Part 5 in a series of 5 for NextGen Journal
Read Part 1, Part 2, Part 3, and Part 4

Scientists can never say that something is 100% certain, but they can come pretty close. After a while, a theory becomes so strong that the academic community accepts it and moves on to more interesting problems. Replicating an experiment for the thousandth time just isn’t a good use of scientific resources. For example, conducting a medical trial to confirm that smoking increases one’s risk of cancer is no longer very useful; we covered that decades ago. Instead, a medical trial to test the effectiveness of different strategies to help people quit smoking will lead to much greater scientific and societal benefit.

In the same manner, scientists have known since the 1970s that human emissions of greenhouse gases are exerting a warming force on the climate. More recently, the warming started to show up, in certain patterns that confirm it is caused by our activities. These facts are no longer controversial in the scientific community (the opinion pages of newspapers are another story, though). While they will always have a tiny bit of uncertainty, it’s time to move on to more interesting problems. So where are the real uncertainties? What are the new frontiers of climate science?

First of all, projections of climate change depend on what the world decides to do about climate change – a metric that is more uncertain than any of the physics underlying our understanding of the problem. If we collectively step up and reduce our emissions, both quickly and significantly, the world won’t warm too much. If we ignore the problem and do nothing, it will warm a great deal. At this point, our actions could go either way.

Additionally, even though we know the world is going to warm, we don’t know exactly how much, even given a particular emission scenario. We don’t know exactly how sensitive the climate system is, because it’s quite a complex beast. However, using climate models and historical data, we can get an idea. Here is a probability density function for climate sensitivity: the greater the area under the curve at a specific point on the x-axis, the greater the probability that the climate sensitivity is equal to that value of x (IPCC, 2007):

This curve shows us that climate sensitivity is most likely around 3 degrees Celsius for every doubling of atmospheric carbon dixoide, since that’s where the area peaks. There’s a small chance that it’s less than that, so the world might warm a little less. But there’s a greater chance that climate sensitivity is greater than 3 degrees so the world will warm more. So this graph tells us something kind of scary: if we’re wrong about climate sensitivity being about 3 degrees, we’re probably wrong in the direction we don’t want – that is, the problem being worse than we expect. This metric has a lot to do with positive feedbacks (“vicious cycles” of warming) in the climate system.

Another area of uncertainty is precipitation. Temperature is a lot easier to forecast than precipitation, both regionally and globally. With global warming, the extra thermal energy in the climate system will lead to more water in the air, so there will be more precipitation overall – but the extra energy also drives evaporation of surface water to increase. Some areas will experience flooding, and some will experience drought; many areas will experience some of each, depending on the time of year. In summary, we will have more of each extreme when it comes to precipitation, but the when and where is highly uncertain.

Scientists are also unsure about the rate and extent of future sea level rise. Warming causes the sea to rise for two different reasons:

  1. Water expands as it warms, which is easy to model;
  2. Glaciers and ice sheets melt and fall into the ocean, which is very difficult to model.

If we cause the Earth to warm indefinitely, all the ice in the world will turn into water, but we won’t get that far (hopefully). So how much ice will melt, and how fast will it go? This depends on feedbacks in the climate system, glacial dynamics, and many other phenomena that are quantitatively poorly understood.

These examples of uncertainty in climate science, just a few of many, don’t give us an excuse to do nothing about the problem. As Brian, a Master’s student from Canada, wrote, “You don’t have to have the seventh decimal place filled in to see that the number isn’t looking good.”. We know that there is a problem, and it might be somewhat better or somewhat worse than scientists are currently predicting, but it won’t go away. As we noted above, in many cases it’s more likely to be worse than it is to be better. Even a shallow understanding of the implications of “worse” should be enough for anyone to see the necessity of action.


8 thoughts on “Uncertainty

  1. Should’a, could’a, would’a, but didn’t, can’t and won’t.

    It ought to be obvious, even to the disbelievers in climate change, that they’re in for a big rate hike on fuel, and a little independence from the grid would be wise in dollars alone.

