This is a little tangential to the topic you have researched, but I wonder whether improving forecasting capacity with an eye on abrupt sunlight reduction scenarios (like nuclear winter) would be cost-effective. I have heard that the current models are good to predict global variations (e.g. of temperature), but not so much to get the local variations right (apart from relatively large local phenomena like monsoons). I suppose it would be quite important to figure out in the 1st few months after the nuclear war how the wheather would evolve in the next few years in order to minitigate the food shortfall.
Hi Vasco—sorry for the slow response! I don’t think I have a particularly satisfactory answer for you on this topic, but I think my instinctive response is that the potential could be limited, depending on whether we think the patterns that underly the algorithms for forecasting now will be the same/​ be able to be quickly adapted for these scenarios. My understanding is that forecasting depends on predicting the physics in the atmosphere (which may not change), as well as interactions with the land/​oceans (which may be affected, e.g. changing landscapes due to death of flora/​fauna). If this scenario is one that we haven’t experienced before, maybe we could simply update the parameters based on new observations and the models would work, but maybe we would need to do research to improve the methods that are in use, which could take time. Another limitation could be that I’m not sure how well models could predict the evolution over several years, as you mention—our focus was on <6 months and we found limited accuracy.
Perhaps just one other thing to flag is that doing forecasting at somewhere like ECMWF uses a huge amount of energy, so we’d want to think about the trade-offs with other needs!
Hi,
This is a little tangential to the topic you have researched, but I wonder whether improving forecasting capacity with an eye on abrupt sunlight reduction scenarios (like nuclear winter) would be cost-effective. I have heard that the current models are good to predict global variations (e.g. of temperature), but not so much to get the local variations right (apart from relatively large local phenomena like monsoons). I suppose it would be quite important to figure out in the 1st few months after the nuclear war how the wheather would evolve in the next few years in order to minitigate the food shortfall.
Hi Vasco—sorry for the slow response! I don’t think I have a particularly satisfactory answer for you on this topic, but I think my instinctive response is that the potential could be limited, depending on whether we think the patterns that underly the algorithms for forecasting now will be the same/​ be able to be quickly adapted for these scenarios. My understanding is that forecasting depends on predicting the physics in the atmosphere (which may not change), as well as interactions with the land/​oceans (which may be affected, e.g. changing landscapes due to death of flora/​fauna). If this scenario is one that we haven’t experienced before, maybe we could simply update the parameters based on new observations and the models would work, but maybe we would need to do research to improve the methods that are in use, which could take time. Another limitation could be that I’m not sure how well models could predict the evolution over several years, as you mention—our focus was on <6 months and we found limited accuracy.
Perhaps just one other thing to flag is that doing forecasting at somewhere like ECMWF uses a huge amount of energy, so we’d want to think about the trade-offs with other needs!
Thanks for the feedback!