Claim: How the IPCC arrived at climate sensitivity of about 3 deg C instead of 1.2 deg C.

This is not “How the IPCC arrived at climate sensitivity of about 3 deg C instead of 1.2 deg C.”
If you want to know how the IPCC arrived at climate sensitivity of about 3 deg C, I recommend reading the IPCC report: http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch8s8-6.html

The multi-decadal oscillation of the GMST has been described by Swanson et al [1]:
“Temperatures reached a relative maximum around 1940, cooled until the mid 1970s, and have warmed from that point to the present. Radiative forcings due to solar variations, volcanoes, and aerosols have often been invoked as explanations for this non-monotonic variation (4). However, it is possible that long-term natural variability, rooted in changes in the ocean circulation, underlies much of this variability over multiple decades (8–12).”
After removing the multi-decadal oscillation, Wu et al have reported their result for the long-term warming rate [2]:
“…the rapidity of the warming in the late twentieth century was a result of concurrence of a secular warming trend and the warming phase of a multidecadal (~65-year period) oscillatory variation and we estimated the contribution of the former to be about 0.08 deg C per decade since ~1980.”
This long-term warming rate result of 0.08 deg C/decade by Wu et al has been confirmed by Tung and Zhou [3]:
“The underlying net anthropogenic warming rate in the industrial era is found to have been steady since 1910 at 0.07–0.08 °C/decade, with superimposed AMO-related ups and downs that included the early 20th century warming, the cooling of the 1960s and 1970s, the accelerated warming of the 1980s and 1990s, and the recent slowing of the warming rates.”
References
[1] Swanson et al. (2009)
Long-term natural variability and 20th century climate change
http://www.pnas.org/content/106/38/16120.full.pdf+html
[2] Wu et al. (2011)
On the time-varying trend in global-mean surface temperature
http://bit.ly/10ry70o
[3] Tung and Zhou (2012)
Using data to attribute episodes of warming and cooling in instrumental records
http://www.pnas.org/content/110/6/2058

“Positive feedback of the climate is not supported by the data.” Which is why it has taken more than $100 billion in research grants, and over two decades of overt government pressure, to “balance” the bad data with the reluctant physical reality. OUR ‘outcome based education’ system has achieved the desired low information voter, with near total scientific illiteracy. OUR advocacy media has marketed the fable. OUR grant system has bribed the more than willing university system and sustainable product industry. OUR EPA extortion network has provided the muscle, combining to create the BIGGEST BUNCO OPERATION of all time. Thought far from the founders intentions, you must admit OUR crime syndicate government is the most elaborate in all history. Unfortunately for the public, you are not the beneficiaries of OUR racketeering.

Well, at least your heading in the right direction. Alert me when you get to climate sensitivity of zero and I’ll pay attention.

Baa Humbug says”Alert me when you get to climate sensitivity of zero and I’ll pay attention”
You may be interested in my extremely simplistic approach to this issue. Since no-one has measured a CO2 signal in any modern temperature/time graph, from standard singal to nosie ratio physics, there is a strong indication that the climate sensitivity of CO2 is indistinguishable from zero.

To: Kelvin Vaughan
OK, I read the 4th paragraph, which says:
“There is much debate worldwide over natural resource allocations, this is partly due to increasing scarcity (depletion of resources) but also because the exportation of natural resources is the basis for many economies (particularly for developed nations such as Australia).”
We sometimes forget that all of the world’s natural resources are controlled by national governments, except for privately owned mineral rights on privately owned land in the United States. Ironically, it is the individual owners of the small portion of private mineral interests in the United States who have turned the energy market upside down with fracked natural gas, drastically reducing energy costs for everyone on the globe.
If you want to change the way natural resources are used globally, look to leadership in national governments. If they are elected they have the power to do what you want, even though they would like for you to think it is private energy sector interests who to blame for everything. If they are not elected by you, you might want to think about changing your government to some form of elected representative democracy. The last place you want to look to for solutions is the United Nations and the IPCC, which is about as far as you can get from a democracy. Think of the UN as a World Chamber of Commerce, originally designed and still controlled by the world’s dictators and world’s financial elite — the 1% of the 1% of the 1%.

“To remove the warming rate due to the multidecadal oscillation of about 60 years cycle, least squares trend of 60 years period from 1945 to 2004 is calculated.”
Shouldn’t the trend over a complete 60 year cycle be zero?

Its pretty easy to see by looking at the two earlier rises (1850 – 1880) (1910 – 1945), and comparing them to the (1975 – 2000) rise, that the MOST you get is 1C per doubling. I don’t really like the analysis method presented here, though.

This is not how the IPCC arrived at 3C. They give the reasons on their website for anyone who wants to look them up.

Re Jim Cripwell — shouldn’t that be “singal to nosie raito” ??

Willis you beat me to it. Damn.

