Speaking of doubling down on a disintegrating paradigm, one path of retreat from unfavourable real-world data is into the models. Regrettably their predictions have not been very good, although some people insist that they secretly have. Including the press. When this year’s Nobel Prize in Physics went to Syukuro Manabe, Klaus Hasselmann and Giorgio Parisi, NBC rushed to tell us it was “for work that helps understand complex physical systems, such as Earth’s changing climate. Manabe and Hasselmann were awarded jointly for the physical modeling of the climate, ‘quantifying variability and reliably predicting global warming.’” If so, it’s strange that it should happen just as, Judith Curry observes in a post pointedly titled “IPCC AR6: Breaking the hegemony of global climate models”, the IPCC itself is backing away slowly from the overheated models and accompanying rhetoric.
National Geographic is all in and then some on the climate modelers win prize angle, with a headline “Nobel Prize in Physics Awarded for Study of Humanity’s Role in Changing Climate” and a deck saying “The work of Syukuro Manabe, Klaus Hasselmann and Giorgio Parisi ‘demonstrate [sic] that our knowledge about the climate rests on a solid scientific foundation,’ the committee said.” Then the actual text begins “Three scientists received the Nobel Prize in Physics on Tuesday for work that is essential to understanding how the Earth’s climate is changing, pinpointing the effect of human behavior on those changes and ultimately predicting the impact of global warming.”
Wow. Pinpointing. That’s even better than “attribution science” that paints with a brush. As for “ultimately predicting”, would that be the one from 13 climate scientists about how “Our climate projections for 2500 show an Earth that is alien to humans”? Point that pin somewhere else, will ya?
As Curry notes, “If we harken back to the IPCC AR4 (2007), global climate models ruled, as exemplified by this quote: ‘There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above.’” However by the time of the next “Assessment Report” in 2013, “Some hints of concern about what the global climate models are producing were provided in the AR5. With regards to climate sensitivity, the AR5 included this statement in a footnote to the SPM: ‘No best estimate for equilibrium climate sensitivity can now be given because of a lack of agreement on values across assessed lines of evidence and studies.’” And “The IPCC AR6 takes what was begun in the AR5 much further.”
Specifically, she explains, AR6 lowers the top end for ECS or “Equilibrium Climate Sensitivity”, the temperature increase to be expected from any doubling of atmospheric CO2, from 4.5 to 4. It also increases the bottom end from 1.5 to 2.5, of which she says “I do not agree with their rationale” but her main point is that the rationale is not that the models say so. Rather, “A substantial number of the [latest-generation] CMIP6 models are running way too hot, which has been noted in many publications” so instead of looking to the models for guidance on ECS, the IPCC looks at actual 20th-century data (including filtering the models and using only those that “reasonably simulate the 20th century”).
In short, the more complicated the models become, the more obvious it is that they are a substitute for reality not a depiction of it. Which is unfortunate, but it isn’t any less true for being so.
NBC reports that Parisi got half of the Physics prize “for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales.” And it is important, because a great many things we would really like to understand, a word here meaning “solve using the tools of linear algebra that have powered science from triumph to triumph for hundreds of years”, are mathematically disorderly.
The trouble is that certain kinds of mathematical disorder resist modeling. As the IPCC itself said in an unguarded moment back in 2001, “In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.” The problem here wasn’t that the models weren’t yet complex enough or the computers fast enough. It’s that systems that are “Sensitively Dependent on Initial Conditions” cannot be modeled because you can never get the inputs sufficiently precise. (One outfit that uses the word “skeptical” to mean “not skeptical at all” claims that the IPCC didn’t mean what it seemed to say because it went on to talk about “the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions” but if you think that means anything other than we’ll run lots of models then take a guess you’re not very skeptical.)
The chair of the Nobel Committee for Physics, Thors Hans Hansson, waved away the uncertainty by insisting that “We can predict what is happening with the climate in the future if we know how to encode the chaotic weather”. But we reply as the Spartans did to Philip of Macedon, “If”. (In fact we will add that the IPCC’s admission was not just that weather is chaotic but so is climate, which makes the challenge infinitely harder.) And we’ll believe it when the predictions are more accurate than random guesses rather than worse, as they typically are now.
We’re not holding our breath. NBC also said, “Manabe, a senior meteorologist at Princeton University, demonstrated how increased levels of carbon dioxide in the atmosphere lead to higher temperatures. His work in the 1960s led the development of physical models of the Earth’s climate, and his work laid the foundation for the development of current climate models.” OK. But as our Crystal Ball video series has shown, the models that made predictions long enough ago that they can be compared with real-world results, the sort based on discoveries from the 1960s, are worse than random.
NBC added that “Hasselmann, a researcher in climate dynamics and professor emeritus at the Max Planck Institute for Meteorology in Germany, created a model that links weather and climate – answering the question of why climate models can be reliable despite weather being changeable and chaotic.” So here instead we quote Puddleglum, specifically his crack “Got to start by finding it, have we? Not allowed to start by looking for it, I suppose?” It would have been nice if Hasselmann had answered the question of whether climate models can ever be reliable despite weather being chaotic because the point here is not that the predictions are good and we want to know why, it’s that they are no good and we want to know why not.
Also, since the question of expertise is frequently flung in the faces of climate skeptics, we feel justified in pointing out that the NBC story was written by NBC’s “Middle East Digital Reporter”, whatever that title is supposed to mean, and that her degree from “Sciences Po” should not be taken to indicate that she studied sciences; that institution is “A World-Class University in the Social Sciences” where she did a double Master’s in Journalism and International Relations to complement her BA from Trinity College Dublin in History and Italian. If she is aware of chaos theory it was not evident in her piece.