On attribution

How do we know what caused climate to change – or even if anything did?

This is a central question with respect to recent temperature trends, but of course it is much more general and applies to a whole range of climate changes over all time scales. Judging from comments we receive here and discussions elsewhere on the web, there is a fair amount of confusion about how this process works and what can (and cannot) be said with confidence. For instance, many people appear to (incorrectly) think that attribution is just based on a naive correlation of the global mean temperature, or that it is impossible to do unless a change is ‘unprecedented’ or that the answers are based on our lack of imagination about other causes.

In fact the process is more sophisticated than these misconceptions imply and I’ll go over the main issues below. But the executive summary is this:

  • You can’t do attribution based only on statistics
  • Attribution has nothing to do with something being “unprecedented”
  • You always need a model of some sort
  • The more distinct the fingerprint of a particular cause is, the easier it is to detect

Note that it helps enormously to think about attribution in contexts that don’t have anything to do with anthropogenic causes. For some reason that allows people to think a little bit more clearly about the problem.

First off, think about the difference between attribution in an observational science like climatology (or cosmology etc.) compared to a lab-based science (microbiology or materials science). In a laboratory, it’s relatively easy to demonstrate cause and effect: you set up the experiments – and if what you expect is a real phenomenon, you should be able to replicate it over and over again and get enough examples to demonstrate convincingly that a particular cause has a particular effect. Note that you can’t demonstrate that a particular effect can have only that cause, but should you see that effect in the real world and suspect that your cause is also present, then you can make a pretty good (though not 100%) case that a specific cause is to blame.

Why do you need a laboratory to do this? It is because the real world is always noisy – there is always something else going on that makes our (reductionist) theories less applicable than we’d like. Outside, we don’t get to perfectly stabilise the temperature and pressure, we don’t control the turbulence in the initial state, and we can’t shield the apparatus from cosmic rays etc. In the lab, we can do all of those things and ensure that (hopefully) we can boil the experiment down to its essentials. There is of course still ‘noise’ – imprecision in measuring instruments etc. and so you need to do it many times under slightly different conditions to be sure that your cause really does give the effect you are looking for.

The key to this kind of attribution is repetition, and this is where it should become obvious that for observational sciences, you are generally going to have to find a different way forward, since we don’t generally get to rerun the Holocene, or the Big Bang or the 20th Century (thankfully).

Repetition can be useful when you have repeating events in Nature – the ice age cycles, tides, volcanic eruptions, the seasons etc. These give you a chance to integrate over any unrelated confounding effects to get at the signal. For the impacts of volcanic eruptions in general, this has definitely been a useful technique (from Robock and Mao (1992) to Shindell et al (2004)). But many of the events that have occurred in geologic history are singular, or perhaps they’ve occurred more frequently but we only have good observations from one manifestation – the Paleocene-Eocene Thermal Maximum, the KT impact event, the 8.2 kyr event, the Little Ice Age etc. – and so another approach is required.

Page 1 of 3 | Next page