Guest Post By Bob Tisdale
For the past few years, we’ve been showing in numerous blog posts that the observed multidecadal variations in sea surface temperatures of the North Atlantic (known as the Atlantic Multidecadal Oscillation) are not represented by the forced components of the climate models stored in the CMIP5 archive (which were used by the IPCC for their 5th Assessment Report). We’ve done this by using the Trenberth and Shea (2006) method of determining the Atlantic Multidecadal Oscillation, in which global sea surface temperature anomalies (60S-60N) are subtracted from the sea surface temperature anomalies of the North Atlantic (0-60N, 80W-0). As shown in Figure 1, sea surface temperature data show multidecadal variations in the North Atlantic above and beyond those of the global data, while the climate model outputs, represented by the multi-model mean of the models stored in the CMIP5 archive, do not. (See the post here regarding the use of the multi-model mean.) We’ll continue to use the North Atlantic as an example throughout this post for simplicity sake.
Figure 1 (Figure 3 from the post Questions the Media Should Be Asking the IPCC – The Hiatus in Warming.)
Michael Mann and associates have attempted to revise the definition of multidecadal variability in their new paper Steinman et al. (2015) Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures. Michael Mann goes on to describe their efforts in the RealClimate post Climate Oscillations and the Global Warming Faux Pause. There Mann writes:
We propose and test an alternative method for identifying these oscillations, which makes use of the climate simulations used in the most recent IPCC report (the so-called “CMIP5” simulations). These simulations are used to estimate the component of temperature changes due to increasing greenhouse gas concentrations and other human impacts plus the effects of volcanic eruptions and observed changes in solar output. When all those influences are removed, the only thing remaining should be internal oscillations. We show that our method gives the correct answer when tested with climate model simulations.
It appears their grand assumption is that the outputs of the climate models stored in the CMIP5 archive can be used as a reference for how surface temperatures should actually have warmed…when, as shown as an example in Figure 1, climate models show no skill at being able to simulate the multidecadal variability of North Atlantic. (There are posts linked at the end of this article that show climate models are not capable of simulating sea surface temperatures over multidecadal time frames, including the satellite era.)
Let’s take a different look at what Steinman et al. have done. Figure 2 compares the model and observed sea surface temperature anomalies of the North Atlantic for the period of 1880 to 2014. The data are represented by the NOAA ERSST.v3b dataset, and the models are represented by the multi-model mean of the climate models stored in the CMIP5 archive. Both the model outputs and the sea surface temperature data have been smoothed with 121-month filters, the same filtering used by NOAA for their AMO data.
Figure 2
As illustrated, the data indicate the surfaces of the North Atlantic are capable of warming and cooling at rates that are very different over multidecadal periods than the forced component of the climate models. The forced component is represented by the multi-model mean. (Once again, see the post here about the use of the multi-model mean.) In fact, the surfaces of the North Atlantic warmed from about 1910 to about 1940 at a rate that was much higher than hindcast by the models. They then cooled from about 1940 to the mid-1970s at a rate that was very different than the models. Not too surprisingly, as a result of their programming, the models then align much better during the period after the mid-1970s.
Steinman et al., according to Mann’s blog post, have subtracted the models from the data. This assumes that all of the warming since the mid-1970s is caused by the forcings used to drive the climate models. That’s a monumental assumption when the data have indicated the surfaces of the North Atlantic are capable of warming at rates that are much higher than the forced component on the models. In other words, they’re assuming that the North Atlantic since the mid-1970s has not once again warmed at a rate that is much higher than forced by manmade greenhouse gases.
What Steiman et al. have done is similar to subtracting an exponential curve from a sine wave…where the upswing in the exponential curve aligns with the last minimum to maximum of the sine wave…without first establishing a relationship between the two totally different curves.
MICHAEL MANN PRESENTED A CLEAR INDICATION OF HOW POORLY CLIMATE MODELS SIMULATE MULTIDECADAL SURFACE TEMPERATURE VARIABILITY
I had to laugh when I saw the following illustration presented in Michael Mann’s blog post at RealClimate. I assume it’s from Steinman et al. In it, the simulations of the surface temperatures (represented by the multi-model mean of CMIP5-archived models) of the North Atlantic, North Pacific and Northern Hemisphere surface temperatures have been subtracted from the data. That illustration clearly shows that the climate models in the CMIP5 archive are not capable of simulating the multidecadal variations in the sea surface temperatures of the North Atlantic and the North Pacific or the surface temperatures of the Northern Hemisphere.
Figure 3
In other words, that illustration presents model failings.
