A Peer-Reviewed Statistical Analysis of the 2020 Election

By Andy May

Stephen Dinan of the Washington Times reported on a new peer-reviewed paper that analyzes the results of the 2020 election and found Biden received 255,000 excess votes. It has been accepted for publication by the journal Public Choice and was written by Dr. John R. Lott of the Crime Prevention Research Center. The linked pdf may not match the final printed version of the paper that will appear in the journal, but it is the copy that was peer-reviewed.

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Atmospheric Fingerprint

By Andy May

The IPCC believes that the change in solar radiation over the past 60 years nets to zero and has no trend beyond the normal ~11-year solar cycle. Professor David Karoly writes that we can confidently exclude the Sun as a contributor to recent warming because, if it were, the stratosphere would be warming as a result, and instead it is cooling. The cooling of the stratosphere, when the troposphere warms, is sometimes called the “atmospheric fingerprint” of human-caused global warming. Karoly was a pioneer in this area of research (Karoly, 1989) and (Karoly, 1987).

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By Andy May

Comments on my recent posts concerning HadCRUT5 surface and UAH Lower Troposphere temperatures often degrade to a comparison of the accuracy of RSS and UAH satellite temperatures. Some seem to believe that RSS is more accurate than UAH, when radiosonde data shows the reverse is true. So, this is a short post to briefly cover the issue.

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AR6 and Sea Level, Part 3, A Statistically Valid Forecast

By Andy May

In Part 1 of this series, we examined the data and analysis that was presented in AR6 to support their conclusion that sea level rise is accelerating. In Part 2 we looked at a serious examination of the observational record for sea level rise over the past 120 years and the modeled components of that rise. We concluded in Part 1 that the statistical evidence presented in AR6 for acceleration was crude and cherry-picked. In Part 2 we saw that the error in both the estimates of sea level rise and in estimating the components of that rise is very large. The error precluded determining acceleration with any confidence, but the data revealed an approximately 60-year oscillation of the rate of sea level rise that matches known natural ocean cycles.

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AR6 and Sea Level, Part 2: The complexity of measuring GMSL

By Andy May

Thomas Frederikse and colleagues published a study of sea level data, considering both tide gauges and satellite data in 2020 (Frederikse, et al., 2020). This paper is frequently cited in the Chapter 9 AR6 sea level discussion. They found that there are many causes of global and regional sea level change that need to be considered. Land over much of the Northern Hemisphere is still rebounding from the melting of the massive glaciers they supported during the Last Glacial Maximum. This causes many northern tide gauges to record sea level falling as the land rises. Further, dam construction during the twentieth century caused water to be withheld from the oceans and stored in reservoirs on land, especially between 1960 and 1980. They also tell us that previous assessments of sea level were unable to reconcile observations with the calculated contributions of ice-mass loss, dam construction, and thermal expansion of water. As mentioned in Part 1 of this series, observed sea level change is very small, so this is not surprising. Yearly changes are below the measurement accuracy of the instruments.

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AR6 and Sea Level Rise, Part 1

By Andy May

This is the first of a three-part series on the IPCC’s discussion of sea level rise in their latest report, AR6 (IPCC, 2021). The report claims that the rate of sea level rise is accelerating. It is fair to ask why they think this, what evidence do they offer?

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Northern and Southern Hemisphere Warming

By Andy May

It is common for the news media and consensus scientists to report global average surface temperatures without mentioning that both the warming rate and average temperatures vary a lot around the Earth over one year. For example, Earth’s global average surface temperature varies about 7° F every year. The rate also varies by hemisphere. Phil Jones and colleagues[1] show that the global average monthly temperature, from 1961-1990, was about 54° F in January and 61° F in July. In the Northern Hemisphere it varied from 46° F in January to 70° F in July, the respective average temperatures in the Southern Hemisphere are 61° F and 51° F.

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Satellite and Surface Temperatures

By Andy May

In the Great Climate Change Debate between Professor David Karoly and Professor Will Happer, Glenn Tamblyn was called upon to finish the consensus side of the debate after Karoly backed out. The details are described in my latest book. The debate contained an illuminating exchange of opinions on satellite versus surface temperature measurements. This is Glenn Tamblyn’s opinion:

“Stitching together raw data from multiple satellites is very complex. Thus, the satellite datasets are much less accurate than the surface temperature datasets.

Professor Happer’s stronger emphasis on satellite temperature measurements does not agree with the experts on the subject.”

(Tamblyn, 2021b, pp. 7-8)
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Comparing AR5 to AR6

By Andy May

The IPCC AR5 report was published in 2013 and the CMIP5 climate models they used, have been shown to predict faster warming than observed in the tropical troposphere at a statistically significant level by Ross McKitrick and John Christy.[1] This problem is acknowledged and discussed in the latest AR6 report, published in 2021, but brushed aside as unimportant. In AR6, the IPCC observed:

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Climate Model Democracy

By Andy May

In my last post, I explained how the IPCC attempts to use climate models to show humans have caused the recent global warming. Models are useful for testing scientific ideas, but they are not proof an idea is correct unless they successfully and accurately predict future events. See the story of Arthur Eddington’s test of Einstein’s theory of relativity here. In the computer modeling world, a world I worked in for 42 years, choosing one model, that matches observations best, is normal best practice. I have not seen a good explanation for why CMIP5 and CMIP6 produce ensemble model means. It seems to be a political solution to a scientific problem. This is addressed in AR6 in Chapter 1,[1] where they refer to averaging multiple models, without considering their accuracy or mutual independence, as “model democracy.” It is unclear if they are being sarcastic.

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