Some comments not addressing your question:
I think you are holding climate science to a far higher standard than the other physical sciences.
In the physical sciences, blinded experiments are rarely done. The need for blinding is greatly reduced compared against the medical and social sciences as the data measured is far more objective and observers usually can't influence the results. I've done experiments where I basically start recording data, turn a valve, and from that point on, the result is outside of my control as long as I'm watching from a distance of 10 feet or so. That's often not the case in the medical and social sciences. In my experience, blind experiments in the physical sciences take the form of a blind prediction challenge where a bunch of teams are asked to predict what a certain experiment will do before the experiment is run. This is a good practice that I advocate. I also am interested in blind data analysis and "fake-data simulation" as Andrew Gelman calls it.
There also are a lot of times in the physical sciences where duplicating the "full scale" case is not possible for various reasons (cost, legality for nuclear weapons testing, etc.). Instead they do experiments on scale models (which might not have similitude [0]) or parts of the full problem. This is not ideal, and I do think the researchers could do better, but the situation is unavoidable. Again, there's no reason to single out climate science on this.
I also want to strongly push back on the "computers models are basically mathematical assumptions, not physical laws" part. I'm a mechanical engineer who works in computational fluid dynamics. The models I use have a lot of overlap with climate models, but don't get as much scrutiny even when they are of similar reliability. A large computer model like a climate model has a lot of components, some of which could be regarded as very reliable (likely what you mean as "physical laws") and some of which are less reliable. But even the less reliable components are not assumptions. They always are backed up by some data, perhaps not as a comprehensive as is wanted, yes, but some data.
[0] https://en.wikipedia.org/wiki/Similitude
I think you are holding climate science to a far higher standard than the other physical sciences.
In the physical sciences, blinded experiments are rarely done. The need for blinding is greatly reduced compared against the medical and social sciences as the data measured is far more objective and observers usually can't influence the results. I've done experiments where I basically start recording data, turn a valve, and from that point on, the result is outside of my control as long as I'm watching from a distance of 10 feet or so. That's often not the case in the medical and social sciences. In my experience, blind experiments in the physical sciences take the form of a blind prediction challenge where a bunch of teams are asked to predict what a certain experiment will do before the experiment is run. This is a good practice that I advocate. I also am interested in blind data analysis and "fake-data simulation" as Andrew Gelman calls it.
There also are a lot of times in the physical sciences where duplicating the "full scale" case is not possible for various reasons (cost, legality for nuclear weapons testing, etc.). Instead they do experiments on scale models (which might not have similitude [0]) or parts of the full problem. This is not ideal, and I do think the researchers could do better, but the situation is unavoidable. Again, there's no reason to single out climate science on this.
I also want to strongly push back on the "computers models are basically mathematical assumptions, not physical laws" part. I'm a mechanical engineer who works in computational fluid dynamics. The models I use have a lot of overlap with climate models, but don't get as much scrutiny even when they are of similar reliability. A large computer model like a climate model has a lot of components, some of which could be regarded as very reliable (likely what you mean as "physical laws") and some of which are less reliable. But even the less reliable components are not assumptions. They always are backed up by some data, perhaps not as a comprehensive as is wanted, yes, but some data.
[0] https://en.wikipedia.org/wiki/Similitude
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