Indeed in your circumlocutive manner you have described a task akin to building a sand castle one grain of sand at a time. You're getting into the territory of pascals demon. If you could produce such a sophisticated model why would you even bother with the weather since you'd be able to produce so much more than than that!

What you describe sounds quixotic. Tilting at windmills. I would be very disappointed indeed to discover that climate science floundered in the face of trying to do the impossible.

The history of innovation is littered with stories of impasse quickly followed by the discovery of a short cut and then progress. It's all about short cuts. When I play chess I don't evaluate all possible moves - when I play poker I don't count the cards. Yet - I can play both quite effectively. How is this? Because dispite my incapability to grasp all the variables I can develop quite sophisticated models with effective predictive power.

Nobody expects the sciences to be exhaustive down to the atomic level. Not all sciences are physics. All that is required is a framework into which all extant knowledge can be integrated; methodology for gathering new data and a means to extrapolate predictions.

As a software engineer I'm never expected to write x86 assembly language to implement a functioning web site. All I need to do is read a book on HTML look at a few examples online and then off I go. I can probably do a better web site than the guy writing the ASM code for a living.

That is what using your NS equations is like for computing your boat hull. Far better off reviewing other people's accounts of what works and putting them together - testing - and seeing if it works. Yes there are a great number of edge cases you may never be able to account for but there are only so many hours in a day and you need to produce something that works for the case in point - not all possible situations.

And so it goes with climate science - you can't possibly predict everything to this minutist detail - you can only observe and measure, observe and measure, catalogue, and integrate and continue as infinitum continuously refining your models. Some stuff won't integrate like other stuff but eventually you'll get to a point where you start to have some predictable capacity. By definition this approach can never be "complete" but as we discuss "completeness" isn't possible.

But what we do have from our catalogued observations and measurements is a huuuuge body of knowledge about what "actually does happen". Nothing is ever that wildly out of the blue that our existing knowledge and modelling can't have some premonition of it. There are exceptions of course but like we say without doing the impossible it isn't possible to foresee these ...

But what we can do is, to within a certain degree of certainty make predictions about what is likely to happen. Like if you're swimming in a lake in Africa or something and you see a shimmering movement on the water do you wait to compute that it's just an agglomeration of leaves? Do you wait to see if it moves with the characteristic vortices of a crocodile or so you just GET THE FUCK OUT OF THE WATER straight away?

I know which is be doing. Even if it means all my friends laughing at me because sure I'll feel a little bit embarrassed but in the alternative reality where it was a crocodile but I gave up trying to exhaustively model what it was I have died a horrible and brutal death.

So as much as I appreciate your educational pedigree and thank you for sharing your valuable time with me I do remain sceptical of your scepticism. Though you have conclusively demonstrated what we cannot know you have said little to explain what we do and our corroborating experiences heretofore. Hope this doesn't come across as too much of a circumlocutive gish gallop I've been trying to type this on my phone on the train without recourse to any notes or any editing capabilities. Sorry 'bout that ;-)