Temperatures in the Flat Middle of the USA

Last week I noted the curious drop in temperature along the Eastern Seaboard in the period from around 1950 to 1965. The question then arises as to whether there are other pieces of information in the data that I have accumulated, and so, out of curiosity, today I will look at the strip of states in the middle of the country and compare their average temperature with that of the East Coast. (Checking to see if my memory that there wasn't quite the same drop is, in fact, true).

I am trying to stay away from mountains in this strip, so I will pick Minnesota, Wisconsin, Michigan, Iowa, Illinois, Indiana, Kansas, Missouri, Oklahoma, Arkansas, Texas, Louisiana, Mississippi, and Alabama. And, while there are some hills in the selection, just for simple characterization I am going to call these the “Flat Mid States.”

Interestingly all these months since I first looked at Arkansas, there is still a problem downloading the data for Rohwer from the USHCN, so I used the GISS values instead (recognizing that they are a little manipulated, but since I am averaging over 15 stations, reckoning that the “adjustment” won’t make that much difference.)

Looking at the average plot just averaging the state average values, calculated in earlier posts, one gets, for the homogenized data:

Averaged temperature in the Mid States using state average homogenized temperatures

When one uses the TOBS data, rather than the homogenized values, then the curve changes to

Averaged temperature in the Mid States using state average TOBS temperatures

If I weigh the results by the stations in the state (there are a total of 405 stations in this series), then the result, using TOBS data becomes:

Averaged temperature in the Mid States using state average TOBS temperatures, weighted by number of stations

The effective result of the weighting in the above plot is just to average all the station data. When, however, the area of the states is considered, bearing in mind that apart from Texas they are all much the same size, then the result becomes:

Averaged temperature in the Mid States using state average TOBS temperatures, weighted by state area.

It should be noted that the trend over the range of data is sensibly zero, i.e. there has been no temperature increase on average for these states, over the past 110 years.

The drop in temperature, so clear in the data for the Atlantic States is not as prolonged here, falling from 57.9 deg F in 1954 to 55.1 deg in 1960. Comparing the two plots:

Average temperature in the Flat Mid states (upper green) , relative to those of the Atlantic Shore states (lower red), averaging states weighted by area.

Note that there is a definite rise in temperature along the sea siding states. Also the larger size of Texas tends to give a larger weight to the south here, and thus the overall higher average.

Looking at the individual trends for each state in this set, I have again divided it into two sets to make it easier to distinguish the individual lines.

Average temperatures for the Northern set of states in the Flat Mid region

Note that Wisconsin and Michigan virtually overlap.

Average temperatures for the Southern set of states in the Flat Mid region
Here it is Arkansas and Oklahoma that are almost superimposed.

I will go on to look at other regions and compare them to see how temperature averages differ around the country, but will leave you with the usual comparison.

Difference between the average value for the USHCN homogenized values after the original TOBS values have been subtracted.

Temperature correlation with population and the number of years to use

In the last few posts on state historic temperatures, I have changed the basis for the correlation between local station temperature and population from using an average of the station temperature over the whole period to just using the average of the past 30 years. However I chose a 30-year interval without any real basis for the choice. So I thought I would pause the state temperature reviews this week, to see what the best period would be to use for the temperature averages. Because the population data is relatively current (I am using the population given on the citi-data websites) the longer the time interval then the less likely an accurate correlation will be. On the other hand the likely scatter in individual station data might suggest that the longer the time interval the more accurate the correlation.

Note I was about half-way through writing this post when I read the Bishop’s post (Bishop Hill, not mine) on Lord Beddington’s comments on the UHI, specifically

Most stations are not affected by the urban heat island effect and there are well-established ways of taking the effect into account for stations that are (such as comparing temperatures on still and windy nights and excluding urban stations.).

I will have more comment on this statement later in the post, but the results that I am discussing demonstrably show, as I work through them, that the good Lord was wrong. (As I will discuss a little more below). But to return to answering the question first . . .

Starting with the last year of data (2009) I calculated the average temperature for each station and plotted this against the population data. (Though I did this for a number of states I will use the data from Minnesota as the initial state for illustrative purposes).

Temperature as a function of station population for Minnesota, using only the last recorded year temperature is reported.

Including a trendline on the plot, I then recorded the constant (35.714), the coefficient (0.7021) and the regression coefficient (0.16893) against the number of years used to calculate the average (in this case one). I then increased the number of years to two, by averaging 2008 and 2009 data, and plotted the curve again. I repeated this, incrementing the number of years in the average by one, until I was averaging 35 years in the plot.

