Here’s a quick question. I’ve seen references to a repository for IMTRB (something like that) for the dendro crowd. But, does there exist, or is there even any push for, a centralized repository for scripts, data files, etc, for all climate scientists to use? It’s great that you make things available, but computers crash, go down, etc. It would be nice if the NSF or some other entity provided funding for a central repository, where one could search for articles, and download all relevant scripts, data, etc. At paper published with NSF funds would be required to provide all said data/scripts, etc., to such a repository. People make it out to sound so difficult, but it’s a simple task, storage is cheap, and it ought to be done.

]]>It’s not that hard to make the codes turnkey i.e. eliminating internal references to directories on my computer and ensuring that all relevant materials are online somewhere. So it’s something that I need to do, should have done at the time and will do some time soon.

I made this scripts operable with external references (as it had refs to files on my computer) and it’s been online at http://data.climateaudit.org/scripts/huybers for a couple of weeks now. I added some other calcs as well into the script. This is a cross-reference.

]]>There is a geospatial procedure called Gaussian unconditional Gaussian simulation (Wackernagel, 2003) which ought to be possible to extend to address the problem you describe. ]]>

But white noise or not, I find it very difficult to obtain RE less than zero for NH AD1820 step. Which makes me wonder, what does Mann mean by

]]>Any positive value of beta is statistically significant at greater than 99% confidence as established from Monte Carlo simulations.

I’m not sure though whether covariance is preserved by VARFIMA only *after* the whitening, that is, in the VARMA step. And I don’t think that is generally covariance or correlation preserving. On the other hand, maybe that’s what it needs for RE significance: reproduce the covariance after the whitening, that is, use VARFIMA. Unfortunately I’m not too familiar with that stuff.

They say it’s algorithmically complex, but if there is a program floating around then why not use it? However, given the shortness of the series I wouldn’t go much further than VARFIMA(1,d,1). And given the low verification RE levels the effort might not be worth it from the start: Low RE is bad, be it significant or not.

]]>*Modeling and Generating Multivariate Time Series with Arbitrary Marginals Using a Vector Autoregressive Technique* by Deler and Nelson

wherein is an equation for covariance of VAR(1) process , for zero-lag

where is autoregressive coefficient matrix and and is covariance matrix of driving noise (white in time). Using this I found one solution for your problem:

You have N proxy series, each of length n.

1. Assume that all N proxy series are AR(1) processes with one-lag correlation p. Estimate p from the proxy data.

2. Draw samples (N vectors) from multivariate normal distribution, with specified NXN correlation matrix (use mvrnorm, for example). Correlation matrix is estimated from the proxy data (between series correlation).

3. Use these samples as driving noise for N separate AR(1) processes. As per the equation above, the correlation matrix will remain the same.

]]>Sure, they are able to do it by detrending the series, see the third column in calibration for steps 1400-1500.

But I assume that MBH98 Fig 3 top is obtained by using all 112 proxy series and 11 TPCs (INVR+variance match). And it is claimed that even in that case positive beta is significant. I find it very hard to obtain negative beta with white noise, and I don’t think that redness in any direction will change this.

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