Willis, I think what he’s doing is assuming that the period of natural variation is (exactly) 60 years and thus by looking at (any) 60 period you should average out the cycle and get a more representative slope.
It’s a pretty crude way of fitting a 60y cosine + linear trend model to the data.
Crude but effective.
I would not say it was that rigorous and it has a lot of obvious weaknesses but it is a way to show what I have called “cosine warming”:
http://climategrog.wordpress.com/?attachment_id=209

According to Nicola Scafetta:
“The climate system is clearly characterized by a 60-year cycle. We have seen statistically compatible periods of cooling during 1880-1910, 1940-1970, 2000-(2030 ?) and warming during 1850-1880, 1910-1940, 1970-2000.”
[See HadCRUT4 Global monthly mean temperature anomalies 1850-2013.09 (°C) + linear trends + 13 months mean (WoodForTrees – Observatorio ARVAL)]:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1850/to:2012.84/plot/hadcrut3gl/from:1850/to:1880/trend/plot/hadcrut3gl/from:1880/to:1910/trend/plot/hadcrut3gl/from:1910/to:1940/trend/plot/hadcrut3gl/from:1940/to:1970/trend/plot/hadcrut3gl/from:1970/to:2001/trend/plot/hadcrut3gl/from:2001/to:2012.84/trend/plot/hadcrut3gl/mean:13

Looking at derivates of SST and co2 is much more informative.
http://climategrog.wordpress.com/?attachment_id=223
Most of the inter-annual changes is explained by temperature causing CO2 out-gassing , not CO2 changing temperature.
http://climategrog.wordpress.com/?attachment_id=233
The short term proportionality between temp and rate of change of SST shows 1 degree deviation from equilibrium conditions causes 8 ppm/year of CO2 out-gassing from the oceans.
It also shows that there is a very rapid response time that pretty much destroys the IPCC’s claim of 100-1000 year residency of anthropogenic CO2.

Girma,
In your third figure you display a straight line fit to curvy data; why not add the model fit on which you base your calculation, that is, the linear trend plus the 60 yr. oscillation?
Your title is misleading. What you have is a different estimate of climate sensitivity to CO2, not “how” the IPCC derived their estimate.

Another interesting feature of the rate of change plot is that d/dt( CO2 ) has a also taken a “pause” since 1997:
http://climategrog.wordpress.com/?attachment_id=223
ie CO2 is still rising but no longer accelerating. So if CO2 was driving temperature we would still be seeing rising temps.
On the other hand since is it rate of change of CO2 that is affected by temperature, this period confirms it is temp driving CO2 and not the opposite.
There is more to be gained from the long term relationship that shows a ratio of 4 rather than 8 between dT/dt and d2/dt2(CO2) .
That should tell us more about the proportion of emissions that do/don’t get absorbed and possible inverse effect on temps. This is probably a proportional+differential relationship that needs more thought and study.
I don’t think this is going to be explained by hacking about of WTF.org.

Don Easterbrook
I am not saying CO2 is causing the warming. I believe it is the warming that is causing the increase in CO2 concentration, as the vostok ice cores show. The CO2 concentration will drop when the temperature falls.

This is a fundamentally wrong calculation. You must take account of the thermal inertia. Otherwise, you largely underestimate the sensitivity. This is first year heat transfer physics by the way,
In addition, fitting a long period oscillation will remove some energy form the linear component even if the true periodic signal is zero because both function are not necessarily orthogonal on such period of time. Basic, Fourier analysis. First year again.

Monckton of Brenchley says: “This was one of the reasons why they sneered at Lindzen & Choi (2009, 2011), who studied sensitivity empirically over about 25 years.”
LC2011 looked at very short snippets of time within that period. That probably tells us something useful but should not be read as being representative of longer periods.
I think the method was rigorous but care is needed in interpreting what it shows.

http://www.woodfortrees.org/plot/hadsst2gl/from:1958/mean:12/mean:9/mean:7/derivative/normalise/plot/esrl-co2/derivative:0.003/derivative:-1.03/mean:12/mean:9/mean:7/normalise

The formula you used for CO2 ‘forcing’ yes. That is not how they calculate sensitivity. That comes from a number of sources mainly climate models. I don’t think they are right , just that it is incorrect to say what you presented is how they do it.
Though what you present is a fairly good caricature.
You have not commented on the fact that your method is still including “cosine warming”:
http://climategrog.wordpress.com/?attachment_id=209
If you go from peak to peak you may get what you were intending to get , and it will be a lot less.
That may be interesting.

Girma says:
My result is based the following climate pattern of the 20th century:
http://bit.ly/15FKX0n
What result does your calculation give for the previous cycle 1875-1940?

I’m with Girma. I don’t know why everyone else has got their panties in a bunch.
The Team thought that they had the whole “CO2 causes terrifying warming” thing sorted because the warming from about 1970 to about 2000 matched their calculations of sensitivity (assuming that temps would be flatlining if it wasn’t for the increase in CO2.)
But if you look at the whole pattern from about 1880, there’s a very obvious oscillation:
http://www.woodfortrees.org/plot/gistemp/plot/hadcrut4gl/from:1880
If you look at the overall trend then clearly the Team’s sensitivity sums were nonsense.

Like I said, crude but effective. If you can back it up by a more rigorous study that makes it more credible. That backs up you 0.08 K/decade.
IPCC also bring all sorts exaggerated volcanic cooling, parametrised water vapour feedbacks at stuff in as well and it all goes wrong.
You are assuming no such effects in the way you calculate your sensitivity. So they are not doing the same thing but ignoring the cyclic variation. Saying they do is inaccurate, as a few people have pointed out.
You also stated you think temp causes CO2 but carry on calculating CO2 causing temps.
Since the mechanism is very different and depends on d/dt CO2 these are not interchangeable.
I think you need to look at that.

