The end of model democracy ? An editorial comment

Imagine you are hosting a garden party tomorrow and you are trying to decide whether or not to put up a tent against the rain. You read the weather forecast in the newspaper and you ask the farmer next door, and you look at the sky (knowing that persistence is often not a bad weather forecast). So you get three predictions, but how would you aggregate them? Would you average them with equal weight? You might trust the forecast model more (or less) than the farmer, not because you understand how either of them generates their prediction, but because of your past experience in similar situations. But why seek advice from more than one source in the first place? We intuitively assume that the combined information from multiple sources improves our understanding and therefore our ability to decide. Now having read one newspaper forecast already, would a second and a third one increase your confidence? That seems unlikely, because you know that all newspaper forecasts are based on one of only a few numerical weather prediction models. Now once you have decided on a set of forecasts, and irrespective of whether they agree or not, you will have to synthesize the different pieces of information and decide about the tent for the party. The optimal decision probably involves more than just the most likely prediction. If the damage without the tent is likely to be large, and if putting up the tent is easy, then you might go for the tent in a case of large prediction uncertainty even if the most likely outcome is no rain. Although it may seem far-fetched at first, the problem of climate projection is in fact similar in many respects to the garden party situation discussed above. So far, projections from multiple climate models were often aggregated into simple averages, standard deviations and ranges. One example is the recent Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC),