Guest Post by Willis Eschenbach
In the leaked version of the upcoming United Nations Intergovernmental Panel on Climate Change (UN IPCC) Fifth Assessment Report (AR5) Chapter 1, we find the following claims regarding volcanoes.
The forcing from stratospheric volcanic aerosols can have a large impact on the climate for some years after volcanic eruptions. Several small eruptions have caused an RF for the years 2008−2011 of −0.10 [–0.13 to –0.07] W m–2, approximately double the 1999−2002 volcanic aerosol RF.
and
The observed reduction in warming trend over the period 1998–2012 as compared to the period 1951–2012, is due in roughly equal measure to a cooling contribution from internal variability and a reduced 2 trend in radiative forcing (medium confidence). The reduced trend in radiative forcing is primarily due 3 to volcanic eruptions and the downward phase of the current solar cycle.
Now, before I discuss these claims about volcanoes, let me remind folks that regarding the climate, I’m neither a skeptic nor am I a warmist.
I am a climate heretic. I say that the current climate paradigm, that forcing determines temperature, is incorrect. I hold that changes in forcing only marginally and briefly affect the temperature. Instead, I say that a host of emergent thermostatic phenomena act
quickly to cool the planet when it is too warm, and to warm it when it is too cool.
One of the corollaries of this position is that the effects of volcanic eruptions on global climate will be very, very small. Although I’ve demonstrated this before, Anthony recently pointed me to an updated volcanic forcing database, by Sato et al. Figure 1 shows the amount of forcing from the historical volcanoes.
Figure 1. Monthly changes in radiative forcing (downwelling radiation) resulting from historical volcanic eruptions. The two large recent spikes are from El Chichon (1983) and Pinatubo (1992) eruptions. You can see the average forcing of -0.1 W/m2 from 2008-2011 mentioned by the IPCC above. These are the equilibrium forcings Fe, and not the instantaneous forcing Fi.
Note that the forcings are negative, because the eruptions inject reflective aerosols into the stratosphere. These aerosols reflect the sunlight, and the forcing is reduced. So the question is … do these fairly large known volcanic forcings actually have any effect on the global surface air temperature, and if so how much?
To answer the question, we can use linear regression to calculate the actual effect of the changes in forcing on the temperature. Figure 2 shows the HadCRUT4 monthly global surface average air temperature.
Figure 2. Monthly surface air temperatures anomalies, from the HadCRUT4 dataset. The purple line shows a centered Gaussian average with a full width at half maximum (FWHM) of 8 years.
One problem with doing this particular linear regression is that the volcanic forcing is approximately trendless, while the temperature has risen overall. We are interested in the short-term (within four years or so) changes in temperature due to the volcanoes. So what we can do to get rid of the long-term trend is to only consider the temperature variations around the average for that historical time. To do that, we subtract the Gaussian average from the actual data, leaving what are called the “residuals”:
Figure 3. Residual anomalies, after subtracting out the centered 8-year FWHM gaussian average.
As you can see, these residuals still contain all of the short-term variations, including whatever the volcanoes might or might not have done to the temperature. And as you can also see, there is little sign of the claimed cooling from the eruptions. There is certainly no obvious sign of even the largest eruptions. To verify that, here is the same temperature data overlaid on the volcanic forcing. Note the different scales on the two sides.
Figure 4. Volcanic forcing (red), with the HadCRUT4 temperature residual overlaid.
While some volcanoes line up with temperature changes, some show increases after the eruptions. In addition, the largest eruptions don’t seem correlated with proportionately large drops in temperatures.
So now we can start looking at how much the volcanic forcing is actually affecting the temperature. The raw linear regression yields the following results.
R^2 = 0.01 (a measure from zero to one of how much effect the volcanoes have on temperature) "p" value of R^2 = 0.03 (a measure from zero to one how likely it is that the results occurred by chance) (adjusted for autocorrelation). Trend = 0.04°C per W/m2, OR 0.13°C per doubling of CO2 (how much the temperature varies with the volcanic forcing) "p" value of the TREND = 0.02 (a measure from zero to one how likely it is that the results occurred by chance) (adjusted for autocorrelation).
So … what does that mean? Well, it’s a most interesting and unusual result. It strongly confirms a very tiny effect. I don’t encounter that very often in climate science. It simultaneously says that yes, volcanoes do affect the temperature … and yet, the effect is vanishingly small—only about a tenth of a degree per doubling of CO2.
