# A Longer Look at Climate Sensitivity

Guest Post by Willis Eschenbach

After I published my previous post, “An Observational Estimate of Climate Sensitivity“, a number of people objected that I was just looking at the average annual cycle. On a time scale of decades, they said, things are very different, and the climate sensitivity is much larger. So I decided to repeat my analysis without using the annual averages that I used in my last post. Figure 1 shows that result for the Northern Hemisphere (NH) and the Southern Hemisphere (SH):

Figure 1. Temperatures calculated using solely the variations in solar input (net solar energy after albedo reflections). The observations are so well matched by the calculations that you cannot see the lines showing the observations, because they are hidden by the lines showing the calculations. The two hemispheres have different time constants (tau) and climate sensitivities (lambda). For the NH, the time constant is 1.9 months, and the climate sensitivity is 0.30°C for a doubling of CO2. The corresponding figures for the SH are 2.4 months and 0.14°C for a doubling of CO2.

I did this using the same lagged model as in my previous post, but applied to the actual data rather than the averages. Please see that post and the associated spreadsheet for the calculation details. Now, there are a number of interesting things about this graph.

First, despite the nay-sayers, the climate sensitivities I used in my previous post do an excellent job of calculating the temperature changes over a decade and a half. Over the period of record the NH temperature rose by 0.4°C, and the model calculated that quite exactly. In the SH, there was almost no rise at all, and the model calculated that very accurately as well.

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

Third, the greenhouse gases are generally considered to be “well-mixed”, so a variety of explanations have been put forward to explain the differences in hemispherical temperature trends … when in fact, the albedo and the sun explain the different trends very well.

Fourth, there is no statistically significant trend in the residuals (calculated minus observations) for either the NH or the SH.

Fifth, I have been saying for many years now that the climate responds to disturbances and changes in the forcing by counteracting them. For example, I have held that the effect of volcanoes on the climate is wildly overestimated in the climate models, because the albedo changes to balance things back out.

We are fortunate in that this dataset encompasses one of the largest volcanic eruptions in modern times, that of Pinatubo … can you pick it out in the record shown in Figure 1? I can’t, and I say that the reason is that the clouds respond immediately to such a disturbance in a thermostatic fashion.

Sixth, if there were actually a longer time constant (tau), or a larger climate sensitivity (lambda) over decade-long periods, then it would show up in the NH residuals but not the SH residuals. This is because there is a trend in the NH and basically no trend in the SH. But the calculations using the given time constants and sensitivities were able to capture both hemispheres very accurately. The RMS error of the residuals is only a couple tenths of a degree.

OK, folks, there it is, tear it apart … but please remember that this is science, and that the game is to attack the science, not the person doing the science.

Also, note that it is meaningless to say my results are a “joke” or are “nonsense”. The results fit the observations extremely well. If you don’t like that, well, you need to find, identify, and point out the errors in my data, my logic, or my mathematics.

All the best,

w.

PS—I’ve been told many times, as though it settled the argument, that nobody has ever produced a model that explains the temperature rise without including anthropogenic contributions from CO2 and the like … well, the model above explains a 0.5°C/decade rise in the ’80s and ’90s, the very rise people are worried about, without any anthropogenic contribution at all.

[UPDATE: My thanks to Stephen Rasey who alertly noted below that my calculation of the trend was being thrown off slightly by end-point effects. I have corrected the graphic and related references to the trend. It makes no difference to the calculations or my conclusions. -w.]

[UPDATE: My thanks to Paul_K, who pointed out that my formula was slightly wrong.  I was using

∆T(k) = λ ∆F(k)/τ + ∆T(k-1) * exp(-1 / τ)

when I should have been using

∆T(k) = λ ∆F(k)(1 – exp(-1/ τ)) + ∆T(k-1) * exp(-1 / τ)

The result of the error is that I have underestimated the sensitivity slightly, while everything else remains the same. Instead of the sensitivities for the SH and the NH being 0.04°C per W/m2 and 0.08°C per W/m2 respectively, the correct sensitivities should have been 0.05°C per W/m2 and 0.10°C per W/m2.

-w.]

## 228 thoughts on “A Longer Look at Climate Sensitivity”

1. Kev-in-Uk says:

Hah!
Now all you have to do is add a couple of adjustments, one -ve (but small), lets call it adjustment X – and one +ve (but larger than the other one) and call it adjustment CO2 – and hey presto, you will have a model that demonstrates AGW quite well!
If you bury the adjustments as several ‘forcings’ (all estimated of course, and probably calculated from other variables in direct and indirectly proportional manners) you can make these fudge factors extremely difficult to find. Bingo, you now have the next model for the next round of IPCC projections…….

2. MaineIdea says:

The gauntlet has been thrown. I for one, will not pick it.

3. Richard M says:

I’ve mentioned before that GHGs may work as little thermostats. They have both a warming and cooling effect. The thing that determines the “setting” of the thermostat is the amount of energy entering the system. That is, the sun and clouds.

4. Physics Major says:

Touché, Willis. Well played.

5. tchannon says:

Any have a working link to NASA servers Willis cites in the other post?



6. Joachim Seifert says:

Willis, wait only a few more months – your PS is BS, skim over my
booklet: Climate IS explained and calculated with examples from over
50,000 years of paleoclimate…..
My new paper will outline and calculate 4 different climate forcing
mechanisms over the past 10,000 years – this as announcement
to you…..
I can say: all climate mechanisms have to be proven over LONG
time spans, over a minimum of several 1,000 years……
All engaging in short term curve fitting of less than this time span is
just curve twisting….or call it meteorology but not climate science…
See my new paper coming out towards the end of this year …..
BTW: your volcano part is very good, it coincides with my findings
Regards meanwhile
JS

7. kuhnkat says:

Willis,

you are not a Climate Scientist. That means they will not listen to your nonconsensus view. Heck, MoshPup might stop talking to you cause it shows that RT isn’t the be all and end all of climate!!

HAHAHAHAHAHAHAHAHAHAHAHAHAHAHA

Nice.

8. RobW says:

“but applied to the actual data…”

We don’t need no stinkin” data (need to wear a sombrero while saying) {sarc}

9. Truthseeker says:

“Second, the sun plus the albedo were all that were necessary to make these calculations.” and “… when in fact, the albedo and the sun explain the different trends very well.” seem to be an elegant (if limited) confirmation of Nikolov and Zeller’s theories of climate and temperature.

Just sayin …

10. Michael D Smith says:

nobody has ever produced a model that explains the temperature rise without including anthropogenic contributions from CO2 and the like

I don’t believe I’ve ever seen one that explains the temperature WITH including them either.

11. I have this nagging question about phase of the data in regards to the analysis window. It looks like it starts in Jan and ends in December. Would you get the same slope if you went from Jan to Jan? Or Feb to Jan? Or July to June?

My guess is that you will not get the same slope with these different windows, but how much of a difference is it?

12. Reason for selection of start & end dates?
My apologies if I missed it if stated before.
I’ve been studying hot year 1998 and it has some peculiar properties, so I’m keen to see what follows. There was a reason for the hot year and the explanation is either in your parameters, or a different forcing. It appears to have a NH vs. SH difference.

13. gnomish says:

bravo Willis.
your thinker runs smooth and cool.

14. Thank you for this post. I’m very glad to see the albedo being given the attention it deserves. When it comes to wondering how the albedo would respond to a doubling of CO2, it’s not so difficult to build a simulation of the evaporation cycle, in which a slight warming of the Earth causes more evaporation, which leads inevitably to more clouds, and therefore less sunlight reaching the Earth’s surface. Some collaborators and I did this, and produced a straightforward result: without clouds, a CO2 doubling would indeed result in 2 or 3 C warming of the surface, but with the effect of clouds, the warming drops to around 0.5 C. You will find more details here.

To get such a simulation to work, you need to model the formation of rain and clouds, but there’s nothing particularly difficult about it. Perhaps you and your collaborators could do something similar, and see if you come up with the same answers. It appears to us that the positive feedback in the IPCC models arises from the correct assumption that water vapor will increase with CO2 doubling, thus increasing the CO2-induced warming, but at the same time failing to allow for the inevitable increase in cloud cover that results from increased water vapor. The increased cloud cover is a much more powerful effect, because even 3 mm of water as cloud droplets will reflect of order half the incoming sunlight, while the same amount of water in vapor form effects the outgoing radiation by only a few percent.

Or so it seems to us, it would be nice to have someone else apply themselves to the problem.

15. Willis Eschenbach says:

tchannon says:
May 31, 2012 at 5:55 pm

Any have a working link to NASA servers Willis cites in the other post?

Sorry, tchannon, my bad, try this NASA link. I’ve fixed it in the other post as well.

w.

16. Willis Eschenbach says:

Joachim Seifert says:
May 31, 2012 at 6:01 pm

Willis, wait only a few more months – your PS is BS …

Joachim, let me remind you of what I said above:

Also, note that it is meaningless to say my results are a “joke” or are “nonsense”. The results fit the observations extremely well. If you don’t like that, well, you need to find, identify, and point out the errors in my data, my logic, or my mathematics.

So you’ll forgive me if I just laugh and pay no attention at all. Come back when you have something more than your insults to back up your claims.

w.

• Joachim Seifert says:

Willis, you can laugh as want… but I repeat: The climate forcing
claims that it does not exist +
and I gave you the ISSN number various times….. nothing new to you….
To short term – long term analysis: Everybody in the climate field
agrees (consensus?) that the longer the time frame, the higher the
accuracy, therefore
…we have to stay on the millenial time level to produce meaningful
climate forcing calculations……..
Rahmstorf recently had an analysis over a 30-year time span, trying to
prove Warmist forcings….
If you do analysis on a shorter than 1 millenium time frame, you will
see in a few months, that all short term forcing jiggling is nothing but
for the Katz….
Dont get mad, I know you will scrutinize my coming paper and I do not
want to put you in a negative mood (if you are not already)…..
JS

17. Willis Eschenbach says:

Truthseeker says:
May 31, 2012 at 6:26 pm

“Second, the sun plus the albedo were all that were necessary to make these calculations.” and “… when in fact, the albedo and the sun explain the different trends very well.” seem to be an elegant (if limited) confirmation of Nikolov and Zeller’s theories of climate and temperature.

Just saying …

Not is any way, shape, or form. I cannot be strong enough in saying that this work has absolutely nothing to do with the bogus theories and claims of N&Z. It doesn’t confirm them, nor does it falsify them, it has no connection with them at any point.

If that’s not clear enough, let me know …

w.

18. Willis Eschenbach says:

Stephen Rasey says:
May 31, 2012 at 6:46 pm

I have this nagging question about phase of the data in regards to the analysis window. It looks like it starts in Jan and ends in December. Would you get the same slope if you went from Jan to Jan? Or Feb to Jan? Or July to June?
My guess is that you will not get the same slope with these different windows, but how much of a difference is it?

I don’t see what difference it would make if the trend is different. The point is that the calculations almost exactly match the observations, not what the trend is … but the data is in the spreadsheet if you want to take a look.

Geoff Sherrington says:
May 31, 2012 at 7:02 pm

Reason for selection of start & end dates?
My apologies if I missed it if stated before.
I’ve been studying hot year 1998 and it has some peculiar properties, so I’m keen to see what follows. There was a reason for the hot year and the explanation is either in your parameters, or a different forcing. It appears to have a NH vs. SH difference.

That is all of the albedo data in the Hatzianastassiou paper, so that’s what I used … I’d love to have more.

w.

19. Camburn says:

W:
I am tired, and did a quick read of your post. AT 1st glance, it looks pretty good.
I have to be missing something tho as this does not support the AGW theme and the consensus developed.

Or…….could it be the consensus is similiar to Alfred Wegener days? He was right, but highly ostrasized.

20. Willis Eschenbach says:

Stephen Rasey says:
May 31, 2012 at 6:46 pm

I have this nagging question about phase of the data in regards to the analysis window. It looks like it starts in Jan and ends in December. Would you get the same slope if you went from Jan to Jan? Or Feb to Jan? Or July to June?

I just looked at the NH data, and removed the monthly average variations (climatology) from the data. That gives a trend of 0.3°C per decade rather than 0.5°C per decade. As you would expect (because they are 180° out of phase), it has the opposite effect in the SH, increasing the trend slightly to 0.09°C/decade.

As I said above, however, this makes no difference to my conclusions from the analysis, because they are not based on or dependent in any way on the trend.

Good eye, though, well spotted, my thanks …

w.

21. BarryW says:

So given the time frame of your calculation, the temperature rise and the .3 deg C per doubling for the NH, how much of the temperature rise is “attributable” to CO2?

22. Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature.

Surely, if you measure albedo from top of atmosphere, it comprises the net of aerosols, BC, aerosol indirect effect (clouds), land use, snow and ice, plus some other things.

Although perhaps that’s implicit in what you wrote.

BTW, you have a typo in the graphic – Observerver.

23. Maus says:

Willis, I’m going to assume that the spell-checkers here will catch any flaws in your model and I’ll run on the assumption that your model is sound. What I’m curious about is how this compares to the other climate models running around out there and there fit on back-casting.

That is, does your model fit as well as other models? Does it fit better? Or does it fit worse?

‘Model’ being the oft-equivocated word that means ‘theory’ and I’m interested in whether or not yours is preferable on parsimony for an equal fit (Occam), better fit, or whether it doesn’t make as good a match as the competing theories.

I’m not here talking about the physical or philosophical underpinnings of any specific model. Just correlation and the buzzword notion of science preferring the closer over the farther, and thereafter the simple over the epicycle. Thanks in advance for any time spent in reply. And a great many thanks for the time spent huddling over spreadsheets already.

24. Lance Wallace says:

Geoff Sherrington says:
May 31, 2012 at 7:02 pm

Reason for selection of start & end dates?
My apologies if I missed it if stated before.
I’ve been studying hot year 1998 and it has some peculiar properties, so I’m keen to see what follows. There was a reason for the hot year and the explanation is either in your parameters, or a different forcing. It appears to have a NH vs. SH difference.

The Hatzianastassiou reference refers to the years 1984-1997 whereas Willis uses the phrase 1984-1998. Both are correct I suppose, since the period appears from Hatzianastassiou’s graphs to end about December 1997, but there is an ambiguity. Anyway, it is too bad that the “hot” year of 1998 just missed being included.

25. Lance Wallace says:

Willis, looking at your datasheet it appears to still have the term exp(-1/tau). As one commentator pointed out, this is impermissible–exponents cannot have a dimension. Usually these sorts of terms appear as exp(-t/tau). Can you fix your datasheet properly?

26. Willis..I’m reminded of the time I was given a paper on a variational method in transient heat transfer…with the 1/4 of the semester grade contingient upon elucidating the method shown in the 4 page paper…and then applying it to a “sample” problem.

I walked in to my “night” in the graduate heat transfer course, and started with the 38 pages of “overheads” (yes, I’m dating myself, I hate that..I have to use a mirror…)and one hour and 10 minutes later I concluded. I apologized to the professor, Dr. Lu (R.I.P.) and said, “I had all I could do to figure out what this fellow had condensed in these 4 pages…I did NOT have time to apply this method to a “sample problem”.

Dr. Lu used it as an object lesson. ‘Papers are very condensed, they oft times lack many details to truly explain the method they present.’…He went on to say, ‘They can contain valuable information, but it may take much effort to find out WHAT that information is!’..

HA! I think the same as your presentation. To really understand your method, takes more than looking at the write up for 1/2 hour, and spouting out a instant judgement based on one’s EMOTIONS rather than intellect.

I just ask that you FORMALIZE this concept in a longer and more detailed writing, perhaps a 150 page PDF, with citations!

Max

27. Lance Wallace says:

Willis–

Regarding the need for providing a standard error–if you are working in Excel and used Solver to determine tau, there is a software package called SolverAid that will supply uncertainty estimates in addition to the solution. The program was developed by Robert de Levie and described in his book Advanced Excel. I have found the 2nd Edition to be very helpful. There is a Third Edition now out and information is here: http://www.bowdoin.edu/~rdelevie/excellaneous/

28. Willis Eschenbach says:

BarryW says:
May 31, 2012 at 7:50 pm

So given the time frame of your calculation, the temperature rise and the .3 deg C per doubling for the NH, how much of the temperature rise is “attributable” to CO2?

Well, since my numbers fit the data very well with no CO2 involved, and furthermore there is no significant trend in the residuals, I’d say about zero is attributable to CO2 …

w.

29. Willis Eschenbach says:

Lance Wallace says:
May 31, 2012 at 8:01 pm

… The Hatzianastassiou reference refers to the years 1984-1997 whereas Willis uses the phrase 1984-1998. Both are correct I suppose, since the period appears from Hatzianastassiou’s graphs to end about December 1997, but there is an ambiguity. Anyway, it is too bad that the “hot” year of 1998 just missed being included.

You are correct, 1998 is not included, the data runs to December 1997. Sorry for the confusion.

w.

30. Willis Eschenbach says:

Lance Wallace says:
May 31, 2012 at 8:04 pm

Willis, looking at your datasheet it appears to still have the term exp(-1/tau). As one commentator pointed out, this is impermissible–exponents cannot have a dimension. Usually these sorts of terms appear as exp(-t/tau). Can you fix your datasheet properly?

Thanks, Lance. The “1” simply means that “t” = 1, so it has the units of months and cancels out the units of the tau (months) and so it is dimensionless.

w.

31. AJ says:

I’m not a big fan of a “one-box” constant tau with an instantaneous factor added in. In a retarded attempt to come up with an “infinite box” model, I chose to have the *apparent* tau being a log function of time. That is, tau ~ 4/3 ln(1+time). My *apparent* tau is based on the lag given different cycle periods. Doing some contrived curve fitting, I come up with a lag of just under 6hrs on a daily cycle, 2 months on a yearly cycle, 5 months on the ENSO cycle, and 6.5 months on the 11yr Solar cycle.

It’s a bit cryptic, but here are my calculations:

32. Graeme W says:

Willis Eschenbach says:
May 31, 2012 at 8:13 pm

BarryW says:
May 31, 2012 at 7:50 pm

So given the time frame of your calculation, the temperature rise and the .3 deg C per doubling for the NH, how much of the temperature rise is “attributable” to CO2?

Well, since my numbers fit the data very well with no CO2 involved, and furthermore there is no significant trend in the residuals, I’d say about zero is attributable to CO2 …

w.

Playing Devil’s Advocate, CO2 can affect the albedo, so it’s included. The theory is that an increase in temperature due to CO2 will result in increased water vapour being held in the atmosphere, which can manifest as clouds, altering the albedo.

So this model doesn’t impact on AGW concepts – it’s a much more empirical analysis of temperature based on how much energy from the input source (the sun) gets absorbed by the Earth (determined by albedo). Conceptually, straight forward and hard to refute. The next level of discussion is then to determine what impact various factors (such as CO2, aerosols, etc) have on the Earth’s albedo. It would be interesting is to see if there are any existing studies on that subject.

33. cba says:

Willis,

Again, thanks for the work you’ve put in. I don’t think I’m going to have time to go through the details. However, one of the comments indicated that the albedo information ended in 1997. There is another source of albedo information that might help expand that a bit. Goode and Palle’ have some papers for their Earth Shine project that determines Earth’s albedo from Earth Shine light reflecting off the Moon (unlit area) and as I recall, they infer some albedo information from cloud cover records to extend albedo records beyond the very limited measurements available.

34. Truthseeker says:

Willis you said …

“Not is any way, shape, or form. I cannot be strong enough in saying that this work has absolutely nothing to do with the bogus theories and claims of N&Z. It doesn’t confirm them, nor does it falsify them, it has no connection with them at any point.

If that’s not clear enough, let me know …”

It is certainly clear to me who is doing the ranting …

35. David Gould says:

Willis,

One issue that immediately springs to mind is your exclusion of factors that we know affect the climate, such as aerosols. This has been pointed out to me before by others when I made an attempt to determine climate sensitivity without taking them into account. If we know from physics that something has an effect, then excluding that from a model is a potential problem for that model.

It might mean – for example – that it is entirely a coincidence that your model explains the temperature changes. Or it might not be a coincidence: it might mean that albedo changes are effects that are a close proxy for the temperature changes, meaning that you will get a pretty good match. But if this was the case this would render your model invalid for its purpose – all you would be matching would be temperature change verus temperature change.

I am not saying that that is what has happened here; just that excluding things that we know have physical effects from models of those physical effects can be problematic.

36. ferd berple says:

David Gould says:
May 31, 2012 at 9:38 pm
One issue that immediately springs to mind is your exclusion of factors that we know affect the climate, such as aerosols.
=====
It is accounted for in its effect on albedo, or there would be residuals during aerosol peaks such as volcanoes.

In effect they are being counted twice if you also include them separately with a non-zero coefficient, which will lead to an induced error in the model.

37. David Gould says:

Willis,

If you have a look at the GISS annual snow albedo data and plot it against the GISS temperature data, you get an r^2 value of 0.8. Depending on which you plot as the dependent variable, either the snow albedo changes explain much of the annual temperature variation over the last 130 years or the temperature variation explains much of the snow albedo change.

Snow albedo is not the only albedo factor, of course.

38. Stephen Wilde says:

I agree with the conclusion, namely that:

“the climate responds to disturbances and changes in the forcing by counteracting them.”

and that it is all down to albedo variations affecting the amount of solar energy able to penetrate the oceans.

The difficulty is that cloudiness decreased during the late 20th century warming period and is now increasing with a cessation of that warming whereas Willis’s proposition requires more clouds when it is warming so that the warming is offset by the increased albedo.

That is a serious problem which must be addressed.

My solution is to propose that instead of increased cloudiness from warming such warming comes from reduced cloudiness caused by an expansion of the tropical air masses allowing more solar energy into the oceans by intensifying the subtropical high pressure cells, pushing the entire air circulation poleward to give more zonal / poleward jets and reduced cloudiness overall.

That reduction of cloudiness and consequent warming from more energy getting into the oceans is then offset by an increase in the hydrological cycle via my GLOBAL version of Willis’s own Thermostat Hypothesis whereby there is an increase of convective overturning along the ITCZ AND an intensification of cyclogenesis along the more zonal but also more vigorous mid latitude jet streams.

The thing is that such intensification of the hydrological cycle is accompanied by reduced GLOBAL cloudiness because the increased activity is compressed into smaller surface areas by the more zonal air flow configuration.

Thus more energy getting into the oceans is caused by the expanded tropical air masses with a reduction of cloudiness but the poleward shifting of the entire air circulation pattern then increases the rate of energy transfer from surface to space for a zero or near zero effect on the equilibrium temperature of the entire system.

Expansion of the tropical air masses can result either from faster energy release by the oceans OR changes in the vertical temperature profile of the atmosphere caused by variations in the mix of particles and wavelengths from the sun influencing ozone concentrations differently at different levels.

Climate variability is just a consequence of the continual interplay between the solar and oceanic influences as each of them varies in relation to the other all the time. The positions, sizes and intensities of the permanent climate zones shift about over time as part of the balancing process. All observed climate variations can be explained by that mechanism.

Changes in GHG amounts would have a similar effect but too small to measure compared to solar and oceanic variability.

The proposition of Nikolov and Zeller is relevant in that it provides a basic physical mechanism for the necessary redistribution of the surface air pressure pattern but I am aware of Willis’s disagreement on that issue which can be left for another time so as to avoid derailing this thread.

39. ferd berple says:

Willis I remove my earlier objection to the seasonal lag in your earlier work. The longer lag and lower sensitivity in the SH are consistent with the reduced land mass and antarctic ice cap.

Coupled with the earlier work, this is an extremely powerful result because it shows a consistency between seasonal and annual sensitivity, in spite of different lags. What is truly remarkable is the closeness of the fit, as it suggests that climate forgings are nowhere near as complicated as suspected.

To predict future global average temperatures one one need be able to predict:
1. CO2 levels
2. Solar output
3. Global albedo

Thus, if one want to control climate change, if may be much more cost effective to control the earth’s albedo than CO2 levels. Especially as the sun’s output is largely unpredictable and outside our control, and CO2 levels are directly tied to economic performance and thus hard to modify without also affecting the economic climate.

40. ferd berple says:

Kevan Hashemi says:
May 31, 2012 at 7:19 pm
Perhaps you and your collaborators could do something similar, and see if you come up with the same answers.
=====
It would appear this is what Willis has done. Rather than calculating what change the clouds have in albedo, he has simply taken the observed change in albedo as the calculated effect. Not only for clouds, but for the net sum of all effects such as clouds, volcanoes, carbon black, etc, etc. Since the underlying assumption of climate science is that net W/M2 is the ultimate driver of global temperature, he has reduced the problem to simplest terms to enable a solution.

41. ferd berple says:

Stephen Wilde says:
May 31, 2012 at 10:22 pm
Willis’s proposition requires more clouds when it is warming so that the warming is offset by the increased albedo.
====
I don’t see that anywhere in the model. Willis makes no stipulations as to the cause of the change in albedo. He has simply asked if the observe change in temperature, CO2 and albedo are closely related. And it would appear they are very closely related, with a modest amount of warming predicted for a doubling of CO2.

The lack of residuals is compelling evidence that there are no significant hidden variables. In other words, we don’t need to add any factors or assumption to improve the fit, because the fit is much better than anything yet found anywhere in any climate model.

The proof of the pudding will come when the model moves outside of the “training” area. It could be that Willis is simply curve fitting in which case the model will have no predictive ability. However, if it maintains the low residuals as it moves into the unknown, then Willis has the making of a new scientific theory.

Potentially this is the breakthrough in understanding that is missing in climate science. Or, like the climate models, it could prove to be useless. Predicting accelerated warming at the exact point warming leveled.

42. ferd berple says:

What I like about Willi’s approach is that it holds true to Occam’s Razor
from wikibible
Occam’s razor (also written as Ockham’s razor, Latin lex parsimoniae) is the law of parsimony, economy or succinctness. It is a principle urging one to select among competing hypotheses that which makes the fewest assumptions and thereby offers the simplest explanation of the effect.

43. I think this is a ‘spoof’ post.
You can manipulate data to give desirable result. Here is one I produced to show that ‘doubling C02 will lead to 3C of warming’
with graph Sp.giff on http://www.vukcevic.talktalk.net/00f.htm

44. richard telford says:

I doubt that it trivial to detect long time constants in a record where nearly all the variance is at intra annual scales. It is practically certain that Eschenbach’s method severely underestimates climate sensitivity. There are several possible ways to demonstrate this:  A sensitivity analysis. If realistic time constants are added to the analysis, do they appreciably change the results. If not, you cannot exclude them. Simulated data. If you repeat the analysis with simulated climate data from a model with known climate sensitivity is the estimate of model sensitivity underestimated. This could be done with anything from an energy balance model to a fully coupled CGM, and does not assume that the models are correct (it is axiomatic that they, like all models , are wrong but possibly useful).  You could try to reconcile your estimate of climate sensitivity with the climate changes over the last 100000 years. Is it possible to generate a glacial maximum several degrees colder than modern with so low a climate sensitivity. I predict Eschenbach will do none of these, prefering to amuse the crowd with cheap numerical tricks. If he could show a climate sensitivity with robust methods, it would be published immediately in Science.

45. Stephen Richards says:

Hi Willis

Thanks for your work, I really don’t know where you find the time.

What I find interesting about all your work is that it points to a very stable climate system; Now, when you think about it that fact is, in effect, a necessary conclusion of 4.5billion years of catastrophes and we and the planet are still here.

So, how does an ice age form? From all your work there would seem to be only one possibiltity, THE SUN. You have shown, really clearly, that the climate is so stable that only changing it’s fundamental energy source could radically change it cyclically.

46. mb says:

I’m wondering about something here. It seems to me that this model is time independent, so that you will get the same constant trend forever, and hindcasting you will also get the same constant trend in the past. Which we didn’t have.

So if you do explain the current warming, you will have to explain the previous lack of warming.

Maybe I misunderstand the model.

47. Willis:

Thankyou for this. It is a direct rebuttal of KR and Latimer Alder in the previous thread who claimed need for a “two-box” model to emulate longer-term changes than the instantaneous changes. They did not have an argument as is demonstrated by – at the end of that thread – their abandoning rational argument and resorting to insult and abuse.

You now show by demonstration that they were wrong when they claimed your “one-box” model does not emulate changes other than only instantaneous changes. As you say

First, despite the nay-sayers, the climate sensitivities I used in my previous post do an excellent job of calculating the temperature changes over a decade and a half. Over the period of record the NH temperature rose by 0.4°C, and the model calculated that quite exactly.

I note that detractors have responded in this thread by saying you need to assess very long time periods. That was predictable. This response is most clearly expressed by Richard Telford who says (at May 31, 2012 at 11:54 pm)

I doubt that it trivial to detect long time constants in a record where nearly all the variance is at intra annual scales. It is practically certain that Eschenbach’s method severely underestimates climate sensitivity. There are several possible ways to demonstrate this: A sensitivity analysis. If realistic time constants are added to the analysis, do they appreciably change the results. If not, you cannot exclude them.

His assertion is wrong: you have obtained a realistic result by excluding them so his assertion is a falsehood. It is his duty to justify testing of “realistic time constants” (whatever they are). He is ‘blowing smoke’ until he shows the effect of adding such “realistic time constants” and explains the need for them.

And you disproved the arm-waving by your detractors who claimed NH and SH were different so your model “must” be wrong when you report

In the SH, there was almost no rise at all, and the model calculated that very accurately as well.

You state the important indication of your model when you say

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

Yes, you have demonstrated that
Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

However, an ability to attribute a factor as a cause of a change only demonstrates the possibility that the factor is the cause of the change. An ability to attribute a factor as a cause of a change does NOT demonstrate that the factor is the true cause in part or in whole.

As I argued in the previous thread, there are other determinations of climate sensitivity than yours and – at present – there is no way to determine which is ‘right’. But your determination does not agree with what some people want to think is ‘true’.

So, you now find yourself in the same situation I have been in for a decade.
• I have been showing that the recent rise in atmospheric CO2 concentration can be attributed to factors other than anthropogenic CO2 (and have been vilified for it).

• You are showing the recent rise in global temperature can be attributed to factors other than the rise in atmospheric CO2 concentration (and probably will be vilified for it).

I advise that you fasten your seat belt: you are in for a bumpy ride.

Richard

48. Willis Eschenbach says:

ferd berple says:
May 31, 2012 at 10:50 pm

The proof of the pudding will come when the model moves outside of the “training” area. It could be that Willis is simply curve fitting in which case the model will have no predictive ability. However, if it maintains the low residuals as it moves into the unknown, then Willis has the making of a new scientific theory.

Actually we can do some of that now. Below I have calculated the standard deviation of the residuals. The first row shows when all of the data is used for the training.

The next row shows the “in sample” results when the first half is used for training, and is then used to evaluate the “out of sample” second half.

The final row shows when the second half is used for the training.

As you can see, there’s not much difference in the size of the residuals whether you use all, just the first half, or just the second half for the training.

w.

49. Moderator:

My post seems to have gone into the bin. Please find it. Thanking you in anticipation.

Richard

50. Stephen Wilde says:

I said:

“Willis’s proposition requires more clouds when it is warming so that the warming is offset by the increased albedo.”

ferd berple said:

” I don’t see that anywhere in the model.”

I think it is implied by this comment from Willis:

“because the albedo changes to balance things back out.”

Furthermore the observed decrease in cloudiness during the period of a warming troposphere is also contrary to AGW theory. More warmth is supposed to give more evaporation and more clouds which is said to be a positive feedback despite any increase in global albedo.

In reality the troposphere warms when clouds decrease and cools when clouds increase. However I would argue that system energy content remains almost exactly the same because by far the vast majority of energy in the Earth system is in the oceans and the temperature of the troposphere as a whole is related primarily to the rate of flow of energy through it between oceans and space.

51. Willis Eschenbach says:

richard telford says:
May 31, 2012 at 11:54 pm

I doubt that it trivial to detect long time constants in a record where nearly all the variance is at intra annual scales. It is practically certain that Eschenbach’s method severely underestimates climate sensitivity.

“You doubt” and you are “practically certain”? Is that supposed to impress me? I am impressed by facts, by logical arguments, by math, by results.

People telling me what they doubt? Not so much …

There are several possible ways to demonstrate this:

A sensitivity analysis. If realistic time constants are added to the analysis, do they appreciably change the results. If not, you cannot exclude them.

Well, I’m not sure what you call a “realistic time constant”, but let’s say it is 8 years months. [NOTE: written in error as “years”]

As you can see, with a time constant of that length it doesn’t respond anywhere near fast enough to match the annual changes. Nor is it successful in replicating the overall trend in the NH. Finally, there is a large difference between NH errors and SH errors, indicating structural problems … so yes, using a larger time constant definitely and appreciably changes the results.

Simulated data. If you repeat the analysis with simulated climate data from a model with known climate sensitivity is the estimate of model sensitivity underestimated. This could be done with anything from an energy balance model to a fully coupled CGM, and does not assume that the models are correct (it is axiomatic that they, like all models , are wrong but possibly useful).

Actually, I’ve done that, and reported on it here and here. I get a much larger climate sensitivity (lambda) and time constant (tau) for the response of the model to the model’s forcing than I do when I look at real data … so what? I fear that just means that their models give unreasonable results.

You could try to reconcile your estimate of climate sensitivity with the climate changes over the last 100000 years. Is it possible to generate a glacial maximum several degrees colder than modern with so low a climate sensitivity.

The question is not necessarily climate sensitivity. It is what the climate is sensitive to … which is the albedo. However, the conditions for instigating an ice age are not well understood by anyone, and we have no albedo data for that time, so I can’t see how to even do the test you suggest.

I predict Eschenbach will do none of these, prefering to amuse the crowd with cheap numerical tricks. If he could show a climate sensitivity with robust methods, it would be published immediately in Science.

Well, I’ve done two of the three things you requested, and I see no way to do the third since we don’t understand what causes ice ages, and we have no data on albedo during ice ages.

I will gladly accept your apology for your spiteful, snide, unwarranted, and untrue comments. Or, alternatively, I’m happy to ignore any future posts from you.

w.

52. Willis Eschenbach says:

mb says:
June 1, 2012 at 12:51 am

I’m wondering about something here. It seems to me that this model is time independent, so that you will get the same constant trend forever, and hindcasting you will also get the same constant trend in the past. Which we didn’t have.

So if you do explain the current warming, you will have to explain the previous lack of warming.

Maybe I misunderstand the model.

Thanks, mb. The model merely specifies what the temperature change will be from a certain change in the albedo. As a result, It can’t be used to forecast anything, because we don’t know the future state of the albedo. What I’ve done is hindcasting, because from the past albedo and the sun’s annual variations I’ve hindcasted past temperatures, with a very good degree of accuracy.

All the best,

w.

53. Stephen Wilde says:

Willis said:

“The question is not necessarily climate sensitivity. It is what the climate is sensitive to … which is the albedo”

Correct as to both points.

We have a scenario whereby the system is highly sensitive to albedo because that affects the amount of energy getting into the oceans.

However, that high sensitivity exerts a negative response to ANY other forcing changes and so results in a very insensitive system overall.

The climate ‘price’ of the negative response is a shift in the surface air pressure pattern and the permanent climate zones.

Sizeable shifts from variability in sun and oceans as per MWP and LIA but miniscule from human CO2 emissions.

54. OK. It is now clear that the post I sent at ~12.50 your time has vanished. I am now sending it again.

Willis:

Thankyou for this. It is a direct rebuttal of KR and Latimer Alder in the previous thread who claimed need for a “two-box” model to emulate longer-term changes than the instantaneous changes. They did not have an argument as is demonstrated by – at the end of that thread – their abandoning rational argument and resorting to insult and abuse.

You now show by demonstration that they were wrong when they claimed your “one-box” model does not emulate changes other than only instantaneous changes. As you say

First, despite the nay-sayers, the climate sensitivities I used in my previous post do an excellent job of calculating the temperature changes over a decade and a half. Over the period of record the NH temperature rose by 0.4°C, and the model calculated that quite exactly.

I note that detractors have responded in this thread by saying you need to assess very long time periods. That was predictable. This response is most clearly expressed by Richard Telford who says (at May 31, 2012 at 11:54 pm)

I doubt that it trivial to detect long time constants in a record where nearly all the variance is at intra annual scales. It is practically certain that Eschenbach’s method severely underestimates climate sensitivity. There are several possible ways to demonstrate this: A sensitivity analysis. If realistic time constants are added to the analysis, do they appreciably change the results. If not, you cannot exclude them.

His assertion is wrong: you have obtained a realistic result by excluding them so his assertion is a falsehood. It is his duty to justify testing of “realistic time constants” (whatever they are). He is ‘blowing smoke’ until he shows the effect of adding such “realistic time constants” and explains the need for them.

And you disproved the arm-waving by your detractors who claimed NH and SH were different so your model “must” be wrong when you report

In the SH, there was almost no rise at all, and the model calculated that very accurately as well.

You state the important indication of your model when you say

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

Yes, you have demonstrated that
Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

However, an ability to attribute a factor as a cause of a change only demonstrates the possibility that the factor is the cause of the change. An ability to attribute a factor as a cause of a change does NOT demonstrate that the factor is the true cause in part or in whole.

As I argued in the previous thread, there are other determinations of climate sensitivity than yours and – at present – there is no way to determine which is ‘right’. But your determination does not agree with what some people want to think is ‘true’.

So, you now find yourself in the same situation I have been in for a decade.
• I have been showing that the recent rise in atmospheric CO2 concentration can be attributed to factors other than anthropogenic CO2 (and have been vilified for it).

• You are showing the recent rise in global temperature can be attributed to factors other than the rise in atmospheric CO2 concentration (and probably will be vilified for it).

I advise that you fasten your seat belt: you are in for a bumpy ride.

Richard

55. Moderator this is my third attempt to send the post I first sent at ~12.50

Willis:

Thankyou for this. It is a direct rebuttal of KR and Latimer Alder in the previous thread who claimed need for a “two-box” model to emulate longer-term changes than the instantaneous changes. They did not have an argument as is demonstrated by – at the end of that thread – their abandoning rational argument and resorting to insult and abuse.

You now show by demonstration that they were wrong when they claimed your “one-box” model does not emulate changes other than only instantaneous changes. As you say

First, despite the nay-sayers, the climate sensitivities I used in my previous post do an excellent job of calculating the temperature changes over a decade and a half. Over the period of record the NH temperature rose by 0.4°C, and the model calculated that quite exactly.

I note that detractors have responded in this thread by saying you need to assess very long time periods. That was predictable. This response is most clearly expressed by Richard Telford who says (at May 31, 2012 at 11:54 pm)

I doubt that it trivial to detect long time constants in a record where nearly all the variance is at intra annual scales. It is practically certain that Eschenbach’s method severely underestimates climate sensitivity. There are several possible ways to demonstrate this: A sensitivity analysis. If realistic time constants are added to the analysis, do they appreciably change the results. If not, you cannot exclude them.

His assertion is wrong: you have obtained a realistic result by excluding them so his assertion is a falsehood. It is his duty to justify testing of “realistic time constants” (whatever they are). He is ‘blowing smoke’ until he shows the effect of adding such “realistic time constants” and explains the need for them.

And you disproved the arm-waving by your detractors who claimed NH and SH were different so your model “must” be wrong when you report

In the SH, there was almost no rise at all, and the model calculated that very accurately as well.

You state the important indication of your model when you say

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

Yes, you have demonstrated that
Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

However, an ability to attribute a factor as a cause of a change only demonstrates the possibility that the factor is the cause of the change. An ability to attribute a factor as a cause of a change does NOT demonstrate that the factor is the true cause in part or in whole.

As I argued in the previous thread, there are other determinations of climate sensitivity than yours and – at present – there is no way to determine which is ‘right’. But your determination does not agree with what some people want to think is ‘true’.

So, you now find yourself in the same situation I have been in for a decade.
• I have been showing that the recent rise in atmospheric CO2 concentration can be attributed to factors other than anthropogenic CO2 (and have been vilified for it).

• You are showing the recent rise in global temperature can be attributed to factors other than the rise in atmospheric CO2 concentration (and probably will be vilified for it).

I advise that you fasten your seat belt: you are in for a bumpy ride.

Richard

56. Moderator:

I have made 3 attempts to send a post that I first sent at ~12.50

All these posts have vanished. I would be grateful for your help.

Richard

57. Stephen Wilde says:

“What I’ve done is hindcasting, because from the past albedo and the sun’s annual variations I’ve hindcasted past temperatures, with a very good degree of accuracy.”

That is very important.

It follows that if one could accurately measure changing global albedo from whatever reason one could assess whether energy input to the oceans is increasing or decreasing at any given time.

At some stage it should then be possible to ascertain the level of albedo that represents a stable system.

Then, linking climate zone positioning to albedo level as modified by ocean cycles would give a reasonable method of anticipating regional climate changes.

Currently, I observe increasing cloudiness, greater jetstream meridionality and cooling mid latitudes with a slight equatorward drift of the climate zones.

58. richard telford says:

Eschenbach appears to have a reading problem. I suggested that ” realistic time constants are *added* to the analysis”. Instead he *replaced* the short time constant with a longer time constant. Not surprisingly, a long time constant cannot replicate the intra-annual variability. But why only one time constant? There must be a range of time constants associated with different components of the system, for example surface vs sub surface ocean.

His previous work on modelled data are irrelevant to his current error as they are analyses of annual rather than monthly data. Not surprisingly, they don’t find a sub-annual time constant. This does not indicate that the models are generating “unreasonable results”. If you analyse two decades of monthly model data you will find a time constant of a couple of months, necessary to fit the annual cycle, regardless of the climate sensitivity of the model. That your method will be unable to find the climate sensitivity of the model will show that it is unfit for purpose, a cheap numerical trick.

59. Kasuha says:

Too bad I don’t have time now to study this in detail, it looks really, really interesting. I’d have one suggestion though – what about instead of dividing the earth to two hemispheres, dividing it to sea and land? Different sensitivities for land and sea make a bit more physical sense than different sensitivities for two hemispheres.

60. steveta_uk says:

So it appears this very simple model shows that GST depends only only two things: TSI and albedo.

Leif can give us all we need to know (and much more) on TSI.

That leaves albedo. Various people have pointed out land use changes have had a significant impact on NH albedo (Pielke Sr in particular). Links between aerosols and volcanos and albedo are probably reasonably well understood if not well evaluated.

So we’re left with the link, if any, between CO2 and albedo. Roy’s cloud work, and some of Willis’s tropical storm work, imply -ve feedback to minimise the effects. I assume the CAGW crowd would claim increased temps result in reduced cloud formation hence increased temps. Is there an argument that increased IR capture by CO2/H2O would reduce cloud cover? If all it does is change the height of cloud cover, albedo is largely uneffected.

And what about ice? Increased temps might reduce ice/snow cover, and hence alter albedo and hence drive Willis’s curve. But that assumes the temp changed first – and Willis has shown it can’t change first – the lags are too small.

Too hard for me.

61. richard telford:

I am responding to your post at June 1, 2012 at 2:18 am that begins by saying:

Eschenbach appears to have a reading problem. I suggested that ” realistic time constants are *added* to the analysis”. Instead he *replaced* the short time constant with a longer time constant. Not surprisingly, a long time constant cannot replicate the intra-annual variability. But why only one time constant? There must be a range of time constants associated with different components of the system, for example surface vs sub surface ocean.

If there is a “reading problem” then it is yours, not his.

I point you to my post at June 1, 2012 at 12:52 am. I there quoted what you had said verbatim (I note that Willis also quoted your same words in his answer to you) and I wrote to Willis saying of your words

His assertion is wrong: you have obtained a realistic result by excluding them so his assertion is a falsehood. It is his duty to justify testing of “realistic time constants” (whatever they are). He is ‘blowing smoke’ until he shows the effect of adding such “realistic time constants” and explains the need for them.

Your entire response says of Willis’ analysis

His previous work on modelled data are irrelevant to his current error as they are analyses of annual rather than monthly data. Not surprisingly, they don’t find a sub-annual time constant. This does not indicate that the models are generating “unreasonable results”. If you analyse two decades of monthly model data you will find a time constant of a couple of months, necessary to fit the annual cycle, regardless of the climate sensitivity of the model. That your method will be unable to find the climate sensitivity of the model will show that it is unfit for purpose, a cheap numerical trick.

That does NOT state “realistic time constants” and it does not justify them.

You are still ‘blowing smoke’.

Richard

62. JohanS says:

Albedo changes with CO2 concentration, Willis. Albedo is all frequencies not just the visible ones. More CO2 makes the planet effectively “darker” in the CO2 absorption bands. I use the quotes around “darker” because those bands are invisible to the naked eye and require a spectrograph to “see” them.

So you were rolling CO2 into your calculation without realizing it.

63. mfo says:

Willis, I can’t comment on the formula other than that it has a delightfully convincing clarity.

As a child and country bumpkin I was always being told that nature abhors a vacuum. Not in the Aristotle sense but as a metaphor for the fact that nature always seemed to rebalance itself in the countryside around us. I’m a fan of yourThermostat Hypothesis as illustrated by tropical cloud formation. I like the idea that the climate contains homeostatic mechanisms.

The first thing I noticed in this and your previous post was that the time span, 1984 to 1998, was the same as the test case for your theory that “changes in albedo help regulate the temperature and keep it within a narrow range”, using the eruption of Pinutabo. I was suprised that only one comment in the first thread mentioned Pinutabo.

Like many others I considered this latest formula an addition to your Thermostat Hypothesis as neatly explained in an earlier post:
https://wattsupwiththat.com/2009/06/14/the-thermostat-hypothesis/

If “climate scientists” were truly open to new ideas they would be very excited by a hypothesis which demonstrates that carbon dioxide from fossil fuels will not make the planet uninhabitable even though it might bring an end to grant money burning holes in their pockets.

64. JohanS says:

Google albedo. The #2 hit (wikipedia is #1) is the link below. It is a technical definition of albedo and discusses the critical difference between bond albedo and geometric albedo.

http://hyperphysics.phy-astr.gsu.edu/hbase/phyopt/albedo.html

My emphasis:

Albedo
The term albedo (Latin for white) is commonly used to applied to the overall average reflection coefficient of an object. For example, the albedo of the Earth is 0.39 (Kaufmann) and this affects the equilibrium temperature of the Earth. The greenhouse effect, by trapping infrared radiation, can lower the albedo of the earth and cause global warming.

The albedo of an object will determine its visual brightness when viewed with reflected light. For example, the planets are viewed by reflected sunlight and their brightness depends upon the amount of light received from the sun and their albedo. Mercury receives the maximum amount of sunlight, but its albedo is only 0.1 so it is not as bright as it would be with a higher albedo.

In more technical treatments of albedo, such as that of de Pater and Lissauer, a distinction is made between “bond albedo” and “geometric albedo”, the numbers quoted above being geometric albedos. The geometric albedo is defined as the amount of radiation relative to that from a flat Lambertian surface which is an ideal reflector at all wavelengths. The bond albedo is the total radiation reflected from an object compared to the total incident radiation from the Sun. The bond albedo for the Earth is given as 0.29 by de Pater and Lissauer, compared to their value of 0.37 for the geometrical albedo.

65. Kev-in-Uk says:

Ok, so I’m a little confused – on the one hand, a short term analysis clearly shows that a simple model can replicate observations but Willis seemingly accepts this is not going to demonstrate how ice ages occur, i.e. climate sensitivity over longer timescales.
Either the climate and our available data is ‘isolated’ into the now familiar cycles, and is chopped up for analysis ‘within’ those cycles – OR, the climate is taken as one big fecking cycle (i.e. ice age+ scale) with sub-cycles within and we ‘look’ at the whole thing. This seems to be the way of some of the thinking – but I feel it is wrong, the subcycles are the things to concentrate on in respect of anthropogenic influences as it is reasonable to assume that if there is a significant anthropogenic influence it will be evident within any sub-cycle? – i.e. whether the temps are going up or down, if they are going up, the AGW effect will increase the rate, if they are going down, the AGW effect will decrease the rate.

I think what Willis has done is demonstrate that short term cycles are easily replicated with a simple model but as others have commented, this is of no real ‘use’ if there are ‘other’ effects which the model does not and cannot consider.
I agree with Willis that his work appears to demonstrate a ‘low’ overall sensitivity (but only over the short timescale) and to me, this low sensitivity is reasonably logical. However, we KNOW that something(s) MUST happen to cause the KNOWN greater range in temperatures and these
something(s) must kick the sensitivity up (or down) as they occur.

