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’
==================================
Johannes Herbst writes at: http://klimawandler.blogspot.de/
Gareth Phillips says:
February 7, 2014 at 9:33 am
The point I am making is the unusual situations are becoming more frequent, and floods such as we see in the Somerset levels, cold in the US and weird weather elsewhere seems to be what we expect as being normal, not the 100-1 shot we may have previously thought .
I know of no data to support that statement. To the contrary all objective data have shown the opposite. Comments and anecdotal information are not reliable. As a start, let me refer you to
http://judithcurry.com/2013/12/11/hearing-a-factual-look-at-the-relationship-between-climate-and-weather/#more-14006
which has references to actual data that refutes your statement.
“According to the Intergovernmental Panel on Climate Change (IPCC), there is “high agreement” among leading experts that long-term trends in weather disasters are not due to human-caused climate change.”
As you can see in the references above the data shows actual decrease in floods, cold, “weird weather”, etc. If you have other data I would like to see it.
I would further like to see any data (not claims, models, or conversation) you have showing an anthropogenic link to climate.
May I make a suggestion? It would be very useful if we could leave phrases such as “delusional” out of reasoned debate altogether, especially when applying it to one of the debators, even where it is your sincere belief that it is true. It is ad hominem and irrelevant to the truth or falsity of any assertion, it accomplishes nothing (but to get the back up of the one being called delusional and make them less likely to acknowledge any error or weakness in their argument, since that would only serve to prove that they are “crazy” and not just “mistaken”.
I personally will freely admit to being mistaken, even mistaken frequently, about ever so many things. I might even be mistaken about being mistaken, as in many cases I say things I myself am hardly certain of to see if I can rationally defend them and to draw out somebody else’s knowledge and ability to support a counter position.
I rather hope that I’m not crazy, and while I do have a thick skin, I’d still at least somewhat resent being called crazy, especially in an argument where the true parsing of the accusation is “We disagree! You are wrong! I am right!”. Note well that this is completely irrelevant to whether or not I AM wrong, right, in between, and I’m happy to concede that we disagree without disagreement being tied to insanity or delusion.
I have tried to be respectful to Gareth as I have offered refutation of his metaphors and belief that bad weather in one part of England is proof of climate change in a non-stationary system. Note that I’m not asserting that his belief is delusional, only that he is conflating weather with climate, something that the press openly encourages by reporting every single “extreme” weather event around the world as proof of climate change in spite of the fact that the normal climate sets records of extreme weather every day somewhere on the planet (unsurprising, given the miniscule interval over which we have records to find extrema within and the enormous number of locations). Citing individual weather extremes to prove climate change is a form of egregious cherrypicking, and is why we do not trust anecdotal claims to support hypotheses in science.
Now if one looks up the work of the Pielkes, who IIRC study precisely that — the statistical distribution of extreme weather events of various sorts — you will learn that there is no statistically defensible evidence for an increase in the violence, frequency, energy, or any other metric of extreme weather. No there are not more hurricanes than usual. Nor tornados. Nor are hurricanes on average stronger. Nor tornadoes. Nor are there excessive numbers of droughts (that’s a hard record to reach, by the way, as there have been some doozy droughts in the past!). Or floods. There is absolutely no statistically defensible reason to think that the weather is getting worse, anywhere.
This is a simple matter of fact. If you disagree, Gareth, then I would respectfully ask you to produce the study the finds otherwise, and look carefully at the degree of significance for the claim. Given the number of times people look for some statistically significant measure of “climate change” presumably caused by humans, it is literally inevitable that they will find some metric or another where there is a “significant” effect. This, however, is the result of data dredging (look it up, Wikipedia will educate you as will the xkcd comic “Green Jelly Beans cause Acne”). Data dredging is simply using statistics to put a patina of respectability on good old anecdotal evidence by finding SOME anecdotal evidence SOMEWHERE that is statistically significant while ignoring all of the rest that shows no effect whatsoever or even a negative effect (e.g. cat 3 storms making landfall in the Atlantic basin).
In the meantime, perhaps we could all hold off on the name calling, invective, ad hominem, and so on. I very much doubt that Gareth is trying to fool us or that his beliefs are not sincere. I merely challenge those beliefs. Are they defensible?
