YOU CAN'T CAPTURE THE ESSENTIAL ELEMENTS.

Power Line's John Hinderaker, in "Why Climate Models Don't Work", points readers to Greg Chapman's "The Dirty Secrets inside the Black Box Climate Models."  It's familiar ground to regular readers of Power Line, or of Cold Spring Shops.  All the same, it's useful to have the message close to the top in case new readers (for some reason, Blogger recorded a lot of page views just before the election, and daily traffic is still a little higher: thanks for sticking around, whoever you are).

Here's the key.  "The short form of Steven Hayward's argument is 'in a complex adaptive system it is impossible to capture the behavior of the system without fully describing it in your model.'"  You can't capture the movement of the atmosphere at the molecular level, or even at the basketball level.  Here's Mr Chapman.

In order to represent the earth in a computer model, a grid of cells is constructed from the bottom of the ocean to the top of the atmosphere. Within each cell, the component properties, such as temperature, pressure, solids, liquids and vapour, are uniform.

The size of the cells varies between models and within models. Ideally, they should be as small as possible as properties vary continuously in the real world, but the resolution is constrained by computing power. Typically, the cell area is around 100×100 km² even though there is considerable atmospheric variation over such distances, requiring each of the physical properties within the cell to be averaged to a single value. This introduces an unavoidable error into the models even before they start to run.

It's a non-trivial avoidable error: are there no tornadoes, one tornado, or several tornadoes in two counties of Iowa, which is one way of thinking about a cell a hundred kilometers on a side and however high the atmosphere is?  Or, as I put it in another old post,

The fundamental problem remains one of computing capacity. If you attempt to capture the essential elements, the complexity of the adaptive system we call the atmosphere puts the computers in a position of taking longer to anticipate the changes than it does for the actual changes to manifest themselves. Me again. "Put another way, it would take longer to extrapolate a century-long climate emergency on the computer than it would take for the emergency to materialize. And rewriting the code in something other than FORTRAN would not change that reality. Let the fun begin."

Instead, you have no tornadoes, or one tornado, or a tornado outbreak, in one of your cells.  Now, to get some idea of how wild the future climate will be, you have to initialize the cells.  What with?  Your best guess, because it's good practice to see how well the simulation stacks up against reality, but at the beginning of the Industrial Revolution, there weren't a lot of weather stations.  If you could get something that looks like current reality from a variety of initial conditions, that's encouraging, but what you will get is chaos.  Mr Chapman confirms.  "The climate system is chaotic which essentially means any model will be a poor predictor of the future – you can’t even make a model of a lottery ball machine (which is a comparatively a much simpler and smaller interacting system) and use it to predict the outcome of the next draw."  But your grant, or perhaps your policy shop's future, depends on a prediction anyway, and off you go.  Perhaps that's permissible.  "There's nothing wrong with something being amiss in the models, as long as the models provide a better explanation of reality than a coin flip, and as long as the models aren't misused. There is nothing wrong with a scientific tool having large out-of-sample forecast errors, as long as that tool isn't being used as a guide to public policy."

But the whole point of the climate modeling is to have a guide to public policy.  Oops.