A few weeks ago, we were having one of many conversations on this blog about the subject of climate change. In the comments, I said the following
The climate is clearly changing. There is nothing unusual about this. The climate is always changing. I’m happy to concede that the trend in recent decades has been to hotter temperatures. Again, nothing unprecedented about that. The world has hot periods and cold periods. The trend seems to have slowed or reversed over the last few years. This is not a short enough period of time to prove anything, but it does make you wonder how strong the trend is. Some of the data analysis that purports to show the trend has been presented in ways that deliberately or otherwise state the data in such ways that appear to indicate the trend is stronger than it is, and/or choose starting points and data series lengths that appear to show the trend as more abnormal than it is, in my opinion.
Again, with the impact of human activity, I am happy to concede an impact exists. There is a lot of human activity – it must have some impact on the climate. Whether it is a significant impact is another question.
Having those two thoughts, you look for a correlation, and find one between CO2 in the atmosphere and average temperature. One can be found, although it is not clear whether it is a causal relationship (CO2 levels vary historically before significant human activity existed, and a lot of the time CO2 increases seem to trail temperature changes rather than the other way round).
So how much are higher temperatures caused by higher CO2 levels, and how much of the increased CO2 level caused by human activity? The answer to the last question is clearly “quite a lot”, but that is not an answer to the question “How much?” Is it “70%? 90%? 100%? 120%? To be able to come up with a meaningful model, we have to have a good numerical answer, and we don’t remotely.
As to what impact increased CO2 levels have on average temperatures, there is much greater uncertainty. Basically you have to enter a fudge factor into your model, see how well it models the past, and hope you can then model the future successfully. A few people have created models that can just about model the past, but that doesn’t mean you have the mechanism right – it just means you have found a mathematical function that fits the points on your curve.
As it is, we have a few extremely crude mathematical / computer models that suppose mechanisms that go from human activity to CO2 release to global warming. They don’t agree with one another, and they are incredibly crude. (The Earth’s atmosphere is an extremely complex system. These models only have a tiny fraction of its complexity). They have a poor record of predicting the future.
The science of global warming ultimately boils down to saying that “The level of warming is unprecedented”. “Human releases of CO2 into the atmosphere are unprecedented”. “Therefore, the second causes the first”. This isn’t an inherently ridiculous thing to say. If climate change really is unprecedented then we would look for other unprecedented things as likely causes and human activity would be the likely one. We could then look for mechanisms and solutions, but we would largely be doing so with our eyes closed.
I will listen to somebody who more or less says this and that the risks of global warming are so great that we must do something about them, but somebody who simply states that the science is settled and beyond discussion is frankly not even worth arguing with.
In response, I received a mocking reply from a true believer, saying more or less that if I knew so much about it, why didn’t I publish papers in a refereed journal myself, and he was sure that a Nobel Prize would be beckoning. There was no attempt to address anything I said – merely an observation that what I was saying did not have the approval of the clique controlling the argument.
In a way this was odd, because I was not actually claiming to know anything about the workings of the climate: only about the likely limitations of the methodology of climate scientists.
As it happens, once, in another life, I was a research scientist. At least, I did a Ph.D. in a field not a million miles away from climate science before departing for other parts of the economy. I have written one or two computer models of physical systems. And as it happens, they are hard. It is possible to use all the computational power you have in simply modelling something tiny: the vortices around the tip of an aircraft wing, say. As the systems you have become larger and larger you make more and more approximations and more and more assumptions that particular terms in equations are small and will be small in the future because they have been small in the past. There comes a point where models more from theoretical to empirical. You end up basically extrapolating from the recent past to the near future. In systems containing a lot of nonlinearity, factors that have not had macroscopic impacts in the past can suddenly flare up and become dominant in the future. Sometimes it is possible to figure out just when this will happen and add these effects to your models at the right time. Sometimes it isn’t. The earth and its climate is a huge system. In places it is highly nonlinear. It is horribly hard to model.