Temperature as a function of station population for Minnesota, using an average of the last 35 years for the temperature value.

Beyond this point I stepped the number of years in 5-year increments out to 70 at which point I stopped. Then I plotted the three values in turn against the number of years in the average:

The influence of the number of years used in the average on the coefficient of the log of population for Minnesota

One thing to remember is that as more years are included, the added years are likely to be at a lower temperature, but if the temperature change is evenly distributed around the state, then the coefficient should remain the same. If, however, there is a change over time that differs from station to station, then the slope will change over time. Keeping that thought in mind, let’s now look at the change in the constant as the number of years is increased.

Variation in the base temperature in the correlation of temperature and population for Minnesota, as a function of the number of years used in determining the average temperature.

The third plot is the correlation coefficient, or r-squared value:

The variation in the regression coefficient (r-squared) determined for a plot of temperature with population for Minnesota, as a function of the number of years used to determine the average temperature.

The last plot suggests that (ignoring the one high value at year 22) the best selection for the number of years to include in the calculation is 14. However, before settling on this value I decided to check the values for other states.

The next state, moving backwards in the series, that I checked was Texas. When I had first made these calculations I had included the GISS station data as well as the USHCN station data in the calculation of population effect. One reason for this is that the GISS stations are, by and large, in the cities with the larger populations in each state. But as I have gone round the states in many states at least one of the stations that GISS uses has only data that starts in 1948. This also will affect the overall averages. Texas is not a state that has a good correlation between temperature and population, but, out of curiosity I ran the calculations described above for Texas both combining GISS and USHCN stations, and using only USHCN TOBS data. (I used Texas for this comparative analysis since it has a significant number of GISS stations). The correlation coefficient was significantly better when the GISS data was removed, and so I removed those values from all the correlations.

(This meant, inter alia, redoing the MN calculations, since this was originally calculated including the GISS values – the plots above, and from this point on, will not include the GISS values).

Texas values underwent a significant change where more than 10 years were included, and this is most clearly seen with the r-squared values.

The variation in the regression coefficient (r-squared) determined for a plot of temperature with population for Texas, as a function of the number of years used to determine the average temperature.

Part of the reason for this is that there were a couple of stations that only had data until about 2000, and when these were included they reduced the correlation. Without those values the correlation was higher, and interestingly, the coefficient was somewhat similar to that of Minnesota.

The influence of the number of years used in the average on the coefficient of the log of population for Texas

The next state that I looked at was Oklahoma, and ran the same procedure to see how changing the number of years included changed the trendline numbers. The plots that I obtained for the regression and population coefficients, using the same procedure, were as follows:

The variation in the regression coefficient (r-squared) determined for a plot of temperature with population for Oklahoma, as a function of the number of years used to determine the average temperature.

This would suggest either a dozen or twenty-three years should be used to compute the average temperature, looking at the two peaks. When the population coefficient curve is examined, below, it can be seen that at the point where the highest regression occurs, the value of the coefficient is around 0.65.

The influence of the number of years used in the average on the coefficient of the log of population for Oklahoma

The next state, moving steadily North from Oklahoma, is Kansas. This spoils the pattern since, although there are good correlations with latitude and elevation (the other two most significant contributors to varied temperature distribution around the state) there is not a good correlation with population. I did not do as many checks on the number of years for this state, as you can see below, but it is interesting that again there are peaks in the r-squared values at about 10 and 20 year intervals.

The variation in the regression coefficient (r-squared) determined for a plot of temperature with population for Kansas, as a function of the number of years used to determine the average temperature.

The correlation coefficient for the log population was about 0.1 in both cases.

Moving North again to Nebraska, one can run the same derivations and get:

The variation in the regression coefficient (r-squared) determined for a plot of temperature with population for Nebraska, as a function of the number of years used to determine the average temperature.

Which again suggests that either 10 or 20-22 years to derive an average would be better than 30.

The influence of the number of years used in the average on the coefficient of the log of population for Nebraska

Which again gives a coefficient value, in both cases, of between 0.6 and 0.7.

I am not going to belabor the point. In this series I have now looked at the temperature data for just over half of the contiguous states of the Union. It would appear that there is a statistical substantiation for the correlation between temperature and population around the measuring stations of those states of the form:

Temperature = state base temperature + a.logn (local population)

Where a is a constant with a value between 0.5 and 0.8.

