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
RC Saumarez:
Your post at September 25, 2013 at 1:40 am is a waste of space in the thread.
Your post is abusive, offensive and arrogant but it is devoid of any useful information.
Please say if you have genuine criticisms of Willis’ work because many – including me – would like to know of them. But assertions that you would have done something else, your “suspicions”, and your unfounded insults of Willis only provide information about you.
This may surprise you, but nobody is interested in you. Many are interested in Willis’ analysis.
Richard
“This may surprise you, but nobody is interested in you. Many are interested in Willis’ analysis.”
Richard, this may surprise you but you are not authorised to speak for everyone reading and commenting here.
I am very interested in RC Saumarez’s comments which I generally find technically interesting. As with all I read, I check whether is makes sense and ties in with my understanding.
I find Willis has been unnecessarily off hand in a lot of his comments to RC, even though he may be correct on some points. A bit of cool and civility on all sides would be appreciated.
Hopefully Willis will have time to ignore posters like Salvatore and time to address technical criticism raised by Paul_K, Frank and myself.
Greg Goodman:
At September 25, 2013 at 4:58 am you say to me
Thankyou for that. Clearly, in reading the post from RC Saumarez which I was replying (it is at September 25, 2013 at 1:40 am) I must have missed something. I have read it again, and I still fail to discern anything in that post which is “technically interesting”. I absolutely fail to see any specific criticism of the analysis by Willis.
Perhaps you could explain what RC Saumarez said in that post among his abusive, offensive and arrogant remarks which you found to be “technically interesting” because that would be helpful?
Thanking you in advance.
Richard
Richard,
Thank you for the replies — if most of the heat over top of a land surface radiates into space prior to diffusing towards colder sections of the atmosphere, that would indeed invalidate my hypothesis completely. I’ll hunt down a primer and start cracking so I can follow this more competently.
Thanks again for taking the time and effort, I appreciate it.
This is a demonstation of overshoot involving two coupled boxes of infinite volume. Box 1 is filled at a constant rate of an arbitrary unit. Box 2 is filled from box 1 at an accelerating rate that is calculated as the previous rate plus a fraction of the difference between the volumes in boxes 1 and 2. The chart shows boxes 1 and 2 swapping overshoot/undershoot positions.
https://docs.google.com/spreadsheet/ccc?key=0AiP3g3LokjjZdFN0d1pfWUNTMk5DVVUwcTNnTmEwdmc&usp=sharing
I see this as a sort of “demonstration of concept” which could help explain “the pause”. Box 1 being the well mixed layer and box two the ocean depths. Due to inertia convection is at first slow but then accelerates caring the surface heat to the depths.
Paul_K: Thanks for the reply. If one is playing Willis’s game of “spot the volcano”, it appears to be much easier to do so using the satellite records (some of which have been adjusted for El Nino) than the surface temperature record Willis is showing. Since you analyze several years worth of monthly data, it is possible that you would find that your parameters provide a reasonable good fit for all of the temperature records. However, a quick glance at the surface temperature record in Troy’s post (which appears to have been amplified two-fold “2*GISS) might create some doubt.
Troy, Wigley and probably other have analyzed the surface temperature record, but you may have been the only one to apply a model that fits a model with all of the relevant heat energy fluxes to all of the observations. I hope you have tried to publish: your work appears to be unique in this regard. And Pinatubo is the largest and best observed perturbation of climate in history. If you plot your calculated temperature data vs the observed temperatures from several different sources, you might be able to narrow the range of ECS. If you plot your calculated temperature data vs the observed data from several different sources, you might be able to narrow the range of ECS
Grrr. I just spent three hours writing an extended response on this thread only to get the dread “failed to upload” error from WordPress. Double Grrr.