If we were to invert those curves, by subtracting reality (data) from computer-aided speculation (models), the resulting differences would show how greatly the models have overestimated the warming of the North Pacific and Northern Hemisphere in recent years.
What were they thinking? We’d let that go by without calling it to everyone’s attention?
Thank you, Michael Mann and Steinman et al (2015). You’ve made my day.
OTHER REFERENCES
We’ve illustrated and discussed how poorly climate models simulate sea surface temperatures in the posts:
- Alarmists Bizarrely Claim “Just what AGW predicts” about the Record High Global Sea Surface Temperatures in 2014
- IPCC Still Delusional about Carbon Dioxide
For more information on the Atlantic Multidecadal Oscillation, refer to the NOAA Frequently Asked Questions About the Atlantic Multidecadal Oscillation (AMO) webpage and the posts:
- An Introduction To ENSO, AMO, and PDO — Part 2
- Multidecadal Variations and Sea Surface Temperature Reconstructions
CLOSING
Some readers might think that Steinman et al. is nothing more than misdirection, a.k.a. smoke and mirrors. What do you think?
Thanks to blogger “Alec aka Daffy Duck” for the heads-up.
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This should be in your reference section as it is the most educational piece that I’ve seen on the subject of climate models.
http://wattsupwiththat.com/2015/02/24/are-climate-modelers-scientists/
Important aspects of the AMO variability ignored by the climate models:
North Atlantic decadal and Multidecadal Oscillation AMO (de-trended N. Atlantic SST) can be successfully explained and numerically represented by solar- geomagnetic interactions.
http://www.vukcevic.talktalk.net/GSCp.gif
N. Hemisphere’s climate is under control of the polar and sub-tropical jet-streams, whereby the long term zonal-merdional positioning of jet streams depends on the extent and strength of three primary cells (Pollar, Ferrel and Hadley).
http://www.srh.noaa.gov/jetstream//global/images/jetstream3.jpg
Since the equatorial temperature changes little, it is the Arctic temperature which moves jet streams latitudinal location.
Solar magnetic activity reaches the Earth’s poles in form of geomagnetic storms. NASA: “a two-hour average sub-storm releases total energy of five hundred thousand billion (5 x 10^14) Joules. That’s approximately equivalent to the energy of a magnitude 5.5 earthquake”
This is in form of the electric current ionising upper layers of the atmosphere, whereby the atmospheric flow is affected by the changes in the resultant magnetic field (Lorentz law). The Earth’s field (i.e. magnetospheres shielding) is not constant (the internal oscillations are due to the cores differential rotation – see J. Dickey, JPL).
The strength the solar incursions is modulated by the interactions of two fields, since it is strongest at the poles, effect on the polar vortex and subsequently the Arctic’s jet stream would be most strongest.
Geomagnetic effect is also clearly demonstrated in the Arctic temperatures up-trend and its multidecadal oscillations with correlation factor R2>0.8.
http://www.vukcevic.talktalk.net/AT-GMF1.gif
Inevitable conclusion must be:
It is the sun!
Vuk,
Encyclopedia Americana. Danbury, CT: Grolier, 1995: 532.
By today’s standards the two bombs dropped on a Japan were small — equivalent to 15,000 tons of TNT in the case of the Hiroshima bomb and 20,000 tons in the case of the Nagasaki bomb.
In international standard units (SI), one ton of TNT is equal to 4.184 × 109 joule (J)
Hiroshima bomb TNT 15000
ton-TNT to Joules 4.18E+09
Joules total 6.276E+13
a two-hour average sub-storm releases total energy of five hundred thousand billion (5 x 10^14) Joules. That’s approximately equivalent to the energy of a magnitude 5.5 earthquake”
5.00E+14 / 6.276E+13 = 7.97 in the correct ‘climate science’ equivalency.
Please revise your 5.5 earthquakes to 8 Hiroshima bombs so all of us will then understand the power.
Good post by the way.
Is it the sun ?
Svalgaard :” As the magnetospheric ring current and the auroral electrojets and their return currents that are responsible for geomagnetic activity have generally North-South directed magnetic effects (strongest at night), the daytime variation of the Y or East component is a suitable proxy for the strength of
the SR ionospheric current system..”
But what all this has to do with the AMO ?
Data shows direct correlation of the AMO to the Y or East component
http://www.vukcevic.talktalk.net/GMEC-AMO.gif
at latitude of 60N the home of the polar jet-stream
http://www.srh.noaa.gov/jetstream//global/images/jetstream3.jpg
Is it possible that these could become strong enough to cause extended glaciation/ice ages??
