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Eh, atmospheric CO2 (and other GHGs) is just another input parameter. I don't see any reason that the model wouldn't be able to incorporate all of the data. I
by kaibee 2y ago
Eh, atmospheric CO2 (and other GHGs) is just another input parameter. I don't see any reason that the model wouldn't be able to incorporate all of the data. If the climate system is just getting more chaotic, well, you're still running multiple projections, and you'll see that in increased variance.
- lainga 2y agoBecause we have well-measured inputs for CO2 between basically 250 PPM and $current_day PPM. Do you see how this statement > Eh, atmospheric CO2 (and other GHGs) is just another input parameter only works for CO2 inputs outside our measurements, if the climate response to those inputs is linear (and thus predictable from the responses we have already seen)? I claim that the climate response to CO2 forcing is, in fact, strongly nonlinear, and further that it's nonlinear for other "unusual inputs" - not just CO2 - things like sea surface temperature or unusually low pressure troughs. So-called extreme weather. I can't bring up good citations at the moment, sorry, but here's a somewhat exaggerated thought experiment: Take an AI model trained on weather measurements ~1970-2024. Also take a model of the primitive fluid equations on a rotating sphere. What predictions might you expect from each one for an asteroid hitting an empty patch of the West Pacific?