By Andy May

In 2016, I published a post entitled “Facts and Theories.” It has been one of my most popular posts and often reblogged. I updated the post extensively for my new book, Politics and Climate Change: A History. This post is a condensed version of what is in the book.

Sometimes people ask climate skeptics if they believe in evolution or gravity. They want to ridicule our skepticism by equating human-caused, aka anthropogenic, climate change to evolution or gravity. Evolution and gravity are facts and anthropogenic climate change is a hypothesis. Equating “climate change” to gravity or evolution is valid, as all three are facts. Climate changes, gravity holds us to Earth’s surface and species evolve.

Karl Popper, the famous philosopher, would say that these observed phenomena are not scientific hypotheses or theories because they are not falsifiable (Popper, 1962). How can you prove or disprove that climate changes?

There are other ideas that Popper calls pseudoscience. These are ideas that are framed in such a way that no matter what one observes, the observation can be seen to confirm the idea. Popper offers Marx’s theory of history as an example. Popper observes “that a Marxist could not open a newspaper without finding, on every page, confirming evidence” for the theory. Freud’s theories were the same, every clinical case was a confirmation of Freud’s ideas. It was precisely this fact, that evidence always fit these ideas, that was their weakness. A theory that is not refutable by any conceivable event, is not scientific (Popper, 1962, pp. 35-36). Astrology is another example.

Popper asked himself in 1919 how Marxism, Freud and astrology differed from truly scientific ideas like Newton’s law of gravity or Einstein’s theory of relativity. He realized that the latter could be tested and proven false. He was inspired by Frank Dyson, Andrew Crommelin and Arthur Eddington’s confirmation of Einstein’s theory of relativity during the solar eclipse of May 29th, 1919. Einstein’s theory predicted that starlight would curve around the Sun, due to gravity. Newton’s Law of Gravity also predicts a deflection, but Einstein’s theory predicted a deflection twice as large. Their observations during the eclipse showed that it happened exactly as Einstein predicted (Coles, 2019).

This was the first real confirmation of Einstein’s theory and it was based on a risky prediction. A confirmation of a theory must include a risky prediction of things that cannot or will happen if the theory is true. Theories should predict things successfully and they should forbid things and the more they forbid the better. Confirmations do not prove a theory, but they allow it to survive.

Popper draws a bright line between science and pseudoscience. Scientific hypotheses and theories predict what will happen and what will not happen if the idea is true. Pseudoscience draws no such line.

In other words, if a war happens and someone became rich from it, that does not verify Marx’s view of history. Marx would have had to predict the man would become rich and would have to admit that if the man stayed poor, he was wrong. We must be able to imagine how the theory can be disproven.

Gravity and evolution have generally accepted theories of how they work. Einstein developed our current scientific theory of gravity. Newton provided us with his descriptive “Law of Gravitation.” Newton’s law tells us what gravity does and it is useful, but it tells us nothing about how it works. For that we need Einstein’s theory of relativity.

In the scientific community, for both a law and a theory, a single conflicting experiment or observation invalidates them. Stories exist that either Einstein or Popper once said something like:

“No amount of experimentation can ever prove me right; a single experiment can prove me wrong.” (author unknown)

Both said similar things, both believed that no scientific theory is ever proven, they can only be disproven. So, let us examine our topics in that light. Newton’s descriptive law of gravity, based on mass and distance, are there any exceptions? Only on solar-system-sized scales, near black holes and on small atomic scales. In everyday life on Earth, Newton’s law works fine. How about Einstein’s theory of gravity (Relativity), any exceptions? None that we know of at any scale.

How about evolution? Species evolve, we can see that in the geological record (Jepson, Mayr, & Simpson, 1949). We can also watch it happen in some quickly reproducing species (Wilcox, 2011) and (Soltis & Soltis, 1989). Thus, we could describe evolution as a fact. It happens, but we cannot describe how without more work. Early theories of the evolutionary process include Charles Darwin’s theory of natural selection (Darwin, 1859) and Jean-Baptiste Lamarck’s theory of heritable species adaptation due to external environmental stresses. Lamarck did not originate the idea of heritable adaptation; it was commonly believed long before he was born. But he did incorporate it into his ideas on new species evolution.

