GCM's are actually models. Suppose you were designing the Titanic. You might make a scale model, which, with suitably scaled dimensions (Reynolds number etc) could be a good model indeed. It would respond to various forcings (propellor thrust, wind, wave motion) just like the real boat. You would test it with various scenarios. Hurricanes, maybe listing, maybe even icebergs. It can tell you many useful things. But it won't tell you whether the Titanic will hit an iceberg. It just doesn't have that sort of information.
So it is with GCM's. They too will tell you how the Earth's climate will respond to forcings. You can subject them to scenarios. But they won't predict weather. They aren't initialized to do that. And, famously, weather is chaotic. You can't actually predict it for very long from initial conditions. If models are doing their job, they will be chaotic too. You can't use them to solve an initial value problem.
How GCMs are usedSo I'd better say a bit more about how GCM's are traditionally used - then I'll get onto a recent alternative, which is part of the motive for this post. Because GCM's can't reliably work from initial conditions, they are usually started well back in time from the period of interest. This is also common in CFD (computational fluid dynamics). Flows in CFD are also chaotic, and not usually solved as initial value problems. As with GCM's, a forceful reason is that an initial state just isn't known.
So people "wind back". The idea is to start with conditions that aren't expected to correspond to a measured state. rather, they strive for physical consistency. If you are modelling a near-incompressible fluid (explicitly), you'll want to make sure that the density is what it should be, and the velocity is divergence-free. Otherwise, explosion.
In fact, even that won't be right, but the artificial initial effects will work out over time, and the flow will come into some sort of balance with the forcings. Then you can look at variations.
So that is why a GCM, in its normal running, can't predict an El Nino. Good ones do El Nino's well, but with no synchronicity with events on Earth. A good illustration is this video from GFDL:
Note that the months are numbered, but no year is given. It's a depiction of an El Nino, but not for any particular time. Though the months are significant - El Nino is weakly coupled to the annual cycle.
Here is another SST Movie from GFDL
Again it shows all sorts of interesting motions. These are not predictions of actual events, and they are not the consequence of any initial conditions. They are the results of forcings, fluid properties and topography. But it is actually telling you a lot about real world currents (and SST).
Decadal forecastingThis is the recent alternative that I mentioned. People are trying to get useful information for years in advance from initial conditions. Meehl wrote a paper in 2009, basically with a prospectus, and wrote a review earlier this year, subtitled "an update from the trenches".
Are we there yet? No, and I think success is still uncertain. For details, I can only suggest reading the paper, but here is a summary quote:
"It remains an important question as to whether or not decadal climate predictions will end up providing useful information to a wide group of stakeholders. Indications now are that temperature, with a greater signal-to-noise ratio, shows the most promise, with precipitation being more challenging. These two quantities are typically the ones that have been addressed so far in the literature. Since sources of skill are time dependent, it is important to emphasize that for the first 5 or so years of a decadal prediction, skill could come from the initial state, and after that skill arises because of the external forcing, with some regions having potentially greater skill than others. Further quantification with other variables needs to be done and applied in reliability studies, which are just now beginning, in order to demonstrate usefulness of decadal climate predictions."
So it sounds like five years max, at the moment. We'll see.