The code includes non-Monte Carlo way to compute the ‘90%, 95%, 99%

significance levels’. The scaling part still needs help from CA statisticians, but I

suspect that the MBH98 statement ‘The associated confidence limits are approximately constant between sliding 200-year windows’ is there to add some HS-ness to the CO2 in the bottom panel:

(larger image )

This might be outdated topic (nostalgia isn’t what it used to be!). But in this kind of statistical attribution exercises I see a large gap between the attributions (natural factors cannot explain the recent warming!) and the ability to predict the future:

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If Earth’s rotation (length of day) is affected by solar factors, and if John’s work above shows a correlation between length of day and NH temperature reconstructions for recent centuries, then maybe there’s a clue somewhere in this about a solar / climate relationship.

]]>Length of day on a decadal scale, per this article , seems to be related to activity in Earth’s core, which in turn could be forced by internal dynamics and/or solar system activity and/or surface events. How that ties to surface temperature, though, is unclear.

Perhaps an alternate factor affecting length of day is that warming oceans expand, which would move them farther from Earth’s center, which would slow the planet’s rotation.

Something to ponder.

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I had become frustrated that I could not determine what a good fit was for the given data. I decided to try to break the signal into a bunch of frequency bands because I was curious how the regression coefficients of the drivers was dependent upon the frequency band. I decided to graph each band first before trying any fitting technique.

The three bands were a low frequency band which results from about a 20 year smoothing. A mid frequency band that results from a frequencies in the period of 5 years and 20 years and a high frequency band where the frequency is less then a 5 years period.

Each filter is almost linear phase. The filter produces signals that are quite orthogonal and extremely linearly independent. In figure 1-A the solid line is the original northern hemisphere temperature signal and the dotted line is the sum of the three temperature signals produced by the filters. As you can see the sum of the three bands nearly perfectly adds up to the original signal.

What is clear from the graphs is the low frequency signal looks much more like the graph of the length of day then the carbon dioxide signal. If carbon dioxide was the principle driver then the drip in temperature between 1750 and 1920 would be very difficult to explain. Conversely the length of day fell between 1750 and 1820 and then started to rise again after 1900. One wonders why the length of day would impact the earth much more then carbon dioxide but the evidence is hard to ignore.

The mid frequency band looks like it has frequency components similar to the sunspots cycle but the relationship does not appear to be linear. The high frequency band looks difficult to explain. My best bet is cloud cover.

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