Current epigenetic research (Nature, 2020) shows that Darwin and Lamarck were both right and that evolution involves both processes. For a summary of recent research into the epigenetic component of evolution see this Oxford Journal article (Mendizabal, Keller, Zeng, & Yi, 2014). Natural selection plays an important role in extinction, since species who cannot adapt to a new environment extirpate. Lamarckian-type heritable adaptation plays a critical role in how new, more robust varieties and species evolve.

Lamarck first presented his new idea that that the various species on Earth gradually evolved from the simplest to the most complex in two lectures on May 17th, 1802 at the Paris Museum of Natural History. The first lecture was to his students and the second to his fellow professors. The second was accompanied by a report (Lamarck, 1802). As Richard Burkhardt, a historian at the University of Illinois describes, Lamarck’s ideas were ground-breaking and revolutionized biology, but this was not recognized at the time (Burkhardt, 2013).

Modern DNA research describes how adaptations can be inherited. John Smythies of the University of California and his colleagues explain that environmental stress normally leaves a creatures DNA unaltered, but sperm do not just carry DNA to the ovum, they also carry a wide variety of RNA molecules, which regulate the expression and the timing of various parts of the DNA. Stress affects these RNA molecules and they affect the development and characteristics of the offspring (Smythies, Edelstein, & Ramachandran, 2014).

As science progresses, well-established facts and scientific laws rarely change but theories do evolve. Facts and laws are easily dismissed when contradictory data are gathered and, sometimes, reinstated as we learn more. The modern theory of evolution is a good example of where competing theories can merge into one and a dismissed theory can be reinstated.

Most scientific theories begin as hypotheses. A hypothesis is best described as an idea of what might be causing a specific event to occur. As discussed above, both hypotheses and theories must be falsifiable. “Climate change” is not falsifiable, it is not a scientific hypothesis or a theory. Popper would describe “climate change” as pseudoscience since any weather event can be, and often is, interpreted as supporting the idea, much like the Marxist with his newspaper.

Man-made or anthropogenic climate change is a proper scientific hypothesis since it is falsifiable. Science is mostly skepticism. We look for what does not fit, we poke at established facts and laws, at theories and hypotheses. We try and find flaws; we check the numbers. Worse, science done properly means we spend more time proving ourselves and others wrong than we do proving we are right. Life is tough sometimes and scientists rarely win popularity contests.

Table 1, below, is a table of phrases. Each is identified as a fact, theory, law, hypothesis, or simply an idea. We can see that anthropogenic climate change and the possibility of an anthropogenic climate change catastrophe are not comparable to the theories of relativity and evolution. Anthropogenic climate change is more than an idea, it is based on some observations and reasonable models of the process. But none of the climate models have successfully predicted global warming with any accuracy. The theories of relativity and evolution have each made successful predictions with great accuracy and precision.

As Popper said, the proponents of anthropogenic climate change must make risky predictions that become true to claim their hypothesis is a valid theory. Anthropogenic climate change is still a work-in-progress and not a scientific theory. It is certainly not a fact.

Only validated and reproducible models and experiments, with no exceptions, can be used to support a scientific theory. The opinions of scientists and politicians are not relevant. This is not to say that anthropogenic climate change or the possibility of an anthropogenic climate change disaster are disproven, it is just to say that no valid evidence exists to support these hypotheses.

The idea of man-made climate change causing a catastrophe at the scale of Islamic terrorism or weapons of mass destruction, as John Kerry claimed in 2014 (Almasy, 2014), is pure speculation. The models used to compute human influence on global average surface temperature don’t match observations, this is easily seen in Figure 1 which is John Christy’s graph of IPCC climate model predictions versus satellite and weather balloon observations (Christy, 2016). Satellite and weather balloon measurements are independent of one another and they are independent of the various surface temperature datasets, like HadCRUT4 shown in Figure 2. All the curves on the plot have been smoothed with five-year moving averages. The five-year averages are to remove short-term weather events, like El Niños and La Niñas (NOAA, 2020). Climate is normally defined as changes over 30 years or longer.

Figure 1. A comparison of IPCC/CMIP5 climate model predictions to 3 satellite and 4 weather balloon datasets. The graph is from John Christy’s 2016 testimony to the House Committee on Science, Space and Technology (Christy, 2016).

