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Statistical Methods for Quantifying the Effect of the El Niño–Southern Oscillation on Wind Power in the

Northern Great Plains of the United States

by

Bret R. Harper, Richard W. Katz and Robert C. Harriss

REPRINTED FROM

WIND ENGINEERINGVOLUME 31, NO. 3, 2007

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Wind 31-3-Harper 3/9/07 2:49 pm

Statistical Methods for Quantifying the Effect of theEl Niño–Southern Oscillation on Wind Power in theNorthern Great Plains of the United States

Bret R. Harper1, Richard W. Katz2 and Robert C. Harriss3

1Energy and Resources Group, University of California at Berkeley,310 Barrows Hall, Berkeley, CA 94720-3050. Email: [email protected] Telephone/FAX: +1 510 6421640/+1 510 64210852Institute for Study of Society and Environment, National Center for Atmospheric Research,Boulder, CO 803073Houston Advanced Research Center,The Woodlands,TX 77381

WIND ENGINEERING VOLUME 31, NO. 3, 2007 PP 123–137 123

ABSTRACTThe El Niño-Southern Oscillation (ENSO) phenomenon is a well-known source of inter-

annual climate variability for both precipitation and temperature in the northern Great

Plains of the United States. In order to determine if ENSO has similar impacts on wind speed

and wind power, we applied statistical methods, including the sign test and resampling, to

hourly airport wind speed measurements for the past half century at two airports in North

Dakota and at another two in South Dakota. There is strong statistical evidence that ENSO

has a small effect on wind speeds in some months, which affects the amount of wind power

a typical utility scale wind turbine can produce. During the El Niño phase, we found monthly

mean wind power production to decrease generally, consistent with the mean wind speed

decreasing and the probability of a low-speed wind event increasing, at two locations in

South Dakota. The ENSO connection was weaker and harder to detect in North Dakota, but

we found evidence that the sign of the change appeared generally to be consistent with that

in South Dakota. These effects were smaller than the general variability of wind speeds, but

nevertheless were detectable as ‘signals within the noise’.

1. INTRODUCTIONSix of the seven poorest counties in the United States are located in the northern Great Plains

area. There are many stresses for these residents, including climate variability, economic

volatility, and market pressures that reduce the profitability of small farms. Climate

variability and change has the possibility of affecting, either positively or negatively, many

economic sectors in the Great Plains, including agriculture, ranching and livestock, natural

ecosystems, and water resources.

Wind power may offer a much-needed economic boost in rural areas of the northern Great

Plains, which has the largest natural wind resource in the United States . As the wind energy

installations expand in the northern Great Plains, issues related to the site-specific

dependability and the economics of intermittent wind energy resources are going to become

increasingly crucial to utility planning. Although modern wind turbines have long lifetimes,

the site planning data used to estimate potential energy production are commonly based on

as little as one year of data (e.g. Essa and Embaby 2005, Rehman 2005). Energy production

potential could be either over- or under-estimated if inter-annual variability influences on-site

Wind 31-3_final 3/9/07 2:27 pm Page 123

measurements in an unknown way. There are obviously financial and grid management

consequences if a site does not produce as much energy as expected, but there are

consequences if the site produces more energy than expected as well.

The El Niño-Southern Oscillation (ENSO) phenomenon is the “most prominent year-to-year

climate variation on Earth” (McPhaden et al. 2006) and a known source of inter-annual

variability in the northern Great Plains. Many past studies have shown the Great Plains’

association of ENSO with temperature and precipitation (Ropelewski and Halpert 1986; Sittel

1994, Ting and Wang 1997; Montroy et al. 1998, Schubert et al. 2004), and severe storms (Bove

et al. 1998; Etkin et al. 2001). These studies indicate that the ENSO signal tends to be the

strongest during the months of January though April, with El Niño conditions corresponding

to warmer weather and La Niña weather corresponding to colder weather on the northern

Great Plains. This is believed to be largely a result of the polar jet stream being further North

during El Niño and more variable during La Niña allowing cold polar air to penetrate further

South. Although there are other important sources of variability in North America such as the

Pacific/North American (PNA) circulation pattern (Wallace and Gutzler 1981), for sake of

example, we focus only on ENSO.