BFL hi,
Polar jet stream’s trajectory (as far as I can ascertain) appear to be swung from ‘zonal’ to ‘meridional’ direction by the geomagnetic field just west of the Hudson Bay (affected by geomagnetic storms) and the Icelandic Low, the atmospheric semi-permanent system. In the winter it is located to the south-west of Greenland, controlled by the Atlantic drift current down-welling but in the summer months moves to Iceland’s north.
Let’s assume that for some reason (e.g. the Arctic warm current inflow across Greenland- Scotland ridge is weakened, leading to increase in the summer see ice) the northern summer down-welling may cease. In such case the polar jet stream may get stuck in the strong merdional flow for many years. The process would be self reinforcing by positive feedback. Great Lakes ice would persist thought the summer months providing initial conditions for the onset of the next Ice Age.
The jet stream’s controlling factor can be clearly deduced from the fact that during the last glacial, most if not all of Siberia was free of the ice sheet while N. America was under incredible 3,000 meters tick ice.
http://www.qpg.geog.cam.ac.uk/research/projects/englishchannelformation/1453389260_3dcecb561c.jpg
Illustration is from the University of Cambridge, thus we can assume it is the best knowledge available.
Looks like more circular logic from the AGW propagandists.
You mean epicyclic reasoning
I have a question. Since someone is on a “Witch Hunt” Who funded the research paper? Is there a past conflict of “interest”. If so, then the authors should be ban from summiting any research paper fired from their jobs.
More garbage by Mann and his associated clowns.
Coulrophobia.
Subtracting the models from the data only changes the sign of the result. Either way it shows that the models have completely failed to capture the oscillations. Steinman et al. must have applied some serious smoothing to the differences in Figure 3.
Or it suggests that they were just modeling a model, (or models), in the first place
Yeah Fig 3 almost more like a theoretical sketch than actual data as much as it has been smoothed. The “uncertainties” for the temperature impacts of the PDO since 2000 are basically non-existent, too, lol.
Remember when the acolytes used model runs with “only natural forcings” as sunlight and volcanoes and compared them to model runs which included GHGs to demonstrate the only way to get temperature variations the likes of which we were observing is to have GHG forcings included? And now that they need a convenient excuse for the pause, “internal variability” is involved, lol.
Mann and others are providing the “it’s worse than we thought” excuse about how it’s REALLY going to warm when the “pause” ends.
Forget that they poo-poo’d any idea that natural variability could be so dramatic. It somehow can cancel all the warming from the current high levels of CO2 in the atmosphere but was claimed to be basically negligible back when CO2 levels were lower.
It’s amazing that they are given any credibility at all anymore.
Thus we see how amazing flexible “Settled Science” can be… 🙂
Let’s wait and see how much more flexible Mann & Co will grow when finally “The Pause” will end differently than they hope, namely by changing in a colder than warmer climate… 😉
Our hapless friend Michael Mann is stuck in the web of lies of his own making. I want to see him scrambling some more for the straws he needs.
We could have a field day with Mann’s post. Lets see how the warmists respond around the blogosphere.
Well, according to the Guardian (where I am currently banned form posting after querying how the UEA CRU fossil fuel funding was different from Dr Soon’s),
So it’s state of the art models then.
Makes more sense than any state of science.
The gospel of global warming has been made into the bible of Common Core must know and follow 13th commandment. Never mind the reality of the world tilting into an overdue return of Ice Age showing its fangs with full display of ferocity. Michael Mann is riding the same wave of opportunistic deception akin to certain Trofim Lysenko, a favorite and recipient of Stalin most wanted medal.
Faux pause, you say? Then it was a faux rise also.
Faux up ?
Faux pas?
Definitely fauxed up.
This post shows a fundamental misunderstanding about how climate models work. You would never expect for climate models to all simulate multidecadal variability in the same phase and magnitude simply because these are process-based models that have their own synoptics and various forcings as input. This is why taking a large ensemble should average out all the natural variability leaving only the forced response. However, the forced response in the models is underestimated because the lack of updated forcings for the historical runs – e.g. many (almost all) models assume no volcanic forcing past 2005 whereas there’s strong evidence of a moderate forcing. Unless you have the forcings correct the forced response the approach of detrending using the models will cause some attenuation of the actual signal.
Yeah, that’s it. Climate scientists have been looking for every possible explanation for “the pause,” and they haven’t found a way to account for volcanic forcing over the past decade. What exactly is the time lag in determining volcanic activity, and when can we expect to see this added to the models? Why didn’t the peer-reviewers catch that? Won’t make Steinman et al 2015 just as silly when these “moderate forcings” are added.
Does the climate not vary internally on time scales longer than decades?
Not if you are selling a product that requires precise parameters to be believed.
