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Comment by mhh__ | Hacker News Reader

One thing that is worth keeping in mind is that the end result can end up still being right.

Academic Fortran is horrible but it's also (hopefully) maintained by people who know what they're looking for — egregious errors are like errors, things like "this is constant when it shouldn't be" sounds more like a modelling assumption.

People got relatively good climate (well, temperature) estimates from the 80s (if you input the right CO2 data for the future), but I'd image the modern ones more subtle predictions could be quite wobbly (i.e. people complain about them having way too many parameters but I'd imagine many of these are for specific "tasks" rather than massively dominating the overall model)

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There are two individual claims, that should be inspected individually:

1. Data from the field showing warming up / other real world observations. I'm not disputing this. This has been corroborated by many different methods without any clear single point of failure.

2. Given 1, what is the probability that the reason for the observed effects are CO2.

This is the crucial policy question, and the problem is that the evidence for 2 is actually purely from these models.

It's easy to take the conjunction fallacy here and immediately jump to 1&2 being more probable than 1 alone, but it's wrong thinking.

The problem is only magnified, in my opinion, by the fact that only few parameters have even had the same kind of detailed analysis that CO2 had. And then the code has clear bias towards the narrative, and the forcing of CO2 in the model is extremely detailed in the parts where the narrative says it's important (radiation forcing) while neglecting many other effects, and taking various things that actually depend on CO2 to be constant.

And finally the problem is that the policy decisions are the complete opposite if 2 is wrong: if we're heading towards more chaotic weather and CO2 isn't the culprit, you would want to rely on fossil fuels instead of the weather (a.k.a solar and wind) for energy.

The correlation with CO2 output is very very good.

The Berkely earth group went in with basically exactly the same question, did everything from scratch and broadly replicated the IPCC curves

> The correlation with CO2 output is very very good.

Sorry to recite a common wisdom that "correlation does not mean causation".

Ergo all causal hypotheses are unfalsifiable? After all if a model fits the data well, that's just correlation.

Do you have a competing theory? No one has been able to propose one that fits as well as greenhouse gases, and god knows people have tried.

However, there is a strong first principles model on greenhouse gases effects in the atmosphere: CO2, Methane, and others. All produced as byproducts of agriculture and industrial revolution technologies with ballpark accounting going back to 1800s. In addition there are mass balances accounting for carbon uptake by biomass from satellite imagery.

This isn’t a shot in the dark using excel linest(atmospheric _co2, global_average_temperature) trend plots and calling it a day.

There is sound first principles model to solar thermal energy retention. It may not predict the global average temperature in the next 15 years within 0.2C but you have got to be kidding me bringing up that intro to statistics caveat “correlation!=causation”.

Like do you know how inaccurate most physics modeling is and still tends to be useful?

> One thing that is worth keeping in mind is that the end result can end up still being right.

“ On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.” - Charles Babbage

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