Willis
I have not claimed a constant pattern before 1869. As the climate forcing changes, the pattern also changes.
If you include the time before 1869, the residual will have a trend.
Here is a simple fit to the 21-years and 63-years moving average since 1869 with R^2 = 0.998 and R^2 = 1, respectively.
http://orssengo.com/GlobalWarming/ClimateSensitivityOfOnePointThreeDegC.png
The above graph shows clearly the climate pattern since 1869.
144 years is sufficient to establish climate relationship during that time.

Girma , look at rate of change of hadcrut and best.
http://www.woodfortrees.org/plot/hadcrut3vgl/mean:252/mean:188/mean:141/derivative/plot/best/mean:252/mean:188/mean:141/derivative/plot/esrl-co2/scale:0.003/offset:-1.03/detrend:-0.22/from:1982/derivative/mean:12/mean:9/mean:7
The hadcrut that you chose to use has a acceleration that would not be explained by the ln(co2) formula.
You have to have a credible model for you data. What you are trying to do is fit a cos+linear and use linear for your sensitivity. There is a significant upward curve in your plot an that is the acceleration we see here. Your cos plus linear would have a level cosine as derivative
BEST show a deceleration and a strong 30 y as well a 60y then goes sky high (probably residual UHI)
The whole idea of linear CO2 AGW does not fit the data, not even with a 60 year cosine. I agree having one is whole lot better than not but the model is just wrong. No sense in trying attribution until there is a reasonable model.
Now have a look at CO2 (I’ve filtered out 12m seasonal). There’s a lot of action in there too and guess what … when you look it actually shows it’s caused by temperature and not the other way around.
You actually suggested this was the case in one of you comments but it’s not what your post is about. I again suggest you try to take a look.
http://climategrog.wordpress.com/?attachment_id=233

BTW, its not “converting” anything, it is three successive filters

Its the feedback assumptions/theories that take the 1.1C/1.2C to a total of 3.0C per doubling.
Here us a little table showing how it is actually calculated. The feedbacks are over 2.2 W/m2 per 1.0C increase in temperatures. 1.1C of doubled CO2/GHG causes a feedback increase of 8% in water vapor, a 2% decline in cloud optical thickness and reduced Albedo as ice melts. The first round of these feedbacks produces another increase in temperatures of 0.7C,
That 0.7C increase in temperature from feedbacks produces another round of feedbacks increasing the temperature another 0.45C, Then there is round #3 of feedbacks on the feedbacks so on and so on. Pretty soon, the tropopause has increased its forcing by 11.7 W/m2 and temps here are 3.0C higher. The theory assumes the surface will increase by something like the tropopause (although there is small increase in the lapse rate counted in the feedback numbers already).
http://s2.postimg.org/xkjw426dl/Stefan_Boltzmann_3_0_C_doubling.png
However, if the feedbacks are much less than this 2.2 W/m2/K, we get much less warming. And here is something rarely talked about, if the feedbacks are much more than this 2.2 W/m2/K, there is actually a runaway greenhouse effect. The actual feedbacks only need to be something like 3.0 W/m2/K and we are at 11C of warming per doubling. Bump them up to 4.0 W/m2/K or so and the oceans boil off eventually.
The feedbacks numbers have been carefully chosen to keep the numbers at 3.0C per doubling.
So far, water vapor is coming in less that 50% of that predicted, and cloud optical thickness is probably Zero as far as we can tell.
http://s13.postimg.org/7dk4nfh6f/Hadcrut4_vs_TCWV_Scatter.png
http://s9.postimg.org/y8o23z2rz/UAH_RSS_vs_TCWV_Scatter.png
Feedbacks strength versus how much warming we get.
http://s24.postimg.org/7jjj2kcgl/Feedback_Strength.png

Mike Jonas
Thank you for your reasoned comments.
Here is a published paper that arrive at recent warming rate of 0.08 deg C/decade:
“The underlying net anthropogenic warming rate in the industrial era is found to have been steady since 1910 at 0.07–0.08 °C/decade, with superimposed AMO-related ups and downs that included the early 20th century warming, the cooling of the 1960s and 1970s, the accelerated warming of the 1980s and 1990s, and the recent slowing of the warming rates.”
Tung and Zhou (2012)
Using data to attribute episodes of warming and cooling in instrumental records
http://www.pnas.org/content/110/6/2058

Willis, is Girma really your “friend”? If not, the repeated use of that title may be construed as somewhat condescending and, with your stature, that is really not necessary.

Mike sez: “His unspoken argument is that you have to take a temperature trend over complete cycle(s) in order to remove the cyclical effect”
Just a caution here; the Cosine Warming effect can occur with a single full cycle. It is mainly dependent on the starting and end points. If they aren’t the same, an apparent linear slope occurs.

Thanks Alexej . I tried googling his name some time last year a drew a blank.

Correlation is not causation.

@Greg Goodman
Would this help?
http://www.geocities.ws/girmao/resume.htm

Your end point is almost a decade old.
Determining climate sensitivity to a given forcing via observed temperature change over the past 60yrs is silly for obvious reasons.
You need to look at spectral dampening through the TOA boundary (since all radiative forcings are derived at the TOA). The data in that regard (CERES) makes clear the fact that there has indeed been a minor dampening in the CO2 spectrum, yet total OLWR has actually increased from 1979-present, by over 1W/m^2. It can be determined that at least 94% of the warming observed since 1979 was naturally forced via a slight reduction in low level cloud cover, of the cumulus/cumulonimbus type, and depleted upper tropospheric/stratospheric H2O/O^3, possibly stemming from a weakening magnetic field or increase in the ferocity of the solar wind up until solar cycle 23.

