Since last October, this series has been sitting at the rather awkward number of 19 (or “XIX”) posts. Time to round it off at an even XX.
For those new to this topic, the Greatest Scientific Fraud Of All Time is the systematic downward adjustment of early-year temperatures in order to create a fake enhanced warming trend, the better to bamboozle voters and politicians to go along with extreme measures to try to avert the impending “climate crisis.” Prior posts in this series have documented large and unexplained downward adjustments at hundreds of stations around the world that are used by official government organizations (in the US, primarily NOAA and NASA) to wipe out early-year high temperatures and thereby proclaim that the latest month or year is “the hottest ever!” To read all prior posts in this series, go to this link.
You might ask, with the extensive exposure of these unsupportable downward adjustments of early-year temperatures by official government organizations — accompanied by highly credible accusations of scientific fraud — haven’t the adjusters been cowed by now into a smidgeon of honesty? It sure doesn’t look that way.
The latest news comes out of Australia, via the website of Joanne Nova. Nova’s February 17 post is titled “History keeps getting colder — ACORN2 raises Australia’s warming rate by over 20%.” “ACORN2” is a newly revised and updated temperature series for Australia, with temperatures going back to 1910 based on records from 112 weather stations on the continent, some 57 of which have records that go back all the way to the 1910 start date. “ACORN” stands for Australian Climate Observations Reference Network. The ACORN2 data compilation is so called to distinguish it from ACORN1, which was only released some 7 years ago in 2012. The people who put out these things are the Australian Bureau of Meteorology.
According to Nova, the latest temperature adjustments were released “oh-so-quietly.” I guess that the plan is just to start using the new figures as the historical comparisons and bet that journalists will be too stupid or ignorant to figure out that the earlier temperatures have been altered. That’s actually a pretty good bet. However, down in Australia they do have a hard-working group of independent researchers who are on top of this issue. One of them is Nova, and another is Chris Gillham. Gillham has done his own very detailed analysis of the adjustments in the ACORN2 report, and has also put up a post on same at Watts Up With That. So there is plenty of information out there for intelligent people to make an independent judgment.
A few excerpts from Nova:
Once again we find that the oldest thermometers were apparently reading artificially high, even though many were newish in 1910 and placed in approved Stevenson screens. This is also despite the additional urban warming effect of a population that grew 400% since then. What are the odds?! Fortunately . . ., sorry scientists have uncovered the true readings from the old biased thermometers which they explain carefully in a 67 page impenetrable document. . . . The new ACORN version has nearly doubled the rate of warming in the minima of the longest running stations.
Nova has put together several charts to show the magnitude of the adjustments, not only from ACORN1 to ACORN2, but also from the prior AWAP compilation to ACORN1. To no one’s surprise, each round of adjustments makes the earlier years cooler, and thus enhances the apparent warming trend. Here is Nova’s chart showing the amount of warming from the beginning to the end of the series, for each of AWAP, ACORN1 and ACORN2, and for minimum, mean and maximum temperatures:
For example, the average minimum temperature had increased over the century covered by 0.84 deg C in the AWAP series. That increased to 1.02 deg C in the ACORN1 series, and to 1.22 deg C in the ACORN2 series.
You need to go over to Gillham’s work to see how these changes derive mostly from decreases in early-year temperatures. Here is a chart from Gillham on the changes to minimum temperatures at the 57 stations that go back all the way to the 1910 start:
As you can see, the “raw” and “v1” temperatures tend to be close — sometimes one higher, sometimes the other. But v2 is significantly lower across the board in the earlier years. Then, suddenly, in the recent years, it tracks the “raw” almost perfectly.
Do they offer a justification for these downward adjustments? Yes, but nothing remotely satisfactory. The one-word explanation is “homogenization.” OK, we understand what that is. For example, sometimes a station moves, and that causes a discontinuity, where, say, the new location is systematically 0.1 deg C lower than the old. An adjustment needs to be made. But these sorts of adjustments should cancel out. How is it possible that every time some official meteorological organization anywhere in the world makes some of these “homogenization” adjustments, the result is that earlier years get colder and the supposed “global warming” trend gets enhanced — always to support a narrative of “climate crisis.”
Well, fortunately, this time the Australian Bureau of Meteorology has put out a very long 57-page document explaining what they have done. Here it is. Is it any help?
As far as I am concerned, this is the definitive proof of the fraud. If this were even an attempt at real, credible science, the proponents would put out a document complete with the details of the adjustments — and all of their computer code — so that an independent researcher could replicate the work. Nothing like that is here. This is pure bafflegab. Nova calls it “impenetrable,” which is way too nice a word as far as I’m concerned. Let me give you a small taste:
3. HOMOGENISATION METHODS
3.1 Detection of inhomogeneities - use of multiple detection methods in parallel
In version 1 of ACORN-SAT, a single statistical method for detection of inhomogeneities was used (Trewin, 2012). This method was based closely on the Pairwise Homogenisation Algorithm (PHA) developed by Menne and Williams (2009), and involves pairwise comparison of data between the candidate station and all sufficiently well-correlated stations in the region, with the Standard Normal Homogeneity Test (SNHT) (Alexandersson, 1986) used to identify significant breakpoints in the difference series. The test was carried out separately on monthly mean anomalies (as a single time series with 12 data points per year), and seasonal mean anomalies, with a breakpoint flagged for further assessment if it was identified in either the monthly series, or (within a window of ± 1 year) in at least two of the four seasons. Further details of the implementation of the PHA in the ACORN-SAT dataset are available in Trewin (2012).
A range of other detection methods have been developed in recent years, many of which were the subject of the COST-HOME intercomparison project (Venema et al., 2012). Three of these methods were selected for use in ACORN-SAT version 2, the selection primarily based on ease of implementation. These methods were:
HOMER version 2.6, joint detection (Mestre et al., 2013)
MASH version 3.03 (Szentimrey, 2008).
RHTests version 4 (Wang et al., 2010).
All of these methods, which use different statistical approaches, have been successfully used across a range of networks since their development. Further details on their implementation are given in Appendix C.
My favorite part is that reference at the end to “Appendix C.” This document has no Appendix C. There are three appendices, numbered Appendix 1, Appendix 2 and Appendix 3. That’s about the intellectual level we are dealing with.
Anyway, try going to this document and see if you can figure out what they are doing. Believe me, you can’t.
And finally: over the years as I have accumulated posts on this topic, several commenters have suggested that I must be alleging some kind of conspiracy among government climate scientists in making these adjustments. I mean, without that, how does it come about that the Australians just happen to be making the exact same kinds of adjustments as NASA, NOAA, and for that matter, as the Brits at the Hadley Center in the UK?
If your brain is wondering how that could be, I would suggest that we have the same kind of phenomenon going on here as the hate crime hoax phenomenon. How does Jussie Smollett just happen to fake a hate crime playing right into the progressive narrative of the moment — just as did the Duke lacrosse team hoaxer, and the Virginia fraternity hoaxer, and the Harvard Law School black tape hoaxers, and many dozens of others? (Here is a compilation of some 15 recent hate crime hoaxes.) Did they all coordinate in one grand conspiracy? Or did they all just realize what was needed from them to support their “team” and its narrative?
DEAR READERS: I have no idea why this piece has been formatted in two columns. I’ll try to fix it tomorrow.
UPDATE, February 20: I think I fixed the problem.