Does a small change in average temperature lead to a large change at the poles? – Is it good?
Kip Hansen’s guest essay – August 30, 2022
One of the major misleading claims made by the Climate Group and various climate propaganda journals is that “the general mantra that has emerged in this area of climate science: “A small change in the average means a large change in the extremes.” (See more here and here). “Patrick Brown at Breakthrough Institute wrote about this issue recently in “Effective climate communication about extremes should not sacrifice clarity in the name of persuasion”.
This mantra is supported and promoted with images like these (there are quite a few, the reader can simply glance at them, but should notice the obvious difference):
Pretty convincing, isn’t it? That’s the problem – it’s convincing without being true.
What does the temperature distribution of a real weather station look like? I have chosen pseudo-randomly from National Center for Environmental Information feature called Daily summary map. I chose a station near the center of the United States – Atlanta Missouri, station code: USR0000MATL. I downloaded daily summaries (including BILLIONMAXIMUMBILLIONMINand TAVG ) from August 2003 to August 2022, nineteen yearsand, after removing the error codes in TAVG column (values 600 and 599) and convert the value (listed in tenths of a degree Celsius) to degrees Celsius with decimals [ 241 to 24.1°C ] MSExcel created one chart of values. I use the histogram, an “approximate representation of allotment numerical data”, with a bin of 2 degrees Celsius to clearly illustrate a real-world example of the distribution of temperature data.
We are interested in the hottest of hot summer days. In 19 years, they saw only 15 days with average temperatures above 30°C (86°F).… Less than one day a year.
Compare this actual station data distribution, above, with the theoretical “normal distribution“Used by climate enthusiasts in their graphics at the beginning of the essay –
Not a very good match is it? Not sure, page up to see the normal distributions used in the propaganda messages, which all look like this. (left picture)
Seems suitable for people on the street, if they don’t pay close attention.
Here’s a version of dw.com, switched to show “small increase in average”. But what they show is moving the whole distribution right side full 20 to 25%. They don’t mention that this eliminates almost all very cold weather – but that’s not their point. The Climate Center folks use the same trick – shifting the 25% average to the hot side.
If we are talking about 1°C declared on Global Warming in the last 100 years, here’s what happens with real station data:
What changed when we added one degree Celsius to each and every daily average? The whole shift to the right, to the warmer side. The long-term mean (all daily values) also increased by 1 degree.
How many extra hot days – extremely hot days? NOBODY
Before the small average increase, there were 15 days above 30°C (86°F) with only one of the days above 33°C (about 90°F).
After the small increase, there will still be only 15 days (out of 6,860) with an average above 30°C and still only one over 33°C, still less than one day per year.
Why? Two reasons: 1) a small change produces only a small change in a linear system like the average daily temperature, and 2) shape of the real distribution looks like the real temperature in Central America and nothing like the normal distribution.
Summer days average between 21 and 29°C (70 and 85°F) with very few really hot days, with averages above 31°C (88°F), only 15 out of 6860. And, visually , spring, autumn and some Winter days average between 0 and 20 °C in equal numbers. The cold days0 down to -21°C is much more crowded than hot days.
The fat tail is long on the cold side, the short tail sloping on the hot side. An average increase of 2°C doesn’t change the results much… and the effect depends entirely on whether one draws the line (in degrees) between hot summer days and extremely hot summer days. Hotter days still add only one degree.
Looking at TILLmaximum and Tmaximum+1, we see a slightly different shape, but the overall pattern is similar:
Very few (27 out of 6860) days hotter than 37°C (99°F) in 19 years are shown (about 1.5 days per year), and as the average increases by 1°C, the same number of days ( still about 1.5 days per year above 38°C or 100°F) but the highest temperature is 42°C (107°F), one day in 19 years.
To make an important change for the people of Atlanta, Missouri, a big change in the number (or temperature) of the hottest days each year, Daily average, daily average in Atlanta, Missouri will have to change 6 °C to the hot side; thus moving the column higher from 27 to 29°C to the range of 33 to 35°C and the same shift of Tmaximum hottest days in the 90s high and 100s lowest.
Conclusion:
Looking at the actual distribution of daily temperatures at an actual weather station, over a period of 19 years, we find that a small change in average temperature cause only a small change on hotter days.
Small changes in climate on average do not produce large changes in temperature.
The average temperature is not normally distributed.
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Regular readers will know that I don’t think average temperatures are entirely appropriate for assessing climate changes, even short-term and locally. Here I use a long-term average just to follow CliSci’s conventions and to illustrate their meme spoofing.
In my opinion a better way to look at the temperature is to look at the Tmax and Tmin shown on the same image. It is acceptable to display Tavg (Tmax + Tmin/2) as a visualization tool, although it is of no particular significance as it does not convert to a graph of day temperature, which will carry more information . Here’s 2004’s Atlanta Missouri:
We see that Atlanta had a week-long thaw at the end of December, then dropped back to very cold temperatures of -10 to -20°C. The “annual average” would miss the fact that there this agriculturally important body.
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Author’s comment:
Philosophically, the problem is that climate enthusiasts pretend that the “climate average” (of any metric) is normally distributed random data. As in “A random variable with a Gaussian distribution is assumed to be normal distributionand is called normal deviation. “There’s no scientific reason to believe it’s true, the evidence is otherwise, but they use it to try and create a [false] perspective on climate change.
Only in non-linear dynamicsin which systems can exhibit sensitive dependent initial conditions, Do we see how small changes make big changes? For the general climate temperature, the opposite is suspected, that is, the temperature can manifest strange long-term stability with similar variation over different time scaleses.
Start comments with “Kip…” if speaking to me.
Thanks for reading.
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