The line going through the observations is the Russian model “INM-CM4” (Volodin, Dianskii, & Gusev, 2010). It is the only model that comes close matching observations. INM-CM4, over longer periods, does very well at hindcasting observed temperatures. Ron Clutz is a blogger and Canadian management consultant with a degree in chemistry from Stanford. Clutz has studied INM-CM4 and has written that it uses a CO2 forcing response (ECS) that is 37% lower than the other models, roughly 2°C per doubling of CO2. It also uses a much higher deep ocean heat capacity (climate system inertia) and it exactly matches lower tropospheric water content and is biased low above that. The other models are biased high (Clutz, 2015). The Russian model predicts future temperature increases at a rate of about 1°C/century, not at all alarming and much lower than the predictions of the other models. The average of the other models predicts warming of 2.15°C/century. The observed linear warming trend for the globe, according the UAH (University of Alabama in Huntsville) satellite record, since 1979, is 1.3°C per century at the time of this writing (April 4, 2020) (Spencer, 2020). Figure 2 shows that warming, according to the Met Hadley Center/Climatic Research Unit over the past century has been about 0.8⁰C.

Figure 2. The Met Office Hadley Center and the Climatic Research Unit at the University of East Anglia global average temperature reconstruction since 1850. It is divided into warming and cooling periods. Overall, it shows ~0.8°C warming in the 20th century.

One can consider each climate model, shown in Figure 1, model to be a digital experiment. The range of predicted warming from these digital experiments is over one degree from 1979 to 2025. This exceeds the average CMIP5 (Coupled Model Intercomparison Project 5) predicted warming of one degree since 1979. Compare the CMIP5 prediction to the actual warming trend of 0.5°C, as measured by UAH and reported by Roy Spencer (Spencer, 2020). The range of model results and the comparison to actual measurements does not give us confidence in the accuracy of the models. Yet, the IPCC uses the difference between the mean model temperature predictions with and without computed human impact since 1950 to compute the human influence on climate (Bindoff & Stott, 2013, p. 879). In Figure 3, after Bindoff and Stott, their Figure 10.1, page 879, we see the CMIP3 (AR4) model runs as faint light-blue lines, the CMIP5 (AR5) model runs as faint yellow lines and the model averages as heavier blue (CMIP3-AR4) and red (CMIP5-AR5) lines. Overlain on the plot are surface temperature measurements as a heavy black line.

In Figure 3, graph (a), the models use a scenario that the IPCC believes represents both natural and human climate forcings. In graph (b) they use a model scenario that they believe represents only natural (that is, non-human) climate forcings.

Figure 3. IPCC AR5 Figure 10.1, page 879. The graphs illustrate how the IPCC computes the human influence on climate. The red and blue lines are models assuming no human forcing (b) and with human forcing (a). The black lines are observed temperatures.

The graphs are quite small and cover over 150 years, but even so, significant departures of the observed temperatures from the model mean are quite apparent from 1910 to 1940 and from 2000 to 2010. Further the range of model results is annoyingly large. The Figure 3(b) graph shows a flat natural climate trend and all the observed temperature increase from 1950 to today is attributed to human influence. This result has generated a lot of criticism from Willie Soon, Ronan Connolly and Michael Connolly (Soon, Connolly, & Connolly, 2015), as well as Judith Curry, Marcia Wyatt (Wyatt & Curry, 2014), and others. Soon, Connolly, and Connolly (SCC15) believe the IPCC chose an inappropriate model of the variation in the Sun’s output (TSI or total solar irradiance).

There are many models of solar variation in the peer reviewed literature and which is correct is a topic of vigorous debate. Eight recent models are presented in Figure 8 of SCC15 (see our Figure 4). Only low solar variability models (those on the right of Figure 4) are used by the IPCC to compute man’s influence on climate although just as much evidence exists for the higher variability models on the left. The scales used in the graphs are all the same, but the top and bottom values vary. At minimum, the IPCC should have run two cases, one for high variability and one for low. SCC15 clearly shows that the model used makes a large difference in the calculation of human influence on the climate.

Figure 4. Various peer-reviewed models of solar variability over the past 200 years. The IPCC uses low variability solar models like those on the right to compute natural variability so they can derive human influence as is shown in Figure 3. Source: (Soon, Connolly, & Connolly, 2015).

