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).

Texas combined temperatures

This is one of an evolving series in which I am looking at the temperatures over the past 115 years in the different states of America. I started in Missouri, and have gradually changed the format of the post as I have found different things in my exploration of the data. Last week in looking at Oklahoma I changed the plot which compares the population around the measuring station to compare it with only the average of the last 30-years of temperature data, rather than the entire 115 years. This does not mean that the last 115 years of data is not valuable, though once I started collecting the GISS records for the state, I found that longer term range does not appear to concern them as much. Of the thirteen GISS stations listed for Texas, there are only three with a temperature record over the full 115 years. The remaining ten stations only have records since 1948.

There are 49 USHCN stations in Texas, ranging from Albany to Weatherford, and they are spread not quite evenly over the state.

Distribution of USHCN stations over Texas (USHCN )

Looking for the GISS station record for Austin I found that there are 3 different records in the GISS file the longest one, however, (1895 to 2009) is for an Austin at a different set of co-ordinates (39.5 N, 117.1 W) to the city which is the logical choice. Of course the currently used site (Austin/Robert 30.3 N 97.7 W) only has data from 1948, but then, so do the others I mentioned. Houston, for example, has four stations, but the one that GISS has chosen to keep maintaining records for has only been around since 1948. While it may be that I am more cynical than most it should be noted that, if one looks at the record for, for example, the GISS stations in Abilene and Amarillo, there are very clear higher temperature spikes in 1933 and 1934, above the most recent high temperatures.

Abilene TX temperatures (GISS)

Amarillo temperatures over the last century (GISS)

However if one only plots the temperatures since 1948 (as most GISS stations do) then there is no evidence that the temperatures were, in that record, higher than the recent past. For example consider the case of Lubbock as an example.

Historic temperature record for Lubbock TX (GISS)

Looking at the overall temperature differences between the GISS temperature average, and that of the homogenized USHCN temperatures, there is a clear trend, although heavily influenced by the changing number of stations in 1948.

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

For Texas as a whole, of the average of the temperatures measured, adjusted for time of observation only (the TOBS temperatures) are used, then the state temperature appears to have declined slightly over the past 115 years.

Average temperature in Texas from 1895.

Texas is 790 miles long and 660 miles wide, including the bit that sticks up almost to Kansas and down past parts of Mexico. Thus it extends from 93.5 deg W to 106.5 deg W, and from 25.2 degrees N to 36.5 degrees N. is the second largest state, behind only Alaska in size. The mean elevation of the state is 518 m, and it runs from sea-level up to 2,666 m (Guadalupe Park). The average USHCN station is at a height of 438 m, while the average GISS station is at a height of 375.9 m. The Center of the state by latitude is sensibly at 31.25 degrees N. (both GISS and USHCN stations center on 31.3 degrees).

Turning to the usual geographic parameters, first latitude:

Correlation of temperature with latitude for Texas

Temperature correlation with longitude for Texas

However as the land rises to the West, this is explained within the relation to elevation:

Texas correlation with elevation

Using the 30-year temperature average for each station and plotting this against population, does not, for this state, give much of a correlation in this case

.Texas correlation of the last 30-year average temperature with population

And then there is the usual:

Difference between the reported USHCN homogenized data for Texas and the raw data corrected for time of observation (TOBS).

Sorry this is a little rushed today but a little excitement in the hotel with a fire alarm bringing us all down to the lobby and slowing things related to leaving. Nothing relative to the disasters of the rest of the world, but taking time.