I haven’t yet gone back and done the above calculation for all the states yet which would allow a more refined estimate of the value, nor have I yet done the correlation to the three parameters (latitude, elevation and population) together – though it is likely that the latitude and elevation influence the state base temperature, how they interact to control station temperature is not yet clear.

But for now I feel very comfortable stating that Lord Beddington’s statement is demonstrably, factually and terminologically inexact. (To misquote Sir Winston Churchill).

Kansas combined temperatures (revisited)

As I move around the states looking at the differences between the GISS station temperatures, the USHCN homogenized temperatures and the original raw temperature data (albeit modified by a correction for the Time of Observation (TOBS), we have returned to Kansas, which I had originally written about before the TOBS data became available, and then written again looking at that data.

Since then the style of the presentations has changed a little bit, so I am going to redo the Kansas post, and just put the combined temperature reference into the list down the right side of the site, so as to bring it (and later some of the other states) into the same format as I have now evolved. So to begin , Kansas has 31 USHCN stations, that are reasonably evenly distributed around the state:

USHCN station locations in Kansas (USHCN)

Back when I did the initial TOBS data analysis I was still sufficiently naïve that I did not realize how, by manipulation of the station picks, GISS could manipulate the temperatures that it reports. Chiefio has explained both how the current selection raises the overall U..S.A. station record by 0.6 deg C, over the USHCN average, by selective deletion of stations, for 2008, for example. There is also a new set of 59 GISS stations that have been added, but these do not have the historic data that I have been comparing to, so I am going to ignore those additions for the rest of this series. However there is one additional point that this study has brought out about how NASA shall we say “fudges” their data.

I first mentioned, when looking at Idaho’s temperatures, the GISS habit of marrying a short-term station with a long-term one. E.M. Smith (Chiefio) has done a detailed post explaining how, by using this type of combination it is possible to generate a trend that does not exist in the initial data. What has since been interesting, as I have continued with this series, is finding just how many state temperature situations that applies to. And the first one that I noticed, but not then realizing the reason, was Kansas, the second state that I had looked at There are 5 GISS stations on the list, including Witchita , Topeka, Concordia,, Dodge City and Goodland. It is Goodland that only has data since 1948.

Goodland KS GISS station data

Because of the small number of stations, the impact of the partial temperature record is significant on the relative difference between the GISS and USHCN average values (using the homogenized USHCN data for this)

Difference between the average GISS station temperature and that of the homogenized USHCN stations

Turning to the TOBS data, and looking at the average temperature change in the state, over time, the temperature rise is 0.85 degrees per century (the homogenized data would suggest a rise of 1.2 degrees.)

Average TOBS temperatures for Kansas (USHCN)

The geography of Kansas is that it is 400 miles long and 210 miles wide, running from roughly 94.5 deg W to 102 deg W, and 37 deg N to 40 deg N. The highest point is at 1,231 m, and the average elevation is at 609.6 m. (The average USHCN station is at 511 m and the average GISS station at 586 m).

Looking therefore at the variation in temperature with the geographical parameters of the state:

Effect of station latitude on recorded temperatures in Kansas

This is a state where the effect of latitude is clearly defined. Given that the state elevation is steadily rising to the West, as we move towards the Rockies it is not surprising that we see:


The stronger and real correlation is with the changing elevation



There was not, in the original search for data, much problem in finding population numbers

The effect of current population size on average station temperature for Kansas

Note that for the curve above, because it is the only source of information for the larger cities in the state, I have included the GISS station data.

And finally there is the difference that shows the effect of USHCN homogenization of the data relative to that recorded and adjusted only for time of observation.


I am replacing the original two posts of Kansas on the comprehensive list with this post so as to give a greater consistency to the formatting.

Kansas temperatures revisited - the TOBS effect

Having restarted the analysis of the temperature data, the second state along the road is Kansas. In the original post on the topic I had used the homogenized data, and had ignored (through ignorance of location) one of the GISS sites. So we will redo the data and see if there are any changes in the conclusions. In case you are new to this I am comparing the GISS station average data for the different states with the averages from the USHCN sites for each state.

Having looked at the three different sets of data available (Raw, corrected for time of observation (TOBS) and homogenized) in this set I am going to use the TOBS set.

