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.
Pamela Gray said:
“Flat out, the capacity of the oceans as the driver of variations, driven by changes in equatorial trade wind systems (which itself starts with the Coriolis affect) to affect all other parameters, is simply overwhelmingly stronger” (than the solar effects).
Agreed and I have said as much more than once. However the top down solar effect modulates that larger oceanic effect and the further poleward the oceans try to push the jets the greater the resistance from the then prevailing solar setup. After all, the oceans could never push the jets all the way to the pole. Its like pushing against a rubbery substance, the resistance increases as the pressure increases so it doesn’t really matter that the oceans are much more powerful than the sun, the strain is dissipated by the change in the speed of the hydrological cycle which increases faster the more poleward the jets shift.
The latitudinal position of the jets is a function of the speed of the global hydrological cycle at any given moment.
Steven Mosher says:
October 1, 2010 at 4:24 pm
Let’s just start with the fundamental physics which all these approaches ignore. GHGs will cause warming. Aerosols will cause cooling. If you leave those two out of your equations, you are going to be wrong. One way or another, you are going to be wrong.
It appears to me that Steve has now suffered the same degree of over-confidence in climate causes/effects that has infected many other scientists.
I’ve yet to see him or any other promoter of GHGs provide a proof that Miskolczi is wrong. Yet, they have no trouble making assertions that assume he is wrong.
So, Steve, where is your proof that Miskolczi is wrong? If one exists I’d love to see it so I can discount his hypothesis. So far, I’ve seen nothing.
I won’t even start on the aerosols which can cause both warming and cooling and no one seems to understand the net effect, except for Steve and the IPCC.
Bob, I don’t assert that the stratosphere drives the troposphere.
Merely that the temperature of the stratosphere affects the strength of the inversion at the tropopause which affects the air circulation below.
Furthermore that the solar effect from above merely constrains the oceanic effect from below and vice versa.
Neither the sun nor oceans get a free pass. Both are essential to understanding the variability of the eneregy flow up through the troposphere.
Show me a warming stratosphere when the jets move poleward or a cooling stratosphere when the jets move equatorward on a decadal timescale.
It cannot happen because a stronger inversion at the tropopause always inhibits upward energy transfer so as to increase the high pressure cells around the poles and drive the polar oscillation negative. Some short term phenomena excepted.
vukcevic says:
October 2, 2010 at 8:00 am
“Position of the jet stream is directly related to heat release from the Gulf Stream into atmosphere, it takes place in northern latitudes (~ 60 N, Greenland-Iceland area)”
Vuk, you need to consider it globally.
“[…] smoothing always increases correlation. Here is our recipe for generating spurious results:
1. Start with two absolutely unrelated time series which show no correlation,
2. Smooth one or both series,
3. Recompute the correlation;
4. If the correlation is not yet “statistically significant”, repeat 2 and 3 until it is.
This recipe is guaranteed.”
This is just flat out wrong.
I’ve spent far more than enough time around academic statisticians to know with absolute confidence that they could benefit from a series of conversations with advanced physical geographers about the nature of the problems encountered in the latter field.
In all the lectures I had in stats, whether undergrad or grad, there was no mention of grain & extent (not the same thing as resolution or sampling units). I once sat in on a meeting in which a statistical consultant was “advising” a biologist about an ecological field survey. It was hideous. There was zero awareness of heterogeneity & scale-dependent pattern-variations in the spatial domain.
I found it ironic that (most of) the statisticians would teach Simpson’s Paradox and then make little or no effort to recognize it when & where it arises – (too much automatic reliance on inference with insufficient attention to exploratory data analysis & diagnostics to check assumptions).
Most humorous of all are those who make flawed aggressive attacks on temporal smoothing while contentedly practicing haphazard &/or arbitrary spatial smoothing.
—
Stephen Wilde, how do clouds fit into your narrative? Don’t they, like the day & the year, play a role in terrestrial insolation (not to be confused with solar irradiance)?
Richard M.
“So, Steve, where is your proof that Miskolczi is wrong?”
