Comments on: Warmest in a Millll-yun Years

By: UC

UC — Tue, 03 Oct 2006 04:31:00 +0000

#170

Thanks for the link, the raw data surely helps! But I’m still not sure if we can compute that 0.5 C without seeing the full 12-proxy reconstruction. Weighting by 2/3 is mentioned, but does it have a theoretical basis even if redness is p=2/3? Maybe that is because they use RE and not RMS. RMS would catch that extra variance due to redness without any extra tricks, right? (if the sample window is large enough). Still a bit confused.

Oh, IMHO, UC, as a Ph.D. student, you should not look too closely to Mann&Lees 96, it may give you too many bad ideas

OK;) Median smoothing of spectrum will remove that peak right away. So, ‘background noise’ can never be very red.

By: Jean S

Jean S — Mon, 02 Oct 2006 17:26:35 +0000

re #160/#164: UC, great, I think you are after finally figuring out the ’99 confidence secret! I knew it had to be something simple. If you have time, check (the 27.4 Update part of) this post (IGNORE these columns), I think that might give the rest of the secret.

Oh, IMHO, UC, as a Ph.D. student, you should not look too closely to Mann&Lees 96, it may give you too many bad ideas

By: fFreddy

fFreddy — Mon, 02 Oct 2006 16:44:52 +0000

Re #168
Wow – their best work to date.

By: Michael Jankowski

Michael Jankowski — Mon, 02 Oct 2006 14:40:51 +0000

165: Yeah, but there are other places like RC or NPR which try to act serious

You must have missed this RC piece.

By: TCO

TCO — Sun, 01 Oct 2006 17:24:30 +0000

165: Yeah, but there are other places like RC or NPR which try to act serious, but don’t have people doing math and such. I’d much rather be smart and off-color then dumb and have a stick up my ass.

By: UC

UC — Sun, 01 Oct 2006 17:04:23 +0000

#163

If one uses optimal estimation techniques for a NH temperature reconstruction, assuming that NH temperatue time series is nice (ie ergodic, widesense stationary,gausssian) than the optimal estimate for the time series with no or poorly correlated proxies will always have a hockeystick shape.

That’s where we need to think about splicing. But if you assume the average to be 1890 value or something like that, then yes. Theoretical AR1 wanders around 0, in real life we have additional random constant to estimate.

#164

If p of the calibration residuals really is around 0.67… That’s something. p of global temp during calibration period is 0.64, proxy noise is not red they say, 12 thermometers couldn’t bring the 2-sigma down to 0.5 C, ozone in the stratosphere affects tree rings, but CO2 doesn’t… Well, then we can just forget MBH99.

By: Boris

Boris — Sun, 01 Oct 2006 16:35:24 +0000

I would expect that a site that wanted to be taken seriously in the scientific world would not resort to mocking via mid-90s comedic icons. My presumptions are apparently off base.

It just seems so bloggy.

By: Steve McIntyre

Steve McIntyre — Sat, 30 Sep 2006 21:24:37 +0000

#159. Looks like you may have figured this out!! I’ll take a look over the week-end.

By: Phil B.

Phil B. — Sat, 30 Sep 2006 21:24:22 +0000

#Re 162, UC, good luck with your PhD studies, I personally use extended Kalman filters extensively, appreciating that they’re suboptimal. I need to run and pack as my wife and I are off to Mexico for a week, but a thought for you. If one uses optimal estimation techniques for a NH temperature reconstruction, assuming that NH temperatue time series is nice (ie ergodic, widesense stationary,gausssian) than the optimal estimate for the time series with no or poorly correlated proxies will always have a hockeystick shape.

By: UC

UC — Sat, 30 Sep 2006 20:08:23 +0000

#161

Thks. Good guess, quite close. Still a student, though (PhD candidate who is spending too much time here? ). Linear optimal estimation with Gaussian inputs is fun, but things get tricky in real life when we have non-linear systems with non-Gaussian inputs.

Homework for math geeks: using the first eq in #160 show that is a good approximation of random walk power spectrum.