Can we improve on that result? Yes, although not a whole lot. As our estimate improves, we’d expect a better R^2 and a larger trend. To do this, we note that we wouldn’t expect to find an instantaneous effect from the eruptions. It takes time for the land and ocean to heat and cool. So we’d expect a lagged effect. To investigate that, we can calculate the R^2 for a variety of time lags. I usually include negative lags as well to make sure I’m looking at a real phenomenon. Here’s the result:
Figure 5. Analysis of the effects of lagging the results of the volcanic forcing.
That’s a lovely result, sharply peaked. It shows that as expected, after a volcano, it takes about seven-eight months for the maximum effects to be felt.
Including the lag, of course, gives us new results for the linear regress, viz:
R^2 = 0.03 [previously 0.01] "p" value of R^2 = 0.02 (adjusted for autocorrelation) [previously 0.03] Trend = 0.05°C per W/m2, OR 0.18 ± 0.02°C per doubling of CO2 [previously 0.13°C/doubling] "p" value of the Trend = 0.001 (adjusted for autocorrelation). [previously 0.02]
As expected, both the R^2 and the trend have increased. In addition the p-values have improved, particularly for the trend. At the end of the day, what we have is a calculated climate sensitivity (change in temperature with forcing) which is only about two-tenths of a degree per doubling of CO2.
Here are the conclusions that I can draw from this analysis.
1) The effect of volcanic eruptions is far smaller than generally assumed. Even the largest volcanoes make only a small difference in the temperature. This agrees with my eight previous analyses (see list in the Notes). For those who have questions about this current analysis, let me suggest that you read through all of my previous analyses, as this is far from my only evidence that volcanoes have very little effect on temperature.
2) As Figure 5 shows, the delay in the effects of the temperature is on the order of seven or eight months from the eruption. This is verified by a complete lagged analysis (see the Notes below). That analysis also gives the same value for the climate sensitivity, about two tenths of a degree per doubling.
3) However, this is not the whole story. The reason that the temperature change after an eruption is so small is that the effect is quickly neutralized by the homeostatic nature of the climate.
Finally, to return to the question of the IPCC Fifth Assessment Report, it says:
There is very high confidence that models reproduce the more rapid warming in the second half of the 20th century, and the cooling immediately following large volcanic eruptions.
Since there is almost no cooling that follows large volcanic eruptions … whatever the models are doing, they’re doing it wrong. You can clearly see the volcanic eruptions in the model results … but you can’t see them at all in the actual data.
The amazing thing to me is that this urban legend about volcanoes having some big effect on the global average temperature is so hard to kill. I’ve analyzed it from a host of directions, and I can’t find any substance there at all … but it is widely believed.
I ascribe this to an oddity of the climate control system … it’s invisible. For example, I’ve shown that the time of onset of tropical clouds has a huge effect on incoming solar radiation, with a change of about ten minutes in onset time being enough to counteract a doubling of CO2. But no one would ever notice such a small change.
So we can see the cooling effect of the volcanoes where it is occurring … but what we can’t see is the response of the rest of the climate system to that cooling. And so, the myth of the volcanic fingerprints stays alive, despite lots of evidence that while they have large local effects, their global effect is trivially small.
Best to all,
w.
PS—The IPCC claims that the explanation for the “pause” in warming is half due to “natural variations”, a quarter is solar, and a quarter is from volcanoes. Here’s the truly bizarre part. In the last couple decades, using round numbers, the IPCC predicted about 0.4°C of warming … which hasn’t happened. So if a quarter of that (0.1°C) is volcanoes, and the recent volcanic forcing is (by their own numbers) about 0.1 W/m2, they’re saying that the climate sensitivity is 3.7° per doubling of CO2.
Of course, if that were the case we’d have seen a drop of about 3°C from Pinatubo … and I fear that I don’t see that in the records.
They just throw out these claims … but they don’t run the numbers, and they don’t think them through to the end.
Notes and Data
For the value of the forcing, I have not used the instantaneous value of the volcanic forcing, which is called “Fi“. Instead, I’ve used the effective forcing “Fe“, which is the value of the forcing after the system has completely adjusted to the changes. As you might expect, Fi is larger than Fe. See the spreadsheet containing the data for the details.
As a result, what I have calculated here is NOT the transient climate response (TCR). It is the equilibrium climate sensitivity (ECS).
