| Let's think about this. Let's assume that when a river gets redirected, a scientist goes and investigates it (which seems reasonable), and is asked what is the chance that it happened by natural causes, or is due to global warming. Based on the analysis that @acover shared with us, we can see that out of a thousand such dispatches, 995 will be due to global warming and 5 will be due to natural variability. So it seems correct for that scientist to conclude "It's 99.5% due to global warming.". In fact, saying that it's .5% due to natural variability is equivalent. You are probably thinking of the jelly bean situation, where a test is performed to detect something within a sample population. The test has a failure rate, and the population has a base rate of occurrence. In this case (thanks @acover), they are directly calculating the base rate. Now, you could also ask "How many times in a year will a scientist be dispatched and be wrong", which is a again a different question (and is closer to the scenario you described). |
The paper appears to create a model for a single glacier's retreat. Selects the parameters of this model by analyzing all glaciers. Then determines the retreat of this glacier is unlikely under this model.
The issue is that there is a sampling bias. This glacier is not like most glaciers [I assume]. It was not randomly sampled from all glaciers. It was selected because of a river diversion due to glacial retreat.
What did they do to compensate for this sampling bias? I don't see it.