The foregoing posts summarize why I believe that the CAGW Hypothesis is wrong. However, I am willing to consider in good faith arguments, evidence, or ideas I may have missed. Please feel free to share them, keeping in mind the Rules I laid out in Section 1. Thank you.
Archive for September, 2008
The last argument I see commonly made in support of CAGW is the argument that some authority has accepted the CAGW hypothesis.
However, on closer inspection, it seems that most of these statements contain equivocal language and/or employ the same bait and switch mentioned in Rule 1.1 below. For example, according to Wikipedia, here’s what the American Meteorological Society said:
There is now clear evidence that the mean annual temperature at the Earth’s surface, averaged over the entire globe, has been increasing in the past 200 years. There is also clear evidence that the abundance of greenhouse gases in the atmosphere has increased over the same period. In the past decade, significant progress has been made toward a better understanding of the climate system and toward improved projections of long-term climate change… Human activities have become a major source of environmental change. Of great urgency are the climate consequences of the increasing atmospheric abundance of greenhouse gases… Because greenhouse gases continue to increase, we are, in effect, conducting a global climate experiment, neither planned nor controlled, the results of which may present unprecedented challenges to our wisdom and foresight as well as have significant impacts on our natural and societal systems
Sorry, but that is NOT an acceptance of CAGW. Read it carefully, and here’s the test: Assume for the moment that CAGW is incorrect. Does it necessarily follow that the statement was wrong? No!.
In any event, situations can and do arise where the “experts” are wrong. So even if lots of experts unequivocally endorsed the CAGW hypothesis, it would be interesting, but not necessarily determinative of anything.
Finally, it’s worth noting that the statements and actions of an organization do not necessarily represent the views of the organization’s membership. Situations can and do arise where a representative group or committee decides issues differently than would be decided by the people it represents. For example, one might ask why Switzerland did not join the European Union. It seems that unlike other European nations, the issue of Swiss membership in the European Union was put to a vote of individual citizens. As opposed to a vote of the Swiss legislature. The kind of person who goes through the trouble of ending up on a legislature or executive committee is not your average person.
Objections / FAQ
6.1 How dare you — a non-scientist — challenge the authority of actual scientists?
Very easily. For one thing, it’s a lot easier to have the knowledege and thinking ability to see serious problems with a hypothesis than to be reasonably satisfied that the hypothesis is correct. The former requires you to break only one link in the chain.
Indeed, I suspect that a lot of these climate scientists probably have backgrounds in statistics and modeling which are not too different from mine. (I took numerous advanced classes in statistics and modeling as an undergraduate.)
In any event, you (the person reading this and objecting) are probably not a scientist either. Do you feel you are qualified to go against such scientists as Richard Lindzen and Bob Carter?
Ultimately, anybody’s arguments must stand or fall on their own merits – regardless of the person’s credentials. But in any event, if you believe (as a non-scientist) that non-scientists are not qualified to evaluate the CAGW hypothesis, then there is no need for you to debate the issue with me.
The next set of evidence frequently cited in support of CAGW is that some aspect of weather, climate, or whatever is unprecedented. Essentially, the argument is that if something significant happens with the weather which has not happened before, then that thing is likely to be a result of mankind’s activities.
However, on closer inspection, these “unprecedented” claims usually turn out to be either (1) false; (2) unsupported by the evidence; or (3) not based on a sufficiently long time period to mean much, if anything.
For example, it is often said that temperatures in the 1990s were the highest since instrumental temperature monitoring began in the 19th century. Assuming this is true, the time period in question does not seem long enough to mean much, since we have been coming out of the Little Ice Age.
Similarly, it has been claimed that the 1990s were the warmest decade in 1000 years. However, the evidentiary support for this is rather weak. Since temperatures were not measured with thermometers 1000 years ago, you have to guesstimate temperatures using “proxies,” like tree rings. How do we know that we are interpreting these proxies correctly? Or even that they can be used to measure past temperatures at all?
The obvious way to check these proxy methods is to use the same methods to estimate recent temperatures, and then to compare the results to intstrumental records. As far as I know, proxies have not done so well when checked this way. This is the so-called “divergence problem.” So any claim that current temperatures are unprecedentedly warm is at best a guess.
At the same time, there does exist evidence of warm temperatures in the past. The classic example of this is the VIking colonization of Greenland during the Medieval Warm Period. Of course, one can debate the significance of this and similar evidence. But the bottom line is that there is not a good case at this point that recent temperatures are unprecedentedly warm.
Objections / FAQ
5.1 Even if one proxy study is flawed, we can overcome this problem by averaging the results of many studies.
We cannot for the same reason as that set forth in 4.4.
5.2 Ok, so maybe recent temperatures are not unprecedentedly warm — what matters is the rate of change. Recent temperatures are rising at an unprecedentedly high rate.
Show me proof. And don’t compare proxy measures with instrumental measures. That’s not an apples-to-apples comparison. If proxy measures are understating the magnitude of recent temperature increases, it’s likely that they are understating the magnitude of past increases. Indeed, this would make sense as proxy measures – such as tree rings – are based on biological systems, subject to homeostasis.
