Joe D’Aleo and Don Easterbrook have produced a new paper for SPPI. This graph of US Mean temperature versus the AMO and PDO ocean cycles is prominently featured:
I particularly liked the regression forecast fit:
They have this caveat:
Note this data plot started in 1905 because the PDO was only available from 1900. The divergence 2000 and after was either (1) greenhouse warming finally kicking in or (2) an issue with the new USHCN version 2 data.
Hmm. I’m betting USHCNv2.
Abstract:
Perlwitz etal (2009) used computer model suites to contend that the 2008 North American cooling was naturally induced as a result of the continent’s sensitivity to widespread cooling of the tropical (La Nina) and northeastern Pacific sea surface temperatures.
But they concluded from their models that warming is likely to resume in coming years and that climate is unlikely to embark upon a prolonged period of cooling. We here show how their models fail to recognize the multidecadal behavior of sea surface temperatures in the Pacific Basin, which determines the frequency of El Ninos and La Ninas and suggests that the cooling will likely continue for several decades. We show how this will be reinforced with multidecadal shift in the Atlantic.
Here’s the paper you can download:
UPDATE: The goodness of fit, seems almost too good. There may be a reason. I’m reminded in comments of this article by statistician William Briggs – (thanks Mosh)
Do not smooth times series, you hockey puck!
Where he points out:
Now I’m going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses! If the data is measured with error, you might attempt to model it (which means smooth it) in an attempt to estimate the measurement error, but even in these rare cases you have to have an outside (the learned word is “exogenous”) estimate of that error, that is, one not based on your current data.
If, in a moment of insanity, you do smooth time series data and you do use it as input to other analyses, you dramatically increase the probability of fooling yourself! This is because smoothing induces spurious signals—signals that look real to other analytical methods. No matter what you will be too certain of your final results! Mann et al. first dramatically smoothed their series, then analyzed them separately. Regardless of whether their thesis is true—whether there really is a dramatic increase in temperature lately—it is guaranteed that they are now too certain of their conclusion.
Perhaps Mr. Briggs can have a look and expound in comments. I only have the output, not the method. But let’s find out and determine how good the “fit” truly is. – Anthony
UPDATE: Statistician Matt Briggs responds in depth here. He says:
I want to stress that if D&E did not smooth their data, the correlation would not have been as high; but as high as it would have been, it would still have been expected. All that smoothing has done here is artificially inflated the confidence D&E have in their results. It does not change the fact that AMO + PDO is well correlated with air temperature.
Add in the ‘sunspot integral’ for an even better R^2 = .96
http://hockeyschtick.blogspot.com/2010/01/climate-modeling-ocean-oscillations.html
Then it is clear that the temperature drives the PDO and AMO
[ryanm: i sense a bit of sarcasm, but i would posit that our knowledge of the forcing mechanisms for both the AMO and PDO are poorly understood. It is a distinct possibility that PDO+AMO and the global temperature increase are NOT independent and/or caused by a totally different mechanism, i.e. solar forcing]
Got Coal? Wood? Nukes?…
Found a typo on the page 21 of the pdf:
Three possible projections are shown: (1) moderate cooling (similar to the 1945 to 1977 cooling); (2) deeper cooling (similar to the
1945 to 19771880 to 1915 cooling); or (3) severe cooling (similar to the 1790 to 1830 cooling).Liked the article.
Found a typo on the page 21 of the pdf:
Three possible projections are shown: (1) moderate cooling (similar to the 1945 to 1977 cooling); (2) deeper cooling (similar to the
1945 to 19771880 to 1915 cooling); or (3) severe cooling (similar to the 1790 to 1830 cooling).Liked the article.
Nice work.
ONE Graph shows what causes temperatures to rise and it’s not CO2!!! It’s the Pacific and Atlantic Oceans! This is with a squared correlation coefficient of 0.85 which is about as good as you can get. All the climate models never get anywhere near that close.
NATURE has thus once again falsified the alleged alarmist AGW hypothesis.
Wow, I stand corrected… a better fit of a Squared Correlation Coefficient R2 of 0.96 when the solar activity is added in!!!
Amazing! I guessed some years ago that there was a relation between AMO/PDO and the mean but never calculated it out – I would never guess the correlation is quite that high.
Looks like it satisfies the Tchebycheff criterion of a good fit, too, of an equal ripple property of variation about the mean
AMO+PDO= temperature variation = Climate Science’s GCM’s have finally met their match?/sarc.
AMO and PDO are both headed down; we’ve had an extended solar minimum. D’Aleo Easterbrook will be proven wrong if the 2010-2012 temperature average is not significantly below the 2000-2009 average.
I really have no queston that CO2 is less of a problem than the AGW alarmists like to claim.
Which is not to say it is *no* problem. If they are exaggerating by 50%, it is still worth some of our time to consider how to minimize that impact, IMO.
The other question that often occurs to me, as much as I am a believer in the impact of the AMO, PDO, ENSO, etc. . . is to what degree CO2 can impact those cycles out of their historic rhthyms. I haven’t heard a satisfactory answer to those questions yet.
More study? Yes, by all means. . . remake the world economy? Not yet.
uncanny
The fundamental difference that will be pointed out by the alarmists is that their leaders (Hansen et al) use the GISS surface station adjusted data and this paper uses the UAH satellite data. Hansen is already on record saying that the temperatures have been increasing without stop since the 70s, despite what the satellite data shows. The 2010 temperature increase from the 70s is only 0.4K or so in this paper, while Hansen has >0.8K. A difference that should invalidate one position.
I admire the skeptical position that science and data trump computer modelling. A prediction is called for, an honest one, not a “projection” as weasled by the IPCC. Money and mouths and all that. Yet this paper has that “today state” problem. Not that I agree with the GISS today-state, but that is as it is.
The comment that the PDO-AMO warming and cooling events are laid on top of a general warming from the Little Ice Age is a hidden gem and stone for tripping over. While true, the general warming trend from the LIA is not one that the IPCC agrees with. The IPCC model shows a halt or even cooling post 1988: the “normal” condition. Thus all warming since 1988 is CO2 related. This paper has as a base assumption that the post-LIA warming is not finished.
So, two positions for the warmists to argue invalidates this paper: 1) the recent satellite temperature history is wrong, and 2) warming from the LIA stopped by 1990.
I have argued that the global warming debate/war is all about the integrity of the original data and certain assumptions. The warmists accept the “peer-reviewed” data and modelling, and therefore accept the IPCC conclusions as they are internally consistent and replicable (as shown by Lucia et al). The skeptics question the data and some assumptions. We’re both think the other is tilting at a windmill, while we are the one facing a dragon.
(P.S. For the record, I’m with St. George, and Hansen is with Don Quixote.)
Wonderfully done, and very clear. I think you’ve ‘nailed it’.
I would suggest looking at the work by folks like Ian Wilson that show a LOD correlation with PDO cycles. It would help to show where the PDO changes come from and complete the ‘causality loop’…
As we have several correlated slope reversals, it is very hard to dismiss this relationship as a coincidence. If this paper cannot be refuted, I think it will be a more telling blow to the IPCC consensus than the McShane and Wyner paper.
Nice work. Repetition of work of late prof. Marcel Leroux. This hypothesis can be found in his books on climatology. Prof. Leroux states (based on observations) that changes in insolation and enegy flux to the Earth influence meridional exhcnge of air from polar regions. When polar region cools then the exchange intensifies; as an effect the PDO indices have larger amplitudes, and the temperaatures change as well (less solar = cooler). As quoted here researchers, one should look into Sun, clouds and large scale circulation (mostly atmospheric).
I’m having trouble opening this .pdf
Does the entire paper show global figures?
If it doesn’t I’m not entirely sure how this can be understood as saying anything more than oceanic temps on either side of continental USA have an effect on mean temps, which is not entirely unexpected.
Good paper. All that matters now is what the Sun does next. Currently, I see it waffling between a 1900 event and a Dalton event, meaning that if enough time goes by and it does not ramp up, it could slip behind even the Dalton Minimum.
What is interesting about the Maunder is that the Decadal Oscillations were held down in the La Nina territory for the duration.
It (the SC) looks like a straw that breaks the camels humps.
It’s just numerology.
sarc off 😉
This kind of thing always overlooks the acceleration of the CO2 effect.
The CO2 increase from 1940-1970 was about 25 ppm.
2000-2030: about 90 ppm expected.
I expect the CO2 increase rate will overwhelm this cyclical effect, since it has almost quadrupled since the last cooling cycle (if indeed the cooling was all oceans and no aerosols, which is also somewhat dubious), and nobody has accounted for that in these articles that talk about the 60-year cycle.
@ur momisugly E.M.Smith September 30, 2010 at 9:08 pm:
That would be lovely to know that.
I’ve been trying to find what causes the El Niño, and haven’t found anything on it. Everyone seems to assume the El Niño is the cause of other things – as if it just appears out of nowhere.
I have some ideas, but those and $3.00 will buy me a cup of coffee.
Oh dear:
“Figure 18: With 22 point smoothing, the correlation of US temperatures and the ocean
multidecadal oscillations is clear with an r-squared of 0.85 ”
1. the underlying indices are based on SST. temperature
2. we already know the SST is fairly well correlated with the temps you see over land.
3. Ws Briggs http://wmbriggs.com/blog/?p=735
“Somebody at Steve McIntyre’s Climate Audit kindly linked to an old article of mine entitled “Do not smooth series, you hockey puck!”, a warning that Don Rickles might give.
Smoothing creates artificially high correlations between any two smoothed series. Take two randomly generated sets of numbers, pretend they are time series, and then calculate the correlation between the two. Should be close to 0 because, obviously, there is no relation between the two sets. After all, we made them up.
But start smoothing those series and then calculate the correlation between the two smoothed series. You will always find that the correlation between the two smoothed series is larger than between the non-smoothed series. Further, the more smoothing, the higher the correlation. The same warning applies to series that will be used for forecast verification, …
One graph did say it all. You do not prove anything by taking one times series that is built from SST temperatures and a second times series of temperatures, smooth them and then regress. Except that a smoothed times series of temperatures correlates with another smoothed series of temperatures.
Steven Mosher says:
September 30, 2010 at 10:18 pm
Oh dear: […] the more smoothing, the higher the correlation.
Oh dear, indeed! Don’t compute r-squared on smoothed data. Especially not if the smoothing is over many points [22 in this case].
Meanwhile, there is related good news — the salmon are returning to the Fraser and Adams Rivers B.C.
In a report about the large number of salmon entering the Fraser River this year compared to last year John Reynolds, holder of the Tom Buell BC Leadership Chair in Salmon Conservation and Management at Simon Fraser University, says:
“The Fraser River salmon runs are much smaller than those of the Adams River, which pass in October. This year’s Adams River run is currently forecast at a hefty 8 million salmon.
“Every family should go see the Adams River this year,” said Reynolds, “It’s one of the great ecological spectacles.””
See:
http://thetyee.ca/Blogs/TheHook/Environment/2010/08/07/Reynolds-Salmon-Run/
The PDF contains much of the straight data, so I wouldn’t discount it just because of the smoothing comparison.
The favoring of El Nino/La Nina during the various stages of Solar Cycles is more interesting.
The one thing I would not be certain of is whether the PDO & MEI turned into thier cold phases in 1997. Looks more like 2007 to me. 1997 is where it starts tailing off.