I am sure folk here understand the +ve and -ve feedback analogies using a concave or convex surface respectively? (I trust I don’t need to explain this!) but the way to think of the whole climate might be to think of a rippled bowl, with crests and troughs circular and radiating out from the centre.

If you consider the depths/heights of the troughs and crests as being variable – its easy to see how the earths climate can ‘drop’ into a different ring around the centre and the climate within this ‘ring’ will be slightly different.

Taking my rather wittering analogy a stage further, the different rings (i.e. distances from the centre) could be comparable to major cycles, like Milankovich, etc…..
We all know about the change in the earths axial tilt, etc, effects of the moon-earth distance, etc,etc – if you take my analogy even further, these major astronomical/planetary type changes are tilting the plane upon which the bowl sits – thereby tipping the ball into the next trough/ring.
My (probably badly made) point is that the climate needs to be considered within each phase of its development and cannot be viewed as a whole ‘range’.

Gawd, I hope this makes sense (it made sense in my mind! LOL)

66. charlie says:

Willis,
I like the simple approach and I too think albedo is a very important factor to look at.
But, your conclusion about Pinatubo is wrong, I think. Since large volcano eruptions are supposed to cool the earth by changing the albedo, they are included indirectly in your model. If Pinatubo did affect the albedo, the temperature did follow as your model predicted. If your model had failed to predict temperatures after the eruption, it would have been a reason to question your model.

67. JohanS says:

Another reference. I know I’ll meet a lot of resistance from laymen here including the author but there really is nothing new in this posting. Albedo is just another unit of measure for the earth’s equilibrium temperature and it should therefore come as no surprise that they should line up perfectly.

http://hyperphysics.phy-astr.gsu.edu/hbase/thermo/grnhse.html

My emphasis:

An issue of major concern is the possible effect of the burning of fossil fuels and other contributers to the increase of carbon dioxide in the atmosphere. The action of carbon dioxide and other greenhouse gases in trapping infrared radiation is called the greenhouse effect. It may measurably increase the overall average temperature of the Earth, which could have disastrous consequences. Sometimes the effects of the greenhouse effect are stated in terms of the albedo of the Earth, the overall average reflection coefficient.

68. Nylo says:

It can be accepted from this essay that TSI and Albedo are all you need to predict temperature change. However, this doesn’t confirm that CO2 has no effect on temperatures, as it has NOT been demonstrated that it has no effect on the albedo (in fact, we skeptics like to theorize the opposite, that it has, and it is a negative feedback). However, one would need to look at the data and say, if warmists say that CO2 is supposed to change the Albedo in a way that increases the warming above the one produced by the GH effect alone… well, why hasn’t it?

69. JohanS:

You make a good point in your post at June 1, 2012 at 2:57 am but, with respect, you misstate it.

Observed albedo is in the visible wavelengths so effective reflection by GHGs is not relevant to Willis’ analysis. However, as you say, it is possible that Willis’ analysis DOES include effect of increased GHGs such as CO2. I explain this as follows.

If an increase to atmospheric GHG concentration affects the hydrological cycle then the increase may alter cloud cover with resulting change to albedo. Thus, albedo is a proxy for atmospheric GHG concentration.

Please note that I am NOT claiming a change to albedo IS a proxy for atmospheric GHG concentration. I am only pointing out the possibility that it may be.

And this possibility is only one of several possibilities why an ability to attribute sunlight and albedo as the cause of global temperature change does not mean they are the cause in part or in whole.

I think Willis has shown that sunlight and albedo are the cause of global temperature change. But there are reasons why I could be wrong.

Richard

70. Peter Miller says:

As someone mentioned earlier, this could be a classic case of Occam’s Razor at work.

Occam’s razor (also written as Ockham’s razor, Latin lex parsimoniae) is the law of parsimony, economy or succinctness. It is a principle urging one to select among competing hypotheses that which makes the fewest assumptions and thereby offers the simplest explanation of the effect.

I have recently had personal experience in trying to solve the geological controls over the structures, which acted as conduits for mineralization into a large base metal deposit. At times, I felt very much like a ‘climate scientist’, producing ever more complex theories and models, which would never explain 100% of what we were seeing in the drill cores.

After four years of struggle, we finally had an epiphany a few weeks ago and now the explanation for what we see in our drill cores is really very simple.

I suspect Willis may have stumbled over something which has the potential to put the global warming industry out of business. If he has, the world owes him a great debt of thanks. However, as can be seen in a couple of instances here, there will be plenty of shrill objections from those only interested in maintaining the health of the hugely expensive and utterly pointless global warming industry.

71. Ian W says:

Graeme W says:
May 31, 2012 at 8:59 pm

Willis Eschenbach says:
May 31, 2012 at 8:13 pm

BarryW says:
May 31, 2012 at 7:50 pm

So given the time frame of your calculation, the temperature rise and the .3 deg C per doubling for the NH, how much of the temperature rise is “attributable” to CO2?

Well, since my numbers fit the data very well with no CO2 involved, and furthermore there is no significant trend in the residuals, I’d say about zero is attributable to CO2 …

w.

Playing Devil’s Advocate, CO2 can affect the albedo, so it’s included. The theory is that an increase in temperature due to CO2 will result in increased water vapour being held in the atmosphere, which can manifest as clouds, altering the albedo.

So this model doesn’t impact on AGW concepts – it’s a much more empirical analysis of temperature based on how much energy from the input source (the sun) gets absorbed by the Earth (determined by albedo). Conceptually, straight forward and hard to refute. The next level of discussion is then to determine what impact various factors (such as CO2, aerosols, etc) have on the Earth’s albedo. It would be interesting is to see if there are any existing studies on that subject.

Devils advocate is a dangerous game :-)

1. The extra water vapor in the AGW theory would show up – it is claimed and in all the models as a tropospheric hotspot – there is no tropospheric hotspot.
2. It would appear that atmospheric humidity is actually dropping rather than “rising in response to temperature.” – Moreover, a drop in humidity by reducing atmospheric enthalpy could lead to a rise in atmospheric temperature without any heat being ‘trapped’ at all.

72. JohanS says:

richardscourtney says:
June 1, 2012 at 4:10 am

“You make a good point in your post at June 1, 2012 at 2:57 am but, with respect, you misstate it.”

I didn’t misstate anything. I parroted, with citations and quotes, an award-winning online physics reference used by millions of people annually. If you believe there were any misstatements I suggest you take it up with the creator of the resource whose contact information is listed at:

http://hyperphysics.phy-astr.gsu.edu/hbase/hframe.html

Contact

Carl R. (Rod) Nave
Department of Physics and Astronomy
Georgia State University
Atlanta, Georgia 30302-4106

Email: RodNave@gsu.edu

73. Richard M says:

The question will come up so I think it needs to be addressed. If the temperature is only influenced by albedo and TSI then does that mean there is no greenhouse effect? And, if there is, then why doesn’t its effect show up here.

My own view, given earlier, is that GHGs work to provide a thermostatic effect. Hence, once the temperature gets close to the “setting” then adding more GHGs will have no effect. As long as sufficient GHGs exist in the atmosphere the thermostat works fine and adding more GHGs won’t make any difference. This means that N&Z claims are not sufficient and that theory can be ignored. However, it does mean the planetary correlations they found may be meaningful since they are essentially based on the same factors as Willis has determined (since the gravity and mass of the atmosphere are more or less constant for the Earth at this time).

The bottom line is GHGs are required to raise the temperature of the Earth above 255K, the GHE is real but only half the story and, finally, adding more GHGs will have almost no impact on climate.

74. richard telford says:

richardscourtney says:
June 1, 2012 at 2:51 am
—————————–
Apologies for missing your earlier post.

Eschenbach has produced a method that is incapable of finding the correct climate sensitivity. He and you appear to be incapable of realising that. He is not the first to fall into this trap: Schwartz (2007) created a method with similar failings. The comment by Foster et al (2007) on Schwartz (2007) (http://www.jamstec.go.jp/frsgc/research/d5/jdannan/comment_on_schwartz.pdf) is relevant to Eschenbach latest half-backed effort.

You seem allergic to the idea that climate is more complex than a one-box model. How long would I have to search before I could find a quote from you or your fellow lobbyists proclaiming that climate models are insufficiently complex? Do you not notice the contradiction here, or do you not care? To claim that a one -box model is sufficient, is to claim that either all components of the climate system (such as the subsurface ocean, and the atmosphere) warm at the same rate, or that the more slowly changing parts of the system cannot subsequently affect the other parts. Which of these two absurd positions do you hold?

75. RomanM says:

Willis, I wrote a post on the proper way to calculate trends for seasonal (and anomalised data) here several years ago.

The trends calculated are unaffected by the starting and ending months of the data.

76. Allan MacRae says:

I found my calcs from 2005 that gave a climate sensitity to CO2 (doubling) of 0.3C.- as stated in my post on a related earlier thread.

But what if temperature primarily drives CO2, not the reverse, as I proposed in January 2008?

Conclusions:
CO2 drives temperature,
AND
Tail wags Dog.

News at 11.

77. Ian W says:

JohanS says:
June 1, 2012 at 3:49 am

Another reference. I know I’ll meet a lot of resistance from laymen here including the author but there really is nothing new in this posting. Albedo is just another unit of measure for the earth’s equilibrium temperature and it should therefore come as no surprise that they should line up perfectly.

http://hyperphysics.phy-astr.gsu.edu/hbase/thermo/grnhse.html

My emphasis:

An issue of major concern is the possible effect of the burning of fossil fuels and other contributers to the increase of carbon dioxide in the atmosphere. The action of carbon dioxide and other greenhouse gases in trapping infrared radiation is called the greenhouse effect. It may measurably increase the overall average temperature of the Earth, which could have disastrous consequences. Sometimes the effects of the greenhouse effect are stated in terms of the albedo of the Earth, the overall average reflection coefficient.

JohanS if you are able to point to observed metrics that the infra red radiation from the Earth is reduced by GHG you will be the first person to do so. However, there are observations that show that the outgoing long wave radiation appears not to be attenuated in this way.

The quotes you have provided are unsupported hypotheses unless they can provide observational evidence. . .

78. Stephen Wilde says:

“If the temperature is only influenced by albedo and TSI”

I would say that temperature at the surface is set by surface atmospheric pressure and TSI but albedo changes the amount of solar energy (TSI) that gets into the oceans AND is involved in the rate at which energy flows from oceans to space.

Low albedo allows more energy into the oceans due to the widening of the equatorial air masses but also represents a faster hydrological cycle working to reduce or cancel out the effect of more energy into the oceans.

High albedo allows less energy into the oceans due to the narrowing of the equatorial air masses but also represents a slower hydrological cycle working to reduce or cancel the effect of less energy into the oceans.

Thus high sensitivity to albedo changes but low sensitivity overall because the albedo changes apply a negative system response to any other forcing effect.

Oceanic heat release from below and solar changes from above (other than raw TSI) both work to alter the width of the equatorial air masses in relation to the size of the polar air masses. Sometimes offsetting and sometimes supplementing one another in a constant dance around the equilibrium system energy content set by surface pressure and TSI.

When the polar air masses are ‘winning’ the troposphere is cooling and when the equatorial air masses are ‘winning’ the troposphere is warming.

79. joeldshore says:

Willis Eschenbach says:

After I published my previous post, “An Observational Estimate of Climate Sensitivity“, a number of people objected that I was just looking at the average annual cycle. On a time scale of decades, they said, things are very different, and the climate sensitivity is much larger. So I decided to repeat my analysis without using the annual averages that I used in my last post.

I think you are misstating the objections to your original post. I don’t think I saw anyone complain that you were looking at the average annual cycle rather than the actual annual cycle over several years. Whether you look at the average annual cycle or the annual cycle over several years, you are still looking at a cycle of a particular frequency. I would in fact be shocked if your model fit the data for the average annual cycle but failed for the annual cycle when looked at over the whole data set…which is pretty much just a repetition of that cycle over and over again! I don’t see how that would be possible.

The one thing that is new here is the attempt to see how well you do with the linear trend in the data over time. However, there are things about that which are unclear to me: You claim to have fit the linear trend well but you don’t show in detail how well that is or how your fit to that trend changes as you change the time constant….Or, if you can use a longer time constant coupled with a greater sensitivity in order to do well with that trend. My point is that I don’t believe this trend over time is providing much of a constraint on your model. So, you really aren’t showing anything different from what you were showing us before.

As has been pointed out to you, there are ways you really could test your model, such as seeing if the model with the same time constant and lambda_0 can accurately represent the diurnal cycle…or, looking to see if your model with these fitting parameters can correctly diagnose the ACTUAL KNOWN sensitivity in a climate model.

80. Bill Illis says:

Global warming theory depends on increased GHGs reducing Albedo (clouds and ice surfaces and vegetation).

Low cloud cover declines and high cloud cover increases which provides 1.0 W/m2/C of feedback (and results in 0.6C of the 3.0C per doubling expected). Ice and vegetation are lower and take longer to have an impact. Clouds might only take a day or two.

—–

Now the Earth’s Albedo certainly does change through time. How much does it increase in the ice ages for example. What about the Cretaceous when there is no ice and the ocean covers 35% of the continents. Snowball Earth conditions produce an Albedo of 50% versus 30% today.

Clouds are the big uncertainty. They are over 50% of the total Albedo at -53.0 W/m2. Doubled CO2 +3.7 W/m2. The cloud feedback signal can easily impact your doubled CO2 temperature impact by +/- 100%.

81. wsbriggs says:

RomanM says:
June 1, 2012 at 5:45 am

Thanks for the info on the calculation method, not only was it informative in and of itself. The comments, particularly of Nick Stokes, show how hard some of the AGW tribe work to avoid correct analysis of the problem. Wow, the whole show in a microcosm.

82. donkeygod says:

Excellent paper, and thanks very much for it. One of Irving Copi’s elements of a successful hypothesis — the one too many climate scientists forget or ignore — is simplicity. All other things being equal (and that’s something you show pretty well), the simpler model, has to be the best. This approach looks like a bloody good bet to confusticate and bebother The Team. If albedo and insolation are good enough to explain observations on their own then, in logic, anything else should be considered superfluous … at least until somebody shows that some of the various forcings improve the model. Apparently, they don’t … at least in relatively recent times. I accept that the model’s not predictive, but surely insolation follows some fairly well-known trends. And I imagine future albedo can be predicted from trends as well … within limits: testable over the next decade or three, and subject to correction with real-time measurements as they come in. I suppose one could estimate albedo and insolation in past as well, though maybe without much precision. (Better than tree rings, anyhow.) This looks like a significant advance: something against which a lot of dodgy ‘research’ might be tested. One wonders why it hasn’t been done before (or, if it has, in which swamp the results were buried). Very well done, and I hope you’re able to whack a few thick skulls with it.

83. cba says:

ice ages have usually been attributed to orbital mechanics, variations in the Earth’s tilt and orbital path. this would lead to a very precise cycle of glaciation and thawing. However, while cyclical, the timing is not that precise and it doesn’t align really well to the orbital mechanics. It would seem other items are actually the trigger for both glaciation and for melting. Albedo changes make for a tremendous opportunity to explain this as well as short term variations.
As stated above, bond albedo is the type of albedo we need to describe the amount of power being absorbed by Earth and its atmosphere, (1-a)*incoming solar. Bond albedo is all scattered/reflected energy coming from a body at all wavelengths of the em spectrum. Earth is around 0.3 venus is about 0.9, and the moon is about 0.123 (wikipedia bond albedo page has the references).
Venus is total cloud cover while the moon is essentially rock and dirt of the same materials as Earth. Earth is 70% liquid water on the surface and 62% cloud cover obscuring the surface leaving rock, ice/snow, dirt/sand, and vegetation to be a small fraction of the total area.
If Earth had 0.9 bond albedo like Venus but located on the surface as in a cloudless snow ball Earth scenario, Earth’s mean temperature would be down around 157K or 116 deg C below freezing. This is the Stefan Boltzman T for the emission of 34 W/m^2.
If Earth had an albedo of 0.123 like the Moon, its surface T average would have to be only about 269K, or -4 deg C were it not for atmospheric effects warming the surface. This is the Stefan Boltzman T for the emission of 299 W/m^2.
Earth’s actual albedo of 0.3 which, without an atmosphere, would require a surface radiating temperature of 255K, -18 deg C which is what is required to emit 239 W/m^2. Note that the average incoming power minus albedo reflected power is (1-0.3)*341 = 239 W/m^2.
Earth’s albedo is quite complex due to its unique make up. Ice (opaque) and fresh snow are quite high, possibly in the 0.6 to 0.8 range. Old snow is much lower and clear ice approaches the albedo of water which is under 0.04. Liquid water is under 0.04 for high angles of incidence for the incoming light energy. For low angles it increases dramatically but then there is not much power coming in per unit area at such low incidence angles. The remaining surface, under 30% is land and if it is rock, is likely to be similar to the lunar albedo, 0.12. Sand, especially wet sand, can be over 0.3 and vegetation also runs in the 0.1 to 0.2 range. However, at any one time around 62% of the Earth is covered in clouds and thick ones totally obscure surface albedo, substituting their own instead. Clouds range from around 0.4 to 0.9 albedo, depending on thickness and type of cloud. Note too that scattering in the atmosphere amounts to around a 0.04 albedo contribution as I recall.
This all adds up to our 0.3 albedo with the usual interglacial snow and ice contribution and land use changes being hardly worth mentioning as part of the whole. That 70% ocean coverage on the surface means that all land is a small part of the whole, especially considering that cloud cover of 60% amounts to the vast majority of our albedo. Snow and ice have little impact globally because it is located now at higher elevations where the incoming solar power per unit area is far less and where open water’s albedo starts to become significant. Getting rid of our clouds and atmospheric scattering (while keeping the ice and oceans) would result in about 0.09 albedo which is significantly less than the Moon’s 0.123 albedo.
All of this combined with Willis’ calculations points to a negative feedback or more aptly, a setpoint control system for Earth’s climate. Cloud cover (albedo) is the dominating factor and it adjusts by water cycle (evaporation, clouds, rain) for the incoming power and regulates the temperature rather well for changes in incoming power. However, during glaciation periods the feedback loop gets fouled up due to lots of extra fresh ice and snow which short circuits this cloud controlling feedback. Having lots of fresh ice and snow at lower lattitudes means that the albedo can stay very much the same with and without clouds present.
As the ice and snow ages and packs down the albedo drops. Also it gets dirty with soot from forest fires and volcanoes, and gets dirty from incoming cosmic debris and possibly windstorm born dust and dirt and sand until eventually one also starts to get melting pools which results in very low albedo of liquid water and the low albedo of clear ice, exacerbated by a lack of cloud cover due to the low temperatures. These are what eventually gets us out of the ice age glaciation period.
As for getting into one, there are always the possibility of a quiet sun, changes in extra solar cosmic rays that affect cloud cover and cloud albedo (due to cloud forming nuclei size variations affecting the reflectivity), orbital parameters, volcanic emissions, forest fires, and cosmic debris impacts and plain old serious weather pattern flukes that can combine into a perfect storm of albedo increase that drives us into starting a glaciation cycle.

84. richard telford:

Your post addressed to me at June 1, 2012 at 5:41 am does not address the points I have repeatedly put to you. Instead, it provides twaddle like this

You seem allergic to the idea that climate is more complex than a one-box model.

NO!
I am allergic to unjustified stupidity.

Willis’ has demonstrated that a “one box” model works.
You claim a more complex model is needed.

Richard

85. JohanS:

re. your post addressed to me at June 1, 2012 at 5:20 am.

Either you have misunderstood Carl R. (Rod) Nave or he is wrong.
In either case I have no intention of contacting him.

Richard

86. joeldshore:

You really are good at ignoring everything which does not fit your prejudices. For example, at June 1, 2012 at 6:38 am you assert to Willis.

As has been pointed out to you, there are ways you really could test your model, such as seeing if the model with the same time constant and lambda_0 can accurately represent the diurnal cycle…or, looking to see if your model with these fitting parameters can correctly diagnose the ACTUAL KNOWN sensitivity in a climate model.

You know that is nonsense because it was repeatedly explained to you in the previous thread.

There is no “ACTUAL KNOWN sensitivity”.
Each climate model uses a different value of climate sensitivity.
Willis has assessed reality so a climate model can be tested against Willis result.
A climate model is a representation of an idea about reality so it tells NOTHING about the validity of Willis’ result.

Richard

87. The following is an attempt to find some common ground between Willis and Dr Telford, which might foster further discussion.

Dr Telford correctly notes that sceptical commentators have often criticised the climate models as being too simple to adequately model the extraordinarily complex system that is global climate. He seems disturbed that Willis has produced a model which is almost trivially simple when compared to the other climate models.

The root of the complexity is that a large number of factors contribute to our climate and their relationships frequently appear very complex.

If I understand Willis correctly, I believe he is taking a different approach. He postulates that a good approximation may be reached with a very small number of parameters; excluding many factors may as either
1. Sufficiently small to be ignored or
2. Bound to other (included) parameters, such that the included parameters might act as a proxy for the affect of the excluded parameters.

Because this is intended as a fresh approach, its inconsistency with earlier approaches does not inherently invalidate it.

If we wish to say that Willis’ simplification is invalid, we need to identify specific ways in which it fails, rather than just dismissing it because it is different.

If the above is characterisation is correct, may I encourage Dr Telford (and others who share his opinion) to identify specific flaws.

88. richard telford says:

richardscourtney says:
June 1, 2012 at 8:19 am
————–
If Eschenbach had done his analysis on a week of hourly data from Epsom, he would have found a time constant of a few hours, with no evidence that longer time constants are required. Would you still claim that a one-box model was sufficient, even though the results of hourly, monthly and annual data all suggest different time constants?

I hope the advice you claim to give to MPs is of better quality than what you write here. Or does advising MPs amount to sending uninvited letters?

89. Phil. says:

Willis Eschenbach says:
May 30, 2012 at 12:55 pm
[UPDATE—ERROR] I erroneously stated above that the climate sensitivity found in my analysis of the climate models was 0.3 for a doubling of CO2. In fact, that was the sensitivity in degrees per W/m2, which means that the sensitivity for a doubling of CO2 is 1.1°C. -w.

Willis, in the previous analysis you got 0.4 and 0.2 which averaged to 0.3 then subsequently posted the above correction are the results you post here actually ºC or ºC/W/m2, or is it just coincidence that the magnitudes are so similar? Thanks

90. Robbie says:

Climate sensitivity of 0.3° C/decade (Figure 1) for the NH. That’s 3°C/century for the NH. And 0.14°C/decade for the SH. That’s 1.4°C/century for the SH. Averaging it out at 1.4+3.0=4.4/2=2.2°C global temperature increase for a doubling of CO2. And that’s not catastrophic?
It means we will definitely skip the coming Ice Age and the so-called coming global cooling is out of the question if we continue to burn fossil fuels as usual.
Thanks for the info Mr. Eschenbach: I made a mistake in my comment in your previous post. I thought you meant a sensitivity of just 0.3°C for a complete CO2 doubling and not a trend per decade.

91. Willis Eschenbach says:

richard telford says:
June 1, 2012 at 2:18 am

Eschenbach appears to have a reading problem. I suggested that ” realistic time constants are *added* to the analysis”. Instead he *replaced* the short time constant with a longer time constant. Not surprisingly, a long time constant cannot replicate the intra-annual variability. But why only one time constant? There must be a range of time constants associated with different components of the system, for example surface vs sub surface ocean.

Richard, I’m done with you. Your attitude is insufferable, as is your assumption that I am acting in bad faith. And referring to me as “Eschenbach”, as if I wasn’t here, is a calculated insult.

I have provided all of the data and calculations for you or anyone else to examine. If you want to do something with it, please do so, and report back. If you think that there are a range of time constants, that’s quite possible, I don’t deny that. So if that’s your claim, then get up off of your dead okole, buckle down, do the work, and demonstrate that to us. I have demonstrated that for the time periods in question (one month to fourteen years) a single time constant does a fine job of replicating both the larger trend in the NH and the smaller trend in the SH, as well as the monthly variations. Does this establish that my model is correct? No, it just shows that it does a good job.

