Mathematical models are used – and used successfully – everyday in Engineering, Science, Medicine and Business. Their usefulness is determined – and some are extremely useful – by knowing their limitations and acknowledging that they only represent an approximation of real complex systems. Actual measurements always override the model results and whenever reality does not agree with model predictions it is usually mandatory to adjust the model. Where the adjustments can only be made by using “fudge factors” it is usually necessary to revisit the simplifying assumptions used to formulate the model in the first place.
But this is not how Climate Modelling Works. Reality or actual measurements are not allowed to disturb the model or its results for the far future. Fudge factors galore are introduced to patch over the differences when they appear. The adjustments to the model are just sufficient to cover the observed difference to reality but such that the long-term “result” is maintained.
The assumption that carbon dioxide has a significant role to play in global warming is itself hypothetical. Climate models start with that as an assumption. They don’t address whether there is a link between the two. Some level of warming is assumed to be the consequence of a doubling the carbon dioxide concentration in the atmosphere. For the last 17 years global temperature has stood still while carbon dioxide concentration has increased dramatically. There is actually more evidence to hypothesise that there is no link (or a very weak link) between carbon dioxide and global warming than that there is. Nevertheless all climate models start with the built-in assumption that the link exists. And then use the results of the model as proof that the link exists! They are not just cyclical arguments – they are incestuous – or do I mean cannibalistic.
It is bad enough that economic models, developed to count the cost of carbon dioxide are first based on some hypothetical magnitude of the link between carbon dioxide emission and global warming as their starting point. But it gets worse. These “integrated assessment” models then themselves are strewn with new assumptions and further cyclical logic as to how the costs ensue.
A new paper by Prof. Robert Pindyck for the National Bureau of Economic Research takes a less than admiring look at the Integrated Assessment Climate models and their uselessness.
Robert S. Pindyck, Climate Change Policy: What Do the Models Tell Us?, NBER Working Paper No. 19244
Issued in July 2013
(A pdf of the full paper is here: Climate-Change-Policy-What-Do-the-Models-Tell-Us)
Abstract: Very little. A plethora of integrated assessment models (IAMs) have been constructed and used to estimate the social cost of carbon (SCC) and evaluate alternative abatement policies. These models have crucial flaws that make them close to useless as tools for policy analysis: certain inputs (e.g. the discount rate) are arbitrary, but have huge effects on the SCC estimates the models produce; the models’ descriptions of the impact of climate change are completely ad hoc, with no theoretical or empirical foundation; and the models can tell us nothing about the most important driver of the SCC, the possibility of a catastrophic climate outcome. IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.
Even though his assumptions about “climate sensitivity” are somewhat optimistic, he is more concerned with the assumptions made to try and develop the “damage” function to enable the cost to be estimated:
When assessing climate sensitivity, we at least have scientific results to rely on, and can argue coherently about the probability distribution that is most consistent with those results. When it comes to the damage function, however, we know almost nothing, so developers of IAMs [Integrated Assessment Models] can do little more than make up functional forms and corresponding parameter values. And that is pretty much what they have done. …..
But remember that neither of these loss functions is based on any economic (or other) theory. Nor are the loss functions that appear in other IAMs. They are just arbitrary functions, made up to describe how GDP goes down when T goes up.
…. Theory can’t help us, Nor is data available that could be used to estimate or even roughly calibrate the parameters. As a result, the choice of values for these parameters is essentially guesswork. The usual approach is to select values such that L(T ) for T in the range of 2◦C to 4◦C is consistent with common wisdom regarding the damages that are likely to occur for small to moderate increases in temperature.
…… For example, Nordhaus (2008) points out (page 51) that the 2007 IPCC report states that “global mean losses could be 1–5% GDP for 4◦C of warming.” But where did the IPCC get those numbers? From its own survey of several IAMs. Yes, it’s a bit circular.
The bottom line here is that the damage functions used in most IAMs are completely made up, with no theoretical or empirical foundation. That might not matter much if we are looking at temperature increases of 2 or 3◦C, because there is a rough consensus (perhaps completely wrong) that damages will be small at those levels of warming. The problem is that these damage functions tell us nothing about what to expect if temperature increases are larger, e.g., 5◦C or more.19 Putting T = 5 or T = 7 into eqn. (3) or (4) is a completely meaningless exercise. And yet that is exactly what is being done when IAMs are used to analyze climate policy.
And he concludes:
I have argued that IAMs are of little or no value for evaluating alternative climate change policies and estimating the SCC. On the contrary, an IAM-based analysis suggests a level of knowledge and precision that is nonexistent, and allows the modeler to obtain almost any desired result because key inputs can be chosen arbitrarily.
As I have explained, the physical mechanisms that determine climate sensitivity involve crucial feedback loops, and the parameter values that determine the strength of those feedback loops are largely unknown. When it comes to the impact of climate change, we know even less. IAM damage functions are completely made up, with no theoretical or empirical foundation. They simply reflect common beliefs (which might be wrong) regarding the impact of 2◦C or 3◦C of warming, and can tell us nothing about what might happen if the temperature increases by 5◦C or more. And yet those damage functions are taken seriously when IAMs are used to analyze climate policy. Finally, IAMs tell us nothing about the likelihood and nature of catastrophic outcomes, but it is just such outcomes that matter most for climate change policy. Probably the best we can do at this point is come up with plausible estimates for probabilities and possible impacts of catastrophic outcomes. Doing otherwise is to delude ourselves.
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