“It is a real horrible place.” That’s how NASA atmospheric scientist Paul Newman describes an alternative world:
A NASA study about ozone-munching chemicals from aerosol sprays and refrigeration used a computer model to play a game of what-if. What if the world 22 years ago didn’t agree to cut back on chlorofluorocarbons which cause a seasonal ozone hole to form near the South Pole?
…In mid-latitudes like Washington, DNA-damaging ultraviolet radiation would have increased more than sixfold. Just 5 minutes in the summer sunshine would have caused a sunburn, instead of 15. Typical midsummer UV levels, now around 10 or 11, would have soared to 30. Summer thunderstorms in the Northern Hemisphere would have been much stronger.
Nice to know that a little foresight can pay off. There must be a lesson. What could it be? Oh:
Newman, the co-chair of the protocol’s scientific panel, said the study provides hope that the world can do the same thing on another looming but even harder to solve environmental problem: Global warming.
[Image: This is not a spray can by badjonni]
9 thoughts on “‘A world avoided’: Banning CFCs 22 years ago paid off”
Truly frightening! And they learned all that from a computer model as well! Then it certainly must be true. Computer models never lie, you see. The biggest ozone holes over Antarctica recently occurred for natural reasons, then, not CFCs, since CFCs are banned. Perhaps that was always the case, but then that would not fit the narrative, so no! Ozone hole, global warming, silent spring, mass starvation by the billions in the 1980s. All prevented by a bit of timely action, no doubt. Run the models, you’ll see.
Ah, I’ve heard about these flawed computer models, but it seems no one can ever tell me what’s wrong with them – probably due to me not having spent years studying climate science, I guess. Maybe you could be the one to explain to me the errors in method, Raul? Layman’s terms, preferably, but I’ll settle for links to peer-reviewed papers as long as they’re not too over my head.
Paul, maybe I can help you out. First of all, you almost answered your own question when you said “probably due to me not having spent years studying climate science!” Paul, have you personally ever created, or even used, a computer model of a real-world physical system? Note that this is not a rhetorical question. I prepare physics-based computer models as part of my job. I model a variety of electromagnetic phenomena and I also design & model electromagnetic hardware, ranging from simple arrays of magnets and iron to complex microwave-guiding and radiating structures. And unlike climate, the physics I employ is very, very well understood, all based on Maxwell’s equations (see http://en.wikipedia.org/wiki/Maxwell's_equations). Of course, when I have to create a model for an even marginally-complex system, I have to make various approximations, since my computer (despite its impressive computing power) still isn’t powerful enough to include all the details. Most of the time the approximations that I choose work well. But sometimes, despite my more than 20 years of experience, my models generate faulty predictions! I realize that given your faith in other computer models (which, of course, are created by mere-mortal scientists who went to good schools and received good grades, just like me), you must find my claim here (i.e., that some of my models turn out to be wrong) to be almost impossible to believe! Right? But as my fellow scientists know, we often have no choice but to do experiments, to be sure. Now let’s consider the Earth’s climate, shall we? It is, in my humble view, more than a trillion times more times complex than any system I have ever attempted to model. And let’s add to that that the physical phenomena it involves are not nearly as well understood as electromagnetics. In fact, the critical equations that govern the climate, and also the inputs and constraints, are only partially known! I’d estimate that the state of climate modeling today is roughly comparable to the state of electromagnetic modeling back in mid 19th century, before the discovery of radio waves. After all, the electromagnetic scientists of the 19th century had “computers” (aka, slide rules, log tables, and by-hand calculations) back then, which were not up to the task of modeling electromagnetic interactions in much detail, but they were every bit as clever as our scientists are today. And oh yes, they were still debating the final form of the laws of electromagnetics. So sure enough, they made a lot of mistakes, just like climate-change scientists do today! Or… perhaps you’d prefer a medical analogy? Untold billions of dollars have been spent studying cancer, at both macroscopic and microscopic levels, for many decades, and by many of the world’s best minds. And yet, there are still many kinds of cancer, even common ones, that are barely understood at all, and still incurable. Isn’t it utterly amazing that all those scientists still can’t truly understand cancer, despite the fact that cancer is, (in my opinion) vastly-simpler (and far easier to study via experiments!) than the Earth’s climate. So in summary, forgive me, Paul, if I find it extremely easy to believe that there just might be an itty-bitty possibility that the computer models commonly used to predict climate-change might not be completely accurate representations of reality. Yes, computer models are useful. But no serious scientist considers them infallible.
You shouldn’t entirely discount climatologically computer models. That said, they shouldn’t be taken as gospel truth either. Most models run via two methods. Either they take correlations and extrapolate them, or they take theoretical calculations and plug in the numbers (or they do a little bit of both).
The correlation method is actually a pretty good way to determine what will happen if you repeat something. So long as you snag up all of the relevant variables, massive multivariate correlation do a decent job. The danger with such a method though is that once you leave the place where you have data, your model becomes highly uncertain. A good example of this is the recent financial crisis. Stuff got AAA credit ratings because historical data stated that it would take a one in a thousand year crisis to make certain derivatives as worthless as they are now. The truth is that what really was happening is that the market tipped slightly out of the realm where the correlations had data and it turns out that that “one in a thousand year” crisis was wasn’t actually one in a thousand years.
