Category Archives: Climate science

Myth Makers in Lab Coats

02 Friday Apr 2021

Posted by in Climate science, Research Bias, Science

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The prestige of some elements of the science community has taken a beating during the pandemic due to hugely erroneous predictions, contradictory pronouncements, and misplaced confidence in interventions that have proven futile. We know that medical science has suffered from a replication crisis, and other areas of inquiry like climate science have been compromised by politicization. So it seemed timely when a friend sent me this brief exposition of how “scientific myths” are sometimes created, authored by Lee Jussim in Psychology Today. It’s a real indictment of the publication process in scientific journals, and one can well imagine the impact these biases have on journalists, who themselves are prone to exaggeration in their efforts to produce “hot” stories.

The graphic above appears in Jussim’s article, taken from a Cambridge study of reporting and citation biases in research on treatments for depression. But as Jussim asserts, the biases at play here are not “remotely restricted to antidepressant research”.

The first column of dots represent trial results submitted to journals for publication. A green dot signifies a positive result: that the treatment or intervention was associated with significantly improved patient outcomes. The red dots are trials in which the results were either inconclusive or the treatment was associated with detrimental outcomes. The trials were split about equally between positive and non-positive findings, but far fewer of the trials with non-positive findings were published. From the study:

“While all but one of the positive trials (98%) were published, only 25 (48%) of the negative trials were published. Hence, 77 trials were published, of which 25 (32%) were negative.“

The third column shows that even within the set of published trials, certain negative results were NOT reported or secondary outcomes were elevated to primary emphasis:

“Ten negative trials, however, became ‘positive’ in the published literature, by omitting unfavorable outcomes or switching the status of the primary and secondary outcomes.“

The authors went further by classifying whether the published narrative put a “positive spin” on inconclusive or negative results (yellow dots):

“… only four (5%) of 77 published trials unambiguously reported that the treatment was not more effective than placebo in that particular trial.“

Finally, the last column represents citations of the published trials in subsequent research, where the size of the dots corresponds to different levels of citation:

“Compounding the problem, positive trials were cited three times as frequently as negative trials (92 v. 32 citations. … Altogether, these results show that the effects of different biases accumulate to hide non- significant results from view.“

As Jussim concludes, it’s safe to say these biases are not confined to antidepressant research. He also writes of the “canonization effect”, which occurs when certain conclusions become widely accepted by scientists:

“It is not that [the] underlying research is ‘invalid.’ It is that [the] full scope of findings is mixed, but that the mixed nature of those findings does not make it into what gets canonized.“

I would say canonization applies more broadly across areas of research. For example, in climate research, empirics often take a back seat to theoretical models “calibrated” over short historical records. The theoretical models often incorporate “canonized” climate change doctrine which, on climatological timescales, can only be classified as speculative. Of course, the media and public has difficulty distinguishing this practice from real empirics.

All this is compounded by the institutional biases introduced by the grant-making process, the politicization of certain areas of science (another source of publication bias), and mission creep within government bureaucracies. In fact, some of these agencies control the very data upon which much research is based (the National Oceanic and Atmospheric Administration, for example), and there is credible evidence that this information has been systematically distorted over time.

The authors of the Cambridge study discuss efforts to mitigate the biases in published research. Unfortunately, reforms have met with mixed success at best. The anti-depressant research reflects tendencies that are all too human and perhaps financially motivated. Add to that the political motivation underlying the conduct of broad areas of research and the dimensions of the problem seem almost insurmountable without a fundamental revolution of ethics within the scientific community. For now, the biases have made “follow the science” into something of a joke.

Climate Activists Run From Rigor

02 Tuesday Jul 2019

Posted by in Climate science

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Climate activists are seemingly averse to empiricism, and to the scientific method for that matter. Esteemed climatologist John Christy makes that point abundantly clear in a recent speech entitled “Putting Climate Change Claims To the Test“. Christy was one of two lead authors of the third Assessment Report (AR3) of the United Nations Intergovernmental Panel on Climate Change (IPCC), published in 2001, but his research into systematic discrepancies between climate models and actual temperature trends put him in the doghouse with the IPCC. He hasn’t been asked to serve as a lead author since. The transcript linked above is awkward in a few spots where Christy makes informal references to slides, which do, however, accompany and align with the transcript. He covers a lot of ground in this speech, but I’ll cover just a few points. Read the whole thing!

Christy notes that all of the climate models used by the IPCC have substantially over-predicted temperatures for the past thirty years, by an average across models of more than 2.5 times! He measured the errors 17 years ago and again recently, and the magnitude of those errors was almost identical. Yet little progress has been made in correcting the climate models. And why bother? The press simply won’t report the errors, and the IPCC and the activist community are too enraptured by their religious, end-of-days narrative to give it up.

Christy uses a stylized global “energy budget” to illustrate the various sources of climate forcing. He attributes the climate model errors to a failure to adequately account for the escape of energy from the atmosphere into space. He also demonstrates that the magnitude of carbon forcing from human activity represents a tiny contribution to the impact of Earth’s total energy forcings.

Another major point from Christy is that the climate research community has lost its scientific bearings. The very title of his speech refers to testable hypotheses, which is what real science is all about. Christy provides a punchy quote from Richard Feynman on this issue: “Science is a belief in the ignorance of experts.” Today it is routine for climate scientists to report results based on extrapolations from models hinging on mere assumptions they claim to have backtested. Those backtests are often based on flimsy standards and tend to receive little scrutiny, just as long as they are consistent with the so-called “expert consensus”. In other words, those claims amount to a big “what-if” exercise, and the underlying assumptions often lack rigorous testing. Christy goes on:

“Michael Crichton says that in science consensus is irrelevant, what is relevant is reproducible results, consensus is inappropriate. So, as an aside, there’s a strange thing happening in climate science: the proliferation of unfalsifiable claims, in other words the unfalsifiable hypothesis.  Remember I said that scientific method: you make a claim and the claim has to be testable and falsifiable and then you check and see if it’s the real thing.

Well here’s the claim. Whatever happens is consistent with global warming.  Maybe it’s snow or no snow. More hurricanes? Less hurricanes? This method says wait for something to happen and then claim that human-caused warming is to blame. That’s the unfalsifiable hypothesis and it has no information value and there is no way to test it, it has no testable parameter and so it is not science. The unfalsifiable hypothesis predicts anything is possible therefore nothing is testable.“

In other words, today climate research is infested with “fake science”. Christy marches through a variety of climate-related phenomena in his speech, offering evidence on each that is sometimes mixed but often contrary to the implications of climate models and claims made by activists. The big picture is nothing short of damning to the catastrophic warming narrative. Yet the dire scenarios feared by the Chicken Littles of the world, which never come to pass, continue to be reported eagerly by the media and pressed for costly political action.