The prediction, although uncertain, was terrifying: A vast system of Atlantic currents called the Atlantic Meridional Overturning Circulation (AMOC) that shapes weather around the world could collapse around 2057. Behind this theory were two sibling scientists, Susanne and Peter Ditlevsen, and their work had kindled an international debate about one of the planet’s most feared tipping points.
When San Francisco–based journalist Gregory Barber first read about their startling prediction, he thought he had found a character-driven climate story. But as Barber reported a piece for Quanta Magazine, he became intrigued by a more basic and unsettling question: What, exactly, is a tipping point? He realized that although the phrase “tipping point” seems intuitive to most of us (think of tipping over a chair), we don’t actually know what a tipping point is. He decided to turn his story into an explanatory one—taking us deep into the math of tipping points and showing us how important they are and how uncertain they can be.
That shift changed the story. He spoke to Rohini Subrahmanyam about how he wove complex math into an explanatory story on climate change while keeping his readers hooked. (This interview has been edited for length and clarity.)
What made you believe an explanatory piece on tipping points would be more intriguing than the story about the scientists and their predictions?
There was a lot to explain and a lot to understand, so I was motivated to do an explanatory piece. If there was a broader understanding of what a tipping point was and some of the mathematics—which actually isn’t all that complicated under the hood—then we could think about tipping points in a way that was less alarmist. As soon as you understand all the uncertainties that go into these mathematical models, it doesn’t change reality, but you feel like you know what’s actually going on, how to evaluate these predictions and the way that people are talking about big changes in the climate.
Climate models that are used to predict tipping points are uncertain, as you mentioned, but their consequences are important to convey to a broad audience. How do you convince readers that it’s important to care about something that we don’t or can’t know for sure?
Models are simplifications of reality; they’re a matter of the choices we make. In speaking with the scientists who are producing these models, it’s helpful that they’re extremely upfront about the uncertainties, but they’re also not shy about the overall conclusions about the risks that we’re putting ourselves under if we continue warming this climate.
The idea is to make the reader comfortable with the uncertainties in the way that scientists are—that was my overarching goal of doing this explanatory approach. [Instead of stating] simply, “things are uncertain,” [my goal is to] really pull people into the thinking behind that uncertainty, so that hopefully they don’t just throw their hands up.
When you are covering stories about something as alarming as climate tipping points, how do you make sure to not overstate the implications of the study you are covering?
I feel like tipping point reporting specifically is one of the rare cases in climate change reporting where it is possible to be alarmist because the uncertainties are just so large and the implications are so great.
I’m putting [the readers] in the middle of what’s already a pretty fierce debate over this particular study that had a conclusion that some people say is alarmist, but people are critiquing that conclusion and saying, Here are all the different reasons why, if you change this model slightly, it gives you a very different conclusion and it gives you a very different date or timeframe on the potential collapse of this climate system.
You started the piece with the work of Soviet climatologist Mikhail Budyko in the 1960s. He proposed that a tipping point 600 million years ago could have caused the entire planet to freeze over. Was there any particular reason behind the framing?
I just really liked the Budyko story. I think that there was something about going with the most extreme example first, this prehistoric example of the most radical kind of tipping that you could think of in a climate system, where the world is shifting between the sort of temperate state that we’re in now and this frozen-ish state. I liked it as a really clear indicator of just how far tipping could go.
In terms of why I didn’t start with AMOC, to be honest, I wanted the story to be a bit broader. I wanted the story to be more about the language of tipping points and the math of tipping points.
One gratifying aspect of doing an explanatory piece is that people keep coming back to it. It’s a bit more general, like the science behind something that’s not going away. For any long piece, you need to think about how you’re going to invite people in beyond that first section. I worried about losing people who felt like they already knew the story based off of our exposure to the AMOC debate. This historical point can be a way in for people to be a little bit surprised by the ways in which these climate conversations were happening in a very different context, in a very different time.
Your story takes us from the mathematics underlying bifurcations to catastrophic climate phenomena. How did you balance the technical aspects and the real-world impacts?
I try to let the history of mathematics tell a good part of that more technical story. [I wrote about Henri] Poincaré doing his experiments that start off the science of bifurcations, which is the study of how gradual changes can lead to sudden shifts in a complex system.
In the case of Christopher Zeeman [a British mathematician who became influential in the 1960s and ‘70s], the way I start off telling his story is through his Christmas lecture, which continues to be a big lecture series in the U.K. that’s televised. He did one of his talks on catastrophe theory. What I tried to do there was let Zeeman himself talk us through some of the technical details of his science. He has this device called the catastrophe machine, which is basically like a pulley system that can be gradually tugged and then at some point it’s tugged too far and suddenly the whole system snaps, and you get the sudden change of a bifurcation illustrated right there.