But in the real world…..yes
Robert Way
What “strong evidence” of “a moderate forcing” (since 2005)? There has been NO change on atmospheric clarity since the early 1993-94 eruption!
http://www.esrl.noaa.gov/gmd/webdata/grad/mloapt/mlo_transmission.gif
How can you (anyone) insert a moderate forcing into the solution to a problem (CO2 has risen strongly but atmospheric temperatures have not changed) when the “symptom” of the moderate volcanic forcing (a dirtier atmosphere) is entirely absent?
+ several million
Robert Way: This is why taking a large ensemble should average out all the natural variability leaving only the forced response.
The ensemble mean is an unbiased estimate of a population defined from the model, its variants, and the parameter estimates with their uncertainties. But what has that population mean got to do with nature? We know that the components of the model are supposed to be based on physics, but some parameters are more or less guessed at. Almost all the model runs to date are higher than the data that they might have predicted and might be tested against, and the mean is significantly discrepant from the data. The claim that the ensemble mean is the “forced response” is not supported by anything more than the hope that it is.
.Steinman et al essentially show that if the ensemble mean is assumed to be an accurate representation of the “forced response”, then the PDO, AMO, and NMO can be redefined to “correct” the model and bring it close to the data. The circularity is obvious. The result is important only if there can be collected a bunch of independent evidence (independent of the model), confirming that these are good estimates of PDO, AMO, and NMO.
This is explained in their supporting online material.
Robert Way, I think model variability is a better term to use than natural variability in your post above.
“This is why taking a large ensemble should average out all the natural variability leaving only the forced response.”
The average of fantasy being fixation.
Robert Way says: “This post shows a fundamental misunderstanding about how climate models work.”
There’s no misunderstanding on my part, Robert. You obviously have misunderstood what was presented in this post. I’ve illustrated in simple terms what Steinman et al. have done. You, on the other hand, have failed to grasp Steinman et al, as evidenced by your comments at RealClimate and Michael Mann’s replies to you.
Robert Way says: “You would never expect for climate models to all simulate multidecadal variability in the same phase and magnitude simply because these are process-based models that have their own synoptics and various forcings as input.”
You have low expectations, Robert. Maybe the modelers need to change their expectations too. Because what they’re giving us is crap. Maybe the modelers need to initialize their hindcasts/forecasts from actual conditions, so that they can prove the model are capable of simulating Earth’s climate. Otherwise, it appears to many of us that the modelers avoid initializing from existing conditions because the models would fail in those efforts.
As illustrated and discussed in the post, climate modelers are assuming the latest upswing in surface temperatures is caused by manmade greenhouse gases, etc., when the instrument temperature records and climate models indicate the surfaces of the Northern Hemisphere, the North Atlantic, and North Pacific are capable of warming at rates that are comparable to the recent warming without being forced to do so.
What I find remarkable, Robert, is that the climate science community expects us to believe their climate model-based projects of future climate when the models have shown no skill at being able to simulate past climate….and, as Trenberth reminded us back in 2007, that climate models are not simulating Earth’s climate as it exists now or as it has existed at any time in the past.
Others have responded to the rest of your comment about volcanic aerosols.
Regards
So, averaging a bunch of wrong guesses leads, a priori, to the correct guess? Sweet – almost like magic!
Hey … maybe I should generate several tax forms all showing the government owes me huge rebates and then average them. That way I can claim it is reality and the government should not be able to object …. right? 😉
Climate models can be useful in understanding how one discreet component influences climate, how much that influence is, how it stacks up against other influences and the chaotic relationship between the influences is unknown. To claim otherwise is to be dishonest, ignorant, or delusional.
I can in my sink determine how much of an alkaline substance to add to a volume of water to change the alkaline level to a certain level. I can then calculate how much alkaline is required to change the oceans the same amount and subsequently use a model to forecast multiple time lines using various amounts and various substances to determine when the alkaline change predicted will occur. Simple math and chemistry right? Yes, but no relationship to reality. Oceans are much more complex than simulations that artificially add various alkaline substances to it over time. A simple example but this is how climate modelers using limited knowledge make extravagant claims disassociated with reality.
The fundamental misunderstanding is climate modelers having little understanding of how little their climate models simulate.
So to summarize; ten wrongs averaged do make a right?
good for you jim g cut right to the heart of the issue!
Robert, if models work as you describe, they are no more representative of the climate than the programmer’s ability to think up inputs and their forcing effects. In short, if the programmer thinks CO2 forcing effect is 4 degrees per doubling, an ensemble of runs will ‘predict’ that because it is built in.
Another programmer who thinks that CO2 doubling will lead to 1.0 degree of warming will find, all said and done, that their ensemble of runs gives 1.0 as the answer.
