Comments on: Proof of AGW http://landshape.org/enm/proof-of-agw/ The Power of Numeracy Mon, 30 Jan 2012 17:00:00 +0000 hourly 1 http://wordpress.org/?v=3.3.1 By: Andrew http://landshape.org/enm/proof-of-agw/#comment-1539 Andrew Mon, 29 Jun 2009 22:40:24 +0000 http://landshape.org/enm/?p=2514#comment-1539 David, I think it is a fun little result and a cool statistical tool but:"But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air."Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise David, I think it is a fun little result and a cool statistical tool but:”But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air.”Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise

]]> By: davids99us http://landshape.org/enm/proof-of-agw/#comment-1540 davids99us Mon, 29 Jun 2009 19:12:15 +0000 http://landshape.org/enm/?p=2514#comment-1540 I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong. I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong.

]]> By: Andrew http://landshape.org/enm/proof-of-agw/#comment-1538 Andrew Mon, 29 Jun 2009 17:40:24 +0000 http://landshape.org/enm/?p=2514#comment-1538 David, I think it is a fun little result and a cool statistical tool but:"But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air."Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise David, I think it is a fun little result and a cool statistical tool but:”But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air.”Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise

]]> By: Andrew http://landshape.org/enm/proof-of-agw/#comment-11361 Andrew Mon, 29 Jun 2009 17:40:00 +0000 http://landshape.org/enm/?p=2514#comment-11361 David, I think it is a fun little result and a cool statistical tool but: "But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air." Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise David, I think it is a fun little result and a cool statistical tool but:
“But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air.”
Is indeed good advice. I suspect most of it is just interesting looking nonsense-in scientific terms, noise

]]> By: davids99us http://landshape.org/enm/proof-of-agw/#comment-1537 davids99us Mon, 29 Jun 2009 14:12:15 +0000 http://landshape.org/enm/?p=2514#comment-1537 I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong. I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong.

]]> By: Anonymous http://landshape.org/enm/proof-of-agw/#comment-11360 Anonymous Mon, 29 Jun 2009 14:12:00 +0000 http://landshape.org/enm/?p=2514#comment-11360 I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong. I would need to study the linear algebra to say anything more intelligent on this topic. I am not saying you are wrong.

]]> By: Nick Stokes http://landshape.org/enm/proof-of-agw/#comment-1536 Nick Stokes Mon, 29 Jun 2009 11:25:01 +0000 http://landshape.org/enm/?p=2514#comment-1536 David,No, there is an implied extension of L/2 at each end of the range. You need that data to calculate the endpoint autocorrelation matrix that SSA finds the eigenvalues of. MRC makes an explicit extrapolation. As I said above, I imagined that if you don't supply anything, it will assume it to be zero, and this will be shown up by shifting the reference point (by subtracting 1, say). However, this didn't happen, so I can only assume SSA pads by extending the last value at each end as a constant. David,No, there is an implied extension of L/2 at each end of the range. You need that data to calculate the endpoint autocorrelation matrix that SSA finds the eigenvalues of. MRC makes an explicit extrapolation. As I said above, I imagined that if you don't supply anything, it will assume it to be zero, and this will be shown up by shifting the reference point (by subtracting 1, say). However, this didn't happen, so I can only assume SSA pads by extending the last value at each end as a constant.

]]> By: Nick Stokes http://landshape.org/enm/proof-of-agw/#comment-11357 Nick Stokes Mon, 29 Jun 2009 11:25:00 +0000 http://landshape.org/enm/?p=2514#comment-11357 David, No, there is an implied extension of L/2 at each end of the range. You need that data to calculate the endpoint autocorrelation matrix that SSA finds the eigenvalues of. MRC makes an explicit extrapolation. As I said above, I imagined that if you don't supply anything, it will assume it to be zero, and this will be shown up by shifting the reference point (by subtracting 1, say). However, this didn't happen, so I can only assume SSA pads by extending the last value at each end as a constant. David,
No, there is an implied extension of L/2 at each end of the range. You need that data to calculate the endpoint autocorrelation matrix that SSA finds the eigenvalues of. MRC makes an explicit extrapolation. As I said above, I imagined that if you don’t supply anything, it will assume it to be zero, and this will be shown up by shifting the reference point (by subtracting 1, say). However, this didn’t happen, so I can only assume SSA pads by extending the last value at each end as a constant.

]]> By: davids99us http://landshape.org/enm/proof-of-agw/#comment-1535 davids99us Mon, 29 Jun 2009 01:30:32 +0000 http://landshape.org/enm/?p=2514#comment-1535 Nick the Rahmstorf analysis you refer to has different parameters. The minimum roughness criterion (MRC) made by appending a new slope onto the dataset, and the 11 year embedding period. Here, the data is not extended, just pure SSA, and the embedding period is L/2 by default. The amount of downturn would vary with the embedding parameter, so it is contingent, but not an artifact. SSA is like a matrix of principle components that moves along the dataset, so the size of the matrix (embedding period) affects the bendiness of the first component. But it doesnt need an extension of data. That a Mann one-off designed specifically to ensure the continuation of increasing temperatures to the end of the data series (its in the paper). Nick the Rahmstorf analysis you refer to has different parameters. The minimum roughness criterion (MRC) made by appending a new slope onto the dataset, and the 11 year embedding period. Here, the data is not extended, just pure SSA, and the embedding period is L/2 by default. The amount of downturn would vary with the embedding parameter, so it is contingent, but not an artifact. SSA is like a matrix of principle components that moves along the dataset, so the size of the matrix (embedding period) affects the bendiness of the first component. But it doesnt need an extension of data. That a Mann one-off designed specifically to ensure the continuation of increasing temperatures to the end of the data series (its in the paper).

]]> By: Anonymous http://landshape.org/enm/proof-of-agw/#comment-11349 Anonymous Mon, 29 Jun 2009 01:30:00 +0000 http://landshape.org/enm/?p=2514#comment-11349 Nick the Rahmstorf analysis you refer to has different parameters. The minimum roughness criterion (MRC) made by appending a new slope onto the dataset, and the 11 year embedding period. Here, the data is not extended, just pure SSA, and the embedding period is L/2 by default. The amount of downturn would vary with the embedding parameter, so it is contingent, but not an artifact. SSA is like a matrix of principle components that moves along the dataset, so the size of the matrix (embedding period) affects the bendiness of the first component. But it doesnt need an extension of data. That a Mann one-off designed specifically to ensure the continuation of increasing temperatures to the end of the data series (its in the paper). Nick the Rahmstorf analysis you refer to has different parameters. The minimum roughness criterion (MRC) made by appending a new slope onto the dataset, and the 11 year embedding period.

Here, the data is not extended, just pure SSA, and the embedding period is L/2 by default. The amount of downturn would vary with the embedding parameter, so it is contingent, but not an artifact.

SSA is like a matrix of principle components that moves along the dataset, so the size of the matrix (embedding period) affects the bendiness of the first component. But it doesnt need an extension of data. That a Mann one-off designed specifically to ensure the continuation of increasing temperatures to the end of the data series (its in the paper).

]]>