State temperatures - Missouri corrected for observation time

The data that I have been correlating in regard to the temperatures of the different recording stations in the different states of the Union, now comes in three flavors, rather than the one that I started with. The three are the Raw data, data that has been adjusted for the time of day of observation (TOBS), and that which has been homogenized to account for the urban heat island effect, among other things.

However the little data that I have massaged from about 8 states suggests that those massaging the data who ignored town sizes below 10,000 folk were neglecting a sensitivity that the data was beginning to repetitively indicate. Thus it does not seem appropriate to continue with using the “homogenized” data of the set that was the only one available at the U.S. Historical Climate Network (USHCN) when I started . On the other hand, a strong case can be made that the adjustment that the USHCN has made to correct for the varying time of days at which the readings were taken is a legitimate one. If one goes to the USHCN website and selects the time of day corrected data (under the listing TOBS for time of observation) then one can, in the same way as I did last week create a table of information using this simple correction to the Raw data. And so, I will see, this week, whether making that change will affect the trends in the data.

Using the procedures that I have outlined, therefore, I have just done that, and will now continue to work back through the states I have already covered (marking the list with either RAW or TOBS) and beyond using this less manipulated data. For Missouri, you may recall that my original concern was for the difference between the GISS stations and the USHCN. Last week I posted the comparison of the Raw data and it showed that changing the data and the measuring stations both made a difference.

The blue line is for the homogenized data, the red is for the raw.

And if I now look at the difference using the TOBS data I get:


A slight upward trend, but not that significant. As for the temperatures in Missouri, over the past 100 years, with the correction – really there is no trend, it has been relatively stable:


I do note that the highest temperatures were some decades ago.

Actually a little surprise in looking at the changes in standard deviation. They had been reducing over time, with both the raw and homogenized data, but when the TOBS values are used, then there is a slight increase over time.


And for the four parameters that had started to appear to have an impact on relative temperature across the states. First there is latitude:


For which there is not much change. Then there is longitude:


And though the state is relatively flat though it rises toward the west, the change due to elevation:


Which gives a better correlation (and one more logically based) than longitude.

And finally that of population, which I have shown both with log and normal scales, but for tonight – because it emphasizes the effect of towns below 10,000 in size, I’ll use the normal one again.


And so, starting with this as the new baseline of a mid-western state, let’s head off West again, and see what the TOBS data will show.

Restarting the Temperature reviews - Missouri RAW data

On Saturdays I have started looking at the temperatures of different states, as they have changed over the past hundred years. In February I started with Missouri which, apart from being where the current center of the population of the United States resides, happens to be where I live. Having been an experimental researcher for my professional life, I decided to see what the data really said, in light of the claims of both sides in the climate change debate.

When I began I had some little idea of what I might find but in the main did not want to prejudge the issue. But thought it would be fun to make the odd hypothesis and then see if the data sustained it. So far I have covered about ten states, they are listed at the very bottom of the column to the right. However, as I was writing up the information for Indiana I found that the data being presented at the US Historical Climate Network (USHCN), had changed. The data that I had been using in the reviews to that point had been adjusted before being posted, as I noted:

However, note that a subset of GHCN, the United States Historical Climatology Network (USHCN), has been adjusted via a homogenization intended to remove urban warming and other artifacts [Karl et al., 1990; Peterson and Vose, 1997].

Since one of the things that I was finding was that there remained an urban size effect in the data, even after homogenization, and that I did not know what the “other artifacts” were, it would have been nice to have access to the raw data. Well that information is now available. I did not use it, at the time I wrote the post about Indiana, since there are significant gaps in the data table provided. However, for today’s post I have gone back to Missouri, to see if the conclusions change, when raw data is used instead of homogenized.

Basically what I have done, to make my life easier was to duplicate the Missouri data table, which I described forming back in the original post I then acquired the data for the RAW mean temperatures, as I described for the Indiana post, and just pasted them into the table over the original data. Doing it this way meant that the curves were already set up, and did not have to be created again.

Then, because I want to be able to plot information from the two sets of data to seen what the differences are, I copied the entire data sheet containing the RAW data, and pasted it as a second sheet in the Excel file containing the original homogenized data. So that is where we start our investigation for the day.

