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/
Richard, you are meandering from your argument, but at least you haven’t called me sweety. I am one of course but only in my private life. Professionally, the term is inappropriate and I hesitated to refer to Bart as Bartie. A weak moment on my part.
RichardLH says:
February 10, 2014 at 10:44 am
Richard, I use a host of techniques from a variety of other disciplines. In fact, climate science is the most interdisciplinary of sciences.
Which is why I said “I get nervous” rather than “I cast off” … because in addition, climate science is also perhaps the messiest of sciences. To start with, it’s about as non-linear as you can get.
We like to think of the world as linear, and we’ve made great strides by doing so. It’s a good technique, but it has its limits.
However, in climate science saying “non-linear phenomena” is like saying “non-elephant biology”, it’s the majority of what’s going on.
There are a host of complications in climate science, many of which involve the fact that much of the system is composed of evanescent emergent phenomena which only appear above a certain generally temperature-based threshold and act to cool the surface in dozens of ways … which means we are dealing with a system composed of entities rather than a system of continuously varying parameters. Nature doesn’t do “continuously varying” all that well. Instead, nature does edges and “rose-moles” as the poet put it,
GLORY be to God for dappled things— For skies of couple-colour as a brinded cow; For rose-moles all in stipple upon trout that swim; Fresh-firecoal chestnut-falls; finches’ wings; Landscape plotted and pieced ...… and climate, with its “skies of couple-color” and “landscape plotted and pieced”, is among the ultimate in non-linearity.
Faced with this, of course, our scienfic response is to take refuge in the linear … hey, we’re human. So we draw straight-line trends, and some are happy to extend them to the year 2080, linearity roolz, and so you hear things like “well, I know it’s not linear, but we can approximate it as linear over this range” … sorry, but unfortunately, that just ignores the hard reality that there is no linear approximation to an edge.
As a result, while all kinds of methods from many disciplines need to be used, we cannot do so blindly. We need to pick and choose very carefully when we start taking techniques from other fields and applying them to the denizens of the climate zoo …
And in particular, we can’t say “Well, it’s easy to find the elephant in the linear room of electronic signal processing, so what’s the problem in climate?” … which is a paraphrase, but I think an accurate one, of Bart’s comment above.
As always,
w.
RichardLH says:
February 10, 2014 at 11:05 am
There is good evidence that we cannot model clouds very well which are a large part of the ‘instantaneous acceptance/reflection ratio ‘ so all is not that simple.
We don’t need to model anything. The simple calculation uses the relation dS/S = 4 dT/T where S is the energy input and T is the temperature. Taking observed dS/S as 0.0013, you get dT/T = 0.000313 or with T=289K, dT = 0.09K. The ‘acceptance/reflection ratio’ is already in the value of 289 K. The only assumption is that those values do not vary much over a cycle. This is borne out by observations. So it is actually simple.
lsvalgaard says:
February 10, 2014 at 10:44 am
“We are looking for how the energy input drives temperature.”
It is not energy. It is power. It is Watts. You can call it whatever you want, of course. But, sloppy language leads to sloppy thoughts.
The empirically determined lag is only one month, so the ‘long-term’ variation should follow the variation of the energy input [e.g. as measure by TSI].”
An empirically determined mean lag is one month. The rest does not follow.
The long term variation should follow the variation in integrated power, filtered through the transmission function which relates input power to stored energy.
Pamela Gray says:
February 10, 2014 at 10:51 am
“if you cannot observe something correlating between a tiny solar and the temperature trend change AND that has a plausible mechanistic connection, you must acquit. IE Solar parameters are not responsible for the warming or cooling trend.”
A) The Sun is a major suspect. It is the only significant source of heat available.
B) A lack of a 0th order correlation is not conclusive. Even lack of an arbitrary order correlation is not conclusive.
When you blow through a whistle, does the wind in your breath correlate to the pitch you will hear coming out of the whistle? Of course not. If it did, you wouldn’t need the whistle. Yet, the whistle sounds with a clear and piercing note. Should we then conclude that you are not actually causing the whistle to make its sound, that it simply happens spontaneously, and there is no significance to the fact that it happens to do so right when you start blowing into it?
Pamela Gray says:
February 10, 2014 at 11:08 am
“A weak moment on my part.”
Obviously “sweetie” set you off. Sorry. To me, it is a term of endearment.
RichardLC, TOA solar heating is well-known, which is what Leif refers to (about 340 watts per square meter of solar energy arriving at the top of the atmosphere and will vary in general terms by the amount Leif states). It is intrinsic parameters (Earth sourced) that are not well known and mechanized. Solar heating at the surface is called Insolation (short for incident or incoming solar radiation) and is a measure of solar radiation energy received on a given surface area. The first measure is pure solar (extrinsic), the second is a combination of solar (extrinsic) and atmospheric/topographic (intrinsic) factors. Don’t confuse the two. Leif refers to TOA solar heating only.
Bart says:
February 10, 2014 at 11:16 am
You can call it whatever you want, of course. But, sloppy language leads to sloppy thoughts.
Not my term, but the term commonly used, so therefore, by definition, correct, and not due to sloppy thoughts
An empirically determined mean lag is one month.
‘The’ lag as observed for the time where we have actual measurements
The long term variation should follow the variation in integrated power, filtered through the transmission function which relates input power to stored energy.
The common usage is ‘energy input’, not ‘power input’. Did you consult the links I gave you. Why not?
B) A lack of a 0th order correlation is not conclusive. Even lack of an arbitrary order correlation is not conclusive.
If there is an effect it should be observable. If it is not, any ‘effect’ need not be of concern. That is the issue, not whether some unobservable effect might exist.
Bart, apology accepted. Pamela will do just fine.
Pamela Gray says:
February 10, 2014 at 11:17 am
“…TOA solar heating is well-known…”
Over what time interval? How does the uncertainty increase as you go farther into the past? At what point does it become purely speculative? And, how do those time intervals relate to the very long term climatic intervals of interest?
lsvalgaard says:
February 10, 2014 at 10:56 am
Thanks as always, Leif. Surface temperature average global upwelling radiation is ≈ 400 W/m2. Variation in TSI over the cycle is ~ 1/4 W/m2 averaged over the globe. We get about a 400/340 = 18% bump from the TOA solar to the surface via the greenhouse effect, so we’ll call the surface increase 1.2 * .25 = 0.3 watts. (Note that this ignores the fact that increasing/decreasing solar is counteracted by increasing/decreasing clouds.)
That gives us:
And indeed, that rounds to about a tenth of a degree as you say. So ignoring the cloud response, you and I get the same numbers.
My question, however, is … who has presented “good evidence” of such a cycle in the temperature record? A citation would be valuable here …
Gotta run,
w.
Pamela Gray says:
February 10, 2014 at 11:17 am
“RichardLC (sic), TOA solar heating is well-known, which is what Leif refers to (about 340 watts per square meter of solar energy arriving at the top of the atmosphere and will vary in general terms by the amount Leif states).”
I do rather know that. I was careful to state ‘TOA to surface’ for the acceptance/reflection ratio
so as to include all the other phenomena as well. That was no accident.
I agree that the tiny delta in the Solar output is unlikely to cause much direct delta in the temperatures. I must get my humour meter (re)checked.
W. It would be buried in the noise and cannot be extrapolated, since all temperature components end up as a single entity “heat” on a thermometer so therefore are tagged with the same tag (by the way brilliant thought).
lsvalgaard says:
February 10, 2014 at 11:25 am
“‘The’ lag as observed for the time where we have actual measurements”
A mean lag as observed. This is like claiming the observed global mean temperature tells you how warm it was in Peoria.
“Did you consult the links I gave you. Why not?”
Because it is sloppy, and I do not care to use sloppy nomenclature just because someone else is doing so. This is power. It is energy per unit of time. As I said, it is as different as velocity versus position.
“If there is an effect it should be observable. If it is not, any ‘effect’ need not be of concern.”
But, it does not follow that, just because some people say they cannot see it, it is of no concern. You have then arbitrarily constrained the range of observable phenomena to a specific subset of possibilities.
Pamela Gray says:
February 10, 2014 at 11:25 am
“Pamela will do just fine.”
Then, please, do not address me as “LOL”.
Willis Eschenbach says:
February 10, 2014 at 11:09 am
“Richard, I use a host of techniques from a variety of other disciplines. In fact, climate science is the most interdisciplinary of sciences.
Which is why I said “I get nervous” rather than “I cast off” … because in addition, climate science is also perhaps the messiest of sciences. To start with, it’s about as non-linear as you can get.
We like to think of the world as linear, and we’ve made great strides by doing so. It’s a good technique, but it has its limits.”
I know. It is just I thought you were casting your net a little too wide. Just a little bit of Hyperbole is all.
oops RichardLH. My bad.
Willis: Please note that I am a great critic of Linear Functions.
Linear Trend = Tangent to the curve = Flat Earth 🙂
Pamela Gray says:
February 10, 2014 at 11:35 am
“oops RichardLH. My bad.”
NP
Willis Eschenbach says:
February 10, 2014 at 11:27 am
My question, however, is … who has presented “good evidence” of such a cycle in the temperature record? A citation would be valuable here …
Many people has shown that. The estimates range from 0.05 to 0.2 K. Here is Lean’s estimate:
http://www.leif.org/EOS/LeanRindCauses.pdf
lsvalgaard says:
February 10, 2014 at 11:15 am
“The ‘acceptance/reflection ratio’ is already in the value of 289 K. The only assumption is that those values do not vary much over a cycle.”
