Most people have a fairly clear position on human-induced climate change, despite the vast majority of people not knowing much about the scientific basis of the climate models used to study it. That’s understandable – they are very complicated indeed and not accessible for everyone.
In one sense it doesn’t matter if climate models are only understood by climate modellers, as long as they can interpret their research findings for others to understand. But it does leave them open to the suggestion that their research is somehow “inferior” to that of scientists who don’t use models – despite the fact that models are used in every field of science, from economics to astrophysics.
The essence of science is to model our world, so it follows that understanding the underlying model is essential in understanding the predictions and its uncertainties.
Modelling the world
The state-of-the-art climate models on which groups such as the Intergovernmental Panel on Climate Change base their climate projections are developed from the models used in weather forecasting. Weather models primarily deal with questions such as “where does the wind blow from?” and “will it rain?”
These are essentially questions about how the atmosphere will circulate for the coming few days. This is why these kind of models are called “general circulation models” (GCMs). When extrapolating the climate, we essentially use longer-term forecasts of the same thing.
Today’s GCMs are probably the most complex and advanced scientific models ever created. But their complexity also makes them hard for the general public to understand, which in turn makes it less likely they will have faith in the models’ forecasts. To the general public (and even to other members of the climate research community) these GCMs are essentially black boxes – you put something in (such as increased CO₂ concentration) and you get a response out (warming temperatures).
These responses might be easier for the public to understand if we step back from the most complex models and instead use one that is easier to understand. There is no better way to learn than doing, so my lab has designed a climate model that people can try out for themselves.
A simpler climate model
A simpler approach of modelling the climate is often based on the first law of thermodynamics: the conservation of energy. It models how energy is going in or out of the system and by this it is modelling the energy balance of the surface of the earth. These models are called “Energy balance” models.
The new Monash Simple Climate Model (MSCM), which my research team developed, allows students and the public to use a real climate model to do their own climate simulations. It provides a simple model of the average global climate and its response to external factors such as changes in sunlight or CO₂ concentration.
The MSCM allows you to study the results of more than 2,000 different model experiments in an interactive way. You can take the climate apart, and see how it responds to different climate change scenarios. It also provides educational tutorials about the climate, climate models and climate change, and even some fun puzzles.
Experimental simulations are a key method in science. They allow you to address “what if?” questions that are not easily answered simply by observing the real world or by experimenting with it.
Using our model you can, for instance, address the question of what would happen if you [took away all clouds](http://monash.edu/research/simple-climate-model/mscm/greb/cgi-bin/dmc_i18n.py?activetab=undefined&version=Basic&locale=EN&atmosphere=1&clouds=1&co2=1&heat_diff=1&heat_adv=1&albedo=1&hydro=1&vapour_diff=1&vapour_adv=1&ocean=1&model=0&atmosphere_s=1&clouds_s=0&co2_s=1&heat_diff_s=1&heat_adv_s=1&albedo_s=1&hydro_s=1&vapour_diff_s=1&vapour_adv_s=1&ocean_s=1&model_s=0&lat=&lon=®ions=0&location=Global%20mean%20(default). And how else are we to learn about clouds’ role in the climate system unless we see what happens when they’re not there?
Much as some people deride modelling as “not real science”, we only have one Earth and one climate, so we can’t do “real” experiments with it. By using models, we can test important questions such as what would happen if we double the concentration of CO₂ in the atmosphere. In fact this may not be the best example, as we are well on the way to doing this experiment in real life – although the models should hopefully warn us of the impacts in time for us to avoid the real thing.
Speaking of the future, a publicly accessible climate model could also be used in schools, helping to equip future generations with a better understanding of our climate system. For a generation that will grow up in a fast-changing climate, this will educate the public about what’s in store, and might also inspire the next generation of scientific modellers – of the climate as well as in many other areas of science.