Climatists Mistake Means for Ends

As of 2024, data on natural changes in temperature, rainfall, and sea level
do not show any statistically significant difference to historical records.

From Science Matters (rclutz.com)

By Ron Clutz

Roy Gilbert exposes the fundamental mistaken thinking regarded global warming/climate change.  His Spectator Australia article is Conceptual Error in Climate Change Analysis.  H/T John Ray  Excerpts in italics with my bolds and added images.

It is often said that the ‘science is in on climate change’. Is it? We should always adhere to the principle of the ‘working hypothesis’ and have an open mind on scientific questions no matter how well-recognised the researchers are. In the study of science, there is always the chance new information can come along to cause a rethink.

A common error in problem-solving and policy development is to confuse
a technical strategy for a desired client outcome.

Our Climate Change Minister could be accused of this. Reducing emissions is a ‘strategy’, not the fundamental desired client outcome. With the mission ‘to reduce carbon emissions’ by increasing renewable energy, the way to assess performance is to concentrate on measuring emission reduction, and then to follow this up with how quickly the renewables are built and their cost (wind farms, solar panels, transmission lines).

Instead of the current strategy-driven mission, a fundamental client outcome statement would be: ‘To protect against, and where possible, prevent damage from extreme off-trend fluctuations in climate.’ How would you go about managing your program using this mission statement?

First, you gather accurate temperature, rainfall, and weather measurements. They are the valid and fundamental ‘outcome’ measures – not data on CO2 emissions. If there is an undeniable and dangerous increase in temperature and rainfall, more cyclones, and a clear and unabated rise in sea level, then the possible cause must be thoroughly identified. Depending on the answer, you would adopt appropriate mitigation strategies, or strategies that adapt to weather patterns and temperature levels.

Another principle of problem-solving is to map out the total picture and not be driven by ideology. The Climate Change Minister should consider possible causes other than human-induced emissions. It was announced in April 2023 that coronal cones 20 times larger than Earth have been discovered and may cause a massive outburst of energy from the sun. What could be the implications for our planet? Ask solar physicists.

Chief scientist in applied helio-physics at John Hopkins, Ian Cohen, has suggested that solar storms could take out satellites, cut power and shut down the internet. In 1972 a solar storm caused 4,000 magnetically sensitive mines in water off Vietnam to detonate. Earth is said to be entering a period of peak activity as part of an eleven-year cycle. It is suggested this potentially could be more violent than the solar cycles of the past three decades. Now that would be something for climate scientists to really worry about…

With respect to the world’s temperature, there are several sources that claim to present the precise figure. One says the 2023 average global temperature was 1.45c above the 1950-90 average. Another says since 1880, Earth’s temperature has increased by 0.08c. Another says during the last 50 years the increase is 0.13c. To the unscientific mind, these temperatures do not appear to be verging on catastrophic boiling us all to death.

As of 2024, data on natural changes in temperature, rainfall, and sea level
do not show any statistically significant difference to historical records.

There are respected scientists who question the current climate orthodoxy. Physicist Prof. William Happer of Princeton University and Prof. Richard Lindzen, Earth, Atmospheric and Planetary Sciences at MIT have argued science demonstrates there is no climate-related risk caused by fossil fuels and CO2, and that 600 million years of CO2 and temperature data contradicts the theory that high levels of CO2 will cause catastrophic global warming. They state reliable scientific theories come from validating theoretical predictions with observations, not consensus, peer review, government opinion, or manipulated data.

In July 2023, the International Monetary Fund cancelled a planned talk on climate change by 2022 Nobel physicist John Clauser when they learned he had stated publicly: ‘I can confidently say there is no real climate crisis, and that climate change does not cause extreme weather events. The IPCC is one of the worst sources of dangerous disinformation.’  Clauser pointed out that the US Environmental Protection Authority has charts that show a heatwave Index going back to 1895, showing heatwaves were more common before the 1960s and especially in the 1930s.

