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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.
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).
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.
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]](http://wattsupwiththat.files.wordpress.com/2013/01/clip_image0026.jpg "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.
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.
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/
“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.
Yes, hardly a bombshell, just a new statistical appraisal of the record with the obvious conclusion.
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.
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.
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.
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.
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.
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.
@ 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.
…; 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.
I heard Mike M. and James H. both say, “Ouch”.
“…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.
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.
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.
“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.
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.
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.
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).
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.)
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?
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?
Interesting but they are Jews living Israel. Guess what the first line of attack will be
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.
Moderators Spelling mistake above “br” should be “be”.
[Please be more specific. — mod.]
[Reply: Fixed. previous entry with Israel. -ModE]
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.
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….
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
Lubos, that’s common language for these types tests to simply indicate sign.
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.
” 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.
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.
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.
rgb
rgb
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.
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.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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
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.
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.
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.”
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…
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.
This should give Steve McIntyre something to do when he gets home from Asia. It will be interesting to see what his take is on the statistical methods employed.
Anthony, do what you can to keep this paper from Mosher – he might just wax in-eloquently.
One of many problems I have with reconstructions of things like irradiance and temperature is that they never seem to come with an error analysis. The curves above (wherever they are from) are no exception — we see a simple jiggy line that is supposed to be “temperature”, or “irradiance”, or “CO_2 level” back to 1880, with no error bars at all.
Yet those error bars have to exist, and have to be rather large for anything inferred by means of proxies or measurements with crude instrumentation at the beginning of the 20th century or earlier).
It would actually be really lovely to have honest error bars — with any reasonable interpretation of error — both in the figures and, one would expect, in use in the statistical analysis of the timeseries. Otherwise one has a compounding of assumptions and errors.
Lacking both error bars and a single set of solar data that the entire community endorses within those error bars (so that the error bars reflect among other things disagreement within that community) it is going to be impossible to create a statistical study of solar state and global climate that means anything at all. This (lack of a) model then becomes an important Bayesian prior in further statistical analysis of possible causes, because the amount of warming one attributes to CO_2 clearly has to depend to some extent on how much you attribute to insolation, so if the latter is uncertain the former is even more uncertain (and vice versa). Errors tend to grow like SE = \sqrt(SE_1^ + SE_2^2) after all, and that’s for simple linear models with favorable assumptions — it can be much worse.
Lief, is there a set of solar data that everybody in the solar community endorses (or at least, has their disagreements duly entered in in the form of additional uncertainty in the claims)? Or is it really just a matter of flipping coins and grabbing a paper at random? Picking the paper (either way) that produces the conclusions you want to assert?
It would be very useful to see this computation redone not for the 1880-2012 data, but only for the e.g. 1979-2012 data that is moderately reliable. The idea is actually a good one and I’ve looked at it myself — the problem can be reduced to looking at global temperature and the e.g. Mauna Loa CO_2 curve and comparing them — there are obviously lots of ways of mapping the one monotonic function (CO_2) into a linear (over this timescale) model for temperature plus noise, but the noise is them many times larger than the trend, the fit interval is short, and one ignores completely the dynamics of the noise. It is clear at a glance, however, that there is no short-run correlation between changes in temperature and CO_2 level — only the weak trend over the entire timeseries, which puts 2012 as the 9th warmest year in 33, remarkably close to both mean and median. I don’t need to do student t to measure p to tell you that p for the null hypothesis is not going to be reassuringly low over the entire interval.
They should also attempt a nonstationary timeseries analysis, treating the temperature like a Hurst-Kolmogorov variable with a possibly directed stochastic noise term wrt to eh hypothesized drivers.
rgb
Very thorough, a brilliant paper.
It’s about time someone stepped up to do this kind of unit root test. There are thousands of such studies published all the time on other topics.
You clown !!!
To all the negative commentators above, I will remind you that ALL of the top research departments of the world’s central banks use this methodology and result format.
rgbatduke says:
January 3, 2013 at 12:32 pm
Leif, is there a set of solar data that everybody in the solar community endorses (or at least, has their disagreements duly entered in in the form of additional uncertainty in the claims)? Or is it really just a matter of flipping coins and grabbing a paper at random? Picking the paper (either way) that produces the conclusions you want to assert?
There is no such set(s). I am leading two workshops [involving the foremost researchers in the solar community] to produce such sets:
1) http://www.leif.org/research/Svalgaard_ISSI_Proposal_Base.pdf
2) http://ssnworkshop.wikia.com/wiki/Home
Our work is not finished yet, although we do have some preliminary findings [basically what I have been talking about for some time here on WUWT].
