I am just a simple emergency physician, but I know when I’m dealing with people who haven’t a clue.
The science is settled, we just don’t understand all the elements of the settled science and can’t model them too well? When something goes wrong with the predictions the warmists seem to be able to come up with something that they didn’t mention before, like aerosols.
For all the high tech computational systems, data collection, communication and billions of bucks they don’t seem to have gotten much farther than Arrhenius’ estimates.
The graph, which everyone who follows this blog should be familiar with by now, clearly illustrates the absurdity of the IPCC’s subjective (pronounced http://www.howjsay.com/index.php?word=fraudulent) assessment of the certainty of AGW.
A model is supposed to include and recognize variables.
Every time the warmist models don’t work out, they come up with an explanation (really, more of a complaint) such as too much rain, too little rain, a stronger La Nina than expected, etc. Shouldn’t the models already include all the variables that nature throws at us – since the models claim to predict what nature will do in the future (get warmer).
Basically, the warming modelers claims can be reduced to this absurd way of thinking: ” Our predictions and models are all correct – it’s nature that’s wrong.”
Have faith! The fever will happen!
Maybe a prayer to the goddess Ixchel, goddess of reason.
Ilya Zaliapin, Michael Ghil: Another Look at Climate Sensitivity, Nonlin. Proc. Geophys., 17, 113-122, 2010. (http://arxiv.org/abs/1003.0253 , full paper is dowloadable in pdf and post script formats for free, just look to the right-hand side.)
This paper indicates that the models they’re they’ve been using can’t get it right, because their fundamental assumptions regarding the calculations themselves are wrong.
From the abstract: “We revisit a recent claim that the Earth’s climate system is characterized by sensitive dependence to parameters; in particular, that the system exhibits an asymmetric, large-amplitude response to normally distributed feedback forcing. Such a response would imply irreducible uncertainty in climate change predictions and thus have notable implications for climate science and climate-related policy making. We show that equilibrium climate sensitivity in all generality does not support such an intrinsic indeterminacy; the latter appears only in essentially linear systems. The main flaw in the analysis that led to this claim is inappropriate linearization of an intrinsically nonlinear model; there is no room for physical interpretations or policy conclusions based on this mathematical error.” (emphasis mine)
For those of you who have a minute and aren’t scared of some math, take the time to read this thing. Some of the implications are eye-opening, including that if they HAD been using a non-linear standard for their calculations, they might have actually been able to get some pretty good results out of their models, even without perfectly accurate data.
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