Climate proxies are often selected on the basis of their correlations with instrumental temperature data, as in the reconstruction underlying the analysis of Hegerl et al. Using such proxies in regression models to reconstruct past temperatures leads to selection bias, resulting in an overestimation of the correlation between proxies and temperatures and an underestimation of uncertainties

Seems to me that’s all the ipcc-types have tried to do with CO2 concentrations as proxies for temperatures, and have only ended up proving it doesn’t work, as the quote predicts.

C. E. Forest, M. R. Allen, A. P. Sokolov and P. H. Stone
Constraining climate model properties using optimal fingerprint detection methods, Climate Dynamics Issue Volume 18, Numbers 3-4 / December, 2001

As is well known, is is difficult to estimate the true unforced climate system variability from observations and therefore, climate models are run with constant external forcings for 1000 years to provide estimates of climate variability to be used in significance tests of climate change detection.

That’s the way to go.

Willis,

takes some time to go this through. Hegerl et al:

Thus, the likelihood of the reconstruction given each set of model parameters and forcings is estimated from the probability that its residual variability is statistically indistinguishable from the best-fit residual (5), given internal climate variability and non-climatic random errors in proxy data.

Maybe MBH99 CIs are the easy thing to solve after all, IGNORE1 is almost clear now, 2/3 scale of IGNORE2 is still a mystery

…but the easiest way to see the problem is to look at http://data.climateaudit.org/pdf/NAS.M&M.pdf Figure 2. Average doesn’t work at all. And it means that CVM using all those proxies doesn’t work at all (or that the instrumental record is flawed

Essentially the same message is now printed in Nature. Kind of a footnote, it is not Schneider’s main critique..

All the best,

w

I couldn’t find a free version.

Thanks,

w.

It seems to me that the real problem is that there’s no way to choose whether proxies are selected at random or because en suite they give a signal.

Similar statement is published in Nature (446, March 07) by Schneider:

Climate proxies are often selected on the basis of their correlations with instrumental temperature data, as in the reconstruction underlying the analysis of Hegerl et al. Using such proxies in regression models to reconstruct past temperatures leads to selection bias, resulting in an overestimation of the correlation between proxies and temperatures and an underestimation of uncertainties

It seems that RC people are too busy to ‘refute’ this, as refuting Svensmark and Great Global Warming Swindle program takes all their time..

We assume that the sensitivity of the proxy composite is stable, not that the temperature itself is stable.

in reply to my statements that

Assumption of Stationarity: The CVM method assumes that the variance in the climate
is stationary. Eduardo Zorita comments that “In the case of a stationary signal and
stationary noise: once the variance is matched in a calibration period, under stationarity
assumptions, its is matched in all periods.” http://tinyurl.com/yd8pmv

However, the Union Reconstruction is used to show that the recent temperature is
significantly higher than the historical temperature, viz: “The reconstructions evaluated
in this study show considerable disagreement during the 16th century. The new 18
proxy reconstruction implies 21-year mean temperatures close to 0.6 K below the AD
1866 to 1970 mean.” This, of course, means that the assumption of stationarity is
unfounded. In fact, there is a large signal in the variance of the Union Reconstruction
itself, which is shown in SOM Figure 2. As this figure shows, the standard deviation of
the UR varies by a factor of two over the time period, as well as containing an overall
trend. Thus, the assumption of stationarity is not supported by the data.

and

Assumption of Stable Global Temperature Field: One of the underlying, and untested,
assumptions of the CVM method is that the global temperature field is stable over
time. That is to say, will a CVM average that works in one century necessarily work in
another? As noted in Review 2, the assumption of stationarity in the variance is not
supported by the data, so there is no a priori reason for assuming that a CVM average
will work over a multi-century period. This assumption needs justification.

does not address the issues at all. I must admit, the more I see of these fine professional “scientists”, the happier I am to be an amateur scientist. It’s much easier to admit when I’m wrong (which happens more than I would like, but is unavoidable in a new science such as the study of climate), as I have no professional ego to maintain. When I’m wrong … I’m wrong, and life goes on.

Sigh …

w.