Jo Nova writes:
Murry Salby was sacked from Macquarie University, and Macquarie struggled to explain why, among other things, it was necessary to abandon, and strand him in Paris and hold a “misconduct” meeting in his absence. Since then he has been subject to attacks related to his previous employment. I’ve asked him to respond, which he has at length in a PDF (see below). The figures listed below refer to that PDF, which encompasses 15 years of events.
I don’t have the resources (unlike the National Science Foundation, the NSF) to investigate it all, but wanted to give Murry the right of reply.
On closer inspection the NSF report used by people to attack Salby does not appear to be the balanced, impartial analysis I would have expected. Indeed the hyperbolic language based on insubstantial evidence is disturbing to say the least. Because of the long detailed nature of this I cannot draw conclusions, except to say that any scientist who responds to a question about Murry Salby’s work with a reference to his employment is no scientist.
Remember the NSF report was supposedly an inhouse private document. It was marked “Confidential”, subject to the Privacy Act, with disclosure outside the NSF prohibited except through FOI. Desmog vaguely suggest there “must have been an FOI”, but there are no links to support that. In the end, a confidential, low standard, internal document with legalistic sounding words, may have been “leaked” to those in search of a character attack.
My summary of his reply:
See: http://joannenova.com.au/2013/08/murry-salby-responds-to-the-attacks-on-his-record/
The PDF:
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Nick Stokes says:
August 14, 2013 at 2:52 pm
“No, the trend is arbitrarily variable, via your T0 (-0.4). And the curvature is arbitrarily variable by the multiplier of Gi (your 0.2).”
I meant the trend in rate. That trend in rate matches the trend in the rate of measured concentration when you scale by the same scaling factor you use to match the variational components. That trend integrates into the curvature, but that curvature is not arbitrarily variable. It is chosen such that the variational components in temperature and rate of change of CO2 match.
Ferdinand Engelbeen says:
August 14, 2013 at 3:10 pm
“Indeed, that is what is found in the observations: the change in CO2 increase rate lags the temperature increase after e.g. an El NIño with 6-9 months. Thus my “model” reflects reality…”
This is getting really tedious. It lags by 90 degrees. In every frequency component, it lags by 90 degrees. That is because CO2 is proportional to the integrated temperature relative to a particular baseline.
There is no uniform lag in time, only in phase. And, that 90 degree phase shift indicates an integration. You cannot have a constant 90 degree phase shift without having an integration. This can be proven analytically via the Bode phase-gain integral.
Bart,
“That trend integrates into the curvature, but that curvature is not arbitrarily variable.”
It is. You have a relation
C= a3* Σ Gi + Σ a2 + a1
where C is CO2, Gi GISSLO, and a2 can be identified with a3*( your equilibrium temp). You choose a3 (0.2) and choose eq temp (0.4, and thus a2), and match a1. Regression does the same, to minimise LS.
I’m saying that you can get a very similar result, no worse, with
C= b3* Σ t + Σ b2 + b1
where t is time, again choosing the b’s to optimise. The information contained in Gi isn’t helping.
richardscourtney says: August 14, 2013 at 2:19 pm
“I assume you were joking, but just to be sure I write to ask you to either confirm you were joking”
Of course not. The plot shows gains in air CO2 and the amounts we emitted. Gains rose with emissions, but were a fraction. And you think the presence of CO2 in the air needs special explanation?
I think of this scene. Three people, A,B,C see a leaky bucket with a hose running water into it. A notes that it is half full of water and wonders where it came from. B says, well, from the hose. But C says, no! The bucket is sitting in a puddle, and has holes in it. I can prove water flows through holes. The water must have come from the puddle.
Nick Stokes says:
August 14, 2013 at 3:52 pm
“I’m saying that you can get a very similar result, no worse…”
But, it is much worse. When you differentiate b3* Σ t, you only get b3*t. You don’t get all the variational components.
The variation is the ups and downs. In this plot, you see the big blips at, e.g., just past 1970, up and down around 1990, one at just before 2000, and all the other little bumps and burbles in between? Those are important markers. They match with that value of what you are calling a3.
This is what I have been saying: the curvature matches and the variational terms match, with just that one value for a3.