Heck, I’m not claiming that my model is the be-all and end-all. For starters, I suspect that a “fat-tailed” distribution would do a better job than a standard exponential distribution. Or there may be some other model entirely that works better, perhaps your model, I don’t know. I just do what I can and put it out for comment.

But insulting me is not the way forwards.

w.

92. Gail Combs says:

cba says:
June 1, 2012 at 7:20 am

ice ages have usually been attributed to orbital mechanics, variations in the Earth’s tilt and orbital path. this would lead to a very precise cycle of glaciation and thawing. However, while cyclical, the timing is not that precise and it doesn’t align really well to the orbital mechanics…..
___________________________________________________________
You missed the more recent developments that take care off that problem.

Luboš Motl has a good article with links to the 2006 paper In defense of Milankovitch by Gerard Roe

What you are referring to is shown in the graph of Ice Volume vs the June theoretically calculated insolation at 65 °N. This is the GRAPH. As Motl says “The graphs above are just unimpressive. A lag of 8,000 years has to be added by hand to make it at least remotely plausible. There’s no real agreement”

…the basic correct observation is the following: If you suddenly get more sunshine near the Arctic circle, you don’t immediately change the ice volume. Instead, you increase the rate with which the ice volume is decreasing (ice is melting). Isn’t this comment trivial?

Nigel Calder knew that this was the right comparison to be made back in 1974.

So when the correct comparison is made, the rate of change, the derivative of the ice volume you get this GRAPH

…And you clearly get a spectacular agreement between the theoretically calculated insolation curve (cyan) and the derivatives of the reconstructed ice volumes (white). Moreover, this model requires no lag to be adjusted and no significant CO2 forcing to be added if you want to reproduce the data very well. Roe explicitly mentions – even in the abstract – that CO2 is not needed….

So I am sorry but that dog don’t hunt no more.

93. Willis Eschenbach says:

JohanS says:
June 1, 2012 at 2:57 am

Albedo changes with CO2 concentration, Willis.

Cite?

Albedo is all frequencies not just the visible ones. More CO2 makes the planet effectively “darker” in the CO2 absorption bands. I use the quotes around “darker” because those bands are invisible to the naked eye and require a spectrograph to “see” them.

So you were rolling CO2 into your calculation without realizing it.

While there is reflectivity or “albedo” in the longwave frequencies, it is typically quite small for solids and liquids, and even smaller for gases. Clouds and liquid water, for example, are essentially a blackbody at longwave frequencies, with very little being reflected. And for gaseous CO2 itself, the albedo is effective zero.

Several people have pointed out that the albedo contains the signals of e.g. the indirect aerosol effect [effect of aerosols on clouds], and the land-use change. They are correct.

But CO2 directly? Not happening.

Indirectly? Perhaps, through some theoretical CO2 –> temperature increase –> greater evaporation –> more clouds –> greater albedo … but there are a host of assumptions in that, and it doesn’t work on short time scales (e.g. decadal or shorter) because the CO2 changes are small in that length of time.

w.

94. cba says: “ice ages have usually been attributed to orbital mechanics, variations in the Earth’s tilt and orbital path. this would lead to a very precise cycle of glaciation and thawing. However, while cyclical, the timing is not that precise and it doesn’t align really well to the orbital mechanics.”

This is because the variations in summer insolation in the Northern Hemisphere correlate with the time derivative of the global ice volume, not the ice volume itself. It’s quite clear that the orbital theory is right:

http://earthweb.ess.washington.edu/roe/GerardWeb/Publications_files/Roe_Milankovitch_GRL06.pdf

95. Willis Eschenbach says:

JohanS says:
June 1, 2012 at 3:20 am

Google albedo. The #2 hit (wikipedia is #1) is the link below. It is a technical definition of albedo and discusses the critical difference between bond albedo and geometric albedo.

http://hyperphysics.phy-astr.gsu.edu/hbase/phyopt/albedo.html

My emphasis:

Albedo
The term albedo (Latin for white) is commonly used to applied to the overall average reflection coefficient of an object. For example, the albedo of the Earth is 0.39 (Kaufmann) and this affects the equilibrium temperature of the Earth. The greenhouse effect, by trapping infrared radiation, can lower the albedo of the earth and cause global warming.

Thanks, Johan. I’m sorry, but that’s just the uncited, unsupported, and unsubstantiated opinion of one man, an associate professor at Georgia State. It is certainly not the definition used by most scientists.

For example, the following result on google says:

Albedo definition
Albedo is the fraction of solar energy (shortwave radiation) reflected from the Earth back into space.

Notice that it says nothing about longwave radiation. This is the common definition of albedo, the one most people use.

More to the point, it is the definition of albedo used by the authors of the study from which I took the albedo measurements that I used. They are talking about albedo as shortwave reflection, not longwave. So no, I am not “rolling CO2 into my calculations” as you claim, because the figures I am using are only of shortwave albedo.

There’s an interesting discussion of albedo and long-wave radiation here. Generally, they are inversely related, but this is because of clouds affecting the longwave radiation, it’s not from CO2.

All the best,

w.

96. Matthew R Marler says:

Also, note that it is meaningless to say my results are a “joke” or are “nonsense”. The results fit the observations extremely well. If you don’t like that, well, you need to find, identify, and point out the errors in my data, my logic, or my mathematics.

I agree. The test will be in the future, say perhaps 20 years from now. The record of F and T, hence their first differences, will be available. True “predicted” temperatures for the future years and months will be available, conditional on the observed changes in F, and the predicted temperatures will be tested against the observed temperatures.

What’s peculiar about the model is that the change in temperature for May (or any month) depends on the change in forcing for May, rather than on the mean or other integral of forcing for May or the preceding April. A change in forcing has about 90% of its total effect during the month of the change in forcing (the 90% approximation is from your spreadsheet, showing the decay of the effect in subsequent months.)

lambda and tau of your model are strictly monotonic functions of the parameters a and b of a linear vector autoregressive model (displayed in my posts on the earlier thread), and don’t have anything to do with lag or delay. Any pair of strictly monotonic functions would do as well.

97. Willis Eschenbach says:

charlie says:
June 1, 2012 at 3:24 am

Willis,
I like the simple approach and I too think albedo is a very important factor to look at.
But, your conclusion about Pinatubo is wrong, I think. Since large volcano eruptions are supposed to cool the earth by changing the albedo, they are included indirectly in your model. If Pinatubo did affect the albedo, the temperature did follow as your model predicted. If your model had failed to predict temperatures after the eruption, it would have been a reason to question your model.

Thanks, charlie. Yes, the volcanic eruptions are supposed to cool the earth by increasing the albedo.

But the data shows no such spike in the albedo after the eruption. This means that when the eruption happens and drives up the albedo, the albedo elsewhere on the planet must be reducing to compensate. Otherwise, we’d see the spike in the albedo and the corresponding temperature drop. Since we see neither, the climate must be responding to the eruption by a general lowering of the albedo. This makes sense, since in general the albedo decreases when temperature goes down.

w.

98. Matthew R Marler says:

willis at 12:53: As you can see, there’s not much difference in the size of the residuals whether you use all, just the first half, or just the second half for the training.

Thanks for that.

99. Willis Eschenbach says:

RomanM says:
June 1, 2012 at 5:45 am

Willis, I wrote a post on the proper way to calculate trends for seasonal (and anomalised data) here several years ago.

The trends calculated are unaffected by the starting and ending months of the data.

A most fascinating post, RomanM, many thanks for that.

w.

The solar magnetic flux operates the gate (magnetosphere) which controls the amount of GCR flux which modulates cloud cover. A giant MOSFET.

101. Stephen Wilde says:

“since in general the albedo decreases when temperature goes down.”

Does it ?

Cloudiness decreased during the late 20th century warming and is now on the increase again:

https://wattsupwiththat.com/2007/10/17/earths-albedo-tells-a-interesting-story/

“This means that when the eruption happens and drives up the albedo, the albedo elsewhere on the planet must be reducing to compensate.”

Most likely warmed air resulting directly or indirectly from the eruption descends elsewhere and the adiabatic warming in the descending column reduces cloudiness there to offset the increased cloudiness caused by the eruption or its products.

102. Willis Eschenbach says:

joeldshore says:
June 1, 2012 at 6:38 am

… The one thing that is new here is the attempt to see how well you do with the linear trend in the data over time. However, there are things about that which are unclear to me: You claim to have fit the linear trend well but you don’t show in detail how well that is or how your fit to that trend changes as you change the time constant….Or, if you can use a longer time constant coupled with a greater sensitivity in order to do well with that trend. My point is that I don’t believe this trend over time is providing much of a constraint on your model. So, you really aren’t showing anything different from what you were showing us before.

Thanks, Joel. As I showed above, if I increase the time constant to 8 months the fit to the trend becomes very poor. So contrary to your claim, the trend does in fact provide a strong constraint on the model.

As has been pointed out to you, there are ways you really could test your model, such as seeing if the model with the same time constant and lambda_0 can accurately represent the diurnal cycle…or, looking to see if your model with these fitting parameters can correctly diagnose the ACTUAL KNOWN sensitivity in a climate model.

If you have temperature and albedo data for e.g. the northern hemisphere on an hourly basis, I’d be glad to use it to test the model. I don’t have the data, and don’t know where to get it, but if you do I’m more than happy to give it a shot. I doubt if it will work at that resolution … but then neither does the time constant of the CCSM3 climate model, which is on the order of 2 years from memory.

Finally, you keep talking about the “ACTUAL KNOWN” sensitivity of the climate models … but I can emulate the climate models with extremely good accuracy, with or without correcting for their illusions about volcanoes, using a much, much lower sensitivity than what you call the “ACTUAL KNOWN” sensitivity of the models.

Why the difference? I haven’t a clue, but it doesn’t give me much faith in the “ACTUAL KNOWN” sensitivity that seems to be neither actual nor known, but only claimed by the modelers. In fact, I don’t have a clue exactly how they are calculating their ACTUAL KNOWN sensitivity … and I would guess that you don’t either. Oh, I know in general, temperature change divided by forcing in some sense, but their results don’t agree with my actual calculations using their actual forcings and actual resulting temperature outputs. Perhaps you believe things simply because some modeler claims that they are true … me, not so much.

Joel, it appears that you don’t believe my results regarding emulating the climate models, but you cannot find any flaw in them. As a result, before making further claims about the ACTUAL KNOWN sensitivity of the models, I would encourage you to use a simple lagged model to calculate the sensitivity for yourself. See if you get the ACTUAL KNOWN sensitivity, or some other number …

w.

103. Willis Eschenbach says:

Michael J says:
June 1, 2012 at 8:44 am

The following is an attempt to find some common ground between Willis and Dr Telford, which might foster further discussion.

I have no interest in further discussion with him. He went out of his way to be insulting, snide and dismissive. I said he could apologize or the discussion is over. He refused to apologize. I have not even read any of his successive comments. End of story.

w.

104. Willis Eschenbach says:

Phil. says:
June 1, 2012 at 8:52 am

Willis Eschenbach says:
May 30, 2012 at 12:55 pm

[UPDATE—ERROR] I erroneously stated above that the climate sensitivity found in my analysis of the climate models was 0.3 for a doubling of CO2. In fact, that was the sensitivity in degrees per W/m2, which means that the sensitivity for a doubling of CO2 is 1.1°C. -w.

Willis, in the previous analysis you got 0.4 and 0.2 which averaged to 0.3 then subsequently posted the above correction are the results you post here actually ºC or ºC/W/m2, or is it just coincidence that the magnitudes are so similar? Thanks

The sensitivity for a doubling of CO2 in this analysis is about 0.3°/doubling. The sensitivity found by analyzing the climate models is about 1.1°C/doubling.

w.

105. Willis Eschenbach says:

Matthew R Marler says:
June 1, 2012 at 9:48 am

lambda and tau of your model are strictly monotonic functions of the parameters a and b of a linear vector autoregressive model (displayed in my posts on the earlier thread), and don’t have anything to do with lag or delay. Any pair of strictly monotonic functions would do as well.

Thanks, Matthew. In the earlier thread, you said:

Now you have: ΔT(n+1) = λ∆F(n+1)/τ + ΔT(n) exp(-1/ τ)

which could as easily have been written as: ΔT(n+1) = a∆F(n+1) + bΔT(n).

In this formulation, you have:

a = λ/ τ

b = exp(-1/ τ)

I don’t see why you’d want to do that. As it stands, lambda is the sensitivity and tau is the time constant. If you combine them in the form you suggest, what advantage do you gain? When you do that, you just have to unscramble them to extract the time constant and the sensitivity. I don’t get the point. What am I missing?

w.

106. Paul Vaughan says:

@Stephen Wilde

Are you sure global albedo is the right metric?
(…or are you just attempting simplified narrative?)

For example, what is the effect of cloud cover in the summer polar-day (around-the-clock low-angle sunshine) vs. in the winter polar-night (around-the-clock darkness) vs. at the equator — etc.?

Do you assume spatial uniformity of cloud cover impact?

If the effect you claim exists, it will be:
a) imprinted on Earth rotation data via the Law of Conservation of Angular Momentum.
b) detectable via careful hierarchical application of Central Limit Theorem.

The signature I’ve seen in Earth rotation & global wind data relates to poleward tropical sea surface temperature gradients. (Bill Illis has volunteered related illustrations in past WUWT discussions. Also see the work of Jean Dickey at NASA JPL.)

Regards.

107. Those discussing “time constant” may wish to read:
Scafetta N., 2008. Comment on Heat capacity, time constant, and sensitivity of Earth’s climate system’ by Schwartz. Journal of Geophysical Research 113, D15104. DOI: 10.1029/2007JD009586.

Thus, Figures 1c, 2c, and 3 show that within a time scale of 1–2 years the climate is characterized by a fast time response of about 5 months while for time scales larger than 1–2 years up to 20 years the climate system is characterized by a slower response with a measured time constant of about 8 ± 2 years, which may correspond to 12 ± 3 years by taking into account the statistical bias.

108. Willis:

Please do not be upset by Telford. All his posts demonstrate he is not acting in good faith, and there is no purpose in trying to guess why he is acting as he is.

Others are criticising your model in a variety of ways in genuine attempt to falsify it. Be assured that your work really is sufficiently interesting to warrant such attention. And be flattered that it attracts so much constructive criticism.

Richard

109. Matthew R Marler says:

Willis: I don’t get the point.

Since any strictly monotonic function can replace exp(-1/ τ), there is no reason to think of τ as a time constant; it’s merely a fitted parameter to make b = exp(-1/ τ), and a = λ/ τ where a and b are the coefficient in a standard vector linear autoregressive model. You could have b=3^τ and a=sqrt(τ),

Why would you want to do this? there is no principled reason to prefer one pair of invertible equations to another, subject to the constraint that if you search hard enough you can find some reparameterizations where the estimation procedure is unstable, and those you would prefer to avoid.

The estimate for τ has no meaning other than it is the estimate that gives the best fit with this model. It’s not “months” or “years” or anything like that.

This is a really great topic. It may have even prompted me to dust off my old Quantum text book from an Upper Division Physics course back in the day. ;)

111. Stephen Wilde says:

“Are you sure global albedo is the right metric?
(…or are you just attempting simplified narrative?)”

Since albedo either increases or decreases (never remaining the same for long) the right metric is the netted out result of all factors influencing albedo globally.

If the right observations are recorded accurately enough over a long enough time I am sure that the relationships I describe will become apparent and widely accepted.

Since I first promulgated such ideas there have been numeroius papers which appear supportive and many contributors here and elsewhere have been setting out similar if less complete formulations.

A few years ago my propositions were ‘way out there’. Now, not so much.

112. Willis Eschenbach says:

Matthew R Marler says:
June 1, 2012 at 2:05 pm

Willis:

I don’t get the point.

Since any strictly monotonic function can replace exp(-1/ τ), there is no reason to think of τ as a time constant; it’s merely a fitted parameter to make b = exp(-1/ τ), and a = λ/ τ where a and b are the coefficient in a standard vector linear autoregressive model. You could have b=3^τ and a=sqrt(τ),

Matthew, the standard form for exponential decay over time is exp(-t/tau), where “t” is the elapsed time. Tau in this case is the time constant, which in fact is the “e-folding time”, or the time it takes for the amount to decay to 1/e. In other words, when t = tau, it resolves to exp(-1), which is 1/e.

In this particular instance, I’m only using it at time t=1, because this is a simple model. However, the amount “tau” actually is a time constant, the e-folding time, in whatever units of time you are working in. In my case, that’s months.

w.

113. Robbie says:

richardscourtney says on June 1, 2012 at 8:36 am

“There is no “ACTUAL KNOWN sensitivity”.
Each climate model uses a different value of climate sensitivity.
Willis has assessed reality so a climate model can be tested against Willis result.”

Some simple questions: If there is no actual known sensitivity then why is Mr. Eschenbach projecting his ‘real world’ assessment into the future? That doesn’t make any sense. Besides CO2 is still increasing and if Mr. Eschenbach is already claiming a 3°C/century trend for the Northern Hemisphere I would not want to know what the trend will be when CO2 has doubled. That’s gonna be catastrophic for the Northern Hemisphere.
How would you establish sensitivity when CO2 is still increasing? Even if CO2 will stabilize in the atmosphere next year (for example) can we be sure that the warming stops immediately or is there still some more warming in the pipeline (what some well respected climate scientists claim) when climatic conditions turn to warming again?
What Mr. Eschenbach has done is making a climatic sensitivity assessment of some real world data in the early phases of CO2 increase in a “Cold” Earth.

114. Robbie:

At June 1, 2012 at 3:38 pm you say to me:

Some simple questions:

If there is no actual known sensitivity then why is Mr. Eschenbach projecting his ‘real world’ assessment into the future? That doesn’t make any sense.

He is NOT “projecting his ‘real world’ assessment into the future“ and he said he is not at June 1, 2012 at 1:31 am where he wrote:

The model merely specifies what the temperature change will be from a certain change in the albedo. As a result, It can’t be used to forecast anything, because we don’t know the future state of the albedo

Besides CO2 is still increasing and if Mr. Eschenbach is already claiming a 3°C/century trend for the Northern Hemisphere I would not want to know what the trend will be when CO2 has doubled. That’s gonna be catastrophic for the Northern Hemisphere.

Clearly, you have not read what he wrote. His model implies that atmospheric CO2 concentration is irrelevant. He wrote;

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

How would you establish sensitivity when CO2 is still increasing?

Willis’ model is a determination of climate “sensitivity when CO2 is still increasing”.

Even if CO2 will stabilize in the atmosphere next year (for example) can we be sure that the warming stops immediately or is there still some more warming in the pipeline (what some well respected climate scientists claim) when climatic conditions turn to warming again?

If you accept the implication of Willis model then atmospheric CO2 concentration is not relevant so your questions are misplaced.

What Mr. Eschenbach has done is making a climatic sensitivity assessment of some real world data in the early phases of CO2 increase in a “Cold” Earth.

No. What Mr. Eschenbach has done is to assess climatic sensitivity of the Earth which exists.

Now, let me ask you some questions.

And
Why have you asked me these questions and not asked them of Willis Eschenbach when it is his work and not mine?

Richard

115. D. J. Hawkins says:

Robbie says:
June 1, 2012 at 3:38 pm
richardscourtney says on June 1, 2012 at 8:36 am

“There is no “ACTUAL KNOWN sensitivity”.
Each climate model uses a different value of climate sensitivity.
Willis has assessed reality so a climate model can be tested against Willis result.”

Some simple questions: If there is no actual known sensitivity then why is Mr. Eschenbach projecting his ‘real world’ assessment into the future? That doesn’t make any sense. Besides CO2 is still increasing and if Mr. Eschenbach is already claiming a 3°C/century trend for the Northern Hemisphere I would not want to know what the trend will be when CO2 has doubled. That’s gonna be catastrophic for the Northern Hemisphere.
How would you establish sensitivity when CO2 is still increasing? Even if CO2 will stabilize in the atmosphere next year (for example) can we be sure that the warming stops immediately or is there still some more warming in the pipeline (what some well respected climate scientists claim) when climatic conditions turn to warming again?
What Mr. Eschenbach has done is making a climatic sensitivity assessment of some real world data in the early phases of CO2 increase in a “Cold” Earth.

Please re-read his post. Willis isn’t talking about 0.3°/C per decade, but 0.3°/C per doubling of CO2. If he is correct, I can live with that.

116. Robbie says:

richardscourtney says on June 1, 2012 at 4:14 pm

And
Why have you asked me these questions and not asked them of Willis Eschenbach when it is his work and not mine?”

First question: Just read under Figure 1: “For the NH, the time constant is 1.9 months, and the climate sensitivity is 0.30°C for a doubling of CO2. The corresponding figures for the SH are 2.4 months and 0.14°C for a doubling of CO2.”
A doubling of CO2! That hasn’t happened yet. So yes he is projecting his assessment into the future. Mr. Eschenbach is clearly claiming something about CO2 sensitivity. Besides he hasn’t responded to me yet.

117. Robbie says:

D. J. Hawkins says:
June 1, 2012 at 4:59 pm

“Please re-read his post. Willis isn’t talking about 0.3°/C per decade, but 0.3°/C per doubling of CO2. If he is correct, I can live with that.”

Have you seen Figure 1 closely and what it reads underneath? It is claiming a 0.3°C/decade trend for the NH.
Mr. Eschenbach is wrong if he means a 0.3°C per doubling of CO2. It means a negative feedback (probably due to water vapor – what else?) of more than 75%. And that is 100% wrong. It isn’t happening now and it won’t happen in the future.
I’ve explained that here: https://wattsupwiththat.com/2012/05/29/an-observational-estimate-of-climate-sensitivity/

118. Willis Eschenbach says:

Robbie says:
June 1, 2012 at 3:38 pm

richardscourtney says on June 1, 2012 at 8:36 am

“There is no “ACTUAL KNOWN sensitivity”.
Each climate model uses a different value of climate sensitivity.
Willis has assessed reality so a climate model can be tested against Willis result.”

Some simple questions: If there is no actual known sensitivity then why is Mr. Eschenbach projecting his ‘real world’ assessment into the future? That doesn’t make any sense.

Robbie, I’m not following you. I’ve shown results from 1984-1997 inclusive … where am I projecting anything into the future? I see that you also say:

“For the NH, the time constant is 1.9 months, and the climate sensitivity is 0.30°C for a doubling of CO2. The corresponding figures for the SH are 2.4 months and 0.14°C for a doubling of CO2.”

A doubling of CO2! That hasn’t happened yet. So yes he is projecting his assessment into the future. Mr. Eschenbach is clearly claiming something about CO2 sensitivity. Besides he hasn’t responded to me yet.

The measurement “for a doubling of CO2” is not projecting into the future. It is merely a way of measuring climate sensitivity. It can be measured in °C for each additional W/m2 of forcing. More generally, it is measured in °C for each doubling of CO2, which is said to result in 3.7 W/m2 of addition forcing. So “per doubling of CO2” means nothing about the future, it is just a way to measure climate sensitivity that merely means “per 3.7 W/m2 of additional forcing.”

Thanks,

w.

119. Matthew R Marler says:

oops, I wrote: A change in forcing has about 90% of its total effect during the month of the change in forcing (the 90% approximation is from your spreadsheet, showing the decay of the effect in subsequent months.)

Willis wrote: Matthew, the standard form for exponential decay over time is exp(-t/tau),

I appreciate that. but since t is held constant, the functional form of the function of tau is irrelevant, and as I wrote any pair of invertible transforms is equivalent to the linear vector autoregressive model.

120. Willis Eschenbach says:

Robbie says:
June 1, 2012 at 5:24 pm

Mr. Eschenbach is wrong if he means a 0.3°C per doubling of CO2. It means a negative feedback (probably due to water vapor – what else?) of more than 75%. And that is 100% wrong. It isn’t happening now and it won’t happen in the future.
I’ve explained that here: https://wattsupwiththat.com/2012/05/29/an-observational-estimate-of-climate-sensitivity/

First, I’m just reporting my results. I see that you don’t like them, and I see that you think if you say that very loudly and with great vehemence, it will make you right … unfortunately, your passion is not relevant.

In the other thread you refer to, you gave the standard explanation, which is that water vapor will be the dominant feedback, and it is strongly positive. Me, I think that the dominant feedback is clouds and thunderstorms, and they are strongly negative.