Gareth?
rgb
Gareth, could you please point out to me (and all of us) the anthropogenic CO2 signal in this graph from the Met Office:
http://notalotofpeopleknowthat.files.wordpress.com/2014/01/image26.png
Gareth Phillips says:
February 7, 2014 at 3:12 am
It went up, it stayed up.It has not got statistically warmer for some time, but the patients temperature is still high and the fact that it has not risen any further is neither here nor there. Our planet is still pyrexial. With patients we prescribe anti-pyrexials such as Asprin or Paracetamol. Is there a prescription for the planet, or is everyone happy to see it stay heated? If so, the conveyor belt of storms experienced since last year in the UK will have to be accepted as quite normal as well as other climatic changes yet to be seen.
This is rubbish. The earth’s temperature changes, both upwards and downwards. Implying that an upward change measured over a short period of time is indicative of a “sickness” that can be cured is preposterous. Also, you are making the implication that a particular snapshot of temperature is “normal” and current temperatures have departed from “normal”, which is also preposterous.
Leif Svalgaard says:
February 7, 2014 at 9:54 am
Leif, while I generally agree with you, this claim (that in the long run TSI rules the temperature) ignores the obvious—the thermally driven response of cloud increase, which cuts down the amount of energy hitting the earth. TSI goes up … clouds go up … temperature stays the same. I have demonstrated this, using actual observations, in many ways and forms.
What you have claimed is as mechanistic as claiming that the strength of the sun controls the temperature of the human body, so if we go out into the sunshine, in the long run we’ll end up with a body temperature a couple of degrees warmer than if we stay in the shade …
That kind of thinking works fine in a mechanistic universe. On the other hand, in our universe where a straight line is NOT the favored distance between two points, it doesn’t work … see the endless meandering of rivers as one of many examples of times when nature doesn’t follow the obvious, linearly-related, straight-line path that our mechanistic understanding always wishes it would follow …
w.
Gareth Phillips – Please just answer this one question… what is the ideal ‘average’ temperature of the earth?
rgbatduke:
At February 7, 2014 at 12:39 pm you ask
His “beliefs” are his own. His assertions about the tragedy in the Somerset Levels are NOT defensible.
Furthermore, his claim that he said other than he did when his words were specifically quoted is either delusion or deliberate lie. In either case, correction is not amenable to reason.
But the fact is that the campaign posed by his ilk – and which he still tries to defend – has caused a disaster for hundreds of families, and failure to expose that truth to onlookers assists continue of many similar campaigns which are being conducted.
This is not merely some academic discussion. It is a fight against an insane philosophy which places a bird sanctuary above the lives and homes of hundreds of people.
Richard
rgb, Gareth will not be able to refute you regarding the data associated with the recent rise in CO2 and “extreme” weather events, obviously.
He will, however, show up on the next thread on this subject arguing as if he never read your post or looked at the data, with the same arguments. At that point I will make the same point I made above, which was not invective, which is why I prefaced my comment by saying I was not being facetious. There is a syndrome associated with this exact type of behavior – continuing to not be able to get something out of your head when it has been repeatedly pointed out that you are wrong.
He accuses me of saying that because I don’t agree with him when, in fact, it’s the hard scientific data that does not agree with him. I just passed on the data to him via my link to Paul Homewood’s post.
Of course there are other alternatives, such as he has a CAGW agenda to spout, or that he just can’t grasp actual scientific data.
RichardLH says:
February 7, 2014 at 10:39 am
OK, you’ve unburdened yourself. In the best California New-Age fashion you’ve shared your data-free, uncited, unsubstantiated belief in something that you don’t even begin to describe but still think exists … and you wonder if it might be capital-G “Gravity” that is the ‘lever’ …
Richard, do you actually think that Gravito-babble means something? Did you go to one of those schools where everyone gets a prize, so no one feels left out?
And more to the point, do you think anyone here cares about your puerile handwaving theories?
Come back when you have something to say that has some solidity, some numbers, some real hypothesis, something other than childish wonderings. So far, you’re just wasting electrons, and your vacuous comments suck the very oxygen from the scientific air.
w.
Gareth Phillips:
My post at February 7, 2014 at 11:47 am is here. It cites your words where you have repeatedly stated falsehoods that have been repeatedly refuted and quotes your words I had answered but you claim to have not written.
At February 7, 2014 at 12:07 pm you have replied to that saying in total
Oh, I learn all the time. For example, today I have learned that you are a contemptible eco-loon who never learns from his mistakes and just repeats them time after time.
