Uncertainty and Black Swans Cloud the Climate Models' Use of the Past to Predict the Future

A black swan, says Nicholas Nassim Taleb who popularized the term, is a hard-to-predict event with a large impact.

The nearly impenetrable Navier–Stokes equations are the basis for complex computer programs commonly known as global climate models [GCMs] along with sea ice and land-surface components. There are almost as many GCMs as there are modelers, but all of them are selected as useful for future projections because they do a decent job of looking backward at how climate changed in the historic record leading up to the present. The fact that those changes that are happening today over decades took thousands of years in the past is one of the systemic reasons for the uncertainty surrounding the GCM's projections.

GCMs—use a bottom-up approach. They divide the Earth and its atmosphere into a grid which generates an enormous number of calculations in order to imitate the climate system and the multiple influences upon it. The IPCC estimates and those most often referred to by the 'consensus' are GCMs. Another widely used type—energy-balance models—are simpler. They are top-down, treating the Earth as a single unit or as two hemispheres, and representing the whole climate with a few equations reflecting things such as changes in greenhouse gases, volcanic aerosols and global temperatures.

The latest gold standard are the coupled atmosphere-ocean GCMs where climate sensitivity is an emergent property: it is not a model parameter, but rather a result of a combination of the particular GCM and its parameters. By contrast, simpler energy-balance models usually have climate sensitivity as an explicit parameter and attempt to solve the problem in the other direction. In short, the different sorts of climate model measure somewhat different things.

Very complex interdependent systems suffer not only from sensitive dependence on initial conditions but also from the differences between Nature and the models employed in representing it. Finding the 'equilibrium climate sensitivity' is the goal of all the climate models and modelers, however for all the feedback mechanisms and other uncertainties, positive and negative to work their way through in Nature takes centuries whereas public policy makers want answers yesterday.

No model – whether a wind-tunnel model for designing aircraft, an economic model, a weather model or a climate model for projecting global warming – perfectly reproduces the system being modeled. But all models have their place. Complex science often depends on our attempts to describe the world with simple models. In this context, GCMs are capable of reproducing the general features of the observed global climate over the past century and are therefore useful in showing us the best directions to focus our attention on. Models may be a good guide as to whether to bring an umbrella or sunglasses to work tomorrow but they are far to cloudy to base public policy and our environment's future on.

Tomorrow The Mud Report will dive into the complex uncertainties that drive the scientists mad, the known unknowns. Then we'll take a look at the chaotic feedback mechanisms, the unknown unknowns, and various black swan events that expose the error bars of all types of scientific modeling as wishful thinking.