I haven’t actually looked at Judith Curry’s blog for a while, but popped across there and noticed a guest post about energy budgets, climate system domains, and internal variability. One reason why we think that we can actually do long-term climate modelling is that the evolution of our climate depends mostly on the boundary conditions, rather than the initial conditions. The basic suggestion in the guest post on Judith Curry’s blog is that this is wrong. It’s essentially a convoluted but chaos argument.
I probably shouldn’t bother rebutting this, but I’m waiting for someone to come and service my boiler, and I’ve been thinking about this a little, so thought I would write a quick post. Essentially, if we want to make predictions about the weather a few days in advance, then the initial conditions are important. These are things like temperatures, pressures, winds, clouds, etc. You put these initial conditions, which you get from actual measurements, into the simulation and run it forward in time. You might also perturb these slightly to see how this influences the output, but you keep it close to the known initial conditions.
Climate modelling, on the other hand, is not trying to make predictions about the weather, but is trying to understand what is typical. We would generally regard the climate as being an average of some property (temperature, for example) over a suitably large region and a suitably large time interval. It turns out that this depends less on the initial values of the system, than on the boundary values. The boundary values are the conditions that constrain the climate over the long-term and are things like how much energy we get from the Sun, how much is reflected back into space, how much energy is radiated from the surface, and how much of this escapes into space. The latter depends on the composition of the atmosphere, and so this is often more associated with a boundary value, rather than being regarded as an initial value.
A key point is that the system will always tend towards a state in which the amount of energy coming in, matches the amount going out into space, and that this state depends mostly on the boundary conditions. This quasi-equilibrium state will then set things like the surface temperatures, latitudonal temperature gradients, large-scale circulation patterns, and how much energy is in the system. Hence, it will determine the typical properties of the climate.
The counter-argument is that the system is inherently chaotic and, therefore, we cannot make long-term predictions. This is true for weather predictions, but not for climate modelling. Even if we could get very accurate initial conditions, there would still be a limit to how far in advance we could predict the weather. The climate, however, doesn’t depend very strongly on the initial conditions, and so this property doesn’t impact climate modelling in the same way as it does weather modelling.
A few additional comments. Even though climate modelling is more a boundary value problem, than an initial value problem, doesn’t mean that the initial conditions don’t matter. The exact path that we follow will depend on the initial conditions. However, if we were to consider numerous simulations with different initial condition, but the same boundary conditions, then we would expect the typical climate to be similar. This also doesn’t mean that the non-linear, chaotic nature of the system can’t have an impact on climate. It is possible that the non-linear dynamics could lead to some big change in some circulation pattern that could substantially influence the climate. Dansgaard Oeschger events may be an example of exactly this (although these are still ultimately associated with a change in one of the boundary conditions). It’s just that this is probably unlikely; we don’t often see shifts in climate that we can’t associate with a change in one of the boundary conditions.
I’ve written this quite quickly, so may not have explained it as well as I could have. Probably also worth reading the posts I link to below. I will add that part of the problem may be that when communicating publicly it’s often necessary to provide reasonably simple explanations. You can’t possibly provide all the details and complexities when trying to explain something like this to a non-expert audience. A consequence of this, though, is that people can then pick holes in the explanation, if they’re not willing to accept that it’s been intentionally simplified.