Guest essay by Johannes Herbst
There is a much discussed graph in the blogosphere from ‘Tamino’ (Grant Foster), which aims to prove that there is no delay or pause or decline in global warming.
He states: Twelve of sixteen were hotter than expected even according to the still-warming prediction, and all sixteen were above the no-warming prediction:
Let’s get a larger picture:
- We see the red HADCRUT4 graph, coming downwards a bit from 1960 to 1975, and inclining steeper beyond 2000, with a slight drop of about the last 10 years.
- We see a blue trend, rising at the alarming rate of 0.4°C within only one decade! This was the time when some scientists started to worry about global warming.
- We see the green trend, used by the blogger Tamino in the first graphic, rising less than 0.1°C per decade.
- Below we see the Sunspot Numbers, pulsing in a frequency of about 11 years. Comparing it with the red temperature graph, we see the same pattern of 11 years pulsing. It shows clear evidence that temperature is linked to the sunspot activity.
Tamino started his trend at high sun activity and it stopped at low activity. Therefore the weak increase during 18 years.
Which leads us to the question: How long should a time be for observing climate change? If we look at the sunspot activity and the clear pattern it produces in the temperature graph, the answer is: 11 years or a multiple of it.
Or we can measure from any point of:
·high sun activity to one of the following
·low sun activity to one of the following
·rising sun activity to one of the following
·declining sun activity to one of the following
to eliminate the pattern of sunspot numbers.
Let’s try it out:
The last point of observation of the trend is between 2003 and 2014, about 2008. But even here we can see the trend has changed.
We do not know about the future. An downward trend seems possible, but a sharp rise is predicted from some others, which would destroy our musings so far.
Just being curious: How would the graph look with satellite data? Let’s check RSS.
Really interesting. The top of both graph appears to be at 2003 or 2004. HADCRUT4 shows a 0.05°C decline, RSS a 0.1°C per decade.
A simple way for smoothing a curve
There is a more simple way for averaging patterns (like the influence of sunspots). I added a 132 months average (11 years). This means at every spot of the graph all neighboring data (5.5 years to the left and 5.5 years to the right) are averaged. This also means that the graph will stop 5.5 years from the beginning or the end. And voila, the curve is the same as with our method in the previous post to measure at the same slope of a pattern.
As I said before the top of the curve is about 2003, and our last point of observation of a 11 years pattern is 2008. From 2008 to 2003 is only 5 years. This downtrend, even averaged, is somehow too short for a long time forecast. But anyway, the sharp acceleration of the the 1975-2000 period has stopped and the warming even halted – for the moment.
Note: I gave the running average graph (pale lilac) an offset of 0.2°C to get it out of the mess of all the trend lines.
If Tamino would have smoothed the 11years sun influence of the temperature graph before plotting the trend like done here at WFT, his green trend would be would be the same incline like the blue 33 year trend:
Even smoother
Having learned how to double and triple smooth a curve, I tried it as well on this graph:
We learned from Judith Curry’s Blog that on the top of a single smoothed curve a trough appears. So the dent at 2004 seems to be the center of the 132 month’s smoothed wave. I double smoothed the curve and reached 2004 as well, now eliminating the dent.
Note: Each smoothing cuts away the end of the graph by half of the smoothing span. So with every smoothing the curve gets shorter. But even the not visible data are already included in the visible curve.
According to the data, after removing all the “noise” (especially the 11 year’s sun activity cycle) 2004 was the very top of the 60 years sine wave and we are progressing downwards now for 10 years.
If you are not aware about the 60 years cycle, I just have used HADCRUT4 and smoothed the 11 years sunspot activity, which influences the temperature in a significant way.
We can clearly see the tops and bottoms of the wave at about 1880, 1910, 1940, 1970, and 2000. If this pattern repeats, the we will have 20 more years going down – more or less steep. About ten years of the 30 year down slope are already gone.