Marcia Wyatt and Judith Curry (Wyatt & Curry, 2014) or WC14 believe that natural temperature variation due to long term natural cycles is not represented correctly in Figure 3(b). Their “Stadium Wave” (Wyatt, 2014) suggests that considerable natural warming was taking place in the 1980s and 1990s. If the long term (approximately 30-year half cycle) oscillations described in WC14 were incorporated into Figure 3(b) the amount of warming attributed to humans would be much less. Marcia Wyatt does consider variation in total solar irradiance to be a possible cause.

Any computer climate model must establish a track record before its output is used in calculations. The planet Earth is simply too complex and natural climate oscillations are poorly understood. If natural oscillations cannot be predicted they cannot be subtracted from observations to compute the human influence on climate. The debate is not whether humans influence climate, the debate is over how much we contribute and whether the additional warming is dangerous. The jury is still out. Certainly, the case for an impending catastrophe has not been made as this requires two speculative jumps. First, we need to assume that humans drive climate change, second, we need to assume this will lead to a catastrophe. One can predict a possible catastrophe if the most extreme model predictions are correct, but observations show they are not. Only INM-CM4 matches observations reasonably well and INM-CM4 does not predict anything remotely close to a catastrophe.

In the study of the process of evolution the problem is the same. Some believe that the dominant process is natural selection and epigenetic change is minor. Some believe the opposite. Everyone believes that both play a role. As in climate science, figuring out which process is dominant is tough.

Recent climate history (the “pause” in warming and the recent slow rate of warming) suggests that we have plenty of time to get our arms around this problem before doing anything drastic like destroying the fossil fuel industry and sending billions of people into poverty due to a lack of affordable energy. We owe a lot to cheap fossil fuels today. This was a point made by Roger Revelle and colleagues in 1988 and it is still true. If the projections in WC14 are correct, the “pause” may go on for quite a while, giving us much more time.

In summary, science is a process of disproving ideas that purport to show how natural events occur and why. Science cannot be used to prove anything. Scientific ideas and hypotheses can be proposed, but they must be falsifiable. If no one can disprove an idea, it survives. If it remains viable over a significant period, the idea becomes a theory.

Thus, climate scientists have not proven that humans control the climate with atmospheric emissions, nor could they ever do so. They also have not disproven that nature controls climate. This is their task, something they must do, if they expect to ever show humans are controlling it. There is abundant evidence that nature and solar variation play a large role in climate change. There is also quite a lot of evidence that greenhouse gases play a small role in influencing global warming as shown by Lindzen and Choi (Lindzen & Choi, 2011), Lewis and Curry (Lewis & Curry, 2018) and (Lewis & Curry, 2015). The median value and best estimate computed by Lewis and Curry is 1.5°C per doubling of CO2 (Lewis & Curry, 2018). This is a little less than the sensitivity (~2°C) computed from the Russian INM-CM4 climate model (Clutz, 2015). Their value is much less than the sensitivity computed from the average of the other climate models (~3.1°C). Lindzen and Choi compute an even smaller value, roughly 0.44°C per doubling of CO2 (ECS).

It cannot be said that these papers and other works by climate skeptics disprove the idea that humans have more control over Earth’s climate than nature and the Sun, but they do cast considerable doubt on the idea. There is no data that supports the idea of an impending climate catastrophe of any kind. There are ways to create a climate model that shows problematic warming in the far future, but a model can be constructed to do anything you want.

We have tried to show how science works, from a scientist’s perspective. Then we used this methodology to show the state of climate science in 2020. Climate scientists are vigorously debating the causes of climate change now and in the future. Alarmists have used models to predict an impending climate disaster. The skeptics have used observations to empirically calculate a much smaller effect of CO2 on climate. Traditionally and practically, observations rule. It seems unlikely that burning fossil fuels is dangerous.

Nothing is settled, nothing is proven, and nothing is disproven. This is a work in progress.

This is an abbreviated excerpt from my new book, Politics and Climate Change: A History.

To download the bibliography, click here.

Andy May, now retired, was a petrophysicist for 42 years. He has worked on oil, gas and CO2 fields in the USA, Argentina, Brazil, Indonesia, Thailand, China, UK North Sea, Canada, Mexico, Venezuela and Russia. He specializes in fractured reservoirs, wireline and core image interpretation and capillary pressure analysis, besides conventional log analysis. He is proficient in Terrastation, Geolog and Powerlog software. His full resume can be found on linkedin or here: AndyMay