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

Ah, seems you’ve done at the same time, fine. Any thought on what they show or a mod to take care of phase wrapping around?
PS thanks for hacking the code, I’d only done the first one so, that’s saved some time.
AJ. your comment on the 2008 plot in the initial graph set:
“For comparison purposes, here’s a plot of the fitted curve to the actual signal for year 2008 data. Does this model underestimate the amplitude once the signal starts to become lost?”
I’d say not. I think it appears to correctly shows the reduced 12mo amplitude but does not model the 6mo component.
I’d suggest the 6mo is a rectified 12mo not a 6mo sine. That’s why it appeared as a “notch”.
What I find rather surprising at first view is that this signal is so significant at depth in the extra-tropics , when the initial jump-to-conclusion would be to associate this with tropical insolation cycle.
Now since this is less marked in the multi-year means, maybe it is not exactly 6mo and/or there’s a phase drift.
Digging around in the depths is proving very interesting. Using the segment AJ has called program 2 in his revised code, linked above, confirms that the even numbered years are more interesting.
It all seems to be happening below 300m with a clear pattern emerging at 1000m
2008 I’ve already commented has a strong 6mo signal, the second harmonic. Also 2004 has strong 2nd and 3rd, and 2010 has strong 3rd. An this is just looking at one lat band at 42.5 S.
It seems the depths are not as deep and still as we are lead to believe (or assume).
Willis said:
“Now add an argon atmosphere, which is transparent to infrared. The pressureheads claim that the pressure of that atmosphere will warm the surface.
But if it does so, the surface will radiate more … and at that point it’s radiating more than it is absorbing, and that’s not possible on a continual basis.
That’s the proof that no such mechanism based on pressure can heat the surface. It would violate the law of conservation of energy, constantly emitting more than it is receiving.”
Consider pressure as a proxy for density which is a function of mass and gravity.
The more dense the argon at the surface the more it will exchange energy with the surface by non radiative processes.
The surface then has to radiate energy to space AND exchange energy with the argon.
Thus the surface temperature can (indeed must) be higher without offending the laws of thermodynamics.
It is true that what the surface receives from the argon over time matches what it passes back to the argon but nonetheless the surface temperature must be higher so that notwithstanding the argon/surface exchange it STILL has enough energy left over to radiate as much out at ToA as is received at ToA.
Then consider that when the argon atmosphere expands or contracts the density at the surface decreases or increases and the amount of energy exchanged by non radiative processes will change too.
That results in circulation changes so as not to upset ToA equilibrium.
All composition variations can do is change the volume of the atmosphere which then adjusts the density at the surface which also adjusts the temperature at the surface with an equal and negative thermal response for zero net effect on surface temperature but instead a circulation adjustment.
Greg Goodman says:
September 26, 2013 at 9:12 pm
Re: NA’s in longitudinal start/count fields
Yep… That was a “feature” I corrected. I didn’t notice it because I was selecting all longitudes. At the time I may have been wondering what to do when the selection crosses the prime meridian, which still isn’t resolved. Should only matter if you’re looking specifically at the Southeast Atlantic, North Sea, etc.
I was wondering if the win.graph function would work on linux. That was back in the “old days”. Nowadays I use the dev functions.
As far as phase wrapping, my thought would be to check if point1 was in the 4th quarter and point2 was in the 1st, then put point2 in a fifth quarter. Same with point1 being in 5th quarter and point2 in 1st quarter. Kinda cludgy, but looks like it would work. There’s probably more elegant solutions using slopes.
I think the amplitude is lost once the signal becomes noisy. I better metric would be to calculate the variance or stdev as that would give you the power across the full spectrum of frequencies:
https://sites.google.com/site/climateadj/ocean_variance
I wouldn’t be surprised if the 6mo tropical signal migrates poleward. I’d also bet Chladni patterns emerge.
Good to see you find the digging interesting. Happy hunting.
I’ve hacked AJ’s program2 to show the annual cycle at 1000m for each year.
http://climategrog.files.wordpress.com/2013/09/argo-multi_-42p5s_1000m.png
There is a clear 2y alternation between a mainly 12mo seasonal cycle and a more complex variation dominated by higher harmonics.
BTW anyone explain what a “potential temperature” is ? I find lots of mentions of the term without an explanation. Straight temp is also there.
http://climategrog.files.wordpress.com/2013/09/argo-multi_-42p5s_1000m1.png
plus 2011,2012 😉
Greg… now that I’ve thought about it for more than 2 seconds and have had my coffee, here is a way of handling the phase wrap. Note, I was measuring phase in days (assuming a 365 day year).
If the difference between two years is outside of 365/2 days, add or subtract 365 days:
lag = c(200, 250, 300, 350, 40, 90, 40, 350, 300)
lagdiff = diff(lag)
x = lagdiff (365/2)
lagdiff[x] = lagdiff[x] – 365
lagdiff = c(lag[1],lagdiff)
lag = cumsum(lagdiff)
Greg… from what I understand potential temperature is adjusted to a standard pressure level. Not as important in the ocean as it is in the atmosphere.
http://en.wikipedia.org/wiki/Potential_temperature
crap… angle brackets wrecked my code
Let’s try this again with .lt., .gt.
lag = c(200, 250, 300, 350, 40, 90, 40, 350, 300)
lagdiff = diff(lag)
x = lagdiff .lt. -(365/2)
lagdiff[x] = lagdiff[x] + 365
x = lagdiff .gt. (365/2)
lagdiff[x] = lagdiff[x] – 365
lagdiff = c(lag[1],lagdiff)
lag = cumsum(lagdiff)
Greg… your plots still have a 1 in 128 chance of being a fluke… so I wouldn’t get too excited 🙂
Unless you were looking for an alternating pattern that started off with heads instead of tails. Then the chances of a fluke would be 1 in 256 🙂
AJ, thanks for the code suggestion. I’ll try to test it later. It would be interesting to see the phase without the flip.