Now, what I’ve done above is provide this funny thing called “evidence supporting my claim”. You have provided merely an explanation and a strong re-statement of your claim.

If you don’t like my evidence, restating your claims over and over won’t change anything. You need to either find holes in my logic, math, data, or procedures, or find evidence that supports your claims. Please be clear than results from global climate models are not evidence …

My best to you in your search for evidence to bolster your claims,

w.

121. Willis Eschenbach says:

Matthew R Marler says:
June 1, 2012 at 5:51 pm

Willis wrote:

Matthew, the standard form for exponential decay over time is exp(-t/tau),

I appreciate that. but since t is held constant, the functional form of the function of tau is irrelevant, and as I wrote any pair of invertible transforms is equivalent to the linear vector autoregressive model.

Yes, but your claim is that tau is NOT a time constant. You said:

… there is no reason to think of τ as a time constant

… so are you now agreeing with me that in fact it is a time constant?

I use lambda and tau, Matthew, because in fact they are the sensitivity and the time constant. You can replace them with “a” and “b”, as you point out, where a = lambda/tau and b=e-1/tau … but then you just have to reverse the replacement to extract the sensitivity and the time constant. I don’t get why you’d want to do that. What’s the advantage?

w.

122. D. J. Hawkins says:

Robbie says:
June 1, 2012 at 5:24 pm
D. J. Hawkins says:
June 1, 2012 at 4:59 pm

“Please re-read his post. Willis isn’t talking about 0.3°/C per decade, but 0.3°/C per doubling of CO2. If he is correct, I can live with that.”

Have you seen Figure 1 closely and what it reads underneath? It is claiming a 0.3°C/decade trend for the NH.
Mr. Eschenbach is wrong if he means a 0.3°C per doubling of CO2. It means a negative feedback (probably due to water vapor – what else?) of more than 75%. And that is 100% wrong. It isn’t happening now and it won’t happen in the future.
I’ve explained that here: https://wattsupwiththat.com/2012/05/29/an-observational-estimate-of-climate-sensitivity/

I see the legend on the chart you are refering to, and concede the point regarding the trend line. However, the data that Willis analyzed in this case happens to encompass the steepest part of the late 20th century warming. His model explains this warming using albedo and TSI. Now, CO2 effects that alter albedo are thus folded into his model, but it is incorrect to a priori ascribe all the changes in albedo to CO2. He also specifically eschews making any predictions, so extrapolating the slope over the next 100 years is making claims for the model that the model maker does not. Plus, if you look at the last 15 years of temperature, there is significant flattening already. Granted, “past performance is no guarantee of future results”, but if you think it’s OK to extrapolate, I can play that game too.

In addition, in the note below Figure 1, you can see Willis’ conclusion regarding a doubling of CO2 and it’s effects. I looked at your response in his previous post, and I’ve seen the elements thereof elsewhere. The problem is that all those elements IIRC are “estimates” of the “consensus” and not directly measured values. They are WAG’s and nothing more. “He who builds his house on sand,” and all that. As one note, global humidity is more or less the same over the last 40 years, so the primary feedback mechanism of the AGW thesis is conspicuously absent. I think you need a new epicycle on your model.

123. joeldshore says:

D. J. Hawkins says:

As one note, global humidity is more or less the same over the last 40 years, so the primary feedback mechanism of the AGW thesis is conspicuously absent.

The water vapor feedback is now well-verified. Those who desire a low climate sensitivity basically have to put all their hopes in a strongly-negative cloud feedback.

124. joeldshore says:

Thanks, Joel. As I showed above, if I increase the time constant to 8 months the fit to the trend becomes very poor. So contrary to your claim, the trend does in fact provide a strong constraint on the model.

Well, I would expect that you might be able to get the trend fit better if, as I said, you varied the sensitivity and the time constant. Admittedly, the fit to the phase of the annual cycle will still be off…but that is because we are trying to approximate multiple time scales by a single time scale.

But also, what are your errorbars on those trends? In particular, what is mainly responsible for the trend in the forcing…Is it the albedo…and how accurate are those measurements? I’m a bit skeptical that you are not stretching the data beyond its capabilities in looking at these small trends over time. (I think the data is fine for the annual cycle, where the forcing will clearly be dominated by the change in solar angle.)

If you have temperature and albedo data for e.g. the northern hemisphere on an hourly basis, I’d be glad to use it to test the model. I don’t have the data, and don’t know where to get it, but if you do I’m more than happy to give it a shot. I doubt if it will work at that resolution … but then neither does the time constant of the CCSM3 climate model, which is on the order of 2 years from memory.

You really don’t need albedo data…The sun either being there in the sky or being below the horizon is what is going to dominate the forcing for the diurnal cycle. And, you can estimate temperature variations or find some data somewhere. The point is to just get a rough estimate, which in fact Jim D already provided for you in the other thread. You could quibble about his numbers but not enough to escape the fact that the estimate one would get for the climate sensitivity using the diurnal cycle would be considerably lower than the estimate that one gets using the annual cycle, just as one would expect from basic physical principles. There is no great mystery here…I think that we all understand that the reason we don’t get frigidly cold at night is because of the considerable damping of the solar forcing that occurs due to the thermal inertia of the atmosphere and oceans.

Finally, you keep talking about the “ACTUAL KNOWN” sensitivity of the climate models … but I can emulate the climate models with extremely good accuracy, with or without correcting for their illusions about volcanoes, using a much, much lower sensitivity than what you call the “ACTUAL KNOWN” sensitivity of the models.

Why the difference? I haven’t a clue, but it doesn’t give me much faith in the “ACTUAL KNOWN” sensitivity that seems to be neither actual nor known, but only claimed by the modelers.

The reason for the difference, or most of it anyway, is pretty obvious. Your estimate is more like an estimate of the transient climate response than the equilibrium climate sensitivity. The equilibrium climate sensitivity is obtained by doubling CO2 and then allowing the model to run long enough that it actually equilibrates. As you can see in Table 8.2 of the IPCC AR4 Working Group 1 report, transient climate responses and equilibrium climate sensitivities are a fair bit different. For example, the GISS Model ER has an ECS of 2.7 degC but a TCR of only 1.5 degC. So, really the only part of things that is any mystery at all is why you got 1.1 deg C instead of 1.5 degC…but that’s not too big a difference (and what you have computed is indeed not exactly a transient climate response, which has a specific definition…but it is much closer to being that than to being an equilibrium climate sensitivity).

Joel, it appears that you don’t believe my results regarding emulating the climate models, but you cannot find any flaw in them. As a result, before making further claims about the ACTUAL KNOWN sensitivity of the models, I would encourage you to use a simple lagged model to calculate the sensitivity for yourself. See if you get the ACTUAL KNOWN sensitivity, or some other number …

The whole point is that a simple lagged model (or simple one-box model) does not correctly diagnose the climate sensitivity. It is because the climate system is complicated and operates on a variety of different time scales. You need to include AT LEAST 2 independent time scales to do any justice to this.

You seem to be making the mistake that skeptics often accuse climate scientists of making of putting too much faith in a model. You are putting too much faith in your very simple model, which is too simple to describe even full-scale climate models and is certainly too simple to describe reality. The equilibrium climate sensitivity that you diagnose with the model is not correct because neither the real world nor the climate models obey the simple picture of having a single relaxation time.

125. ferd berple says:

Willis Eschenbach says:
June 1, 2012 at 12:53 am
As you can see, there’s not much difference in the size of the residuals whether you use all, just the first half, or just the second half for the training.
=========
Willis, I’m impressed by these results. My caution is that the residuals are almost too good. So, I would be very cautious that there isn’t a math error. This will reveal itself quick enough going forward.

What is going to bother many people is what bothers all experts. Once you have seen the answer it looks so simple you wonder why someone didn’t think of it earlier. But of course all advances are like that. Once you have been shown the answer it looks so simple it is obvious.

However, if what you have discovered holds, then billions of dollars in computer models and climate science is going to be shown to be worthless rubbish. In which case a lot of people’s jobs are on the line and they are going to fight tooth and nail to try and rubbish your result.

Not because the result is wrong, but because it is a direct threat to the welfare of themselves, their families and their standing and prestige in the community. They will fight.

Gird your loins and formalize this work. Make the prediction going forward – that is the only true test – and if it turns out to be correct there can be no arguments. Propose 3 test as did Einstein for GR. The rule of three. http://en.wikipedia.org/wiki/Rule_of_three_%28writing%29

126. ferd berple says:

Looking up the rule of three I came across a statistical rule of three which would appear to apply to climate science. It is interesting the climate is weather averaged over 30 years, which is the same interval for the rule of three.

http://en.wikipedia.org/wiki/Rule_of_three_%28medicine%29
In the statistical analysis of clinical trials, the rule of three states that if no major adverse events occurred in a group of n people, then the interval from 0 to 3/n can be used as a 95% Confidence interval that for the probability that a corresponding major event will arise for a single new individual. This is an approximate result, but is a very good approximation when n is greater than 30.

127. DocMartyn says:

Willis, I have tried to think of an internal control for you analysis.
Would it be a lot of work to just analyze the belly of the beast between the Tropics of Cancer and Capricorn? Here I would expect the time constant to be smaller than when comparing the the hemispheres. In this band one should get the least swing between winter/summer.

128. Paul Vaughan says:

Stephen Wilde (June 1, 2012 at 2:22 pm) wrote:
“Since I first promulgated such ideas there have been numeroius papers which appear supportive and many contributors here and elsewhere have been setting out similar if less complete formulations.
A few years ago my propositions were ‘way out there’. Now, not so much.”

I’m digging through some older articles on solar-terrestrial circulatory morphology and its evident that some had remarkably clear vision at least 3 decades ago. Did their peers sufficiently understand & appreciate? Possibly not. The publicly projected narrative seems to be that lack of atmospheric angular momentum records for the early 20th century leaves experts with some particularly nagging worries. I see one – possibly 2 – workaround(s) – (details sometime down the road….)

Thanks for your regular contributions Stephen.

Best Regards.

129. P. Solar says:

Willis , this is very interesting. It does suggest that climate is inherently stable and dominated by strong neg feedbacks , not the artificially invented positive feedbacks fed into the models.

“But the calculations using the given time constants and sensitivities were able to capture both hemispheres very accurately. The RMS error of the residuals is only a couple tenths of a percent.”

Err, wasn’t that “a couple of tenths of a degree” ?! Not quite the same level of accuracy.

“The fit is actually quite good, with an RMS error of only 0.2°C and 0.1°C for the NH and the SH respectively.”

One thing you do need to add for this to be meaningful is some uncertainty calculation. What is the uncertainty of the source data and how do changes within that range affect your results.

In fact, someone else pointed out that using 3.2 W/m2 gave a significantly higher climate sensitivity ( 1.6 C IIRC). So what is the uncertainty in that figure and how does its range affect your result?

This is one of the biggest problems in climate “science”, there is a total lack of regard for uncertainty evaluation ( or totally fictitious ones are often provided when they are given).

One other thing you could try is to separate out the tropics rather than doing a simple NH,SH split. The tropics have a 6m cycle in irradiance, not and annual one. This may account for the way your fits have noticeable deviations near the ends of the ellipses.

It would also seem to be a significant omission that you do not seem to state anywhere just what “temperature” you are using in all this.

Overall this is a great article. The astounding simplicity and the very small residuals suggests you are on the right track.

One more point, forcing due to doubling CO2 is referring to pre-industrial levels. The next doubling will have less effect. Atmospheric CO2 conc is well above the “linear” log relation to gas concentration where each doubling causes the same effect.

Nice post.

130. Joeldshore:

At June 1, 2012 at 6:31 pm you write:

The water vapor feedback is now well-verified. Those who desire a low climate sensitivity basically have to put all their hopes in a strongly-negative cloud feedback.

No and no.
The ‘tropospheric hotspot’ is missing. That missing elevated temperature at altitude in the tropics is the anticipated result of a water vapour feedback.

You have linked to abstracts of papers which claim to have determined increased water vapour at altitude in the tropics. But there has been no accelerated warming at altitude relative to the surface in the tropics. In other words, the increased humidity has NOT provided a positive feedback.

So, if the papers you cite are right then you have cited evidence which shows
The water vapour feedback is now REFUTED.

Richard

131. P. Solar says:

“Matthew, the standard form for exponential decay over time is exp(-t/tau), where “t” is the elapsed time. ”

For those having trouble grasping this , it should be written exp(-delta_t / tau), since in this case the formula is given in a iterative form, so delta_t is the time interval from T(n) to T(n+1) ie , in this case one month. The exponential is dimensionless as it should be and tau is in the same time unit as the “1”.

132. lgl says:

Willis

133. Robbie says:

D. J. Hawkins says:
June 1, 2012 at 6:14 pm

“As one note, global humidity is more or less the same over the last 40 years…”

Can you back that claim with sources? I can and it’s the opposite of what you are saying.
– Dai 2006 – Recent Climatology, Variability, and Trends in Global Surface Humidity.
Take a good look at Figure 11. It shows global humidity. What’s the trend?
– Willett et al 2008 – Recent Changes in Surface Humidity: Development of the HadCRUH Dataset.
“Between 1973 and 2003 surface specific humidity has increased significantly over the globe, tropics, and
Northern Hemisphere.”

134. bean says:

Willis,
Accepting the fact that solar radiation and albedo explain the data, it would seem that the next logical step would be to examine the linkage, if any, between albedo and the rest of the “forcing agents” used by the AGW models?
Bean

135. Robbie says:

Willis Eschenbach says:
June 1, 2012 at 6:04 pm

“I see that you don’t like them, and I see that you think if you say that very loudly and with great vehemence, it will make you right … unfortunately, your passion is not relevant.
In the other thread you refer to, you gave the standard explanation, which is that water vapor will be the dominant feedback, and it is strongly positive. Me, I think that the dominant feedback is clouds and thunderstorms, and they are strongly negative.”

First the evidence:
Schmidt et al 2010 – Attribution of the present‐day total greenhouse effect.
“The actual mean surface temperature is larger (by around 33°C, assuming a constant planetary albedo) due to the absorption and emission of long‐wave (LW) radiation in the atmosphere by a number of different “greenhouse” substances.”
That’s all we need 33°C. Without “greenhouse” substances Earth would be -18°C. It’s not it’s 15°C. Every well respected climate scientist accepts that.
Here is another graph coming from Roy Spencer’s Climate Confusion

It simply states that if clouds were removed from the atmosphere and everything else stays equal the temperature will rise to 60°C due to the greenhouse effect. That given fact is known in the climate world and accepted by many, if not all, climate scientists.
So clouds do cause a 58% cooling ((60-15)/78(total greenhouse warming)x100%) and not the 45% I claimed. I made a calculation error there. Yes clouds cause cooling (58%) (which I accept), but the net effect will be warming on top of the 2xCO2 warming effect or the total warming from 2xCO2 and water vapor together. Nobody knows that exactly. (Schmidt et al 2010 “For instance, one cannot simply take the attribution to CO2 of the total greenhouse effect (20% of 33°C) and project that onto a 2 × CO2 scenario.”) The extra clouds won’t cause the huge negative feedback you believe in. At least not in the real world.

Now to some of your quotes: “Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.”
Just to name a few papers: Solanki 2003 – Can solar variability explain global warming since 1970?, Lockwood 2008 – Recent changes in solar outputs and the global mean surface temperature. III. Analysis of contributions to global mean air surface temperature rise, Benestad 2009 – Solar trends and global warming, Pittock 2009 – Can solar variations explain variations in the Earth’s climate?, etc etc etc. If you want some more just ask for it. The list is so long that one can rule out that the Sun will be sufficient enough to cause the temperature changes over that time period.
And: “For example, I have held that the effect of volcanoes on the climate is wildly overestimated in the climate models, because the albedo changes to balance things back out.”
What causes the albedo to change? I can see Pinatubo very clearly here:

but not in cloud variations here:

Furthermore: “I can’t, and I say that the reason is that the clouds respond immediately to such a disturbance in a thermostatic fashion.”
Name your source for that statement please. I cannot see the cloud response in my presented graph. Can you?
How can clouds respond to such a disturbance when you have just ruled out aerosols, volcanic forcing and indirect aerosols? It is aerosols emitted by volcanoes which cause volcanic cooling.
http://vulcan.wr.usgs.gov/Glossary/VolcWeather/description_volcanoes_and_weather.html
“increases in volcanism that could have thrown more airborne volcanic material into the stratosphere, thereby creating a dust veil and lowered temperatures.”

136. joeldshore says:

richardscourtney says:

The ‘tropospheric hotspot’ is missing. That missing elevated temperature at altitude in the tropics is the anticipated result of a water vapour feedback.

You have linked to abstracts of papers which claim to have determined increased water vapour at altitude in the tropics. But there has been no accelerated warming at altitude relative to the surface in the tropics. In other words, the increased humidity has NOT provided a positive feedback.

So, if the papers you cite are right then you have cited evidence which shows
The water vapour feedback is now REFUTED.

That is very impressive amount of incorrect science to pack into just a few sentences!

(1) It is quite debatable that the “hotspot” is missing. The analyses / re-analyses of the different radiosonde and satellite data sets show quite different results for the multidecadal trends in the tropical troposphere. This is because both the satellite and radiosonde data have serious issues that can produce artifacts in these long term trends. And, in fact, for fluctuations in temperature on monthly to yearly time scales where artifacts are not an issue in the data, the tropical tropospheric amplification is well-confirmed.

(2) The “hotspot” (enhanced warming at altitude predicted for the tropical troposphere) is not a result of the water vapor feedback, i.e., it has absolutely nothing to do with water vapor absorbing additional greenhouse gases. Rather, it is just a result of the basic physics of the lapse rate as long as it follows the moist adiabatic lapse rate. Hence, your reasoning that this “refutes” the water vapor feedback is completely spacious.

(3) As Isaac Held has explained, although the predicted enhancement of warming at altitude is predicted to increase the water vapor feedback somewhat over its value in the absence of this effect, it also produces the lapse rate feedback, a negative feedback in the climate models that occurs because the “hotspot” means the surface does not need to warm as much as it otherwise would in order to increase the radiation emitted back out into space and restore radiative balance. And, in fact, it turns out that this lapse rate feedback produced is predicted to be a bit larger in magnitude than the enhancement of the water vapor feedback. Hence, the net effect of the purported absence of the “hotspot”, were it to prove real, would be that the climate models are probably slightly underestimating, not overestimating, the climate sensitivity.

137. joeldshore says:

Steve Keohane says:

Atmospheric RH% is going down. http://i38.tinypic.com/30bedtg.jpg

(1) Relative humidity going down is not incompatible with absolute humidity going up. (Most climate models predict relative humidity to stay about constant or decrease a bit overall as the climate warms.)

(2) You give no source or other information for the data set you show but I believe you have cherry-picked a particular re-analysis of radiosonde data with known severe problems. This data does not agree with the much better satellite data available (and I think it even disagrees with other re-analyses of the radiosonde data).

138. pochas says:

joeldshore says:
June 2, 2012 at 7:41 am

“That is very impressive amount of incorrect science to pack into just a few sentences!”

Followed by three paragraphs of incorrect science.

139. Pamela Gray says:

Well Joel, today in the high desert plains of Oregon, it is freaking cold! Snow is predicted at pass level by Tuesday. So I tell you what, when the day comes that that hot spot starts to warm my lilly white —–, I will agree with you. Till then, you are arguing for a signal that my tomato plants have no knowledge of as they sit sulking in their pots on my porch…she said kindly.

140. ferd berple says:

joeldshore says:
June 2, 2012 at 7:41 am
Hence, the net effect of the purported absence of the “hotspot”, were it to prove real, would be that the climate models are probably slightly underestimating, not overestimating, the climate sensitivity.
========
In which case, temperatures would not have flat-lined post WWII and post 2000, when industrialization skyrocketed. The would not have increased sharply during the 20’s and 30′ and during the 80’s and 90’s when there was nothing remarkable happening with CO2.

What we do have today is a world in which we are feeding 7 billion people with only minor problems with famine as compared to 50 years ago in which we had a constant struggle to feed 3 billion. Over this same period food prices have dropped in real dollar terms. This is completely with odds with the predictions of virtually all “experts” in high places.

The problem is that “doom and gloom” forecasts sell, so they get more publicity than the facts. The facts are simple. Mainstream Climate Science predicted an accelerating warming post 2000. It didn’t happen and there are no signs that it will be happening anytime in the near future. In science this is graded as a FAIL. The theory failed in its prediction, thus the theory as stated is wrong.

Now we see the situation where climate science is trying to rationalize the failed prediction after the facts. All scientific theories are equally correct in those circumstances. Every theory can be adjusted to fit the past. It is a meaningless exercise. The one and only test that has meaning is for a theory to predict something that is unexpected and thus hard to predict.

141. Willis Eschenbach says:

P. Solar says:
June 2, 2012 at 12:49 am

Willis , this is very interesting. It does suggest that climate is inherently stable and dominated by strong neg feedbacks , not the artificially invented positive feedbacks fed into the models.

“But the calculations using the given time constants and sensitivities were able to capture both hemispheres very accurately. The RMS error of the residuals is only a couple tenths of a percent.”

Err, wasn’t that “a couple of tenths of a degree” ?! Not quite the same level of accuracy.

Thanks, fixed. Please note that a couple tenths of a degree RMS error in a system running at ~ 288K means my calculation of the temperature based solely on available sunlight is accurate to about 7 hundredths of one percent … just sayin’ …

w.

142. Willis Eschenbach says:

P. Solar says:
June 2, 2012 at 12:49 am

… One thing you do need to add for this to be meaningful is some uncertainty calculation. What is the uncertainty of the source data and how do changes within that range affect your results.

The source doesn’t give any uncertainty figures for the albedo, so I’m unable to do that.

In fact, someone else pointed out that using 3.2 W/m2 gave a significantly higher climate sensitivity ( 1.6 C IIRC). So what is the uncertainty in that figure and how does its range affect your result?

I’m a bit mystified as to how you got from 3.7 w/m2 as the equilibrium climate sensitivity to 0.4C and 0.2C for the northern and southern hemispheres respectively. If you use the normally accepted conversion of 3.2 W /m2/ C you get the value of 1.16 C per CO2e doubling, which is closer to normally accepted value of sensitivity without positive feedback.

The gentleman is totally confused. First, 3.7 W/m2 is not the “equilibrium climate sensitivity” as he claims, it is the IPCC value for the expected change in DLR from a doubling of CO2. Unfortunately, I’ve never seen any uncertainty figure for that either. Next, there is no “normally accepted conversion of 3.2 W /m2/ C”, that makes no sense. Finally, I don’t have a clue how he gets from that to “the value of 1.16°C per CO2e doubling”, that’s totally opaque. The IPCC gives the expected warming from a doubling of CO2 as being 1.5°C—4.5°C, or more recently as 2°C—4.5°C, but I’ve never seen 1.16

So I don’t have a clue what either of you are trying to say. I use 3.7 W/m2 per doubling purely so my numbers can be compared to the results from other studies, which also use 3.7 W/m2 per doubling … so there’s no need for an uncertainty figure on that.

This is one of the biggest problems in climate “science”, there is a total lack of regard for uncertainty evaluation ( or totally fictitious ones are often provided when they are given).

I couldn’t agree more.

One other thing you could try is to separate out the tropics rather than doing a simple NH,SH split. The tropics have a 6m cycle in irradiance, not and annual one. This may account for the way your fits have noticeable deviations near the ends of the ellipses.

I love how people always tell me that I should try this and that … how many times do I have to say I don’t have the data? What I’m using is the only albedo dataset I can find that is split by hemisphere. I know of no such dataset for just the tropics. I know of no such dataset for other time periods. If anyone comes up with one I’ll be very happy to analyze it, but until then …

You are the first person to comment on the error at the end of the ellipses, which has been puzzling me as well. The deviation at the end of the ellipses actually occurs mostly at the cold end, and not the hot end. I suspect that there are a couple reasons for the error. One is the effect of the melting/freezing of ice/snow, which involves the transfer of energy with no change in temperature. The other is that I suspect that the climate sensitivity (lambda) is a function of absolute temperatures, rather than being a constant. Always more to investigate … and little time in which to do it.

It would also seem to be a significant omission that you do not seem to state anywhere just what “temperature” you are using in all this.

Overall this is a great article. The astounding simplicity and the very small residuals suggests you are on the right track.