Despite the growing importance of wind climatology for the wind power industry, ENSO’s

effect (as well as that of other circulation patterns) on wind speed has received little attention

in the reviewed literature. Enloe et al. (2004) have previously documented the ENSO impacts

on extreme winds over the entire United States. These authors, however, did not have sufficient

data to draw any conclusions about the impacts of ENSO on wind in the northern Great Plains.

Romero-Centeno et al. (2003) have previously documented an ENSO signal on wind speeds

through the Isthmus of Tehuantepec in Mexico, but as far as we can tell, inter-annual wind-

speed variability has not been examined in the United States other than by Enloe et al. (2004).

In order to help fill this void, our study uses statistical methods to explore inter-annual

variability using airport wind speed data. As an example, we look at the potential role of El

Niño as a source of inter-annual variability in the wind speed and wind power production at

four sites in the states of North Dakota and South Dakota. First, we determine the apparent

effects of ENSO on wind speeds at these locations. Next, we determine whether these

apparent teleconnections between ENSO and wind speeds are, in fact, real and not simply

artifacts of the data we used. Then we quantify how much ENSO changes the wind speed,

taking into account the uncertainty in this relationship. Finally, we quantify how much ENSO

changes the wind power production.

2. DATAWe obtained hourly wind speed data for four airport anemometers located at Huron Regional

Airport (44.38°N, 98.22°W), SD; Pierre Municipal Airport (44.38°N, 100.28°W), SD; Bismark

Airport (46.81°N, 100.78°W), ND; and Williston Airport (48.16°N, 103.63°W), ND from the TD 6421

Enhanced Hourly Wind Station Data for the Contiguous United States dataset developed by the

National Climate Data Center (see Fig. 1). The NCDC datasets include metadata that help to

ensure that any changes in the immediate environment of the recording instruments (such as

new buildings and change of instrument location) are accounted for. Thus the particular dataset

includes elevation homogenization of the near-surface wind time series based on anemometer

elevations changes for all stations. The wind speed data were measured at the original

anemometer height (units of m sec-1 x 10 in the dataset). The data were recorded as discrete

speeds with a precision of approximately 0.5 m sec-1. We selected these sites to examine wind

characteristics along a range of latitudes and longitudes in the northern Great Plains.

124 STATISTICAL METHODS FOR QUANTIFYING THE EFFECT OF THE EL NIÑO–SOUTHERN

OSCILLATION ON WIND POWER IN THE NORTHERN GREAT PLAINS OF THE UNITED STATES


All four of the stations have some missing data (Table 1), but are relatively complete in their

record. The metadata helped us evaluate whether changes in the station surroundings or any

physical movement of the station had occurred. We also checked for unusual wind

distributions patterns that may indicate corrupted data. Such analyses did not indicate any

obvious problems with the quality of the wind data at these locations.

WIND ENGINEERING VOLUME 31, NO. 3, 2007 125

Table 1. Stations used in studyStation Location Completeness Dates UsedBismarck Airport 89.3% 1 January 1950 – 31 December 1999Huron Regional Airport 89.0% 1 January 1950 – 31 December 1999Pierre Municipal Airport 89.0% 1 January 1950 – 31 December 1999Williston Airport 84.5% 1 January 1962 – 31 December 1999

Figure 1. a) North Dakota Map. Note the locations of Bismarck in the South central and Williston in the

North West part of the state. b) South Dakota Map. Note the locations of Pierre in the central

and Huron in the east central part of the state.

a

b


Our study was also constrained by the ENSO index data we used, which spanned the period

from January 1950 – December 1999 obtained from Trenberth (1997) and updated to 1999.

Extremes in ENSO typically develop during Northern Hemisphere summer, climax in the fall,

and subside the following spring. We summarize the periods of ENSO events used in this study

in Table 2.

METHODOLOGYOur approach, which is commonly applied in teleconnections research, involves dividing the

time series of the variable of interest (e.g. wind speed) into groups based on the simultaneous

value of another variable (e.g. occurrence/non-occurrence of an El Niño event). Then we

compared the median values, as well as other statistical characteristics of the groups, using

box plots. This approach is more appropriate than using an ordinary correlation because we

suspected the relationship between tropical SST and North American wind speeds may be

non-linear and because wind speeds are not normally distributed, but positively skewed.

Additionally, the extremes wind speeds are of particular importance because the amount of

power produced is very sensitive to the frequency of high and low wind speeds.

