Sources of reconstruction uncertainty in CRU temperature proxy reconstructions

Here is a list of all the sources of error, variability, and uncertainty and whether those sources—as far as I can see: which means I might be wrong, but willing to be corrected—are properly accounted for by the CRU crew, and its likely effects on the certainty we have in proxy reconstructions:

1. Source: The proxy relationship with temperature is assumed constant through time. Accounted: No. Effects: entirely unknown, but should boost uncertainty.
2. Source: The proxy relationship with temperature is assumed constant through space. Accounted: No. Effects: A tree ring from California might not have the same temperature relationship as one from Greece. Boosts uncertainty.
3. Source: The proxies are measured with error (the “on average” correlation mentioned above). Accounted: No. Effects: certainly boosts uncertainty.
4. Source: Groups of proxies are sometimes smoothed before input to models. Accounted: No. Effect: a potentially huge source of error; smoothing always increases “signal”, even when those signals aren’t truly there. Boost uncertainty by a lot.
5. Source: The choice of the model m(). Accounted: No. Effect: results are always stated the model is true; potentially huge source of error. Boost uncertainty by a lot.
6. Source: The choice of the model m() error term. Accounted: Yes. Effect: the one area where we can be confident of the statistics.
7. Source: The results are stated as estimates of β Accounted: No. Effects: most classical (frequentist and Bayesian) procedures state uncertainty results about parameters not about actual, physical observables. Boost uncertainty by anywhere from two to ten times.
8. Source: The computer code is complex. multi-part, and multi-authored. Accounted: No. Effects: many areas for error to creep in; code is unaudited. Obviously boost uncertainty.
9. Source: Humans with a point of view release results. Accounted: No. Effects: judging by the tone of the CRU emails, and what is as stake, certainly boost uncertainty.

There you have it: all the potential sources of uncertainty (I’ve no doubt forgotten something), only one of which is accounted for in interpreting results. Like I’ve been saying all along: too many people are too certain of too many things.