Willis
But no, now that I’ve shown that your “multidecadal oscillation” pattern doesn’t exist further back than 1869, now you are saying it’s just a temporary pattern, might disappear tomorrow … but if so, what is the justification for removing it?
The quality of the instrumental temperature records is poor before 1880s as stated by Phil Jones here:
Temperature data for the period 1860-1880 are more uncertain, because of sparser coverage, than for later periods in the 20th Century.
http://news.bbc.co.uk/2/hi/8511670.stm
However, here is a published paper that shows the multidecadal oscillation extends back to 1400 years:
A signature of persistent natural thermohaline circulation cycles in
observed climate
Knight et al.
Analyses of global climate from measurements dating
back to the nineteenth century show an ‘Atlantic
Multidecadal Oscillation’ (AMO) as a leading large-scale
pattern of multidecadal variability in surface temperature.
Yet it is not possible to determine whether these fluctuations
are genuinely oscillatory from the relatively short
observational record alone. Using a 1400 year climate
model calculation, we are able to simulate the observed
pattern and amplitude of the AMO. The results imply the
AMO is a genuine quasi-periodic cycle of internal climate
variability persisting for many centuries, and is related to
variability in the oceanic thermohaline circulation (THC).
This relationship suggests we can attempt to reconstruct
past THC changes, and we infer an increase in THC
strength over the last 25 years. Potential predictability
associated with the mode implies natural THC and
AMO decreases over the next few decades independent
of anthropogenic climate change.
….
The quasi-periodic nature of the model’s AMO
suggests that in the absence of external forcings at least,
there is some predictability of the THC, AMO and global
and Northern Hemisphere mean temperatures for several
decades into the future. We utilise this to forecast decreasing
THC strength in the next few decades. This natural
reduction would accelerate anticipated anthropogenic THC
weakening, and the associated AMO change would partially
offset expected Northern Hemisphere warming. This effect
needs to be taken into account in producing more realistic
predictions of future climate change.

Maybe I just misunderstood something in the math here, but something seems amiss:
The 5.1 in the climate-sensitivity calculation comes from using CO2 concentrations from 1974 and 2004, but it is used with the temperature change from 1945 to 2004 at the bottom. When looking at the response of temperature to CO2, shouldn’t the changes in each over the same time-period be used?

Stephen
When looking at the response of temperature to CO2, shouldn’t the changes in each over the same time-period be used?
The problem is the CO2 concentration is a smooth monotonic curve. However, the Annual GMST data is not. It has a clear oscillation of about 60 years. To remove this oscillation, you need to smooth the Annual GMST curve with longer period of about 60 years. Here is the annual global mean temperature smoothed with a 63-years moving average showing the pattern.
http://bit.ly/1109qyb
This graph shows the warming based on the 30-years least squares trend of 0.6 deg C is greater than that based on the long-term trend of 0.24 deg C.
My main point is that the long-term trend should be used to estimate climate sensitivity, not the 30-years least squares trend.

“My main point is that the long-term trend should be used to estimate climate sensitivity, not the 30-years least squares trend.”
That is a very valid point.
Now if you want tp propose a cos+linear model, why not fit one directly using OLS, rather than doing a questionable filter?

Greg
I agree the method is an approximate one, but I have checked it agrees with complicated published results.
Here is my argument.
I have shown below how the 60-years least squares trend relates to the long-term secular GMST. It shows the 60-years least squares trend is tangent to the secular GMST.
http://bit.ly/110bM02
As a result, the change in temperature from 1974 to 2004 may be estimated from the 0.08 deg C/decade warming rate of the 60-years least squares trend.

before calling CO2 “monotonic” you ought to have a look at:
http://climategrog.wordpress.com/?attachment_id=233

UPDATE
To respond to the comments, I have included the following graph
http://bit.ly/16GPCm3
I have got a better estimate of the warming of the long-term smoothed GMST using a least squares trend from 1949 to 2005 as shown in the above graph, which shows the least squares trend coincides with the Secular GMST curve for the period from 1974 to 2005. For this case, the warming rate of the least squares trend for the period from 1949 to 2005 is 0.09 deg C/decade.
This gives dT = 0.09 * 3 = 0.27 deg C, and the improved climate sensitivity estimate is
CS = 5.1*0.27 = 1.4 deg C.
That is an increase in Secular GMST of 1.4 deg C for doubling of CO2 based on the instrumental records.

Greg
With that kind of baggage you should be able to fit a simple model without relying upon trivial detrending and runny mean filters available at WTF.org.
I want others to do it for themselves with available data and software online.
How many can determine for themselves a climate sensitivity of 1.4 deg C from the data and software easily available online as described in my essay?

“I want others to do it for themselves with available data and software online.”
Well that would be nice. But that is really not an acceptable excuse for not doing it properly. WTF.org is so basic you just can’t do this sort of analysis. Maybe there are better online tools or you could use a spreadsheet.
You’re taking the log of two points on the CO2 curve , that gives you the increased “forcing” at the end of the period. You seem to be applying that across the whole period.
What you need is the integral of the instantaneous forcing over the period. It’s not correct.

“I have done the analysis as you suggested. ”
Well you haven’t. But it’s not my job to try and force you to do something you don’t want or are not capable of doing.
I’m tired. Bedtime.