I will summarize. RCS’s blog post was excellent, but fails to properly recognize that e.g. the Laplace transform of the ACF is very likely “rich” and that Laplace transforms connect power laws to multiple exponential laws to arbitrary decay function laws that aren’t really either one. RCS also fails to note that Willis’ result of “much less effect than existing GCMs incorporate” for volcanic forcing is very likely to survive tweaking the specific assumptions made concerning autocorrelation, even if some part of it is self-fulfilling prophecy caused by the selected filtering. That much is already apparent from playing the “spot the volcano” game. I think he has successfully shown that there is a quantitative basis for rejecting the assertion that volcanoes have a large or persistent impact on the climate, and I personally heartily applaud the application of the fluctuation-dissipation theorem to climate science, which I think RCS would agree we need more of, not less.
Willis does not, I think, claim that his results are unique and correct (which would be a ridiculous claim given that he’s looking at only one dimension of a highly multivariate system with lots of confounding climate “impulses” e.g. ENSO events that overlap with the volcanic events), only that they are reasonable and defensible and likely to NOT be particularly sensitive to his specific assumptions concerning the climate autocorrelation, although given the paucity of the data one cannot rule out the possibility that confounding impulse-driven variation may have lowered his estimates of the system response by adding similar-scale noise. It would be very interesting to do the same analysis on volcanoes AND ENSO at the same time (for example) although there may not be enough data on ENSO to support this over a long enough time frame, and still more interesting to include still more “impulse” forcing changes if they can be found in the data. Examination of autocorrelation of these impulses is surely a common goal of RCS and Willis, as that is HOW once can objectively determine what the structure of the ACF, even though the analysis is doubtless significantly hampered by the (lack of) data — at most a short time series of higher quality data and an intermediate time series of much lower quality data.
Now, if we could all stop being pointlessly rude to one another, that would be lovely. Yes, RCS, you have some mad skills in statistics. I’m perfectly happy to acknowledge them, after reading your paper. Indeed, I share your interest in Hurst-Kolmogorov processes and think that in the end, it may be Koutsoyiannis who leads us out of the desert of ill-founded climate models and into the green oases of models that at least are capable of getting the various correlation times right, or as right as we can so far determine given the inadequate, sucky, indifferently manipulated data.
In a sane world, one would have done the work you and Koutsoyiannis appear to be doing long BEFORE building the FIRST climate model, as without it one doesn’t even have an empirical handle on what reservoirs are actually important, what timescales matter. The GCM community is paying the price by having to postulate a huge reservoir for “missing heat” with very long time constants and so far moderately unbelievable mixing dynamics: “The Ocean”. This, in turn, will make ALL of their past computations wrong as well, as the Ocean wasn’t born yesterday and it didn’t “suddenly” decide to confound GCMs, they were wrong even before they were wrong if indeed it is an important and formerly neglected factor.
I especially like your “reservoir” model. It will join the “coupled capacitor” model that might be used to model the multiple exponential alternative to the power law. Indeed, it would be fun to build a reservoir model not with smooth sloping sides but with smooth sloping sides with e.g. holes drilled through them, with multiple reservoirs with seepage from one into another (and down to “ground”), with a stream that fills them through different pipes that themselves work better or worse as the water level(s) in the various reservoir(s) change(s). Then one might get SOME idea of just how complex things like the KT diagram really are, and how egregious the splitting up of insolation into the various channels under the assumption that the channel couplings don’t vary with the state of the system really is.
Now, if we could all try very hard to be excellent to one another instead of rude and reactive, perhaps we could make better progress. I’m just saying…
rgb
RC Saumarez says:
September 25, 2013 at 1:40 am
RC, I’ve very politely invited you to do the analysis your way. You’ve declined … no surprise there. Instead, you say that given two simple datasets and being asked to determine how much one of them affects the other “it would take several months” just to get your teeth into the problem … OK, in that case I’m glad I didn’t hire you to do the job.
But for a man who appears to have no idea how to actually solve the problem, you are surprisingly willing to take random meaningless potshots at the two different ways that I’ve gone about it. I used standard linear regression, and I’ve used a lagged exponential model.
