I've got a background in this field and I am very surprised to see this published in Nature. The model presented is purely statistical with no representation of the underlying physics. When we are dealing with a phenomenon that is driven by well-understood physical laws (e.g. geophysical fluid dynamics, radiation physics etc) then these physical models are the most reliable basis for prediction. When I say physical m…
As a lay-bystander to the climate debate, I get a little spooked when I see comments like this. Like 99.9% of people, the science of complex climate systems is beyond me as are the de facto black box models that project climate change. Instead I use “appears in a credible journal” as a basis for trusting the apparent direction of travel and I’m disconcerted when I see that proxy challenged. I also, I am somewhat sorr…
Not climate scientist, but researcher in ML (another highly hyped and arguably more noisy field).
After Bourbon, the hard truth is expertise + don't.
Unfortunately journals/conferences/venues are an extremely noisy mechanism which in general aren't realistically much better than arxiv (at least over here). Peer review isn't journals/conferences, it is peers reading and evaluating the works. Reviewers are doing a service and often not giving a work significant time as they got other stuff to do. The only real way to know if a paper is valid or not is to be an expert in the subject matter (more focused than the field) and to read it __and the code__. All too often it is a nuanced point that is the crux of a paper, and would be entirely missed if you're not deep in that subcommunity. I know this answer sucks, but that's what it is.
Fwiw, I wouldn't realistically change your strategy as a layman. It's probably the best you got. Maybe only thing is don't think "journal publication == 100% true" but rather "journal publication == probably right." The process is noisy and there's no way around this.