Scientists have built a theory of climate change from multiple lines of consistent, high-quality evidence. Just as we are confident that penguins can’t fly and that our skydiving scientist plummets under gravity, we are also confident in our understanding of human-induced climate change.
But simple statements implicating the human causes of climate change contain many complexities. The specifics are important too – what’s in store for the future? And how sure are we of these future climate impacts?
Even with our increasingly sophisticated knowledge of the climate system, there are aspects where questions remain and uncertainties we need to consider. Scientists need to be able to express these nuances in scientific findings, which means quantifying how much we understand.
We can again think of the evidence of climate change as ajigsaw puzzle. All information has at least a small degree of uncertainty, so we can imagine each puzzle piece is imperfect: they might be a little bit out of focus, or perhaps slightly discoloured. But despite their imperfections, they are still recognisable.
These are tangible uncertainties, our “known unknowns”. The same measurements of the same phenomena will always be slightly different. There are limits on our computing capacity and representing the earth’s complex physical systems with numbers is complicated.
These uncertainties can be quantified and accounted for as part of the scientific process. We have a good idea of just how blurry or discoloured these pieces are and what they are showing us.
Then there are the bits of the puzzle we don’t even realise are missing yet. These are “unknown unknowns”. They are bits of our puzzle at the limits of our reasoning or modelling, so their uncertainties can’t be quantified.
In science, there are complexities in all questions. For example, we can ask what kind of changes we will see in temperature extremes in coming decades. Scientists are confident that temperature extremes will become more frequent, but in order to express some of the complexity encompassed in that outcome, scales of likelihood are used describe the level of certainty.
We are “virtually certain” (99-100% probability) we will see an increase in the frequency and severity of extreme high temperatures. Here, the uncertainty of a particular finding has been quantified in probabilistic terms.
Using probabilistic thinking comes naturally to us in everyday life and we use it to weigh up risks. For example, we decide whether to take an umbrella to work based on the likelihood of rain.
But despite our familiarity with probabilities, the language of uncertainty can be confusing. When taken out of a scientific context, “uncertainties” seem to indicate that scientists are just plain wrong.
In scientific discourse, “uncertainty” does not imply that the science is unreliable. Instead, uncertainty is about probabilities and likelihoods that describe our understanding of a particular outcome.
Like those unexpected rainy days that keep us guessing, scientific information is never completely, unequivocally certain. All our attempts to understand complex systems and future changes come with uncertainty. So, we do not, and should not, draw conclusions from a single piece of evidence. We discern the picture of climate change by looking at all our puzzle pieces together.
Ultimately, the only scientific certainty is uncertainty. At times, these inevitable scientific uncertainties have been framed as synonymous with doubt and used to try to discredit findings.
But uncertainty is not a weakness of science. Rather than eroding our scientific confidence in human-caused climate change, using probabilities to talk about scientific uncertainties allows scientists to communicate findings more precisely and transparently.
Source: The Conversation, story by Sophie Lewis and Ailie Gallant