You responded, for example, that my model did not properly account for autocorrelation. I went to the trouble of posting the ACF of the results and my emulation … they are only slightly different. In response you’ve said:
Right … we’re looking at monthly data, we have been from the start, and now you don’t answer questions because I didn’t say the units are months. Do I have to say “Mother may I” as in the kid’s game as well? RC, I’m not going to hold your hand and blow in your ear. If you want to pass on answering and that’s your ludicrous excuse, I’ll take it that you are unwilling to admit I’m right, that there is very little difference between the ACFs of the model results and my emulation of those results.
In any case, you’re positive both of those methods are the wrong way to go about it (despite the fact that they give very, very similar answers), but you don’t know the right way, and it would take you a couple of months to get your teeth into the problem … got it.
Please come back when you’ve found your teeth, put them in, and gotten those choppers sunk into the problem. At that point I’m sure your comments will make more sense.
Until then? Well, I suppose you could continue to spew abuse at me if that’s what floats your boat. I have to warn you that hurling vitriol doesn’t do much good for your reputation as a brilliant PhD signal analyst … but I suppose if it takes you two months to compare two simple signals, your reputation isn’t doing all that well anyhow.
RC, I asked you how you would do it because from the way you’ve been going on here, I actually thought you knew a better way, and I’m always willing to learn.
However, it seems you don’t know a better way.
When you find one, I’m still more than happy to hear it. My suggestion would be to concentrate on the puzzle, and let the ad hominems slide. Yeah, I’m a jerk, and I don’t take insults well, and I don’t like being patronized, and I don’t suffer fools gladly … so what?
Me being a jerk doesn’t help your position at all. You’ve made lots of claims that you knew much, much better than me how to go about solving the puzzle … and despite all your claims, when I say “OK, show us your better way”, you say it will take you two months to just start chewing on the problem.
And fair enough, I may not see the true complexity, it may take that long, I’m not a PhD signal analyst … so when you’ve not only gotten your teeth into the problem, but have chewed it up and digested it, come back and reveal all. That should at least give us some peace until the end of November, at which point you can truly put me in my place with the elegance of your solution.
w.
One more comment: Indeed, the interesting thing about the “sloping side” reservoir model is that one can actually map it, I think, into things like the SBE by selecting the right shape for the reservoir sides. This still won’t give you nonlinear coupling and will only crudely model feedback — where increasing CO_2 might be equivalent to increasing the thickness of one PART of the side(s) of the reservoir, which might actually effectively decrease the thickness of other sides as increased flow metaphorically forces resistive particles apart or it might equally metaphorically increase drag — positive or negative feedback ASIDE from the variation in the height of water in the reservoir.
I’ll have to think about this some more. In the end, the actual model is oversimplified and wrong no matter what, as the actual dynamics are going to be closer to the transport model with multiple local “reservoirs” all with their own distinct time constant(s) you allude to in your blog post and that correspond crudely to GCMs, but to the extent that one wishes to describe some sort of “average” flow through an open system with crude linearizations as a toy model or metaphor it is still useful.
rgb
AJ: “Due to inertia convection is at first slow but then accelerates caring the surface heat to the depths.”
I have no basis for commenting on how plausible a significant inertial component is, but in the absence of comments from any of this site’s heavy hitters I’ll put my two cents’ worth in: to my untutored eye the fact that your (third-order) system includes no dissipative elements somewhat detracts from its persuasiveness as a proof of concept. What would help is some basis for believing that the inertia involved is large in comparison with dissipation. (I’m perfectly willing to be convinced that it is, but it doesn’t as an initial proposition commend itself to my intuition.)
Just in case you find it relevant, it seems to me that a somewhat separate question is whether lower depths can be increasing in temperature while the surface’s temperature drops. I would say such a proposition ought not to be controversial, even if you don’t assume much inertia. If you reproduce the “tautochrone” shown here: http://wattsupwiththat.com/2012/06/18/time-lags-in-the-climate-system/ for different times of the day, you’ll be able to infer from the different depths’ phase differences that such a result can follow from diffusion alone.
Robert Brown says:
September 25, 2013 at 9:45 am
It seems to be connected to the “Copy” key, that if I don’t copy it before hitting the “POST COMMENT” button the odds of it failing to upload increase dramatically … need a statistical analysis of that to be sure.