None of this is to say that well constructed models of such systems cannot be useful. However, they are inevitably uncertain. They are inevitably approximate. Everyone who has ever worked with one knows this.
A second thing one learns from working as a research scientist is that people in research labs resemble people in other workplaces. Petty fiefdoms exist. People stab one another in the back. Some people do better work than others. People will have different levels of respect for the work of other researchers. Some people rise to the top through doing good work. Others rise to the top through playing good politics (good researchers generally hate such people, but they none the less manage it). People at opposite ends of the corridor hate one another. If one is going to work in a particular team, one must work within the culture and beliefs of that team. In one’s work, you often have to start with whatever the person before you left behind.
In scientific research involving computer modelling and data analysis, this often leads to computer models consisting of layer on layer of code crufted on top of lower layers that are not well (or at all) understood. Data does get lost, or assumed to be correct because the previous person used it and there is no real way to verify it. Supposedly impartial journals do become captive of a particular point of view. People’s whole careers do become dependent on a particular interpretation of the results, and it then becomes very hard for them to back down. People become more and more certain of their results when the personal cost of abandoning them gets greater and greater.
However, once again as in any other workplace, good work still happens amongst all this. If there are six different cliques in different places, they will compete with one another until the truth comes out. If there are six different journals, then they won’t all become captive to the same clique, and eventually the one with the best and most meaningful results will become the most prestigious. There will be enough ability to move between teams that younger scientists will not necessarily be caught in a particular viewpoint because of who they work with. Politics will be horrible. Much bad work will be done. It will be a messy process, but it will generally be understood who does the best work, and the truth will come out. Really good researchers will be able to figure out what in the crufty codebase is good and what is not, and get meaningful results anyway.
This, fundamentally, has been my problem with the science of global warming – the denial of the messiness of it all. We have been told that “The Science is Settled” by men in white coats in ivory towers, and that we are “denialists” and unworthy of being listened to, if we dare to question the process or to state the obvious – that science is a messy and uncertain process and that as a consequence of being a very hard problem, modelling the climate is going to give answers with huge margins of error and huge unpredictability. (Nicholas Naseem Taleb would say it’s a system highly susceptible to Black Swans, and he would be right).
Which was why, when I was cc’ed on an e-mail last Thursday stating that there was a huge leak of data from CRU at the University of East Anglia, I pretty much knew what it contained and I haven’t been remotely surprised by anything we have learned. There is lots of politics, lots of bad work, lots of crufty code, and lots of uncertainty and disagreement.
The scandal here, is the pretence that this was ever not so. The careerist political side of this unit, mixed in with an unholy political alliance of Greens, Luddites, politicians with hidden agendas or at least vested interests in Climate Change being real, managed to create an environment in which the normal competition and disagreement between teams of scientists has not been allowed to take place. To even suggest that climate scientists behave like other scientists and to ask them to fully explain their work, has been to be opposed to their noble efforts to save the planet. “The Science is Settled” means that this is not necessary.
We have learned little in the last week that we should not have been already aware of, but perhaps now that it is out in the open, we can have a proper debate. This should hopefully be a relief, and if we are going to discuss policy for the whole world, it is required to be entirely out in the open.
Almost certainly, there are good researchers and good work being done in climate science, even at
CRU. Hopefully we now have the opportunity to identify it amongst all the cruft. Once it is out in the open that the science is not settled, we can do some good science. Good science in this field gives results with large margins of error. Remember that. If the researchers will not admit this right up front, then they are probably not worth listening to.
There is of course a second global warming related assumption that many of us have been called names for questioning. This is that a Kyoto Protocol type solution is the appropriate response to it. Bjorn Lomborg has bravely attacked this line, but much more time needs to be spent on properly quantifying the benefits of warmer temperatures as well as the negatives, and the economic costs of fighting climate change by slowing the speed of our technological civilization. Technical fixes down the line (when and if necessary) may well be a better strategy. This is particularly so if we acknowledge that we are very uncertain as to what is happening. And we need to say this, over and over.