Amazingly, there is some resistance among our ‘users’ to our attempt to create a vetted, agreed upon data set. It seems to be most convenient to have several [and wrong] sets to pick from to support everyone’s pet conclusions.
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 remind that I’m a layman here, but I wonder what they would have concluded if Watts et al had been included?
We are pretty sure. But we are not 100% sure
and if you want to prove us wrong – heres how
how long have I waited to hear this. And, guys, I dont even care if you are wrong, just that admission that you are not walking on water is fantastic. reality at last
Resourceguy says:
“To all the negative commentators above, I will remind you that ALL of the top research departments of the world’s central banks use this methodology and result format.”
With the ‘banking collapse’ a few years ago and the state of the world economy at the moment that should at least give you a pause for thought.
Resourceguy,
And looking at the global economy, I would say it’s working really well. /sarc
Just one fundamental flaw of many. Atmospheric CO2 concentration is not a random variable. It is almost completely deterministic. There is measurement error and year to year variability, but those factors are small compared to the deterministic change. We know where it comes from and how much is emitted each year. Applying a unit root test to this data without removing the deterministic trend is therefore invalid.
Did they forget to polynomially cointegrate ocean heat?
DeWitt Payne:
At January 3, 2013 at 2:15 pm you assert
You “know where it comes from”? Really? How?
I don’t know if the cause of the recent rise in atmospheric CO2 concentration is entirely anthropogenic, or entirely natural, or a combination of anthropogenic and natural causes. But I want to know.
At present it is not possible to know the cause of the recent rise in atmospheric CO2 concentration, and people who think they “know” the cause are mistaken because at present the available data can be modeled as being entirely caused by each of a variety of causes both anthropogenic and natural.
(ref. Rorsch A, Courtney RS & Thoenes D, ‘The Interaction of Climate Change and the Carbon Dioxide Cycle’ E&E v16no2 (2005) ).
The econometrics paper under discussion may or may not be found to contain many flaws (time will tell) but it displays a refreshing willingness to admit we don’t “know” anything about the climate issue with certainty.
Richard
SORCE Science Meeting, 18-19th September, 2012
http://lasp.colorado.edu/sorce/news/2012ScienceMeeting/docs/presentations/S2-01_Ineson_sorce2012.pdf
Could all of this, have only achieved a firm grasp of the obvious?
Well this is good look at things from the statistical angle. The technique is robust but, as we all know, the period of reliable data is far too short to draw any conclusions.
It is not proof of anything but adds to the debate and does prove the uncertainty that is present in clmate science at the moment.
The result could have been predicted just by eyeballing the various data sets actually. There is clearly not a good correlation between CO2, temperature, aerosols and black carbon since 1880.
However, it is always useful to have this backed up by robust methodology.
Alan
richardscourtney,
E&E and you’re a co-author? Pull the other one.
As long as we’re self-referencing: http://noconsensus.wordpress.com/2010/03/04/where-has-all-the-carbon-gone/
Anthony:
Andre Bijerk and Joanna Ballard might be two of the best, but perhaps credit for bring Beenstock, et al. to WUWT-land should go to the following contributor to Tips and Notes:
Brian H says:
December 9, 2012 at 10:19 pm
h/t DirkH;
http://economics.huji.ac.il/facultye/beenstock/Nature_Paper091209.pdf
This means,
crucially, that a doubling of greenhouse gas forcings does not permanently increase
global temperature.
From there, it was easy to track down the published paper, also as noted soon thereafter in Tips and Notes.
@R Taylor
With me getting a veritable firehose of comments and email each day sometimes it simply is a matter of who gets my attention first. I regret I cannot read every comment and email I get.
DirkH says:
January 3, 2013 at 12:06 pm
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?
———————–
I am glad I amused you, but it how realistically the CGMs model climate is irrelevant for the type of analysis I am proposing. What is relevant is that there is a time series of global temperatures and a time series CO2 and other forcing that generated this temperature series in the model. If Beenstock et al’s method cannot find the relationship between CO2 and temperature in the model, then it cannot be trusted if it cannot find the relationship in the real world.
And then there’s Ferdinand Englebeen’s page: http://www.ferdinand-engelbeen.be/klimaat/co2_measurements.html#The_mass_balance
Also, The Carbon Cycle, T.M.L. Wigley and D.S. Schimel ed., Cambridge University Press, 2000.
The evidence, as opposed to speculative assertions, is overwhelming that humans have caused the atmospheric CO2 level to increase from the preindustrial level of 280 ppmv.
DeWitt Payne,
True. Human activity has added [harmless, beneficial] CO2 to the atmosphere. It is still a very tiny trace gas, and it will never be a significant part of the atmosphere.
And yet, the planet has been up to 8ºC warmer repeatedly in the geologic past, without regard to CO2 levels. We are currently in a geologically cool period [top of the chart]. Ferdinand Engelbeen has also stated that the rise in CO2 is harmless. Thus, there is no need whatever to reduce CO2 to pre-industrial levels.
On net balance, more CO2 is better for the biosphere, and there is no verifiable, scientifically testable global harm as a result of the rise in CO2. Really, it’s all good.
How many more such studies will it take before the United Nations instructs the IPCC to abandon its quest of proving anthropogenic global warming and to concentrate on natural variability and theories such as Henrik Svensmark’s cloud theory, which is backed by observational data and b experimentation?
I don’t think anyone is arguing that humans have or haven’t added CO2 to the atmosphere. The argument is over how sensitive the climate is to it. The IPCC climate sensitivity numbers are basically speculative and observations over time have shown them to be false. We get about 1C of warming for each doubling of CO2 in the atmosphere. Most of that warming from pre-industrial levels has already happened. CO2 impact is logarithmic and each ton of CO2 added to the atmosphere has LESS impact than the ton before it had. To get 1C of warming from today’s level, we would have to double atmospheric CO2 from today’s level. The “feedbacks” that the IPCC has speculated to exist haven’t turned up in real life. We are spending hundreds of billions and fleecing the populations of this planet based on predictions of a fairytale.
Wake me up when we get to 1600ppm of CO2 … but we will never make it that far. China currently has 30 nuclear power plants under construction in various phases of completion. US CO2 emissions are falling, China’s emissions will begin to fall in about another 10 years as more of their nuclear generation comes online, Excess CO2 above Earth’s equilibrium amount begins to come out of the atmosphere as soon as the emissions are stopped. I doubt we will even get to 800 ppm, let alone 1600. But more importantly, nobody has shown a good reason to even reduce CO2 emissions. Why should we? Nobody has shown what PORTION of warming since the end of the LIA is due to CO2 to my satisfaction. They are simply running around talking about what “could” happen in the future based purely on climate models programmed with speculative feedbacks to CO2 increases.
It’s theft. It’s a racket. It is robbery.
Mervyn says:
January 3, 2013 at 5:22 pm
concentrate on natural variability and theories such as Henrik Svensmark’s cloud theory, which is backed by observational data
Actually it is not: http://www.leif.org/EOS/swsc120049-GCR-Clouds,pdf
“it is clear that there is no robust evidence of a widespread link between the cosmic ray flux and clouds” and
“In this paper we have examined the evidence of a CR-cloud relationship from direct and indirect observations of cloud recorded from satellite- and ground-based measurement techniques. Overall, the current satellite cloud datasets do not provide evidence supporting the existence of a solar-cloud link. Although some positive evidence exists in ground-based studies, these are all from highly localized data and are suggested to operate via global electric circuit based mechanisms: the effects of which may depend on numerous factors and vary greatly from one location to the next. Consequently, it is unclear what the overall implications of these localized findings are. By virtue of a lack of strong evidence detected from the numerous satellite- and ground-based studies, it is clear that if a solar-cloud link exists the effects are likely to be low amplitude and could not have contributed appreciably to recent [anthropogenic] climate changes.”
Mervyn says:
January 3, 2013 at 5:22 pm
concentrate on natural variability and theories such as Henrik Svensmark’s cloud theory, which is backed by observational data
Actually it is not: http://www.leif.org/EOS/swsc120049-GCR-Clouds.pdf
The paper is from Israel. So following the logic of the alarmists anybody who disagrees with it is a Holocaust denying anti Semite who loves Hitler.
DeWitt Payne says:
January 3, 2013 at 4:47 pm
“And then there’s Ferdinand Englebeen’s page:”
I don’t know what Ferdinand Englebeen is smoking, but the Mass Balance argument for attributing the increase in atmospheric carbon dioxide to anthropogenic causes is anything but overwhelming. The argument seems to be based on the premise that the natural CO2 fluxes into and out of the atmosphere remain unchanged regardless of atmospheric CO2 concentration. IMHO this is not particularly good logic. It also violates Le Chatelier’s principle.
According to the carbon cycle theory, there are natural carbon fluxes into and out of the atmosphere that are on-going and continuous. The argument for Mass Balance goes something like this: When humans add X amount of CO2 to the atmosphere in any given year, these natural fluxes adjust themselves such that X/2 of the added amount is removed and X/2 remains (forever, or at least for a very long time). Because the amount of additional CO2 is less than the amount added by humans, Mass Balance says the increase must be due solely to anthropogenic CO2.
Suppose in the following year there is no anthropogenic CO2 added to the atmosphere. What happens to the X/2 quantity of anthropogenic CO2 from the previous year that is still in the atmosphere? According to Mass Balance, this added CO2 remains as a permanent increase to the atmospheric CO2 concentration. What happens to the natural fluxes? They presumably stay in balance, as they were in the year before the anthropogenic CO2 addition. Mass Balance therefore seems to be saying the fluxes into and out of the atmosphere will remain the same as they were two years ago, before anthropogenic CO2 was added. This despite the fact that the atmospheric CO2 concentration has increased.
So where is the equilibrium shift that, according to Le Chatelier’s principle, counteracts this increase in concentration? Or does Le Chatelier’s principle not apply in this case?
The obvious gets a paper. Of course their ‘might’ have been a ‘temporary’ effect when co2 rise and temp rise happened to correspond. So, their might be a ‘temporary’
Any common sense observer has seen that our rise in temps in the later part of the 20th century continues a non-remarkable trend since the end of the little ice age.
I expect many more papers like this to give some face saving backing away from the prior positions of dangerous AGW. I still find it silly this couching of even a paper like this that says, this doesn’t mean there isn’t a temporary AGW effect – but rather just saying the models have not been rigorously tested with statistics.
Wait, can guys from Israel be “deniers”?
I get so confused…
Friends, I’m just a layman with a limited understanding of advanced statistics, but it seems that the biggest bone of contention lies with the claim that carbon forcing has a limited shelf life. Does this not essentially affirm the warmest position regarding the warming effect of GHG’s?
If the findings are valid, then forcing will continue for forty years after GHG’s stabilize. Alternatively, how can it be claimed on one hand that forcing occurs for a limited period, but does not cointegrate with global temp?
If the statistical methodogy is correct, then the paper essentially proves that one of the data sets is inaccurate. Obviously, the lowest hanging fruit is Mann’s proxy data.
Red, I’ll take Michal Mann for $250.
Figure 1 uses untrue temperature data, including falsely depicting 2007 as 0.6 degrees Celsius warmer than 1980 when the actual figure from satellite data was not more than 0.3 degrees warmer. There was not more than half as much warming over that period. Partially correcting the graph improves the relationship of temperature with solar activity, turning the graph of figure 1 into http://s9.postimage.org/yrkytofyn/fixedplotb.jpg (click to enlarge).
One of the most common fallacies in much of what gets called science these days is style over substance. Like GIGO, superficially formal writing and numbers can be only misleading illusions, falsely impressing viewers yet irrelevant when the basic data and assumptions are off. What happens is a little like the story of the Emperor’s New Clothes: Most people fear seeming unsophisticated and hesitate to criticize such.
However, one of the most important things I ever learned (in another context) was not just to calculate but to state my assumptions before calculation, recognizing that the internal correctness of the math itself was simply irrelevant unless the starting assumptions were correct.
Implicit unstated assumptions in this paper include (incorrectly) treating Hansen’s GISS as a trustworthy temperature source. Actually such is utterly compromised.*
* (A simple example is http://www.giss.nasa.gov/research/briefs/hansen_07/fig1x.gif versus http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.D.gif , where the former shows shows the 5-year mean of U.S. temperature in the high point of the 1980s was 0.4 degrees Celsius cooler than such in the 1930s but the latter is fudged to make the same less than 0.1 degrees Celsius apart).
Get a statistics expert to analyze the paper in a conventional manner, and the illusion of a sophisticated criticism can be increased. But unless they are intelligent enough and sufficiently far from typical naivety (or bias) to criticize starting assumptions and input data rather than treating them as a given, such would be inferior to even my quick casual comments here. (Peer review can be junk due to narrow-minded analysis; an example in another context is how a paper was published and applauded for claiming a 40% decline in phytoplankton over the past several decades, when so much as a look at fish catches, let alone contradictions from other plankton measurements, would show such to be BS, a bit like Mann’s hockey stick was not properly flagged for blatant contradiction to about everything of relevance published before the era of politicized science).
With that said, despite an incorrect sun versus temperature depiction, this paper happens to be correct on lack of correlation of temperature with CO2, though that can be seen in other ways more blatantly, like http://wattsupwiththat.com/2012/04/11/does-co2-correlate-with-temperature-history-a-look-at-multiple-timescales-in-the-context-of-the-shakun-et-al-paper/
The paper may be helpful in a way. Much of the CAGW movement’s more naive follower population is comprised of people who utterly fall for superficial appeals to authority, so perhaps the contradiction with CAGW claims could help disrupt their mindsets. But it is style over substance, especially in the context of how multiple climate forcings combine (especially solar / GCR variation like http://s13.postimage.org/ka0rmuwgn/gcrclouds.gif and http://s10.postimage.org/l9gokvp09/composite.jpg though with El Nino echos of past ocean heat back to the atmosphere).
@davidmhoffer says:
January 3, 2013 at 10:50 am
________________
Nice analogy
willb,
That’s not how it works. I suggest you read my article from The Air Vent to see how the Bern model works in practice. For example, the amount that remains in the atmosphere isn’t half. That’s the apparent value because anthropogenic emission is continually increasing. If human emissions were to cease instantly, the atmospheric CO2 concentration would decay to a level that would be the preindustrial level plus about 15% of the total amount emitted. The initial decay would be rapid, but it would take hundreds to thousands of years to reach a new steady state because the full mixing of the ocean takes that long. This graph is about what the CO2 level would have done if all human emissions ceased in 2005. The new steady state value would be about 320 ppmv. And you’re neglecting the isotope ratio and oxygen concentration data that is in agreement with the source of the increase being mostly fossil fuel combustion.
That should be 22% not 15% so about 335 ppmv, not 320 ppmv, at steady state.
Good catch Henry.
Rob Ricket: Thanks.
DeWitt Payne says:
January 3, 2013 at 2:15 pm
“Atmospheric CO2 concentration is not a random variable. It is almost completely deterministic. There is measurement error and year to year variability, but those factors are small compared to the deterministic change. We know where it comes from and how much is emitted each year.”
And, we know that part (the anthropogenic input) has negligible impact on the overall concentration. The data show that atmospheric CO2 concentration is almost completely driven by surface temperatures. In this WoodForTrees plot, it is clear that CO2 is dominated by an affine-in-temperature differential equation of the form
dCO2/dt = k*(T – To)
where “k” is a coupling constant, “T” is the global temperature anomaly, and “To” is an equilibrium temperature. Here is another such comparison with GISTEMP. Any of the major temperature sets will generally do, as they are all more-or-less affinely related. Any time you have a continuous flow into and out of a system which can be modulated by a particular variable, you can get an integral relationship of this sort for the residual which gets left behind.
Here is an example of what you get when you integrate the relationship. Clearly, there is very little room for human influence on the measured concentration. It is simply inconsistent with the data. Some quibble that the linear term is an artifact of the choice of “To”, and that provides most of the match in the integrated output. But, there has to be some value of “To”, because the temperature anomaly “T” is measured relative to an arbitrary baseline. More importantly, it has no effect on the slope of the CO2 rate of change, which matches the temperature slope quite well, when you choose “k” to match the variation, the peaks and valleys, between the time series. And, since the anthropogenic input rate itself has a slope, there is, again, no room for it to any level of significance.
Since differentiation necessarily imparts a 90 degree phase advance, it follows that coincidence between the peaks and valleys in the temperature and the rate of change of CO2 implies that CO2 lags temperature, and therefore the direction of causality is temperature-to-CO2. Or, one may consider that on a more elementary level, it would be absurd to argue that the temperature depends on the rate at which CO2 is changing, and not the overall level and, again, we conclude the direction of causality is temperature-to-CO2.
It also necessarily follows that the Earth’s mechanisms for sequestering CO2 have been grossly underestimated, the residence time conversely grossly overestimated, and the natural flows into the system grossly underestimated, as well. It is hardly surprising given that such estimates have been largely paper exercises without closed loop confirmation. This is what happens when you guess at an answer, and decide if it is right or not by taking a vote: Fiasco.
If human emissions were to cease instantly, the atmospheric CO2 concentration would decay to a level that would be the preindustrial level plus about 15% of the total amount emitted. The initial decay would be rapid, but it would take hundreds to thousands of years to reach a new steady state because the full mixing of the ocean takes that long.
So the sinks get rid of most of it quickly, but “know” to leave the other 15% for thousands of years? That is beyond ridiculous.
If this method was correct, each time a serious volcanic eruption occurred in which there was a significant amount of surplus CO2 over normal then 15% of the excess would fail to be absorbed. Over time, then, CO2 would inexorably rise.
That “it would take hundreds to thousands of years to reach a new steady state” is a red herring. The excess would be absorbed into quicker sinks and only then slowly equilibrate with the slower ones. That one sink is not yet in equilibrium has no bearing on whether the other ones are mopping up any significant excess.
The only way the slowness of the oceans will have any effect is if the other sinks are full.
DeWitt Payne says:
January 3, 2013 at 8:02 pm
These falacious arguments have been hammered out numerous times on these boards, often with the participation of Ferdinand. The “mass balance” argument begs the question – it only holds if you a priori assume that the source of the rise observed in the 20th century is attributable to humans. Here is a repeat of a previous response many, many moons ago to others:
———————————————————-
————————————————
This was written prior to my discovery (Allan MacRae, who posts here occasionally, has claim to discovering it first, I hasten to say) of the strong and compelling correlation between the rate of CO2 and temperatures. We do, in fact, know more, with this added bit of information. We know that UA is quite close to A, and we know that atmospheric CO2 concentration is largely temperature driven.
Bart says:
January 3, 2013 at 8:35 pm
>>>>>>>>>>>>>>>>>>
Wow. Folks, anyone who skipped through Bart’s post because it was long and technical…. I highly recommend going back and looking at those graphs.
Adam
The paper is from Israel. So following the logic of the alarmists anybody who disagrees with it is a Holocaust denying anti Semite who loves Hitler.
You should be disgusted with yourself, belittling the Holocaust and anti-Semitism like you do. The Holocaust is not, repeat NOT, a climate argument toy.
I am disgusted that the moderator lets comments like that through.
[Reply: we moderate with a light touch here. Heavier moderation leads to censorship. You have the right to respond to comments you disagree with. — mod.]
Dear aaron and Steveta_uk,
be sure that I’ve noticed that the word “fail” is pretty much the only English word that is being used in these contexts these days. It’s cultural, indeed. But I am still convinced that it affects the listeners’ and readers’ thinking because they inevitably create an emotional association of the possible results with “good” and “evil” or “success” and “failure”.
So yes, my comment was a recommendation to change the culture and favorite formulations in the English language… Incidentally, there are many contexts in which I think that exactly the opposite “emotional message” is appropriate and desirable. In those cases, I replace the word “fail” by “refuse”. For example, statistical tests refused to find a global warming smoking gun in this case. This sounds like someone wanted these tests to do a dirty job but these tests have some human rights and they just didn’t want to obey. 😉 They refused because there’s ultimately no empirically detectable CO2-caused global warming anywhere, after all.
Aside from “fail” and “refuse”, there also exist more neutral verbs, obviously.
All the best
Lubos
Bart says:
January 3, 2013 at 8:35 pm
The Keeling curve shows a steady rise with a roughly sinusoidal pattern superimposed on it. (It’s available in many places; here is one of them: http://scrippsco2.ucsd.edu/program_history/keeling_curve_lessons_3.html) This sinusoid has a period of one year, and an amplitude of about 5 ppm. The standard interpretation for this is that there is a long-term in CO2 concentrations due to fossil fuel burning, and a series of short-term seasonal variations due to uptake of CO2 during spring and summer, and a release of CO2 from decaying plant matter in the fall and winter. It’s not surprising that these natural processes would be well correlated with temperture, both in terms of the seasons themselves, and also for warmer or cooler years. For these, CO2 shoud follow Northern hemisphere temperature (there’s more land, and more plant life, in the NH) but with a 180 degree phase shift, and then perhaps some lag on top of that.
The derivative of CO2 concentration will mostly follow the seasonal variations, but these are themselves averaged out by a 24-month running average in the graphs that Bart referenced. The 90-degree phase shift will show up (although for some higher frequencies, with period less than one year, there will be a 180 degree phase shift due to the 24-month average). However, these higher frequencies will actually show up as lower frequencies (see Nyquist-Shannon sampling theorem). A glance at the curve shows that these lower frequencies should not cause too much trouble, but it would be a good idea to check to be sure. As for the time domain, going from one point to the next in the series of the derivative of CO2 concentration represents moving the average, i.e., adding a data value a year in the future while dropping one a year in the past. Finally, the first graph uses Hadcrut4sh, which is a Southern hemisphere data set, and so adds a 180 degree phase shift in temperature, for comparison with the CO2 fluctuations, which are predominantly a Northern hemisphere phenomenon.
It’s not obvious what to make of the resulting comparisons, in terms of fluctuations, phase shifts, etc. What is obvious, however, is that the shorter-term fluctuations that dominate the derivative of CO2 concentration (fluctuations in terms of a few years) should show the effects of similar variations in natural processes. They should also show short-term variations in anthropogenic CO2, if there actually are any. It’s to be expected that natural processes, governed at least partly by temperature, would show up in the derivative of CO2. But anthropogenic CO2 would show up mostly as a steady value (an offset) in the derivative of CO2, if it’s the relatively constant rise that people seem to think that it is, and that the Keeling curve indicates.
DeWitt Payne says:
January 3, 2013 at 8:02 pm
“And you’re neglecting the isotope ratio and oxygen concentration data that is in agreement with the source of the increase being mostly fossil fuel combustion.”
A lot of people will have a very hard time taking seriously any discussion about atmospheric CO2 concentration if it neglects isotope concentrations. Since plants take up C-12 in preference to C-13, either deforestation or burning of fossil fuels (ancient plants) tends to put more C-12 into the atmosphere, along with less C-13. Measurements of carbon isotopes in atmospheric CO2 show a decreasing concentration of C-13, consistent with the notion that increased CO2 arises from these anthropogenic sources.
Yes but…
If a gas mixture that contains CO2 is irradiated by a source at, say, 15°C it will absorb more or less energy in relation with the CO2 concentration. To enable the release of this absorbed energy to the environment (the outer space) the source temperature must change, e.g. increase by approx 0.54°C if the CO2 concentration is doubling. This is primary forcing, just physics.
As it is undisputed that CO2 concentration went up: where did the additional absorbed energy go if no global temperature change is correlated with it?
The paper is very interesting because it points to a need for other interpretations of climate change (or no change) than the monomaniac AGW theory.
As write the authors in their conclusion: This is not an argument about physics, but an argument about data interpretation.
richard telford says:
January 3, 2013 at 4:47 pm
“I am glad I amused you, but it how realistically the CGMs model climate is irrelevant for the type of analysis I am proposing. What is relevant is that there is a time series of global temperatures and a time series CO2 and other forcing that generated this temperature series in the model. If Beenstock et al’s method cannot find the relationship between CO2 and temperature in the model, then it cannot be trusted if it cannot find the relationship in the real world.”
Go ahead, show that this guy was wrong.
http://en.wikipedia.org/wiki/Granger_causality
I don’t understand this paper but my instincts are saying its fiddling with statistics that are divorced from the physics of the system.
Might be illuminating to build a fake black box model that includes some degree of causality, originating from multiple sources and mushed up with random variation and multiple response time scales. Then apply this same kind of analysis to see if it correctly identifies the underlying multiple causes. If it can’t the methodology is broken.
Lubos, I love the idea of a test refusing to produce the required result.
It’s similar to the use of the work “but” in logical statements. “A AND NOT B” gives exactly the same result as “A BUT NOT B”. But somehow using “BUT” implies a level of dissapointment. Like “Christmas but no presents.”
“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 suspect that the reason for this temporary “jump” is due to measurement bias. We measure temperatures where people LIVE and are producing energy, not in the uninhabited boondocks. When economic activity increases, we use more energy, which ultimately winds up as “waste” heat. That temporary jump is is a result of measuring the increase in local waste heat produced. No increase in economic activity implies no increase in waste heat, therefore no increase in temperatures regardless of what CO2 does.
DeWitt Payne:
My post at January 3, 2013 at 2:48 pm asked you to justify your silly assertion that you knew the cause of recent rise in atmospheric CO2 concentration and it said
Your reply to me at January 3, 2013 at 4:32 pm says in total
So, you ignore peer reviewed work because I contributed to it, and you cite your blog post which is twaddle.
The isotope data shows a change in the correct direction for it to have been induced by the anthropogenic emission (there is a 50:50 chance that it would be in the correct direction) but it has the wrong magnitude by a factor of ~6 if it were induced by the anthropogenic emission. There is no reason to suppose that any of the isotope change was induced by the anthropogenic emission when most of it cannot have been.
The facts are that the recent rise in atmospheric CO2 concentration can be modeled in a variety of ways as having a purely natural or a purely anthropogenic cause.
Each of the models in our paper matches the available empirical data without use of any ‘fiddle-factor’ such as the ‘5-year smoothing’ the UN Intergovernmental Panel on Climate Change (IPCC) uses to get the Bern Model to agree with the empirical data.
So, if one of the six models of our paper is adopted then there is a 5:1 probability that the choice is wrong. And other models are probably also possible. And the six models each give a different indication of future atmospheric CO2 concentration for the same future anthropogenic emission of carbon dioxide. Three of our models assumed a purely anthropogenic cause of the recent rise in atmospheric CO2 concentration and the other three assumed a purely natural cause.
