Those who believe climate change is a real problem should present their case with more epistemic responsibility, by which I mean fewer appeals to authority. “Appeal to authority” is an informal fallacy, which I’m sorry to say is exemplified by this article, both in its appeal to the expertise of climate scientists, and to the non-expertise of their critics.
To understand why many genuine sceptics are doubtful about climate science, we have to ask some simple but deep questions about what empirical evidence is, what makes a good scientific theory worth believing, and so on. To talk about “overwhelming evidence” in an apparent vacuum of answers to those questions is not good enough.
A quick answer to the question of what makes a scientific theory worth believing is that is has great explanatory or predictive power — or best of all, both. Examples: evolutionary theory has remarkable explanatory power (which is why Daniel Dennett called it “universal acid”) but rather limited ability to predict the future. By contrast, quantum theory throws up more puzzles than it solves, so its explanatory power is ambiguous at best, but it more than makes up for that shortcoming with its extraordinary predictive power. With both evolutionary theory and quantum theory, we can be very confident that we are “on to something”, even though we may be wrong about the finer details, or have real conceptual difficulties with the currently-available interpretations of the formalisms.
A not-so-quick answer to the same question adds why predictive power is important. Only by yielding checkable predictions can a scientific theory be tested. In testing, the theory (in conjunction with numerous other theories and assumptions) implies something that can be observed. If that something actually is observed, the theory is “corroborated”, which is good news for the theory. And if it isn’t, that’s not such good news for the theory.
Note that this corroboration by observation is nothing like its “being implied by” observation. Observations can’t imply theories that describe unobservable things — in other words, scientific theories aren’t “based on” observations. Rather, our reason for thinking a scientific theory is true is: it would be an odd “coincidence” for it to be able to pass tests yet still be substantially false. The more varied and cunningly devised such tests can get, and the more of an “amazing coincidence” the theory’s falsity would become if it still passes them, the more confident we can be that it actually is true, or at least approximately true.
Evidence for a scientific theory is a special case of evidence for any belief — it consists of other beliefs. As the believer engages with the real world, together these beliefs in effect “test” the belief in question, and if it “passes” the “test” it probably actually corresponds to (i.e. its content accurately describes) the real world. It “works” because it’s probably true. In slogan form, evidence consists of “everything seeming to hang together well”. A good scientific theory is consistent with observations that would expose it as false if it were false — in other words it “stuck its neck out and survived” a trial by observation. More generally, a good belief meshes smoothly with our other beliefs. So we have good evidence for any scientific theory which has passed tests, and which exhibits various “theoretical virtues” such as simplicity, modesty, etc. (i.e. the tell-tale marks of its meshing smoothly with our other beliefs).
Unfortunately, there is a long philosophical tradition of supposing that evidence consists of something else, namely, a theory or belief’s “being based on” something more certain, usually what is fancifully thought of as “raw data” of sense experience. The ubiquity of that supposition reveals itself in everyday language, in which words like ‘grounds’, ‘foundation’ and ‘basis’ are practically synonymous with ‘evidence’. Such words are indeed appropriate in mathematics, where theorems are genuinely based on (i.e. implied by) axioms, but in everyday discourse they are deeply misleading.
That tradition was effectively discredited in the twentieth century by mainstream philosophers of science and a few exceptional figures such as Wittgenstein and WVO Quine. But even in the absence of sophisticated philosophical analysis, it should be obvious from animal behaviour that many animals have knowledge — and their knowledge has nothing to do with their beliefs being “based on data”. Instead it depends on their beliefs being sustained by reliable processes connecting brain and physical surroundings in the real world.
Despite all that, the assumption that evidence in general and scientific evidence in particular is a matter of being “based on data” lives on at the periphery of science — in disciplines like psychology, where routine appeals are made to “studies” which work as starting-points for the genesis of theory rather than tests for the justification of theory. The guiding assumption of these questionable disciplines is that empirical evidence typically takes the form of extrapolation from a sample rather than passing a test. We don’t find that assumption in mainstream sciences like physics, chemistry, biology or in any branch of engineering. But we do find it in climate science.
Although some aspects of climate science (such as the greenhouse effect) belong to mainstream physics and borrow the legitimacy of its methodology, a large part of climate science involves the construction of computer models that are intended to mimic the earth’s climate. They are shaped to fit the “data” of past climate, “data” consisting of both actual measurements and — alarm bells should ring here — “proxy measurements” derived (with a high dependence on theory) from tree-rings, ice-cores, lake-beds, etc.
