Is Climate Modelling Science?

At first glance this seems like a strange question. Isn’t science precisely the quantification of observations into a theory or model and then using that to make predictions? Yes. And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements? Yes, again. So the fact that climate modelling was recently singled out as being somehow non-scientific seems absurd.

Granted, the author of the statement in question has little idea of what climate modelling is, or how or why it’s done. However, that his statement can be quoted in a major US newspaper says much about the level of public knowledge concerning climate change and the models used to try and understand it. So I will try here to demonstrate how the science of climate modelling works, and yes, why it is a little different from some other kinds of science (not that there’s anything wrong with that!).

Climate is complex. Since climatologists don’t have access to hundreds of Earth’s to observe and experiment with, they need virtual laboratories that allow ideas to be tested in a controlled manner. The huge range of physical processes that are involved are encapsulated in what are called General Circulation Models (or GCMs). These models consist of connected sub-modules that deal with radiative transfer, the circulation of the atmosphere and oceans, the physics of moist convection and cloud formation, sea ice, soil moisture and the like. They contain our best current understanding for how the physical processes interact (for instance, how evaporation depends on the wind and surface temperature, or how clouds depend on the humidity and vertical motion) while conserving basic quantities like energy, mass and momentum. These estimates are based on physical theories and empirical observations made around the world. However, some processes occur at scales too small to be captured at the grid-size available in these (necessarily global) models. These so-called ‘sub-gridscale’ processes therefore need to be ‘parameterised’.

A good example is related to clouds. Obviously, in an actual cloud, the relative humidity is close to 100%, but at a grid box scale of 100′s of km, the mean humidity – even if there are quite a few clouds – will be substantially less. Thus a parameterisation is needed that relates the large scale mean values, to actual distribution of clouds in a grid box that one would expect. There are of course many different ways to do that, and the many modelling groups (in the US, Europe, Japan, Australia etc.) may each make different assumptions and come up with slightly different results.

It’s important to note what these models are not good for. They aren’t any good for your local weather, or the temperature of the water at the nearest beach or for the wind in downtown Manhattan, because these are small scale features, affected by very local conditions. However, if you go up to the regional scale and beyond (i.e. Western Europe as a whole, the continental US) you start to expect better correlations.

One of the most important features of complex systems is that most of their interesting behaviour is emergent. It’s often found that the large scale behaviour is not a priori predictable from the small scale interactions that make up the system. So it is with climate models. If a change is made to the cloud parameterisation, it is difficult to tell ahead of time what impact that will have on, for instance, the climate sensitivity. This is because the number of possible feedback pathways (both positive and negative) is literally uncountable. You just have to put it in, let it physics work itself out and see what the effect is.

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