The stuff about seasons and such is poorly explained and I don’t see how it adds more degrees of freedom to deal with the short sample period (given ENSO).

In addition, the agnosticism on Watts’s earlier one year analysis is troubling. It’s like he doesn’t want to call out “our side”.

Well guys…our side should be truth.

Well, that is surely a sneaky way to hide it. You’re right, though, when I turn my head sideways it becomes quite obvious — thanks Bruce. ROTFL!!!

Sort of… think of this more like the number of cycles you have to have before you can detect them. Works for any frequency if you think of it that way. You are right that you need 2x PDO’s to detect any signal, 2x of 11 year solar, etc. thus nyquist still applies.

And you’re right in that nyquist is certainly more associated with frequency domain stuff like audio CD’s and 44.1 KHz sampling to be able to reliably sample 20 KHz audio.

I was actually being a little facetious. I assumed TMP meant temperature and the “National” in “National Summary” probably wasn’t Guatemala. It’s just that it looks so odd without the hockey stick on the right end, heh.

USA. Temperature

Here, we are discussing problems detecting low frequency oscillations. Detecting the lowest frequencies is also difficult.

And the arguments over whether or not they exist are impossible to fully resolve without a long data sets.

On the low frequency side: We know there are 11 years solar cycles; that means if you don’t know when they start or end, you would need to sample for at least 11 years to average over a cycle. Suppose someone thinks this matters a lot. To disprove that, and convince them, you need to collect data over several 11 year periods and show it to them. (Others might be convinced by order of magnitude estimates– but in come cases in AGW discussions, these order of magnitude estimates pre-suppose that we can estimate the effects we are arguing over.)

How much solar variablity matters to the final computation of the underlying trend in C/century depends on the magnitude of the solar variability relative to other forcings.

AGW theory says the solar variability is now piddly compared to the underlying trend due GHG forcing. But to test that claim empirically, we need to collect data over at least one or two solar cycles. If we can detect the effect of solar variations, that’s a problem for AGW theory. If we can’t detect the effect of solar variations, that tends to confirm AGW. ( Of course, due to volcanism and other factors, we might need many solar cycles!)

Similarly, if the PDO is thought (by anyone) to affect GMST significantly, and we wanted to convince them they were wrong, we would need to measure GMST over at least one or two PDOs and see whether or not the PDO influence could be detected, and to show its effect is less than the over all trend.

Once again, the only way to empirical way to test whether PDO matters enough to affect any underlying trend due to AGW is to get data over several PDO cycles.

Just exactly what does the graph in your 9:02:32 post show, and for what geographic area?

Bob B and randomengineer, the frequency of events varies. ENSO 3 to 7 years and PDO 25 to 30 years. I would think for something like PDO you would need at least 3 cycles and maybe 4 to see the net effect on climate and we wont go into solar cycles of 200 years 400 years and so forth.

Lee , yes we are still waiting for the formula and data for hinge points.

“Dell (11:55:56), your referenced article is analysing US land temperature.”

“The US is not the world.”

Yes it based up temps in the US, and not the world, but the US temp data set is the most wide spread land, complete long term data set in the world. Do we really know what actual temps were in the first half of the 20th Century in third world countries? Yet much of the pre 1950 world temps are based on estimates, not real temps.

If you can show me a global temp data base that is based entirely on actual temps, and not guesstimates for pre-1950, then we can talk century long Global temps, and solar trends.

But instead, we have the very persons who are promoting global warming alarmism, who are estimating much of the pre-record global temps. The fact that there is so much discrepancy the between the patterns of the US actual data and the global estimated data pre-1950, yet post 1960, we see almost identicle patterns, which would make the pre-1950 global temp estimates highly suspect.

I was looking at your post on your site.

What struck me is the implication that the .2C per decade warming is relatively recent … and therefore CO2 could be the culprit.

What about 1910 to 1940? Isn’t that a .2C per decade rise?

What would happen if you used 1940 as a starting point? Would not the rise drop in half?

Starting points and end points matter.

Picking the mid 1970’s as a starting point maximizes warming.
Picking the 1940’s would minimize it.
Picking 1998 flattens it.
Starting in 1934 in the USA should show a flat line.
Ignoring 1910 to 1940 is in my opinion wrongfully aids the faulty theory that CO2 is the culprit.

I like this graph: http://www7.ncdc.noaa.gov/CDO/cdodivisionalselect.cmd?nationSelect=110&regionSelect=101&startMonthSelect=01&startYearSelect=1988&endMonthSelect=01&endYearSelect=2008&outputRadio=staticGraph&staticGraphElementSelect=TMP&filterSelect=00&method=doStaticGraphOutput&reqtype=nation

It clearly says” You want to spend 20 trillion to fix WHAT?????? There is nothing to fix.

Its sems intuitive that if you are looking for a DC bias of sorts that you cant find it by sampling over a period that is dominated by “natural” weather frequencies..

Conceptually, if you have events that last 7 years, then your not going
to get any good look at the underlying bias unless you sample out beyond that period ( 2x being a good guess, hat tip to nyquist) .

Maybe LUCIA will look at this.

I believe you.

You’re stating it’s a Nyquist problem; you need N times the frequency (3-7 years) to see a worthwhile signal. Sample interval needs to be N times that of the highest frequency you intend to detect where N should be at least double.

So let’s assume you’re correct.

This is exactly why the Tamino chart Lee shows is worthless; if you look at ENSO and PDO and solar cycles then you have the same underlying Nyquist problem. Anything less than say 120 years of data is suspect; you can’t see the PDO/solar/ENSO signal effect otherwise.

I see what you intended to show now. I posted comments over there about a few caveats.

Plot A vs D as a scatter plot, and change chart type to an area plot. Lots of regular oscillations after 1882 – much noisier before.

You lose the first two years of data due to the method of calculation (n has to >=24), so the first plotted data point is December 1851. Then the timeline needs to be adjusted so that any events concur with their dates rather than the 12/2=6 month displacement that this method leads to

So what does it really tell us apart from a smoothed down version of the anomalies?
They go up and down and sweep out areas, which could be measured by integration, but it seems a little OTT.
I look forward to Basil’s Part II.