a seasonal character—and hence vary from month to month—there is no easy way to reconcile the difference in both magnitude and sign when comparing wind speed change projections from one GCM to another. For example, in the case of Yakima in May, both the GISS and MPI models suggest a likely increase in wind speeds by about 5–6%. At the same time, however, the GFDL model suggests a 1% decrease and the MRI model projects a decrease of 4%. Noting that wind power potential is proportional to the cube of the wind speed this corresponds to a wind power impact that ranges from an increase of 19% to a decrease of 12%. 20%

Clearly, the discrepancies in direct GCM output for future climate scenarios is problematic from the standpoint of understanding potential risks and opportunities presented by a changing climate. When the downscaling models developed from current climate observations and the 20th century GCM simulation scenario (20c3m) are applied to the future climate scenarios, these discrepancies can be largely removed from the resulting downscaled estimates of wind speeds. Fig. 5 is analogous to Fig. 4, only it represents the downscaled version of the A1B scenario. What is immediately evident in this figure is the general consensus obtained from each of the downscaled GCMs for any month and location. Returning to the earlier example of wind speeds in Yakima for May under the A1B 20%

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Fig. 6. Future changes (2050–present) in monthly wind speed averages at the five weather station sites using statistically downscaled results from the SRES A2 Scenario.

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scenario, we note that all downscaled models are generally in agreement that wind speeds will decrease by 4–6%. Fig. 6 shows the downscaled wind speeds for the A2 scenario. The results shown in this figure are similar to those for the downscaled A1B scenario (Fig. 5) except that the magnitudes of changes (both positive and negative) are generally a bit larger. 6. Implications for wind power The downscaled climate change results presented above are in general agreement that in the Pacific Northwest US wind speeds are likely to decrease in the months of April–October and will decrease to a lesser extent—or potentially increase—in the winter months. The magnitude of change in the wind power resource is difficult to assess directly from these results as the analysis is applicable to 80%

the specific weather station locations where it was developed. At the same time, however, it seems reasonable to combine the results from the four models and five Northwest locations into a single assessment of the likely range in changes in wind speeds under both of the climate change scenarios investigated. The conversion from wind speed to power density (power per area, W/m2) involves taking wind speed values and first scaling from the weather station reference height of 10 m up to 50 m which is a reasonable hub height for commercially sized wind turbines and also the height of choice for current wind resource maps. This scaling was facilitated by the use of the power law profile [34] for wind speeds:  a UðzÞ z ¼ , (3) Uðzr Þ zr 80%

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Fig. 7. Estimates of change in monthly wind power resources under the SRES A1B scenario. These data represent the combination of all four GCM simulations for each of the five stations. The line represents the average of the downscaled GCM results.

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where z and zr are the height of interest and reference height, respectively. While the power law exponent a is highly variable, a value of 1/7 is commonly used in wind power assessments and was selected for use in the present power density calculations. The scaled values were then compared to a minimum turbine cut-in speed. If a particular wind speed value was under the cut-in speed, it would be reduced to 0 to indicate that it does not contribute to generation of power. While each wind turbine design has its own cut-in speed, a typical value for commercial turbines is 5 m/s, and was used for the cut-in speed in our power estimate calculations [34]. The power density was then calculated using P 1 ¼ rU 3 , A 2

(4)

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where FU(d) is the wind speed change factor for the dth day of the year, n1 and n2 are the number of years of daily data in the current and future downscaled scenarios,

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where P/A (W/m2) is the power density, r is the air density (1.225 kg/m3, sea-level, 15 1C) and U is the wind speed (m/s) [34]. In order to create scenarios of changes in power density under climate change, we developed a method for mapping the daily-resolution downscaled GCM output to the hourly level. To do so, we first determined a daily wind speed change factor as the ratio of downscaled future to downscaled current climate wind speeds: P ð1=n2 Þ n2 DSU2 ðdÞ P F U ðdÞ ¼ , (5) ð1=n2 Þ n1 DSU1 ðdÞ

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Fig. 8. Estimates of change in monthly wind power resources under the SRES A2 scenario. These data represent the combination of all four GCM simulations for each of the five stations. The line represents the average of the downscaled GCM results.