Wllis
The relationship between temperature and and CO2 is between the Secular GMST (the monotonously increasing long-term GMST that has similar shape as the CO2 concentration and the sea level rise) and the CO2 concentration. The multidecadal oscillation of about 63 deg C period should not be considered as they are transient.
Willis, plotting the Secular GMST and ln (CO2) gives you a linear relationship given by
T = 1.871*ln(CO2/320.09)
That is the relationship between HadCRUT4 and the Mauna Loa datasets. T is the simple fit to the 63-years moving average of the annual GMST and CO2 is the annual CO2 concentration.
Please try it and see if it works. It has worked for me.
The equation for T since 1869 is
T = 0.5*t1*(year-1895)^2 + t2*(year-1895) + t3
where
t1 = 5.477*10^(-5) deg C/year^2
t2 = 2.990*10^(-3) deg C/year
t3 = -0.344 deg C
Here is the graph for the relationship between the model and the annual GMST:
http://orssengo.com/GlobalWarming/GmstPatternOf20thCentury.png

stacase
The annual CO2 is related to the 63-years moving average GMST with an R^2 = 0.99
Here is the correlation:
http://www.woodfortrees.org/plot/hadcrut4gl/mean:732/from:1901/normalise/plot/esrl-co2/compress:12/normalise/offset:0.615/detrend:-0.125

“””””…..IPCC:
“Since IPCC’s first report in 1990, assessed projections have suggested global average temperature increases between about 0.15°C and 0.3°C per decade for 1990 to 2005. This can now be compared with observed values of about 0.2°C per decade, strengthening confidence in near-term projections.”……””””””
So how does that jibe with the CRU public declaration that there has been NO statistically significant warming in the last 17 (now 18 ) years.
So if there was all of 0.6 deg. C rise since 1974, it must all have happened before 1995, because there has been none since.
Why do people keep on insisting on a link to CO2, when the data (real observed measurements) show there is no cause/effect connection (either way) whatsoever..
Keeping on repeating that old mantra under the authority of a PhD shingle doesn’t make it any more believable. The data tells the story; CO2 and Temperature can and do move in either the same or opposite directions, or both, and there is no link.

“””””……Girma says:
May 18, 2013 at 7:50 pm
Greg
I have done the analysis as you suggested. Here is my final result, which shows the linear relationship between Secular GMST (after the multidecdal oscillation has been removed) and the logarithm of the CO2 concentration.
http://orssengo.com/GlobalWarming/ClimateSensitivityOfOnePointThreeDegC.png
From the above graph, the slope of the linear relationship for the period from 1974 to 2004 is
k = dT/(ln C2 – ln C1) = (0.31 – 0.06)/(ln 377 – ln 331) = 0.25 / ln(377/331) = 0.25 / 0.130 = 1.923
From k = dT/(ln(C2/C1)), for doubling of CO2 we have
k = CS/ln 2
Therefore CS = k * ln 2 = 1.923 * ln 2 = 1.923 * 0.693 = 1.3 deg C for doubling of CO2…..”””””
So Girma, have you tried to fit the data, since say IGY in 1957/8 into a mathematical relationship of the form:-
y = mx + c, where y and x represent mean global surface Temperature, and Mauna Loa atmospheric CO2 abundance, either in that order, or in the reverse order (flip T and CO2), and show that it is any less likely to be true that your logarithmic form. For that matter, have you tried to fit the T and CO2 data to a formula of the form:-
(CO2)2 – (CO2)1 = a log (T2/T1) and shown it is any less plausible than your formula.
And for the piece de resistance try fitting the GMST and MLCO2 to an equation of the form:-
y = a.exp (-1/mx^2) +y0 since 1957/8, and of course x and y represent GMST and MLCO2 in either order (or both).
And show that is any less plausible than your logarithmic relationship.
remember only real measured data between 1957 and 2013; no computer simulated estimates, running averages, filtration residues or any such crap.; just the actual data.
For a bonus, try fitting either GMST or CO2, or both since 1957, to the average Telephone number in the Manhattan Telephone directories published between 1957, and 2013.

Don Easterbrook says:
May 18, 2013 at 6:51 am
“…the notion that temperature is a function of CO2 is invalided [sic] until you first show a cause-and-effect relationship between the two!”
The cause/effect of CO2 and temperature has been known for 150 years. What is it about variation around a secular trend that seems to elude you? JP

@ Greg Goodman on May 18, 2013 at 3:56 pm:
Okay, you’re agreeing you screwed up on the “doubled differential” part. That’s a start. Now to the rest.

You argue from ignorance . Go and inform yourself about filter design ,frequency response, and phase distortion. Look at the response of a single running mean, a gaussian and the triple running mean.

From A Handbook of Numerical and Statistical Techniques: With examples mainly from the life sciences by J.H. Pollard, 1977, starting at pg 26, Simple methods of smoothing crude data:

There is a difficulty, however: as well as smoothing a set of data, running averages distort values that are already smooth. (…)
…
We therefore note the following three points concerning running averages:
1. They reduce irregularities and fluctuations.
2. They distort values already smooth.
3. They do not provide values at the beginning and end of a table.
The smoothed values in fig. 4.2.1 can be made even smoother by repeating the running-average process, but further values will be lost from both ends of the table and the danger of distortion becomes greater.

