Wrong
I was not talking about statistical models, I WAS talking about physical models. These use systems of partial differential equations (PDEs) to model the physics of the climate, just as weather models use PDEs to model the physics of weather. Anybody who knows about PDEs (I have a BS in math and computer science) knows that initial conditions matter a great deal . It's also true that in solving these equations on a computer the accuracy and granularity of the data matters a great deal. It's why you need the largest supercomputers to model either the climate or the weather, because the amount of data that has to be crunched is so huge.
Climate models aren't as detailed as weather models because we just don't know nearly enough about the climate as we do the weather. They make large simplifications about wind, ocean currents, and how they interact, for example. Nobody is even sure how clouds affect the climate. In contrast, meteorologists get a new run of their models against the changing weather about every week. We only have the one run of the climate, and our accurate data only goes back 150 years or so (which really isn't long enough to qualify as a climate run).
To say that climate models "don't depend on data to make predictions" is wrong. EVERY science theory depends on data. It's not valid until the theory matches the data (it's the core of the scientific method). Why else would climate scientists spend so much time on tree rings and ice cores? Why would we be sending up satellites and establishing thousands of ground stations to record data?
As for initial conditions, climate scientists often talk about "tipping points". If the Arctic ice cap disappears, the earth may suddenly absorb too much solar energy and heat up enormously. Or the permafrost may melt and suddenly release a lot of methane. So if your initial conditions are on one side of a tipping point or another, it makes a big difference. Initial conditions do converge, but not always to the same place. Data accuracy matters a lot.