First, there is no such thing as proof in science. That you would ask the question indicates that we do not have a common understanding from which to even begin a discussion. Simply, if you expect proof out of science or out of me, then you will never be satisfied. I could, just as easily say “where’s your proof that he is right” and you would see we would be at a wonderful impass. But start here if you insist:
http://bartonpaullevenson.com/Miskolczi.html
PS ( yes aerosols can cause warming and cooling, thanks for reinforcing my point! any simplified model that FAILS to take these into account and fails to treat them accurately, is wrong from the start.. Its like this When you see a model that predicts temperature that doesnt have aerosols… especially when you know they have mixed effects, when you see a model like that, well you know its wrong. THANKS!
Paul Vaughan says:
October 2, 2010 at 11:25 am
“Stephen Wilde, how do clouds fit into your narrative? Don’t they, like the day & the year, play a role in terrestrial insolation (not to be confused with solar irradiance)?”
All the main cloud bands linked to the jets and the ITCZ move latitudinally with the air circulation systems.
As they do so global albedo changes because of the changing angle of incidence of solar energy onto the clouds.
“Earthshine and FD analyses show contemporaneous and
climatologically significant increases in the Earth’s reflectance from the out-
set of our earthshine measurements beginning in late 1998”
from here:
http://www.bbso.njit.edu/
That change was contemporaneous with the shift of the jets equatorward which I first noticed around 2000.
Cloud quantities might also vary but I see the latitudinal positioning as the major factor.
And see here:
http://wattsupwiththat.com/2007/10/17/earths-albedo-tells-a-interesting-story/
The albedo trend broadly follows the shift of jetstreams latitudinally. First downward as the jets moved towards the poles until about 1998 and then upward as the jets moved equatorward again.
Steven Mosher says: October 1, 2010 at 4:24 pm
GHGs will cause warming.
Mr. Mosher perhaps you should revisit your science library.
GHG do not do what you suggest, to the degree you whish us to believe.
Here is an example for you to ponder:
Atmosphere of planet Mars is made almost entirely from CO2.
On change from day to night its atmosphere, at height of only couple of feet above ground, drops from +80C to – 200C within few minutes!
Anthony writes,
“one graph says it all”
A quick look at the annual AMO, PDO and global temperature indexes says a bit more.
– Annual PDO and AMO indexes do not correlate at all over their common period, 1856-2009. Since they measure totally different things, what’s the rationale for adding them together?
– Annual PDO correlates not at all with GISTEMP or HadCRUT over their common period, 1880-2009, and adds nothing but noise to a regression model.
– Annual AMO correlates only r2 = .19 with GISTEMP (or .14 with HadCRUT), so those graphs say somthing quite different from the r2 = .85 achieved by aggressively smoothing, truncating the time series, and looking only at US temperatures above. What little resemblance there is between the raw AMO and global temperature occurs across a narrow range between about 1940 and 1970.
But on a longer scale, temperature has been trending unevenly upward while AMO goes up and down. Thus, an “AMO predicts temperature” model (way too simple, but even so a couple of steps beyond eyeballing smoothed and truncated series) predicts warmer-than-observed temperatures before about 1940, and colder-than-observed temperatures after 1975.
In other words, neither of these oscillations forecast the observed warming trend. If you detrend the temperatures, *then* PDO and AMO show more evident effects on interannual variations. Many authors have noticed this.
I’ve just had a chance to look at the following:
http://scienceandpublicpolicy.org/images/stories/papers/reprint/multidecadal_tendencies.pdf
A few cautionary notes:
1) The Figure 23 extrapolation (into the future) is fanciful rubbish.
2) The date-boundaries chosen for the PDO “cool” & “warm” narrative are at odds with observation for the early 20th century. (See Figures 3 & 23.)
Elaboration on the 2nd note:
The authors suggest the following PDO-phase narrative:
Negative: 1880-1915
Positive: 1915-1945
However, the empirical PDO series suggests the following:
Negative: ~1870-1896 (with a notable exception [or near-exception, depending on the timescale considered] ~1885-1892)
Positive: ~1896-1909
Negative: ~1909-1926
Positive: ~1926-1943
Such discrepancies beg the question:
“Which” PDO? – (i.e. the scientific one or the colloquial one?)
