http://initforthegold.blogspot.com/2007/07/b-schools-vs-scientists.html

It is perfectly possible to find people who will place substantial bets about things climate models actually say.

If people would like to make bets that the IPCC position is overstated then they will find takers, but only if they bet against things that the IPCC consensus position actually states.

Mr. Killian’s argument, however, is also wrong.

While what we do in the next few years will greatly affect whether the final outcome is disastrous, they will have little effect over the next ten years climate trajectory. For good or ill, it has taken a century to get on this path and it will take decades of effort to get off it.

Whether or not we adopt a serious climate change policy, the science unfortunately indicates that it will have little impact on the climate for some considerable time.

$10,000 might sound like a lot of money to the working schmo but on the scale of the bet is not much.

The real stunt by this huckster of a professor is that Gore loses either way.

If the efforts to abate global warming fail then Gore wins. But the scale is such that if nothing is done within a decade it will be too late. So we lose the world while Gore wins a trivial bet.

If the efforts succeed then Gore loses the bet.

This huckster wants Gore to bet that he will fail. Gore would have to want to fail to accept the bet!

Legitimate challenge? No way!

http://julesandjames.blogspot.com/2007/06/more-on-20000-bet.html

He write (my excerpt)

“, he is using the most obvious and trivial trick, that he appeared to have ruled out with his talk of forecasting climate change on this page. In fact, the terms of his challenge refer to forecasting annual mean temperatures at a handful of points, using raw model output. The trivial trick here is that of course the models do not directly represent local temperature (typical resolution is ~300km horizontally) and they also have significant regional biases, so meaningfully relating their output to local temperature requires at a minimum some sort of bias correction and/or downscaling. Such bias adjustment is an entirely routine procedure in many branches of forecasting, it is inconceivable that Armstrong does not realise this.

The other big problem is the time scale: the bet is for 1-10 year forecasts. While there are probably some people who can produce usable forecasts over at least the seasonal to annual time scale (and maybe further in some cases), on the whole these aren’t the same people as those doing 100 year projections. The GCMs used in the IPCC report don’t have any proper initialisation scheme that would enable them to make meaningful annual forecasts, and no-one has ever claimed that they do. From their point of view, whether one year is warmer than the last is basically a matter of chance, and a “persistence” forecast is a pretty reasonable reasonable choice.

A much fairer test of the models would be to look at something like a 20 year trend for global mean temperature (and possibly at a more regional scale: I haven’t looked in detail at this). Armstrong claims to be amenable to altering his terms: I’ve emailed him with these points and will report on his response. Based on what I have read, I’m not optimistic. It reads like a cheap publicity stunt rather than serious challenge.”

How will you respond?

The professor seems to be confusing weather and climate. Nobody claims to be able to make ten year weather forecasts, anywhere.

He’s challenging whether any “global climate model” can make pinpoint ten year weather predictions. What part of “global” isn’t understood here?

He’s asking people to focus (for ten years) on only ten instruments, throwing out virtually all the information available, asking to bet only a tiny subset of the available information.

A very small signal will be lost in the annual variability. This is well known in climate history; see note 33 here: http://www.aip.org/history/climate/20ctrend.htm#N_33_

Only with a very large collection of data can a small signal be reliably detected in a noisy environment, as mentioned here for example:
http://tamino.wordpress.com/2007/07/05/the-power-of-large-numbers/

As marketing this is very clever.