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respectively, and DSU2 and DSU1 are the corresponding daily downscaled wind speed values. The average hourly wind speed (scaled to hub height) from the observational record was then calculated to create a corresponding set of 8760 hourly values of hub height observed current wind speeds—OCU(h). The daily wind speed change factor was then multiplied by the hourly average observed wind speed for each hour of the year to create a year of hub-height average hourly wind speeds under climate change— CCU(h): CCUðhÞ ¼ F U ðdÞ  OCUðhÞ.

(6)

The power density for each hour was then calculated from Eq. (4) and summed to create monthly and annual totals. Monthly power densities under current and future climate scenarios are compared in Fig. 7 for the A1B scenario and in Fig. 8 for the A2 scenario. For both scenarios and for all models, the projected future wind power potential is reduced over current power potential by 40% or more in the spring and summer months. The impact in winter and fall months is more variable with some models and cities showing increased wind power potential. 7. Discussion and conclusions From the results presented here, it is clear that the direct output of wind data from GCMs is not representative of the wind speeds at specific observation sites studied. Furthermore, when multiple GCMs are used to assess potential changes in regional wind statistics, these models often differ significantly not only in the magnitude of the projected changes, but also in terms of the sign of the change. Upon statistically downscaling the four GCMs, we were able to significantly improve the accuracy of current climate wind speed estimates. When these downscaled models were applied to future climate scenarios, we found that the inconsistencies in projections of changes in wind statistics were significantly reduced. As a result, we have some confidence in the statistically downscaled scenarios of future wind speeds for the Northwest US. These scenarios suggest that under a warmed climate, the wind power resource in the Northwest US may decrease by up to 40% in the spring and summer months. In winter months, the results are less consistent, with most sites indicating less of a reduction in wind power resource. The results for Yakima, in fact, indicate a significant potential increase in wind power resources. It must be emphasized, however, that Yakima is the one site for which the various models disagree significantly with respect to changes in winter months. The GFDL model, for example, shows an increase in wind power potential greater than 80% for November–February. In contrast, however, the MPI model predicts more than a 30% reduction in wind power potential for Yakima in 3 out of 4 winter months. As a result of these conflicting results, we must conclude that

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there is still a high degree of uncertainty with respect to how the future climate scenarios will impact winter wind power resources in and around Yakima. Hence, in assessing likely climate change impacts on wind power resources for the Northwest United States, we feel more confident relying on the more consistent story told by results for the other four Northwest cities. As far as wind power implications are concerned, the model results for either of the SRES scenarios were similar. In most cases, the impacts of the A2 scenario were slightly larger than those for the A1B scenario, but these interscenario differences were smaller than the inter-model differences, even after downscaling. Acknowledgments The authors wish to acknowledge the GCM modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model output, and the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The multi-model data archive is supported by the Office of Science, US Department of Energy. One author, M.S., wishes to acknowledge Portland State University for a student research award. References [1] Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hansen JE, editors. Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press; 2007. [2] Harrison GP, Wallace AR. Climate sensitivity of marine energy. Renewable Energy 2005;30:1801–17. [3] Vena¨la¨inen A, Koskela J, Turunen MA, Vehvila¨inen B, Forsius J, Ja¨rvinen P, et al. The influence of climate change on energy production and heating energy demand in Finland. Energy Environ 2004;15:93–109. [4] Pryor SC, Barthelmie RJ, Kjellstro¨m E. Potential climate change impact on wind energy resources in Northern Europe: analyses using a regional climate model. Climate Dyn 2005;25:815–35. [5] Pryor SC, Schoof JT, Barthelmie RJ. Empirical downscaling of wind speed probabilty distributions. J Geophys Res D: Atmos 2005;110:1–12 [article no. D19109]. [6] Pryor SC, Schoof JT, Barthelmie RJ. Climate change impacts on wind speeds and wind energy density in Northern Europe: empirical downscaling of multiple AOGCMs. Climate Res 2005;29:183–98. [7] Segal M, Takle ES, Pan Z, Arritt RW. On the potential change in wind power over the US due to increases of atmospheric greenhouse gases. Renewable Energy 2001;24:235–43. [8] Sailor DJ, Rosen JN, Hu T, Li X. A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renewable Energy 1999;19:359–78. [9] Breslow PB, Sailor DJ. Vulnerability of wind power resources to climate change in the continental United States. Renewable Energy 2002;27:585–98. [10] Belyeu K, de Azua CR. Annual US wind power rankings track industry’s rapid growth. American Wind Energy Association (AWEA), /http://www.awea.orgS; 2007. [11] Gipe P. Wind energy comes of age. New York: Wiley; 1995.

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