Climate change? Nothing to worry about.

Mark Buchanan
Mar 30, 2015 · 5 min read

At least, if you’re happy to ignore what we know about physics

Two weeks ago I wrote a short column in Bloomberg referring to a recent draft paper on climate change circulated by Cliff Asness and Aaron Brown, both of the hedge fund AQR. Fortune magazine ran an article about the Asness-Brown paper under the title “Top hedge fund manager: Global warming isn’t a danger.” When I read that, I thought “What?” For that is not the message any honest reader could possibly take away from the paper. Asness and Brown undertook a certain intellectual exercise, motivated by their impression that lots of people believe that the record of temperatures over the past century, taken by itself, implies that we face dangerous climate change. They show that it doesn’t, but readily admit that climate scientists don’t just work from this one set of recent temperatures, but use lots of other data too, and also take everything we know about the physics of the Earth system into account as well.

Some readers took my column as a direct criticism of the Asness-Brown work, despite my effort to emphasize just how honestly I think their paper was written. Maybe my tone was wrong. One Ross Kaminsky certainly took me to be some species of knee jerk lefty “warmist” unable to handle the obvious truth that climate change is just a hoax perpetrated by scientists to get more research money.

So let me try, in the simplest terms possibly, to clarify my point. My concern isn’t with the Asness-Brown paper itself, as the authors make every effort to be clear and cautious, but with the potential for misunderstanding on the part of others, especially others who, like the editors at Fortune (it would appear), may actually prefer to mis-understand. Asness and Brown themselves, in a postscript to their paper (added 16 March), have also complained about the misleading perspective furthered by Fortune.

Anyway, for clarification, let’s use an analogy. Imagine there’s a black box with a red light fixed to it. The light flashes every second or so on average, but in a highly irregular and unpredictable way. Some people argue that the flashing is getting more frequent with time, and showing larger fluctuations from its average behavior. Others say, no way, that’s an illusion, it’s always been irregular and these apparent changes are only insignificant and temporary fluctuations.

Two sets of people set to work to figure out if the pattern of flashing really is changing, and to predict how much we should expect it to change in the near future, if at all. The two teams go about their work in very different ways. Team A decides to work just with the mathematical pattern of flashing recorded over a not-too-distant interval of the past — say, one week. The other team, Team B, also uses that information, but decides to supplement it with other recordings of the flashing pattern from further in the past, some going back months, even years. Team B goes further too, using X-ray, MRI and ultrasound imaging of the box to work out a detailed, but certainly incomplete, picture of what goes on inside the box — gears and electronics and other stuff — to produce the flashing. They do experiments outside of the box to tease apart these mechanisms, and to get insight into how different mechanisms might interact within the box.

As the system turns out to be highly complex, Team B also starts to build replicas of the box, as well as large-scale computer models designed to simulate the interplay of all the mechanisms inside of the box. They test and refine both the replicas and the simulations over time using real data from the box. The members of Team B, knowing how easy it is for people to confuse themselves, and to believe they understand more than they really do, also splits itself into a number of sub-teams which compete against each other on standard data sets so they can get objective measures of improvement of these simulations over time. Who can run a simulation, based on plausible mechanisms, which can reproduce what the box did between 10 and 12 week ago? How does a model, trained on that interval, do if applied to other intervals later on? In this way, Team B slowly builds up a capacity for understanding what goes on in the box, and for predicting how it will likely behave next.

Now, suppose Team A and Team B make predictions for what they think is most likely to happen to the flashing pattern in the near future — say over the next 5 weeks. Both would acknowledge that the task is difficult given the complexity of the system. But which team do you think is more likely to make the better prediction? I think most people would naturally choose Team B, as they’re using a much richer set of information and data about the box and it’s behavior than Team A. They’re taking into account lots of things that Team A is not. Usually, the more information one brings to bear on a problem, the better one does on that problem. Indeed, most of the theories developed by Team A based on the short time series alone can be immediately shown to be highly unlikely by comparison with other data studied by Team B.

This situation is directly analogous to the analyses offered by Asness-Brown (Team A) and climate scientists around the world (Team B). It’s not that Asness and Brown don’t know about all this other information, of course. It’s just that, in the present paper, making the most accurate prediction IS NOT their purpose. They limit their analysis to the temperature record from 1880 to the present because, as they see it, lots of people mistakenly believe that this temperature record alone is strong evidence for rapid and highly problematic rising temperatures over the next century. Indeed, they cite a number of papers which they suggest make this error. Their analysis aims to stop such confusion, by showing quite clearly that simple mathematical extrapolation of the temperature record alone does not suggest such rapidly rising temperatures. They make it very clear that they’re leaving all the other information to the side, and acknowledge that this other information might very well lead to a very different conclusion. But that’s another matter, and not the topic of their paper.

This basic logic is all I wanted to make clear in my column, and mainly because, within a few days of the paper being circulated, the editors at Fortune had already completely missed the point. I’m quite certain a few other people will miss it as well. In fact, I wouldn’t be surprised if the Asness-Brown draft soon becomes widely cited within certain circles as delivering a definitive disproof to claims of rapid warming, despite the authors best efforts to disavow such interpretations.

I actually quite enjoyed reading their paper because of it’s profound clarity. They offer a completely legitimate analysis, and if many people really do think the temperature record alone suggests rapid warming, then this work ought to come as an excellent corrective.

However, on the broader question of what we should expect from future climate based on the best analysis of all information, I think (and so I believe do Cliff Asness and Aaron Brown) that the projections of Team B, the world’s climate science community, ought to be considered as likely to be the more accurate.

Bull Market

A collection of finance and business writing by…

Mark Buchanan

Written by

Physicist and author, former editor with Nature and New Scientist. Columnist for Bloomberg Views and Nature Physics. New book is Forecast (Bloomsbury Press)

Bull Market

A collection of finance and business writing by @alexisgoldstein, @delong, @dsquareddigest, @DuncanWeldon, @felixsalmon, @jamesykwak, @Mark__Buchanan, @WhelanKarl

More From Medium

More from Bull Market

Top on Medium

Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch
Follow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore
Get unlimited access to the best stories on Medium — and support writers while you’re at it. Just $5/month. Upgrade