Thank you kindly. In the rush to find errors, many folks seem to have overlooked the fact that as far as I know, this is clearly both the most accurate and the simplest emulation of earth’s temperature that has been done to date. Sure it has limitations, and as many have pointed out it may fail outside the temporal range (1 month to 14 years) of my study, but within that range it is shockingly accurate.

One more point, forcing due to doubling CO2 is referring to pre-industrial levels. The next doubling will have less effect. Atmospheric CO2 conc is well above the “linear” log relation to gas concentration where each doubling causes the same effect.

Actually, it makes no difference where you refer to as the starting point for the CO2 doubling. You get the same numbers regardless of where you start. Also, you say “the next doubling will have less effect”. This is not true. Since the changes are logarithmic, each doubling (within the range seen on Earth) will have the same effect. I know of no evidence to support your claim that “Atmospheric CO2 conc is well above the “linear” log relation to gas concentration where each doubling causes the same effect” … cite? Let me say that MODTRAN disagrees with you …

Nice post.

My thanks again,

w.

143. Babsy says:

ferd berple says:
June 2, 2012 at 9:08 am

“The problem is that “doom and gloom” forecasts sell, so they get more publicity than the facts. The facts are simple. Mainstream Climate Science predicted an accelerating warming post 2000. It didn’t happen and there are no signs that it will be happening anytime in the near future. In science this is graded as a FAIL. The theory failed in its prediction, thus the theory as stated is wrong.

Now we see the situation where climate science is trying to rationalize the failed prediction after the facts. All scientific theories are equally correct in those circumstances. Every theory can be adjusted to fit the past. It is a meaningless exercise. The one and only test that has meaning is for a theory to predict something that is unexpected and thus hard to predict.”

Furthermore, they care not one whit whether their ‘predictions’ are correct or not. The issue has been politicized. The US could be energy self sufficient tomorrow but in doing so the left would lose “Devil Oil’ as a scapegoat and they can’t allow that to happen. “Forcing’ is BS. it exists in an equation and in computer programs but there is no way to experimentally increase CO2 in a vessel containing air that results in an increase the temperature inside the vessel which is what they claim exists in the atmosphere. I vote we not give them one damn penny more and throw the SOBs out on their arses (SOB is Texan for “Sweet Old Boy”) until they can produce, verify, and reproduce again an experiment that proves their fundamental statement of an increase in atmospheric CO2 concentration leads to an increase in the Earth’s atmosphere’s temperature.

144. ferd berple says:

Willi’s have found an interesting correlation that tends to show that climate forcing’s are constrained by very simple observational parameters. The correlation itself says nothing about the cause of the correlation.

The correlation shows that solar activity, albedo and CO2 may be highly accurate predictive proxies for temperature. We see this for example in the earth’s tides. The tides are driven by gravity, but we use the position of the sun, moon and jupiter as predictive proxies for the effects of gravity on earth’s oceans.

Based on these observational parameters, this gives us a highly accurate prediction of the tides. A much more accurate prediction that can be achieved by using any theory of gravity to predict the tides. Even though we “know” that gravity is forcing the tides, it does a very poor job of predicting the tides.

Nothing in Willi’s work says that solar activity, albedo or CO2 are “directly” the forcing mechanisms. Rather, what Willis is showing is that whatever the forcing mechanisms, temperature is constrained by simple observational parameters. Exactly like we see with the tides. Gravity is the forcing mechanism, but tidal height is constrained by the position of the sun and planets, not by gravitational forcings.

We can argue and speculate all day about “why” such constraints might exists, but this will not change the numbers. This really is the crucial point. “Why” something happens can never be completely answered in science. However, even without understanding the “why”, science can answer “what”, “when” and “where”. This is how science is validated. Not because it satisfies human curiosity to know “why”. Rather, theory is validated based on its ability to predict “what”, “when” and “where”.

In contrast, pseudo science is based largely on explaining the “why”, with little or no ability to predict “what”, “when” and “where” better than a toss of a coin.

145. Matthew R Marler says:

Willis: I don’t get why you’d want to do that. What’s the advantage?

There is no advantage either way: an infinite number of two-parameter models are equivalent. To put it differently, you have a non-linear lagged regression model where an equivalent linear lagged regression model will do.

It may be that a and b estimates have smaller correlations than lambda and tau estimates; I’ll know that later.

If I understand your simulations correctly, from your spreadsheet, almost 100% of the effect of a step change in forcing occurs in just 10 months after the step change. If that is so, then the long-term sensitivity equals the short-term sensitivity. Is that a fair interpretation of your model output?

146. Willis Eschenbach says:

joeldshore says:
June 2, 2012 at 8:05 am

Steve Keohane says:

Atmospheric RH% is going down. http://i38.tinypic.com/30bedtg.jpg

(1) Relative humidity going down is not incompatible with absolute humidity going up. (Most climate models predict relative humidity to stay about constant or decrease a bit overall as the climate warms.)

Joel, you’re disagreeing with the citations you provided earlier. One of them says:

The observed moistening is consistent with model simulations and corresponds approximately to a constant relative humidity increase in upper tropospheric moisture.

So it’s claiming that observations show a constant increase in relative humidity, and it says that the models agree with it. But you say the models show stable or decreasing relative humidity …

Finally, FWIW (which may not be much), the model results from the NCEP Reanalysis Model are here … they show variations but no overall trend in either relative or specific humidity over the last decades.

So I’d say that despite your certainty, the question is still wide open …

Is there water vapor feedback? Me, I’d say yes, but it is overwhelmed by the clouds. Note in my analysis, for example, that it spans a fairly wide temperature range. During the summer we must get much more moisture in the air than in the winter … but despite that, the resulting temperature is well modeled using a constant climate sensitivity. This observational evidence strongly supports the secondary role of water vapor in determining the temperature.

You still haven’t grasped the nettle, Joel. I’ve hindcasted the temperature, with shocking accuracy, using nothing but the clouds and the sun. This means that whatever other feedbacks and mechanisms might be involved, there is very, very little left for them to explain … and thus it indicates that the other mechanisms play only a very small role in determining temperature.

w.

147. Willis Eschenbach says:

lgl says:
June 2, 2012 at 4:44 am

Willis

Thanks, lgl. You are right, but remember that these are Watts per square metre, not total amounts. As a result, they should be “2* Net Sun Global”, because you cannot simply add them, you need to average them instead.

w.

148. ferd berple says:

Overall this is a great article. The astounding simplicity and the very small residuals suggests you are on the right track.
=====
Agreed. The accuracy of the result suggests either Willis has discovered a truly remarkable correlation, or there is a nonsense in the math, or that we have a statistical fluke of low probability.

Many have argued that the complexity of the climate system prevents accurate prediction from first principles as is being attempted in the climate models. The problem is computer run times for many (most) problems grows exponentially with problem size, making them impractical top solve on computers.

In fact,much of computer science is concerned with discovering efficient algorithms. Computer methods that do not grow exponentially with run times. Otherwise, you have no option but to reduce resolution, as is done in the climate models. However, this leads to problems of reliability in the answers, as has been seen.

Another method is to tackle the problem from a completely different direction. To bypass first principles and look for a “rule of thumb”. Something that gives a quick, reliable, but less precise answer. In other words, an answer that it close to correct all the time. As compared to an answer that it very precise, but sometimes spectacularly wrong.

This is what I see in the work Willis has done. He has found what appears to be a very good rule of thumb to predict temperature, that appears to be more reliable than the method of first principles used by climate models.

What would I believe be an interesting exercise to validate the model would be for Willis to run the model forward, to provide a range of prediction of future temperatures based on IPCC scenarios for CO2, solar activity and albedo and see where that leads.

Have the prediction down on paper and plot it as we go forward, with its own page on WUWT to track performance for the whole world to see. A contest between the “Willis Method” or “Eschenbach Technique” and the best of the climate models. Keep a running score on the accuracy of the various techniques.

149. Bart says:

I agree with others that you have made a reasonable case that albedo is the response variable which has the greatest control over temperature, but you have not provided evidence showing that CO2 contribution to albedo change is negligible. Given the narrow bands in which CO2 affects the albedo, this should be straightforward in and of itself, but then you hit the warmist claim of positive feedback with water vapor, and since water vapor contributes significantly to albedo, we are right back to the fundamental argument over whether there is a positive feedback or not.

So, all in all, it appears to me that you have put forward another way of looking at the problem, but have not really resolved anything in a way which would force the opposing side out of their bunkers.

150. ferd berple says:

Bart says:
June 2, 2012 at 10:33 am
So, all in all, it appears to me that you have put forward another way of looking at the problem, but have not really resolved anything in a way which would force the opposing side out of their bunkers.
=====
Agreed, because everyone is concentrating on “why” without simply getting on with the science. Everyone agrees that the sun, albedo and CO2 are in some fashion related to temperature. All that is really being argued is how much and in what direction. And folks are pulling out pieces of the puzzle to support their arguments.

However, what Willis has done is to draw a line on the table that says “it doesn’t matter how many pieces there are to the puzzle, it doesn’t matter how big the pieces are, it doesn’t matter their color, size,or shape. No matter what, when assembled correctly, the puzzle will fit snugly within the area drawn, with very little error”

What Willis has really shown is that understanding the mechanism by which the various forcings determine temperature is not required to accurately predict temperature. We know this to be true for the tides. Willis has now demonstrated this to be true for global temperature.

What Willis has shown is that it is possible to accurately predict future temperatures as we currently do now with the tides. From very basic observational evidence using simple, low-cost computer models.

What Willis has shown is that the hundreds of millions of dollars spent on complex solutions was money spent going in the wrong direction. The solution lay not in mastering complexity, but in mastering simplicity.

151. Bart:

Your post at June 2, 2012 at 10:33 am concludes by saying to Willis:

So, all in all, it appears to me that you have put forward another way of looking at the problem, but have not really resolved anything in a way which would force the opposing side out of their bunkers.

I strongly agree. I remind that I concluded my above post June 1, 2012 at 12:52 am by saying to Willis:

So, you now find yourself in the same situation I have been in for a decade.
• I have been showing that the recent rise in atmospheric CO2 concentration can be attributed to factors other than anthropogenic CO2 (and have been vilified for it).

• You are showing the recent rise in global temperature can be attributed to factors other than the rise in atmospheric CO2 concentration (and probably will be vilified for it).

I advise that you fasten your seat belt: you are in for a bumpy ride.

Richard

152. ferd berple says:

Another analogy comes to mind. Everyone is arguing how many angels (forcings) are dancing on the head of a pin (temperature). Some say the pin is large because there are lots of angels. Some say the pin is small because there are few angels. Some say there are many angels but the angels are small. Others say there are few angels but the angels are large.

Willis has shown that no mater how many angels, the pin is “this big”. Since the real question we are trying to answer is the size of the pin, not the number of angels, it is time to stop arguing over the number of angels as a means of measuring the size of the pin.

The number of angels and their size is not needed. We know the size of the pin, from the song the angels are singing (solar, albedo, co2).

153. Steve Keohane says:

joeldshore says: June 2, 2012 at 8:05 am

Steve Keohane says:

Atmospheric RH% is going down. http://i38.tinypic.com/30bedtg.jpg

(1) Relative humidity going down is not incompatible with absolute humidity going up. (Most climate models predict relative humidity to stay about constant or decrease a bit overall as the climate warms.)

(2) You give no source or other information for the data set you show but I believe you have cherry-picked a particular re-analysis of radiosonde data with known severe problems. This data does not agree with the much better satellite data available (and I think it even disagrees with other re-analyses of the radiosonde data).

Sorry to disrupt your fantasy, I did not cherry pick anything. Here is an updated version of that graph, I simply stored these two graphs when I came across them in 2008 and 2012, I did not generate them. http://i48.tinypic.com/2qlfnzn.jpg
I understood them to be US gov’t data.
Here is another albeit shorter time frame and different pressures from NOAA

154. ferd berple:

With respect, you are misunderstanding the situation created by Willis analysis.

For example, at June 2, 2012 at 10:55 am you say;

Everyone agrees that the sun, albedo and CO2 are in some fashion related to temperature.

But Willis’ analysis denies that.
As Willis says himself

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

So, he specifically states that
“Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.”
And he specifically lists “CO2” was something he “did not use” in his analysis.

This implies that CO2 is NOT a significant variable which affects global temperature.

But, as I said in my post at June 1, 2012 at 12:52 am

However, an ability to attribute a factor as a cause of a change only demonstrates the possibility that the factor is the cause of the change. An ability to attribute a factor as a cause of a change does NOT demonstrate that the factor is the true cause in part or in whole.

And, importantly, as I said in my post at June 1, 2012 at 4:10 am

If an increase to atmospheric GHG concentration affects the hydrological cycle then the increase may alter cloud cover with resulting change to albedo. Thus, albedo is a proxy for atmospheric GHG concentration.

Please note that I am NOT claiming a change to albedo IS a proxy for atmospheric GHG concentration. I am only pointing out the possibility that it may be.

So, Willis analysis implies the effect of atmospheric CO2 concentration is not significant to recent global temperature change but does not prove it.

Furthermore, at June 2, 2012 at 10:55 am you suggest

What Willis has shown is that it is possible to accurately predict future temperatures as we currently do now with the tides.

No, Willis has NOT shown that. Indeed, he refutes that saying at June 1, 2012 at 1:31 am

The model merely specifies what the temperature change will be from a certain change in the albedo. As a result, It can’t be used to forecast anything, because we don’t know the future state of the albedo.

Willis’ analysis is VERY important. So, understanding what his analysis does and what it does not do are also important.

Richard

155. Interstellar Bill says:

An expanded version of this should be in Nature Climate Change,
instead of this month’s sprawling pile of modelling articles:
Quantifying Future Climate Change (7 pages)
Evaluation of Climate Models Using Paleoclimate Data (8 pages)
Multistability and Critical Thresholds of the Greenland Ice Sheet (4)
Overestimation of Mediterranean summer temperature projections due to MODEL DEFICIENCIES (imagine seeing that phrase at all, in a title yet!)

This is atop the usual pile of Doomsday-Soon articles!

Though you need a subscription if you actually want to bother reading these,
the incestuous list of references suffices to spell out the closed-circle aspect of this field.

How much longer before the rank odor of sanctimonious fraud is so overwhelming that actual science, such as this article, penetrates into that rotten field to bring long-overdue sanity?

When will the supercomputers they egregiously misuse be re-purposed for useful work?

156. robm says:

Willis,
As usual, a very thought provoking post. What you touch on here I see as an extension of your post on ‘The cold equations’ (Jan 28 2011). Together these seem to point to a very good path for growing a model from the simple to the complex by adding terms to eliminate discrepancies. What I like about your approach is that it can be directly related to physical quantities. With an electrical background I kind of see it as building up an ‘analog computer and solving the equations digitally. The climate system is obviously a system of many modes, each having its own time constant. But it is still frequently found informative to explore simple single time constant approximations and then extend them.
Ok. So I took your single time constant model which seems to show encouraging results and looked at it as an energy storage unit of capacity C=mass times heat capacity., a current source (in electrical terms , which is the solar energy input. And a coupling out of the system. This output coupling is the parameter which determines your tau value. This coupling in the present case is a function of Co2, moisture and all the rest. Your model assumes this constant at some value (a place to start). When I use The sigma*T^4 formula for energy out. I find that I have to adjust (epsilon if you like) to a value of ~ 0.6 to get the temperature to rise to the measured level.
An extension here could be this as adjusting the temperature to the TOA level. A resistance to TOA and a small atmosphere storage reservoir could be added to incorporate a more physical model. Also the ocean could be broken into shallow /deep storage with resistance between.
In the ultra simple model above The heat capacity adjusted to roughly the equivalent of 70 M depth of water matches the temperature phase and amplitude.
This is all pretty quick, so I hope there are not too many mistakes.
I see Bart is on the thread. Help us out here Bart, I think this is along the lines of the systems analysis approach that you advocate.
The Matlab/Octave code for a crude step integration solver is attached. The result is the figure below

% from W. Eschenbach post may 31,2012.
%These data are in a 10 column array named at;
%C=1e4;%Guess 1 atm air columnheat capacity
C=2.9e9/15;%700M depth of water(adjusted by divisor)
sigma=5.67e-8;%Boltzmann constant
%B=0.3;%Bond albedo
Tn=260;%Start the calculation at this temperature
%otherwise build up ts long due to T^4 (not a lineat eq).
delt=1;%time increment (mo)
t=at(:,1);
B=at(:,3);
nsam=length(t);
%T=at(8,:);%global average temperature (C)
T=zeros(size(t));
I=(1-B).*at(:,6);%average global solar flux (W/M^2)
Tm=at(:,9);%global temp
%hold
for n=1:nsam;
Tn=Tn+3600*24*30*delt*(I(n)-0.615*sigma*Tn^4)/C;
T(n)=Tn;
endfor
plot(t,T-273,’r’)
hold
plot(t,Tm,’b’);
plot(t,(I-mean(I))/10+mean(Tm),’g’)

157. Bart says:

richardscourtney says:
June 2, 2012 at 11:13 am

In a sane and rational world, the shoe would be on the other foot: the alarmists would have to prove other factors were not responsible, not the realists having to prove CO2 is not.

I conclude from this evidence that we do not live in a sane and rational world.

“• I have been showing that the recent rise in atmospheric CO2 concentration can be attributed to factors other than anthropogenic CO2 (and have been vilified for it).”

And, I have recently shown that it must be attributable to other factors, though you disagree, and I’m sure neither one of us wants to revisit that issue at this time. Interested parties can review the debate at this thread.

158. Hi Willis,

As argues before, with the annual cycle you are just calculating the frequency response of a simgle frequency, , which doesn’t have an effect on multidecadal periods. Other people also arrived at 0.1 K/wm-2 for the annual cycle so your value isn’t new either

I suggest you read the recent paper by J. H. van Hateren who considers the complete spectral response one of the conclusions reads

” The transient climate response (response after 70 years of 1 % yearly rise of CO2 concentration) is 1.5 ± 0.2 °C.”

http://rankexploits.com/musings/2012/empirically-based-estimate-of-climate-sensitivity/

the online paper
J. H. van Hateren, 2012, A fractal climate response function can simulate global average temperature trends of the modern era and the past millennium, Climate Dynamics, 10.1007/s00382-012-1375-3

159. cba says:

Willis,

Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.

I fully agree with your first sentence and third sentence. but you are not quite right on the second. When you used a real albedo measurement, you included the effects of aerosols, volcanic forcing, black carbon, land use, snow and ice albedo and EVERY other forcing that has scattering and reflection as its operational effect. I agree too that methane and co2 are not part of your model, except for what, if any, measureable effects upon temperature exist which are actually caused by them. Considering that your model describes or tracks the real world temperatures to the extent that around 90% or better is covered, it would seem there is extremely little contribution left for ghgs to be a part of after the albedo factor. For those detractors, that 90% means that 90% of the temperature variation, trends and fluctuations, are described by Willis’ model which means that this vast majority of temperature variation is not caused by co2.
As I recall, our beloved CAGW fanatics (Hansen et al ???) have managed to create a model that explains only around 30% of the variation which is usually not considered to be statistically significant – except in catastrophic climate disaster research papers. Of course, there is also the factor of causality which is not determined – which means the CAGW fanatics don’t actually know if co2 level increase changes cause warming or whether warming causes co2 level increases. Chemists on the other hand know what happens when you warm a liquid that has dissolved gases present which tends to support the warming causing co2 gas increases. The causality in Willis’ model is a bit more straight forward and actually makes sense.

160. Hans Erren:

Thankyou for your providing a post at June 2, 2012 at 1:17 pm which includes evidence to support your statement that “with the annual cycle you are just calculating the frequency response of a simgle frequency”. However, you ignore the fact that Willis Eschenbach has proved by demonstration that his method emulates global and hemispheric changes for the period from 1984 to 1998. Therefore, he has demonstrated that “a single frequency” is a sufficient model.

Importantly, your post misses the main point. Willis Eschenbach has shown that
“Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period” (i.e. from 1984 to 1998).
The effect of rising CO2 is not a necessary input to his model.

Hence, those who want to claim e.g.
” The transient climate response (response after 70 years of 1 % yearly rise of CO2 concentration) is 1.5 ± 0.2 °C.”
need to explain why the analysis of Willis Eschenbach holds for the period of 1984 to 1998 but does not apply “after 70 years”.

Richard

161. cba:

re your post at June 2, 2012 at 2:17 pm, please read my post at June 2, 2012 at 12:03 pm.

Richard

162. joeldshore says:

Steve Keohane says:

Sorry to disrupt your fantasy, I did not cherry pick anything. Here is an updated version of that graph, I simply stored these two graphs when I came across them in 2008 and 2012, I did not generate them. http://i48.tinypic.com/2qlfnzn.jpg
I understood them to be US gov’t data.

In other words, you came across some data that agrees with what you want to believe. So, despite the fact that it disagrees with lots of data better suited for the purpose of looking at long-term trends and you have no idea where it is from or what the caveats and issues associated with it are, you conclude things from it that are in contradiction both with what the scientific community has concluded and basic physical principles.

If that is not a cherry pick, I don’t know what is!

163. Richard, 14 years is way too short to satisfy the Nyquist theorem for a centenial signal response.

164. joeldshore says:

Willis Eschenbach says:

So it’s claiming that observations show a constant increase in relative humidity, and it says that the models agree with it. But you say the models show stable or decreasing relative humidity …

Actually, I think the quote that you took from Soden is just poorly worded. If you read it in the context of the rest of the paper, it is clear that he is not saying they show a constant increase in the relative humidity. He is saying that they show an increase in upper tropospheric moisture that is equivalent to what you get if you assume that the relative humidity remains constant as things warm. He certainly should have worded that better, but if you read the rest of the paper, it will be obvious that this is what he was saying. [I talked about even slightly decreasing relative humidity as being compatible with the models because Dessler and coauthors wrote a paper in which he found a small decrease in relative humidity with time from the satellite data and thought that this was perhaps a bit incompatible with what the climate models predicted. However, in another paper a few years later, they actually checked what various models predicted and found that overall on average they did predict a slight decrease in relative humidity (although I think, within the variability of the models, it also included a completely stable relative humidity).]

Finally, FWIW (which may not be much), the model results from the NCEP Reanalysis Model are here … they show variations but no overall trend in either relative or specific humidity over the last decades.

Yes, which is why this NCEP reanalysis has been so popular with AGW skeptics. But, there are apparently known severe issues with the long-term trends in that reanalysis and it is contradicted by lots of other evidence from satellites (and other reanalyses too, I think) that show the moistening trend.

During the summer we must get much more moisture in the air than in the winter … but despite that, the resulting temperature is well modeled using a constant climate sensitivity. This observational evidence strongly supports the secondary role of water vapor in determining the temperature.

Statements like this drive me batty. If you think the seasonal cycle is in contradiction with the role of water vapor, then you should be able to demonstrate this, for example, by showing that climate models exaggerate the seasonal cycle as a result of the important role that the water vapor feedback plays in them. But, of course, that isn’t true since, as I noted, the analysis that I know of that looked at seasonal cycles that I linked to previously concluded that it supports climate sensitivity right in the IPCC range.

You still haven’t grasped the nettle, Joel. I’ve hindcasted the temperature, with shocking accuracy, using nothing but the clouds and the sun. This means that whatever other feedbacks and mechanisms might be involved, there is very, very little left for them to explain … and thus it indicates that the other mechanisms play only a very small role in determining temperature.

It may shock you but it doesn’t shock me. Yes, a simple model with a single time scale can do a good job at modeling the seasonal cycle, which is undoubtably dominated by solar effects. As to how well you did with the overall trend, I already told me the reasons for being skeptical about that; I doubt if the albedo data is even good enough to get that without large error bars.

And, your model doesn’t speak at all to what feedbacks are or are not present.

165. cba says:

Hans Erren says:
June 2, 2012 at 1:17 pm
“I suggest you read the recent paper by J. H. van Hateren who considers the complete spectral response one of the conclusions reads

” The transient climate response (response after 70 years of 1 % yearly rise of CO2 concentration) is 1.5 ± 0.2 °C.”