Your description is correct, and that is why I don’t believe the output from the models. If they operated isolated from and were not tuned using the past, and agreed with the past, I could believe their forecasts of future temperatures. In fact they are trained using the past, and are unable to predict the present “out of sample”. That is a fatal failure, in my book.
Only by incorporating a pretty comprehensive set of solar and geomagnetic inputs will they come close to replicating the past and present. When that happens (and it will) the influence of CO2 will be seen, at its present level and rate of change, to be quite a minor, though real, influence.
Robert,
I agree with the first half of your comment, but I don’t follow your claim that the forced response in models is underestimated due to omission of post 2005 volcanic forcing (which anyway seems a minor factor). Surely its omission leads to models [further] overestimating the forced response?
BTW, I thought your comments at RealClimate made very good points. I’m glad you realise what rubbish the Steinman paper is. And well done for bringing up the Booth paper – which is certainly relevant to the natural AMO vs aerosol N Atlantic previous cooling debate – and the devastating critique of it by Zhang et al.
Yes, it’s just the models variance from reality…but if you smooth it and make it look pretty, you have some of the humps and dips in the right places and can pretend it’s meaningful.
Can you imagine using these curves to represent “internal variability” and trying to get something published in 2000 arguing that a measurable amount of the observed warming from 1980 to 1990 (eyeball says 0.2 deg C…0.1 from AMO, 0.05 from each of the two others) was due to “internal variability?”
I can just see Michael Mann showing Figure 1 to a group at the IPCC, touting the model predictions, looking like Jim Carrey’s character “The Pet Detective” as he points to the figure and says “LLLLLLIKE a GLOVE!”
I can also see him bent over and [trimmed. .mod].
I bet Josh would have fun with that one…
Mann: “We show that our method gives the correct answer when tested with climate model simulations.”
There is something wrong with those people. I don’t know what the name for it is, but there’s something wrong with them. They substitute their software-outputs for reality.
I do not say this as an insult: I am merely describing what I observe. Over and over and over again, when purporting to verify an idea, they compare the idea, not to real-life observations, but to their software-outputs.
I worked in software for two decades. I programmed mostly for business applications, accounts receivable, payables, payroll, inventory, and the like. I cannot imagine writing software that does not have to be checked against something in the real world.
But Mann et al. actually seem to believe that their computer simulations are more real than reality. Isn’t that why they check their new ideas against software-outputs?
So, I conclude there is something wrong with those people. Is there a name for what’s wrong with them? Or are my observations about them incorrect? I ask these questions quite sincerely.
Look up NPD, some items there.
http://www.outofthefog.net/Disorders/NPD.html
The answer is they believe in “consensus reality”, not “objective reality”.
In other words ‘make believe’ is more real to them than truth, call it wishful thinking.
Feynman would say, “They are still waiting and wondering why the airplanes haven’t landed yet.”
I read the same sentence and came to the conclusion that the problem is they believe the “correct answer” is what the model simulations say it is without regard to any reality.
All hail the mighty models.
My guess is as good as the model guess; therefore it is right.
I do not know what it is called, but it is related to this precept: “It is difficult to get a man to understand something when his salary depends upon his not understanding it.” Upton Sinclair.
Thanks, everybody, for your thoughts.
Disconfirmed expectancy. Other than buying a book about it Wikipedia covers it. It explains why the cult of global warming deny temps have not been rising, have become shrill, attack people that point out their failed prophecy, the need to have followers to prop up their beliefs, and explains the attitude that even if global warming is a hoax, the actions taken will benefit the earth. It explains a lot, in my opinion.
“But Mann et al. actually seem to believe that their computer simulations are more real than reality. Isn’t that why they check their new ideas against software-outputs?” Mike knows where his bread comes from, and will do what needs to be done for him to survive another day and be paid for it and for tomorrow as well. It has nothing to do with any science in Newton understanding of it.
I believe it is called Delusion, believing your own lies.
I agree this post shows a fundamental misunderstanding of climate models. The point of Steinman et al. is that there is an underlying trend (called AGW), with natural variability on top of it. What we have seen during the “faux pause” is natural variability that offsets the continued upward trend. As soon as the natural cycle is on the upswing, we will see a “double whammy” effect and rapid warming.
You mean that “natural variability” that was ignored by climate scientists in the past? Where was that in their previous model work? Oh wait, they obviously didn’t have a clue how the climate actually works but you now want us to believe they’ve had a revelation and have been blessed with divine knowledge. Yeah, what flavor kool-aid are you imbibing?