One more pattern
There is also a double bump visible at the downward slopes of about 10/10 years up and down. By looking closer you will see a hunch of it even at the upward slope. If we are now at the beginning of the downward slope – which could last 30 years – we could experience these bumps as well.
Going back further
Unfortunately we have no global temperature records before 1850. But we have one from a single station in Germany. The Hohenpeissenberg in Bavaria, not influenced from ocean winds or towns.
http://commons.wikimedia.org/wiki/File:Temperaturreihe_Hoher_Pei%C3%9Fenberg.PNG
Sure, it’s only one single station, but the measurements were continuously with no pause, and we can get somehow an idea by looking at the whole picture. Not in terms of 100% perfection, but just seeing the trends. The global climate surely had it’s influence here as well.
What we see is a short upward trend of about ten years, a downward slope of 100 years of about 1°C, an upward trend for another 100 years, and about 10 years going slightly down. Looks like an about 200 years wave. We can’t see far at both sides of the curve, but if this Pattern is repeating, this would only mean: We are now on the downward slope. Possibly for the next hundred years, if there is nothing additional at work.
The article of Greg Goodman about mean smoothers can be read here:
Data corruption by running mean ‘smoothers’
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Johannes Herbst writes at: http://klimawandler.blogspot.de/
” If you take the integral over the heating-cooling cycle it is constant, so T is constant?”
You were the one that insisted the analogy compared to a climate that had been running for billions of years. so yes over [an] integral number of heating cycles it’s constant since you imposed that condition.
The cooling cycle matches the warming cycle hence the triangular waveform.
“proper analysis….. based on the energy input/loss per unit time..”
That would be the _time integral_ of the “energy input/loss per unit time” if you want a temperature.
Enough of this shit, Good night Willis , hope you manage to make sense of it.
Bart says:
February 9, 2014 at 5:18 pm
If we can all agree that it is not disproved, but that the case for proving it does not, at this time at least, rise to the level to persuade Leif, then I think the argument is over.
There never was an argument, just a pissing contest based on nitpicking, e.g. over Energy vs. Power. In the scientific community concerned with climate and the Sun the discussion is held in terms of Energy input [expressed in W/m2] and nobody uses the term ‘power’ for this [I gave a link for this, and can give hundreds more], so ‘correct’ is what is being used, not what one likes it to be.
Greg Goodman says:
February 9, 2014 at 5:20 pm
You were the one that insisted the analogy compared to a climate that had been running for billions of years. so yes over an integral number of heating cycles it’s constant since you imposed that condition.
Which is proper if you want it to be an analogy of the real climate.
In the real case, the heating-cooling cycle for a place on the Earth is a day so the triangular waveform washes out over, say, over a month or a year, or a solar cycle, so monthly [yearly, …] averages should correlate with the energy input [as it does] per unit time for any given location, and averaged over the globe there would no triangular waveform left regardless of the interval chosen.
Bart says:
February 9, 2014 at 5:18 pm
I would personally amend that to say “no immediate 1:1 correlation”, or “no 0th order correlation” as a stated previously, or something along those lines of simple and straightforward correlation.
As the lag [found empirically – see Lean – as giving the best best in a multivariate analysis] between TSI and Temps is of the order of one month over timescales of the decades over which we have actual observations of TSI, correlation of yearly values would be for all practical purposes ‘immediate, 1:1 0th order’.
So, are we all agreed here now that there is an important variation in the UV coming from the sun? This does not necessarily affect TSI that much, as it just shifts the distribution curve a bit. My own investigations and those of William Arnold confirm this inner change within TSI comes as a result of some solar or solar/planetary cycles.
So TSI is relatively constant, but the amount of ozone / peroxides / nitric oxides produced TOA is not. Therefore the amount of (subsequent) back radiation caused by these substances results in a variation of the amount of radiation that ends up in the oceans, specifically of the type that heats the oceans,
This is the only logical explanation I can think of for the observed drop in maximum temperatures, which seems to follow an A-C curve.
http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
.
