The Greatest Scientific Fraud Of All Time — Part XXX

Friday’s post principally reported on the recent (February 8, 2022) article by O’Neill et al., in Atmospheres, “Evaluation of the Homogenization Adjustments Applied to European Temperature Records in the Global Historical Climatology Network Dataset.”

In the piece, O’Neill et al., dramatically demonstrate that the NOAA/NCEI “homogenization” algorithm is wildly off the mark in its intended mission of identifying and correcting for supposed “discontinuities” or “breakpoints” in weather station location or instrumentation to provide a more accurate world temperature history.

At the same time, although not mentioned in O’Neill et al., the NOAA/NCEI algorithm is wildly successful in generating a world temperature history time series in the iconic hockey stick form to support the desired narrative of climate alarm.

What should be done? O’Neill et al., for reasons that I cannot completely understand, has bought into the idea that having a group of government-paid experts correct the temperature record with a “homogenization” algorithm was and is a good idea; therefore, we just need to tweak this effort a little to get it right.

From O’Neill, et al.:

[W]e are definitely not criticizing the overall temperature homogenization project. We also stress that the efforts of Menne & Williams (2009) in developing the PHA . . . to try and correct for both undocumented and documented non-climatic biases were commendable.

Long-term temperature records are well-known to be frequently contaminated by various non-climatic biases arising from station moves . . ., changes in instrumentation . . ., siting quality . . ., times of observation . . ., urbanization . . ., etc.

Therefore, if we are interested in using these records to study regional, hemispheric or global temperature trends, it is important to accurately account for these biases.

Sorry, but no. This statement betrays hopeless naïveté about the processes by which government bureaucracies work.

Or perhaps inserting this statement into the piece was the price of getting it published in a peer-reviewed journal that, like all academic journals in the climate field today, will suppress any piece that overtly challenges “consensus” climate science.

Whichever of those two it is, the fact is that any collection of government bureaucrats, given the job to “adjust” temperature data, will “adjust” it in the way that best enhances the prospects for growth of the staff and budget of the bureaucracy.

The chances that scientific integrity and accuracy might intrude into the process are essentially nil.

Is there any possibility that a future Republican administration with a healthy skepticism about the climate alarm movement could do anything about this?

For starters, note that President Trump, despite his climate skepticism and his focus on what he called “energy dominance,” never even drained a drop out of this particular corner of the swamp.

It took Trump until September 2020 — just a few months before the end of his term — to finally appoint two climate skeptics, David Legates and Ryan Maue, to NOAA to look into what they were doing. Before they really got started, Trump was out and so were they.

Even if a new Republican President in 2025 got started on his first day, the idea that he could quickly — or even within four years — get an honestly “homogenized” temperature record out of NOAA/NCEI is a fantasy.

The existing bureaucracy would fight him at every turn, and claim that all efforts were “anti-science.” Those bureaucrats mostly have civil service protection and cannot be fired.

And there doesn’t even exist enough climate skeptics with the requisite expertise to redo the homogenization algorithm in an honest way.

But here are some things that can be done:

• Do an audit of the existing “homogenization” efforts. Come out with a report that points to five or ten or twenty obvious flaws in the current algorithm. There are at least that many. The O’Neill et al., work gives a good starting point. Also, there are many stations with good records of long-term cooling that have been “homogenized” into long-term warming. Put the “homogenizers” on the hot seat to attempt to explain how that has happened.
• After the report comes out, announce that the government has lost confidence in the people who have been doing this work. If they can’t be fired, transfer them to some other function. Don’t let the people stay together as a team. Transfer some to one place, and some to another, preferably in different cities that are distant from each other.
• Also after the report comes out, announce that the U.S. government is no longer relying on this temperature series for policymaking purposes. It’s just too inaccurate. Take down the website in its current form, and all promotion of the series as something providing scary information about “hottest month ever” and the like. Leave only a link to hard data in a raw form useful only to “experts” with infinite time on their hands.
• Stop reporting the results of the USHCN/GHCN temperature series to the hundredth of a degree C. The idea that this series — much of which historically comes from thermometers that only record to the nearest full degree — is accurate to one-hundredth of a degree is completely absurd. The reporting to an accuracy of a hundredth of a degree is what gives NOAA the ability to claim that a given month was the “hottest ever” when it says temperature went from an anomaly of 1.03 deg to 1.04 deg. I suggest reporting only to an accuracy of 0.5 of a degree. That way, the series would have the same temperature anomaly for months or years on end.
• Put error bars around whatever figures are reported. Appoint a task force to come up with the appropriate width of the error bars. There should be some kind of sophisticated statistical model to generate this, but I would think that error bars of +/- 0.5 deg C are eminently justifiable. Again, that would make it impossible to claim that a given month is the “hottest ever,” unless there has been some sort of really significant jump.

I’m sure that others can come up with some other good suggestions, but this should be a good start.

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