Section 4.6 Responses

4. It is alleged that there has been an improper bias in selecting and adjusting data so as to favour the anthropogenic global warming hypothesis and details of sites and the data adjustments have not been made adequately available It is alleged that instrumental data has been selected preferentially to include data from warmer, urban in contrast to rural sites; that the rationale for the choice of high/low latitude sites is poor; and that the processes by which data has been corrected, accepted and rejected are complex and unclear.

QUESTIONS TO ADDRESS

 6. What means do you use to test the coherence of the datasets?

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One Response to “Section 4.6 Responses”

  1. Jimchip Says:

    Matbe here for a technical discussion of the statistics commonly used for detrending, autocorrelation

    http://listserv.arizona.edu/cgi-bin/wa?A2=ind1001&L=itrdbfor&T=0&P=5570

    Assuming you want to relate these ring widths to something else, like
    climate data, you have to somehow remove the autocorrelation structure from
    each core (or tree), by removing the age-related trend. You can use a
    linear filter, a function that relates ring width to cambial age (e.g.
    negative exponential or linear equation), a cubic spline, an autogressive
    model, or something like a first difference, depending on the
    autocorrelation structure of the ring widths The goal is to obtain as
    serially uncorrelated a series as possible, for each series. You can then
    calculate any statistic you want from the resulting data, with n = 1000 in
    your example. You can also detrend the mean of the 10 series instead, and n
    would then = 100. Keep in mind though, that the relationship with the,
    e.g. climate, data will very much depend on how you detrend the ring data.

    The other way is to simply calculate the lag 1 autoregressive coefficient
    (r) and plug it into an equation that computes the effective number of
    degrees of freedom, which is of the form Neffective = (1-r)/(1+r). That
    assumes that the autocorrelation structure is dominated by a lag 1 effect.
    Your standard error calculation is then based on Neffective, not the
    original n.

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