Climate Physicists Face the Ghosts in Their Machines: Clouds (quantamagazine.org) 25
Climate scientists trying to predict how much hotter the planet will get have long grappled with a surprisingly stubborn problem -- clouds, which both reflect sunlight and trap heat, account for more than half the variation between climate predictions and are the main reason warming projections for the next 50 years range from 2 to 6 degrees Celsius.
Two research groups are now racing to close that gap using AI, though they disagree sharply on method. Tapio Schneider at Caltech built CLIMA, a model that uses machine learning to optimize cloud parameters within traditional physics equations; it will be unveiled at a conference in Japan in March. Chris Bretherton at the Allen Institute for AI took a different path -- his ACE2 neural network, released in 2024, learns from 50 years of atmospheric data and largely bypasses physics equations altogether.
Two research groups are now racing to close that gap using AI, though they disagree sharply on method. Tapio Schneider at Caltech built CLIMA, a model that uses machine learning to optimize cloud parameters within traditional physics equations; it will be unveiled at a conference in Japan in March. Chris Bretherton at the Allen Institute for AI took a different path -- his ACE2 neural network, released in 2024, learns from 50 years of atmospheric data and largely bypasses physics equations altogether.
wait... (Score:2)
"Two research groups are now racing to close that gap using AI, though they disagree sharply on method."
Isn't AI supposed to be THE METHOD? Don't you use AI to improve the model? And are they "racing"?
"...his ACE2 neural network, released in 2024, learns from 50 years of atmospheric data and largely bypasses physics equations altogether."
Seems unlikely, but by doing precisely what? Producing an alternative model? Why does that matter? Isn't the result what matters?
There are climate models. You judge t
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There are climate models. You judge them on the reliability of their predictions.
Right. But if they are admitting to some major uncertainties in the models, they aren't ready to use them as the basis of trillion dollar investments. So keep working. The science isn't done yet.
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"But if they are admitting to some major uncertainties in the models, they aren't ready to use them as the basis of trillion dollar investments."
Conveniently conflating two different groups as "they", then lying about one of them.
"The science isn't done yet."
Science is never done, that's not a statement of whether science IN THIS CASE is good enough.
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whether science IN THIS CASE is good enough.
account for more than half the variation between climate predictions and are the main reason warming projections for the next 50 years range from 2 to 6 degrees Celsius.
It appears not to be. One tail of the probability curve is over in ice age territory.
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"Climate is notoriously complex set of wild interactions that are unknown at best."
At worst, not at best. Climate's "set" also includes very ordinary "interactions" that are well understood, you're just misrepresenting that to support your narrative.
"Predictions of climate crisis have always been wildly off, because the goal is to scare people into action."
False, followed by a blatant lie.
Also, a complete and abject failure to address the subject. The subject is the use of AI to improve climate models, no
Re: wait... (Score:5, Interesting)
Really? Which climate predictions are we talking about here?
Hansenâ(TM)s 1988 models inaccurately predicted emissions levels, but when adjusted for actual emissions levels using the same methodology turns out to be fairly accurate to reality.
The IPCC report from 1990 predicted 0.3Â of warming per year, which tracks well with the 0.2-0.3Â per year weâ(TM)ve seen.
Early predictions of arctic ice melt predicted that the
Volume of arctic sea ice would have fallen by 35% by now, it has fallen by 40% - pretty accurate.
1990s predictions of sea level rise predicted 18cm by now, its risen by 20-25cm - so somewhat conservative but the right trend.
The one prediction that I think it would be possible to point to as âoewrongâ is the idea that freak weather events would increase as sea temperatures rose. The rate of such events has turned out not to rise, however, the severity has risen instead, so this one is a bit off but not significantly.
Re: wait... (Score:2)
Another way of phrasing this:
Two projects aim to come up with new models. One is using vast amounts of data and back propagation to learn the values of various coefficients in an existing model. Another is using vast amounts of data and back propogation to come up with a new model that is a vast array of linear equations combined with sigmoid functions.
Phrased that way, it becomes clear that the former is likely to be far more useful for extracting understanding of whatâ(TM)s going on, while the latt
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Alchemy? (Score:4, Insightful)
At what point does this cease to be science and starts becoming alchemy? What is the cutoff point?
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It was never science. Modeling is only science if the model is actually tested. These models claim to predict what will happen in 50-100 years. They will become science only after someone checks the predictions in 50-100 years.
Re: Alchemy? (Score:3)
Yes and no - they claim to predict things *over* the next 50 years. We can check them each and every second until 50 years passes with increasing confidence (or lack there of) in them. The predictions made in the 80s and 90s have so far turned out to be largely accurate, so seems like weâ(TM)re more in the science box than the alchemy one.
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This ignores both how models are tested and verified against existing data, and how reliable these models have been over the past decades.
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They've been making predictions for 50 years.
With very close to a 100% failure rate.
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FTFY
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> With very close to a 100% failure rate.
Bullshit! While specific forecasts about specific places have been wrong, in general warming continues and the sea is gradually rising. You are cherry-picking after the fact.
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We call it Climate Ouroboros.
The models are tuned quite literally to create today's weather from yesterday's inputs.
This is how they calibrate them.
And then the 'climate scientists' turn around, run today's weather through them and - voila! - warming!
"Proved" by models.
If these models didn't show warming, you would throw them out as broken models and look for another.
The early IPCC reports didn't even regard water vapor and clouds as - essentially - too complicated to model.
This paper from 2024: https://www [mdpi.com]
Joni Mitchell was right. (Score:2)
We really don't know clouds at all.
Here are some thoughts (Score:2)
Clouds at night keep the heat in. (Keeping it warm)
Clouds in the daytime reflect the heat of the sun, making it cooler.
Cloud computing? (Score:2)