We assigned each month of wind speed data one of three ENSO phases: cold (La Niña),

neutral, or warm (El Niño). Separating the data by ENSO phase, we were able to compute

differences between the cold/warm phase and the neutral phase. The three power

characteristic differences we computed were: 1) mean hourly wind speed 2) probability of a

low hourly wind event and 3) mean hourly wind power production.

Utility scale turbines have a hub height of around 80 m and, therefore, typically experience

higher wind speeds than those at the height where weather stations record wind data. An

approach commonly used to extrapolate 10 m wind speed data to 80 m is the power-law

relation (available at http://rredc.nrel.gov/wind/pubs/atlas),

(1)α)()(Rzz

RVzV =



Table 2. Listings of El Niño and La Niña events after 1950 as defined by SST’s in the Nino3.4 region and exceeding ± 0.4˚C threshold. The starting and ending month of each eventis given, along with the duration in months. Table originally published in Trenberth 1997

with tables updated through 1999El Niño events La Niña eventsBegin End Duration/month Begin End Duration/monthAug-51 Feb-52 7 Mar-50 Feb-51 12Mar-53 Nov-53 9 Jun-54 Mar-56 22Apr-57 Jan-58 15 May-56 Nov-56 7Jun-63 Feb-64 9 May-64 Jan-65 9May-65 Jun-66 14 Jul-70 Jan-72 19Sep-68 Mar70 19 Jun-73 Jun-74 13Apr-72 Mar-73 12 Sep-74 Apr-76 20Aug-76 Mar-77 8 Sep-84 Jun-85 10Jul-77 Jan-78 7 May-88 Jun-89 14Oct-79 Apr-80 7 Sep-95 Mar-96 7Apr-82 Jul-83 16 Jul-98 Dec-99 18Aug-86 Feb-88 19Mar-91 Jul-92 17Feb-93 Sep-93 8Jun-94 Mar-95 10Apr-97 Apr-98 13


where V(z) is wind speed at elevation z above the topographical surface (80 m in this case, i.e.

V(80)), VR

is wind speed at the reference elevation zR

(10 m above the topographical surface

in the rest of this paper), and α (typically 1/7) is the friction coefficient (Archer and Jacobson

2003).

Once we corrected the wind speed for height, we used a power curve for a typical utility

scale turbine (Fig. 2) to calculate the power produced from the wind speed at 80m. We used a

power curve for the NORDEX N60 1.3-MW turbine approximated by a fourth-order

polynomial. Note that most utility scale turbines only produce power between 4 m/s and 25

m/s. This non-linear relationship is similar for almost all modern wind turbines, with a range

of wind speeds for which they extract power from the wind; outside this range, no power is

produced.

We then tested the statistical significance of the apparent ENSO effects on these power

characteristics. Our statistical analysis involved four different stages: 1) global test of

significance, 2) local test of significance, 3) confidence interval for effect size, and 4)

distributional analysis. The main tool used to analyze the dataset was the open source

statistical programming language R (R Project for Statistical Computing, http://www.r-

project.org/). For a review of statistical methods used in teleconnections research, see Brown

and Katz (1991). Statistical characteristics of time series of hourly wind speed are treated in

Brown et al. (1984).

3.1 Global Test of SignificanceThe global test of significance is designed to detect a significant ENSO effect, treating all

months simultaneously. It utilizes only the sign of the result, rather than the magnitude, and is


Figure 2. Power curve for a Nordex N60/1300kW wind turbine (points) and a fitted polynomial curve

(line) used to estimate wind power from wind speed.


often referred to as the “sign test” (Hollander and Wolfe 1998). The sign test indicates whether

there is an ENSO effect in one or more months, but does not necessarily identify which specific

months. We plotted the monthly statistic of interest to give us an annual pattern for each

phase of ENSO. Simply by recording the sign of the differences, the test tells us whether there

is a statistically significant effect.

Any differences in a monthly statistic are similar to flipping a fair coin 12 times, if there were

no real ENSO effect and if the outcomes for different months were statistically independent.

The outcome of flipping a coin can be represented by a binomial distribution. Thus, there is a

0.0244% chance that this event will result in 12 heads. Likewise, there is a 0.0244% chance that

all tails will result. Combined, there is roughly a 0.049% chance that one would always get the

same result back, either all heads or all tails. This is the same as having, for example, all warm

phase monthly mean statistics result in higher values than all the neutral phase monthly mean

statistics or vice versa. It is less statistically significant if one or more of the warm phase

differences is negative. The probability of zero, one, two, or three points breaking with this

pattern is given in Table 3.

