It’s kinda trivial to show that impacts at 2ºC are much worse than at 1.5ºC. What nobody has done is shown that the difference between 1.6ºC and 1.5ºC is so much more than between 1.5ºC and 1.4ºC, and therefore that this is the key threshold at which we must stop /panic /revolt /give-up-and-party /whatever … What matters is the curvature of the function, to see curvature you need at least three points within the range of choice (acc to Paris 1.5-2.0). So more info about impacts at 1.75ºC could help. I told them this during scenario planning discussions over a decade ago, but the big earth-system-model guys just mumbled about “signal-to-noise-ratio” - they preferred to run RCP8.5 (the extreme high scenario that - fortunately- is looking so implausible it may be dropped from next IPCC round), because the higher the scenario, the more exciting graphics they make, the more funding they get…, but this didn’t help to show why we need more effort from all, at the bottom of the range.
Most of the exact boundaries for ecological tipping points aren’t known with an 0.1ºC precision. I don’t think we’re going to get something like what you are asking for, just knowledge that each incremental increase in temperature is another step into a minefield.
True, but in principle we (i.e. big teams) could make impacts functions for each case and combine by integrating over risk (within which, value judgements also make a big difference - but that’s something to learn too). To fit those functions we needed more studies by ESMs with smaller differences at the low end - if you don’t study it, you won’t know it (hence my point years ago - those models are slow, and big committee-run processes even worse).
Sure, it would be nice to know, but I don’t see knowing as likely to change policy in any kind of meaningful way. That’s going to take changing who holds power, not incremental knowledge.
I agree about that too - and think about how to model these power dynamics.
Maybe you’re suggesting (by this + posting the original) that we influence politics more by emphasising the crisis, regardless of the shape of that curve of increasing impacts. Such logic could also justify why they like to run high scenarios in ESMs. This doesn’t convince people like me, but I know my way of thinking is not so typical, so maybe you are right.
Otoh there are some influential people, especially those who trust black boxes of economic optimisation models (there’s another recent thread about that), for whom the lack of such info could effectively provide an excuse for delayed action.
My impression is a lot of the high-emissions scenarios being used lets you demonstrate that there is an impact easily, even if the actual date of that impact looks to be somewhat later than under the most likely emissions scenarios.
And yes, the economic models are particularly awful; DICE has a bunch of assumptions in it about economic impact not affecting sectors which operate indoors and about damage being in the form of a fraction of GDP for that sector. eg: if agriculture is 3% of the economy by value, and it goes to zero, the overall impact is a 3% cut to economic output, even though no food means no economy.
The human brain is not reasoning with power functions.
While I agree with you I think the root for people not understanding properly, or dont want to is not about how the data is presented its simply in cultural heritage and in general no self motivated thinking.
People shut down when the information is to bipolar to their beliefs and and even if they keep listening they cant comprehend one step further because its too abstract.
It’s kinda trivial to show that impacts at 2ºC are much worse than at 1.5ºC. What nobody has done is shown that the difference between 1.6ºC and 1.5ºC is so much more than between 1.5ºC and 1.4ºC, and therefore that this is the key threshold at which we must stop /panic /revolt /give-up-and-party /whatever … What matters is the curvature of the function, to see curvature you need at least three points within the range of choice (acc to Paris 1.5-2.0). So more info about impacts at 1.75ºC could help. I told them this during scenario planning discussions over a decade ago, but the big earth-system-model guys just mumbled about “signal-to-noise-ratio” - they preferred to run RCP8.5 (the extreme high scenario that - fortunately- is looking so implausible it may be dropped from next IPCC round), because the higher the scenario, the more exciting graphics they make, the more funding they get…, but this didn’t help to show why we need more effort from all, at the bottom of the range.
Most of the exact boundaries for ecological tipping points aren’t known with an 0.1ºC precision. I don’t think we’re going to get something like what you are asking for, just knowledge that each incremental increase in temperature is another step into a minefield.
True, but in principle we (i.e. big teams) could make impacts functions for each case and combine by integrating over risk (within which, value judgements also make a big difference - but that’s something to learn too). To fit those functions we needed more studies by ESMs with smaller differences at the low end - if you don’t study it, you won’t know it (hence my point years ago - those models are slow, and big committee-run processes even worse).
Sure, it would be nice to know, but I don’t see knowing as likely to change policy in any kind of meaningful way. That’s going to take changing who holds power, not incremental knowledge.
I agree about that too - and think about how to model these power dynamics. Maybe you’re suggesting (by this + posting the original) that we influence politics more by emphasising the crisis, regardless of the shape of that curve of increasing impacts. Such logic could also justify why they like to run high scenarios in ESMs. This doesn’t convince people like me, but I know my way of thinking is not so typical, so maybe you are right.
Otoh there are some influential people, especially those who trust black boxes of economic optimisation models (there’s another recent thread about that), for whom the lack of such info could effectively provide an excuse for delayed action.
My impression is a lot of the high-emissions scenarios being used lets you demonstrate that there is an impact easily, even if the actual date of that impact looks to be somewhat later than under the most likely emissions scenarios.
And yes, the economic models are particularly awful; DICE has a bunch of assumptions in it about economic impact not affecting sectors which operate indoors and about damage being in the form of a fraction of GDP for that sector. eg: if agriculture is 3% of the economy by value, and it goes to zero, the overall impact is a 3% cut to economic output, even though no food means no economy.
The human brain is not reasoning with power functions.
While I agree with you I think the root for people not understanding properly, or dont want to is not about how the data is presented its simply in cultural heritage and in general no self motivated thinking.
People shut down when the information is to bipolar to their beliefs and and even if they keep listening they cant comprehend one step further because its too abstract.