Greg: Lief: Willis: Whoever…
This thing about adding or multiplying for frequencies. The true answer is that it is always both.
So we get FM radio type stuff with addition where the frequencies are a long way apart.
And we get moiré interference patterns where the two are closer together.
For frequencies that are neither obviously one or the other often both factors are visible quite easily.
Thus 60 and 4 gives 56, 64 and 240.
2000 and 4 gives 1996, 2004 and 8000.
To be accurate that should read as below I suppose.
Thus 60 and 4 gives 4, 56, 60, 64 and 240.
2000 and 4 gives 4, 1996, 2000, 2004 and 8000.
lsvalgaard says:
February 9, 2014 at 5:37 pm
“There never was an argument, just a pissing contest based on nitpicking, e.g. over Energy vs. Power.”
It’s not a nitpick. Power and energy are as different as velocity and position, as salary and wealth. If I tell you how fast a car is going, can you tell me if it is in California or New York? If I tell you someone is worth $1M, can you tell me his monthly salary? And, vice versa?
lsvalgaard says:
February 9, 2014 at 5:45 pm
“As the lag [found empirically – see Lean – as giving the best best in a multivariate analysis] between TSI and Temps is of the order of…”
There is no single lag. There is a phase response. There may be a mean lag for dominant frequencies, but that is as useful as using mean national income to determine how much money a given person makes.
Defining the problem is half the battle. And, if you are sloppy in defining it, you aren’t going to make any headway.
Bart, it seems to me that using your methods, I would be able to point to elephant tail wagging as the driver of the problem you wish to investigate. There is no more appropriate discipline than research to have pasted on your forehead the acronym KISS.
HenryP, your hypothesis needs one more addition. On what part of the globe are you saying this affect from ozone is most important? Equatorial? Extratropical? Polar? Be careful which you choose. The interaction between globe position and angle of illumination has a nasty habit of making your calculated result not matter. And remember there is a noisy night and day difference as well as location overhead for ozone that could obscure your tiny wriggles. Literally. So again, your calculations would not matter.
Bart says:
February 10, 2014 at 8:46 am
It’s not a nitpick. Power and energy are as different as velocity and position
You are missing the point. The issue was if it was ‘correct’ to use TSI [power] as a measure of the energy input to the system, as in slide 3 of http://www.leif.org/EOS/Nagoya-Lean-2012.pdf
showing the Earth’s Energy Budget [not power] labelled with W/m2
My point was that it is customary to use W/m2 as a measure of Energy. that makes it correct usag, hence nitpicking.
And, if you are sloppy in defining it, you aren’t going to make any headway.
You may safely assume that scientists working in this field are not sloppy morons and that we know what we are doing. When it comes to global temperature forced by solar radiation on timescales of decades there is no lag [well, one month which doesn’t matter for yearly values]. That is the issue when correlation the two variables.
Pamela Gray says:
February 10, 2014 at 8:54 am
Pamela – KISS is an excellent principle when you are designing a system to do something practical. For determining how an extant system operates, not so much. You must make things as simple as possible, but not simpler.
What I (we, as I am sure Greg would concur) am arguing is, in fact, very commonplace. In typical dissipative systems, small low frequency inputs are generally amplified while higher frequencies are severely attenuated. With even a simple one-box system model, power transmission rolls off at -40 dB/decade. Thus, a low frequency input component can easily have 100X the impact of a component a single decade higher. Add in another state to get a resonance, and it can easily soar to 1000X or more.
This is the basis for our entire industry of telecommunications. Out of all the electromagnetic energy bouncing around out there, you are able to tune in a relatively very weak signal on your radio. How can you possibly find the tail of that humongous elephant? You can, and you do, on an everyday basis.