3.2 Local Test of SignificanceIn contrast to the global test, the local test of significance attempts to detect an ENSO effect for

a single given month at a time. It is necessarily less powerful than the global test, because it

makes use of wind specific months rather than all of the data. The local test randomizes the

data (mimicking the grouping by the ENSO phase), and then draws from that random set

without replacement. In other words, the data is simply permuted, or re-ordered, because

there are no repeated values. Permutation procedures focus on the underlying mechanism

that led to the data being distributed between groups (Efron and Tibshirani, 1993).

In order to determine whether the teleconnection between ENSO and wind speeds in the

northern Great Plains that we found is, in fact, statistically significant for any particular month,

we used the permutation approach. By randomly choosing new ENSO states for a particular

month of each year, we computed the same statistics of interest under the hypothesis of no

ENSO effect. This approach allows for autocorrelation because consecutive hours remain

paired together. We performed the permutation test 10,000 times for a particular month and

then determined the level which contained 95% of these differences in the statistic. By

comparing this level to the observed difference in our original statistic of interest, we could say

with 95% confidence whether any apparent effect is real.

3.3 Confidence Interval for Effect SizeThe confidence interval for effect size uses the bootstrap technique to attach uncertainty to a

statistic (Efron and Tibshirani, 1993). Such a confidence interval is useful regardless of

whether or not the ENSO effect is statistically significant (i.e. it can be viewed, more generally,

as a formal method of error analysis). Consider, for example, two groups of wind speed data

that have been assigned, one during an anomalous event, another during a normal event. The

bootstrap approach focuses primarily on the sampling error in estimating a difference

between the two conditions, resulting in a confidence interval for this difference.



Table 3. Binomial probabilities for sign testOccurrence Probability (%) Significance0 or 12 0.049 Significant at 1% level1 or 11 0.635 Significant at 1 % level2 or 10 3.857 Significant at 5% level3 or 9 14.600 Not significant at 10% level


Our approach first created two populations, the first consisting of, for example, all the

warm phase mean monthly statistics and the second consisting of all the neutral phase mean

monthly statistics. We then drew (with replacement) a sample of equal length for the new

“warm” and “neutral” sets. Each of these new sets of data is called a bootstrap sample and

differs from a permutation because we sampled with replacement and therefore could get

repeated values. We repeat this process 10,000 times as before, and thereby obtain 10,000

bootstrap samples. By computing the 2.5 and 97.5 percentiles of the statistic from the 10,000

bootstrap samples, we can infer a 95% confidence interval on the original statistic. In other

words, we are 95% confident that the real difference lies with the provided confidence

interval.

3.4 Distributional AnalysisIt is generally accepted that a wind regime is best represented by the Weibull probability

distribution, with probability density function:

(k>0, V>0, c>1) (2)

where V is the wind speed, c is the scale parameter with the same units as that of wind speed

and k is the dimensionless shape parameter (Basumatary et al. 2005, Dodson 2006). In our

study, we use the Weibull distribution for purely descriptive purposes, examining the effect of

ENSO on the entire distribution of wind speed simultaneously, rather than focusing on a single

wind statistic at a time as in the previous three stages of analysis. For wind speeds the shape

parameter usually ranges between 1 (an exponential distribution; i.e., highly positively

skewed) and 3 (a nearly normal distribution; i.e., only slightly skewed). When two

distributions are compared, the one with a lower shape parameter indicates that low wind

speeds occur more frequently provided the two distributions have the same scale parameter.

The scale parameter has no effect on the shape of the distribution, but does affect the mean

and variance of the distribution.

4. RESULTSFor the four sites analyzed during this study, the largest variations in wind speed occurred in

annual and diurnal cycles (we do not show the diurnal cycles here because their correlation

with ENSO was not significant). The most reliable high winds in the area of study normally

occur in April, whereas average wind speeds are the lowest in July. This accounts for the

overall peak and valley pattern seen in the plots of mean wind speed (Fig. 3). These annual

cycles, along with the diurnal cycles, are the dominant sources of variability in wind speeds.

Further details are discussed below, focusing first on the location of Huron.