Hans, why would you even bother suggesting reading a paper that treats albedo, the most important variable in the whole climate discussion, as a constant. Granted, there are no albedo records that date back 70 years so van Hateren could not properly reconstruct a long term sensitivity at all. In the short term where we actually have some albedo measurements, we know that albedo varies by at least 10%. Anyone trying to determine sensitivity without considering this factor is doing nothing but generating random numbers. It’s like ascribing a 4% variation in TSI just for the heck of it with no data to back it up.

It is just like every other sensitivity calculation that has been done which ignores albedo as a significant variable. The results are worth absolutely nothing at all. It’s like trying to figure the gas mileage of a hybrid car without considering it gets plugged into the power charger every night and the engine only runs when the car batteries get low. Drive it only 10 miles a day and your gas mileage becomes almost infinite but your electric bill goes up significantly.

166. cba says:

joeldshore
“Actually, I think the quote that you took from Soden is just poorly worded. If you read it in the context of the rest of the paper, it is clear that he is not saying they show a constant increase in the relative humidity. He is saying that they show an increase in upper tropospheric moisture that is equivalent to what you get if you assume that the relative humidity remains constant as things warm. He certainly should have worded that better, but if you read the rest of the paper, it will be obvious that this is what he was saying. [I talked about even slightly decreasing relative humidity as being compatible with the models because Dessler and coauthors wrote a paper in which he found a small decrease in relative humidity with…”

OOPS! someone better tell lacis and hansen. They assume something a bit different, where by rising T causes rising humidity and dwindling cloud cover in order to have an unstable positive feedback sufficiently strong to explain how a 1 deg C rise in co2 forcing will cause a 3-6 deg C total temperature rise. It seems that if RH stays constant, the water vapor feedback from a 5 deg C rise over the entire atmospheric column only adds about 30% more h2o vapor which is far less than a doubling and is in fact less than the effect of the co2 doubling – so much for the largest official ipcc feedback.
Hey, your co2 doubling + h2o vapor at constant RH(assuming a 5 deg C T rise) gives you a good 5 or 6 W/m^2 and you only need an additional 20 W/m^2 or so to get to the 5 deg C rise.

And, oh, you also need to explain why there would be a cloud decrease with a substantial increase in h2o vapor in the atmosphere and a slight rise in T. That means since we’re currently at around 62% cloud cover that this is as much as we can get. Drop the T and you start to lose cloud cover. Raise the T (accordning to lacis and hansen) and you start to lose cloud cover. Evidently, the only way Earth can get more than this 60% cloud cover is to have something other than h2o clouds – like venus. SARC OFF/

167. Hans Erren:

Your post at June 2, 2012 at 2:58 pm is an evasion of – and not an answer to – either of the points in my post addressed to you at June 2, 2012 at 2:41 pm.

Richard

168. lgl says:

Thanks Willis
Here is my result for NH

Net Sun: +1 W/m2 (june-june)
Temp: +0.25 C (july-july)
or close to 1 C pr CO2 doubling

169. Richard, apparently you don’t understand why a decade of observations is not sufficient to calculate the amplification factor for a century or a millenium.

Others: I introduced the van Hateren paper because he uses an elegant frequency domain approach, he also uses R scripts. But the most important is that even using Mann reconstructions, van Hateren’s climate sensitivity doesn’t exeed 2 degrees for CO2 doubling. Which is quite promising when using reconstructions that aren’t hockeysticks.

170. ferd berple says:

richardscourtney says:
June 2, 2012 at 12:03 pm
With respect, you are misunderstanding the situation created by Willis analysis.
Second, the sun plus the albedo were all that were necessary to make these calculations.
======
Thanks Richard, I haven’t had the time to check the math. If no CO2 was required in this model, then this implies the CO2 sensitivity is 0 C, given the size of the residuals. But this wasn’t the result, so I’ve missed something.

171. ferd berple says:

ferd berple says:
If no CO2 was required in this model, then this implies the CO2 sensitivity is 0 C,
=========
Or that the effects of CO2 are fully contained within the changes in albedo. That CO2 cannot change temperature except by also changing the albedo, which provides a predictive value for the effects of CO2. However, this doesn’t work for me because it implies that nothing else changes albedo in the meanwhile without our knowledge.

The use of 10 years of data given the method used by Willis doesn`t negate the criticism leveled at his previous post. What he has modeled is the annual variation in temperature of each hemisphere.

First of all, The procedure he used fits high frequency data, with the high amplitude of oscillation, about 20 and 10C peak to peak, using a very simple model, over a period of 10 years, to find a parameter that drives a global temperature trend which measurements show is 0.1 – .2C. The residuals in his fit are much higher than that.

Second using a single lag time for the model is no way to estimate the driver of a long term global temperature trend which takes decades or centuries to reach equilibrium, while short term annual oscillations dominate the data that you are using. Whether one uses a single year or 10 years of data makes no difference to that argument. The only thing you will find is the relationship between the forcing variables and the high frequency oscillations they are causing.

This post is one of the clearest examples of quackery and pseudo science I have seen on this web site..

• Anthony Watts says:

This post is one of the clearest examples of quackery and pseudo science I have seen on this web site..

REPLY: Well then, you have no further reason to comment here. As I say in my policy page, comment as if you were in my home. Since I take exception to your remarks, and because you’ve been banned before, and I relented against my better judgment, I’m motioning you off the sofa, and showing you the door. – Anthony

173. George E. Smith; says:

“””””…………from graeme W………….Playing Devil’s Advocate, CO2 can affect the albedo, so it’s included. The theory is that an increase in temperature due to CO2 will result in increased water vapour being held in the atmosphere, which can manifest as clouds, altering the albedo……….”””””

Well graeme, Willis is saying that there is no evidence that CO2 DOES affect the Temperature; and the WATER VAPOR which you say changes the albedo, is certainly capable of changing the Temperature much more than CO2, so you are using a circular argument. Water vapor increase due to more evaporation leads to more clouds; that’s an observational fact; see Wentz et al SCIENCE for july 7 2007. It’s also a logical necessity, since global precipitation must equal global evaporation, or else the oceans would end up overhead. Both WENTZ (actual real earth observations) and the GCMs predict precip = evap, and both increase 7% for a one deg C rise in global Temperature (observed and predicted; excuse me, projected.

And by the way, there is experimental evidence that there is a high correlation between the existence of precipitation; rain, hail, sleet, snow, frogs, etc and the concurrent existence of clouds, aka water vapor condensations. And yes Wentz et al did also find experimentally that total atmospheric water vapor also increases by 7% for a one deg C rise in global Temperature. The GCMs on the other hand, crash and burn on that score predicting as little as 1/7th of the actual observed amount; well I guess that was a projection aas well.

174. Smokey says:

Willis convinced me long ago that he has a handle on the subject, but the more comments I read by George E. Smith, the more convinced I am that George, like Willis, knows more about climate sensitivity than the entire climate alarmist crowd – doubled and squared. Willis and George know the straight skinny. Their critics… not so much. Really. Not so much.

175. George E. Smith; says:

“””””…..Hans Erren says:

June 2, 2012 at 2:58 pm

Richard, 14 years is way too short to satisfy the Nyquist theorem for a centenial signal response……”””””

Now there’s a startling revelation. The Nyquist Theorem says that a band limited signal (containing NO signal components at frequencies higher than B), can be completely represented and recovered from a sequence of instantaneous samples of the continuous function, taken at time intervals separated by no more than 1/2B.

So Willis has the temerity to calculate values of expected TSI from real known earth orbital parameters, which plot remarkably like a near sinusoidal waveform, and use that for a 14 year comparison with actual known Temperature data, and apparently that violates Nyquist since there is evidently present a much lower climate signal frequency of one cycle per century.

Willis, why don’t you change your analysis, and only take one sample per millenium, and see if that will cure the centennial signal Nyquist problem, that Hans has brought to our attention !

And Willis, is there some way, you can remove the sun variation from your analysis, and see if you can explain the climate data, with just albedo variations. What are we going to do without CO2 to control the people. Leif is always telling us that the sun doesn’t affect the climate, so get rid of it Willis. I think you are on a roll this time Willis !

176. Steve Keohane says:

joeldshore says: June 2, 2012 at 2:58 pm
Wrong again, Joel, the charts are not accrued by concordance with a belief system, they are the only ones I have seen. No picking at all, just the only ones to come along. You certainly spend a lot of energy constructing fantasies.

177. :Hans Erren:

I rise from bed to find your silly comment at June 2, 2012 at 4:39 pm and the rebuttal of it by George E. Smith June 2, 2012 at 9:56 pm.

Richard

178. ferd berple:

re. your posts at June 2, 2012 at 6:07 pm and June 2, 2012 at 6:27 pm.

These matters are explained in my post at June 2, 2012 at 12:03 pm which you say they are answering.

Except that you make a logical error when you say
“this doesn’t work for me because it implies that nothing else changes albedo in the meanwhile without our knowledge.”

No, it does not imply that. It only implies that all responses – both known and unknown – affect albedo.

Richard

179. Willis Eschenbach says:

Bart says:
June 2, 2012 at 10:33 am

I agree with others that you have made a reasonable case that albedo is the response variable which has the greatest control over temperature, but you have not provided evidence showing that CO2 contribution to albedo change is negligible. Given the narrow bands in which CO2 affects the albedo, this should be straightforward in and of itself, but then you hit the warmist claim of positive feedback with water vapor, and since water vapor contributes significantly to albedo, we are right back to the fundamental argument over whether there is a positive feedback or not.

So, all in all, it appears to me that you have put forward another way of looking at the problem, but have not really resolved anything in a way which would force the opposing side out of their bunkers.

I’m not clear what your argument is here, Bart, perhaps my lack. You say I have “not provided evidence showing that the CO2 contribution to albedo change is negligible’ … what does the CO2 contribute to albedo? The only connection from CO2 to clouds I can think of is that (if you believe the standard story)

CO2 –> increased temperature –> increased moisture –> increased clouds

The problem (if I understand your argument) is that the increased clouds decrease the temperature … so I’m not sure what your claim is.

Finally, I don’t think in terms of bunkers, but of what I can do and show. I’ve shown that the net sun (solar minus albedo reflections) is enough information, in and of itself, to very accurately emulate both the annual and decadal changes in the global surface temperature. I think that is an interesting and significant result, and as always, YMMV …

Thanks,

w.

180. Brian H says:

W.;
Yes; as regards CO2, the implication of your findings is that IIF* CO2 can affect albedo, it has a role in determining temperature.

Not otherwise.

*If and only if

181. Robbie says:

Steve Keohane says:
June 2, 2012 at 7:30 am

You showed me a graph of the RH% going down. From what study or website did that graph come from?
I am interested how these scientists came to the opposite conclusion than the ones I cited.
Everyone can fabricate a graph without giving a source for verification.

182. Robbie says:

Mr. Eschenbach says at June 1, 2012 at 6:04 pm

I am going to refer to one of your statements in this forum again: “Me, I think that the dominant feedback is clouds and thunderstorms, and they are strongly negative.”
I responded to you on June 2, 2012 at 7:23 am and I would like to see some sources for your claims you make in your piece (see my comment on June 2 at 7:23)
One of your quotes in the original piece: “but please remember that this is science, and that the game is to attack the science”.
If you want to conduct science you also need to put some sources with your claims or else it is not scientific. You cannot make claims like: “Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period” or “and I say that the reason is that the clouds respond immediately to such a disturbance in a thermostatic fashion” without producing some scientific evidence for it.

We know that the Total Observed Greenhouse Effect (TOGE) is ~ 33°C from the Total Greenhouse Effect (TGE) 33+45=78°C. The graph I delivered by Roy Spencer’s Climate Confusion proves that. (See my comment on June 2 at 7:23 for sources). I don’t know exactly why I have to put sources for that, because it’s textbook climate science that every student in Earth Sciences knows.
33°C is 42% of the TGE which means that clouds cause a 58% negative feedback.
Now explain to me please: If clouds and thunderstorms are strongly negative feedbacks then what greenhouse gas(es) are causing 42% of the TGE?
It can’t be water vapor, because water vapor and thus clouds are strongly negative according to you. They will cause more cooling than warming. So it must be CO2 and CH4 or is there some other mysterious gas I am not aware of that floats somewhere in the atmosphere to cause that 42% of the TGE.
If CO2 and CH4 are responsible for the TOGE it simply means that climate is extremely sensitive to CO2 and CH4 increase.

You can come up with all kinds of beautiful mathematical concepts to try to prove that climate is not sensitive to greenhouse gas forcing, especially CO2 and water vapor, and that the observed warming from 1984-1998 is caused by the sun and albedo change (without explaining really what the cause for the albedo changes are and without producing some scientific sources to back up your claims), but you forget to explain the basic concept of the Greenhouse Effect causing a warm and habitable planet. For without it we would be freezing out here.

183. beng says:

Willis, you should know better. Attacking the warmarxist’s sacred (and absolutely necessary for CAGW) H2O-feedback amplification strikes a nerve & gets them rattled.

Your analysis jives w/all the other empirical (not model) analyses. CO2 effects are not amplified, they are diminished.

184. Steve Keohane says:

joeldshore says:June 2, 2012 at 2:58 pm

Steve Keohane says:

Sorry to disrupt your fantasy, I did not cherry pick anything. Here is an updated version of that graph, I simply stored these two graphs when I came across them in 2008 and 2012, I did not generate them. http://i48.tinypic.com/2qlfnzn.jpg
I understood them to be US gov’t data.

In other words, you came across some data that agrees with what you want to believe. So, despite the fact that it disagrees with lots of data better suited for the purpose of looking at long-term trends and you have no idea where it is from or what the caveats and issues associated with it are, you conclude things from it that are in contradiction both with what the scientific community has concluded and basic physical principles.

If that is not a cherry pick, I don’t know what is!

http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl

185. Stephen Wilde says:

“all responses – both known and unknown – affect albedo”

Yes, but I’d word trhat differently.

All factors – both known and unknown – that change the energy content of the troposphere (mostly latent heat energy from changes in the amount of evaporation) will result in a shift of the climate zones which then affects albedo by changing the length of the lines of air mass mixing between polar and equatorial air masses.

Zonal jets gives shorter for reduced global cloudiness (and albedo). Meridional jets gives longer for increased global cloudiness (and albedo).

So, if CO2 or the sun or ocean cycles or anything else (known or unknown) ADD energy to the troposphere then there is a poleward shift which lets more sun into the oceans due to reduced cloudiness BUT the poleward shift also involves a more intense convective overturning in and around the ITCZ and faster, more intense cyclogenesis and decay along the shorter and more poleward jetstream tracks which transfers the extra energy faster to space so that system energy content remains much the same.

So,

i) All forcings affect the air circulation pattern via a change in the speed or size of the water cycle.

ii) The change in the air circulation pattern affects albedo.

iii) The effect of the change in the speed or size of the water cycle is to offset both the initial forcing AND any change in solar input to the oceans that results from the change in albedo.

Poleward shifting negates system warming by increasing evaporation but the troposphere warms as the energy throughput increases. Ocean energy content remains stable subject only to internal ocean cycles.

Equatorward shifting negates system cooling by reducing evaporation but the troposphere cools as the energy throughput decreases.Ocean energy content remains stable subject only to internal ocean cycles.

Ocean energy content being set by atmospheric pressure plus TSI at top of atmosphere but that is another story.

186. robm says:

Willis,
In my post above I showed a small program, which I believe is equivalent to yours but has the advantage that it makes explicit the dependence of tau on output (radiation / convection) coupling and heat capacity.
In the process of this I have noticed a subtlety which you probably also noticed in your model but did not discuss.
I could not get the amplitude of the temperature variation to match the model without using unreasonably different values for the global heat capacity. This raises a point that I have seen often mentioned on blogs but never considered seriously. One cannot just average temperatures. Thermal energy is the meaningful quantity to average in this kind of problem. Also there is the problem of trying to get two terms of opposite phase (nh and sh temps) to cancel when the accuracy is not that great in either one. The firs issue is the more interesting one in my mind.
So I redid the calculation by weighting the temperature values according to the empirically adjusted heat capacities. When this is done the global mean temperature shows a much smaller variation than that shown in your spread sheet. I believe this low variation corresponds better to the real situation.
I won’t bother the forum with another post of rough draft code. I will post it if anyone is interested.

187. robm says:

Willis,
I’m sorry but on reading it I see that in the post above I did not make it clear that ihe issue I am addressing is that my and (I think) your simple model does not seem to work when applied to to the global data.
In the post above I am talking about combining nh and sh in the model to obtain a global result.

188. Matthew R Marler says:

I thought that I would try this one more time. What you have, Willis, is a plain linear vector autoregressive model for deltat and deltaf. The cross lag of deltat with deltaf is 0, and the lag of deltat with deltat is 1.

189. beng says:

****
Bart says:
June 2, 2012 at 10:33 am

I agree with others that you have made a reasonable case that albedo is the response variable which has the greatest control over temperature, but you have not provided evidence showing that CO2 contribution to albedo change is negligible.
****

Huh? Albedo is the fraction of TSI reflected by the atmosphere/surface combo. CO2 doesn’t reflect anything. Yeah, it changes the spectral-emission properties of the tropopause, but that’s a different issue.

190. joeldshore says:

Steve Keohane says:

http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl

A discussion of the issues in this particular reanalysis in regards to the long term trends in humidity is given here: http://geotest.tamu.edu/userfiles/216/Dessler10.pdf

Not every piece of data (or re-analyzed data) that you can find is trustworthy for every possible purpose. Perhaps NOAA should be more careful to discuss the limitations of their data here, or perhaps those who use it for various purposes need to investigate whether or not it is reliable for that particular purpose.

People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.

191. joeldshore says:

Steve Keohane says:

Wrong again, Joel, the charts are not accrued by concordance with a belief system, they are the only ones I have seen. No picking at all, just the only ones to come along. You certainly spend a lot of energy constructing fantasies.

Fine…So, you personally didn’t cherrypick the data. You just visited places where those who presented the graphs cherrypicked them. So, you are blissfully unaware of scientific data that goes against what you want to believe because you hang out in places that present only cherrypicked data to support your preconceptions.

192. Babsy says:

joeldshore says:
June 3, 2012 at 8:57 am

“People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.”

You mean like Roscoe P. Coal Train?

193. cba says:

“joeldshore says:
June 3, 2012 at 8:57 am

“People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.”

Wow! That’s a really great description of mikie mann and the ‘TEAM’.

194. Stephen Wilde:

Your post at June 3, 2012 at 7:41 am provides a complex but plausible and – importantly – falsifiable hypothesis based on Willis’ findings. It is a clear example of the importance of the need for proper understanding of Willis work which I stated at June 2, 2012 at 12:03 pm saying;

“Willis’ analysis is VERY important. So, understanding what his analysis does and what it does not do are also important.”

Richard

195. There has been several comments with link to various versions of a relative humidity graph showing declining relative humidity in the upper atmosphere. I created the graphs. It is in the “Water Vapour Feedback” section of my “Climate Change Science essay at
http://www.friendsofscience.org/assets/documents/FOS%20Essay/Climate_Change_Science.html#Water_vapour

A link to the NOAA data source is given just above the graph. To recreate the graph use Variable “Relative Humidity”, select “Seasonal average”, First month of season “Jan”, second month “Dec”. Select “Area weight grids”. You can select “analysis level” from 1000 mb to 300 mb.

This is a very large dataset of radiosonde data. It is not cherry picked. The second graph shows specific humidity (g/kg air) at 400 mbar pressure level has declined by 13.5% (best fit line) from 1948 to 2011. Also, satellite data can now measure humidity in the upper troposphere. The third chart shows declining humidity from satellite data in the 300 mb to 500 mb range.

A constant RH in the topical troposphere would cause an enhanced warming at about 8 km, but no such warming is found in any dataset, radiosonde or satellite, thus confirming the declining humidity found by the radiosonde data. Increasing humidity and temperatures in the tropical troposphere would have caused increased hurricane energy, but in fact, global hurricane activity has decreased to the lowest level in 32 years.

Further down in the water vapour feedback section of the essay you will find a graph of specific humidity (g/kg) versus CO2 concentration. It shows water vapour specific humidity declining in the topics 0.11 g/kg, or 13%, from 1960 to 2011. Note the very high R2 correlation of 0.713. The graph is here:

This is important because a line-by-line radiative code shows that an absolute change in the quantity of water vapour in a layer 300 to 400 mb pressure level has 30 times the effect on out-going radiation as the same change near the surface. (The specific humidity vapour at 300 to 400 mb is 5.2% of that near the surface.) It matters where in the atmosphere the humidity changes. Increasing humidity near the surface has very little radiative effect on temperatures.

196. RichardCourtney, well there isn’t anything to address as you still fail to grasp the concept of freqency response of a low pass filter.
Now who is silly here.

197. George E. Smith Let me make this clear, I am a luke warmer.
Looking at a high frequency response is like looking at the response of a tree when you shake it vigourously: It won’t bend a lot. However, if you push it slowly, repeatedly, you can get it into a swing with a so big amplitude that it breaks.

The concept here is eigenfrequency.

Now we can observe a lot of those eigenfrequency harmonics in climate: eg the multidecadal oscillation, the Dangaard-oescher cycle and the ice age cycle. Now if one of those natural cycles tunes in with an external forcing, you get a major climatic effect. The ice age cyclicity is an obvious example.

Unfortunately the annual cycle is not one of those natural climate harmonics, and that is the reason why the response is so low as Willis discovered, and Douglas Hoyt before him.

198. Joe Born says:

I inadvertently submitted the following comment on the previous post rather than here. Basically it appears, contrary to what I would have thought, that Mr. Eschenbach’s approach is likely to understate sensitivity significantly.

Specifically, I applied Mr. Eschenbach’s approach to synthetic data I generated from a system whose step response is 0.2 * [1 – exp(-t/2)] + 0.4 * [1 – exp(-t/50)], i.e., to a system in which lambda is 0.6. At least if I did the math right, Mr. Eschenbach’s approach instead infers a sensitivity of 0.34. The stimulus I applied was a 600-sample record of a sinusoid whose period was 120. (All those dimensionless time values can, for instance, be months.) A similar result was produced from a period-12 sinusoid. The rms error in each case was less than 10^(-6)

I’m not sure what all the conclusions one might draw from this are. But, if I did it right, it seems at a minimum we can conclude that inferring low sensitivity from this experiment, elegant as it was, may not be warranted.

I would be greatly interested in hearing from anyone who has tried something similar; it could be that I just did the math wrong.

199. George E. Smith; says:

I can’t say I have fully followed Willis’ argument, in these two postings, the first using annualized data for TSI solar energy input, and the second; following criticism of that approach; using the continuous and trivially calculated orbital variations in TSI throughout the year, because of the known earth orbital parameters. Willis then seems to be saying that coupling that with available albedo, and Temperature data over a 14 year period, he was able to completely explain the Temperature data with just the albedo data, and the observed Temperature (anomalies) data for that period, with no call for CO2 data input at all.
I should comment here that “albedo” isn’t just a four syllable fancy word for reflectance…. Albedo specifically refers to the reflectance/scattering OF INCOMING SOLAR SPECTRUM ENERGY, back into space. If the externally detected radiation coming from earth is NOT solar spectrum radiation, such as LWIR from the surface, it ISNOT a part of earth’s albedo.
Albedo includes a 2-3% Fresnel reflection from water surfaces; given approximately by
((N-1)/(N+1))^2, where N is the refractive index (1.333) for water. It includes Mie scattering by water droplets in clouds, which is simply ordinary geometrical optical refraction of mostly light, by water droplets.. It’s easy to show that a single water drop, focusses light into an almost 2 pi cone of light exiting from the far side of the drop. A handful of such encounters, and the light is scattered into an isotropic distribution with no preferred direction. For smaller droplets, optical diffraction theory, rather than geometrical (ray ) optics has to be invoked, but the net result of diffraction, is an even more scattered oupput from the droplet. In any case this diffuse brightness of cloouds is NOT optical reflectance, it is scattering, and it is not water absorption, followed by LWIR emission The original wavelength (frequency) of the sunlight is preserved or it isn’t part of albedo. Snow and ice also contribute to albedo. In the case of ice that has melted so it isn’t still snow crystals, the reflectance isn’t much, still pretty much the same 2-3% as for water. And just so nobody raises it, I will say here, that at glancing angles, ice reflectance increases rapidly above the 2% normal incidence value. specifically, R remains almost constant up to the Brewster angle, arct tan(N), at which the reflected beam is completely plane polarized, and then it increases above that that ahppens at about 53 deg (from normal) for water or ice. For snaow, the apparent reflectance is much higher (for fresh snow), but it too is more a scatteing like clouds, rather than reflectance. Snow a few hours old that has had sun on it, has much lower reflectance because of micro surface melting, and refreezing.