” … an underlying trend (called AGW)” which tends to zero, “with natural variability on top of it.” There that now makes sense.
“there is an underlying trend (called AGW) …”.
==============================
http://www.woodfortrees.org/graph/hadcrut4gl/from:1845/mean:24/plot/hadcrut4gl/from:1845/trend
You can call it what you like but it amounts to a long-term trend of ~ 0.5C per century, hardly a reason to send the developed economies into a tailspin; let alone deny the majority of the world’s population the lifestyle currently enjoyed in those economies.
And that’s good news Barry, an upward trend means we are still in the interglacial epoch!
…and where will the extra or kinetic energy be released from for a “double whammy”?
Barry Barry Barry, really?, “a double whammy”. It sound more like magic then science. The models are a complete fail on every level. Now that they (climate alarmists) have discovered ocean cycles they need to learn about them. The earth will still be their teacher. The AMO will turn, and they will not like it.
Barry, nice try at misdirection, but it didn’t work.
You wrote, “The point of Steinman et al. is that there is an underlying trend (called AGW), with natural variability on top of it.”
The flawed assumption in Steinman et al. is that the models properly simulate the “underlying trend (called AGW)”, when they’ve shown no skill at simulating those temperatures.
Barry, then model the natural variability. Your assertion implies that you know what the natural variability is. Natural variability wasn’t discussed much before the models couldn’t match history, now it’s the explain all for the difference. You’re just making it all up as you go along.
Barry from sks says: ” As soon as the natural cycle is on the upswing, we will see a “double whammy” effect and rapid warming.”
I have to be honest and say I wish Barry was correct. A warmer Earth with more CO2 for the initiation of life would be how I would choose to live the remainder of my life, and how I’d like to leave the planet for my descendants. Unfortunately, it looks like we’re headed for a repeat of the 1970’s, or even worse, the 1870’s.
So you believe temperature will drop to the level it was in the seventies?
May I ask why?
Hugh, study the sun and the way oceans run, then take heed of history’s warning.
Barry: Is that a prediction, or just a projection?
Barry- You write “What we have seen during the “faux pause” is natural variability that offsets the continued upward trend.”
Hansen vociferously disagrees with you as recently as 2003:
“As we shall see, the small forces that drove millennial climate changes are now overwhelmed by human forcings.”
Hansen et al., 2003 activist bulletin, Columbia University
But then the gobsmacking pause initiates a rethink from The Hansen:
“”The longevity of the recent protracted solar minimum, at least two years longer than prior minima of the satellite era, makes that solar minimum potentially a potent force for cooling,” Hansen and his co-authors said.”
Hansen et al., “Earth’s energy imbalance and implications”, 2011 activist report.
“The 5-year mean global temperature has been flat for a decade, which we interpret as a combination of natural variability and a slowdown in the growth rate of the net climate forcing…The annual increment in the greenhouse gas forcing (Fig. 5) has declined from about 0.05 W/m2 in the 1980s to about 0.035 W/m2 in recent years.”
Hansen et al, 2013 activist bulletin, Columbia University
Still waiting for Hansen’s rethink on the dead-certain anthropogenic interpretation of warming from 1970 – 2000….
From their supporting online material: Regression Method
To calculate the AMO, PMO, and NMO we 1) regressed the observed mean temperature
series onto the model derived estimate of the forced component, 2) estimated the forced
component of observed variability using the linear model from step 1, then 3) subtracted
the forced component from the observations to isolate the internal variability component.
Everything rides on their model-based redefinition of AMO, PMO, and NMO.
In other words, their results are arbitrary. That’s my take away.
Derivative science: set assumptions as fact, derive impact, conclude results correct.
Computationalally correct, representationally unknown.
They also write, in the SOM:
The AMO, PMO, and NMO amplitudes are seen to be unusually large with the
detrending approach (Fig. S5A). Particularly striking are the very large positive trends in
the AMO and NMO at the end of the series, which were indeed predicted (Figs. 2,S2–S4)
as structural artifacts of the method. The root mean square (RMS) amplitude of the NMO
is 0.14oC, more than twice the simulated amplitude of the hemispheric multidecadal
variability from Knight et al. (3). The AMO and PMO have estimated amplitudes of
0.15oC and 0.09oC, respectively, and show high levels of apparent correlation with each
other (R2=0.563, lag = 0, statistically significant at p=0.05 level for a one-sided test—see
next section for details about the associated calculation). The AMO, PMO, and NMO
collectively give the appearance of a “stadium wave” pattern (18,19), wherein each varies
coherently but at variable relative lag.
