This should be titled seven ways to make a hockey stick. It must have taken a long time to set up seven different regression-ish methods.

Calibration by assumption of what the signal is, cannot be used in proxy literature. If anything changed in climate science I hope it would be that. Shock and recovery on a random network are all I see here. I don’t mind the PC1 so much but again the assumption that the result is temperature is hard to swallow.

The OLS method has the highest DOF and allows the method to make the best fit. Of course more negative thermometers arise (very similar to the optics example in my email). However, negative thermometers are accepted in many of the published results. I don’t understand how some negative thermometers are ok and many are not. If we’re to believe that a flipped proxy is ok, then hell we should run OLS all day long.

Of course negative weights are crap in most proxies and IMO so is scaling the same type of proxy by different levels.

It’s an interesting post, I’ll read it again later today.

The style of your new work has changed, as have TCO’s posts. I wonder if TCO is cooking his brain with Alcohol – really.

In this case, there is much better recovery of low-frequency information, as you can see by comparing the proportion of variance in each scale

After what I’ve learned here, perhaps it should say there is much stronger amplification of low frequency information in contrast to better recovery. – not a criticism.

Mark

Judith should read that post referred to in #187 because your wavelet decomposition scheme would be useful in her attempts to isolate the various high/mid/low frequency components of hurricane occurrence vs. climate data. The 5y and 10y periodicity in hurricane occurrence would go some way to “explaining” (i.e. accounting for) the low count for 2007.

I’m curious if this has been attempted with simple short-time Fourier transform methods (like a spectrogram)? Granted, I must confess to being a fan of wavelets myself (I have history with them), but it would be interesting to see a comparison. Wavelets are good solutions for spiky/intermittent data (I analyzed speech and a heartbeat, for example), but there is a level of regularity in temperature reconstructions (visually apparent) that almost begs for the periodicity of Fourier. Just curious… perhaps an exercise for future efforts of my own?

Mark