William Briggs, professor i statistik, M.S., Atmospheric Science, B.S., Summa Cum Laude, Meteorology and Math, kommer här med en intressant analys ”on quantifying the uncertainty of effects due to global warming” som han kommer att framföra vid en konferens i Spanien den 2-3 april (CRITICAL ASSESSMENT OF CLIMATE CHANGE PREDICTIONS FROM A SCIENTIFIC PERSPECTIVE http://www.fundacionareces.es/cambio_climatico_2008.htm). Konferens anordnas bl.a. i samarbete med The Royal Academy of Sciences of Spain.
Analysen är väldigt teknisk/matematisk och är en genomgång av den statistiska osäkerheten av effekterna av Global Warming.
Abstract och sammanfattning finns nedan
Analysen finns här:
”A month does not go by without some new study appearing in a peer-reviewed journal which purports to demonstrate some ill effect that will be caused by global warming. The effects are conditional on global warming being true, which is itself not certain, and which must be categorized and bounded. Evidence for global warming is in two parts: observations and explanations of those observations, both of which must be faithful, accurate, and useful in predicting new observations. To be such, the observations have to be of the right kind, the locations and timing where and when they were taken should be ideal, and the measurement error should be negligible. The physics of our explanations, both of motion and e.g. heat, must be accurate, the algorithms used to solve and approximate the physics inside software must be good, chaos on the time scale of predictions must be unimportant, and there must be no experimenter effect. None of these categories is certain. As an exercise, bounds are estimated for their certainty and for the unconditional certainty in ill effects. Doing so shows that we are more certain than we should be.”
”It is important to examine these kinds of findings because global warming
is not important by itself: it becomes significant only when its effects are consequential to humans. The distinction between questions like \Will it warm?” and \What will happen if it warms” is under-appreciated or conated. For example, when asking how likely are the results of Bi and Parton’s study, we are apt to confuse the likelihood of global warming as a phenomenon with \more kidney disease etc.” happening because of global warming. When of course the two kinds of questions and likelihoods are entirely separate.
Because of the frequency of confusion, I want to follow the path to Bi and Patron’s conclusions starting from first principles, and untangle and carefully focus on the chain of causation leading up their central claims, and to quantify the uncertainty of the steps along the way. In doing this, I will point out how it is easy to muddle what is being claimed and the uncertainties in those claims.”
My conclusions (which will make more sense, obviously, after you have read the paper) are:
”Attempting to quantify, to the level of precision given, the uncertainties in effects caused by global warming, particularly through the use of mathematical equations that imply a level of certainty which is not felt, can lead to charges that I have done nothing more than build an AGW version of the infamous Drake equation (Drake and Sobel 1992). I would not dispute that argument. I will claim that the estimates I arrived at are at least within an order of magnitude of the actual uncertainties. For example, the probability that AGW is true might not be 0.8, but it is certainly higher than 0.08.
The equations given, then, are not meant to be authoritative or complete. Their purpose is to concentrate attention of what exactly is being asked. It is too easy to conflate questions of what will happen if AGW is true with questions of is AGW true. And it is just as simple to confuse questions of the veracity and accuracy of observations and with the accuracy of the models or their components. People who work on a particular component are often aware of its boundaries and restrictions, and so are more willing to reduce the probability that this component is an adequate description of the physical world, but they are usually likely to assume that the areas on which they do not have daily familiarity are more certain than they are. Ideally, experts in each of the areas I have listed should supply a measure of uncertainty for that area alone. I would welcome a debate and discussion on this topic.
I also would not make the claim that I have accurately listed all the avenues where uncertainty arises (for example, I did not even touch on the uncertainty inherent in classical statistical models). But the ones I did list are relevant, though not necessarily of equal importance. We do have uncertainty in the observations we make and we do have uncertainty in the models of these observations. At the very least, we know empirically that we cannot predict the future perfectly. Further, the claims made about global warming’s effects are also uncertain. Taken together, then, it is indisputable that we are less certain that both global warming and its claimed effects are true than in either AGW or its effects alone.”
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