This is a guest post by Doug Keenan.
Two months ago, I published an op-ed piece in The Wall Street Journal. The piece discussed the record of global temperatures, illustrated in the figure.
The IPCC, and most climate scientists, have claimed that the figure shows a significant increase in temperatures. In order to do so, they make an assumption, known as the “AR1” assumption (from the statistical concept of “first-order autoregression”). The assumption, however, is simply made by proclamation. The failure of the IPCC to present any evidence or logic to support its assumption is a serious violation of basic scientific principles. Moreover, it turns out that the assumption is insupportable: i.e. there is conclusive evidence that the assumption should not be used. (Further details are given in the op-ed piece; what follows assumes background given there.)
Without making some statistical assumption, we cannot analyze the global temperatures. Hence we cannot determine whether temperatures have been significantly increasing. The crucial question, then, is this: what assumption should be used in analyzing the global temperatures?
A research paper that appears to make major progress in finding an answer to that question was published on April 15th. The paper is
Koutsoyiannis D. (2011),
“Hurst–Kolmogorov dynamics as a result of extremal entropy production”,
Physica A, 390: 1424–1432.
Following is a partial summary of what the paper indicates for the global temperature series.
Consider this statement: “The temperature of Earth this year affects the temperature of Earth the next year; for example, if this year is cold, then the next year will probably be colder than average”. That statement is accepted by virtually all climatologists, and it is accepted in the most-recent report from the IPCC (§I.3.A).
Koutsoyiannis (2011) assumes that the above statement can be generalized by replacing “year” with any other time span, e.g. “day”, “minute”, “millennium”. He also assumes that the climate system adheres to the second law of thermodynamics (thus entropy is always maximized). From those assumptions, he calculates the approximate formula that the time series for temperatures must have.
The formula has been known for a long time. It is usually called “stochastic self-similar scaling”, although here Koutsoyiannis calls it “Hurst-Kolmogorov”. It has been known for decades that many climatic processes seem to conform to the formula. Yet the formula has not been accepted for such processes, because there seemed to be a problem with it: it seemed to require e.g. that the temperature from some year a century ago affected the temperature this year—and that is not physically plausible. Koutsoyiannis resolves the problem. The temperature from some year a century ago does not have a significant effect on the temperature this year. Rather, the temperature from the last century has a significant effect on the temperature this century, etc.
Koutsoyiannis' paper actually deals with any system for which his assumptions are reasonable. The paper does not analyze the global temperature series, but previous work of Koutsoyiannis does and shows that the formula fits the series well. If the formula is valid for the series, then the conclusion is that there has not been a significant increase in global temperatures. That is, the changes that appear in the figure above can be reasonably ascribed to chance fluctuations.
That does not mean that carbon emissions have not caused an increase in temperatures. As an illustration, suppose that we have a coin and we want to determine whether the coin is biased. We toss the coin three times, and it comes up Heads, Heads, Heads. Getting three Heads out of three tosses might well occur by chance, or it might be due to the coin being biased. That is, we cannot really conclude anything. Similarly with the global temperature series: under the assumptions of Koutsoyiannis, the series is far too short to conclude anything.
As noted, Koutsoyiannis' paper applies to a wide variety of natural systems. Hence the foregoing applies not just to global temperatures, but also to many more time series. In other words, it is unlikely that we will be able to find any empirical evidence for significant global warming. The case for global warming therefore rests almost entirely on computer simulations of the climate.