RichardLH says:
February 10, 2014 at 3:38 am
And yet, Greg keeps insisting that we get the average of the two frequencies, in this case 32 for the first case, and 1002 for the second case … and when I ask him to explain it, he waves his hands at the whole web and tells me I don’t understand.
Richard, I couldn’t take Greg’s BS any more, but since you agree with me, would you like to try to get Greg to explain how he takes the two frequencies 8.55 and 9.3, beats them together, and gets the average frequency …
w.
lsvalgaard says:
February 10, 2014 at 9:04 am
What we are interested in is retained energy, as that is what determines temperature.
Willis Eschenbach says:
February 10, 2014 at 9:20 am
cos(8.55*t) + cos(9.3*t) = 2*cos(8.925*t)*cos(0.375*t)
Bart says:
February 10, 2014 at 9:20 am
What we are interested in is retained energy, as that is what determines temperature.
Is not responsive to the discussion [‘nitpicking’, lags].
Clarification from above:
“With even a simple one-box system model, power transmission rolls off at -40 dB/decade. “
Amplitude generally rolls off at -20 dB/decade, and the square at -40 dB/decade. But, the formula for dB in amplitude is 20*log10. Power engineers typically use 10*log10 as their scale, so they would say -20 dB/decade still. Just noting it in case of any confusion. The attenuation factor is still 100X in either convention.
Bart says:
February 10, 2014 at 9:10 am
Out of all the electromagnetic energy bouncing around out there, you are able to tune in a relatively very weak signal on your radio.
But, evidently, the climate system is not [as there is no correlation], so your analogy is moot and what you know about radios does not carry over to the climate system.
lsvalgaard says:
February 10, 2014 at 9:30 am
Not all the power is transmitted to become stored energy over the long term.
Example: Let’s say I had a power input of
P = 1 + 100*cos(w*t)
and a transmission function
E = P/(1 + (tau*w)^2)
If tau*w = 100, which of the components of P is going to have greater impact on E?
Answer: the “1”
lsvalgaard says:
February 10, 2014 at 9:34 am
“But, evidently, the climate system is not [as there is no correlation], so your analogy is moot and what you know about radios does not carry over to the climate system.”
Really? Do you actually think you could pick out the signal of your local radio station from a time series plot of the full spectrum of electromagnetic energy in your vicinity? Rotsa’ ruck!
Bart says:
February 10, 2014 at 9:35 am
Example: Let’s say I had a power input of…
Still not responsive, and the ‘correct’ term [by common usage is ‘energy input’, not ‘power input, see http://www.leif.org/EOS/123222295-Lean-Trends.pdf ]. Observationally there is no lag.
Bart says:
February 10, 2014 at 9:38 am
“But, evidently, the climate system is not [as there is no correlation], so your analogy is moot and what you know about radios does not carry over to the climate system.”
Really?
Yes, really!
Your knowledge in one area is not always applicable in other areas, and in particular not in this area.
LOL! In terms of radio frequencies, your “found” elephant tail radio frequency is relatively HUGE in the sea of larger and smaller -EHF runs the range of frequencies from 30 to 300 gigahertz- radio frequencies compared to what you are trying to find in solar parameter teleconnections with Earthly warming or cooling trend parameters. And it is KISSABLY simple to figure that out. Terrible analogy. Terrible. You should know better.
lsvalgaard says:
February 10, 2014 at 9:41 am
If it is Watts, it is power. Plain and simple. Energy is Joules. Power is Joules/sec = Watts. Do I really have to explain this to you?
lsvalgaard says:
February 10, 2014 at 9:45 am
“Yes, really!”
No. Really. You wouldn’t have a prayer. This isn’t even marginally credible.
This is the kind of bluster and bravado which diminishes your credibility on other subjects. You really should avoid it if you want people to listen to you on subjects you genuinely know something about.
Pamela Gray says:
February 10, 2014 at 9:46 am
Not in the time domain, sweetie.
I’m clearly not getting through to the uninitiated. Go on and have a party, guys. I’ve got things to do.