GHCN Part 9: A look at the Daily Minimums Debunks a Basic Assumption of Global Warming

In today's post on the Global Historical Climatology Data I am going to concentrate on daily minimum temperatures for long term stations in North America and Europe. As I mentioned last time the coverage is heavily weighted to the US.

In my last post I talked about the high amount of variance between stations. I conjectured most of those variances were due to localized site changes such as development. I believe that is a safe conjecture.

However, looking at daily minimums yields a different picture. The by site variation is there but it is not as pronounced. The standard deviation of the average of annual station average is only .46. That is a very reasonable value in comparison to my previous data set. The annual range between highest and lowest deviation from station average is consistent on average.

The following chart is the difference by year between the highest and lowest temperatures records for all stations in the study. While there is some variation over time the key point is the lack of any clear trend on average. There is a fluctuation in the magnitude of in year variation but that appears to be due to weather events with in the US in the form of hot and cold waves. Because the data is heavily weighted to the US it is sensitive to such events in the US.

This is the average daily minimum temperature record for all stations as mentioned above. It is a reasonable approximation of individual station records.

The following graph may grab your interest if you are familiar with statistics, especially that brand of statistics used in Quality Engineering. If you are interested in the technique you can Google search for Statistical Process Control. This is a well established methodology which has been in use since the 1950's.

What you see here is my twist on the method. I have transposed the data shown in the preceding chart  by converting it to standard deviations with the overall average normalized to zero. This is nothing more than a graphical test for equality of the means. Confidence intervals are thus easily defined, such as ± 1.96 standard deviations form a 95% confidence interval. The second key indicator of a shift in the mean is the number of consecutive points above or below the zero line.

There is no question about the clear signal of a pattern here. There are also clear evidence of extreme events occurring in 1904, 1917, 1921, 1931, and 1998.

Thus far I see no reason to doubt the veracity or accuracy of these extreme events. They appear to be accurate. They are, however, out of the ordinary. The other interesting observation is how the year to year variability decreased going into the 1940's and then again in the 1960's. That variability increases coming into the 1970's. That is reflected in the chart of annual ranges above.

The conclusions I draw are as follows:

• There is evidence of a regular pattern about 60 or so years in length.
• There is no statistically significant difference between the 1900's and the 1960's. Using my normalized data, the 1960's is warmer by 0.07 standard deviations. This is insignificant.
• There is no significant difference between the 1930's and either the 1990's or the 2000's. The 1930's are warmer than either by .08 standard deviations. This is insignificant.
• If this pattern holds true I would expect to see a low point going into the 2020's. This does appear to be happening, but I would be very careful drawing conclusions from short term data. However, similarities do exist between 1931 to 1942 and 1998 to 2007.

Finally, the last question is why would the daily low temperatures show such a different result? I will hazard a few guesses:

• Daily lows must be unaffected for the most part by site changes which cause higher day time temperatures.
• Structures and surfaces added to a site cause increased temperature due to differences in absorbed energy and in heat capacity or specific heat. Lower heat capacity or specific heat means surfaces and objects achieve a higher temperature for the same energy absorbed than surfaces and objects with higher heat capacities or specific heats. That generally means they cool off more quickly as well. Therefore the extra heat is not retained.
• The effect just described above is the opposite effect where specific heat is relatively higher. The best example of that effect is water. Water in either liquid or gas form has a much higher specific heat than a normal atmospheric gas mixture, concrete, brick, shingles, and so forth. A body of water not only stays cooler during the day than what is on the land, it also cools off much slower.

The lowest temperature of a typical day in most locations normally occurs within an hour of sunrise. In order to systematically affect the daily minimum temperature objects, structures, and surfaces that would retain or produce heat must be added to the site. That is possible, certainly adding a pond or lake next to a climate station could have such an affect.

Conclusion:

This result and the obviously different outcome from my prior study supports the supposition most instances of higher than typical temperature increases are due to site changes as described above.

I would further conclude the daily minimum temperatures provide a far more accurate picture of what is happening with respect to the anthropogenic global warming theory.

The lack of any evidence of a change in heat retained over night, if correct, would debunk the concept added CO2 is causing the surface of the Earth to warm up due to downward IR. The logic behind this assertion is simple. If CO2 truly did act as a greenhouse or a blanket to retard cooling that effect would be demonstrable in progressively higher overnight temperatures. There is no evidence that has occurred.