For confirmation, the same result is obtained by first using the instantaneous forcing Fi to calculate the TCR, and then using the TCR to calculate the ECS.
Further confirmation comes from doing a full interative lagged analysis (not shown), using the formula for a lagged linear relationship, viz:
T2 = T1 + lambda (F2 – F1) (1 – exp(-1/tau)) + exp(-1/tau) (T1 – T0)
where T is temperature, F is forcing, lambda is the proportionality coefficient, and tau is the time constant.
That analysis gives the same result for the trend, 0.18°C/doubling of CO2. The time constant tau was also quite similar, with the best fit at 6.4 months lag between forcing and response.
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In this case it’s the Sato paper, which provides a dataset of optical thicknesses “tau”, and says:
The relation between the optical thickness and the forcings are roughly (See “Efficacy …” below):
instantaneous forcing Fi (W/m2) = -27 τ
adjusted forcing Fa (W/m2) = -25 τ
SST-fixed forcing Fs (W/m2) = -26 τ
effective forcing Fe (W/m2) = -23 τ
And “Efficacy” refers to
Hansen, J., M. Sato, R. Ruedy, L. Nazarenko, A. Lacis, G.A. Schmidt, G. Russell, et al. 2005. Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005/JD005776.
Forcing Data
For details on the volcanic forcings used, see the Sato paper, which provides a dataset of optical thicknesses “tau”, and says:
The relation between the optical thickness and the forcings are roughly (See “Efficacy …” below):
instantaneous forcing Fi (W/m2) = -27 τ
adjusted forcing Fa (W/m2) = -25 τ
SST-fixed forcing Fs (W/m2) = -26 τ
effective forcing Fe (W/m2) = -23 τ
And “Efficacy” refers to
Hansen, J., M. Sato, R. Ruedy, L. Nazarenko, A. Lacis, G.A. Schmidt, G. Russell, et al. 2005. Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005/JD005776.
(Again, remember I’m using their methods, but I’m not claiming that their methods are correct.)
Future Analyses
My next scheme is that I want to gin up some kind of prototype governing system that mimics what it seems the climate system is doing. The issue is that to keep a lagged system on course, you need to have “overshoot”. This means that when the temperature goes below average, it then goes above average, and then finally returns to the prior value. Will I ever do the analysis? Depends on whether something shinier shows up before I get to it … I would love to have about a dozen bright enthusiastic graduate students to hand out this kind of analysis to.
I also want to repeat my analysis using “stacking” of the volcanoes, but using this new data, along with some mathematical method to choose the starting points for the stacking … which turns out to be a bit more difficult than I expected.
Previous posts on the effects of the volcano.
Prediction is hard, especially of the future.
Pinatubo and the Albedo Thermostat
Dronning Maud Meets the Little Ice Age
New Data, Old Claims about Volcanoes
Volcanoes: Active, Inactive and Interactive
Stacked Volcanoes Falsify Models
Greg Goodman: “It is the key word non-linear that makes the overshoot required to preserve the degree.day integral. A linear neg. f/b will not do this.”
While I am almost certain that non-linear effects are the key to the climate’s being limited to a relatively narrow temperature range, it is not true that only non-linear systems exhibit overshoot. Any “two-box” system will overshoot, and some will in fact oscillate.
Example: a system whose response y to a stimulus x is given by
d^2 y / dt^2 + 2 dy/dt + 40 y = 40 x
will exhibit a (decaying) oscillatory response to a step in stimulus.
Stephen Wilde says:
September 23, 2013 at 12:51 am
////////////////////
I accept the point that surface pressure is relevant to the temperature at which bonds are broken. The world would be a different place if atmospheric pressure was different (some consider that it was different at the time of the dinosaurs and there is much research into this and in particlur with respect to flying dinosaurs as well as long necked varieties).
I am not sure what to make of your comment “Having considered the matter further overnight I still don’t see how emergent cloudiness can provide any sort of cap on achievable temperatures because a cap has to be exceeded before the clouds form (at least the types of clouds proposed).” Don’t clouds form over all oceans even those with cold temperatures, no doubt because evaopration begins to take place as soon water is liquid, albeit the rate of evaporation is proportional to its temperature such that the rate of evaporation and hence the volume of evaporated water is greater over the tropical oceans than over high latitude oceans. Doesn’t any cloud block solar irradiance such that cloud formation is a negative feedback, although I accept that the type of cloud (not only its areal extent, but also its volume, water content, vapour & water droplet size, height, time of formation and time of dissipation) influences the extent of that negative feedback. Clouds are infinitely complex (and chaotic in nature notwithstanding that they may have certain key drivers) and without understanding these there is no chance of modelling climate.