5.3 Well, but what about X, which has never happened before?
By all means, show me proof. I would be fascinated to look at it.
The main “evidence” for the CAGW hypothesis is that there exist climate simulations which (1) assume great sensitivity to CO2; and (2) are consistent with past temperatures. The problem with this is that in the universe of possible climate simulations, there must exist “false simulations,” i.e. simulations which are consistent with temperatures of the past 50 to 100 years, but do so by coincidence and are not accurate climate simulations. Such “false simulations” can be expected to diverge from reality and cannot be relied upon to accurately predict the future.
One can see that this is true by observing that there are many climate simulations in use out there with very different assumptions about climate sensitivity, and yet they all track past temperatures pretty well. Clearly at least some of these simulations are “false simulations.”
The fact is that it’s very difficult to simulate complex systems accurately. This is true because different elements of the system interact in many different ways, building up uncertainty at each step and making it increasingly difficult to accurately simulate the system over longer amounts of time. The classic example of this is predicting weather, which is very difficult to do more than a week or so in advance. So the default assumption should be that a simulation is unreliable.
Ok, so how do you know whether you have a false simulation or a good simulation? The answer is very simple: You test it. You have the simulation make predictions. If most of those predictions come true, then you can start having some confidence in the simulation.
Unfortunately, the simulations which have been used to predict CAGW have not been tested in this way. Instead they are tested by seeing how well they compare to past data. But this is silly. It’s very easy to make “predictions” with the benefit of hindsight.
So it turns out that the CAGW hypothesis is mainly based on simulations which are unreliable and untested. This is weak evidence at best.
Objections / FAQ
4.1 But the Simulations used to Predict CAGW are Based on Real Physical Principles
This is a bit like saying that Sunny Delight is “made with real juice,” or that the boardgame Risk is based on real geographical principles. In other words, all of the simulations necessarily contain simplifying assumptions which can lead to incorrect results. Even if the simulations are also based — in part — on undisputed physical laws.
The “physical principles” argument essentially implies that we can trust the simulations because their results follow more or less inexorably from basic first principles. But if this were true, then all of the simulations would give the same results, which they certainly do not. Again, the simulations also contain simplifying assumptions.
4.2 But Some Simulations Have Been Tested – Just Look at Hansen’s Simulation from the 80s
Sure, and Hansen’s simulation has been diverging from reality for some time now. Current temperatures are closest to Hansen’s scenario which assumed that CO2 levels would stabilize, which they have not. Of course it is possible that this result is not statistically significant. But in that case, it only shows that Hansen’s simulation has not been put to the test — if the results really are not significant.
4.3 But Skeptics Have Not Done any Better at Predicting Temperatures
That may very well be true, but this argument is attacking a bit of a strawman. I’m not claiming that I can accurately predict future temperatures — I’m claiming that Jim Hansen cannot. At least not as well as he thinks he can. Put another way, it’s not necessary for me to accurately predict the future to win this debate.
4.4 Perhaps Any One Simulation is Unreliable, But We Can Get Reliable Results by Averaging the Results of Many Simulations
This argument rests on the mistaken assumption that errors and mistakes will always cancel out. The assumption is incorrect because there may be “systematic error.” If most or all of the simulations miss the mark in a similar direction, then the average will be off. And there is no way to know that there does not exist such systematic error. The great Richard Feynman touched on this issue with the following parable:
Nobody was permitted to see the Emperor of China, and the question was, What is the length of the Emperor of China’s nose? To find out, you go all over the country asking people what they think the length of the Emperor of China’s nose is, and you average it. And that would be very “accurate” because you averaged so many people. But it’s no way to find anything out; when you have a very wide range of people who contribute without looking carefully at it, you don’t improve your knowledge of the situation by averaging.
The fact is that there are a lot of aspects of the climate which are still not understood very well by anyone. So averaging different simulations is a lot like the situation in Feynman’s parable.
4.5 Perhaps It’s Impossible to Simulate and Predict Weather over Periods of a Few Years, But It Is Possible over Long Time Periods Like 50 – 100 Years. It is Like the Difference Between Predicting the Percentage of Heads in 10 Coin Flips Versus Predicting the Percentage of Heads in 10,000 Coin Flips
It’s also possible that weather is chaotic over short AND long time periods. There is no a priori reason to believe that weather “evens out” over periods from 50 to 100 years. Indeed, during the Little Ice Age, temperatures were quite cold for well over 100 years. Could anyone have predicted the Little Ice Age beforehand? If the Little Ice Age was predictable, then what caused it? If you cannot answer that question, you must consider the possibility that weather is chaotic and unpredictable even over periods of 50 – 100 years.
At a minimum, nobody has shown me proof, or even compelling evidence, that climate is predictable on 50 – 100 year time periods. So it seems to me that if a butterfly in China can cause a storm in New York, it’s also possible that the same butterfly could cause a little ice age.