The first set of values that I had looked at in the original post was to see if there was a difference between the stations that GISS is using for Missouri, and those for the more numerous and distributed stations in the USHCN. There has been some criticism that the reduction in station numbers by GISS (recognized at their site) would bias the results that they are reporting. To see if this was the case I subtracted the average temperature for the USHCN stations from the average of the 3 GISS stations and plotted the result. That result showed that the difference between the two was slightly reducing over time, implying that the criticism, for the Missouri data, was, perhaps unfounded. This was the original plot:


I now shrink the size of the individual data points, and add the same set of averaged difference between the values per year from Sheet 2 – the RAW data. (Ed. note - I will use a red color for the raw data and blue/purple for the homogenized values). And this is what I get:


Now this second curve (with equation etc to the right) shows a much stronger correlation of temp diff with time, and that, starting in about 1970 that the temperature of the GISS stations rose much more rapidly than the USHCN stations, which is the suspicion that Chiefio had expressed. This is, however, only one state and we will see what happens as we progress through the rest.

Looking at the actual temperatures over the century the curves are slightly more significantly changed. In the old plot (with homogenized data) we found:


With the RAW data, however, the plot is a little different:


Instead of there being a steady (though minor) rise in temperature over time, there is a slight decline – not particularly statistically significant, but on the other hand significant in that the “homogenization” of the data takes a negative trend in the RAW data and adjusts it the other way.

There are, in fact sufficient differences between the two sets of curves that the initial conclusions are not as strongly supported by the raw data. That, at this stage, is merely just worth noting since, for this sort of topic there is a need for much more data before stronger conclusions can be drawn. And so we will work back through the states, noting how the differences in data change the conclusions. And perhaps, after a while, there will be enough information to start to draw stronger conclusions.

But for now I am going to finish this by posting the original plots derived from the “normalized” data with the raw data graphs set below them (with the addition of the longitude graph that I did not show the first time).

Moving on to the second hypothesis, which comes from GISS, and is that temperature is insensitive to adjacent population below a community size of 10,000 folk. This is a plot of row 129 plotted against row 9. I am going to show the plot twice. The first time I am using a log scale for the horizontal axis to cover the range from a population of 30 to that of over a million.


And now I am going to change the scale so that the horizontal scale is linear, and truncate it so that it only shows the data up to a population of 50,000.


Notice how the temperature is much more sensitive to population BELOW a population of 10,000 relative to the sensitivity above that size. Thus the assumption that GISS makes in classifying every town below 10,000 as rural without any sensitivity to population is clearly not correct. That was my original conclusion – here is the data plot when the raw data was used:


Not quite as impressive a correlation, but again the scatter in the data and the form of the regression is still evident, so the conclusion stands, albeit more weakly.

I commented earlier that interestingly this also possibly explains the decline in the temperature difference with time (although it would require inputting data from earlier years census to fully explore the topic). The assumption behind the first two hypotheses was that the larger towns had a greater sensitivity to urban heat, which is getting worse, but in reality, if the smaller towns were growing faster (and require less population change to have an impact on the measured temperature) then they would be gaining temperature, because of that growth, faster than the urban sites – hence the negative slope to the graph. Given that the slope is now changed this thought (while worth remembering as we go to larger data sets is now not substantiated by the raw data analysis)

Which brings me to my hypothesis that the scatter in the data would get larger with time, given the deterioration and urbanization around the weather stations. By using standard deviation to illustrate scatter, the plot, if I am right should have an upward slope, over time.


Hmm! Well it looks as though I got that wrong – it was heading the way I thought until the 1940’s and then it started to bend the other way. Apparently the change from glass thermometers to the automated Maximum/Minimum Temperature System (MMTS) started about then and the changing shape of the curve is perhaps indicative of the spread of the new system. And that conclusion hasn’t changed.


In all these graphs it should be borne in mind that Missouri has had a relatively stable climate over the past hundred and fifteen years or so.

There are also likely influences across the state due to changes in latitude and longitude. And since, with the data table assembled, generating additional plots is easy and relatively fast, we can take a look. Originally I said “ It turns out that Longitude doesn’t have that much effect, but the temperature values are much more sensitive to Latitude than anything else that we have discussed.” Based on these curves:


And


Now when the raw data is plotted, the scatter is reduced, but there are, consistently a couple of outlying data points that influence the correlation. So these are the raw data plots for Latitude and Longitude.




And I’ll add a final plot to the set that wasn’t in the original, but later proved important (and explained some of the longitudinal variation) that of elevation, though this is a relatively flat state.


So with some rather muddy results to start with, off we go on our trek again.