Our regional regression approach yields AMO, PMO, and NMO series that are
dramatically different from those obtained with the detrending approach. Absent now are
the very large positive trends in the AMO and NMO near the end of the series. The
amplitude of the NMO (0.07oC using CMIP5-All) is half that inferred from the
detrending approach. Unlike with the detrending approach, the maximum lagged
correlation between the AMO and PMO (R2=0.334 lag = 3) is no longer statistically
significant.
Consequently, their climate model plus the natural variability yields the observed “faux pause”, with the model-based redefinition of AMO, PMO, and NMO.
What I see in Fig 3 is that AMO and PDO were phased locked until 1995. After that PDO peaked and then really acclerated downward from 2002 onward while AMO continued up. In other words they are no longer phase locked.
That realization says that the warming of the 80’s and 90’s was entirely natural, not “model described CO2 forcing”. Since 2002, the divergence between AMO and PDO has kept temps generally flat, save for the occasional mild La Nina or El Nino of the past 13 years. With that firmly in hand, it says CO2 forcing is lost in the noise of the natural variability of the AMO and PDO tracking in and out of phase over many decades.
BINGO (IMV) http://wattsupwiththat.com/2015/02/26/on-steinman-et-al-2015-michael-mann-and-company-redefine-multidecadal-variability-and-wind-up-illustrating-climate-model-failings/#comment-1870193
This is particularly likely when one measures global atmospheric T via the consistent satellites, vs the ever changing surface data sets. (FUBAR surface record))
joelobryan, a reminder, Steinman et al did not present the PDO.
If only people understood – mixturing, tempering and/or “correcting” actual figures never ever is allowed in theories of science.
And
if
one want to make a computer model, one need to take ALL not parts of every needed parameters AND analyse each one’s premises one by one.
Maybe the Dingo ate my Global Warming!
I am glad some really familiar with AMO and PDO is on this. I have not got a copy of the article yet. I did check the first author’s prior research. He does not seem to have been involved in this area up until now, which strikes me as very odd. Mann on the other hand cut his teeth on the AMO. Something does not feel right about this. It also took a while for this paper to get to press. Given that it addresses a hot topic, the reasons for the delay may be instructive.
I’d love to see the first draft. I’m guessing the author tried to use the real AMO and PDO and result was devastating to the claims of future warming. Hence, the other authors came along and they created the fictional AMO and PMO that would have no effect on future warming since it is derived from models with predetermined warming.
Mr. Tisdale writes:
“In other words, they’re assuming that the North Atlantic since the mid-1970s has not once again warmed at a rate that is much higher than forced by manmade greenhouse gases.”
No more damning criticism of a scientist can be made. And Mr. Tisdale made the criticism stick.
Warmist Climate Science has never been anything if not top down. The standard argument form is Circular Reasoning: hide your conclusion in your premises.
Whats funny is I do not even think they are hiding their conclusion in their premises.
Whats perplexing is why they are not called out by it during peer review.
In the meantime, the AMO has crashed as Gray opined it would. While it was not known as the AMO back in the 1970s, Gray opined we would go into the warming period mid 1990s till about 2020 and then start back down ( this was back in the late 1970s) He nailed it before all this became twisted by people pushing an agenda and coming to a conclusion based on that.
I would love one of these climate scientists that tell me after the fact why it happened to just once, forecast something like we are seeing now with the AMO. Its no different than the guy on Monday Morning telling you why a team won or lost a game, or he could have done better
Joe, would you do a Saturday video on the relationship between the PDO and AMO and how the +/- works? Thx.
This slightly off topic, but Mr. Bastardi you are one of my heros!!!
Ditto
The place I work has a winter betting pool, where we all guess how the winter will turn out: Max snow, min temp, number of days below zero, etc. I base my “guesses” on the weatherbell.com Saturday summary videos and have been kicking butt against the “scientists” I work with. They think my success is akin to the secretary winning the football pool basing her choices on uniform colors. Maybe next year I’ll let them in on my “secret weapon”.
Ben Booth writes a perspective for the Steinman, et al, 2015 paper.
http://www.sciencemag.org/content/347/6225/952.summary
In his Perspective, he uses the figure shown below.
The AMO curve shown does not look correct to me, as I am under the impression the AMO has been positive since about 1990.
http://i57.tinypic.com/157q1f.png
Since 1980, as I read http://www.cgd.ucar.edu/cas/catalog/climind/AMO.html
Since 1985 in http://www.esrl.noaa.gov/psd/cgi-bin/gcos_wgsp/tsanalysis.pl?tstype1=24&tstype2=0&year1=&year2=&itypea=0&axistype=0&anom=0&plotstyle=0&climo1=&climo2=&y1=&y2=&y21=&y22=&length=&lag=&iall=0&iseas=1&mon1=0&mon2=11&Submit=Calculate+Results
The AMO and PMO of the paper are model derived fictions. They have nothing to do with reality.
