The limits of climate models

One unfair criticism of the GCMs goes like this; "if the models can’t predict the weather 2 weeks in advance how can they hope to predict the weather 100 years in advance"?However we are not interested in predicting the weather on perhaps December 25^th 2110. What we want to know is what will be the average weather (i.e climate) in a few decades around 2110.

A more realistic expectation should be that the model will predict a weekly average one week in advance, a monthly average a month in advance and a ten year average ten years in advance etc.

The table below gives an indication of the usefulness of GCMs/weather models for different forecast periods. Over a few days they give predictions which are nothing short of brilliant and a real triumph of modern physics. They are certainly useful for periods of up to a couple of months for predicting phenomenon such as El Niño and La Nina events. However my own experience with forecasting El Niño events is that the GCMs are no better than trivially simple models (Halide and Ridd, 2008) so it is doubtful that the impressive complexity of the GCMs contributes a great deal to accuracy.

At periods greater than a few months, the GCMs fail. They do not simulate the cooling of the '40s to '70s (decadal scale), the Holocene Climatic Optimum (millennial scale) or the large scale events associated with glaciations (10000 year time scale). In the light of such comprehensive failure of the models over periods from 1 to 10000 years, why would one believe that GCMs would be accurate over the 100 year time scales which are of greatest concern to us?

A close examination of the GCMs indicate that despite the horrific complexity of the models, a few processes completely dominate the predictions.

The most important is the well-documented water vapour positive feedback . C0₂ doubling by itself would create a temperature rise of around 1^o, i.e. easily tolerable. However such a warming will allow the atmosphere to increase in absolute humidity, and because water is a very powerful greenhouse gas, this humidity increase will increase the temperature further.

It is this water vapour feedback that is necessary for the nightmare scenarios of a few degrees warming. So in the final analysis, if we can have some faith that the GCMs can handle the water vapour problem properly, albeit with a significant uncertainly, then they are useful.

So how do GCMs deal with humidity, clouds and the water cycle? The water vapour remaining in the atmosphere is the result of two processes working in opposite directions viz evaporation and rainfall. Over long periods, and averaged over the earth, these processes are in exact balance, but if C0₂ is added this balance is slightly perturbed – very slightly.

Evaporation is relatively easy to predict with a tolerable uncertainty but rainfall is a different matter. It is no coincidence that of all the parameters that the Weather Bureau predicts, rainfall is the least reliable. It is difficult to predict even whether it will rain tomorrow let alone how much. The Bureau uses qualitative terms such as "scattered or isolated showers". If they predict "rain", expect anything from bone-dry to a deluge.

Rain is formed by a very complex and poorly understood set of processes. Vertical motion of air often by unstable (and difficult to predict) convection is frequently necessary to adiabatically cool the air until a cloud forms.