Our common sense, or intuition, tends to work in a linear way: input A leads to outcome B. A small increase in A leads to a small increase in B, and if we keep increasing A, B should keep increasing, consistently.
In the case of complex systems, however, this kind of reasoning doesn’t hold true. When there are many variables and effects involved, as with ecosystems or the climate, a small shift in one variable may not have a small outcome, or even a consistent one. Threshold, breaking points, negative and positive feedback loops: even a small change in a complex system can set off a cascade of far-reaching responses, reactions big or small in a host of other variables, all compounded by random effects along the way.
This has implications for how we – the public, the media, politicians – understand the climate and talk about climate change. Just because there has been a steady yearly increase in CO2 without corresponding temperature increases in the past does not, as sceptics would have you believe, mean that man-made CO2 does not or will not have detrimental effects on the earth’s temperature, for instance – a complex system won’t have such simple, predictable relationships.
When systems get complex, so must our thinking and our models. Mathematical modelling has advanced significantly in recent years, and by taking into account all the information we do have about the variables and parameters involved in the delicate, complex system that is our climate, these models can help us begin to understand how each piece impacts the others, to what extent and under what conditions. Models are not always correct, but they can assist us in deepening our understanding; as a famous statistician has said, ‘all models are wrong, but some are helpful’. Something as intricate as our climate requires us to move our thinking beyond simple linear common sense, and models help us do this.
- The Conversation Common sense won’t help you understand climate