This is why it is such a bad idea to focus on the concentration itself. When you do that, you do indeed see only some low-ordered polynomial behavior, shorn of most of the identifying information, at least to our eyes. That is why you should focus on the rate domain. When you do that, you see that b3* Σ t is completely unsatisfactory.
Nick Stokes says:
August 14, 2013 at 4:28 pm
“I think of this scene.”
I think of this one. The three people are standing in a room with a candle burning. Person A notes it has gotten warmer since they first came in. Person B says, “obviously, it is because of the heat coming from the candle.” Person C notes that the candle is quite small, that there has been a noticeable hum which stopped at about the time the temperature seemed to stop rising, and that the light on the thermostat down the hall also went out at about the same time.
Bart,
The difference in the analogies is that with the bucket/hose and CO2/emissions, the obvious cause is amply sufficient to explain the effect. And if you want to dream up an alternative, then, in the bucket case, where did all that water from the hose go?
Lot of arguing here about the relationship between dCO2/dt and temperature. However I notice that you are all using just one dataset, the CO2 measurements from Mauna Loa. Admittedly this is the longest sequence, but there are others, and with a wide geographic spread, from Alaska to the South Pole. What happens if you use these datasets? Since there is discussion about time lags, and since the southern hemispheres readings have a lag compared to the northern ones, I think the first step should have been to see how closely the various datasets can be brought into coincidence by a suitable temporal shift, which would establish how large an offset is plausible in the analysis.
Then you need to check your differentiation – I assume it is numerical, but this is notoriously unstable on non-smooth data, which this is. What method is being used, and how has it been checked?
Then you should check the derivatives of the various CO2 datasets against each other – do they match – do they need temporal offsets – are they the same as the CO2 curves themselves.
Only after that should you be trying to match dCO2/dt with temperature. How much of that has been done?
jimmi_the_dalek says: August 14, 2013 at 7:02 pm
Only after that should you be trying to match dCO2/dt with temperature. How much of that has been done?
Jimmi, I did this in 2008. My simplest analysis examined CO2 data from Barrow Alaska, Mauna Loa, and South Pole, along with Global Average CO2. Many others have done this analysis before me.
From memory:
Mauna Loa is helpful because it is close to the Global Average.
Readings at Barrow show the greatest seasonal amplitude of about 16-18ppm CO2 as I recall. Mauna Loa is intermediate and South Pole is near-zero in seasonal amplitude. This is because the Northern Hemisphere has a much larger landmass than the Southern Hemisphere, and dominates the seasonal CO2 cycle.
Allan,
Yes, but that did not really answer my question. Do the derivatives agree? Where did you publish this? And, since there is now 5 years more data, what has changed since?
From the excellent correlation one can indeed draw the conclusion that ∆T controls ∆CO2 to a certain degree, however one could still argue that a continuous rise of CO2 by 1 ppm/year could be anthropogenic while the charcteristic pattern of ∆CO2 (which correlates with ∆T) rides on top of that (with an average 1 ppm/year to explain the total 2ppm/year increase of CO2) but since the anthropogenic 1ppm increase is constant (presumably) it would not contribute to the correlation pattern. Thus, this serves as a compromise where both Engelbeen and Bart etc. are partially right.
Chris Schoneveld:
re your post at August 15, 2013 at 1:37 am
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390608
Yes! Thankyou.
As I keep saying, using available data nobody can know to what degree either of them is ‘right’.
I write to draw attention to your helpful post.
Richard
“So, in the context of your question, yes, a chart show cause and effect if it is known that there is a causal relationship between two parameters.”
Well that was a rather long winded way of getting around to the same point I’d made several messages ago. Causality is something that has to be established via multiple lines of evidence. A graph, in itself, does not show causality. Let’s start there and then run through the other aspects of your posts.
“Absence of correlation indicates absence of a direct a causal relation between two parameters.”
Even that is a little too strong. Parameter A can have a direct causal relation with parameter B but not show correlation because parameter C also has a direct casual relation with B and the magnitude of change of C means the correlation is not easily observed. For example arsenic poisoning is certainly a cause of death but annual fluctuations in levels of arsenic poisoning aren’t going to show a correlation with annual death rates because motor vehicle accidents and heart disease etc make a more significant impact. Statistically if those other parameters are known we can control for them and identify the correlation. However if those other causes of death somehow weren’t known it would still be fallacious to assume arsenic was not deadly because the correlation couldn’t be demonstrated in annual death rates. Of course with arsenic poisoning we can look at evidence from direct controlled experiments to establish a causal mechanism at a different level of analysis.