You could conjecture as to whether or not temperatures have increase during those overnight hours which precede the daily low point. This data does not address that conjecture.

Until the next time.......

GHCN Post 8: North America and Europe or It Varies. A Lot.

This is my eighth post in this series. I would encourage anyone to start at the first post and go forward. However, this post will serve as a stand alone document. In this post I have taken my experience in exploring the history of Australia and applied it forward to cover North America and Europe.

 

The way to view this study is literally a statistic based survey of the data. Meaning I have created a statistic to quantify, rank, and categorize the data. My statistic is very straight forward. It is simply the net change in temperature between the first and last 10 years of 1900 through 2011 for each station.

 

Below is a list of countries showing the lowest net change, the highest net change, and the number of stations per country.

 

 

This is an old fashioned histogram showing how the stations ranked in terms of over all temperature change. This shows the data falls in a bell shaped curve. The underlying distribution is very close to normal. This means analysis using normal techniques will yield very reasonable estimates. This is significant to a statistician. However, you don't need any statistical knowledge to understand this.

 

The mid line value is between -0.5° and 0.5°. The number of stations showing a overall drop in temperature is 40%. Slightly less than 60% of the stations show an increase. The absolute change is statistically insignificant in 74.6% of the stations.

The following graph shows a normalized look at each category: No significant change, significant warming, and significant cooling. The graph is of rolling 10 year averages. Each plot has been normalized to show the 1900 - 1910 average as zero.

You will note, though the overall slope of each plot is significantly different, the shape of the plots are nearly identical. A random sampling of individual station data shows that condition remains true for each station in the range. For example, Denmark's Greenland station shows the 1990 - 2000 average is the same as the 1930 - 1940 average.

Short term changes, such as the warming into the 1930's, hold true for the vast majority of stations. Other examples of this would be the 1940's temperature jump, the post 1950 temperature drop, and the late 1990's temperature jump.

Long term changes vary significantly.

 

There are a number of conclusions to be drawn from this analysis.

 

There is no statistically significant difference between North America and Europe. Those stations showing significant cooling are just 8% of the total. By that statistic, the expected number of the 17 European stations to show cooling would be just one. The number expected to show significant warming would be three. From a statistical sampling standpoint, 17 is just not a robust enough sample size to yield accurate estimates.

 

Short term changes which appear in the vast number of stations from Canada to the US to Europe are probably hemispheric changes. However, there is no indication these are global changes as there is no evidence of similar changes in Australia. Australia did not experience a 1930's warming trend for example. In fact, the overall pattern in Australia is obviously different from what we see here.

 

The evidence strongly suggests the large variation in overall temperature trends is due to either regional or local factors. As shown in the data table at the beginning, the extremes in variation all come from the US. As noted before, there just aren't enough samples from Europe to form accurate estimates for low percentage conditions.

 

Further evidence suggests most of the differences in overall temperature change are due to local factors. What we see from the US is extreme warming is generally limited to areas with high population growth or high levels of development. Large cities such as San Diego, Washington DC, and Phoenix follow the pattern of significant change. Airports also follow this pattern. However, cities like New Orleans, St Louis, El Paso, and Charleston follow the pattern of no significant change.

 

In Conclusion, based upon the available long term temperature data the case for global warming is very weak. There is evidence to suggest a hemispheric pattern exists. The evidence further suggest this is a cyclical pattern which is evident in localized temperature peaks in the 1930's and the 1990's. However, changes in local site conditions due to human development appear to be the most important factor affecting overall temperature changes. Extreme warming trends are almost certainly due to human induced local changes. 

 

What is unclear at this point is the significance of lower levels of human induced local changes. Assessing this would require examining individual sites to identify a significant sample of sites with no changes. Unfortunately, the US, Canada, and Europe are not nearly as obliging on that kind of information as the Aussies are. I have to admit the Australians have done an excellent job of making site information available. Having the actual coordinates to where the actual testing station resides made that easy. I literally pulled them up on Google Maps and was able to survey the site and surrounding areas.

 

It appears this is about as far down the rabbit hole as I am going to get, at least, not without a lot of work which at this point doesn't appear warranted.

 

Until next time......