4.1 HuronThe global test for significance (sign test) for mean hourly wind speeds at the Huron site

indicates that a statistically significant relationship with ENSO does exist, although it does not

identify specifically the timing or magnitude of this relationship. This can be seen in Figure 3,

which shows that the mean hourly wind speed during the warm (El Niño) phase is lower than

during the neutral phase for every month of the year (i.e., statistically significant at the 1%

level according to the sign test, see Table 3). Additionally, Figure 3 shows that for all months

except May and June, the mean hourly wind speed during the cold (La Niña) phase is also

lower than during the neutral phase (i.e., statistically significant at the 5% level). It is important

−

=− kk

c

V

c

V

c

kVf exp)(

1



to recognize that although the average difference in wind speed is small in magnitude, the

sign test is statistically significant because of the consistency of the direction of the effect. If

our only goal were to establish statistical significance, then this analysis would be sufficient,

but to be useful for wind power applications, the magnitude of the ENSO signal needs to be

quantified as well. Therefore, we now focus on the effects of the warm phase in two months,

April and July, selected to be representative of both the case in which the ENSO signal is

resolvable and the case in which it is not.

In order to determine the timing of the ENSO signal, the local test for significance

(permutation approach) is used. At Huron, the observed difference in April mean hourly wind

speeds between the neutral and warm phase is 0.84 m/s, while the permutation analysis

showed that 95% of the time this difference would be less than 0.54 m/s if there were no real

ENSO effect (i.e. the observed difference is statistically significant at the 5% level; see Table 4,

col. 4). In contrast, the difference in July mean hourly wind speeds between the neutral and

warm phase is 0.07 m/s, while the permutation analysis showed that 95% of the time this

difference would be less than 0.27 m/s (i.e. not statistically significant at the 5% level).

In order to determine the magnitude of the ENSO signal, the confidence interval for effect

size is used. Based on the bootstrap technique, the 95% confidence interval for the difference

(i.e., neutral minus warm phase) in April mean hourly wind speeds is 0.46 – 1.3 m/s (see Table

4, col. 3 and Fig. 3). All of the values in the confidence interval are positive at Huron (and the

data point for the neutral phase is above this range in Fig. 3), consistent with the permutation

analysis indicating a statistically significant effect. By contrast, the observed range in July

mean hourly wind speeds is -0.33 – 0.31 m/s (see Table 4 and Fig. 3). This interval contains both

positive and negative values, consistent with the permutation analysis indicating a lack of

statistical significance.



Figure 3. Mean hourly wind speed by month at Huron conditional on ENSO phase. Dashed vertical line

shows 95% confidence interval for difference between neutral and El Niño phase during April

(Month = 4) and July (Month = 7).

Table 4. Difference in mean hourly wind speed between neutral and warm phase at Huron(Neutral – Warm)

Month Mean wind speed (m/s) 95% confidence interval (m/s) 95% significance level (m/s) Significant at 5% level?April 0.84 0.46 – 1.30 0.54 YesJuly 0.07 –0.33 – 0.31 0.27 No


The observed effects on mean hourly wind speed indicate that ENSO should have an

influence on power production potential as well (Fig. 4). Using the power curve to translate

wind speed into wind power, the sign test again indicates that the difference between the

neutral and warm phase is statistically significant at the 1% level. Continuing with the same

two example months, the difference between neutral and warm phase mean hourly wind

power in April was less than 41 kW 95% of the time in the permutation analysis, while the

actual difference in April mean hourly wind power is 67 kW (i.e. statistically significant at the

5% level, see Table 5). The same difference in July mean wind was less than 22 kW 95% of the

time, while the actual difference in July mean wind power is 17 kW (i.e. not statistically

significant at the 5% level, see Table 5). For these two months, the corresponding 95%

confidence interval for the difference (i.e. neutral minus warm phase) in mean power is

included in Table 5 (col. 3) and shown in Fig. 4. When this analysis is extended to all twelve

months, the decrease in wind power production tends to be of greater magnitude for the

months of January though April as well as September and October.

The observed patterns in low hourly wind events associated with ENSO phases are very

similar in magnitude, though naturally inverted, compared to the patterns observed for mean

hourly wind speed. The warm phase has a higher probability of a low-speed wind event than

the neutral phase for every month of the year (i.e. statistically significant at the 1% level

according to the sign test). The cold phase has a similar pattern for all months except May and

June as shown in Figure 5 (i.e. statistically significant at the 5% level).