Richard
lsvalgaard says:
February 7, 2014 at 12:01 pm
greg says:
February 7, 2014 at 10:47 am
I’d be much more impressed if you could reply to the objection I raised to your equating energy and power, than philosophical comments to “reflect” on.
You are just nit-picking. Since the surface area of the Earth is fixed, the power [TSI] applied every second is the amount of energy received in that second (energy=power * time * area). ‘TSI’ is a convenient short-hand for that.
====
I’m not knit picking , power is the time derivative of energy, they are not interchangeable , neither is one “shorthand’ for the other.
If TSI (power) in some way affects mean surface temperature it is not going to correlate with temperature directly unless the whole effect equilibrates in a time-scale much shorter than a solar cycle.
Willis Eschenbach says:
February 7, 2014 at 1:15 pm
“OK, you’ve unburdened yourself. In the best California New-Age fashion you’ve shared your data-free, uncited, unsubstantiated belief in something that you don’t even begin to describe but still think exists … and you wonder if it might be capital-G “Gravity” that is the ‘lever’ …”
Thank you for your considered, scientific approach to an outline question.
Data free! All I ever do is show data. And summaries of it. Nothing more. Please tell me what you believe are the reasons for the periodicity that is evident in the data. I really want to know.
I seek a mechanism, any mechanism, that will explain what I see. Gravity has to be a candidate. How do you so easily exclude it?
For three solar cycles the oceans release sub-surface heat into the LT, which we record. For three cycles additional heat is transported below the surface, and cold water is brought to the surface, where it is measured as surface temp. I don’t have a reason why, but it is quite clear to me, that there is a link, between the sun, the oceans, and the temps of the LT.
http://www.woodfortrees.org/plot/hadcrut4gl/from:1850/to:2014/plot/hadcrut4gl/from:1880/to:1912/trend/plot/hadcrut4gl/from:1912/to:1942/trend/plot/sidc-ssn/from:1850/to:2014/normalise/offset:-.5/plot/hadcrut4gl/from:1942/to:1975/trend/plot/hadcrut4gl/from:1975/to:2005/trend/plot/hadcrut4gl/from:2002/to:2014/trend/plot/hadcrut4gl/from:1850/to:1880/trend
Russ R. says:
February 7, 2014 at 1:45 pm
“For three solar cycles the oceans release sub-surface heat into the LT, which we record. For three cycles additional heat is transported below the surface, and cold water is brought to the surface, where it is measured as surface temp. I don’t have a reason why, but it is quite clear to me, that there is a link, between the sun, the oceans, and the temps of the LT.”
Perhaps 3 * 11 * 2 = 66 and that (or close approximations to it) has been well observed before?
I look at these plots ; for example HADCruD4gl and see data extremes from +0.8 down to -0.4 , a total range of 1.2 deg. Celsius anomaly (not Temperature).
It’s 1245 PDT in California, and I’ve been up since 0700, when I pulled the blinds and let the morning twilight start shining in. It’s rainy, so we haven’t seen the sun yet, and it’s been almost six hours.
My dining room/kitchen Temperature has already already risen by at least three times that HADCruD4gl total extreme range, for about the last 55 years.
Ho hum ! clearly nothing is happening that doesn’t happen almost any day round where I am.
Nothing to see here, so I don’t waste time or effort smoothing it. I can live with the unsmoothed extremes, and actually not even notice that anything is changing.
These scatter plots are really something.
We start with a set of numbers from -0.6 up to + 1.0; not units at all, but possibly deg. C and maybe our set has 160 numbers in 0.01 deg. C increments; we’ll call that column (a).
Then we take another set of numbers labeled from 1960 up to 2015. Again NO units, but they could in fact be calendar years, so a range of 55 years; possibly in months so maybe 660 numbers. We’ll call that column (b).
Now there is absolutely NO physical connection between column (a) in maybe deg. C , and column (b) in maybe calendar years. But not to worry; with scatter plot, you just plot one number against another number to get a point in the scatter plot.
Now the area of the rectangle would seem to be 1.6 deg. C times 55 years, which equals 88 Deg. C Yrs total area. And each “pixel must be 0.01 deg. C times 1/12 year or 8.333..E-4 deg. C Yrs.
No idea what the SI units fro deg. C Yrs are but who cares.
So now we make up a “data set” of number pairs, by taking “one from column (a)”, and taking “one from column (b)” ; in any order I might add; to get pairs of numbers, quite unrelated to each other.
