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The question that headlines this post has caused great confusion and strife ever since climate change first entered the public consciousness.

From the very beginning, climate deniers set about to exaggerate the degree of uncertainty. As GOP messaging maestro Frank Luntz said in his infamous memo, “Should the public come to believe that the scientific issues are settled, their views about global warming will change accordingly.” Luntz sensed, accurately, that the lay public has a pretty naive, linear view of decisionmaking; they tend to think that understanding and quantifying the risks is the first step, to be completed before moving to action.

This has led climate hawks to emphasize, and sometimes overstate, the degree of certainty around climate change. To counter Luntz, they insist that “the science is settled” and “we have the tools we need to solve the problem.”

This is all … kind of dumb. “Certainty vs. uncertainty” is a red herring. Of course the science isn’t “settled.” Of course substantial uncertainty remains about what will happen and the way to avoid or adapt to it. Of course that doesn’t mean what climate deniers say it means.

For the sake of clarity (and to set up my next post), let’s take a closer look at the uncertainty around climate change — how much there is, what kind there is, what it means for us. We turn, as one does in these situations, to a new white paper from the World Bank. It’s about, among other things, climate and “deep uncertainty.”

First, what we know: We know that CO2 is accumulating in the atmosphere, that it’s causing global temperatures to rise, and that rising temperatures will have substantial (and largely harmful) effects on ecosystems. We’re pretty sure we can already see that signal through the noise of natural weather variations, but the signal is sure to get stronger later this century.

While nothing in science is ever “certain,” scientists have a very high degree of confidence in that stuff. So what’s uncertain?

There are three varieties of uncertainty germane to climate change, and yes, they have jargony names. Since you ask, yes, there will be a quiz later. They are: policy, epistemic, and aleatory uncertainty. (Protip: “aleatory” is fun to say. Try it!)

Policy uncertainty comes into play when trying to predict how our social and political choices will affect future carbon emissions. What we do between now and the end of the century will affect trends in population, technology, and economics, all of which will affect how much carbon we emit. Will we stop subsidizing fossil-fuel exploitation? Will we put a price on carbon, and if so when, and how much? Will we invest more in R&D, and if we do, will there be breakthroughs in clean technology?

I wrote a whole post about this kind of uncertainty once. It cannot be avoided or eradicated, for the simple reason that human beings are unpredictable. We don’t know exactly what will happen because we don’t know exactly what we (or our descendents) will do.

Epistemic uncertainty is about the science, “our imperfect knowledge of the functioning of the climate system and of affected systems.” There’s still a great deal we don’t know about how temperature will respond to rising levels of CO2 (so-called “climate sensitivity”) and, in turn, how ecosystems will respond to rising temperature. Consequently, there’s still a wide range of model projections, particularly when it comes to regional effects. The authors of the World Bank paper note:

The IPCC provides results from 19 global climate models. Even though the models agree on the very big picture (more warming in high latitude than in low latitude; more precipitation in high latitudes; less precipitation around the tropics; more precipitation around the equator), the differences can be huge in some regions (e.g., half the models predict an increase in precipitation over India; half the models predict the opposite; and — as a consequence — the “average model” predicts no change, showing the risk of using an average model).

Here’s another example: “For Ghana, [one model] predicts a 20% increase in precipitation, while [another model] predicts a 30% decrease!”

As much as climate science has advanced in recent years, and continues to advance, the fact remains that our best models project a wide array of possible outcomes.

What’s more, even the full range of model projections doesn’t capture the degree of uncertainty. All our models share certain assumptions and data, some of which will likely be refuted or amended by subsequent science. But which ones? That’s an unknown unknown. So the range of uncertainty is wider than the current range of projections.

Some of the epistemic uncertainty can be reduced, but not all, because of …

Aleatory uncertainty is about natural variability within climate subsystems. Here we find an ineradicable element of chance, or chaos, which is inherently unpredictable. Consequently:

Climate models provide information of statistical nature (averages, variance, likelihood to exceed thresholds, etc.), but they do not provide forecasts, i.e. deterministic prediction of the future. In other words, they can estimate the average number of rainy days in the summers of 2060s, but do not say anything about any given day or even any specific summer.

There’s a lot of work being done to “scale down” climate model results to geographical scales more relevant to decisionmakers — everyone wants to know what to expect in their own backyards — but there’s just an inherent limit to how granular and predictive they can get. Unlike climate, weather is chaotic.

None of these three kinds of uncertainty can be eliminated, so “certainty” is off the table. Interestingly, looking out over the coming century, epistemic and aleatory uncertainty dominate in the short term, but in the long term it is policy uncertainty that looms largest. We are the biggest X factors in our own models. What we choose matters most!

So those are varieties of uncertainty. But what do the authors mean when they talk about deep uncertainty? For uncertainty to be deep, it has to involve a few things.

First, there are multiple models with divergent projections and no real way to judge which is “best.” Analysts cannot even agree on the probability distributions of basic variables and parameters. In short, there are “multiple possible future worlds without known relative probabilities.” We don’t/can’t know what’s going to happen.

And second, there are diverse stakeholders with “divergent but equally valid worldviews.” When the people involved in making decisions have different notions of risk, justice, and value, it can be difficult to even determine what the goal is or what “success” would mean. On climate change, for instance, what are we trying to achieve? Sustainability? Economic growth? Fairness? These things cannot be neatly settled since some differences in worldviews are effectively irreducible.

That’s deep uncertainty: We don’t know what’s going to happen, we don’t share a common way of defining the problem(s), and we can’t agree on what would count as a solution.

I’m also reminded of Jay Rosen’s definition of a “wicked problem.”

Wicked problems have these features: It is hard to say what the problem is, to define it clearly or to tell where it stops and starts. There is no “right” way to view the problem, no definitive formulation. There are many stakeholders, all with their own frames, which they tend to see as exclusively correct. Ask what the problem is and you will get a different answer from each. Someone can always say that the problem is just a symptom of another problem and that someone will not be wrong. The problem is inter-connected to a lot of other problems; pulling them apart is almost impossible. In a word: it’s a mess.

Needless to say, climate change qualifies as a wicked problem around which there is deep uncertainty. So what does this mean for us?

Conservatives, deniers, skeptics, whatever, try to argue that it is unwise to act until we have more certainty. This is bollocks. We know enough to know that we have to do something or we’re screwed.

But surely the presence of deep uncertainty should affect the way we make decisions about climate change, yes? Yes! In fact, that will be the subject of my next post.