The dangers of making assumptions about data

I commend to you this YouTube presentation by Frank Lansner, from October, which explains and updates his 2018 paper, which is unfortunately behind a paywall:

The information presented here appears to me to be potentially very significant in the matter of climate change. In essence Lanser’s team hit on the idea, or else spotted in the data, that the distinction usually made between “land surface temperature” and “ocean surface temperature” is misleading, because many land regions, and therefore temperatures, are greatly affected by air masses moving from the oceans.

Weather stations close to the oceans will essentially be reflecting the temperature changes of the upper ocean. However, the true land temperature, ie that produced by the direct affects of sunshine or global atmospheric temperatures (eg from greenhouse gases) will need to be measured in areas sheltered from the influence of sea temperatures, which in effect means measuring them in inland valleys.

When the team took datasets from across the world, and divided them in this way between ocean- and land-influenced temperatures, two dramatically distinct patterns of climatic temperature change emerged for the last century, which differ markedly from the aggregated, homogenised-data graphs published in most of the literature that leads climate policy.

Essentially, the usual homogenised data produces, if not a hockey stick, then a more or less steady, or slightly rising, temperature, with a great acceleration in the last few decades attributed (of course) to CO2 levels.

Separating out the two types of temperature, however, gives an entirely different picture. The ocean-influenced temperatures show a gradual, and more or less steady, small increase from when records began. It correlates with the massive loss of glacial ice recorded from the end of the eighteenth century on. This likely reflects either the warming of the upper ocean following the end of the little ice age, or perhaps more likely the longer term warming after the end of the ice age. Either way, it predates any possible effect of anthopogenic CO2.

The land temperatures, however, once uncluttered from the influence of the oceans, show a clear sinusoidal pattern with something like a sixty year periodicity: a peak of temperature in the 1920s and 30s (corresponding to historical records and individual memories of excessively hot weather around the world), followed by a cooling thereafter (corresponding, towards the end, with the apocalyptic fears of a new ice age), until the recent upswing, markedly similar to that of the early twentienth century. Since there is little to distinguish this from the earlier warming, nothing warrants pinning it to CO2 levels.

Hence taken individually, neither type of data gives any signal whatsoever of a correlation with rising CO2 levels, and both show evidence of different causes preceding any possible anthpogenic effect.

It is only when the two kinds of data are confused and combined that the earlier land peak is largely ironed out by the lower ocean tempertaures back then, and the illusion is given that unprecedented increase has taken place over the last forty years or so.

This is interesting science, which clearly challenges the whole anthropogenic warming theory by re-examination of the raw data. But the interesting (and saddening) part of the story is that in a number of ways this evidence has been hidden by the pre-existing science, and rendered difficult to see, let alone correct.

In the first place, as Lansner points out, the homogenisation of data has not simply been a question of mixing the different types of signal. Instead, the sheltered valley stations, already by their nature lower in number, have been seen not to match the general pattern, and have therefore often been either excluded, or statistically down-graded, as being anomalous to the “best” data predicted by the theory of anthropogenic warming.

Lansner’s presentation shows how, the more this has been done (or the more a particular study is based close to the oceans) the more the early-century maximum and the mid-century minimum has become invisible.

But by performing this homogenization at a stage prior to writing the articles, and presenting the “corrected” temperatures as if they were data (whilst the raw data languishes in an unpublished database), any alternative interpretation is effectively lost to science.

In some cases, giving even more cause for concern, Lanser shows that the graphs produced in the literature simply cut off the period before the mid-century minimum, thus making the present look unique by omission. This is worrying because it suggests deliberate, rather than inadvertant, bias.

Now, all this is of less importance to the advancement of knowledge if some keen-eyed researcher like Lansner notices a phenomenon, re-examines the original data, and realises another story is hidden there – in this case a story that, if confirmed, might save the world trillions of wasted dollars, millions of deaths from fuel poverty, huge numbers of mental breakdowns amongst teenagers taught to feel doomed, massive exploitation of rare-earth miners in poor nations, mountains of un-recyclable solar panels in future decades… and, of course, the simple fact that living in untruth produces multitudes of unforeseen evils. As we are always told, science advances by learning from its mistakes, so all this can be put right, by science.

But sadly, Landsner begins his presentation by explaining how, in order to produce their paper, they contacted twenty or thirty meteorological sources to obtain the raw temperature data for their re-analysis. The response was, it seems, universally negative. So the paper, and a further one due for publication in the New Year, is far less comprehensive than it would otherwise be.

The refusal to release original data is a recurrent feature of climate science, from “Climategate” onwards, and it is done by scientists themselves, not by politicians, the press, or any other scapegoat. The excuses have ranged from protecting private intellectual property (the same excuse that brings universal condemnation of pharmaceutical companies for suppressing negative research on their drugs) to preventing negative criticism of years of the originators’ scientific work – which is as much as to confess they are interested in their own careers, not in science and, accordingly, not in the public good.

The extreme instance of this attitude is the claim that releasing the data might strengthen the hand of “denialists,” much like those biologists keeping uncertainties over evolutionary theory out of text-books and press-coverage lest creationists’ arms be strengthened. Both positions are religious or political, in a bad sense, in that belief in the theory takes priority over what the data actually shows. If truth favours “denialists,” “creationists” or any other out-group, then that’s the way the cookie crumbles. Give stronger evidence for your own view, or admit you’re wrong.

I’m reminded of Jeremy Corbyn’s words following last week’s complete demolition of his Labour Party in the UK general election: “We won the arguments.” Well yes – I too can win every intellectual argument, provided the criterion of victory is my own belief that I am right. In Corbyn’s case, the likelihood is that the criterion of “winning the argument” is correspondence to Marxist theory, which by definition is correct. As my old boss at the pest control lab used to say, “The people have failed us – we will elect a new people.”

Suppressing information from others to win our arguments, however, is about ideology or personal corruption. It is not just bad science.

It is just bad.