Now we do have one additional rule. we choose to restrict the use of the numbers in column (b), so any number can only be used once. We don’t apply this restriction to column (a) for some unexplained reason. So numbers in column (a) can be used any number of times, or even not at all, while numbers in column (b) can only be used once, but also maybe not at all.
So perhaps it is more logical (but not required) to pick the column (b) number first, and since the order doesn’t matter, why not in ascending order from 1960 on up to 2015. That way we can be sure we use them all, except the ones that aren’t used.
So now we plot our scatter plot of points; pretty much exactly like Willis did the other day, when he plotted yearly anomalies against monthly anomalies on a graph with a years. months area, and pixel size.
Now Willis plotted only the points; they have no size. He did not connect them with zig zag lines.
But for some reason, on deg. C Yrs plots, it is fashionable to join the dots with zig zag lines.
I don’t know why, because there are simply no numbers in column (a) or column (b) that correspond to places in between the dots..
Now the dots in Willis’s plot weren’t joined, but he was able to perform rigorously defined mathematical algorithms on them, to come up with a straight line drawn on the chart.
Now just the other day, Lord Monckton, did a similar operation as Willis; this time on an RSS data set, and he too derived by similar methods , a straight line related to the RSS data set. That line had a zero slope horizontalness, for all intents and purposes, which was the point of Christopher’s analysis, absolutely nothing untoward is happening.
Willis’s scatter plot, was much more exciting; it had a positive left to right slope. Clearly a sign of busy bee like activity. But as I recall, there wasn’t ANY time axis on Willis’s scatter plot; just degrees versus degrees of different anomalies.
But we sure got the sensation that something was happening in Willis’s scatter plot; when there was no activity at all.
Lord Monckton’s plot suggested complete inactivity; absolutely nothing is happening, even though he does have a time axis.
But clearly there was lots of activity in Christopher’s plot. The actual RSS original raw data, was dancing all over the place up and down about the totally dead trend line of His analysis.
So as Willis said, you can draw scatter plots of any set of pairs of numbers whether related or not.
And you can create those pairs of numbers by whatever process you want, and then perform any of these quite rigorous algorithmic processes to create the illusion you want; of nothing at all, in the case of Christopher’s RSS set, or a hive of activity, in Willis’s anomaly-anomaly scatter plot.
The results are quite exact if you do the arithmetic correctly, and quite rigorous, and derive a variety of intrinsic properties of your data set of paired numbers, from column (a) and column (b).
But those results relate to only the set of number pairs themselves.
There are NO numbers of the set, in between plotted points, so NO point in drawing a zig or zag between them. We have zero information as to how the time increment got from March 1980 to April 1980, and what happened to the deg. C anomaly on the way. It might have shot up and back down, or verse vicea. The analysis tells us NOTHING about what goes on in between any two adjacent dots, and the straight line is as unlikely as anything else. We can say whether the anomaly went up or went down between those two months.
But what about December 1959. The analysis, tells us nothing about the anomaly for that month, and moreover, it cannot even tell us whether the Dec. 1959 anomaly was down from Jan 1960 or whether it was up.
Well the same is true at the other end. No analysis on the RSS data set up to Dec. 2015 will tell us what the Jan.2016 anomaly will be, and once again, it won’t tell us whether it goes up, or goes down, or stays the same. Well columns (a) and (b) can have gaps in them; so there might not even be a pair of numbers for Jan. 2016 anomaly. Hopefully the month Jan. 2016 will still be there.
So it’s not about the analysis; that is quite rigorous. It’s about the interpretation, and whether anything is happening or not.
I seek a mechanism, any mechanism, that will explain what I see. Gravity has to be a candidate. How do you so easily exclude it?
Wait, I know, I know! Call on me!
Could it be because gravity is a conservative force and cannot heat or cool planetary air at all?
Note well, this isn’t to minimize the role of gravitation in establishing things like convective rolls and the DALR in an open, externally heated and cooled climate system, but it itself adds nothing whatsoever but a gradient that drives convective processes that have some other free energy sources and sinks.
Also, note well, gravity is not changing. The climate changes all the time.
rgb
rgbatduke says:
February 7, 2014 at 1:57 pm
“Wait, I know, I know! Call on me!
Could it be because gravity is a conservative force and cannot heat or cool planetary air at all?”
The tides come and go all the time you know. Canute tried to point out that fact. He knew but could not convince others.