PDO & AMO are based on fundamentally different stats. Sensible interpretation of PDO requires deep conceptual understanding of factor analysis (a set of multivariate statistical techniques).
Just a thought…
Maybe PDO is not the most straightforward metric with which to underpin a simplified narrative (despite the nice [seemingly-intuitive] ring of “Pacific decadal oscillation”).
Vukevic : “Atmosphere of planet Mars is made almost entirely from CO2.
On change from day to night its atmosphere, at height of only couple of feet above ground, drops from +80C to – 200C within few minutes!”
I see your Mars and raise you Venus.
Your remark says more about the heat capacity of a very thin atmosphere than it does about GHG.
Trying to resume how point by point the arguments from that paper have been found misleading, misrepresenting the reality and how things are working, mixing multiple sources to pull personnal conclusion (not scientific’s one). In other word just another junk from SPPI and D’Aleo.
Please moderator don’t pull that comment as being a personal attack. If so, you’ll have to remove about 90% of your database talking about the IPPC members that most of your poster are insulting every day.
As soon has SPPI is putting up a so called paper, you make big head news with it, but on all occasion they are found to be wrong, misleading, bias. Yet you keep pushing whatever they are publishing.
Re: Gneiss
Let’s keep in mind that AMO is based on detrended North Atlantic SSTs. Also, bear in mind interannual spatiotemporal heterogeneity. Simple linear correlation isn’t the best metric for noticing that various climate indices intermittently flip from coincident phasing to perfect anti-phase (on interannual timescales). Complex (i.e. having a real & imaginary part) cross-wavelet methods are an alternative to the misleading (given the spatiotemporal nature of the phenomena under study) simple linear regression. There most certainly is a very strong relationship between decadal-timescale global average temperatures & AMO, but the decadal pattern in AMO can be found elsewhere in the world too, so focus often seems to get misdirected (for those who aren’t looking around…)
I guess Leif and I will remain in disagreement over the meaning and value of Joseph’s and Donald’s analysis. “Smoothing” is a spline interpolation that (hopefully) satisfies the Tchebycheff criteria of a good fit, meaning the maximum error in the interpolation is minimized and variations above and below the weighted means have equal probability. If one set of data are “smoothed” then they all must be within the same sets of data for which a correlation is derived.
It is easy to think of examples of “unsmoothed” data for which regressions can be made to obtain an r**2 approaching unity – and completely meaningless, it can happen with “smoothed” data of course but not to achieve a unique value of r**2.
Smoothing was done to take out the 11 year periodicity in TSI and other factors to be consistent. Willie Soon uses Hoyt’s data, Willie worked with Willson’s version of Hoyt and Schatten’s data recalibrated to the ACRIMSAT data set (which gave higher values). If Leif doesn’t like Hoyt’s data maybe Leif likes J Lean’s better – but there are problems in those data nevertheless.
In conclusion, I think Joseph and Donald shown something meaningful, if Leif disagrees, then we celebrate our differences too.
jimmi says: October 2, 2010 at 2:57 pm
I see your Mars and raise you Venus.
Mars atmosphere (95% CO2) has 2 to 3 times more CO2/m3 than the Earth, while the Venus atmosphere (also 95%CO2) has many thousands times more CO2/m3.
Vukcevic,
Yes that is correct but it is the total density that matters, not just the composition. Mars’ atmosphere is too thin for a large greenhouse effect i.e the other molecules matter as well.
Also, your temperatures cannot be correct, even if you confused the units and meant Fahrenheit the temperature range is nowhere near that. I suggest you check them.
jimmi says: October 3, 2010 at 3:47 am
………
You could be right. It is direct quote from Stephen Hawking’s Universe, you can find on
http://www.channel4.com/programmes/stephen-hawkings-universe/4od/player/3123808
scroll to 01:04:00. since it is UK programme I naturally assumed its meant C not F.
Of course it is density of whole atmosphere (Venus’ is densest of all inner planets, atm. pressure about 30 times ours) so CO2 per say does very little.