[googthrowaway42]

This is like the 538 election models that swing wildly as more polling data comes in. A model that is so sensitive to new data is a pretty poor model since it should have roughly predicted that new data in the first place.

When you combine that with the fact that it is anathema to question Climate Science orthodoxy and this is wielded for massive political and financial ends, the only rational approach is to be extremely skeptical.

Central to this discussion is the point that climate science is not science in the same way that physics is. There are no repeatable experiments that can be run.

[ooobit2]

You point out a key fault in the approach people employ to derive inferential outputs vs. descriptive outputs. I feel like I could rant about the obvious wide MOE and high alphas that have to be employed for inferential models. But I also feel like it's a matter of convenience. Incredibly smart, well-learned data scientists are either subject to their own bias, to their employer's bias, to the audience's popular appeal, and a number of other skews to usable inferences.

Let's say we have 10 models of Greenland's ice sheet predicting non-linear declines in total ice at 10 different progressive rates. If those 10 models have an MOE in the top quartile, and the rate of change for the relevant variables meets high confidence, I'd say we can reasonably infer melt will progress similar to some of those models. I wouldn't shell out a publication declaring it an accurate model, or even one professionals should use to guide decisions. But, given additional precautions are taken, I would expect a sound decision to consider inferences from the 10 models.

I'm talking a whole different script and procedure for handling inferential outputs, communicating them to stakeholders, and setting fair expectations. It's an approach that people don't use, even though it is necessary. At the very least, it averts the kind of surprise conflicts in data that undermine productive, preventative planning and action.

Instead, like you point out, the approach and the bias inherent to the outputs necessitates extreme skepticism. I remind myself a lot of that line from Rick & Morty, when Rick says, "Sometimes science is more art than science." Data is the same kind of beast, especially as you attempt to work around rudimentary entropy. Physics isn't any less subject to the same context. We already have proof that the thermodynamics we see here on Earth are not uniform across the entire Universe. If you and I were standing at different ends of the Universe, I could as easily call you a quack for your declaration that a dropped ball's speed and trajectory can be predicted by your equation.

Anyway, I could keep going, but we both know the downvotes are self-righteous, self-aggrandizing people who have to be right to as deep an extreme as necessary. It's the only way they can sleep at night, so they can wake up and compound the mess they already compounded the previous day.