The response of the climate models to volcanoes has long been held up as evidence that they do well. Unfortunately, their response matches the volcanoes quite poorly. In addition, some models appear to treat volcanic forcing differently (different sensitivity) than other forcings. I believe that the technical term for that is “cheating”.
I would disagree about the volcano / ENSO joint analysis, unless the analysis recognizes that one is a forcing (volcanoes) and the other is a response. The El Nino/La Nina alteration are the instroke and outstroke of a pump which pumps excess heat to the poles. So when a volcano (or anything else) cuts down the incoming heat, the pump should operate less frequently.
Agreed on all points.
While the “reservoir” model is indeed interesting, as you point out it does not address the fact that flow systems like the climate follow the Constructal Law, which says that the flow system will constantly evolve to increase the interaction between the various flows.
As a result, your example with multiple interactions between the reservoirs that are constantly changing is much closer to the actual reality. See Bejan’s papers here, here, and here on the subject.
And yes, I know I shouldn’t be rude to RC, he just rubs me the wrong way, coming in to tell us very patronizingly that the master has arrived and we should all listen to him and take his words as gospel. Nullius in verba is my motto. But you’re right, I’ll cut him some slack when he comes back in November to tell us how he’d solve the puzzle.
w.
Sooner or later likely sooner there will be another significant volcanic eruption. We will then get to see how much or little effect it will have on global temperatures. I rest my case.
Willis you are correct to point out that the contribution from volcanic activity from the period 2008-2010 or so had no impact on the temperature trend as the IPCC so wrongly points out. I am with you on that.
Joe Born says:
September 25, 2013 at 10:12 am
“… your (third-order) system includes no dissipative elements …”
#########
Yep… it’s an undamped model. Hit it with a pulse and it will ring forever. I find this distracting as well. Any help would be appreciated. Thanks for the comment.
Joe Born says:
September 25, 2013 at 10:12 am
“What would help is some basis for believing that the inertia involved is large in comparison with dissipation.”
##############
I don’t have much to support the proposition that inertia is large in comparison to dissipation, but maybe a couple of plots in the link below might support this. They show temperatures and sea-levels both with a ~60yr cycle, with sea-level trends lagging temperature trends by ~20yrs.
https://sites.google.com/site/climateadj/multiscale-trend-analysis—hadcrut4
In a traditional one-box model, the response cycle can’t lag the forcing cycle by 1/4 cycle and retain an amplitude. If I modify the exponential decay model to introduce a relative acceleration, say exp(-(t^1.5))/tau), then I can push the lag out past 1/4 cycle and still retain an amplitude. Presumably this relative acceleration indicates inertia. Of course I’m assuming that sea-level is a proxy for ocean heat content as well.
So I have two models, one that overshoots but doesn’t dissipate and one that indicates inertia but doesn’t overshoot. Now if only someone could come up with a model that overshoots, dissipates, and creates a response lag of 1/3 cycle. There would still be the matter of only be ~2 cycles of observational data though and the first one is sorta iffy.
AJ: “Any help would be appreciated.”
I can’t think of what I could contribute; as I said, this does not seem to be a very promising avenue (and I didn’t really follow all your comments). But, as I also said, I have no expertise in this area, so it may turn out to be more fruitful than I think.
Joe Born says:
September 25, 2013 at 10:12 am
“If you reproduce the “tautochrone” shown here…”
##############
Well, while I’m playing show and tell, here’s my first attempt using R for an “analysis”. It was simply doing yearly sine fitting on the ARGO data.
https://sites.google.com/site/climateadj/argo-sine-fitting
I started off by looking at 45S, determining the amplitude, phase shift (relative to relative equinox), and mean. Later I expanded it to produce image plots for 55S to 55N. At first I thought the phase might tell me something about uptake, but surmised that it probably wouldn’t (note that my phase plot is mislabeled “ARGO R2 Phase” ugghh..).