“And, as has been repeatedly explained in this thread, global temperature and atmospheric CO2 concentration cohere such that changes in the CO2 follow changes in the temperature at all time scales.”
The relationship that was under discussion was that changes in THE RATE OF CHANGE of CO2 follow changes in the temperature. Your statement there that changes in CO@ur momisugly follow changes in temperature is trivially false. Take 1960 to the present: http://www.woodfortrees.org/plot/esrl-co2/normalise/plot/hadcrut4gl/from:1960/normalise
I forget the actual graph presented but it was essentially this relationship: http://www.woodfortrees.org/plot/esrl-co2/derivative/mean:12/normalise/plot/hadcrut4gl/from:1960/normalise
Which shows rate of change of CO2 pretty much following similar wobbles as the temperature anomaly. Now that is a neat graph but it doesn’t show that CO2 concentration follows (or coheres with) temperature over that time scale. It doesn’t even show that the rate of change of CO2 *follows* temperature – indeed it is easy to find periods were a rise or fall of the rate of change of CO2 precedes a related change in the temperature anomaly.
Nick Stokes:
Thankyou for you reply to me at August 14, 2013 at 4:28 pm
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390278
Bart addressed the error in your reply in his post at August 14, 2013 at 5:01 pm
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390305
Unfortunately, your post to Bart at August 14, 2013 at 6:16 pm.
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390359
indicates that you have missed – or failed to understand – the point.
You claim the anthropogenic CO2 emission is responsible for the observed rise in atmospheric CO2 concentration. But you refuse to understand that when considering a complex system it it is NOT sufficient that a possible explanation is “amply sufficient to explain the effect” . There are often “amply sufficient” and plausible but wrong explanations of complex system behaviour.
If this were merely an abstruse scientific issue then your mistake could be ignored: eventually data and understanding will be obtained to resolve the matter. However, others are now using the same mistake as an excuse to attempt imposition of harmful changes to energy and economic policies world-wide.
If the anthropogenic emission is harmful then responses to that harm need to be considered.
But
If the anthropogenic emission is not harmful then harmful responses to that emission need to be avoided.
In this situation it is essential that there be honest research to determine the true cause of the rise in atmospheric CO2 concentration.
Lysenkoist adoption of “amply sufficient” explanations need to be vigorously opposed.
And the ‘sides’ here represented by Ferdinand and Bart are important. Their promotion of their different interpretations of existing data can point others to needed research to obtain other data and to devise other interpretations of data.
The truth will out. External influences provide a need to rapidly ‘out’ the truth. And determination of the truth is prevented by adoption of Lysenkoist “amply sufficient” explanations.
Richard
Nyq Only:
Your post at August 15, 2013 at 2:13 am
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390622
displays much misunderstanding of the issues I tried to explain to you in my post at August 14, 2013 at 12:39 pm
http://wattsupwiththat.com/2013/08/11/murry-salby-responds-to-critics/#comment-1390029
You had said and asked
And I replied
I then explained that reply and concluded my explanation saying.
To which you have replied
NO!
You asserted, “A chart can only show correlation between two variables”. I explained YOUR ASSERTION IS PLAIN WRONG. And you now claim you said something else!
Nick, there is much else wrong with your reply, too. But the important issue is that if – as you claim – there is a causal relationship between atmospheric CO2 concentration and global temperature then the coherence demonstrates that the temperature is causal. You were asked to provide a chart which showed the opposite coherence. And you are making excuses for your inability to do that.
Also, I refuse to nibble the ‘red herrings’ of your “run through”.
The facts are clear; viz.
You were wrong: a chart can show what you said it cannot.
And you are incapable of providing a chart which is consist with your claims.
Richard
jimmi_the_dalek says: August 14, 2013 at 10:25 pm
Allan,
Yes, but that did not really answer my question. Do the derivatives agree? Where did you publish this? And, since there is now 5 years more data, what has changed since?
___________
Please understand that this work was done five years ago and I ran almost 100 different spreadsheets analyses, so finding the right one took some time.
Yes the derivatives agree although Barrow, with the greatest amplitudes, displays much greater variability, as would be expected.
In summary, the dCO2/dt plots are in-phase and agree.