In addition to these physicists, there are eminent Australian geologists who challenge the CO2 cause theory. Emeritus Prof. Ian Pilmer of the University of Melbourne, and Prof. Michael Asten of Monash University, have argued that throughout the history of the planet, there have been long periods of major change in climate due to natural forces. This would indicate recent human-based emissions may not be the important factor that we have been led to believe.

With respect to measuring emissions (nitrous oxide and methane), there is an expectation that the Intergovernmental Panel on Climate Change would have collected accurate data. Then one reads an independent 2023 report of these greenhouse gas emissions from farm dams in Australia’s irrigation regions, that the measurements had been massively over-estimated by the IPCC by 4 to 5 per cent.

To add further confusion to the issue, a 2023 research paper submitted to the European Physical Journal Plus claimed climate science has become ‘highly politicised’. Italian scientists analysed long-term data on heat, droughts, floods, hurricanes, tornadoes, and ecosystem productivity, and found no clear trend of extreme events. The statements by these scientists would appear worthy of examination. Unfortunately, comments to the publisher by other climate scientists caused the withdrawal of the article.

If activists are correct, and if temperatures and rainfall start to show a significant increase without any influence from natural factors such as the sun or outer atmospheric disturbances, the second ‘outcome’ mission opens your mind to several strategies that could be compared against each other on cost and effectiveness – renewables, outer space satellites capturing solar energy and transmitting to Earth, small nuclear, carbon capture, examine possibility of amalgamating carbon and turning it into a useful product, lower emission coal-fired power stations, hydro, hydrogen fuel cells, a scientific search for a predator for carbon other than trees (or the planting of more trees), and so on.

A valid client ‘outcome’ statement encourages you not to jump to a conclusion
in the initial stages of critical thinking about the cause of any global warming.

If you make a mistake at that point, there are significant productivity implications. Governments could waste a significant amount of money (a catastrophic amount) on a less than optimum strategy. Rather than relying almost entirely on climate scientists who concentrate on carbon emissions, a politician with a mind focused on validity could bring together an inter-disciplinary team – climate scientists, nuclear physicists, solar physicists, atmospheric physicists, examine the moon’s behaviour, plant technologists, oceanographers, geologists, volcanologists, botanists, bushfire specialists and so on. Has any national government followed this approach? Has any Minister for Energy, in any country, expanded their vision beyond their own narrow ideology is a potential danger to their country…?

There are very obvious reasons why some politicians and many rich investors in renewable energy would oppose a serious questioning of the renewable strategy and switching to nuclear instead. If small nuclear was introduced – as is being done in many countries – it would make current renewable energy strategies redundant. That would mean all the billions of dollars spent on wind and solar would have been a waste of money. We wouldn’t need them. Admitting that would be far too embarrassing for any ideological politician and far too financially damaging to any rich wind farm investor obtaining government grants.

If the Sun is found to be the fundamental cause of the problem (variations in energy output, massive infrequent solar flares, and/or variations in distance between Earth and Sun), or if there is a slight tilting of the Earth on its axis, or the Moon changes position, or even disturbance further out in our solar system, you would evaluate adaptation strategies.

It seemed reasonable for some people to assume the vast flooding in 2022 could be attributed to human-induced climate change. There is however, a different possibility … nature. Environment analyst Graham Lloyd explained.

‘The meteorological processes at play are well understood. Three consecutive La Nina weather patterns have left the eastern seaboard soaked and prone to flooding. Triple La Ninas have happened four times in the Bureau of Meteorology’s 120-year record … The Southern Annular Mode is a climate driver that can influence rainfall and temperature. Although wet, the latest BoM figures show that 2022 was the ninth wettest year on record (not the wettest).’

When the above material, stressing the need to examine the total picture in any critical thinking, was shown to a high school Principal, to a high school science teacher and to an environmental engineer, they were all surprised and quite critical that one would want to show this to students. Annoyed actually. One was emphatic…

‘Why waste the students’ time having them look at irrelevant issues?
We KNOW what the problem is. It is CO2 emissions.
And we KNOW what the solution is. It is 100 per cent renewables.’

