RealClimate: On attribution

Gavin at RealClimate discusses attribution -- the relation of an effect to a cause. RealClimate: 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

I’ll go through the details below, but 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.

In the real world we attribute singular events all the time – in court cases for instance – and so we do have practical experience of this. If the evidence linking specific bank-robbers to a robbery is strong, prosecutors can get a conviction without the crimes needing to have been ‘unprecedented’, and without having to specifically prove that everyone else was innocent. What happens instead is that prosecutors (ideally) create a narrative for what they think happened (lets call that a ‘model’ for want of a better word), work out the consequences of that narrative (the suspect should have been seen by that camera at that moment, the DNA at the scene will match a suspect’s sample, the money will be found in the freezer etc.), and they then try and find those consequences in the evidence. It’s obviously important to make sure that the narrative isn’t simply a ‘just-so’ story, in which circumstances are strung together to suggest guilt, but which no further evidence is found to back up that particular story. Indeed these narratives are much more convincing when there is ‘out of sample’ confirmation.

We can generalise this: what is a required is a model of some sort that makes predictions for what should and should not have happened depending on some specific cause, combined with ‘out of sample’ validation of the model of events or phenomena that were not known about or used in the construction of the model.