And that means that first I have to get them, which I do from selecting the Map Sites for Kansas at the USHCN web page, and then clicking on the “Get Monthly Data” icon, selecting the “create a download file of data summarized by year”; and then selecting the “Annual Average Mean Temperature – TOBS,” and hitting the submit button.

This is for Anthony, KS so after downloading the file, which is comma separated (csv), I label it and save it to the desk top. The files for all 31 stations is handled in the same way, and then each is copied and pasted over the data in the master file that I made for the original homogenized data. (The file is then saved with a TOBS label).


Now there was another error that I made in the original file. I thought that only Wichita, Topeka and Concordia were in Kansas, from Chiefio’s list, however there are also Dodge City and Goodland that I did not include. So I need to go to the GISS site and download (and convert) those temperatures. It is mildly irritating to find that the Goodland data only goes back to 1948, so that when one just runs an average comparison of the two sets of data, the shorter series has a quite significant impact. However, with this data series, in contrast to the homogenized data set, there are significant gaps, particularly in the early years in the individual station data. If one goes back and compares the homogenized data plot both lines, although taken to the second power are relatively close to straight, and both show a change in response from positive difference to negative difference around 1950 (which is odd given that the initial plot had three full GISS station data to work with).


Interestingly, with the TOBS correction only in the data, there is realistically no trend with the standard deviations on the data, while there was a declining trend in the earlier plot that is not evident from the TOBS data.


In regard to the overall trend of temperature in the state over the past 115 years:


One thing that I have just noticed, however, in comparing the two sets of data, is that the scale of the vertical axis has changed. The above is the data for the average temp with the only correction being the time of day of observation. This is the plot from the homogenized data set, that I put up last February.


The shapes are close to the same, and the highest temperatures were during the dust bowl years, but the values themselves have changed – how odd. So a quick check back on that data, and there was an error - I had not correctly averaged the initial data set, and when I corrected that the two curves are much closer, with the homogenized set now falling very close to the TOBS in overall average values (homogenized value 54.8 deg, for the TOBS data 54.78 deg) but the rate of temperature increase is now less.

One of the original thoughts related to the correlation with population, and the revised plot of this, shown with a log population scale, shows:


The zero population value was 52.1 deg and the correlation coefficient was at 0.08, which given that the homogenized data is supposed to compensate for population levels is a bit odd, but never mind, we really are still in the early game without huge numbers for the larger cities.

Looking at the three basic parameters that began to evolve (or not) from the data, these are the variation with latitude (which is fairly logical):


The correlation with longitude gets better, relative to Missouri, but that is explicable:


As one moves west through Kansas one starts to approach the Rockies, and the land rises. Thus if one looks at the effect of this rising elevation:


You note, from the r-squared value, that it is likely that the change in elevation is the stronger driver toward temperature drop.

Interestingly with using the TOBS data the correlation with latitude has improved, as has that with elevation, which is much better than that with the homogenized data, though the longitudinal correlation is slightly worse.

So more questions, some trends remain, we will just have to see how this pans out as we move into gold-mining country.

Temperature data for Kansas - does it help?

Well this is the third in what was not planned as a continuing saga. So far some significant climate assumptions haven’t held up so well on examination, so today I am moving from the data for Missouri into Kansas. And the first hypothesis we will look at is that the data and trends for Kansas are the same as for Missouri. As a subsidiary I will put the two sets of data together, and with a total of around 60 stations, see if this improves any of the statistics that we looked at earlier.

Going through the same process that I followed in putting together the data for Missouri, there are 31 stations in the USHCN data base, and with just a little bit of judicious editing I can use the same format that I have already developed to start putting the different plots together. There are in addition 3 stations in the GISS network (Wichita, Topeka and Concordia). The difference between Missouri and Kansas is that while the former has all the GISS stations in the larger metropolises the Kansas GISS data does include one rural station (Concordia – listed as rural in GISS).

Now there is a second modification to the procedures, since I don’t have a map of Kansas from which to get the census data. Instead I went to the Internet, and, for consistency, used the information from the series of city data that can be found on the Web. For example, if I seek “population Anthony Kansas” the top site is www.city-data.com/city/Anthony-Kansas.html. There are similar sites for all the places in both the USHCN and GISS stations, and, for consistency therefore I used the population numbers from this series of sites. (The data is given for 2008).