    It occurred to me just last night–as I stood in the halo of two lights, that if a group of revolutionaries were to go around knocking out street lights that a tremendous amount of fuel could be saved… Oh, if humans could only be still half of the time.

    • Eh, manufacturing replacement street lights costs quite a bit of fuel too… and you know they will be replaced. If only simple solutions worked ;-)

  2. Michael Tobis has often argued that the presence of uncertainty about climate argues for action, not inaction. This is actually a complex issue which can be debated, but the argument boils down to the idea that climate impacts are nonlinear with respect to the climate change itself. That is, even if the climate change isn’t skewed toward high temperatures (as in the climate sensitivity pdfs above), the distribution of climate damages may be. Thus more uncertainty implies more risk (and therefore stronger “insurance” policies, like mitigation, are warranted).

    To see this, consider a normal random variable representing uncertain future warming (e.g., drawn from a distribution with mean 3 C and standard deviation +/- 1 C). Suppose that climate damages grow faster than linearly with temperature, say quadratically: D = c T^2 for some constant c. The distribution of D is not normal, and in fact is right-skewed.

    You can see this analytically. The “risk” is the product of impact and probability. The expected (average) damage is the expected risk, i.e. the integral over all temperature of D times the Gaussian probability function for temperature,

    c T^2 1/sqrt(2 pi sigma^2) exp[-1/2 (T-T0)^2/sigma^2]

    (e.g., with T0=3, sigma=1.)

    If you do this integral, you get E[D] = c(T0^2 + sigma^2).

    From this you can see that with a quadratic damage function, the expected damages increase with sigma, i.e., with the uncertainty about temperature.

  3. “This curve shows us that climate sensitivity is most likely around 2 degrees Celsius ”

    Careful about mode vs. median: the dots on the lines under the graph represent the medians, which are between 2 and 5 degrees C for climate sensitivity. The IPCC stated that “3” was the most likely value, I believe (likely range of 2 to 4.5).

  4. Some really nice articles here Kate! A point about probability density – I think in this case, there’s an equal chance, according to each probability density function, that the climate sensitivity will be below or above the median point (the dots on the lines). You have “But there’s a *greater* chance that climate sensitivity is greater than 3 degrees so the world will warm more.” Actually, there’s an equal chance of the probability being wrong to the lower or the higher end with a median of 3, according to the graphs.

    However, if the sensitivity ends up lower, the shape of the probability density means it won’t be all that much lower (ie very likely it won’t be below 1.5) … conversely, if the sensitivity ends up higher, there is a non-negligible chance that it could be 6 or more. This strongly argues for mitigation, as a sensitivity of 1.5C is still quite enough to worry about (like your quote from Brian) … and we don’t want to think about the consequences of a >6C sensitivity!

  5. I have tuppence:

    1. Skeptical Science pointed me here, which talks about (a rumour of) a soon-to-be-published paper arguing that climate sensitivity is lower than the IPCC AR4 talked about. I can’t recall whether that website is a ‘white hat’ or ‘black hat’ one — which has a bearing on whether the ‘rumour’ is being spread by the denialists; and if it is the latter, then here I am helping to do their dirty work :(

    2. The point I feel that is often lost when people talk about ‘climate sensitivity’ is that this is for each doubling of CO2: I’m pretty sure that in the popular understanding, all the talk about whether we’re going to see a rise of 2, 3, 5, 10 degrees or whatever, it is taken as assumed that whichever number turns out to be the right one, that’s the end of the story; whereas in truth, of course, it most certainly is not the end of the story, since we’re currently on track for far more than just a single double helping of CO2…

    • The paper is Schmittner et al. discussed here. The paper says

      “we estimate a lower median (2.3 K) and reduced uncertainty (1.7–2.6 K 66% probability)”

      i.e., still well within the IPCC uncertainty range. After which the Brian D quote above is again apposite: ““You don’t have to have the seventh decimal place filled in to see that the number isn’t looking good.”


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