The other method is to take a theoretical model based upon natural laws and slap data into it. This method is a lot less likely to completely fail when you extrapolate, but it is also much less certain and results in big error bars.
So, it is the difference between throwing a ball on a windy day and calculating where it will hit using Newtonian laws of motion, or throwing a ball on a windy day and using a the data from the last time you threw a ball on a windy day to guess where it is going to land. The Newtonian laws method is likely to be spot on in an ideal world, but in the less than ideal world it is only as good as the variables you take into account. So, if you just take the basic gravity equations, you are going to be wrong by a lot. If you toss in some equations that take in account the ideal way that the wind effects the ball, you will get closer. If you toss in some more equations to take into account air turbulence, you will be closer still. You will never get everything, but you might get enough to be pretty close. You just constantly run the risk of missing an equation.
The correlation method on the other hand is going to be extremely accurate over the area you have data for. So, if you toss a ball on a day when the wind is 5-10 mph and measure the results and build some correlations, you will likely be very accurate over 5-10 mph. You will in fact probably be more accurate than the guy trying to use Newtonian calculations. Toss the ball in a hurricane though, and your correlations will spit out nonsense and the guy using Newtonian equations is going to be much closer. He still isn’t going to be perfect.
So, where was I going with all of this? Take the climate models as rough shots at reality. They are almost certainly wrong (or at least poorly reported), but almost certain right in terms of the trend they are pointing at. So, when someone declares the earth will heat up 2 degrees in 20 years, you can assume that the authors are idiots or (more likely) reporters are idiots. The model predicts 2 degrees, but you have to slap errors bars of 1 degree around that prediction, and the further you run the model into the future the more uncertain it becomes. Models are useful in that they provide a worthwhile attempt at predicting reality that are closer than just guessing and warm fuzzy feelings. Just realize that that prediction comes with a good deal of uncertainty.
All of which is to say that we’re dealing with possibilities or probabilities, which I’d think would be obvious to “serious” scientists (or even the really frivolous ones who work at NASA?: )
Well, the fallibility of models is implicit in their name, for me at least; I have done some computer modelling in my time, though it was with electronic circuit design, but even so the first thing that is drilled into you in the introductory lessons is that they can only ever *approximate* reality… albeit more powerfully and at greater detail than pen’n’paper work could hope to duplicate in any reasonable timeframe.
If I might extend your own analogy, Robert, and imagine myself diagnosed with a poorly understood form of cancer. The doctor explains the situation to me as best he can, including the admission that he and his profession do not fully understand the causes or ultimate effects of the tumour, and tells me that, with reference to the most advanced research they have access to so far, they have developed a diagnosis and treatment regime that they hope can save my life. Given the option between going with that educated and researched best guess, or instead deciding that the data set the doctors have used is invalidated by its lack of certainty and that my cancer might in fact be benign (or beneficial, or a completely natural phase of my body’s life, or caused by evil spirits and impure thoughts on my part), should I forgo treatment until my cancer is as well understood as Newtonian physics, or should I act on the data provided by people who have devoted their careers to studying and building upon the accumulated medical knowledge of centuries?
In short, all but the scientifically illiterate and sloppy journalists are aware that science – by its very method – never proves anything; on this, I think we are in agreement. Where we disagree in this instance is in the ways we feel we should react to that potentially incomplete data set. Call me impetuous if you will, but when a number of firemen turn up at the door to my house and point out that they can see smoke billowing from my windows and that they suspect my house may be on fire, I’m not going to sit in front of the television until the flames start licking under the living room door.
It seems that this is where we have to agree to disagree; the upset of those who share my pragmatic outlook with those of you who express greater caution may perhaps stem from the fact that, to dip back into the metaphor, we’re all living in that same possibly-burning building. 🙂
Your view is far from pragmatic, Paul, although keep telling yourself it is if it helps. Reputable scientists such as Freeman Dyson promote a far more pragmatic view.
Your view is rather that of a frantic hysteric, screaming at the top of her lungs, “do anything even if it’s wrong, ohmygod! ohmygod!”
Your inability to see yourself that way suggests a remarkable lack of insight, friend, and I say that with all due compassion.
Perspective is a curious thing, I’ll grant you. As is your definition of compassion… but may I take your response to be an admission that we disagree over the way to respond to the available evidence rather than what that evidence says?
Not sure the discussion so far has been that helpful. To address the contrary point, though, it does seem that we avoided a fairly nasty possible future at a fairly modest cost. Even if you concede that the scenario outlined in the article was not certain, I’d argue the cost for avoiding that potential was well worth it.
With climate change, however, it seems both the future scenario and the economic impact of taking various actions are much more tenuous (not arguing whether or not GCC is real, but rather how it will actually play out vs. what the costs of compliance to try to prevent it will be).
Personally, I think little to nothing will ever be done in time to make a difference with GCC because of the pervasive and distributed nature of the source of the problem, and we are better off plowing full speed ahead on developing technologies to sequester carbon and/or ameliorate the negative effect (and I don’t have any illusions about the current state of carbon sequestration as a substitute for other actions — I just don’t think there will ever be the political will to do those other actions, so it’s pointless to sit and debate implementing them).
Comments are closed.