Looks like mainstream press is turning in droves to being at least skeptical. In Google news “global warming” This will definitely be the last IPCC meeting. Even the BBC’s main reporters are beginning to get it
http://www.bbc.co.uk/news/science-environment-24173504
Frank says:
September 23, 2013 at 1:53 am
>>>>>>>
Great input, including a little math to help our understanding. I was trying to put it all together last night, but was too tired and gave up, so was pleased to find your helpful comment this morning.
Thanks!
Greg Goodman:
At September 23, 2013 at 3:36 am you reply to my having said
by saying
The definition I provided is precise and not “vague”. I can state it in mathematical terminology if required but see no reason to do that here.
A negative feedback reduces the magnitude of an effect.
A positive feedback increases the magnitude of an effect.
A governor limits the magnitude of an effect.
No combination of feedbacks and governors reverses the sign of an effect.
Richard
what happens at glaciation and deglaciation is clearly different from what happens in between. There is apparently two stable states ( attractors ) for the climate system. A positive feedback seems to make it snap form one state to the other. We don’t really know what triggers the change-over.
At least two attractors. More likely, the system is highly multistable with attractors all over the place and with at least two MAJOR “attractors of attractors” as it were.
There are even multiple distinct ways the climate system can shift. As you say, when the climate is in a major-bistable critical regime, chance fluctuations can kick the system too far from the currently dominant (say) warm-phase attractor and the system can then descend — probably via a series of transitions to transiently stable intermediate attractors — to one of the many cold-phase attractors sufficiently stable to hold the system once again, or it can start to descend — as perhaps it did during the LIA — but then can pop back up. Bobbles of this sort are clearly visible in the geological record, where even during glacial eras there are stretches of warming that doesn’t reach the critical/tipping point followed by aggressive cooling, or the Younger Dryas, where it warmed to interglacial temperatures but then “suddenly” re-descended into glaciation for close to a thousand years before re-warming into the Holocene proper.
In addition to jumping attractors, the attractors themselves appear capable of secular moment on longer timescales — the stable point itself is no doubt a weak function of a variety of forcings plus a non-Markovian integral over the climate history into the past (of the sort Willis is exploring with his lagged response models above, where it isn’t the state of things “right now” that always matters, sometimes it is the state of things a year, ten years, fifty years past PLUS the state of things right now. The ocean has mixing/turnover phenomena with timescales of centuries on up (in addition to shorter time scales as well).
The pattern of temperature shifts over the “reliable” temperature era (the last 33-50 years) has been periods of relative stability order of 15 years followed by a rapid shift over 2-3 years followed by relative stability. The less reliable thermometric era (HADCRUT-whatever or GISS-whatever) also suggests a pattern of stable temperatures for periods of 1-3 decades, followed by 1-3 decades of warming, a punctuated series of equilibria, with the high frequency noise Willis plots above reflecting motion AROUND the current attractor, not the motion OF the attractors or the jumps between attractors (where the latter two would be very difficult to differentiate without a knowledge of the dimensionality of the space and some feel for the nonlinear functions that describe the locally stable points to gain some insight about how they might vary, appear, disappear as underlying parameters in the climate system change).
However, the fundamental problem with doing the analysis Willis presents so ably above is that it neglects the probable errors. HADCRUT4 actually has an estimated error bar of 0.15 C for present day data. It is at least 0.5 C, if not larger, for most of the rest of the thermometric era, in particular for those parts back in the 19th century and early 20th century. Remember that entire continents were still mostly terra incognita (as far as systematic sampling with reliable thermometers is concerned) well into the 20th century.
The inclusion of probable error into the fits makes it even more difficult to discern the effect of volcanoes or any other secular causes in the temperature trend. If one is trying to resolve a 0.1 C effect in data that is both (possibly) trended (with high autocorrelation) and noisy at 0.5 C, you simply cannot expect to obtain a reliable causal/correlative decomposition. If you like, the p-values Willis obtains are too optimistic — it is even more likely that the almost completely invisible trend he uncovers within the data is there by random chance, because the detrended noise he fits is, in fact, uncertain within a range that is slightly larger than the range of the noise itself.