Table 5. Difference in mean hourly wind power between neutral and warm phase at Huron(Neutral – Warm)

Month Mean wind power (kW) 95% confidence interval (kW) 95% significance level (kW) Significant at 5% level?April 67 41 – 93 41 YesJuly 17 –4 – 40 22 No

Figure 4. Mean hourly wind power by month at Huron conditional on ENSO phase. Dashed vertical line

same as in Figure 3.


For this particular wind statistic, we focus on the effects of the cold phase during the

example months of July and September. The difference in September probability of a low-

speed hourly wind event between the neutral and cold phase is 7.5%, while the permutation

analysis illustrates that 95% of the time this difference would be less than 5.4% if there were no

ENSO effect (i.e. statistically significant at the 5% level, see Table 6). But the difference in July

probability of a low-speed hourly wind event between the same neutral and cold phases is

3.6%, while the permutation analysis indicates that 95% of the time this difference would be

less than 5.4% (i.e. not statistically significant at the 5% level, see Table 6). For these two

months, the corresponding 95 % confidence interval for the difference (i.e. cold minus neutral

phase) in probability of a lull is included in Table 5 (col. 3) and shown in Fig. 4.

As an example, we also produced Weibull curves summarizing the distribution of hourly

wind speed depending on the ENSO phase for the month of April. While the long tail is very

similar for each phase, the low-speed winds differ markedly. Since a wind turbine normally

only produces power above a threshold of 4 m sec-1, Figure 6 illustrates why the neutral phase

conditions produce the most power. The scale parameter of the Weibull distribution tends to

be smaller for the warm phase than for the neutral phase and as a result, the distribution is

shifted toward lower-speed wind.



Figure 5. Probability of low hourly wind events by month at Huron conditional on ENSO phase. Dashed

vertical line shows 95% confidence interval for difference between La Niña and neutral phase

during July (Month = 7) and September (Month = 9).

Table 6. Difference in probability of a low hourly wind event between cold and neutralphase at Huron

(Cold – Neutral)Month Probability of a lull (%) 95% confidence interval (%) 95% significance level Significant at 5% level?July 3.6 –2.6 – 9.7 5.4 NoSeptember 7.5 1.8 – 13.0 5.4 Yes


4.2 Other StationsThe influence of ENSO conditions on wind characteristics at Pierre is more subtle, but the

results are broadly similar to those found at Huron. The warm phase shows a statistically

significant decrease for both the mean hourly wind speed as well as mean hourly power

output (i.e. sign test is statistically significant at 1% level) (Fig. 7). We were not able to identify

a statistically significant effect of ENSO on low wind events; however, the direction of the

warm phase effect appears to be toward a higher probability of a lull when compared to the

normal phase, which is consistent with the other statistics for this station.


Figure 6. Fitted Weibull distributions for hourly wind speed during April at Huron conditional on

ENSO phase.

Figure 7. Mean hourly wind power by month at Pierre conditional on ENSO phase.


The direction of the apparent ENSO effects at the two locations in North Dakota is

generally the same as for Huron, but with smaller magnitudes. The ENSO effect at Williston is

only statistically significant during the cold phase. Generally, the data show a decrease in

mean hourly wind power production during La Niña conditions (Fig. 8a). The direction of the

effect is less clear for both mean hourly wind speed and low wind events, but both statistics

tend to indicate lower-speed winds except during the months of January, February, and

March. We were unable to identify any statistically significant ENSO effect on wind

observations at Bismarck (Fig. 8b), but the direction of the cold phase power production is

noticeably lower than during neutral phase months, especially during the spring. The cold

phase spring wind speeds, as well as the probability of a low-speed wind event, show a similar

pattern.



a

b

Figure 8. Mean hourly wind power by month at (a) Williston and (b) Bismark conditional on ENSO

phase.

Wind 31-3_final 3/9/07 2:27 pm 4

DISCUSSION The wind industry is well aware of the annual wind cycles on the Northern Great Plains. Our

study demonstrates that ENSO’s interannual influence on wind characteristics has

discernable signals that are relevant to wind power planning and siting as well. The robust

nature of our statistical analysis shows the ENSO signal is real, it contributes to the overall

knowledge about the sources of wind speed variability. Although ENSO’s effect on wind speed

is much smaller in magnitude than the annual cycles, it remains an important consideration

because the potential for power production is very sensitive to the probability distribution of

wind speed.

