[baueric]

All responses are so focused on exact predictions. We have high certainty that 50% of flips will be tails over long enough timespan. We don't know what any single flip will be. Climate science works the same way. But climate is not a coin, let's say it's a multisided die and it appears the sides are changing sizes as we compare data year over year.

[hippo22]

I think you’re missing my point: we’re only able to predict large numbers of coin flips because we have an accurate model.

We don’t have an accurate model for weather, so we can’t predict it well.

I don’t see a reason to assume our model for climate is accurate, either.

[felixgallo]

Our models of weather are so accurate that literally trillions of dollars per year bank on them in the agriculture sector, the shipping sector, and everywhere else. Similarly, our models of climate change have been refined and refined, and now are essentially irrefutable.

[Recurecur]

::multiple laughing emojis::

Our “models of climate change” have regularly been falsified at this point. It is absolutely unknown how much “climate change” is attributable to humans right now.

Nor is it actually known what the net favorability of mild warming might be…including the possibility of mitigating the next Ice Age!

[felixgallo]

https://xkcd.com/1732/

[baueric]

Predictive models are not the same as historic data analysis and trend fitting.

Flipping coins: no predictive models, very definitive statistics Weather: +/- 2 week predictive models, 100 years of measurements getting more definitive each year where trend are headed

[trimethylpurine]

Hypothetically. None of what you said is testable, and there is no evidence that models today are any better than before except at matching historic data.

Compare with another topic like, say, evolution. Here outcomes are testable and verifiable because we can observe the theory at work by watching micro-ecosystems, or small animals with fast reproductive cycles.

Meteorology is short term accurate based on a linear regression of data points from historical data. Deviation like "warming" or "cooling" are relative descriptions of how closely aligned one theory is to the line, and how far back the specific model goes along with the number and quality of relevant factors you want to look at.

No matter which model you go with, you're proving the accuracy of a math function at matching historical data, and then hoping that it will match the future. And as we know, none of them match the future very accurately, which tells us there's something wrong with the theory.

This is only slightly better than day trading in the stock market. And much like the stock market, everyone thinks they know better than everyone else but statistically, most fund managers and professional stock callers underperform the market. They earn by selling you on the idea that they have the next model that finally DOES make accurate predictions. They tell you that they know that because this new model matches the historical data more accurately. No shit. Because there's more data now in a growing set of data. So the most recently calculated linear regression is the most accurate.

But we don't know how it works. That's the key here. More data, doesn't mean the theory is better. More accuracy in making predictions about the future, on the other hand, is a strong indicator, and maybe the only indicator, that something is worth believing in. That is to say, it's more likely to be true.

Making overzealous claims about how much we know is not science, it's ignorance. Let's help interest people in science by being cautious about what we claim to know for sure. At least don't claim to know the next 200 years, until we can at least make accurate predictions beyond the next few days.

I majored in biochem with a lot of extra classes I took for fun on environmental chemistry. You?