My answer to them was:

‘The difference between you and me, is that you want to tell the students WHAT to think. I want to teach them HOW to think. I want them to understand insightful thinking. Not to be indoctrinated’.  You can be the judge as to who is on the right track.

Regression attenuation only depends upon the relative noise in “X”

This also impacts the analysis of the urban heat island (UHI).

From Roy Spencer, PhD.

October 11th, 2023 by Roy W. Spencer, Ph. D.

I’m not a statistician, and I am hoping someone out there can tell me where I’m wrong in the assertion represented by the above title. Or, if you know someone expert in statistics, please forward this post to them.

In regression analysis we use statistics to estimate the strength of the relationship between two variables, say X and Y.

Standard least-squares linear regression estimates the strength of the relationship (regression slope “m”) in the equation:

Y = mX + b, where b is the Y-intercept.

In the simplest case of Y = X, we can put in a set of normally distributed random numbers for X in Excel, and the relationship looks like this:

Now, in the real world, our measurements are typically noisy, with a variety of errors in measurement, or variations not due, directly or indirectly, to correlated behavior between X and Y. Importantly, standard least squares regression estimation assumes all of these errors are in Y, and not in X. This issue is seldom addressed by people doing regression analysis.

If we next add an error component to the Y variations, we get this:

In this case, a fairly accurate regression coefficient is obtained (1.003 vs. the true value of 1.000), and if you do many simulations with different noise seeds, you will find the diagnosed slope averages out to 1.000.

But, if there is also noise in the X variable, a low bias in the regression coefficient appears, and this is called “regression attenuation” or “regression dilution”:

This becomes a problem in practical applications because it means that the strength of a relationship diagnosed through regression will be underestimated to the extent that there are errors (or noise) in the X variable. This issue has been described (and “errors in variables” methods for treatment have been advanced) most widely in the medical literature, say in quantifying the relationship between human sodium levels and high blood pressure or heart disease. But the problem will exist in any field of research to the extent that the X measurements are noisy.

One can vary the relative amounts of noise in X and in Y to see just how much the regression slope is reduced. When this is done, the following relationship emerges, where the vertical axis is the regression attenuation coefficient (the ratio of the diagnosed slope to the true slope) and the horizontal axis is how much relative noise is in the X variations:

What you see here is that if you know how much of the X variations are due to noise/errors, then you know how much of a low bias you have in the diagnosed regression coefficient. For example, if noise in X is 20% the size of the signals in X, the underestimate of the regression coefficient is only 4%. But if the noise is the same size as the signal, then the regression slope is underestimated by about 50%.

Noise in Y Doesn’t Matter

But what the 3 different colored curves show is that for Y noise levels ranging from 1% of the Y signal, to 10 times the Y signal (a factor of 1,000 range in the Y noise), there is no effect on the regression slope (except to make its estimate more noisy when the Y noise is very large).

There is a commonly used technique for estimating the regression slope called Deming regression, and it assumes a known ratio between noise in Y versus noise in X. But I don’t see how the noise in Y has any impact on regression attenuation. All one needs is an estimate of the relative amount of noise in X, and then the regression attenuation follows the above curve(s).

Anyway, I hope someone can point out errors in what I have described, and why Deming regression should be used even though my analysis suggests regression attenuation has no dependence on errors in Y.

Why Am I Asking?

This impacts our analysis of the urban heat island (UHI) where we have hundreds of thousands of station pairs where we are relating their temperature difference to their difference in population density. At very low population densities, the correlation coefficients become very small (less than 0.1, so R2 less than 0.01), yet the regression coefficients are quite large, and — apparently — virtually unaffected by attenuation, because virtually all of the noise is in the temperature differences (Y) and not the population difference data (X).