Splendid article, suitably depressing. As one more example, this comment below has been “detected as spam” by the Disqus algorithm on multiple sites. It lasts only a few minutes.
>>It has been a recurring incantation that climate is a chaotic non-linear dynamic system. This has been presented as a challenge for us to eliminate the uncertainties and come to a satisfactory understanding allowing reliable predictions and determinations of causality.
There are recurring murmurs that the uncertainty is not only in the details of the data, but in the process itself. And this has been shown to be correct by a careful statistical analysis comparing temperature and precipitation records at multiple sites in the world and in the USA with the predictions of prestigious climate models* (GCM). All stations spanned at least 100 years of data.
Stations selected for (a) temperature (b) precipitation.
Scientists at the U of Athens, Greece presented their findings and concluded:
“Besides confirming the findings of a previous assessment study that model projections at point scale are poor, results show that the spatially integrated projections are also poor…In a large number of stations, the correlation coefficient has low or even negative values for both temperature and precipitation…At all stations examined, there is not a single model run that successfully reproduces the time series of all variables examined…At the Durban station, South Africa, not a single model output shows the 1.5°C fall in mean annual temperature during 1920–1960; instead, all model outputs show a constant increase…
We think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms. Several publications, a typical example being Rial et al. point out the difficulties that the climate system complexity introduces when we attempt to make predictions. “Complexity” in this context usually refers to the fact that there are many parts comprising the system and many interactions among these parts. This observation is correct, but we take it a step further. We think that it is not merely a matter of high dimensionality, and that it can be misleading to assume that the uncertainty can be reduced if we analyse its “sources” as nonlinearities, feedbacks, thresholds, etc., and attempt to establish causality relationships.”
As a species we are very uncomfortable with uncertainty and we are willing to believe the most arrant nonsense in order to decrease and hopefully eliminate it. As C.S. Peirce said, “It is easy to be certain. One has only to be sufficiently vague.”
(So as information content diminishes, certitude increases until finally, at the limit, we can be absolutely certain about nothing. Thus the attraction of Zen Buddhism.)
Scientists are supposed to be inherently comfortable with uncertainty. And many are, as long as it does not interfere with funding. Thus we note the often profound differences, in scientific papers that have material importance, between the cautious and qualified conclusions and the Summary for Executive Action. Others are simply not immune to the incompatibility of uncertainty with emotional equilibrium.