One thing to note in this collection is the image plot of mean temperature. You’ll notice a relatively warm dome that goes from the tropical surface to the mid-latitudes at depth. I don’t know if this feature has a name, so’ll I’ll just call it the “warm front” (Ekman Pumping Zone, maybe?). When I plotted some temperature trends, the most positive trends were at the poleward edge of the warm front and the most negative trends were at the equatorial edge of this warm front. This indicates that the warm front in moving poleward (note only 6 years of data at the time). In an “overshoot” context, this could imply that the convection profile is changing, possibly accelerating the heat transport to the depths. If this is correct, then sometime in the future this warm front movement could either decelerate or possibly start moving equatorward. Here’s the trend plots I speak of:
https://sites.google.com/site/climateadj/argo-analysis
Anyway, that’s all I have. My guess is that there is a true overshoot/undershoot phenomenon that the model don’t capture.
Joe Born says:
September 25, 2013 at 3:12 pm
“I have no expertise in this area…”
#####
Neither do I, so who knows? Once again, thanks for the comment.
Willis Eschenbach says:
“September 22, 2013 at 8:17 pm
Chuck Nolan says:
September 22, 2013 at 6:19 pm………………….This is why I don’t like what I call “pressureheads” on my threads. I’ve shown you can’t do it, but you keep showing up.”
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Sounds like Willis is channeling Mikey Mann.
I thought this was about learning the answers I didn’t know Willis already had them all.
I guess I’ll get off of “His” thread.
Thanks for all the info Anthony.
Willis can shout at someone else and I’ll “TAKE IT ELSEWHERE!”
Save the name calling for your enemies.
cn
AJ says:
September 25, 2013 at 4:13 pm
Man, if that’s your first attempt in R, I’m very, very impressed. Nice graphics, clean code. I am so stealing great chunks of that.
I’ll have to give that greater study, as I’m not sure what all of that implies. But what a piece of work. I encourage you to write it up so everyone can understand it and submit it as a post to WUWT.
Well done, that man!
w.
Thanks Willis… fill your boots. Mind you, this is provided without warranty!!
🙂
Willis,
While I’m being encouraged, here’s a follow on. Due to noise, I noticed that sine fitting wasn’t very effective at picking up the power of the annual signal. So I switched to a variance method instead and compared my plots against the AR4 models:
https://sites.google.com/site/climateadj/ocean_variance
Most models got the top 100-200m right. Below the thermocline… not so good.
Thanks for the encouragement, but I don’t really do blog authoring. I’d rather write up my own notes and post links in comments where appropriate. If you feel something is blog worthy, feel free to steal away. If you need clarification on something… you got my email address.
Chuck Nolan says:
September 25, 2013 at 4:46 pm (Edit)
Chuck, I’ve given a very clear scientific proof why pressure can’t warm the surface. Here’s the elevator speech. Imagine a blackbody planet evenly heated by a thousand suns spaced equally around the sky. The amount it radiates is equal to the amount it absorbs from the suns.
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.
Now, if you want to dispute that proof, fine. No one has done so successfully to date. But I’m not interested in claims that pressure warms the surface, I’ve proven, not alleged but proven, that it’s not possible. It’s actually one of my cleverest scientific proofs.
Do I have all the answers? By no means, I have more questions than answers. But one of the things I do have the answer to are claims that pressure can warm the surface … it can’t.
Finally, is this “my” thread? Well, I wrote it … and I won’t sit by and see it hijacked by pressureheads. Got no time for that …
So you’re welcome to hang around and join the discussion … but not to make impossible claims. This site is dedicated to science.
All the best,
w.
AJ, thanks for linking the graphs and code, very interesting.
I did not understand what graph you were referring to with the warm bump/ Ekman comment though.
One thing that seems notable in the temp/depth plots that you don’t comment on is that below 200m the cycle appears to 6m not 12m , or at least has a strong 6m component. Now the 6m variation is equatorial/tropical insolation so it seems that mixed layer at 45S had the annual cycle we would expect but deeper is more influenced by tropical energy input. That is a valuable result and surely informs us of something about the system.
should be 6mo and 12mo obviously, sorry for sloppy units.