I got the information on which were the GISS sites in Kansas by using the list provided by Chiefio and as I noted these are in Wichita, Topeka and Concordia. I also added the station height information (from a Google search under “elevation Wichita Kansas” ), since we are moving closer to the Rockies.

So, if you remember there was no significant warming in Missouri over the last 114 years (the data sets are from 1895 on). My initial hypothesis is that this is also true for Kansas. For our purposes an r-squared value of less than 0.05 is considered not to be significant. (I explained this a little last week). And the data says:


And so Kansas is not showing the same trend as Missouri. Here there has been a significant warming over the past 114 years. So how about the other hypotheses that we looked at in that earlier post?

First of all is there a difference between the GISS stations and the overall average for the state. In Missouri that was a 1.19 degree F difference between the GISS stations and the rest. In Kansas this is, on average only a 0.27 deg F difference. Looking at the trend in the data over the years, we find:


And this is also not entirely expected, if one follows conventional UHI theory, since the two largest stations are in the GISS trio, and one might have thought that this would have led to an increase in the GISS temperatures over the rest of the state, which is largely rural.

However, if you remember from the Missouri data, there was a logarithmic relationship between temperature and population, which gave a greater temperature change as small towns grew, over larger city changes. Thus if Kansas, a largely rural state, was seeing a greater proportion of growth in its smaller communities then perhaps this would explain the change.


The significance is still rather low (since as Luis has pointed out, working with data from the rural states reduces the sample size for larger communities). But if we combine the two data sets, what does this do to the correlation?


Now, with more data, there is a significant correlation between population and temperature.

So let’s have a look at a couple of other parameters, there was a very strong (r-squared 0f 0.8) correlation of temperature with latitude, but not much of a correlation with longitude. How does that stand up for the Kansas data?


Hmm! The correlation isn’t nearly as good. So maybe there is another factor coming into play – how about longitude, which was not significant in Missouri?


I suppose this makes a bit of sense. The further west we are going the higher, since we are approaching the Rockies. So after inputting the elevation of the stations we can see if that gives us the same correlation:


And it does not!

Which means, I suppose, that we had better continue this investigation, and move the data acquisition another state West – which was not what I expected when I started writing this, but we’ll leave that investigation until next week.

And one last bit of curiosity, how do the standard deviations hold, over time, with the new state data?


Well we are still getting that improvement in quality with time, which, as I explained initially, may be due to the change from manually reading thermometers to the automated systems being introduced. We will have to see how this holds up as our search for meaning in the data continues.

P.S. As with all the information in this series, if you want a set of the data please let me know, through comments where you want it sent.

Wind in the Rockies is expensive

Government policy can, once decided, be implemented by a combination of laws and financial incentives/disincentives among other means. Thus, for example, when trying to change the ways in which America gets power, the government can limit the amount of coal burned, both by direct fiat, and by making it too expensive (through the cost of permits). The former limit will be established through the caps on the production of GHG, assuming that there is no immediate vast investment in sequestration. There is a fair amount of debate over whether the financial incentive will be through a direct tax or the more indirect route of charging for allocations. Having heard Congressman Waxman’s aide at the EIA meeting, I believe the decision is long over, and that, if anything does come out of this Congress it will be the cap and trade model that will be used.

That having been said, since the nation is growing in numbers, even if per capita use is held constant (as it has sensibly been in California) any reduction in fossil fuel use will need to be replaced with an alternative. At present the most widely touted of these are wind and solar, and the high costs of solar mean that, for many utilities seeking change, wind has been the choice, but even this is, in some cases, proving to be too expensive an option.

Wind farms are becoming more obvious around the country, and their message is strengthened with a steady campaign of adverts. T. Boone Pickens is seen more as a savior than as a salesman. And the Secretary of the Interior currently is pointing out that there is sufficient wind available to replace all the coal-fired power stations in the country. But under all the hype, and after the cranes have come and gone, the change in energy source has to make economic sense. Wind farms will only be established where there is a credible likelihood of their making money for the investors that raise them. The current comments of the Secretary come as hearings get under way around the country.

Salazar said ocean winds along the East Coast can generate 1 million megawatts of power, roughly the equivalent of 3,000 medium-sized coal-fired power plants, or nearly five times the number of coal plants now operating in the United States, according to the Energy Department.

Salazar could not estimate how many windmills might be needed to generate 1 million megawatts of power, saying it would depend on their size and how far from the coast they were located.