You interviewed mathematicians who apply that knowledge to climate change, and also climate scientists who are using the mathematical models in their work. Did you approach these two kinds of interviews differently?
I don’t know if I approached the interviews differently, but I really wanted to highlight some of the pure mathematicians who work on the underlying bifurcation models. I think that they’re often the silent partner to the other researchers who work in fields where maybe it’s more natural to talk to journalists on a regular basis, climate science being a great example of that.
Which aspect of writing or reporting the story was particularly challenging for you?
The biggest challenge for me was that the scientific history gets really muddled by [bifurcation science] being such a broad topic that is defined in different ways and involves different terminology and different ways of thinking between different disciplines. As a result of that, it was very difficult for me to really understand what was important for readers to know about that history.
Typically, it can be such a pleasure to be like, “Here are these crucial moments of the development of the science” and share those narratives from the past. But in this case, it just felt so fragmented. You could go back to the 1970s and talk about Christopher Zeeman and catastrophe theory, and that’s ultimately what I did. But I could have just as easily done more of a deep dive into bifurcation science as it applied to climate science at that time. The ecologists were, in some ways, the first people to apply these models to real-world scenarios. So, I find it really challenging to be faithful in my description of how this field is developing when the field itself has no sort of coherent narrative of how the science is being developed. I can imagine experts in a particular domain reading this piece and being like, “Oh my God, why did he leave out X, Y, Z important paper or concept?” There are certain things that, looking back, I would have loved to have included. But to my editors’ credit, they probably recognized that it would have actually just confused readers, even if my intentions were to be more complete.
Communicating some of the forward-thinking research was also challenging because it gets into the most difficult problems of this type of tipping point analysis. Like, towards the end of the piece, I bring up how even if you can predict some change in the AMOC, people don’t really know what that means. Does it mean that the current shuts off? Does it mean that there’s a change in internal state that’s actually not even observable to people because it’s purely a mathematical change that doesn’t manifest in some kind of catastrophic event?
How did you overcome these challenges?
I think it’s important to think about: What is your goal as a writer? What are you trying to actually tell your audience? And in my case, this is a story about how to, in some sense, think like a mathematician when looking at the range of confusing models that are out there that are describing the changes that are ahead for us in climate change. I wanted people to have some practical takeaway from this piece that is, “Maybe I feel a little bit differently than I did about the headlines that are out there about tipping points.” Once you adopt that framework, then it’s much easier to make those decisions about what you should include in the historical narrative.
Maybe it’s not about constructing a perfect story about how the science was developed. Maybe it’s, “This particular example or anecdote has something bigger to say about how we should think about modeling or the problems that modelers face.”
So that’s why I chose the Christopher Zeeman stuff about catastrophe theory; I mean, it’s a great story as well. I think it’s entertaining just to go through that historical rabbit hole. But then at the same time, it was done for the reason of [how] catastrophe theory has a lot to say about how the public is currently interacting with climate models today. It had all these glimmers of relevance to our current situation that made it make sense as the thing to focus on historically.
What did you feel you learned over the course of working on the story?
I typically gravitate more towards narrative feature writing. So, this was pretty foreign to me—this idea of a long-form piece of explanatory journalism. I found that there’s just a lot that you can do with it that I didn’t expect.
Storytelling and people are the way to convey technical subjects in a much more accessible way because you can move through the technical details along with a person, or you’re observing things with someone. But I don’t think that those techniques have to go away in an explanatory piece. It might be more of a cast of characters across the story or carefully chosen historical moments. Because, on the flip side, you’re no longer beholden to a particular narrative—a particular occurrence to carry us through the entire piece or this specific person to be our sole guide. Instead, you get to make a lot of choices, which can be really challenging, but also really enjoyable.
Do you have any broad advice for other journalists who are trying to communicate uncertainty in climate science or uncertain math and how that relates to real-world scenarios?
The broad advice is to really listen to what the conversation is between the scientists. In some ways, I think that there can be a bit of apprehension about asking about a controversial study, because of the expectation that scientists might not want to talk about it. But generally, scientists do really want to talk about and be heard on that. I think by leaning into the science, the complexity, the debate, and the uncertainty, you can get a lot farther and help readers along.
Rohini Subrahmanyam is a freelance science journalist mostly covering the life sciences, with bylines in Scientific American, Science News, Knowable, Nautilus, and others. She is also a science writer at the Indian Institute of Science, Bengaluru. She has a PhD in biology and postdoctoral experience in developmental biology. Rohini is a TON early-career fellow sponsored by the Burroughs Wellcome Fund. Find Rohini on LinkedIn.
