You have the right general idea about the fit not being adequate. I suspect that their model is far more complex than a simple linear model and of much higher order. The net prediction of future temperatures is a result of how all of these terms combine and it will diverge more and more rapidly from the fitting base data as time progresses. The higher order effects contain the more rapidly changing processes.
Cyclic behavior can be modeled by a series. A good example of this is demonstrated by the infinite series that can be used to construct a sine wave. For small time periods the linear term does a pretty good job of matching the curve. As you move forward in time, the other, higher order terms, become the most significant ones which then allows the overall function to go through its cyclic behavior. The appearance of the temperature pause and the description that it might well last until 2025 and is cyclic strongly suggests that the underlying phenomena responsible for this behavior has been in effect during the rapid temperature rise and could be one of the reasons for the high slope seen. If so, the very dominate earlier seen hockey stick temperature rise has overstated the true underlying increase rate. As corrections are included to the models we may find that mans contributions are overwhelmed by natural effects and that is why I feel that caution is in order. Had there been no long term unexpected pause we may have continued to give unwarranted confidence to the models and their expert constructors. Some day I believe that we will be capable of making predictions about climate change that match the real world, but that day has not arrived. Of course even then the world throws curve balls our way in the form of volcanoes, changing solar activity, and etc. which makes extremely long term predictions a guess at best. Dave -----Original Message----- From: Eric Walker <eric.wal...@gmail.com> To: vortex-l <vortex-l@eskimo.com> Sent: Mon, Aug 25, 2014 2:54 am Subject: Re: [Vo]:global warming? On Sun, Aug 24, 2014 at 6:38 PM, David Roberson <dlrober...@aol.com> wrote: You also probably realize that a polynomial fit to a high power order yields coefficients that vary depending upon the order of the polynomial chosen. Many combinations of coefficients will fit the input/output data over a restricted range. The problem shows up once you use those different coefficients to project the curve forwards into unknown future points. We are now clearly in witness to an example of the type of problem that I am speaking of. ... I think the bad fit to the data you identify could just as likely be an underfit than an overfit; i.e., they have adequately modeled the first-order phenomenon (an increase in temperature) but failed to take into account one or more second-order cyclical trends. Eric
