Over the past weekend, extreme weather events across the continental United States abounded, from a blizzard and flooding in and around Los Angeles to record high temperatures for February in Florida and other parts of the Southeast. Extreme weather is a predicted byproduct of climate change, and we have seen more and more examples of this in the past several years.
But how are we able to predict the increasing prevalence of this extreme weather in the first place? Weather is notoriously much harder to predict than climate, but with the help of mathematics being applied to climate modeling, scientists can observe longer-term trends in the climate as well as accurately forecast patterns of the shorter-term and more fluctuating weather.
We’ve actually come a long way in studying climate over the past 50 or so years, with early advances coming from the advent of chaotic dynamics and multiscale modeling. This column covered the basis of chaos theory in an article last semester, but for a brief refresher, mathematicians use the term “chaotic” to describe systems whose behavior can change drastically with even the slightest of alterations in their initial conditions.
Multiscale modeling is also important for describing complex systems because it tries to capture the different dynamics that can be going on at different scales in the system. When looking at climate, we have, on the smallest scale, particles in the atmosphere interacting with each other; on larger scales, we have phenomena like cloud and front formation which cannot be modeled feasibly by keeping track of every single particle; and for yet larger scales, we have the average temperature and rainfall of a region over many years.
While we have different scales that involve different types of modeling, mathematics still provides us with a way to connect these scales together, and, if we can glean patterns in the smaller scales, we can predict what those patterns engender in the larger ones. This is how scientists figured out that (1) an increase of carbon dioxide and other greenhouse gasses in the atmosphere can and will cause global warming, and that (2) human emissions are the leading cause of this increase.
On the flip side, we can gain an understanding of the smaller scales by studying the largest scales in multiscale modeling, too. This is a little trickier to do, but scientists have also been successful, and it’s in part what has led to a shift from the term “global warming” to the term “climate change.” Scientists know that, while temperatures, on the whole, have risen and will continue to rise, there will nonetheless be fluctuations of this in the weather. The weather will also become more extreme as the buildup of greenhouse gases in the atmosphere continues.
What will be important in the coming years, I predict, will be using multiscale modeling to assess the most effective damage control when it comes to climate change. Humans have already done lots of damage to the Earth, and this has been accelerated exponentially by climate change. To survive as a species and protect the species around us, we will need strategies to mitigate future destruction.
This will be difficult to do, of course, because even the best science likely will not mean much at first to big corporations still making huge profits, or governments neglecting regulations or global agreements, despite the immense toll the Earth is taking from their actions or inactions. Nevertheless, I believe that more practical solutions will arise soon, and the best of them will have involved a great deal of multiscale analysis along the way. The mathematicians and scientists will then rely on activists and politicians to wage a multiscale campaign to implement those solutions.
