Comments on: CRU graph yet again (with R) http://www.win-vector.com/blog/2009/12/cru-graph-yet-again-with-r/?utm_source=rss&utm_medium=rss&utm_campaign=cru-graph-yet-again-with-r The Applied Theorist's Point of View Fri, 27 Aug 2010 05:29:19 +0000 hourly 1 http://wordpress.org/?v=3.0.1 By: Kevin Gaab http://www.win-vector.com/blog/2009/12/cru-graph-yet-again-with-r/comment-page-1/#comment-1663 Kevin Gaab Tue, 15 Dec 2009 01:45:35 +0000 http://www.win-vector.com/blog/?p=1195#comment-1663 A related blog post you might be interested: http://junkcharts.typepad.com/junk_charts/2009/12/the-real-climategate.html There was an interesting article in Science a while back that discussed why the three sigma variance in climate models will probably always be large. I'll have to find it. The argument, if I correctly understood it, was essentially that the propagation of uncertainties in all the component variables (and there are a lot of variables in these models, and they interact in complex, nonlinear ways) is such that the predicted uncertainties in temperature will ALWAYS be of the same magnitude as the mean change in temperature we're trying to model. It wasn't very hopeful that climate model accuracy will ever improve. A related blog post you might be interested:

http://junkcharts.typepad.com/junk_charts/2009/12/the-real-climategate.html

There was an interesting article in Science a while back that discussed why the three sigma variance in climate models will probably always be large. I’ll have to find it. The argument, if I correctly understood it, was essentially that the propagation of uncertainties in all the component variables (and there are a lot of variables in these models, and they interact in complex, nonlinear ways) is such that the predicted uncertainties in temperature will ALWAYS be of the same magnitude as the mean change in temperature we’re trying to model. It wasn’t very hopeful that climate model accuracy will ever improve.

]]> By: Nina Zumel http://www.win-vector.com/blog/2009/12/cru-graph-yet-again-with-r/comment-page-1/#comment-1657 Nina Zumel Mon, 14 Dec 2009 08:02:21 +0000 http://www.win-vector.com/blog/?p=1195#comment-1657 <a href="#comment-1656" rel="nofollow">@jmount </a> : It isn't discussed explicitly in the article above (or in its follow-up article either), but the significance of a regression coefficient is taken against the null hypothesis that the coefficient is zero. In other words, PC5 and PC10 are the only coefficients that are really bounded away from zero in this analysis (well, PC4, PC7, and PC8, too, if you feel generous). That means that most coefficients -- especially the ones that actually matter, the first 3 -- have uncertainty that is of the same magnitude as their estimated value. It's just not a very good model. @jmount : It isn’t discussed explicitly in the article above (or in its follow-up article either), but the significance of a regression coefficient is taken against the null hypothesis that the coefficient is zero. In other words, PC5 and PC10 are the only coefficients that are really bounded away from zero in this analysis (well, PC4, PC7, and PC8, too, if you feel generous).

That means that most coefficients — especially the ones that actually matter, the first 3 — have uncertainty that is of the same magnitude as their estimated value. It’s just not a very good model.

]]> By: jmount http://www.win-vector.com/blog/2009/12/cru-graph-yet-again-with-r/comment-page-1/#comment-1656 jmount Sun, 13 Dec 2009 19:56:56 +0000 http://www.win-vector.com/blog/?p=1195#comment-1656 By the way- the tiny principle components elements tricking the modeling system into thinking they are significant (when they are in fact unimportant noise, just not independent noise as expected by significance calculations) is great example of some of the issues discussed in: http://www.win-vector.com/blog/2009/12/statistics-to-english-translation-part-2a-’significant’-doesn’t-always-mean-’important’/ By the way- the tiny principle components elements tricking the modeling system into thinking they are significant (when they are in fact unimportant noise, just not independent noise as expected by significance calculations) is great example of some of the issues discussed in: http://www.win-vector.com/blog/2009/12/statistics-to-english-translation-part-2a-’significant’-doesn’t-always-mean-’important’/

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