Weakness of AGW Theory- Part 6-A Legal Takedown


The University of Pennsylvania Law School has published Research Paper no. 10-08  titled  “Global Warming Advocacy Science: A Cross Examination” written by Jason Scott Johnston. His technique is a novel way of getting at the truth.  Johnson approaches the question of the validity of Anthropogenic Global Warming (AGW) as if it were in a court of law. While you may know a lot about this topic, my guess is that if you read it you will learn some new things.  I am familiar with the skeptic’s arguments but some of the AGW believer’s arguments were new to me.  Johnston takes a look at the arguments and treats them as if he were cross examining the two sides.

He does a nice job of exposing the weakness of “positive feedback” that is the basis for the computer projections of calamitous happenings if CO2 emissions are not checked. Further that computer projections of future climate are not science.  He highlights the rhetoric used by the alarmists that gets headlines and muddies the waters.

Johnston’s concludes his examination with these thoughts:  (ghg=green house gas)

Even if the reader is at this point persuaded to believe that there remain very important open questions about ghg emissions and global warming, and important areas of disagreement among climate scientists, she may well ask: So what? After all, such a reader might argue, CO2 is a ghg, and if we continue to increase CO2, then it seems clear that despite whatever uncertainty there may be about how much temperatures will increase as a consequence of increasing CO2 in the atmosphere, and about the impacts of such rising temperatures, there is no doubt that temperatures will increase with increasing CO2, and that at some point, such rising temperatures will cause harm, so that one way or another, at one time or another, we simply have to reduce our emissions of CO2.

However beguiling, such an argument not only oversimplifies the policy questions raised by human ghg emissions, it is also misunderstands the significance of the scientific questions revealed by my cross examination for the predictability of anthroprogenically-forced climate change. Consider first the scientific questions. If climate were a simple linear system – with increases in atmospheric CO2 directly and simply determining future warming – then while a detailed understanding of the earth’s climate system might still of scientific interest, there would be little policy justification for expending large amounts of public money to gain such an understanding. But if one thing is clear in climate science it is that the earth’s climate system is not linear, but is instead a highly complex, non-linear system made up of sub-systems – such as the ENSO, and the North Atlantic Oscillation, and the various circulating systems of the oceans – that are themselves highly non-linear. Among other things, such non-linearity means that it may be extremely difficult to separately identify the impact of an external shock to the system – such as what climate scientists call anthropogenic CO2 forcing – from changes that are simply due to natural cycles, or due to other external natural and anthropogenic forces, such as solar variation and human land use changes. Perhaps even more importantly, any given forcing may have impacts that are much larger – in the case of positive feedbacks – or much smaller – in the case of negative feedbacks – than a simple, linear vision of the climate system would suggest. Because of the system’s complexity and non-linearity, without a quite detailed understanding of the system, scientists cannot provide useful guidance regarding the impact on climate of increases in atmospheric ghg concentration.

As a large number of climate scientists have stressed, such an understanding will come about only if theoretical and model-driven predictions are tested against actual observational evidence. This is just to say that to really provide policymakers with the kind of information they need, climate scientists ought to follow the scientific method of developing theories and then testing those theories against the best available evidence. It is here that the cross examination conducted above yields its most valuable lesson, for it reveals what seem to be systematic patterns and practices that diverge from, and problems that impede, the application of basic scientific methods in establishment climate science. Among the most surprising and yet standard practices is a tendency in establishment climate science to simply ignore published studies that develop and/or present evidence tending to disconfirm various predictions or assumptions of the establishment view that increases in CO2 explain virtually all recent climate change.

Perhaps even more troubling, when establishment climate scientists do respond to studies supporting alternative hypotheses to the CO2 primacy view, they more often than not rely upon completely different observational datasets which they say confirm (or at least don’t disconfirm) climate model predictions. The point is important and worth further elucidation: while there are quite a large number of published papers reporting evidence that seems to disconfirm one or another climate model prediction, there is virtually no instance in which establishment climate scientists have taken such disconfirming evidence as an indication that the climate models may simply be wrong. Rather, in every important case, the establishment response is to question the reliability of the disconfirming evidence and then to find other evidence that is consistent with model predictions. Of course, the same point may be made of climate scientists who present the disconfirming studies: they tend to rely upon different datasets than do establishment climate scientists. From either point of view, there seems to be a real problem for climate science: With many crucial, testable predications – as for example the model prediction of differential tropical tropospheric versus surface warming – there is no indication that climate scientists are converging toward the use of standard observational datasets that they agree to be valid and reliable. Without such convergence, the predictions of climate models (and climate change theories more generally) cannot be subject to empirical testing, for it will always be possible for one side in any dispute to use one observational dataset and the other side to use some other observational dataset. Hence perhaps the central policy implication of the cross-examination conducted above is a very concrete and yet perhaps surprising one: public funding for climate science should be concentrated on the development of better, standardized observational datasets that achieve close to universal acceptance as valid and reliable. We should not be using public money to pay for faster and faster computers so that increasingly fine-grained climate models can be subjected to ever larger numbers of simulations until we have got the data to test whether the predictions of existing models are confirmed (or not disconfirmed) by the evidence.

This might seem like a more or less obvious policy recommendation, but if it were taken, it would represent not only a change in climate science funding practices, but also a reaffirmation of the role of basic scientific methodology in guiding publicly funded climate science. As things now stand, the advocates representing the establishment climate science story broadcast (usually with color diagrams) the predictions of climate models as if they were the results of experiments – actual evidence. Alongside these multi-colored multi-century model-simulated time series come stories, anecdotes, and photos – such as the iconic stranded polar bear — dramatically illustrating climate change today. On this rhetorical strategy, the models are to be taken on faith, and the stories and photos as evidence of the models’ truth. Policy carrying potential costs in the trillions of dollars ought not to be based on stories and photos confirming faith in models, but rather on precise and replicable testing of the models’ predictions against solid observational data.

This is a long paper,  some  80 pages, but I suggest that you read the entire document which you can do by clicking here.

Cbdakota

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