I also recall examining some other CO2 measurement sites but did not find those spreadsheets.
I did not publish the work. It did not change the conclusions of my January 2008 icecap.us paper and 2008 was a very difficult year. One major injury, two major surgeries, a 2-month preemie baby delivered by emergency C-section, and lost a fortune in the market crash. Other than that it was a good year.
The paper is located at
http://icecap.us/index.php/go/joes-blog/carbon_dioxide_in_not_the_primary_cause_of_global_warming_the_future_can_no/
I last updated my work in 2010 and did not see any change to that time. I examined the same analysis back to 1958 using Hadcrut3 and the correlations held, although Hadcrut3 showed a possible warming bias of (as I recall) ~0.07C/decade versus UAH LT.
Chris Schoneveld says: August 15, 2013 at 1:37 am
Thank you Chris. I agree with you (and Richard) that the two realities (humanmade CO2 contributions and the “CO2 lags temperature” evidence) are not mutually exclusive, at least in theory.
It is indeed possible, even probable, that the observed increase in atmospheric CO2 has both a natural and a humanmade component, since the counter-assumption that there is absolutely NO humanmade component has a small probability of occurrence.
So it logically becomes a question of the magnitude of the natural versus the humanmade components in the observed CO2 increase. Ferdinand, using the mass balance argument, states that emissions from the combustion of fossil fuels are double the annual CO2 increase in the atmosphere. Richard states the counter-argument far better than I can repeat here.
The fact remains that the ONLY signal I can detect in the data, that survives ALL the complexities of this huge global-scale equation, is that dCO2/dt correlates with temperature T and CO2 LAGS temperature by about 9 months. The fact that this “dCO2/dt vs T signal” survives all this noise suggest to me that it is significant and DOMINANT – the dominant factor in the huge global CO2 flux equation.
In contrast, the global warming alarmists grudgingly accepted that the dCO2/dt vs T signal exists, but dismissed it as a “feedback effect”. I regard their feedback hypo as a ”Cargo Cult” digression, lacking credibility. It is like saying you can hear the piccolo in the orchestra, but you cannot hear the orchestra. I suggest they ARE hearing the orchestra, but refuse to admit it.
I suppose it is possible that the gradual (but not linear) increase in humanmade CO2 emissions is overlain by the dCO2/dt vs T signal, but it seems unusual that the dCO2/dt vs T signal survives intact.
Rapid cooling triggered Bronze-Age collapse and Greek Dark Age
http://iceagenow.info/2013/05/rapid-cooling-triggered-bronze-age-collapse-greek-dark-age/
[Excerpt]
Of course the politically correct verbiage is “climate change.”
Between the 13th and 11th centuries BCE, most Greek Bronze Age Palatial centers were destroyed and/or abandoned throughout the Near East and Aegean, says this paper by Brandon L. Drake
A sharp increase in Northern Hemisphere temperatures preceded the wide-spread systems collapse, while a sharp decrease in temperatures occurred during their abandonment. (Neither of which, I am sure – the increase or the decrease – were caused by humans.)
Mediterranean Sea surface temperatures cooled rapidly during the Late Bronze Age, limiting freshwater flux into the atmosphere and thus reducing precipitation over land, says Drake, of the Department of Anthropology, University of New Mexico.
This cooling and ensuing aridity could have affected areas that were dependent upon high levels of agricultural productivity. The resulting crop declines would have made higher-density populations unsustainable.
Indeed, studies of data from the Mediterranean indicate that the Early Iron Age was more arid than the preceding Bronze Age. The prolonged arid conditions – a centuries-long megadrought, if you will – lasted until the Roman Warm Period.
Those four centuries – known as the ‘Greek Dark Ages’ – were typified by low population levels, rural settlements, population migration, and limited long-distance trade.
The Late Bronze Age collapse is associated with the loss of writing systems such as Linear B, and the extinction of Hatti as both a written and spoken language. Writing and literacy do not return to the Aegean until the end of the ‘Greek Dark Ages’ in 8th century BCE with the spread of the Phoenecian alphabet.
…
Chris Schoneveld says:
August 15, 2013 at 1:37 am
“…while the charcteristic pattern of ∆CO2 (which correlates with ∆T) rides on top of that…”
Misses the point. ∆CO2 does not correlate with ∆T, but with the integral of ∆T. The integration of the slope in ∆T begets the curvature in the observed CO2. Adding in human emissions increases that curvature beyond the level which is observed. Therefore, significant contribution of human inputs is ruled out.