What normally gives us reason to believe that good scientific theories are true does not give us reason to believe that these computer models are accurate. Why? — We noted above that corroboration by observation was not at all the same as being “based on data”. The role of observation is completely different between the two. In the former, a theory “sticks its neck out and survives”, but in the latter, it is deliberately shaped “after the fact” so that it cannot fail to fit with observation. It is often claimed that these models are “tested”, but it is not testing of the sort that gives us reason to believe a scientific theory, i.e. a test which if passed suggests truth and which if failed suggests falsity. Instead, a model is compared with data which were gathered beforehand, and which the model was later adjusted to fit. In the former case, we have a reason to think we are “on to something”; in the latter, we have nothing better than data-fitting. The reasoning involved is not that of the hypothetico-deductive method of guessing and testing, but induction: the most problematic form of reasoning.
The standard scientific method of guessing followed by testing is called “hypothetico-deductive” because observational predictions are drawn from hypotheses using deductive logic (often mathematics). If actual observation confirms the predictions of a hypothesis, then we have a reason to think the hypothesis is true. This pattern is reversed in induction. It starts off with observations and then extrapolates to arrive at a hypothesis or model. Because this is essentially generalisation, the hypotheses or models so arrived at can only describe or mimic the sort of things that were observed directly in the first place. In other words, induction cannot take us beyond the merely observable. Contrast that with mainstream science, whose hypotheses typically describe unobservable and often very exotic things such as subatomic particles and force fields.
It must be admitted that induction does sometimes give us reason to believe its end-product generalisations. It can be a reliable way of forming beliefs, as long as there is a lawlike connection between the property observed in a sample, and membership of the larger class from which the sample was taken. For example, being green is an essential property of emeralds. We can reliably use induction to infer that all emeralds are green from examining a few of them and observing that they are green. But being white is not an essential property of swans, so we can’t reliably use induction to infer that all swans are white from examining a few of them and observing that they are white.
Simple induction can be reliable when applied to some simple lawlike phenomena. But it won’t be reliable when applied to complicated, patternless, un-lawlike phenomena with non-essential properties. No examination of Beethoven’s nine symphonies, however rigorous, will enable us to extrapolate a tenth symphony. The Earth’s climate is huge, very complicated, and chaotic. The human eye can discern few patterns it its routine variability over the decades. This is not the sort of thing induction can reliably extrapolate from. But suppose we give climate science the benefit of the doubt and grant that it can. Then we would have a reason to think the models are accurate as long as they yield some observable predictions that turn out to be true. But apparently they can’t. Inasmuch as these models have been genuinely “tested” at all, they get it wrong — they routinely fail the “tests” because their checkable predictions have been checked and found wanting. And as long as the climate continues not to warm, as it has not been doing now for about fifteen years, they’re getting wronger by the month.
So much for the empirical evidence, which so far seems to be almost entirely negative. What about the so-called “theoretical virtues” mentioned in passing above? Are climate science’s models simple, modest, general, falsifiable? Do they suggest fruitful new lines of inquiry?
To these questions, I think we must answer no, no, no, no, and no, respectively. First, climate science’s models are not simple but spectacularly complicated. Second, what they claim and aim to achieve is strikingly immodest — to mimic the behaviour of something that looks completely random to that master pattern-recognizer, the human eye coupled with the human brain. Third, quite unlike Newton’s laws or that of natural selection, say, the models apply to a very narrow corner of nature, the climate of a single unique planet. Fourth, because the models are adjusted rather than rejected when found not to fit “data” they were designed to fit, and because they are more or less accurate rather than literally true or false, nothing seems to count as a clear “fail”. And if no such “fail” would lead climate scientists to decisively reject a model, that model is in effect unfalsifiable. Fifth, nothing in climate science’s models suggest any new ideas or avenues of exploration in any other science. It’s a rigorous road, but a barren one that doesn’t lead to any new or unexpected places.
Defenders of these models tend to be very dismissive of non-experts, as if the general public don’t have the finesse to judge what goes on in the rarified atmosphere of climate science. Hence the endless, shameless and fallacious references to themselves as “experts”, to the riffraff as thuggish “deniers”, and the ever more authoritarian assertions that the latter are just going to have to take our word for it. I think this is a terrible mistake, as it overlooks the subjective & aesthetic aspects of judgements about truth. Just as the ordinary human mind is a brilliant recognizer of patterns, it’s also pretty good at hearing the “ring of truth”. Non-specialists can see that some scientific theories have an austere beauty, that some of them “work”, and so on. Climate science has been falling out of favour with the general public because they can see it is big, ugly, complicated — and it hasn’t been “working”. So there’s nothing much to persuade us that it’s true or accurate.