The influence of ENSO on South Dakota stations is more consistently statistically

significant than for the stations in North Dakota. In South Dakota, the warm phase exhibits a

statistically significant signal that tends to reduce mean wind speeds and mean wind power

production (using a typical utility scale power curve) while also increasing the probability of

a low wind event. At both Huron and Pierre, the analysis indicates with a high degree of

confidence that the wind power production, which is the main statistic of interest for wind

energy producers, is reduced during particular months in the warm phase of ENSO. This

decrease tends to be of greater magnitude for the months of January though April and again

for September and October. April wind speeds in particular are very likely to be below

average during a warm phase ENSO. The cold phase exhibits a parallel pattern to that of the

warm phase, although it is harder to identify this effect statistically, perhaps because there

were fewer cold-phase months than warm-phase months during the period of study.

At the Huron station, in particular, we are more than 95% confident that the cold phase

reduces mean wind speed and increases the probability of a low-speed wind event. This

decrease was statistically significant during the months of January, September, and

December. With both the warm phase and the cold phase, the Weibull scale parameter is

smaller, indicating that the wind speed distribution is shifted toward the low-speed winds

during these 3 months relative to the same months during the neutral phase (Fig. 5).

In North Dakota, the direction of the ENSO signal resembles that in South Dakota, but its

magnitude is smaller than that in South Dakota and consequently usually does not attain

statistical significance. It is important to remember that not attaining statistical significance

does not imply that there is no signal. A result of no statistical significance only shows that any

signal is likely to be of relatively small magnitude. The strongest North Dakota ENSO signal is

present in the mean wind power statistic at Williston station. Here we are 95% confident that

the cold phase reduces the mean wind power relative to the neutral phase. The magnitude of

this effect attains the highest level of statistical significant during the months of April,

September, and December.

The difference in the statistical significance of ENSO effects between North and South

Dakota may simply reflect the difficulty in detecting a weak, but real signal in the presence of

high variability. On the other hand, perhaps latitude plays some role in determining whether

the cold and warm phase of ENSO reduce wind speeds. Previous investigators have

documented the importance of ENSO events on the seasonal-to-interannual climate

variability in the Northern Great Plains associated with shifts in major dynamical features

such as the jet streams (e.g. Green et al. 1997; Philander 2004). However, many intertwined

issues regarding ENSO dynamics, impacts, forecasting and applications in the Northern Great

Plains remain unresolved (McPhaden et al., 2006). Additionally, other cycles such as the

Pacific/North American, Pacific Decadal, North Atlantic, and Arctic Oscillations could

combine with ENSO to increase variability. Future work could utilize our method to identify



which combination of teleconnections are the most important.

To illustrate the importance of wind resource estimation, take a simple example of a 1.3

MW turbine operating at Huron. Our results suggest that, without inclusion of an El Niño

teleconnection, the resource could be overestimated at maximum capacity by 72 kW per

turbine during an April El Niño event, an error of nearly 6%. With this proportional reduction

at all wind speeds, monthly generation could be 51,840 less kWh per turbine. If the power is

sold for $0.05 per kWh, this represents a monthly reduction of $2,592 per turbine. Random

variations of such magnitude are of course common.

CONCLUSIONThe future contribution of wind power to the North American energy system will depend, in

part at least, on the reliability of forecasts that support the integration of wind into a reliable

system based on diverse distributed resources. ENSO seems to be one source of the general

variability of wind speeds in some months and it is our hope that others will find the methods

we have introduced useful to explore a wider range of teleconnections with wind power, not

only for the northern Great Plains, but for other areas of the United States as well.

ACKNOWLEDGEMENTSThe Significant Opportunities in Atmospheric Research and Science (SOARS) program and

the scientists in the Institute for the Study of Society and Environment (ISSE), both located at

the National Center of Atmospheric Research (NCAR), made this work possible. Special

thanks to Larry McDaniel, Rajul Pandya, and Claudia Tebaldi for their help. We also thank two

anonymous referees for their comments. NCAR is sponsored by the National Science

Foundation (NSF). SOARS is funded by NSF, CIRES, NOAA, and UCAR/NCAR/UOP.

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