J
Jamie
Guest
Tom P wrote:
Jamie
On 12/13/2011 07:18 PM, Orval Fairbairn wrote:
In article<9kpfq4FquqU1@mid.individual.net>,
Tom P<werotizy@freent.dd> wrote:
On 12/13/2011 06:10 AM, Orval Fairbairn wrote:
In article<9kngueFj7qU1@mid.individual.net>,
Tom P<werotizy@freent.dd> wrote:
On 12/13/2011 12:13 AM, Tom P wrote:
On 12/12/2011 05:49 PM, Bret Cahill wrote:
Assume the tree ring data is good.
http://joannenova.com.au/2011/12/chinese-2485-year-tree-ring-study-sh..
.
Bret Cahill
Use of Fourier analysis.
Most would go with the two higher frequencies. The problem is
extrapolating off of the two cycles of the /1300 year frequency.
Bret Cahill
There is another aspect which I cannot understand from the article.
The researchers have presumably found by fourier analysis that
there is
some proxy in the tree-ring data that displays the periodic signals
described- all well and good - but how do they determine the
correlation
between the proxy and the instrumental temperature record? By
Principal
Component Analysis? If so, why are their results any more reliable
than
Briffa's?
Maybe someone with access to the paper can clarify?
Addendum - the paper is accessible, but it refers to yet another paper
for the source of the temperature data for the last 2485 years-
based on
tree-ring analysis, lol. Let the paper chases begin!
BTW I'd like to echo Bret's suspicion as well that particularly
when it
comes to low frequency signals - meaning cycle time comparable with
sample length - you can prove anything you want with fourier analysis.
.... including "hockey stick" tailoffs. If the data are not smooth, the
FFT will go unstable and show a tailoff at the end of the data stream
that does not represent realistic behavior. A common tailoff is the
"hockey stick" shap, popularized by Mann, et. al.
The end of the hockey stick since 1900 is instrumental data. No Fourier
analysis involved. The disputed part is the pre-instrumental part
derived by statistical analysis of proxies, in particular using the PCA
technique. AFAIK no Fourier analysis there either. Correct me if I'm
wrong.
PCA analysis relies on a time overlap between instrumental and proxy
data and attempts to determine which factors in the proxies correlate
with the instrumental data. It' not at all clear to me why anyone
should think that the extremely small changes in global temperatures
should have a detectable effect on tree-ring growth compared with the
major changes in annual growth due to rainfall or cloud cover, let alone
the implicit assumption that there is a linear relationship, without
which the PCA analysis is meaningless.
You need the Fourier analysis just to sort through the data scatter --
even with "instrumental" data, which, BTW, has not covered the Earth
until recent times.
What you have said so far makes little sense. You said:
quote
.... including "hockey stick" tailoffs. If the data are not smooth, the
FFT will go unstable and show a tailoff at the end of the data stream
that does not represent realistic behavior. A common tailoff is the
"hockey stick" shap, popularized by Mann, et. al.
/quote
But the post industrial data is NOT derived from FT analysis, and
moreover Mann's analysis is not based on FFT
So if you say that FFT "goes unstable", why are you now saying that you
need to use it?
Because he's unstable?
Jamie