Great post, and great point about the priors! I have a question about how to use/interpret these which I’d love help with from you or someone else who understands this better than I do.
Can I draw implications of your models about emissions scenarios as defined by the IPCC?
First, can I take the first model to indicate something about how likely various emissions pathways (e.g., RCP 6) are if we take little ‘extra action’? e.g., on the “JH extrapolation” version of business as usual that we’re 95% likely not to reach above the mean RCP 8.5 emissions scenario (6180 Gt), 70% likely not to reach above the mean RCP 6.0 scenario (3885 Gt), etc? (all by 2100)
Second, can I take your second model to indicate something about how how much warming we’d get if we were to reach those emissions scenarios? So if RCP 6.0 is the 70th percentile outcome of business as usual (on the ‘JH extrapolation’ version), can we then take the 70th percentile of the probability density function for one of the sensitivity assumptions (say, the Webster one) for how hot it will get on that version of business as usual + that sensitivity assumption to get the amount of warming predicted for RCP 6.0 -- i.e., 3C?
1. Yes that’s right. (Which scenario is better calibrated is up in the air. The Christensen et al one is an expert estimate but economists are very bad at predicting growth next year, so it’s not clear that an expert survey is better than just extrapolating from the last 30 years.)
2. Yes, that is what the second model is *trying* to do but note that qualification that it is mainly trying to estimate the tail risk, so to get the 95% confidence interval right. In guesstimate, the median concentration isn’t right (given the first model), but the 95% CI is. However, it would be quite easy to make a model estimating the chance of a certain level of warming conditional on a particular level of CO2 concentrations. I have added an example of this to the bottom of the second guesstimate model. If you think, following Rogelj et al, that the most likely current policy scenario is 700ppm, then the 95% confidence interval for warming is 1.6 to 5 degrees, with a median of 2.9 degrees. The chance of more than 6 degrees is about 1%. This shows the effect of priors—on Wagner and Weitzman’s estimate, the chance of >6 degrees is more like 10%
Great post, and great point about the priors! I have a question about how to use/interpret these which I’d love help with from you or someone else who understands this better than I do.
Can I draw implications of your models about emissions scenarios as defined by the IPCC?
First, can I take the first model to indicate something about how likely various emissions pathways (e.g., RCP 6) are if we take little ‘extra action’? e.g., on the “JH extrapolation” version of business as usual that we’re 95% likely not to reach above the mean RCP 8.5 emissions scenario (6180 Gt), 70% likely not to reach above the mean RCP 6.0 scenario (3885 Gt), etc? (all by 2100)
Second, can I take your second model to indicate something about how how much warming we’d get if we were to reach those emissions scenarios? So if RCP 6.0 is the 70th percentile outcome of business as usual (on the ‘JH extrapolation’ version), can we then take the 70th percentile of the probability density function for one of the sensitivity assumptions (say, the Webster one) for how hot it will get on that version of business as usual + that sensitivity assumption to get the amount of warming predicted for RCP 6.0 -- i.e., 3C?
Hi Arden,
1. Yes that’s right. (Which scenario is better calibrated is up in the air. The Christensen et al one is an expert estimate but economists are very bad at predicting growth next year, so it’s not clear that an expert survey is better than just extrapolating from the last 30 years.)
2. Yes, that is what the second model is *trying* to do but note that qualification that it is mainly trying to estimate the tail risk, so to get the 95% confidence interval right. In guesstimate, the median concentration isn’t right (given the first model), but the 95% CI is. However, it would be quite easy to make a model estimating the chance of a certain level of warming conditional on a particular level of CO2 concentrations. I have added an example of this to the bottom of the second guesstimate model. If you think, following Rogelj et al, that the most likely current policy scenario is 700ppm, then the 95% confidence interval for warming is 1.6 to 5 degrees, with a median of 2.9 degrees. The chance of more than 6 degrees is about 1%. This shows the effect of priors—on Wagner and Weitzman’s estimate, the chance of >6 degrees is more like 10%
Thanks, this is helpful!