From Leif Svalgaard to Willis Eschenbach to Roy Spencer, the dangers of repeated averaging are known and warned against. I have known such for a long time. You have already smoothed the data once, and want to do it again, twice. I will need better justification than “You’re stupid, shut up until you learn something.”

Then you may be able to explain to me why the running mean gets nearly all the peaks and troughs perfectly upside down in the RSS data that I posted earlier and that you chose to ignore.

I did not ignore it, but you are speaking from arrogance thus it is understandable you will make all sorts of assumptions about me, that will invariably be in whatever direction further inflates your self-aggrandizement.
Let’s look at running means in smaller bites:
http://www.woodfortrees.org/plot/rss/from:1980/plot/rss/from:1980/mean:15/plot/rss/from:1980/mean:30/plot/rss/from:1980/mean:45/plot/rss/from:1980/mean:60/plot/rss/from:1980/mean:30/mean:22/mean:17
We’ll get rid of the noisy main signal for clarity, the 15-mo running mean shows its shape well enough:
http://www.woodfortrees.org/plot/rss/from:1980/mean:15/plot/rss/from:1980/mean:30/plot/rss/from:1980/mean:45/plot/rss/from:1980/mean:60/plot/rss/from:1980/mean:30/mean:22/mean:17
Your wonderful “triple filter” hugs no better than a simple 30-mo running average, said value being the largest of the ones you specified, while it loses much detail, for no benefit.
As seen in the 15-mo running average, there appears to be an underlying pattern of about five years, often just shorter. Thus 60-mo is a bad choice for a running average, as for example at the trough at approx. 1989.5, it’s averaging the 1987 and 1992 peaks.
Thus the usual sage advice is to only use a running average big enough to “smooth” noisy graph data, as for visual clarity, but stop before you introduce artifacts.
You are trumpeting the marvels of your miraculous “triple filter”, using that graph you said I ignored, when all you’ve basically shown is it’s better to stop at a 30 month running average than to enlarge to 60 months.

Once you have learnt enough to understand that, come back and admit you were talking rather too loudly about something of which you have little understanding or knowledge.

I must assume you are very new to this site, to think a blanket appeal to your authority should mean anything. Especially with the obvious errors you’ve made.
There are authorities on this site I do respect, because they do not have your “shut up and accept my authority” attitude, and are willing to explain things. They have warned against repeated averages as you are advocating, as I have been warned decades ago. You are discussing “filter design ,frequency response, and phase distortion” as if fixed frequencies were involved. Willis Eschenbach warned us about people like you.
If you can get one of them to sign off on your particular number-mangling, I will consider it to have worth, within its obvious demonstrated limited usefulness. But your appeal to your own authority? Your “Cease your ignorant whining, learn something elsewhere, then come apologize to me!” attitude? This site wouldn’t exist if Anthony Watts had accepted that. Neither would Climate Audit, or Jo Nova’s site, or many others.
Climate skepticism wouldn’t have gotten this far if we had adopted the attitude of the opposition as you have done, assuming you are “on our side”. And from the obvious mistakes you are making, I’ll need more assurances that you’re an authority, on much of anything, than your imperious decree.

Whoops, I said on May 19, 2013 at 1:39 am:

Okay, you’re agreeing you screwed up on the “doubled differential” part.

I pulled the same mistake as you, that should be “doubled derivative“, not “differential”. Sorry about that, Greg!

Trying to fit a linear change or a cyclic variation/oscillation to a chaotic system is a nonsense. There may be brief periods where there is ‘a fit’ then the system will change due multiple unknown non-linear non-cyclic interactions and the pattern will not exist any more as expected. Just because we humans like to see a simple pattern doesn’t mean that there is one. Even doing Fourier analyses is just obfuscating the concept that there must be standard repeating patterns that make up the apparent random noise – well you may find some but they will be dependent on the algorithm used and the end points they won’t describe the chaotic system because by definition they expect repeating patterns at various scales from a chaotic system.

IIan W says:
Trying to fit a linear change or a cyclic variation/oscillation to a chaotic system is a nonsense. There may be brief periods where there is ‘a fit’ then the system will change due multiple unknown non-linear non-cyclic interactions and the pattern will not exist any more as expected. Just because we humans like to see a simple pattern doesn’t mean that there is one. Even doing Fourier analyses is just obfuscating the concept that there must be standard repeating patterns that make up the apparent random noise – well you may find some but they will be dependent on the algorithm used and the end points they won’t describe the chaotic system because by definition they expect repeating patterns at various scales from a chaotic system.
===
What you caution is valid as a caution but it is incorrect to go to the other extreme and say that any order or pattern in a chaotic system is illusionary.
Calling a system chaotic is simply a statement of the lack of understand we have of how it works.
That is not a reason for not looking or calling everything “stochastic”
The climate system has many physical feedbacks which will lend to the creation of possible oscillatory behaviour. There are also many periodic drivers of which the daily and annual changes are the most obvious.

Girma says “Willis, plotting the Secular GMST and ln (CO2) gives you a linear relationship given by T = 1.871*ln(CO2/320.09) ”
No, plotting does not give any relationship. What does that is doing a linear regression. Here you have done a linear regression of T on log of co2 ratio.
The thing is that log CO2 represents an additional “forcing” (W/m2) , that will produce a rate of change of temperature (temperature is a measure of energy not power !)
That is why , yesterday, I said you need to integrate. Then you need to account for how much of today’s dT is because of feedbacks to yesterday CO2 etc, etc.
Sorry, if it was as trivial as you are trying to make it , it would have been solved in the 19th century on the back of an envelop.

Surely the best evidence we have comes from the ice cores, which record the temperature and CO2 cycles over nearly the last million years.
As far as I’m aware, the ice cores show no trace of CO2 changes causing corresponding temperature changes. But they consistently show that CO2 changes are driven by temperature changes.
If so, then it strongly suggests that the warming effect of CO2 is close to zero. As the greenhouse effect does – presumably – work in the laboratory, then it also suggests that strong negative feedbacks are dominant in the climate system.
Chris

richard verney says: It is simply impossible to determine a value for climate sensitivity from observation data until absolutely everything is known and understood about natural variation, what its various constituent components are, the forcings of each and every individual component and whether the individual component concerned operates positively or negatively, and the upper and lower bounds of the forcings associated with each and every one of its constituent components.
===
Interesting thoughts. That is certainly true if you hit everything with a 60 filter.
However, I do not think it is as black and white as that. It must be possible to rule out certain extreme values. From there the task is to narrow it down by closer inspection. Until we have a much better understanding that will likely remain very large.
Geologically CO2 has always lagged, this does preclude it acting as a positive feedback. This could be one reason climate tends to flip between glacial and interglacial states.
On a short time-scale temperature seems to determine atmospheric CO2 by out-gassing from oceans:
http://climategrog.wordpress.com/?attachment_id=233
The key question remains at the interdecadal scale and that is where data reliability and politically motived adjustments make an honest scientific enquiry a lot more difficult.

Greg
I have reproduced your result:
http://www.woodfortrees.org/plot/rss/compress:12/derivative/normalise/plot/esrl-co2/compress:12/derivative/derivative/from:1979/normalise
Excellent correlation between global mean temperature and CO2 concentration.
Amazing.
But they are telling us the increase is due to human emission of CO2.
What a ……?

Girma says:
May 19, 2013 at 6:52 am
Greg
I have reproduced your result:
http://www.woodfortrees.org/plot/rss/compress:12/derivative/normalise/plot/esrl-co2/compress:12/derivative/derivative/from:1979/normalise
Excellent correlation between global mean temperature and CO2 concentration.
Amazing.
But they are telling us the increase is due to human emission of CO2.
What a ……?

Perhaps it is a good empirical proof of Henry’s Law.

The finite, positive dT/dt means something is warming the ocean system and causing it to be generally slightly ahead of equilibrium at least during this warming segment.

http://climategrog.wordpress.com/?attachment_id=254
Same data with four year low pass filter applied.
Here the decadal scale variations show CO2 acceleration lagging rate of change of temperature .
The lag is larger at the beginning and end of the record. Varying between about 0.5 years around 1990-95 to >1.5 years at the end.

Greg Goodman at May 19, 2013 at 8:38 am
Around 1990 the graph reveals a dramatic negative acceleration in CO2 and a corresponding negative SST trend. I would have expected to see this dip in 1992 due to the Pinatubo eruption. Does the smoothing cause a shift in dating?
Also – and it may be only me – but I strongly associate CO2 with green and temp with red. For a time, the graph’s reversed expected colors were somewhat disconcerting, a mental illusion which flipped the meaning of the colors back and forth between actual and conditioned.

blueice2hotsea
No the filter will not introduce a spurious shift, it is centred correctly. There will be a slight spreading in width of the peak. However, bear in mind that rate of change drops before the actual temperature.
Max negative rate of change is about when temp goes through zero on the way down.
Having said that even dT/dt can’t drop before the eruption, if is the cause. This shows there’s no significant drop from El Chicon or Mt Pinatubo. Nothing that stands out from the usual ups and downs.
However, there is a very noticeable negative acceleration in CO2 at about the right time for Mt Agung around 1963/64. This stands out as one of the few deviations of the two datasets. A paper was published looking at that in detail which concluded the drop in CO2 could probably be attributed to the eruption.
That is also an explicit recognition of the effect of sea temperature on atmospheric CO2.
CO2 is colourless and temps are only red in alarmist literature. Seems they’ve got you trained with that preconception. 😉
If I had thought about attributing colours, I would have made the sea blue.

Greg Goodman –
When I attempt a similar graph as yours, there is a sharp inflection at 1992. This does not square with 1990 in your graph.
Is there something wrong at WFT? Or what am I doing wrong? I must say that the closer alignment with Pinatubo event is interesting.

“Did we have some global reduction of CO2 leading into the LIA? I don’t think we did.”
We have trouble even getting it to exist in temperature by the time they’ve finished massaging the data but it would be worth looking at. I don’t see why the laws of physics would not have applied during that period.

Greg Goodman
May 19, 2013 at 12:47 pm
Why would I use annual means as data points? Because it doesn’t shift the graph by two years and it saves me pushing all those buttons for a three year running mean.
So I re-did the graph anyway with triple RM and guess what? It looks like this. It still has those 1990 peaks 1992 dips.
I would like to take your word for it that something big happened in 1990 and Pinatubo was inconsequential. But I still need more.

“Because it doesn’t shift the graph by two years ” Well it probably logs it at 1990 instead of 1990.5 so you have 6m shift. Not sure why you are taking about a 2 year shift.
http://climategrog.wordpress.com/?attachment_id=233
I really don’t see huge difference between what you reproduced at WTF.org and my plot , except that you don’t have ICOADS and hadSST2 seems to be offset slightly lower.
“I would like to take your word for it that something big happened in 1990 and Pinatubo was inconsequential. But I still need more.”
Err, what I said was:
This shows there’s no significant drop from El Chicon or Mt Pinatubo. Nothing that stands out from the usual ups and downs.
I’ve just zoomed in on my plot and CO2 drop starts at 1990.55 ICOADS SST is somewhat smoother in form but seems about the same.
PinaTubo was June 1991
The following dip is remarkable only being smaller that average in the record. HadSST2 goes a bit deeper but still nothing more than average.
I’m not sure what you are seeing that you think I am missing.
I would invite you to take Willis’ volcano test. Imagine someone gave you that data and asked you to point out the when a major VE happened. Would you be pointing to 1991 ??

Greg Goodman
Yes. Pinatubo erupted 1991.5. However, your graph dips dramatically in 1989. I am unable to re-produce that precursor dip at WFT. Can you?
re Willis’ volcano test. If the volcanic event released significant SO2 I might look for a strong negative acceleration in temperature. Like this
Note the two strongest candidates are the trenches which occur in 1982 & 1992 – years which are coincident with El Chichón and Pinatubo. respectively. I may 1991 or 1992. Close enough, there is a lag.

blueice2hotsea, GIStemp does seem to dip later and deeper than SST.and CRUtem3
Do the same thing with CRUtem3 and there’s nothing for Mt P and the 1980 dip clearly precedes the eruption.
http://www.woodfortrees.org/plot/crutem3vsh/mean:12/mean:9/mean:7/from:1978/derivative/normalise
Since GISS have made several retrospective adjustments to their data and Hansen also exaggerates volcanic cooling estimations it may well be interesting to look into why.

Girma: What counts is the analysis gives you an EXCELLENT description of the observed data as shown
Yes cos+quadratic does give a reasonably good fit to that part of the data. That’s why I suggested it. However threre is also circa 21y and 11y that will affect the residual you are fitting to CO2 as well as a 160 that hadley processing removes.
http://judithcurry.com/2012/03/15/on-the-adjustments-to-the-hadsst3-data-set-2/
I don’t follow what you are actually fitting to what but it seems you are now comparing the quadratic to linear ( a two point approximation to ln CO2 )
As I pointed out above ln CO2 is an additional radiative forcing , therefore you need to integrate it (or differentiate you T model to get dT/dt) . Since ln CO2 is almost a straight line,it’s integral will be a quadratic. At least you will have something similar to fit.
I’m not endorsing that as a correct evaluation of CS but if you want to go for a direct attribution as you intended in this exercise, that would seem to the appropriate way to do it.
What you are currently doing does not make sense unless you assume that the climate system and the oceans are adjusting almost instantly to the new “forcing” and that dT is the change in equilibrium state. I don’t think you’ll get many backers for that idea on either side of the debate.
The simplest way would be to fit cos+lin to dT/dt and then regress that with ln CO2.
To do it accurately you should do the cos+lin OLS fit to unfiltered data.
That will affect your CS but I can’t guess in which direction.

Willis: “In addition, we have evidence that the climate models, whose programmers do think it is a boundary problem, can’t predict 20 years out, they’ve been quite bad at projecting the future ever since climate stopped warming. This, of course, is because they are merely incrementing machines, reading the inputs (forcings) and doing a linear transform with a lag … and as a result, none of them predicted the current hiatus in the warming.”
I think the failure of current models due to preconceived ideas being allowed to affect the model not the modelling process itself.
Volcanic effects have been exaggerated , as I’ve discussed above and you have said many times there is little evidence that volcanic effect is anything near what the models produce. Either the volcanic input is wrong or the models fail to reproduce climate _insensitivity_ to changes in radiative forcing.
This allows them to put in hypothetical +ve feedbacks to CO2 and it ends up as garbage.
I don’t think the problem in inherent in the modelling process but more to do with personal biases (group thinking) being allow rig the model to produce “expected” outcomes.

Thanks Willis for your response.

I agree with Don Easterbrook’s comments . One cannot make a years forecast based on the trend of the summer months only . Similarly one cannot make a century forecast by only looking at the last 20-30 warm years of a climate cycle that may be 100-120 years long. There are 60 year climate cycles and 100-120 cycles . An analysis of the CET records going back to 1538 by Tony Brown in a previous track showed regular temperature dips every 100-120 years . We are into similar dips like we had 1890,1780, 1670 and 1560. Don has been right all along predicting the global temperatures to drop because he looked at longer term cycles that are apparent in the ice core records .If we want to predict 100 year ahead, we need to look at least 100 years back too. Natural variables that shape these longer term cycles clearly seem to override the effects of CO2

Willis: Yes, you’re 100% right, my error. I made the assumption that since one of them was invertable and one was not, that the order was important … but it’s not, the results are identical.
Your comment was right in suggesting filtering is best done last. Good principal. Just in this case it did not matter.
Interestingly, both the first difference ( which is a trivial kernel convolution ) and a gaussian can be done in one hit. Just use the analytical derivative of the gaussian to make the weighting kernel.
The two point difference is an approximation to the the real derivative of the data. By doing an analytical diff of the gaussian the operation is the same as doing a gaussian on the true diff of the data not the two point approx.
Yeah, I know , I was a sceptical as hell when I first read it but you can check it out. It’s a neat technique.
How much difference it make will depend up on the nature of the data.