I'll pick up John Mashey's comment from the 'relevance' thread, as it illustrates in another way some of what I mean regarding relevance, and about who might know what. He wrote:
As a group, computer scientists are properly placed in the last tier.
Once upon a time, computer scientists often had early backgrounds in natural sciences, before shifting to CMPSC, especially when there were few undergraduate CMPSC degree programs.
This is less true these days, and people so inclined can get through CMPSC degrees with less physics, math, and statistics than one would expect.
Many computer scientists would fit B3 background, K2-K3 level of knowledge on that chart I linked earlier.
On that scale, I only rate myself a K4, which corresponds roughly to Robert's Tier 5. Many CMPSC PhDs would rate no higher than K2 (or even K1, I'm afraid, on climate science).
Of course John is one who has been spending serious effort at learning the science, so although our shortcut puts him on a low tier in this area (he's high for computer science!), the earned knowledge is higher. Best, of course, is to work from the actual knowledge of the individual. On the other hand, presented a list of 60 speakers at a meeting, and seeing few from fields in the upper levels (applicable to the topic at hand), it's not a bad bet that the meeting isn't really about the science (or whatever expertise is involved).
If we're talking specifically about climate modellers, we're talking about people who use computers a lot, and make the computers run for very long periods. So, does that mean that all climate modellers are experts about computers the way that computer scientists are? Absolutely not. Again, different matters. Some climate modellers, particularly those from the early days, are quite knowledgeable about gruesome details of computer science. But, as with computer scientists and climate models, that's not the way to bet.
I'll link again to John's K-scale. A computer scientist spends most time learning about computer science. At low levels, this means things like learning programming languages, how to write simple algorithms, and the like. Move up, and a computer scientist will be learning how to write the programs that turn a program in to something the computer can actually work with (compilers), how to write the system that keeps the computer doing all the sorts of processing you want it to (operating systems), interesting (to computer scientists, at least :-) things about data structures, data bases, syntactic analysis (how to invent programming languages, among other things), abstract algorithms, and ... well probably quite a few more things. It's a long time since I was an undergraduate rooming with the teaching assistant for the operating systems class. Things have changed, I'm sure.
Anyhow, on that scale of computer science knowledge, I probably sit in the K2-K3 level. I use computers a lot. And, on the scale of things in my field, am pretty good with the computer science end of things. But, considered as matters of computer science, things like numerical weather prediction models, ice sheet models, ocean models, climate models, etc., are just not that involved. The inputs take predictable paths through the program (clouds don't get to change their mind about how they behave, unlike what happens when you're making the computer work hard by making it do multiple different taxing operations at the same time and do what you like to the programs as they run). Our programs are very demanding in terms of it takes a lot of processing to get through to the answer. But in the computer science sense, it's fairly simple stuff -- beat on nail with hammer a billion times; here's your hammer and there's the nail, go to it.
The climate science, figuring out how to design the hammer, what exactly the nail looks like, and whether it's a billion times or a trillion you have to whack on it -- that part is quite complex. So, same as you can do well in my fields with only K2-K3 levels of knowledge of computer science, computer scientists can do well in theirs with only K2-K3 knowledge of climate science (or mechanical engineering, or Thai, or Shakespeare, ...).
Again, what the most relevant expertise is depends on what question you're trying to answer or problem you're trying to solve. If you want to write a climate model, you should study a lot of climate science, and a bit of computer science. To write the whole modern model yourself, you'll want to study meteorology, oceanography, glaciology, thermodynamics, radiative transfer, fluid dynamics, turbulence, cloud physics, and at least a bit (these days) of hydrology, limnology, and a good slug of mathematics. On the computer science side, you need to learn how to write in a programming language. That's it. It would be nice to know more, as for all things. But the only thing required, from a computer science standpoint, is a programming language. No need for syntactic analysis, operating system design, or the rest of the list I gave above. Not for climate model building, that is. If you want to solve a different problem, they can be vital. (I include numerical analysis in mathematics -- the field predated the existence of electronic computers. Arguably so did computer science. But the modern field, as with modern climatology, is different than 100 years ago.)
Bad beekeeping, 2015
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