With that said, I do like looking at fluctuation-dissipation in climate models as I think it has a lot to teach us. The response of the climate to a sudden forcing (like a volcano, like a powerful ENSO) in principle gives us a direct look at the shape of the attractor(s) that govern the climate’s current set point.
rgb
richardscourtney: “A Reversal Effect arises in response to a direct effect, and it combines with the direct effect such that the combination has opposite sign to the direct effect (i.e. when the direct effect is +ve the combination is –ve).”
Not sure I follow that, but might the following be a quotidian example?
A water drop hitting a skillet as the skillet only starts to heat up evaporates slowly. As the skillet heats up, subsequent drops evaporate more quickly–but only up to a point. After that, there’s a regime in which increasing skillet temperature causes the drops to evaporate more slowly. (They form little water marbles that roll around.)
richard verney:
Thankyou for your reply to me at September 23, 2013 at 5:30 am.
I apologise to you and anybody else whom I failed to specifically name but ‘lumped as one’ in my reply to requests for explanation of the R&C Effect. This was an error but was not intended as a slight or as any other offence to anyone.
I hope my explanation was adequate, and I read your resulting comment with interest.
Richard
What eruptions are they attributing the current non-warming to? Pinitubo and Chichon? Ridiculous. It’s long been established the effect is short term (1-2 years).
Joe Born:
re your post at September 23, 2013 at 6:30 am
Thankyou for that example. I did not know of it. However, I do not think that is an example of what I am calling a Reversal Effect.
In your example the formation of ‘marbles’ acts to reduce evapouration rate so acts as a negative feedback on evapouration.
[If the formation of ‘marbles’ acted to stop evapouration and to induce condensation then it would have a Reversal Effect on evapouration.]
The R&C and Eschenbach Effects are Reversal Effects on temperature because they induce the system to COOL (n.b. not warm) in response to increased heat input to the system.
Richard
Ouch! I stupidly wrote
If he formation of ‘marbles’ acted to stop evapouration and to induce condensation then it would have a Reversal Effect on condensation.
I intended to write
If the formation of ‘marbles’ acted to stop evapouration and to induce condensation then it would have a Reversal Effect on evapouration.
Sorry. Richard
Milodon, I find that I owe you an apology … but likely not for what you think.
I was reflecting on my statement in the final comment of my post “Missing the Missing Summer.” I had said:
Now, I’ve grown very cautious about what I say, and I couldn’t make sense of that. More to the point, I hadn’t been making any points about Pinatubo above, only about Krakatoa. What I realized was that in my comment I’d written 1992 (thinking wrongly of Pinatubo), and I should have said 1815, the date of Krakatoa.
That makes sense, because in “Missing the Missing Summer” I’d posted the following graphic above my comment:
That graphic was the reason that I made such an absolute claim, which I very rarely do.
So my apologies, Milodon for leading you astray with my incorrect date. I trust that my claim makes more sense now.
All the best,
w.
Willis Eschenbach says:
September 22, 2013 at 6:15 pm
Jim G says:
September 22, 2013 at 5:10 pm
A true super eruption of a super volcano might be at odds with your “self regulating” surface temperature hypothesis.
“We’ve had supervolcanoes in the past, and the temperature has always recovered. Under the models’ view, that wouldn’t happen … with my hypothesis, it would. ”
w.
Willis,
I was not implying that the models were correct. Being better than the models is damning yourself with faint praise. If one takes a long enough view of climate/temperature even the Milankovich cycles do cycle back.
richardscourtney says: The R&C and Eschenbach Effects are Reversal Effects on temperature because they induce the system to COOL (n.b. not warm) in response to increased heat input to the system.
What are you trying to suggest here? Is your “reversal” the overshoot that Willis refers to which is part of a reaction that brings the system back to it’s previous state or are you suggesting a reaction the leaves the system in a settled state, cooler than it was before the perturbation. If that is the case I want to see proof. Not just inventing a name for it.
If it’s the former, it already has a name: non-linear negative feedback.
***
Steven Mosher says:
September 22, 2013 at 6:06 pm
The ECS is what we are mostly interested in, the full response after all feedbacks,
***
All forcing aren’t created equal. IR back-radiation from GHGs can’t penetrate beyond water surfaces, so the effects are immediate (changes in ocean surface temp and atmospheric latent heat from evaporation). Only short-wave solar forcing can be stored in the oceans and have a significant time-lag.
