(1) the effects of climate change are not a probabilty but already occurring so may not be understood as hierarcharally similar to the risk of an ASRS that has not still occurred
Agreed:
â[Global warming] has the potential to result inâand to some extent is already resulting inâincreased natural disasters, increased water and food insecurity, and widespread species extinction and habitat lossâ.
However, I only focussed on the extreme effects of climate change, which are hypothetical as the risks of ASRSs. It also believe that, even from a neartermist perspective, it is also pretty unclear whether climate change is good/âbad due to effects on wild animals.
(2) an ASRS could be more probable in a world exposed to higher tensions related with food shocks occurring as a result of climate change
That makes sense, but I do not think it is a major issue:
my model implicitly considers climate change does not impact the risk from ASRSs via increased risk from nuclear war. I believe this is about right, in agreement with Chapter 12 of John Halsteadâs report on climate change and longtermism:
Most of the indirect risk from climate change flows through unaligned artificial intelligence, engineered pandemics, and unforeseen anthropogenic risks, whose existential risk between 2021 and 2120 is guessed by Toby Ord in The Precicipe to be 100, 33.3, and 33.3 times that of nuclear war. Nevertheless, there is significant uncertainty around these estimates[3].
Conflicts between India and China/âPakistan are the major driver for the risk from climate change, but these only have 7.15 % (= (160 + 350 + 165)/â9,440) of the global nuclear warheads according to these data from Our World in Data (OWID).
I think (3) and (4) are great points!
(3) specifically thinking on the model results, I wonder if any of these models has been able to dissect the effect of lower temperatures to those related with lower radiation and lower rainfall associated with the ASRS (otherwise overall effects could be underestimated); (4) I also wonder about the reliability of these models to represent the effect of temperature reductions vs. to represent the effect of radiation reductions (probably more accurate in representing T reduction and thus, other effects of an ASRS not attenuated by global warmingâreduced rainfall, radiation, UV- , still important in crop failure).
My model does not have an explicit distinction between the effects of temperature, and those of radiation and rainfall. However, the effective soot reduction due to global warming is proportional to a uniform distribution ranging from 0 to 1. The greater the importance of the temperature variation (as opposed to those of radiation and rainfall), the narrower and closer to one the distribution should be. It arguably makes sense to use a wide distribution given our uncertainty. However:
Ideally, one should run the climate and crop models for each level of global warming [thus obtaining updated temperature, radiation and rainfall profiles], since the climate response caused by ASRSs depends on the pre-catastrophe global mean temperature. As an example of why this might be relevant, I do not know whether there is a good symmetry between the regional effects of global cooling and warming.
Thanks for sharing your thoughts, Mariana!
Agreed:
However, I only focussed on the extreme effects of climate change, which are hypothetical as the risks of ASRSs. It also believe that, even from a neartermist perspective, it is also pretty unclear whether climate change is good/âbad due to effects on wild animals.
That makes sense, but I do not think it is a major issue:
I think (3) and (4) are great points!
My model does not have an explicit distinction between the effects of temperature, and those of radiation and rainfall. However, the effective soot reduction due to global warming is proportional to a uniform distribution ranging from 0 to 1. The greater the importance of the temperature variation (as opposed to those of radiation and rainfall), the narrower and closer to one the distribution should be. It arguably makes sense to use a wide distribution given our uncertainty. However: