AGW Bombshell? A new paper shows statistical tests for global warming fails to find statistically significant anthropogenic forcing

graphic_esd_cover_homepage[1]From the journal Earth System Dynamics billed as “An Interactive Open Access Journal of the European Geosciences Union” comes this paper which suggests that the posited AGW forcing effects simply isn’t statistically significant in the observations, but other natural forcings are.

“…We show that although these anthropogenic forcings share a common stochastic trend, this trend is empirically independent of the stochastic trend in temperature and solar irradiance. Therefore, greenhouse gas forcing, aerosols, solar irradiance and global temperature are not polynomially cointegrated. This implies that recent global warming is not statistically significantly related to anthropogenic forcing. On the other hand, we find that greenhouse gas forcing might have had a temporary effect on global temperature.”

This is a most interesting paper, and potentially a bombshell, because they have taken virtually all of the significant observational datasets (including GISS and BEST) along with solar irradiance from Lean and Rind, and CO2, CH4, N2O, aerosols, and even water vapor data and put them all to statistical tests (including Lucia’s favorite, the unit root test) against forcing equations. Amazingly, it seems that they have almost entirely ruled out anthropogenic forcing in the observational data, but allowing for the possibility they could be wrong, say:

“…our rejection of AGW is not absolute; it might be a false positive, and we cannot rule out the possibility that recent global warming has an anthropogenic footprint. However, this possibility is very small, and is not statistically significant at conventional levels.”

I expect folks like Tamino (aka Grant Foster) and other hotheaded statistics wonks will begin an attack on why their premise and tests are no good, but at the same time I look for other less biased stats folks to weigh in and see how well it holds up. My sense of this is that the authors of Beenstock et al have done a pretty good job of ruling out ways they may have fooled themselves. My thanks to Andre Bijkerk and Joanna Ballard for bringing this paper to my attention on Facebook.

The abstract and excerpts from the paper, along with link to the full PDF follows.

Polynomial cointegration tests of anthropogenic impact on global warming

M. Beenstock1, Y. Reingewertz1, and N. Paldor2

1Department of Economics, the Hebrew University of Jerusalem, Mount Scopus Campus, Jerusalem, Israel

2Fredy and Nadine Institute of Earth Sciences, the Hebrew University of Jerusalem, Edmond J. Safra campus, Givat Ram, Jerusalem, Israel

 Abstract. 

We use statistical methods for nonstationary time series to test the anthropogenic interpretation of global warming (AGW), according to which an increase in atmospheric greenhouse gas concentrations raised global temperature in the 20th century. Specifically, the methodology of polynomial cointegration is used to test AGW since during the observation period (1880–2007) global temperature and solar irradiance are stationary in 1st differences whereas greenhouse gases and aerosol forcings are stationary in 2nd differences. We show that although these anthropogenic forcings share a common stochastic trend, this trend is empirically independent of the stochastic trend in temperature and solar irradiance. Therefore, greenhouse gas forcing, aerosols, solar irradiance and global temperature are not polynomially cointegrated. This implies that recent global warming is not statistically significantly related to anthropogenic forcing. On the other hand, we find that greenhouse gas forcing might have had a temporary effect on global temperature.

Introduction

Considering the complexity and variety of the processes that affect Earth’s climate, it is not surprising that a completely satisfactory and accepted account of all the changes that oc- curred in the last century (e.g. temperature changes in the vast area of the Tropics, the balance of CO2 input into the atmosphere, changes in aerosol concentration and size and changes in solar radiation) has yet to be reached (IPCC, AR4, 2007). Of particular interest to the present study are those  processes involved in the greenhouse effect, whereby some of the longwave radiation emitted by Earth is re-absorbed by some of the molecules that make up the atmosphere, such as (in decreasing order of importance): water vapor, car- bon dioxide, methane and nitrous oxide (IPCC, 2007). Even though the most important greenhouse gas is water vapor, the dynamics of its flux in and out of the atmosphere by evaporation, condensation and subsequent precipitation are not understood well enough to be explicitly and exactly quantified. While much of the scientific research into the causes of global warming has been carried out using calibrated gen- eral circulation models (GCMs), since 1997 a new branch of scientific inquiry has developed in which observations of climate change are tested statistically by the method of cointegration (Kaufmann and Stern, 1997, 2002; Stern and Kauf- mann, 1999, 2000; Kaufmann et al., 2006a,b; Liu and Ro- driguez, 2005; Mills, 2009). The method of cointegration, developed in the closing decades of the 20th century, is intended to test for the spurious regression phenomena in non-stationary time series (Phillips, 1986; Engle and Granger, 1987). Non-stationarity arises when the sample moments of a time series (mean, variance, covariance) depend on time. Regression relationships are spurious1 when unrelated non- stationary time series appear to be significantly correlated be- cause they happen to have time trends.

The method of cointegration has been successful in detecting spurious relationships in economic time series data.

Indeed, cointegration has become the standard econometric tool for testing hypotheses with nonstationary data (Maddala, 2001; Greene, 2012). As noted, climatologists too have used cointegration to analyse nonstationary climate data (Kauf- mann and Stern, 1997). Cointegration theory is based on the simple notion that time series might be highly correlated even though there is no causal relation between them. For the relation to be genuine, the residuals from a regression between these time series must be stationary, in which case the time series are “cointegrated”. Since stationary residuals mean- revert to zero, there must be a genuine long-term relationship between the series, which move together over time because they share a common trend. If on the other hand, the resid- uals are nonstationary, the residuals do not mean-revert to zero, the time series do not share a common trend, and the relationship between them is spurious because the time series are not cointegrated. Indeed, the R2 from a regression between nonstationary time series may be as high as 0.99, yet the relation may nonetheless be spurious.

The method of cointegration originally developed by En- gle and Granger (1987) assumes that the nonstationary data are stationary in changes, or first-differences. For example, temperature might be increasing over time, and is there- fore nonstationary, but the change in temperature is station- ary. In the 1990s cointegration theory was extended to the case in which some of the variables have to be differenced twice (i.e. the time series of the change in the change) be- fore they become stationary. This extension is commonly known as polynomial cointegration. Previous analyses of the non-stationarity of climatic time series (e.g. Kaufmann and Stern, 2002; Kaufmann et al., 2006a; Stern and Kaufmann, 1999) have demonstrated that global temperature and solar irradiance are stationary in first differences, whereas green- house gases (GHG, hereafter) are stationary in second differ- ences. In the present study we apply the method of polyno- mial cointegration to test the hypothesis that global warming since 1850 was caused by various anthropogenic phenom- ena. Our results show that GHG forcings and other anthropogenic phenomena do not polynomially cointegrate with global temperature and solar irradiance. Therefore, despite the high correlation between anthropogenic forcings, solar irradiance and global temperature, AGW is not statistically significant. The perceived statistical relation between tem- perature and anthropogenic forcings is therefore a spurious regression phenomenon.

Data and methods

We use annual data (1850–2007) on greenhouse gas (CO2, CH4 and N2O) concentrations and forcings, as well as on forcings for aerosols (black carbon, reflective tropospheric aerosols). We also use annual data (1880–2007) on solar irradiance, water vapor (1880–2003) and global mean tem- perature (sea and land combined 1880–2007). These widely used secondary data are obtained from NASA-GISS (Hansen et al., 1999, 2001). Details of these data may be found in the Data Appendix.

We carry out robustness checks using new reconstructions for solar irradiance from Lean and Rind (2009), for globally averaged temperature from Mann et al. (2008) and for global land surface temperature (1850–2007) from the Berkeley Earth Surface Temperature Study.

Key time series are shown in Fig. 1 where panels a and b show the radiative forcings for three major GHGs, while panel c shows solar irradiance and global temperature. All these variables display positive time trends. However, the time trends in panels a and b appear more nonlinear than their counterparts in panel c. Indeed, statistical tests reported be- low reveal that the trends in panel c are linear, whereas the trends in panels a and b are quadratic. The trend in solar irradiance weakened since 1970, while the trend in temperature weakened temporarily in the 1950s and 1960s.

The statistical analysis of nonstationary time series, such as those in Fig. 1, has two natural stages. The first consists of unit root tests in which the data are classified by their order and type of nonstationarity. If the data are nonstationary, sample moments such as means, variances and co- variances depend upon when the data are sampled, in which event least squares and maximum likelihood estimates of parameters may be spurious. In the second stage, these nonstationary data are used to test hypotheses using the method of cointegration, which is designed to distinguish between genuine and spurious relationships between time series. Since these methods may be unfamiliar to readers of Earth System Dynamics, we provide an overview of key concepts and tests.

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Fig. 1. Time series of the changes that occurred in several variables that affect or represent climate changes during the 20th century. a) Radiative forcings (rf, in units of W m−2) during 1880 to 2007 of CH4 (methane) and CO2 (carbon dioxide); (b) same period as in panel a but for Nitrous-Oxide (N2O); (c) solar irradiance (left ordinate, units of W m−2) and annual global temperature (right ordinate, units of ◦C) during 1880–2003.

[…]

3 Results

3.1 Time series properties of the data

Informal inspection of Fig. 1 suggests that the time series properties of greenhouse gas forcings (panels a and b) are visibly different to those for temperature and solar irradiance (panel c). In panels a and b there is evidence of acceleration, whereas in panel c the two time series appear more stable. In Fig. 2 we plot rfCO2 in first differences, which confirms by eye that rfCO2 is not I (1), particularly since 1940. Similar figures are available for other greenhouse gas forcings. In this section we establish the important result that whereas the first differences of temperature and solar irradiance are trend free, the first differences of the greenhouse gas forcings are not. This is consistent with our central claim that anthropogenic forcings are I (2), whereas temperature and solar irradiance are I (1).

image

Fig. 2. Time series of the first differences of rfCO2.

What we see informally is born out by the formal statistical tests for the variables in Table 1.

image

Although the KPSS and DF-type statistics (ADF, PP and DF-GLS) test different null hypotheses, we successively increase d until they concur. If they concur when d = 1, we classify the variable as I (1), or difference stationary. For the anthropogenic variables concurrence occurs when d = 2. Since the DF-type tests and the KPSS tests reject that these variables are I (1) but do not reject that they are I (2), there is no dilemma here. Matters might have been different if according to the DF-type tests these anthropogenic variables are I (1) but according to KPSS they are I (2).

The required number of augmentations for ADF is moot. The frequently used Schwert criterion uses a standard formula based solely on the number of observations, which is inefficient because it may waste degrees of freedom. As mentioned, we prefer instead to augment the ADF test until its residuals become serially independent according to a la- grange multiplier (LM) test. In most cases 4 augmentations are needed, however, in the cases of rfCO2, rfN2O and stratospheric H2O 8 augmentations are needed. In any case, the classification is robust with respect to augmentations in the range of 2–10. Therefore, we do not think that the number of augmentations affects our classifications. The KPSS and Phillips–Perron statistics use the standard nonparametric Newey-West criteria for calculating robust standard errors. In practice we find that these statistics use about 4 autocorrelations, which is similar to our LM procedure for determining the number of augmentations for ADF.

[…]

Discussion

We have shown that anthropogenic forcings do not polynomially cointegrate with global temperature and solar irradiance. Therefore, data for 1880–2007 do not support the anthropogenic interpretation of global warming during this period. This key result is shown graphically in Fig. 3 where the vertical axis measures the component of global temperature that is unexplained by solar irradiance according to our estimates. In panel a the horizontal axis measures the anomaly in the anthropogenic trend when the latter is derived from forcings of carbon dioxide, methane and nitrous oxide. In panel b the horizontal axis measures this anthropogenic anomaly when apart from these greenhouse gas forcings, it includes tropospheric aerosols and black carbon. Panels a and b both show that there is no relationship between temperature and the anthropogenic anomaly, once the warming effect of solar irradiance is taken into consideration.

However, we find that greenhouse gas forcings might have a temporary effect on global temperature. This result is illustrated in panel c of Fig. 3 in which the horizontal axis measures the change in the estimated anthropogenic trend. Panel c clearly shows that there is a positive relationship between temperature and the change in the anthropogenic anomaly once the warming effect of solar irradiance is taken into consideration.

clip_image002[6]

Fig. 3. Statistical association between (scatter plot of) anthropogenic anomaly (abscissa), and net temperature effect (i.e. temperature minus the estimated solar irradiance effect; ordinates). Panels (a)(c) display the results of the models presented in models 1 and 2 in Table 3 and Eq. (13), respectively. The anthropogenic trend anomaly sums the weighted radiative forcings of the greenhouse gases (CO2, CH4 and N2O). The calculation of the net temperature effect (as defined above) change is calculated by subtracting from the observed temperature in a specific year the product of the solar irradiance in that year times the coefficient obtained from the regression of the particular model equation: 1.763 in the case of model 1 (a); 1.806 in the case of model 2 (b); and 1.508 in the case of Eq. (13) (c).

Currently, most of the evidence supporting AGW theory is obtained by calibration methods and the simulation of GCMs. Calibration shows, e.g. Crowley (2000), that to explain the increase in temperature in the 20th century, and especially since 1970, it is necessary to specify a sufficiently strong anthropogenic effect. However, calibrators do not re- port tests for the statistical significance of this effect, nor do they check whether the effect is spurious. The implication of our results is that the permanent effect is not statistically significant. Nevertheless, there seems to be a temporary anthropogenic effect. If the effect is temporary rather than permanent, a doubling, say, of carbon emissions would have no long-run effect on Earth’s temperature, but it would in- crease it temporarily for some decades. Indeed, the increase in temperature during 1975–1995 and its subsequent stability are in our view related in this way to the acceleration in carbon emissions during the second half of the 20th century (Fig. 2). The policy implications of this result are major since an effect which is temporary is less serious than one that is permanent.

The fact that since the mid 19th century Earth’s temperature is unrelated to anthropogenic forcings does not contravene the laws of thermodynamics, greenhouse theory, or any other physical theory. Given the complexity of Earth’s climate, and our incomplete understanding of it, it is difficult to attribute to carbon emissions and other anthropogenic phenomena the main cause for global warming in the 20th century. This is not an argument about physics, but an argument about data interpretation. Do climate developments during the relatively recent past justify the interpretation that global warming was induced by anthropogenics during this period? Had Earth’s temperature not increased in the 20th century despite the increase in anthropogenic forcings (as was the case during the second half of the 19th century), this would not have constituted evidence against greenhouse theory. However, our results challenge the data interpretation that since 1880 global warming was caused by anthropogenic phenomena.

Nor does the fact that during this period anthropogenic forcings are I (2), i.e. stationary in second differences, whereas Earth’s temperature and solar irradiance are I (1), i.e. stationary in first differences, contravene any physical theory. For physical reasons it might be expected that over the millennia these variables should share the same order of integration; they should all be I (1) or all I (2), otherwise there would be persistent energy imbalance. However, during the last 150 yr there is no physical reason why these variables should share the same order of integration. However, the fact that they do not share the same order of integration over this period means that scientists who make strong interpretations about the anthropogenic causes of recent global warming should be cautious. Our polynomial cointegration tests challenge their interpretation of the data.

Finally, all statistical tests are probabilistic and depend on the specification of the model. Type 1 error refers to the probability of rejecting a hypothesis when it is true (false positive) and type 2 error refers to the probability of not rejecting a hypothesis when it is false (false negative). In our case the type 1 error is very small because anthropogenic forcing is I (1) with very low probability, and temperature is polynomially cointegrated with very low probability. Also we have experimented with a variety of model specifications and estimation methodologies. This means, however, that as with all hypotheses, our rejection of AGW is not absolute; it might be a false positive, and we cannot rule out the possibility that recent global warming has an anthropogenic footprint. However, this possibility is very small, and is not statistically significant at conventional levels.

Full paper: http://www.earth-syst-dynam.net/3/173/2012/esd-3-173-2012.pdf

Data Appendix.

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Jimmy Haigh

This looks just like what those of us with common sense have been saying for donkey’s years: it’s basically got nothing to do with us.

John Peter

And the latest UAH measurements show a further steady decline for December to +0.20 degree C
http://www.drroyspencer.com/2013/01/uah-v5-5-global-temperature-update-for-december-2012-0-20-deg-c/

Stephen Richards

“Indeed, the R2 from a regression between nonstationary time series may be as high as 0.99, yet the relation may nonetheless be spurious”
So, two parameters may corelate to 99% but still not be cause and effect ?
I like this paper. They have detailed their method(s), the data, their caveats, their uncertainties and provided conclusions actually based on the the body of the paper. It’s not like a Crimatology paper at all.

mpainter

Yes, hardly a bombshell, just a new statistical appraisal of the record with the obvious conclusion.

Stephen Richards

Panel c clearly shows that there is a positive relationship between temperature and the change in the anthropogenic anomaly once the warming effect of solar irradiance is taken into consideration
That’s interesting because, it seems to me to say that radiative equilibrium is a very fast process but I still find it difficult to understand how the CO² effect can similate a ‘burst’ of heat energy which then fades at the same time that CO² concentration is steadily increasing.

Vince Causey

Does look rather technical. I would be interested in what Steve McIntyre has to say on their methods – or anyone else with good stats knowledge.

Stephen Richards

mpainter says:
January 3, 2013 at 9:15 am
Yes, hardly a bombshell, just a new statistical appraisal of the record with the obvious conclusion.
I’m not sure that all their conclusions are ‘obvious’. The paper is ‘quite’ unique in it’s analytical method and very thorough.

Brent Buckner

David Stockwell has been following this paper since earlier versions (most recently http://landshape.org/enm/agw-doesnt-cointegrate-beenstocks-challenging-analysis-published/ ).
Thanks for drawing wider attention to it.

Peter Miller

So,in summary:
1. There are too many unknowns to accurately model the Earth’s climate.
2. Man may, or may not, have been responsible for part of the ~0.7 degrees C warming over the past century.
3. The effect of man on our planet’s climate is small and temporary.
Makes sense to me, but these reasonable concepts will give your average climate modeller and/or CAGW cult member a total hissy fit.
I like the fact they use Mann as a reference – I am not sure in what context, but my guess would be to demonstrate statistical BS.

Stephen Richards

I like this paper. For the first time in years, since I studied Lamb’s work, I want to read, inwardly digest and understand a paper on climate. It sure beats the crap out of the BBC-Grauniad journal.

Not only statistically insignificant, not significant by any other measure either. This is all still nothing more then masturbating with in the error band. Since it demonstrates that I guess us AGW atheists will all say amen.

Commons sense says, that there is no difference at all between 1910-1940 warming and 1975-2005 warming, which was even smaller.

BioBob

@ Vince Causey
Don’t expect to get the bottom line on stats because the reality is always ignored but brutally simple.
Virtually ALL land temperature data prior to the age of electronics (and the vast majority since) is worthless for use in statistics since it is all based on single non-random non-replicated samples (n=1 sample per day). N = 1 equals data with unknown variance and unknown error in which virtually all parametric statistical requirements are NOT met. Even a mean is worthless since the shape of real temperature values are unknown and each days temperature population is NOT the same as any other’s.
You don’t need a statistics expert – you only need to look up the known requirements for any particular stats type to see the truth.

It is always a pleasure to see real science being conducted.

Kevin Kilty

…; it might be a false positive…
The authors mean by this the tentative acceptance of the null hypothesis when it is in fact not true. So the meaning appears backward to my way of thinking–it sure is for medical diagnostic tests.
I’m pretty sure there is an anthropogenic component, but it is so small it is lost in the other noise–and this belief animates my appraisal of the issue. Lots of pathological (Irving Langmiur’s term) scientific results originated in trying to interpret a tiny signal that was drowning in noise. By the way, AGW as presented publically itself contains several elements from Langmuir’s list, most prominently the use of ad hoc argumentation to dismiss contrary results.

PRD

I heard Mike M. and James H. both say, “Ouch”.

Jpatrick

“…along with solar irradiance from Lean and Rind, and CO2, NH4, N20…”
I think you mean CH4 instead of NH4
REPLY: yup. fixed

Is this not just another peer reviewed paper that the IPCC must ignore in the AR 5 if the overconfident conclusions of the other 4 reports are not going to be shown to be just plain wrong? After all, new science can be included in the AR 5 up to March 2013.

richard telford

It is a great shame that they did not test if their methods had any statistical power. This would have been easy to do using GCM output and would have greatly strengthened their paper. Without knowing what their type II error rate is, it is impossible to evaluate the paper properly.

D Böehm

Whenever I look at this chart, I question whether human emissions can have the global warming effect claimed by the climate alarmist crowd. There is obviously much we do not know, including all the sources and sinks of atmospheric CO2.
As this paper makes clear, AGW has never been empirically measured. If it exists, it is simply too small to be measurable. At current concentrations, CO2 is only a tiny, bit player; a minuscule 3rd order forcing that is easily swamped by second- and first-order forcings.
More CO2 will not have any effect; the radiative response was used up in the first few dozen ppm. Adding more CO2 is just painting the window again. As we now observe, adding another layer of paint has caused no further global warming.

This makes sense to me because I noticed some time ago that a large spike in temps occurred from 1910-1940 yet there was no significant increase in co2 during this period. Clearly there were other phenomenon at work and it was unclear to me why the ipcc was so willing to discount whatever caused that warming. I realized that the hockey stack may have been the major factor affecting their opinion because essentially they were denying that temperatures had varied in the past therefore natural causes of warming could be discounted but since it is clear that the mwp and lia and numerous other ups and downs occurred in the record its apparent that these other forcings aren’t zero, don’t net out to zero and are nontrivial therefore the assumption that the only thing that could have caused the temperature change from 1978-1998 was anthropogenic is not as simple as thought. Judith curry has pointed out that the attribution is not as clear as thought to be. Numerous studies have shown that this attribution is more complex than was contemplated. The temperature since 1997 has been statistically flat whereas co2 has been climbing. 16 years of non-correlation and its clear that the previous assumed direct and clear association was a fluke therefore the basis for that attribution had to be questioned. This paper simply affirms that the attribution is clearly not as simple as was thought just 5 or 10 years ago. The fact that the association is i(2) implies that there is significant lag (at best) in the correlation. Since the 1978-1998 showed no lag it can be assumed that co2 was not a significant cause of that temperature change. It may have been a factor but then it is likely three is no long term effect of co2 and that the contribution of co2 to that rise was a small portion of that change and is temporary.

MarkW

“That’s interesting because, it seems to me to say that radiative equilibrium is a very fast process but I still find it difficult to understand how the CO² effect can similate a ‘burst’ of heat energy which then fades at the same time that CO² concentration is steadily increasing.”
Could indicate a system with significant negative feedbacks that are delayed in their effects.

Rob Ricket

Do the statements below make sense to anyone? Are the authors inferring there is temporary forcing effect that magically disappears independent of Solar iridescence levels? How should we account for this return to stasis… increased carbon sinking through plant growth?
“Nevertheless, there seems to be a temporary anthropogenic effect. If the effect is temporary rather than permanent, a doubling, say, of carbon emissions would have no long-run effect on Earth’s temperature, but it would in- crease it temporarily for some decades.”
“Indeed, the increase in temperature during 1975–1995 and its subsequent stability are in our view related in this way to the acceleration in carbon emissions during the second half of the 20th century.”

I feel sort of uncomfortable with the verb “fail” in the title because it suggests that finding a man-made fingerprint would be a “success” while finding it’s not there is a “failure”. In this way, the verb introduces a strange bias or lack of impartiality – either because the writer of the word “fail” is partial himself (not the case here) or because he suggests that the authors of the scientific papers are biased in the same way (I don’t see reasons for this accusations, either).

‘The perceived statistical relation between temperature and anthropogenic forcings is therefore a spurious regression phenomenon”
Spurious: Not being what it purports to be; false or fake: “spurious claims” (of a line of reasoning) Apparently but not actually valid: “this spurious reasoning results in nonsense”.
Synonyms: false – sham – counterfeit – bogus – mock – phony
Regression: A return to an earlier stage of life or a supposed previous life, esp. through hypnosis or mental illness, or as a means of escaping.
Phenomenon: A fact or situation that is observed to exist or happen, esp. one whose cause is in question.
This paper hits the nail on the head.

pdtillman

Re: David Stockwell, http://landshape.org/enm/agw-doesnt-cointegrate-beenstocks-challenging-analysis-published/
Stockwell thinks McKitrick was one of their peer-reviewers. Plus,he’s no statistical slouch himself. I hope Wm. Briggs takes a look. And McIntyyre too, of course. Interesting times.

Steveta_uk

I think Lubos is perhaps reading too much into the use of the word “fail” – perhaps a cultural difference is involved.
If I try to find something, but fail, this says nothing about whether I wanted to find it or not. I might be trying to find some lost money (failure=bad) or trying to find lumps in tender parts of my body (failure=good).

Matthew R Marler

I am glad that they finally got it published, even though it is an online journal. They originally intended it for Nature (judging from an annotation at their website when the first draft was posted.)

Michael Moon

Amazing. “Correlation is not causation,” anyone? These guys have stuck their necks out far, far. Will they ever get funded again? Will whoever reviewed this paper ever get funded again? Will the Red Queens of Climatology scream “Off with their heads!!”? Will Michael Mann’s head spontaneously combust? Will Hansen chain himself to these guys’ doors?

Kevin Kilty

Perhaps someone better grounded in these non-stationary tests can confirm what I interpret. The direct effect of CO2 is logarithmic so we should not expect that this effect is I(1); but including feedbacks from water vapor the effect can appear linear over small temperature ranges and thus appear I(1). Yet, the authors separate water vapor as a separate forcing…why then any statistical impact of CO2 being I(2) and temperature being I(1); when it is the log of CO2 that is important?
Is it because a logarithimic non-stationarity should appear sub-I(1)? It certainly would grow more slowly than linear. Am I seeing this correctly?

BrianJay

Interesting but they are Jews living Israel. Guess what the first line of attack will be

Stephen Wilde

If the increase in CO2 did have an effect with a delayed negative response then the shift in the global air circulation to a more zonal pattern and probably also more poleward climate zones and jet stream tracks (as was actually observed) would be a plausible solution because that would alter the rate of energy loss to space.
I don’t put it down to anthropogenic causes at all. They would be barely discernible.
Instead, more solar energy entered the oceans which altered the CO2 absorption / release balance driving atmospheric CO2 up and then the circulation pattern adjusted.
However even that would be a trivial effect swamped by the faster water cycle which resulted from the solar forcing processes that I have described elsewhere.
A natural air circulation response to solar variability has been in control all along subject only to modulation by internal oceanic variability.
Anything else is miniscule in comparison.
And we are now in a more meridional air circulation regime which suggests that the whole process has gone into reverse notwithstanding increasing CO2 emissions.

BrianJay

Moderators Spelling mistake above “br” should be “be”.
[Please be more specific. — mod.]
[Reply: Fixed. previous entry with Israel. -ModE]

mpainter

Rob Ricket says: January 3, 2013 at 9:55 am
Do the statements below make sense to anyone? Are the authors inferring there is temporary forcing effect that magically disappears independent of Solar iridescence levels? How should we account for this return to stasis… increased carbon sinking through plant growth?
“Nevertheless, there seems to be a temporary anthropogenic effect. If the effect is temporary rather than permanent, a doubling, say, of carbon emissions would have no long-run effect on Earth’s temperature, but it would in- crease it temporarily for some decades.”
“Indeed, the increase in temperature during 1975–1995 and its subsequent stability are in our view related in this way to the acceleration in carbon emissions during the second half of the 20th century.”
================================
You have put your finger on the nub.
“a temporary anthropogenic effect” concerning CO2 is a new idea to me. I see nothing more in this paper than a matching of the temperature record against CO2 emissions and observing that there is no correlation except in the 1975-95 trend, to which they baldly attribute a “temporary” effect. This is straining out gnats and swallowing the strainer. All of this heaved up in a fine flourish of improved statistical techniques.

davidmhoffer

Stephen Richards;
That’s interesting because, it seems to me to say that radiative equilibrium is a very fast process but I still find it difficult to understand how the CO² effect can similate a ‘burst’ of heat energy which then fades at the same time that CO² concentration is steadily increasing.
>>>>>>>>>>>>>>
That’s not my interpretation. What they are doing is heavy duty nasty statistics to classify different ripples on the pond. A rock thrown into the pond creates a set of ripples that die out, but then the pond level is pretty much unchanged. But an instantaneous increase in stream flow coming in, even a small one, would also create ripples across the pond surface, but when the ripples die out, the pond level would indeed be higher.
So they’ve classified CO2 as a rock thrown into the pond. Perturbs the system as it is added, but makes very little long term difference once the ripples die out, which fits the observational evidence rather nicely.
On the other hand, they’re trying to identify ripples from half a dozen different things all at once, and I’m not sure they actually have enough data to do that. I can stand on the shore of my favourite lake and tell you if a ripple on the surface came from a kid doing a cannon ball off the dock or a passing power boat. But I can’t look at the surface as a power boat blows by, a kid does a cannon ball off the dock, the dam at the far end gets raised, it starts to rain, wind changes direction and a sea gull takes a dump all at the same time and tell you which ripples belong to what.
Looking forward to commentary by rgbatduke, Leif, and SteveM….

Gail Combs

Stephen Richards says:
January 3, 2013 at 9:17 am
Panel c clearly shows that there is a positive relationship between temperature and the change in the anthropogenic anomaly once the warming effect of solar irradiance is taken into consideration
That’s interesting because, it seems to me to say that radiative equilibrium is a very fast process but I still find it difficult to understand how the CO² effect can similate a ‘burst’ of heat energy which then fades at the same time that CO² concentration is steadily increasing.
>>>>>>>>>>>>>>>>>>>>
I am not sure what the term in statistics is, but A may be correlated with B and B is what actually drives C and not A.
In this instance we know there was a lot of inventing going on in the 1800’s but at least in the USA on farms was still where people were.
1790 Farmers made up about 90% of labor force, population 5,308,483 (1800)
by 1850, Farmers made up 64% of labor force, population 23,191,786
by 1900 Farmers made up 38% of labor force, population 75,994,266
by 1920 Farmers made up 27% of labor force, population 105,710,620
by 1940 Farmers made up 18% of labor force, population 131,820.000
by 1960 Farmers made up 8.3% of labor force, population 180,007,000
by 1980 Farmers made up 3.4% of labor force, population 227,020,000
by 1990 Farmers made up 2.6% of labor force, population 246,081,000
This means not only did the population increase but the population became concentrated in town and cities where there were jobs. The U.S. in 1800 had a per-capita energy consumption of about 90 million Btu. In 1949, U.S. energy use per person stood at 215 million Btu. and now it is 335.9 million BTUs.
Therefore what I think you are seeing is the increase in temperature linked to CO2 was from humans became more ‘concentrated’ in a location and used more energy. This was from the local UHI effect because thermometers were going from rural to city and finally to airports. ( the Climate Scientists classify Airports as ‘rural’) For the last decade there just is no other fiddling available to make the temperatures increase. Especially after the station drop out. link and the oceans are not cooperating.
AIRPORTS:
Digging in the clay: Location Location Location
CHIEFIO: More Airports Hotter Than ‘nearby’ Stations

aaron

Lubos, that’s common language for these types tests to simply indicate sign.

tgmccoy

Can someone smarter than me tell why THIS: http://weather.unisys.com/surface/sfc_daily.php?plot=ssa&inv=0&t=cur isn’t showing cold oceans? Clearly to me-
we seem to have a No Nino conditon….

Stephen Richards says:
January 3, 2013 at 9:17 am
Panel c clearly shows that there is a positive relationship between temperature and the change in the anthropogenic anomaly once the warming effect of solar irradiance is taken into consideration
As long as they use an outdated [really Lean 2005] solar irradiance reconstruction, they can’t remove it in any meaningful way.

Stephen Wilde

” A rock thrown into the pond creates a set of ripples that die out, but then the pond level is pretty much unchanged. But an instantaneous increase in stream flow coming in, even a small one, would also create ripples across the pond surface, but when the ripples die out, the pond level would indeed be higher.”
That is a neat summary of the position that I have been trying to get across for some time.
Increases in atmospheric mass, the strength of the gravitational field or the level of insolation would raise the ‘pond’ level because they change the amount of energy that the system can hold.
Everything else including changes in radiative characteristics just create ripples that die away because they just serve to redistribute energy rather than adding anything to the total energy available.
It is a matter of variable flow rates for multiple components in a single system whereby timing is everything and at base the time that matters is the time it takes for energy in to escape the atmosphere once it has arrived and that time is a function only of mass and gravity with insolation supplying the flow of energy that mass and gravity interferes with.
As far as I know the science relating to gas clouds in space, suns and their formation and planetary gas giants gives no regard to radiative characteristics in determining internal temperatures. It is all a matter of mass and gravity. So it should also be for atmospheres around lumps of rock.
If this paper brings that scenario to the fore it can only be to the good.

Tony McGough

I don’t understand any of the technicalities of the Israeli paper, but gather that it does not consider any physical effects at all – just examines correlations (and lack of correlations) between the seven or eight datasets they acquire (from sound sources). They then conclude that man-made sources have little or no effect on temperatures, provided that they have not found an anomalous false negative.
It’s nice to have these apparently cool-headed statisticians saying what one can simplistically discern from a simple inspection of the CET (central England temperature) record – temperature rises ain’t nothing to do with us, guv.
I would like some of our learned regular contributors to give the paper a shake-down, please: I have learned to appreciate their analyses.

rgbatduke

Good paper. Not sufficient, but a powerful argument. My one concern would be the same one I often use in the other direction — if the Earth’s instantaneous climate state is viewed as a point that is a multidimensional orbit that in the very loosest of terms is “around” some center of quasi-stability — not a stationary Poincare attractor but perhaps a set of attractors in a rugged landscape — then one has to make certain assumptions in order to do the timeseries analysis they suggest. I’m not certain those assumptions are satisfied.
The key question is indeed the one about residuals of first and second order differences, but those have a physical interpretation as being components of a gradient, a partial differential term in a model, or components of a higher order partial derivative.
There are many statistical models that average to zero along any given axis so that there is no single variable linear trend but that have profound multivariate trends. The classic example is the “exclusive or” distribution, a distribution where A exclusive or B has weight 1, while A and B or not A and not B have weight 0. If you look at the distribution along the A axis (alone) it is uniform, and A looks like it is not a predictor. If you look at B alone ditto. Yet from a knowledge of A and B one can predict the outcome perfectly.
This is only the simplest version of this difficulty (non-separability) involving two binary variables, but in statistical modeling it is profound and pervasive. It isn’t clear from their discussion whether or not they assumed separability. If not, the best that they could conclude from their result is that they do not find evidence of a separable (unconditional) contribution from CO_2 compared to e.g. insolation, not that at may not be an important causal factor. I also find it difficult to physically justify CO_2 as producing a local effect that is then neutralized over decades.
So, interesting, powerful argument well made, but not a home run. It does open the way for the future, though.
It also leaves open the question of “which solar data”. Once again, we await Lief, who will remind us that even the I(1) result for insolation depends on which proxy reconstruction of insolation one uses. Back in 1880 they weren’t doing electronics so much. I don’t know if any clever lad or lass used e.g. photographic film to infer solar intensity over the 30+ years before e.g. the photoelectric effect and invention of tubes permitted some sort of direct electron measure, and as we’ve been told repeatedly, sunspot counts are a remarkably inconsistent proxy that it is very difficult to retroactively fix in the process of reconstructing solar state over even/only 1.5 centuries.
So it could be that none of the things studied have a first order effect on the climate, it is basically in some sort of random walk tilted primarily by things yet unstudied. Shades of Koutsoyiannis! Those things could all be driving the climate vigorously back and forth across some quasi-equilibrium as second order stuff, while the primary driver is quietly being ignored.
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DirkH

richard telford says:
January 3, 2013 at 9:47 am
“It is a great shame that they did not test if their methods had any statistical power. This would have been easy to do using GCM output and would have greatly strengthened their paper.”
Thanks, that made me laugh.
Richard, have the computer kiddies in the modeling departments learned how to model convective fronts in the meantime?
Cloud formation?
The QBO?
Oh. I thought so.

apparently no one reads or knows physics. If they did they would know the heat trapping ability if co2 is logarithmic. It’s not possible for it to do what the warmists are trying to convince people it can.

Gail Combs

BrianJay says:
January 3, 2013 at 10:37 am
Interesting but they are Jews living Israel. Guess what the first line of attack will be.
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That does not work because it is “Politically Incorrect” to use race. If they try to connect to the banks it again does not work because of the World Bank (and Robert Watson) are completely entangled with the IPCC and CAGW including the World Banks last Report

DeWitt Payne

Not this again.
This is a dud, not a bombshell. Cointegration tests were designed by economists to rule out spurious correlations between things when there is no known mechanism to relate them . We know that there must be a correlation between surface temperature and ghg concentration. (We do, in fact, know this. Clear sky radiative transfer is considered a solved problem in Physics. See scienceofdoom.com and http://people.su.se/~rcaba/teaching/PhysMetLectNotes.pdf for example.) What we don’t know is the magnitude.
Testing for a trend without detrending the data first reduces the statistical power of the test. Goodness-of-fit calculations are done on the residuals after fitting, not the raw data. There will be more false negatives if you don’t detrend, especially when there may be low frequency oscillations like the AMO influencing the measured temperature (which will also make the raw data look like unit roots are present). There’s a good discussion of this in respect of the C. Franzke paper at The Blackboard.

Paul Westhaver

Would someone send a copy of this paper to that mouth breathing thug, Seth Borenstein at the AP?
With some hope, and some education aids, (blocks, counting sticks, staking rings) he may be able to comprehend it. That goes for Martin Mittelstaedt, no longer doing chicken-little-the-end-of-the-world-is-coming articles for the Globe and Mail.

Dr. Acula

Being well-versed in the Austrian School of economics, I have a pretty low opinion of econometric techniques.
Sorry, but this paper seems to be playing mathematical games to me. It’s not at all obvious why “cointegration tests” should be trusted. What empirical evidence is there to justify using cointegration tests? Why were certain tests used and not their alternatives?
I’m guessing this isn’t really science, but rather the opinion of (perhaps seasoned) econometricists engaging in their art.
It did not take me long to find troubling information about cointegration: http://www.capco.com/capco-institute/capco-journal/journal-32-applied-finance/the-failure-of-financial-econometrics-asses
“This paper demonstrates that the results obtained by using different cointegration tests vary considerably and that they are not robust with respect to model specification. It is also demonstrated that, contrary to what is claimed, cointegration analysis does not allow distinction between spurious relations and genuine ones. Some of the pillars of cointegration analysis are not supported by the results presented in this study.”

Kasuha

Wow, just wow. Not only they exclude anthropogenic impact on temperature, they even provide indirect proof that feedbacks are negative and strong enough to compensate it.
This would be a great reference to add to the upcoming IPCC report, wouldn’t it? I believe it would be no problem to add it if it already references papers which were not even published yet…

DeWitt Payne

rgbatduke,
Charles Greeley Abbot was the first to accurately estimate the value of the solar constant early in the twentieth century using a pyrheliometer mounted on a balloon at 25km altitude. He attempted for something like forty years to measure the variability with little success as his measurements had to be made from the surface. So accurate and precise direct measurement of the solar constant had to wait for satellites. Any inference of the variability of the solar constant using proxies is subject to all the problems of proxy measures in general.