Allan MacRae says:
August 15, 2013 at 4:51 am
“I agree with you (and Richard) that the two realities (humanmade CO2 contributions and the “CO2 lags temperature” evidence) are not mutually exclusive, at least in theory. “
They are mutually exclusive, for the reason given above.
Nyq Only says:
August 15, 2013 at 2:13 am
“The relationship that was under discussion was that changes in THE RATE OF CHANGE of CO2 follow changes in the temperature. “
No! The rate of change of CO2 IS COINCIDENT WITH temperature anomaly. This naturally begets a 90 degree phase lag in absolute CO2 relative to temperature anomaly.
Allan MacRae says: August 15, 2013 at 4:51 am
Allan: “I agree with you (and Richard) that the two realities (humanmade CO2 contributions and the “CO2 lags temperature” evidence) are not mutually exclusive, at least in theory. “
Bart: They are mutually exclusive, for the reason given above.
____________
Please re-read my post Bart.
Perhaps there was a lack of clarity on my part.
Restating:
Humanmade CO2 contributions to atmospheric CO2 growth and the “CO2 lags temperature” phenomenon can both exist at the same time on Earth, at least in theory. In theory, one can overlay the other.
However, the “CO2 lags temperature” signal survives amidst all the noise of the huge CO2 seasonal flux equation, and this fact suggests to me that it is the dominant factor in this equation.
Bart says:
August 15, 2013 at 9:34 am
No! The rate of change of CO2 IS COINCIDENT WITH temperature anomaly. This naturally begets a 90 degree phase lag in absolute CO2 relative to temperature anomaly.
The changes in the rate of change of CO2 follow changes in temperature. That is observed with a lag of 6-9 months. The rate of change of CO2 is coincident with temperature anomaly. That is observed without lag. The question is which one is the real driver.
Any change in temperature will in/decrease the outflux of CO2 from the oceans, which in/decreases the rate of change in the atmosphere. But to give an instantaneous in/decrease in lockstep with the temperature change, one need an enormous change in influx or outflux, which is near impossible to obtain from deep ocean exchanges, which only change with less than 5% for 1 K in temperature change. If the extra CO2 release/absorbance comes from the ocean surface, then it is possible, but limited in time (and quantity).
See the WTF plot…
Allan MacRae says:
August 15, 2013 at 10:26 am
However, the “CO2 lags temperature” signal survives amidst all the noise of the huge CO2 seasonal flux equation, and this fact suggests to me that it is the dominant factor in this equation.
CO2 lags temperature on all time scales, except for the trend over the past 5 decades, where there is no discernable lead or lag. That is a problem for the attribution of the cause of the increase. Be it that other indications show the right direction…
Ferdinand,
You write:
“CO2 lags temperature on all time scales, except for the trend over the past 5 decades, where there is no discernable lead or lag.”
I agree with a lot of what you write. But your comment here is flatly contradicted by empirical evidence. There is a clear, easily discernable lag of CO2 behind temperature changes over the past 5 decades.
“NO!
You asserted, “A chart can only show correlation between two variables”. I explained YOUR ASSERTION IS PLAIN WRONG. And you now claim you said something else!”
Nah – we were discussing causality and the sentence you quoted was in relation to the issue of causality and correlation on which I had made several statements as part of a conversation. You can take my sentence out of context and, for example, you could claim that it is easily refuted by pointing out a graph can show the price of bananas – but that would be just telling the world something about reading comprehension. What you did in your explanation to me was go a long way around the houses back to a point I’d made several messages earlier. Now fair enough it is hard to follow the threads of discussion on a blog posts with lots of comments but that is just something we have to cope with. Back to the bottom line – to establish causality you need more than a graph showing correlation of two variables or even a lag between the two. As far as I can see you agree. If you think I should have worded what I said better then I shan’t disagree with you.
Bart,
Sorry I didn’t mean to say ∆T but meant T anomaly. Yet, I (with my limited knowledge of the subject) don’t see why that would not allow for the possibility that the changes in ∆CO2 could not be derived from a contribution of a linear anthropogenic portion and a varying (with T) natural portion.