[hippo22]

You can’t predict a coin flip because it is random. However, we have an accurate understanding of the random process producing coin flips and therefore, we can make accurate predictions about large quantities of flips.

Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.

Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?

That’s not to say climate change isn’t real, but your analogy doesn’t make sense.

[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.

[triceratops]

> more like weather, where unknown unknowns have outsized impact on accuracy

"Unknown unknowns" aren't the reason weather forecasts are inaccurate.

Weather is path-dependent. Small changes to starting conditions or minor differences between modeled and actual conditions shortly after the simulation begins lead to large differences by the end of the simulation. Errors propagate and magnify.

Over large time periods the errors average out.