I appreciate the 1:128 warning , what needs to be done here is to extract the data and do frequency analysis, not talk about annual flip-flop. That is enough to suggest there’s something worth investigating, it is not a result over 8 individual years.
What is much more interesting is whether there is a clear presence of 2nd and 3rd harmonics. I see suggestions of this but of course it’s easy to be fooled just scanning a few plots by eye.
Greg, I applied my change and it look better. Update plot and source as well:
https://sites.google.com/site/climateadj/argo-sine-fitting-2012
[code language=”r”]
############ avoid phase wrap-arounds
bdiff = diff(b)
x = bdiff < -(365/2)
bdiff[x] = bdiff[x] + 365
x = bdiff >= (365/2)
bdiff[x] = bdiff[x] – 365
bdiff = c(b[1],bdiff)
b = cumsum(bdiff)
############
[/code]
Apparently code can be inserted into wordpress:
http://en.support.wordpress.com/code/posting-source-code/
Now I just gotta fix the longitude selection issue.
Willis… sorry for highjacking your post… this should be the last comment from me
Well I lied. I fixed the longitude select issue (I think). I was able to do a sample in the South Atlantic, from 350 to 10 (i.e. 10W-10E):
https://sites.google.com/site/climateadj/argo-sine-fitting-2012
So in summary you have to replace the getncsample and sinefit functions. The getncsample function now has a subfunction selectncsample that has to be added.
Both programs on the page have been fixed.
Many thanks for the improved code AJ. Being able to limit the sample to separate ocean basins is valuable.
It does look like 2011 was an oddball. It stared like an even year above 300m then veers back to odd year pattern below 400m. I need to pull out complete time series rather than looking at 12m snippets but it’s interesting.
thx.
Something like this will get you a detrended timeseries:
[code language=”r”]
require(ncdf)
require(abind)
##############################################################
############## INSERT FUNCTIONS getncsample and selectncsample
##############################################################
nc<-open.ncdf("argo_2005-2012_grd.nc")
#
#### PARAMETERS ####
#
sy<-01 # 1st year = 1
cy<-08 # number of years
t <- (1:(cy*12))/12
ub<-500 # Upper Level Depth
lb<-500 # Lower Level Depth – ARGO data goes to 2000M
wb<-160 # western boundary
eb<-280 # eastern boundary
samplat<- -45
nb<- samplat + 1
sb<- samplat – 1
#
#### MAIN PROGRAM ####
#
ncsource1 <- "argo"
ptnc <- getncsample(nc, ncsource1, wb, eb, sb, nb, ub, lb, sy, cy)
#
# Get rid of longitudes that have NA values.
#
ptlong <- apply(ptnc,1,function(x)all(!is.na(x)))
ptlong <- which(ptlong==TRUE)
ptsamp <- ptnc[ptlong,,,]
ptsamp <- apply(ptsamp,3,mean)
ptresid <- resid(lm(ptsamp ~ t))
plot(t,ptresid,type=’l’)
[/code]
Anyway, my wife just got back from a road trip, so we got some “catching up” to do. Think I’ll sign out now 🙂
Thanks for the code, should save some time. Fairly strong 4year pattern down at 1000m. Again more like rectified 8 year. At 500m more like a 4 year ramp.
Look in again on Monday, I should have something clearer.
Have a good weekend 😉
I’ve dropped the deeper stuff, though it’s interesting short signals go that deep, amplitude is very small. However, variations around 75m-100m are very interesting compared to the surface.
http://climategrog.wordpress.com/?attachment_id=535
http://climategrog.wordpress.com/?attachment_id=534
http://climategrog.wordpress.com/?attachment_id=533
http://climategrog.wordpress.com/?attachment_id=532
http://climategrog.wordpress.com/?attachment_id=531
http://climategrog.wordpress.com/?attachment_id=530
Often there is larger variation at these depths than at the surface. Also the strong annual peak seems to anti-correlate with these depths.
Even as far north as 25N , at 200m (grey line) the annual cycle peaks as we would see in SH.
At 25S similar suppression of the the surface peak and 200m showing peaks in Jan and June.
These are all 160E to 280E so span trade wind / El Nino east-west variation.
10N is near the ITCZ but still seems dominated by SH like timing.
http://climategrog.wordpress.com/?attachment_id=533
There does not seem to be much evidence of a downward propagating wave from the surface insolation. Often 75m is fairly flat as the surface pattern morphs to the lower annual cycle.
This data really needs to be visualised as an animated 3D plot. I have seen this done but only for very short periods. IIRC that was depth of the thermocline over 2 years.
Nice graphs Greg. Yes the variations below the thermocline are very small. I think the largish variations under the tropics, say between 50-150m, probably have something to do with the trade winds affecting the thermocline, but what do I know?
It would be interesting to see an animated gif of your sample area, say above 200m, with each frame consisting of an image plot of monthly temperature by latitude and depth. Maybe I’ll try that over the coming days.
and here’s the animation I mentioned:
https://sites.google.com/site/climateadj/argo-animation