Disagree, those two fields are extremely unreliable. It takes a lot of things happening simultaneously to be scientific. Yes, those two do make testable predictions, sort of. But that isn't enough.
Modern (computational) epidemiology is rife with unscientific practices. They ignore data that shows a model was invalidated so the fact they make testable predictions isn't really useful. They also engage in a lot of circular reasoning, buggy coding and logical fallacies. During COVID I wrote a whole report on this topic for some politicians [1]. But the biggest issue is "A problem in theory" again - epidemiologists conflate fitting a curve in R with developing a hypothesis, so the field is overrun with overfit models that aren't based on any refinable theory of disease, just misuses of statistics. Even if you prove a paper's predictions were wrong it changes nothing because nothing built on it anyway.
The problem in climatology is that when the models don't fit the data they just change the data and claim victory, e.g.
> The problem in climatology is that when the models don't fit the data they just change the data and claim victory
That is... not remotely true, nor supported by either of the links you shared. (Nor by any cursory look at the state of the world today, with well-anticipated Himalayan glacial floods as front-page news.) You therefore appear to be leaping over the line from healthy skepticism to irrational conspiratorial thinking, so I'm going to stop engaging, sorry.
The two links I give show the problem in action. In 2013 the IPCC was concerned because temperatures had gone sideways for a decade. Der Spiegel's first sentence is "Data shows global temperatures aren't rising the way climate scientists have predicted" and the next says they were trying to "hush it up". In 2015 climatologists announced a new temperature record that replaced the entire 21st century with different data, as reported by Nature.
That's an unambiguous sequence of events. You can't make statements about replicability in an environment where they routinely decide their already published data is wrong post-hoc, without retracting the papers built on that data.
Modern (computational) epidemiology is rife with unscientific practices. They ignore data that shows a model was invalidated so the fact they make testable predictions isn't really useful. They also engage in a lot of circular reasoning, buggy coding and logical fallacies. During COVID I wrote a whole report on this topic for some politicians [1]. But the biggest issue is "A problem in theory" again - epidemiologists conflate fitting a curve in R with developing a hypothesis, so the field is overrun with overfit models that aren't based on any refinable theory of disease, just misuses of statistics. Even if you prove a paper's predictions were wrong it changes nothing because nothing built on it anyway.
The problem in climatology is that when the models don't fit the data they just change the data and claim victory, e.g.
September 2013: https://www.spiegel.de/international/world/climate-scientist...
June 2015: https://www.nature.com/articles/nature.2015.17700
[1] https://plan99.net/~mike/epidemiology.pdf