The oceans are an oscillating system that is in a resonance frequency with the sun. The oceans are always in a dynamic equilibrium, of temp stratification, and mixing due to tides, storms, currents, and wind created swells. They store more heat than they release, until there is a trigger, that causes them to release more than they store. The phases have both positive and negative feedback “weather patterns” that maintain the trend, or oppose it. All that is required, is that the trend, has reached its “capacity to maintain the trend”, and the opposing patterns will reverse the trend.
It is similar to the video of the metronomes that are on a low friction surface. They will eventually find harmony. The oceans are in harmony with the solar cycle, and the temp record, is driven by the storage and release of heat, in the oceans.
Russ, you need to look back further than three cycles or at least look a lot more critically at those cycles. The phase of temp and SSN drift out over time , before WWII they get totally out of phase (one goes up the other goes down).
This was discussed recently in the context of the claim mean sea level correlated to solar cycles, it’s the same thing.
I did a cross-correlation of those two , that is a techniques which sees how the two vary together. I found that there was another close cycle that was causing an interference pattern. This accounts for the phase drift, it came out as about 9.2 years.
http://climategrog.wordpress.com/?attachment_id=760
So if you try to assume it’s just solar dominated it does not work. There are other factors at play.
Looking at the power spectrum of that relationship shows quite a simple spectrum
http://climategrog.wordpress.com/?attachment_id=759
The main peak could well be solar related and shows some spread, as there is in the solar periodicity. There is also 22.6 and broader spread around 5.4 years.
The combination of the latter two could sum to account for the 9.2 value (the average of ‘beat’ frequency).
Sadly, when there are a lot ( or at least several ) factors interacting it requires some spectral detective work to understand how it all goes together. Simple correlation coeffs of one suspected input will not be sufficient to either confirm or refute a suspected influence.
Much of the talk in climatology is of “pseudo-cycles’ because it all seems very irregular and unpredictable. Once we understand how different cycles interact we can start to realise that much of the ‘pseudo’ aspect is just two or three cycles interfering with each other.
BTW I’m using “interference” in the technical spectroscopic or audio sense , not in the common language sense of messing each other up.
RGB “…. but it [gravity] itself adds nothing whatsoever but a gradient that drives convective processes that have some other free energy sources and sinks.”
I agree in principal that gravity can’t heat the earth but it does determine the lapse rate ( the gradient you refer to ). Isn’t it then possible for that change the height and hence temperature of the troposphere thus cloud amount and thus affect the TOA energy budget.
Also changes in convective processes could change surface temps.
Maybe that what you were pointing out.
Also gravity is not constant otherwise we would not have tides and predictable exceptional high tide amplitudes like the once the coincided with Sandy.
Au contraire, it was hypothermic, and is still below optimal. The nadir of the LIA was NOT an ideal, but a crisis to be dreaded.
GE Smith: “Now the dots in Willis’s plot weren’t joined, but he was able to perform rigorously defined mathematical algorithms on them, to come up with a straight line drawn on the chart.”
Sorry, doing linear regression on scatter plots is not a “rigorously defined mathematical algorithms” it’s a rigorously mis-applied algorithm, which almost always gives the wrong answer . (see regression dilution )
It is only valid when you have one controlled variable with minimal error. This is the case for a time series like you had before you started pretending it was a scatter plot.
Misuse of linear regression is one reason by climate sensitivity is over-estimated: slope too small ; CS too big.
Re: greg says:
February 7, 2014 at 2:26 pm
I have decided it is not the actual SSN that matters. That is why the correlations are so poor. It is more about the oceans ability to sustain the current trend. They will maintain a cycle that is releasing heat into the LT, until the negative feedback’s have a slight advantage over the positive ones. That happens at the end of the third cycle. The first and second cycles end, without changing the trend, because they don’t get enough positive feedback from the oceans to overcome, the current trend. The third one ends with a climax of warmth, or cold, that depletes the oceans ability to maintain the current trend, and a new one begins.
Is this really a proper analysis ?
You assume, there is a single variable relationship, which is, that sunspots control temperature. And that’s how you classifiy the signifcance of your p value.
However, in reality, there are multiple influences governing temperature. That correlation and that p value may then not suggest what you think.
For example, if it is assumed that sunspots (or better the AP index) on such timescales would be accountable for 30% of the temperature response, I would contest that this p value would “clearly show” the opposite.
Wouldn’t it be better to compute a regression with multiple variables ? But there are, as well, major difficulties, such as linear dependence of temperature response on each variables, independance of variables and completeness of the variable set.