I really don’t understand the row for climate change. What exactly is meant by “inference” in the data column? I don’t know what you want to count as data, but it seems to me that the data with respect to climate change include increasingly good direct measurements of temperature and greenhouse gas concentrations over the last hundred years or so, whatever goes into the basis of relevant physical and chemical theories (like theories of heat transfer, cloud formation, solar dynamics, and so forth), and measurements of proxies for temperature and greenhouse gas concentrations in the distant past (maybe this is what “inference” is supposed to mean?).
I also don’t understand the ”?” under probability distribution. Are the probability distributions at stake here distributions over credences? If so, then they can be estimated for most any scientist, at least. Are the distributions over frequencies? Then frequencies of what? I suspect we could estimate distributions for lots of climate related things, like severe storms or droughts or record high temperatures. I would be somewhat surprised if such distributions have not already been estimated by climate scientists. Is the issue about calibration? Then the answer seems to be a qualified yes. Groups like the IPCC give probabilistic statements based on their climate models. The climate models could be checked at least on past predictions, e.g. by looking at what the models from 2000 predicted for the period 2001-2011. We might not get a very good sense of how well calibrated the models are, but if the average temperature for each month, say, is a separate datum, then we could check the models by seeing how many of the months fall into the claimed 95% confidence bands, for example. (And just putting down confidence bands in the models should tell you that the climate scientists think that the distribution can be estimated for some sense of probability.)
That’s an interesting point. How precise do you think we have to be with respect to feedbacks in the climate system if we are interested in an existential risk question? And do you have other uncertainties in mind or just uncertainties about feedbacks?
The first thing I thought on reading your reply was that insofar as the evidence supports positive feedbacks, the evidence also supports the claim that there is existential risk from climate change. But then I thought maybe we need to know more about how far away the next equilibrium is—assuming there is one. If we are in or might reach a region where temperature feedback is net positive and we run away to a new equilibrium, how far away will the equilibrium be? Is that the sort of uncertainty you had in mind?
I really don’t understand the row for climate change. What exactly is meant by “inference” in the data column? I don’t know what you want to count as data, but it seems to me that the data with respect to climate change include increasingly good direct measurements of temperature and greenhouse gas concentrations over the last hundred years or so, whatever goes into the basis of relevant physical and chemical theories (like theories of heat transfer, cloud formation, solar dynamics, and so forth), and measurements of proxies for temperature and greenhouse gas concentrations in the distant past (maybe this is what “inference” is supposed to mean?).
I also don’t understand the ”?” under probability distribution. Are the probability distributions at stake here distributions over credences? If so, then they can be estimated for most any scientist, at least. Are the distributions over frequencies? Then frequencies of what? I suspect we could estimate distributions for lots of climate related things, like severe storms or droughts or record high temperatures. I would be somewhat surprised if such distributions have not already been estimated by climate scientists. Is the issue about calibration? Then the answer seems to be a qualified yes. Groups like the IPCC give probabilistic statements based on their climate models. The climate models could be checked at least on past predictions, e.g. by looking at what the models from 2000 predicted for the period 2001-2011. We might not get a very good sense of how well calibrated the models are, but if the average temperature for each month, say, is a separate datum, then we could check the models by seeing how many of the months fall into the claimed 95% confidence bands, for example. (And just putting down confidence bands in the models should tell you that the climate scientists think that the distribution can be estimated for some sense of probability.)
The uncertainties within the models are swamped by uncertainties outside the model—ie whether feedbacks are properly accounted for or not.
I agree that “inference” on its own is very odd. I would have put “inference and observations (delayed feedback)”.
That’s an interesting point. How precise do you think we have to be with respect to feedbacks in the climate system if we are interested in an existential risk question? And do you have other uncertainties in mind or just uncertainties about feedbacks?
The first thing I thought on reading your reply was that insofar as the evidence supports positive feedbacks, the evidence also supports the claim that there is existential risk from climate change. But then I thought maybe we need to know more about how far away the next equilibrium is—assuming there is one. If we are in or might reach a region where temperature feedback is net positive and we run away to a new equilibrium, how far away will the equilibrium be? Is that the sort of uncertainty you had in mind?