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.