So water is a big player in albedo reflectance. Surprisingly many plants such as grasses and such have quite high solar relfectances, as do a lot of rock materials.

So Willis is more or less suggesting that variations in the amount of sunlight reflected back to space largely by water, alters the captured component of solar spectrum energy, in such a fashion as to completely explain the observed Temperature changes at least over that 14 year period.

His analysis, is a more detailed example of a couple of extreme mind experiments I have suggested several times from many years ago.
I suspect that Richard S. Courtney is aware of those posted at other locations, in the past.
I call these two experiments, the “Birdseye” experiment, and the “Venus” experiment. The first is named after the chap (Birdseye) who invented quick frozen foods; legend has it as a result of ice fishing, where the catch was quick frozen right out of the water.

So the aim of the birdseye experiment, is to quick freeze the earth, as an aid to removing all water from the atmosphere; that is every single molecule of water to be removed; even if you have to use tweezers to get the last few.
So the experiment which is done on your X-Box, or Teracomputer, calls for turning off the sun, and lowering earth surface Temperature to zero deg C, unless it is already colder than that, in which case it retains its lower Temperature. Atmospheric water all precipitates out, either as liquid or solid depending on where it is. At zero deg surface Temperature, the oceans do not freeze, because of the saltiness, Now according to legend, if it were not for CO2, the earth would be a frozen ice ball at 255 K, but we will leave all CO2 and other non H2O GHGs intact.
So now we turn the sun back on, and restore all the laws of physics.

Now in the absence of positive water feedback to CO2 global warming, we are all supposed to eventually freeze to death, absent the greenhouse warming of H2O vapor in the atmosphere.

I believe that Peter Humbug actually did this experiment, at least the water removal aspect of it on his tera Playstation, and he said he got all the water back in just 3 months.

So what happened ? Well with no water in the atmosphere, there are no clouds, and no water vapor absorption of any incomingsolar energy, so the albedo, isn’t anything like 0.35, or whatever it usually is, so with TSI at an annual average of 1362 W/m^2, and not much atmospheric reflection and absorption, we have the mother of all global warming forcings, and the surface level solar irradiance is way higher than normal; maybe by 20% or so, some 270 W/m^2 of positive forcing, and since the ocean immediately below the surface is still warm, we now get massibe evaporation from all water bodies in sunlight, and H2O starts to repopulate the atmosphere. The surface Temperatures may go up substantially rather than drop, and that just means more evaporation and more water vapor in the atmosphere. So now the water vapor starts to attenuate some of that incoming sunlight, and the atmospheric warming starts massive convection of warm moist air to higher altitudes, where after sundown, eventually clouds will start to form, and now the albedo of earth starts to creep up again, which will lower the solar energy reaching the ground and the ocean, and moderate the heating.

Well eventually you can see where this is heading; more water in the atmosphere, more clouds more albedo, reduced surface sunlight, as well as of course more water vapor induced greenhouse warming of the atmosphere by surface LWIR captured by H2O, with a trifle of assistance from the pre-existing CO2. Eventually the TSI is cut down to size, and an energy balance is reached at some Temperature which I call the Bridseye Temperature (and state).

I have no idea where the BE Temperature is relative to today’s Temperatures.

So how about that other extreme, the “Venus” experiment.
Well we do the opposite. We crank up the sun, and we raise the whole earth up to say 120 F, and we fill the atmosphere from pole to pole and from the ground to say 50,000 ft with water vapor and clouds, so no blue sky is showing anywhere. Then we set the sun back to normal..

Well we already know that with water saturated clouds from the ground up, there is virtually no sunlight reaching the ground at all, day or night.

So now it is going to get bloody cold on the ground, with no sunlight at all, and it is going to start precipitating; rain, hail, sleet, snow, frogs, whatever. It will rain for 40 days, and 40 nights, until enough water has precipitated out of those all sky clouds, that a little sunlight shows through, and you can start to tell whether it is day or night.
With some sunlight starting to filter through the clouds,it will actually stop cooling, in some places, and as more water precipitates out, the daylight will increase, and it will get a little warmer, and the torrential rains and snow storms will moderate, and the Temperature on the surface will slowly warm p, and more sunlight get through, and clouds break up giving some clear skies. Eventually the sunlight reaching the earth will match the LWIR radiation losses to space, and the Temperature will settle down to some Temperature I call the Venus Temperature. I have no idea where the Venus Temperature is, compared to today’s Temperatures. In particular, I have no idea where the Venus Temperature is relative to the Birdseye Temperature.

One is a steady state Temperature established from a frozen iceball exteme condition, while the other is a steady state Temperature established from a thermal runaway extreme condition.

If the Birdseye Temperature, and the Venus Temperature are different, we can be sure that the condition between those two Temperatures, is unstable, and if the earth climate were located between Birdseye, and Venus, then the system would be driven to one or other of those steady state stable Temperatures; it cannot remain in the unstable regime between them.

So who can provide a rational Physical explanation, for why the earth climate would have two stable states; or even more. There could be several pairs of temperatures, with unstasble regions between the members of the pair, and stable conditions between pairs.

Well I personally don’t know of an explanation for any difference between the Venus Temperature and the Birdseye Temperature. Now remember that we are talking about the earth orbital system as it exists today. We believe from Milankovitch, that major changes in earth orbital parameters would change the system, and crete new states. But with the way things are today, it seems that we are sitting in a feedback regulated stable steady state, where TSI and albedo, regulate the earth Temperature.

Leif S has told us many times, that the sun does not control the earth climate. Well I believe to some extent that is true. If we once had a weak sun, we likely had a different albedo too. And if we fiddle with the CO2, I believe that all we do is shift the albedo, in Willis’ model to some other value. but Temperature wisw, nothing much happens.

It is the direct H2O interplay with TSI through albedo, that regulates earth’s Temperature, and if we had no CO2 in the atmospere, well we would just have a slightly different albedo, but a 255 K ice earth, we would not have.

As for Hans E’s objection to Willis’ model. It is certainly true, that a system, with a hundred year or thousand year or million year resonance, such as thre ice cycles he mentions, will not be significantly driven by an annual cyclic forcing signal, such as Willis used in his analysis; driving a resonant system, that far off frequency will certainly produce small responses.

But Willis, was looking at responses over just 14 years, from data for the actual Temperature and albedo data he has. Wh cares about a quite unrelated response that is clearly due to some other more cataclysmic system change, than elliptical orbital change in instantaneous TSI.

200. Paul_K says:

Willis,
I’ve only just picked up this thread unfortunately. Otherwise I would have commented much earlier on this and your earlier thread.
I think you have drifted off the rails somewhere. You may still have an important finding buried here concerning the relative strength of shortwave variation as a control on the temperature response – but your argument is incoherent mathematically.
The main problem is that the temperature formula you are using here is NOT a solution to the linear feedback equation. It seems clear from your comments about the meaning of lambda and tau, together with your references back to the original matching of the linear feedback equation to the GISS-E results, that you think that it is. Well, it just ain’t.
If you want to apply a single formula for temperature, which DOES represent the solution to the linear feedback equation, you need, using your definition of lambda:-

T(k) = Fk*α *λ + (1-α) * T(k-1)
where Fk is the cumulative forcing at the kth time step,
α = 1 – exp(-DELT/τ)

DELT is the timestep size in years, in your case (1/12). The e-folding time, τ, also has units of years.

You can obtain the above by analytic superposition. (A slightly different, and slightly less accurate form, can be obtained from the convolution integral using a response to a unit step forcing of
T = λ *(1 – exp(-t/τ)) . To understand the difference, check out a series expansion of the exponential term.)

You need to rework your results and see whether (a) you still obtain a great fit and (b) what values of λ and τ you come up with.

201. Willis Eschenbach says:

Hans Erren says:
June 2, 2012 at 1:17 pm

Hi Willis,

As argues before, with the annual cycle you are just calculating the frequency response of a simgle frequency, , which doesn’t have an effect on multidecadal periods.

Hi, Hans, thanks for your comment. It appears you have failed to notice that my analysis also fits the decadal trends of both the NH and the SH. If your claim were true, my method couldn’t possibly do that … and yet it does.

w.

202. Willis Eschenbach says:

Paul_K says:
June 3, 2012 at 11:30 pm

Willis,
I’ve only just picked up this thread unfortunately. Otherwise I would have commented much earlier on this and your earlier thread.
I think you have drifted off the rails somewhere. You may still have an important finding buried here concerning the relative strength of shortwave variation as a control on the temperature response – but your argument is incoherent mathematically.

Riiiiight, it’s “incoherent mathematically”, and yet despite that it correctly calculates both the annual and the decadal temperature variations in both the NH and the SH …

I would say you have a different definition of “incoherent”. I did what I did. You may not like it, or think I have not described it correctly, or something, I’m not sure what. But in fact, what I have done works.

Now, what I have used is

∆T(k) = λ ∆F(k) / τ + ∆T(k-1) * exp(-t / τ)

where lambda is climate sensitivity and tau is the time constant.

If that is incorrect in your view, and it certainly could be, then please give me what you think is the correct formula for ∆T(k).

Many thanks, I await your formula,

w.

203. I like the general idea, which is determining the feedbacks empirically from the the annual cycle.
This will indeed capture all effects which work on this timescale, e.g. water-vapor-, cloud- and snow-albedo-feedback and give a good estimate of the local climate sensitivity.
It gets complicated by (ocean and atmospheric) heat capacity and heat transfers. The implicit assumption seems to be that there’s no net heat transfer between southern and northern hemisphere. To the degree that’s not true, the temperature response and climate sensitivity is underestimated. The results also suggest that the heat capacity is not totally accounted for, because the final climate sensitivity is just the magnitude of the annual cycle. If we locked the earth on SH summer (+100W), would it eventually just get ~5°C warmer? I don’t think so…

204. Hans Erren:

I am replying to your post addressed to me at June 3, 2012 at 2:54 pm. It says;

RichardCourtney, well there isn’t anything to address as you still fail to grasp the concept of freqency response of a low pass filter.
Now who is silly here.

I answer you are “silly here”.
I do “grasp the concept of frequency response of a low pass filter” while you don’t. Willis’ model matches annual and decadal effects. This indicates that any longer frequencies filtered from the model must be of centennial or longer length. Such long frequencies are not relevant to anthropogenic effects (i.e. the purpose of present climatological investigation).

You are using your ignorance (which may be real or feigned) of “the concept of frequency response of a low pass filter” as an excuse to not address my points.

Please address my points or go away. I remind that they are as I stated at June 2, 2012 at 2:41 pm.

Richard

205. If we set a = λ/τ and b = exp(-1/ τ), the formula can be simplified to:
T(n+1) = a*F(n+1) + b*T(n)
Now it’s a little more obvious that you’re calculating an exponential moving average of the flux F.
The equilibrium temperature to a constant flux F, i.e., the climate sensitivity, is then:
T = a/(1-b) * F
It’s kind of estimating the heat capacity and then using it to extrapolate the equilibrium, but it’s not using the physical formulas, e.g. cooling should actually be a function of T^4, etc.
The main result, that the lower SH warming correspondents approximately to its smaller annual cycle is probably a coincidence.

206. Joe Born says:

Paul_K:

I, too, found Mr. Eschenbach’s formula puzzling. The way I eventually explained it to myself is given at https://wattsupwiththat.com/2012/05/29/an-observational-estimate-of-climate-sensitivity/#comment-995842. (I note that a LaTeX infelicity crept into the Dirac-delta-function definition, but presumably that won’t detain you.)

My sense is that in a simple single-pole system his approach overstates the lag by half a sample period but is otherwise reasonably effective. But it seems to underestimate the long-term sensitivity $\lambda$ significantly for some multiple-pole systems. Also, although it does capture trends, the Northern- and Southern Hemisphere trends it captured in his example look to me to be off by 20% and 50% respectively.

207. Ian Biner says:

Um… All EXTREMELY interesting, but has anyone done any calculations on the cumulative thermal effect of burning all the oil/coal/wood/ethanol/-fry-oil/witches that define our industrial civilisation? It seems to me that all this burning of stuff must heat up the atmosphere,and as NASA tells us the amout of radiated heat hasn’t changed (sorry… don’t ask me for a reference… I don’t remember where I read that), then all that heat must have had an effect.

Just a thought.

208. Paul_K says:

Willis,

Jeez,what a grouchy response! And I was only trying to help.

“If that is incorrect in your view, and it certainly could be, then please give me what you think is the correct formula for ∆T(k).”

I gave you the precise formula already in terms of temperature change from time zero. Your deltaT’s are between timesteps. So, to go from my formula to yours, you only need to write the formula for Tk-1 and take the difference. In your format, your formula should then become:-

∆T(k) = λ ∆F(k)(1 – exp(-DELT/ τ)) + ∆T(k-1) * exp(-DELT / τ)

If you want to work in months for tau, then you can continue to set DELT to unity (but I don’t really like it).
The difference between the above formula and your formula then is that the lambda value and the tau values in the above REALLY DO REFLECT the climate sensitivity and e-folding time ( = heat capacity * lambda) as generally derived from the linear feedback equation. If you equate the two versions, you will see that your version of lambda is actually equal to the climate sensitivity times tau times (1 – exp(-1/τ)).

lambda(Willis) = lambda(corrected version) * τ * (1 – exp(-1/τ))

This is why “your” climate sensitivity times a forcing of 3.7 does not give you the same answer as your spreadsheet calculation.
I tested the cumulative version and the incremental version of the above formulas on your spreadsheet and they yield the same result as you obtained in terms of the value of tau but with a revised value of climate sensitivity. Since YOUR version of climate sensitivity and MY version of climate sensitivity are only different by a constant factor for the same value of tau, the modeled (predicted) values of temperature are identical to the values that you obtained.

So this doesn’t change your main conclusions, but does improve mathematical coherence.
Stop grouching and put the check in the mail.
Paul

209. Steve Keohane says:

joeldshore says:June 3, 2012 at 9:01 am

Steve Keohane says:

Wrong again, Joel, the charts are not accrued by concordance with a belief system, they are the only ones I have seen. No picking at all, just the only ones to come along. You certainly spend a lot of energy constructing fantasies.

Fine…So, you personally didn’t cherrypick the data. You just visited places where those who presented the graphs cherrypicked them. So, you are blissfully unaware of scientific data that goes against what you want to believe because you hang out in places that present only cherrypicked data to support your preconceptions.

Interesting you left out that it is THE NOAA dataset, in total. Your line“So, you are blissfully unaware of scientific data that goes against what you want to believe because you hang out in places that present only cherrypicked data to support your preconceptions.”
must then apply to yourself.

210. Paul_K says:

Joe Born,
Nice analysis. You got there the hard way, I think.
See my previous post for an explanation of the difference between the short and long-term estimate of climate sensitivity.

211. Steve Keohane says:

Ken Gregory says: June 3, 2012 at 2:53 pm
Thanks for your post and link. I think RH% is the unspoken elephant in the room. If RH% had been constant, then rising temperature over the past century would indicate warming. Without running the numbers, there could still be a bit of warming. I have been helping a friend with a greenhouse building company, who build in what they call ‘climate batteries’ or heat storage chambers. Basically they are the subsoil, isolated by insulation, with the greenhouse air pumped through, storing heat for the winter and cooling in the summer. I came up with formulas so they could calculate the size of the ‘battery’ needed for the size of the greenhouse. It seems to work empirically. I will try to apply this to the atmosphere to see if the mass change due to RH% Δ overrides the temperature increase insofar as total heat content is concerned.

212. KR says:

“Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.”

If you perform multiple regressions with limited independent variables (leaving out relevant forcings, for example, things we know affect climate energies), you will over-fit the variables you have included. After that, it will be completely unsurprising that you seem not to need any other contributions. And – it will be incorrect.

213. Willis Eschenbach says:

Paul_K says:
June 4, 2012 at 7:02 am

Willis,

Jeez,what a grouchy response! And I was only trying to help.

“If that is incorrect in your view, and it certainly could be, then please give me what you think is the correct formula for ∆T(k).”

I gave you the precise formula already in terms of temperature change from time zero. Your deltaT’s are between timesteps. So, to go from my formula to yours, you only need to write the formula for Tk-1 and take the difference. In your format, your formula should then become:-

∆T(k) = λ ∆F(k)(1 – exp(-DELT/ τ)) + ∆T(k-1) * exp(-DELT / τ)

Paul, calling my work wrong is a simple statement. Calling it “mathematically incoherent” is an insulting attack. You seem surprised that when you attack someone, they bite back.

Moving on to the substantive point, you give the formula:

T(k) = Fk*(1-exp(-DELT/tau) *λ + exp(-DELT/tau) * T(k-1)
where Fk is the cumulative forcing at the kth time step,
α = 1 – exp(-DELT/τ)

I see that you are correct. The difference is quite small, however, and when I re-run the analysis it doesn’t affect the fit. All it affects is the value of lambda. Instead of the climate sensitivities for the SH and the NH being 0.04 and 0.08 °C per W/m2 as I calculated, they are 0.05 and 0.10 °C per W/m2.

Thank you for the correction, Paul. I apologize for grouching at you, but having my work called “incoherent” is a slap in the face. I’ve posted a correction in the head post.

All the best,

w.

214. Joe Born says:

Paul_K:

Thanks for your input on the modeling equation. I had actually wanted to add yet another refinement so that the results would not be off a half period, as I think they tend to be, but my eyes tend to glaze over when I do that stuff, and I doubt that it matters much. .

Anyway, I applied your equation to synthetic data for a system whose step response is 0.2[1-exp(-k/2)] + 0.4[1-exp(k/48)], i.e., to one in which “lambda” should be 0.2 + 0.4 = 0.6, and, as Mr. Eschenbach’s approach did, yours appeared to underestimate the sensitivity significantly. I don’t see this as a problem with the math; to me it suggests that those folks who were cautioning us about latent sensitivity may not be completely off base, at least theoretically.

Since I was using Excel, I generated the output analytically rather than by numerical convolution, and it’s always dangerous for us laymen thus to work without a net, so you may want to go through that exercise yourself.

215. Paul_K says:

Willis,
If I had wanted to insult you, I might have described YOU as incoherent. Or I might be politely describing you as drunk. If I want to say that your argument lacks mathematical coherence then I might describe your ARGUMENT as, well, mathematically incoherent.
These are not fighting words where I come from, merely a description of the problem and an invitation to consider an alternative mathematical proposition – which might turn out to be more, er, coherent, or not as the case may be.
Anyway, I promise you I was not trying to raise your hackles, but that is often the problem with the written word – it lacks a smile.
Paul

216. Paul_K says:

Re:Joe Born says:
June 4, 2012 at 2:42 pm
Hi again, Joe,

I too can’t get interested in the half-timestep problem, it is ultimately (solely) a definitional issue related to the input forcings.

It does not does not surprise me that the solution to a well-defined linear ODE (as proposed by Willis, and which I sought to modify) does not fit your response function (a solution in temperature in this instance) that comes from a different, probably nonlinear system. My guess is that you cannot derive a physically meaningful governing equation which has the response function you propose. But I know your engineering maths is really good from your previous postings, so to cover my bases or as**s, my second guess is that, if you CAN define a meaningful governing equation, then it is an ODE which is non-linear in temperature. (Incidentally if you want to rise to this challenge, I would love to see your answer!)

Well, I have the technology to deal with that, as they say. A two layer ocean model (highly nonlinear) is highly versatile and would yield an excellent approximation to your response function. See Section F here for a description:-
http://rankexploits.com/musings/2011/equilibrium-climate-sensitivity-and-mathturbation-part-2/
But I am not sure that it is necessary or desirable in this instance. One of the strengths of Willis’s argument here lies in the fact that over the historic period a match of the forcings to temperature in the GCMs – at least in those tested so far – can be effected very accurately under the assumption of a simple one-box linear feedback equation. This covers all of the forcings with no unaccounted for flux or temperature differences. This does not mean that one can infer the non-existence of some slow secondary heat flux term (your 24-factor tau term) or even multiple terms, but you can conclude that these terms are not highly relevant to the performance over the shorter timeframes. If then, over the critical shorter period in the same time interval , you can show that behaviour is largely controlled by SW effects (sun and albedo), why would you want to dilute the impact by considering the existence of the far more complicated slow response terms?
Well the answer may be that the focus is on equilibrium climate sensitivity, in which case I am going to take it up with Willis, because this is a non-starter. The focus needs to be on the relative importance of the SW effects vs the LW effects, because this speaks directly and loudly to the ATTRIBUTION argument. It is here that Willis’s findings may be really important.

In summary, I am sure you are right. Neither I nor Willis can approximate your summed exponentials with a two parameter model. It does not matter because Willis can duplicate GCM performance (for several GCMs) over the entire instrumental history with a two-parameter linear feedback model. He can therefore show using the same simple model which emulates GCM performance that most of the temperature rise can be explained by the variation in TSI and albedo over the critical period. For me this is the strength of what he has uncovered here.

217. cba says:

“Ian Biner says:
June 4, 2012 at 3:33 am

Um… All EXTREMELY interesting, but has anyone done any calculations on the cumulative thermal effect of burning all the oil/coal/wood/ethanol/-fry-oil/witches that define our industrial civilisation? It seems to me that all this burning of stuff must heat up the atmosphere,and as NASA tells us the amout of radiated heat hasn’t changed (sorry… don’t ask me for a reference… I don’t remember where I read that), then all that heat must have had an effect.

Just a thought.

The energy contained in the oil coal wood… being burned is quite small compared to just how much energy comes in every second to the Earth. Think about it. All the stuff we burn is organic material like wood which was synthesized in plants by solar energy at some time or another. One of the problems with our modern society is the ability to measure practically anything, regardless of how small or insignificant it is. One can always make a case and throw numbers around about how devastating something is. How about the catastrophic increase in power being created (or would be created) by a universal world wide health exercise plan. Or how about the added extra eating required to supply the energy for all that exercise. Apply those numbers out of context and you can make what sounds like a devastating case. In context of the real world they would qualify as being not relevant to anything climate related.
That is why people have only concerned themselves with co2 emissions which have been grossly hyped. co2 does offer a straightforward calculation of how much effect it might have and it offers a tremendous political club capable of destroying or conquering nations. If there is too much burning going on at there (that is too much consuming going on for the planet to handle) then the totally inferior approaches to government and society which are incapable ultimately of even feeding their people can be hyped as being superior and that must be implemented to “save” the Earth from those evilllll human beans who, left to their own devices will continue to consume the whole planet, buying car after car after car until there’s no more places to store the old ones. (and lots and lots of other such rubbish)

218. Joe Born says:

Paul_K:

Actually, the system I suggesting generating synthetic data from is indeed linear and can readily be imagined as two simple feedback systems mutually coupled, such as the earth’s surface and a lumped-parameter representation of the greenhouse-gas atmosphere (although I find a four-year lag in such a system hard to imagine). Without taking time to get the constants right, I’ll just say its (again, linear) differential equation is of the form $\frac{d^2T}{dt^2} + a_1\frac{dT}{dt}+a_0T = b_1\frac{dF}{dt}+b_0F$.

I’ll agree with you that the response-to-insolation demonstration is important. To the extent that it is intended to establish sensitivity limits, though, it’s far from bullet-proof. That was my point in bringing up the two-(real-) pole system: Mr. Eschenbach’s technique could be very wide of the mark on that score.

219. D. J. Hawkins says:

Ian Biner says:
June 4, 2012 at 3:33 am
Um… All EXTREMELY interesting, but has anyone done any calculations on the cumulative thermal effect of burning all the oil/coal/wood/ethanol/-fry-oil/witches that define our industrial civilisation? It seems to me that all this burning of stuff must heat up the atmosphere,and as NASA tells us the amout of radiated heat hasn’t changed (sorry… don’t ask me for a reference… I don’t remember where I read that), then all that heat must have had an effect.

Just a thought.

174 petawatts vs. 13 terrawatts. Anthro is about 0.007% of natural. Still worried?
http://en.wikipedia.org/wiki/Earth's_energy_budget

• Ian Biner says:

Never worried. It was just a random thought so I turned to all the greater minds here for clarification. Thanks.