Quick question:
How long do this particles stay in the stratosphere, and do their affects change as they fall through the troposphere?
RC Saumarez says:
September 23, 2013 at 12:55 am
Yeah, and you’re also the guy who claimed that two points determine a sine wave, if the time between them is less than half the shortest period in the dataset … and you’re the guy who claimed he can discern a 170.7 year cycle when given only 110 years of data.
Since I demonstrated that the first one of those claims was 100% wrong … and since the second claim is ludicrous on the face of it … well, Richard, I’ve kinda lost trust in your expertise.
Finally, you wrote a whole post that you later claimed was to show me where my math was wrong … but you never mentioned me or any of my claims anywhere in the post. Didn’t stop you from boasting about how you schooled me, despite doing nothing of the sort.
So I believe I’ll pass …
w.
Stephen Wilde says:
September 23, 2013 at 1:13 am
Stephen, YOU are the elephant in the room. Despite being asked twice to take your ignorant claims that pressure warms the earth to some other thread, here you are peddling the same bullshit and stomping your big dumb elephant feet all over a discussion which has NOTHING TO DO WITH YOUR CLAIMS. This thread is about volcanoes, not your stupid ideas.
What is it about PLEASE DISCUSS YOUR CLAIMS ELSEWHERE that you don’t understand? I’ve shown in “A Matter Of Some Gravity” that your theories are in contradiction to the Second Law. If you don’t understand my proof, and why it shows that your ideas won’t work, then read it again. Repeat as necessary until you finally get the picture—neither gravity nor pressure can provide a constant source of heat to warm the surface as you claim, because it violates the Second Law.
And whether you agree with me or not, and whether you understand my proof or not … please don’t come back to argue that you are right. Not interested, sorry.
Don’t go away mad.
Just go away.
w.
@Tintoolman.
Here some points:
1) The model posed by Eschenbach is a simple 1st order ARMA model. It is well known that the autocorrelation fundtion of temperature does not correspnd to this model. Therefore it is incorrect. This has been widely discussed. by McIntyre, Luck and Ludeke and others in the past. See for example:
http://judithcurry.com/2012/02/19/autocorrelation-and-trends/
2) There are significant non-linearities in the climate system and characterisation is made over change in climate. The degree of linearisation possible is unknown and therefore it is unknown whether linear models can be applied to the climate system as has been done here( I suspect not). I would comment that establishment of linearity or non linearity, although extremely difficult using this data is a first step. A non-linear Hurst dynamic model is practically impossible to distinguish from a linear multicompartment model in the global temperature signal. (See link above for details).
3) The errors involved in fitting parameters to a climate model are enormous, even if the data were perfect. If one is going to propose a model, rigorous assessment of that uncertainty is essential. Furthermore,the functions being fitted are highly ill-conditioned, meaning that small changes in the input data will lead to large changes in fitted parameters.
As I have said in past, I am happy to write a criticism of the methods employed here, particularly how one can be misled by spurious linearisation of non-linear systems. Since i was challenged to put up or shut up, I have written an essay on the difficulties in establishing system responses in non-linear systems that might appear to be linear at first sight, sent it to WUTW but it is has clearly not been thought to be suitable for posting, It is difficult to take the argument further
I have said that I am happy to help Mr Eschenbach if he wishes to develop a more elaborate model since this is a constructive approach.
Is the IPCC trying to blame the pause in global warming on a handful of small volcanoes?
MikeN says:
Is the IPCC trying to blame the pause in global warming on a handful of small volcanoes?
Any straw in storm … 😉
The real climate drivers – ocean and solar cycles amplified by levels
Krakatoa was not 1815. That would be Tambora.
[snip – don’t post whole pages in comments with dozens of striped out links to images etc – Anthony]
In the above post to get pictures and graphs just google The real climate drivers -ocean and solar cycles amplified by levels of volcanism.
Willis study is flawed in many ways, one way is he is trying to isolate the effects of volcanos in the climatic system by putting them in isolation against all the zillion other climatic parameters trying to obtain a climate direct cause and effect due to the volcanic eruption itself.Does not work.
The IPCC is also flawed because the Aerosol Optical thickness for the N.H. ws only .01 tau during the time the IPCC claimed volcanic activity and or aerosols was having an impact of slowing down the warming.
Weatherbell Inc. led by Joe Bastardi and Joe D’Aleo see it differently then what Willis is trying to convey from his study. I am in their camp 100%.