I do need to explain my cartoon shorthand better.
Reflection includes clouds, albedo, sparkle, etc. Absorption includes in gas, liquid and solid. That rather does vary all the time and at different heights also.
Let’s move on. My poor attempt obviously failed. 🙁
Pamela Gray says:
February 10, 2014 at 11:08 am
“Richard, you are meandering from your argument, but at least you haven’t called me sweety.”
Not my sort of phrase really 🙂
Which argument? The one where I try to explain that a >15 year LP filter shows up interesting things in the climate data? I’m a bit boring on that one I know.
Doesn’t make it false though.
Bart, you do know that LOL means Laugh Out Loud, yes? And your analogy did indeed make me laugh. You must admit it was a silly one to use, knowing the number of people who frequent this blog who have had experience with frequency detection of one kind or another. Name calling, endearing or not, is another matter. Bartie was the inappropriate name I used and I have apologized for it. Laughing at the various strange theories and analogies offered here for solar connections to the temperature trend is part of the deal when one offers such things. For that I apologies not.
Bart says:
February 10, 2014 at 11:34 am
A mean lag as observed.
The lag as observed.
“Did you consult the links I gave you. Why not?”
Because it is sloppy, and I do not care to use sloppy nomenclature just because someone else is doing so.
You disqualify yourself by such statement. And there is nothing sloppy about it. Energy input is the correct term [as the area and time interval are implicitly given] and the term being used. To used to it, learn.
“If there is an effect it should be observable. If it is not, any ‘effect’ need not be of concern.”
But, it does not follow that, just because some people say they cannot see it, it is of no concern. You have then arbitrarily constrained the range of observable phenomena to a specific subset of possibilities.
You can point me too an analysis that convinces you that there is an effect to be concerned about and I will take a look [this attitude is in contrast to your refusing to look at links]. If there is an effect that need to worry about, it should be clear, large, and unmistakable, otherwise it is of no concern.
RichardLH says:
February 10, 2014 at 11:41 am
Reflection includes clouds, albedo, sparkle, etc. Absorption includes in gas, liquid and solid. That rather does vary all the time and at different heights also.
Unless those things vary systematically and significantly with solar activity they have no systematic effects and wash out. There are no convincing evidence that they do that I know of, but you might correct me on that by linking to what you consider compelling evidence for such variations and that they lead to temperature changes larger than the 0.1 degrees that we observe.
Leif, the data sets were obtained elsewhere by her, some of which have checkered histories. Nonetheless, using the various data sets of the components that cause K I can see how she could attribute overall K to each one. However, one cannot do the reverse by using the temperature series by itself and find separate identifiable signals from the various components that create the heat or lack thereof on the thermometer. So in a way, she a priori set the outcome of her analysis.
Bart:
I write to ask a genuine question. Please note that I am not taking sides: I am trying to understand what you are saying.
At February 9, 2014 at 12:29 pm you say
It seems you are claiming that two parameters (i.e. temperature and TSI) have a causal relationship but they do not correlate. Is that a correct understanding?
If I have understood you correctly, then I fail to see why there is any purpose in considering the putative relationship between “temperature and TSI” which you assert exists. I explain this as follows.
Assuming the putative causal effect does exist then its affect is so small that it is swamped by other effects because otherwise there would be a discernible correlation between “temperature and TSI”. However, if the system were invariate then affects of the putative effect may accumulate over time and, therefore, eventually become non-trivial. But the system does vary and, therefore, any accumulation of affects of the putative effect will be disrupted by variations in the more powerful effects.
Simply, the putative effect is so small that it is swamped by other effects, and affects of the putative effect cannot accumulate because any accumulation will be disrupted by variability of the larger effects.
Hence, if the putative effect does exist then it cannot achieve discernible affects. Why investigate something which – if it exists – cannot be discerned and has no affects which can be discerned? The investigation seems impossible to conduct and the investigation would be purposeless if it were possible.
I am hoping you can explain the matter to me.
Richard
Pamela Gray says:
February 10, 2014 at 11:44 am
“You must admit it was a silly one to use, knowing the number of people who frequent this blog who have had experience with frequency detection of one kind or another.”
Only because the point apparently blew over your head. As I said, you proved my point. You have to resort to sophisticated methods of analysis to detect the signals you are looking for in all the hash. you cannot just “see it” with a glance at the time series.
“Bartie was the inappropriate name I used and I have apologized for it.”
I’m fine with “Bartie”. I’m not fine with using derision as a debating tactic when you are making a non-point.