Mark Rodgers, a spokesman for Cape Wind, which wants to build a wind farm off Cape Cod, Mass., estimates it would take hundreds of thousands of windmills. The average wind turbine today generates 2 to 5 megawatts per unit, he said.

This is the first of four hearings, and focused on the East Coast. But the potential states most likely to benefit can be judged from the DoI plan to create Regional Energy Permitting Centers.

To expedite production of renewable energy on public lands while protecting land, water, and wildlife, Secretary of the Interior Ken Salazar today pledged to create four Renewable Energy Coordination Offices, one each in California, Nevada, Wyoming, and Arizona, along with smaller renewable energy teams in New Mexico, Idaho, Utah, Colorado and Oregon.

You may note that these are all in the West.

While the numbers quoted are large, and the potential benefits of moving to renewable energy are continually being cited, the underlying realities of getting a good return on the investment, at a lower cost than the alternative, is being kept quiet. Unfortunately, as an article in USA Today notes, even with the best will in the world, those benefits don’t always happen. And, in the case of Durango, CO, the utility is moving back to coal, from wind.

For two years, the city of Durango, Colo., bought electricity for all its government buildings from wind farms. The City Council ended that program this year, reverting to electricity derived from coal-burning plants and saving the cash-strapped city about $45,000.

The Durango plan has its roots back in 2007 when the city made the move to wind energy.

Green power currently constitute(d) about 10% of city power purchases. The extra the city pays for green energy will add about $120,000 a year to its electrical bill officials said. But the extra cost will be offset by an energy audit aimed at cutting power consumption. With the wind power option, electric customers pay $1.25 per block of 100-kilowatt-hours in addition to their regular rate. The extra that consumers pay for their power funds investment by power producers in alternative-energy sources such as hydropower, solar and wind. But electricity from all sources flows on the same line. In 2006, Durango used almost 8.15 million kilowatt-hours of power at a cost of $779,000.

At the time, as you may notice, the city recognized that the renewable energy would cost more, but felt that it could make up the difference with improved efficiency. Which is the same sort of argument that we are now getting from the new Administration.

Although the above cite is recent, the change actually occurred last December.

City Manager Ron LeBlanc recommended the city stop buying power from renewable energy sources when it became necessary to cut the 2009 budget by more than $500,000. The city council approved the budget, including his recommendation, earlier this month.

The La Plata electric association charges 80 cents more per 100 kilowatt hours for electricity from solar and wind power. LeBlanc says that adds $45,000 to the city’s annual electric bill.

La Plata does not generate the power itself, but is passing along a program from Tri-State.

The LPEA Green Power program was initiated in 1998 when Tri-State – from which LPEA purchases its power – responded to requests by its member systems, to include a green power option as part of its available resources to end-use consumers. Because of this program, LPEA customers who request purchase of green power receive it at LPEA’s cost from Tri-State, $1.25 per 100 kilowatt-hour block per month. Purchasing one block of Green Power costs consumers less than $.05 per day. To date, LPEA is among the leading purchasers of Green Power in Tri-State’s 44-member cooperative system, supplying nearly 800,000 kilowatt hours of Green Power generation each month.

Tr-State had lowered their costs to 80 cents in January, 2008. . However the company has also carried out an Integrated Resource Plan (IRP) looking at future energy supply, based on anticipated need. That plan concluded:

Tri-State proposes to develop and own two new 700-megawatt supercritical coal-based units at the existing coal-based, 360-megawatt Holcomb Station in western Kansas. The efficient units would include best available control technology to minimize air emissions and activated carbon injection to minimize mercury emissions. The IRP clearly reinforces the need for the first 700-megawatt baseload resource in 2012, which is when Tri-State first unit at Holcomb Station could be online.

Since the first Holcomb unit cannot be brought on line until 2012, Tri-State is left with a significant deficiency in both capacity and energy during the interim period; and its options are somewhat limited. For modeling purposes in this IRP, it is assumed that Tri-State will install combustion turbines (CTs) as soon as possible and purchase energy from the market. However, there are many other options available to Tri-State.

These are the coal-fired power plants that then-Governor Sibelius vetoed three times, with part of her argument being that more than 80% of the energy they would produce would be exported. Which leaves one wondering where the power will come from for Durango, since I am presuming that the turbines are gas-driven, and thus too expensive.

(Um! Yes I know that Durango said that they are going back to coal, and have so moved, but Tri-State still has their supply problem, or will as soon as the economy picks back up without the new stations they were relying on.)