Friday, February 22, 2019

Back to Basic Statistics: Global Warming is Likely An Artifact of Localized Night Time Warming


I am looking at the history of temperature change from the 1900 to 2010 using a sample of 691 stations from the USHCN. The choice of stations was determined by the number of years recorded. These 691 stations have a minimum of 100 annual records.

Since these stations cover a range of temperatures and the number of stations reporting per year is less than 691 for much of this time frame it is necessary to translate the records to deviations from a baseline average. This is less than ideal, however limiting the sample to stations with 111 years reduces the sample size quite a bit.

The question at this point is how to determine an appropriate time interval to determine this base line.

Consider what happens when I use the 1980s to establish this base line. The shape of the average temperature will be the same no matter what baseline I choose. The issue here is what happens to the standard deviations. This indicates my choice of the 80’s as a baseline has changed the shape of the data distribution over time. This shape does not accurately reflect how the data changes.

A test for this is to simply compare the projection against the actual data from 1900 forward. I accomplished this by subtracting decadal averages from the 2000-2009 average for each station and looking at the average and spread for each decade. This shows this projection is only accurate from about 1980 forward. The accuracy decreases progressively from 1980 back to 1900. This was the expected result.






 

Consider what happens when I use the 1920’s to establish my average baseline. Using the same testing methods as before I found this to be reasonably accurate, within the population parameters, from 1920 forward.






 

I decided upon using an average from 1900 to 1960 as a baseline. When tested as above this produced the most accurate results. Part of that comes from the longer length of time used. This evens out smaller sub trends within the data. There is also the issue of number of stations reporting. Any longer time frame begins reducing the projection accuracy.









But what about all those shorter station records?

It should be obvious, based upon how I define my baseline, I can’t add in any records beginning after the baseline time frame. For those stations which began prior to 1960 the accuracy with which they can be located within the record is dependent upon how many years they reported within the baseline period. I can only accurately place a record within this time frame if I accurately transform the data by the station location’s true average from 1900 to 1960.

You can test this by comparing sub averages of samples from the data set. For example, look at the difference by station between their 1900-1960 average and their 1930 to 1960 average. Look at the average and standard deviation of that data set. The standard deviation defines the amount of uncertainty in including stations which began in 1930. The added uncertainty is simply unacceptable. Meaning I can't accurately place the shorter record into the data set because I do not know what its true average really was.
The proper way to handle shorter data sets is to perform this exact same process from a later baseline. Again, you need to maintain as uniform a data set as you can. The same comments concerning shorter data records still apply. To include even more data sets you would look at a shorter time frame. Shorter studies can then be compared against longer studies during coincident time frames. You simply cannot average a record from 1930 to 1950 with a record from 1990 to 2005. That is common sense between two records, that logic applies to many records.

Now the results

The average temperature of this sample did rise by 0.28° C in the 2000’s relative to the 1900 – 1960 baseline. The standard deviation also rose by .25 in the same manner. That translates into and increase of 0.8° C in the spread of the data.




 

Let’s examine how the upper and lower edges defining a projected 90% of the population changed over time.

 
 



As you can see both plots show the warming which occurred going into the 1930’s and the subsequent cooling trend from about 1950 forward. However, the upper bound shows a marked increase from about 1970 forward. This corresponds to a slight decrease in the lower bound. Both boundaries show a period of warming from about 1996 forward.

This pattern is further demonstrated by data consisting of station averages from 2000 to 2009. The indication is, relative to my 1900 to 1960 baseline, there has been an upward skewing of the data.



 

Where does this trend originate

To answer that question, I broke the average down into its component parts, the annual daily minimum and maximum averages. You will notice the trend for maximum temperatures is close to zero overall. It shows the now familiar warming trend into the 1930’s and the cooling after about 1950.





However, the minimums show a warming trend beginning after 1960 which is not evident in the plot above. Now you know why my choice of baseline time frame makes sense. Remember, this has no affect upon the average.




Now we have established, for my 691 station sample, warming after 1960 is almost exclusively associated with the night time lows. We can account for this by development factors.

The following chart shows the results of a correlation study between a development rating factor and the 2000’s average temperature. This development factor is the width of proximate development in miles at the widest point as measured on Google Earth. Since I have defined temperature as a deviation from a 1900-1960 average the 2000’s average is also an average deviation. There does appear to be evidence of correlation. I have a correlation coefficient of 0.72 and a R squared value of .51.



This indicates warming is associated with development, warming is limited to specific locations, warming is evident in the night time lows, and no warming is evident in the daily highs.

This result runs counter to the theory of warming due to increased CO2.

 

You can view the 691 sample sites for this study as well as the 33 sites for the correlation study along with included data and information in interactive map form at the following links.
 
 
 
 
 
 

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