Many of the sciences are actually better understood as more or less rigorous phenomenologies. Sometimes actually applying the formal scientific method is grossly unethical or ludicrously expensive. In cases like that the phenomenological approach lets you still develop something of an understanding of the subject matter.

From a practical perspective, it doesn't matter if the subject is technically formal science or a rigorous phenomenology, what matters is that you can engineer systems using the knowledge of the field.

We have a lot of measurements in astronomy, which often proove or disprove our hypotheses.

I believe that was the implication of the comment you're replying to, whereas the grandparent talking about "Scientific method revolves around hypothesis and tests" ... seems to have a different understanding.

The same is true in climate science.

Also, in case you haven't noticed, we're currently conducting a global scale experiment (though without a control Earth) that is already showing results.

In astronomy one aberrant observation is enough to completely revolutionize the field or destroy a previously esteemed view. It's not based on melting pot models and so a single mistake indicates something is wrong, perhaps fundamentally wrong. Consider that decades before relativity was discovered we knew very well that something was very broken because Mercury's expected orbit was off by 1/100th of a degree per century.

By contrast, how would you propose falsifying a climate model? Each decade or so we gain another chunk of data we can use to try to trial predictions of the past and, to date, they've done quite abysmally except in the most broad sense of a longterm gradual increase in temperatures. For instance from 2001 and 2010 the warming slowed to 0.11C per decade. [1] For the 17 years before then was 0.17C. There was not a single model that predicted this and, in spite of a number of after the fact hypotheses for that apparent decline, there is still no decisive answer. In a science that was more heavily dependent upon testing, this would be a ground-shaking event. But it's effect on climate science has been relatively minimal.

[1] - https://www.scientificamerican.com/article/did-global-warmin...

I mean, all that really proves is that climatology is more complicated than orbital mechanics.

Formal reasoning, scientific or otherwise, is all about reducing a subject to cognitive manageability. If the subject is too complicated to do that, then it shouldn't be considered in the same class as subjects where that is possible.

Or maybe we need to put more effort in, collect more data, build better models, until we can understand it.

I could build a cabin using basic reasoning. I can't build a skyscraper with just basic reasoning. Does that mean we shouldn't build skyscrapers? Or just that we should leave their design to someone who really put the effort into the "cognitive manageability" of it?

And really, when it gets down to it, a lot of the blatantly irrational opposition to the very idea of global warming comes from "cognitive manageability". For people who still think Sky Daddy made the world in six days and cavemen rode dinosaurs, the idea that their car is wrecking the atmosphere is beyond their "cognitive manageability", and it gets flatly rejected.

When I see the people who believe in the scientific method mostly lined up on one side of an issue, and people who reject the scientific method mostly lined up on the other side, it biases me. Which was my original point, that so many people have flipped out about.

The person you're responding to is undoubtedly referring to the ability to link cause with effect. As such "cognitive manageability" doesn't mean simple. Relativity is far from simple, but it's cognitively manageable - as is the fundamental science and engineering knowledge used when designing and ultimately constructing skyscrapers.

To give an example of something that is not cognitively manageable, aside from climate, consider the stock market. We can create a million correlations to the stock market. And these correlations themselves all hold true. And there is an immense, literally unlimited, amount of profit to be made if somebody could combine these correlations into a way that could create meaningful predictive value. There's no doubt that billions, if not trillions, of dollars have been spent in pursuit of such. Yet prediction seems to remain impossible. And that's in a field with a fraction of the variables of climate change.