In the talk I gave this week at the workshop on the CMIP5 experiments, I argued that we should do a better job of explaining how climate science works, especially the day-to-day business of working with models and data. I think we have a widespread problem that people outside of climate science have the wrong mental models about what a climate scientist does. As with any science, the day-to-day work might appear to be chaotic, with scientists dealing with the daily frustrations of working with large, messy datasets, having instruments and models not work the way they’re supposed to, and of course, the occasional mistake that you only discover after months of work. This doesn’t map onto the mental model that many non-scientists have of “how science should be done”, because the view presented in school, and in the media, is that science is about nicely packaged facts. In reality, it’s a messy process of frustrations, dead-end paths, and incremental progress exploring the available evidence.
Some climate scientists I’ve chatted to are nervous about exposing more of this messy day-to-day work. They already feel under constant attack, and they feel that allowing the public to peer under the lid (or if you prefer, to see inside the sausage factory) will only diminish people’s respect for the science. I take the opposite view – the more we present the science as a set of nicely polished results, the more potential there is for the credibility of the science to be undermined when people do manage to peek under the lid (e.g. by publishing internal emails). I think it’s vitally important that we work to clear away some of the incorrect mental models people have of how science is (or should be) done, and give people a better appreciation for how our confidence in scientific results slowly emerges from a slow, messy, collaborative process.
Giving people a better appreciation of how science is done would also help to overcome some of games of ping pong you get in the media, where each new result in a published paper is presented as a startling new discovery, overturning previous research, and (if you’re in the business of selling newspapers, preferably) overturning an entire field. In fact, it’s normal for new published results to turn out to be wrong, and most of the interesting work in science is in reconciling apparently contradictory findings.
The problem is that these incorrect mental models of how science is done are often well entrenched, and the best that we can do is to try to chip away at them, by explaining at every opportunity what scientists actually do. For example, here’s a mental model I’ve encountered from time to time about how climate scientists build models to address the kinds of questions policymakers ask about the need for different kinds of climate policy:
This view suggests that scientists respond to a specific policy question by designing and building software models (preferably testing that the model satisfies its specification), and then running the model to answer the question. This is not the only (or even the most common?) layperson’s view of climate modelling, but the point is that there are many incorrect mental models of how climate models are developed and used, and one of the things we should strive to do is to work towards dislodging some of these by doing a better job of explaining the process.
With respect to climate model development, I’ve written before about how models slowly advance based on a process that roughly mimics the traditional view of “the scientific method” (I should acknowledge, for all the philosophy of science buffs, that there really isn’t a single, “correct” scientific method, but let’s keep that discussion for another day). So here’s how I characterize the day to day work of developing a model:
Most of the effort is spent identifying and diagnosing where the weaknesses in the current model are, and looking for ways to improve them. Each possible improvement then becomes an experiment, in which the experimental hypothesis might look like:
“if I change <piece of code> in <routine>, I expect it to have <specific impact on model error> in <output variable> by <expected margin> because of <tentative theory about climactic processes and how they’re represented in the model>”
The previous version of the model acts as a control, and the modified model is the experimental condition.
But of course, this process isn’t just a random walk – it’s guided at the next level up by a number of influences, because the broader climate science community (and to some extent the meteorological community) are doing all sorts of related research, which then influences model development. In the paper we wrote about the software development processes at the UK Met Office, we portrayed it like this:
But I could go even broader and place this within a context in which a number of longer term observational campaigns (“process studies”) are collecting new types of observational data to investigate climate processes that are still poorly understood. This then involves the interaction several distinct communities. Christian Jakob portrays it like this:
Although the point of Jakob’s paper is to argue that the modelling and process studies communities don’t currently do enough of this kind of interactions, so there’s room for improvement in how the modelling influences the kinds of process studies needed, and how the results from process studies feed back into model development.
So, how else should we be explaining the day-to-day work of climate scientists?