Facepalm. Seriously?
Thanks, Bob.
What were they thinking? They were thinking we have been thoroughly Grubberized and will believe anything they feed us.
Smoke is real, mirrors too, Steinman et al. (2015), not at all.
Here is Steinman’s Fig1A.
http://i57.tinypic.com/2qv85jr.jpg
Here is a closeup shot of the Fig 1A of the last 20 years. The observed is outside the 95% confidence interval. Model fail.
http://i57.tinypic.com/296lzzd.jpg
The Figure 1 legend is here:
http://i57.tinypic.com/2yx3i8o.jpg
Fig 1 B and C here:
(more CMIP 5 failures highlighted)
http://i59.tinypic.com/34es5jt.jpg
That’s why their fictional PMO has a huge drop right at the end.
I thought the models “trained” on the years up to 2005……after that is when the models made future predictions……does anyone know if this is correct
The CMIP5 protocol published in 2009, revised 2011, table 1, ensembles 1.1 and 1.2 were decadal and 3 decadal hindcasts back from 2005. So the parameterizations were to best fit from roughly 1975 to 2005. This is evident from the stuff Ed Hawkins posted in 2013 (use google images). The least divergence between CMIP5 and observed temp (he used HadCru4) is exactly that period.
The Steinman ,Mann Miller paper recognizes that there are serious differences between the NH, N Pacific and N Atlantic SST model runs and the observed temperatures. The authors are particularly concerned to explain the recent “Pause”. They subdivide the ocean system into three separate regional components which they label AMO,NMO and PMO ( somewhat redefined AMO NAO and PDO )
Basically what the paper does is to calculate the differences between models and observations and then attribute the difference to an unexplained “internal variability” in the ocean temperatures. The authors conclude that internal multidecadal variability in NH SST temperatures accounts for the discrepancy between models and observation and that it also likely offset anthropogenic warming over the last decade . They add that this effect will reverse ( at some unspecified date) and add to anthropogenic warming in coming decades.
The AMO PMO and NMO curves in their figure 3c show, more or less, the well known 60 year periodicity in the temperature data. see Figs 15 and 16 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
In other words they are trying to improve the models and save the model forecasts by adding to them
the effects of the PDO ,AMO and NAO.
Unfortunately they continue to make the egregious schoolboy error of tuning their models back about 120 years when the main periodicity is millennial. (Figs5-9 at the link) The recent pause is more accurately described as a cooling since 2003 which date represents a peak in both the 60 year and 1000 year periodicities. I estimate that the cooling trend of the millennial cycle will reverse in about 2650 as opposed to in the coming decades. See the peak at
http://www.woodfortrees.org/plot/rss/from:1980.1/plot/rss/from:1980.1/to:2003.6/trend/plot/rss/from:2003.6/trend
That the Steinman et al paper got through peer review for Science Magazine says much about the current state of establishment science. However in a short comment on the paper in the same Science issue Ben Booth of the Hadley center does sound a refreshingly cautionary ( for Science Mag and Hadley ) note saying that the paper is only useful if the current models accurately represent both the external drivers of past climate and the climate responses to them and that there is reason to be cautious in both of these areas. This comment is an encouraging sign that empirical reality may be finally making an impression on the establishment consciousness. If the expected sharp cooling in 2017-2018 suggested by the drop in the Ap index and Neutron Monitor data in Figs 13 and 14 of the post linked above actually occurs it should just about finish off the whole CAGW meme.
“If the expected sharp cooling in 2017-2018 suggested by the drop in the Ap index and Neutron Monitor data in Figs 13 and 14 of the post linked above actually occurs it should just about finish off the whole CAGW meme.”
I don’t think anything can finish off the cagw meme. If this graph(lifted from your site), didn’t do it, then reality doesn’t matter to them.
3.bp.blogspot.com/-zLZvFvWqy8Y/U8REucSDlfI/AAAAAAAAASg/-f_VHXdfaQY/s1600/CMIP5-73-models-vs-obs-20N-20S-MT-5-yr-means1.png
http://3.bp.blogspot.com/-zLZvFvWqy8Y/U8REucSDlfI/AAAAAAAAASg/-f_VHXdfaQY/s1600/CMIP5-73-models-vs-obs-20N-20S-MT-5-yr-means1.png
Must….adjust…..reality…… to….. fit….. models……(sleepwalk mode).
Just amazing that the AGW groupies are so illogical as to never catch on to the constantly shifting excuses for the model failures.