The study points to higher, not lower, sensitivity (of the climate to a doubling of CO2).
This contrasts with our in-class efforts to understand (and replicate) the empirical results of Lean and Rind and Lean and Kopp, which leaned towards lower sensitivity, and with other recent empirical studies.
But then you have to explain the polar amplification, which we couldn't.
So, and this remains speculative, but important, we most likely have some kind of feedback in place related to the polar amplification. As a result, as I said over and over, we take the L & R results with a pinch of salt and apply them only to the very near future, if at all. And we monitor the heck out of the arctic. And we reduce emissions as fast as we can.
This L & R/L &K sensitivity disparity when compared with studies is only slightly disappointing to me. After all, what did we expect for an climate experiment that is so simple to do we can replicate it in class? At least, by now, students should understand the key variables quite well, having learned to manipulate them and predict outcomes themselves, however crudely. I believe that this result is empowering, especially for math-phobic undergraduate students, and that the process works well as the kind of affective pedagogy needed to overcome such phobia.
If all my students were Calculus III whiz-kids, I'd need a different project and a different pedagogy. I'd probably invest our time in EdGCM instead, and have us run small-scale Stella ® models on the side.
I'm going to post our L & R/L & K model results graph again, just to remind you of what you did while you're studying for the exam, and just because it's such a cool graph.
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