Data that fits all the possible causes is not evidence for the true cause. Data that only fits the true cause would be evidence of the true cause. But the findings in our paper demonstrate that there is no data that only fits either an anthropogenic or a natural cause of the recent rise in atmospheric CO2 concentration. Hence, the only factual statements that can be made on the true cause of the recent rise in atmospheric CO2 concentration are
(a) the recent rise in atmospheric CO2 concentration may have an anthropogenic cause, or a natural cause, or some combination of anthropogenic and natural causes,
but
(b) there is no evidence that the recent rise in atmospheric CO2 concentration has a mostly anthropogenic cause or a mostly natural cause.
Indeed, since you don’t want to read the paper, I will mention a volcanic possibility which the paper does not mention but disproves the certainty with which you delude yourself.
CO2 is in various compartments of the carbon cycle system, and it is exchanged between them. Almost all of the CO2 is in the deep oceans. Much is in the upper ocean surface layer. Much is in the biosphere. Some is in the atmosphere. etc..
The equilibrium state of the carbon cycle system defines the stable distribution of CO2 among the compartments of the system. And at any moment the system is adjusting towards that stable distribution. But the equilibrium state is not a constant: it varies at all time scales.
Any change to the equilibrium state of the carbon cycle system induces a change to the amount of CO2 in the atmosphere. Indeed, this is seen as the ‘seasonal variation’ in the Mauna Loa data. However, some of the mechanisms for exchange between the compartments have rate constants of years and decades. Hence, it takes decades for the system to adjust to an altered equilibrium state.
The observed increase of atmospheric CO2 over recent decades could be an effect of such a change to the equilibrium state. If so, then the cause of the change is not known.
One such unknown variable is volcanic emission of sulphur ions below the sea decades or centuries ago.
The thermohaline circulation carries ocean water through the deeps for centuries before those waters return to ocean surface. The water acquires sulphur ions as it passes undersea volcanoes and it carries that sulphur with it to the ocean surface layer decades or centuries later. The resulting change to sulphur in the ocean surface layer alters the pH of the layer.
An alteration of ocean surface layer pH alters the equilibrium concentration of atmospheric CO2.
A reduction to surface layer pH of only 0.1 (which is much too small to be detectable) would induce more than all the change to atmospheric CO2 concentration of 290 ppmv to ~400 ppmv which has happened since before the industrial revolution.
I don’t know if this volcanic effect has happened, and I doubt that it has. But it demonstrates how changed equilibrium conditions could have had the observed recent effect on atmospheric CO2 concentration whether or not there was a change in temperature and whether or not the anthropogenic CO2 emission existed.
Simply, you are wrong. And it seems you are willfully wrong.
Richard
“do not”
davidmhoffer
Your post at January 3, 2013 at 9:47 pm says
I strongly agree that Bart’s graphs are very informative – everybody needs to see them – but they don’t provide the complete ‘answer’ which Bart assumes.
In my post at January 4, 2013 at 5:52 am I wrote
Bart’s graphs show how the short-term processes immediately respond to the altered system state induced by temperature change.
At issue is the long-term trend in rising atmospheric CO2 concentration.
The dynamics of the system show that the carbon cycle can easily sequester ALL annual CO2 emission (both natural and anthropogenic) of each year, but the long-term rise shows that they don’t. At issue is why they don’t.
The reason for the long-term rise in atmospheric CO2 is probably that some mechanisms of the climate system take decades to fully adjust to an altered system state. Indeed, the ice core records indicate that some mechanisms take centuries to adjust.
There are many possible reasons why the equilibrium state of the carbon cycle has changed: most possibilities are natural phenomena, but the anthropogenic emission is one (improbable) possible reason.
Richard
richard telford says:
January 3, 2013 at 4:47 pm
[…} If Beenstock et al’s method cannot find the relationship between CO2 and temperature in the model, then it cannot be trusted if it cannot find the relationship in the real world.
Sorry, Richard, but that’s a complete logical falacy and displays a serious misunderstanding about the nature of scientific testing (whether statistical or physical). There are many valid tests which have assymetric reliability for positive and negative results.
That’s why two different types of error (type 1 and type 2) exist. . As long as the result a test gives is of the type for which the test is reliable, it doesn’t matter at all what the likelihood of false results of the other type are.
In this case, what that means is that the analysis may well falsely indicate “a relationship” in random data but won’t (or is very unlikely to) indicate “no relationship” in data where causality does exist. So getting a result of “no relationship” in this case is a reliable indication that the data are NOT connected even though it would NOT have been reliable indication that they were connected if it had given a result of “relationship”.
To those negative comments on my methodological reference to the world’s central banks, perhaps you are also confused between regulatory and legislative loopholes in the financial sector and central bank operation. In that sense it is much like the assessment of climate variables in which there is disagreement on what happened even in hindsight. I stand by the soundness of the statistical technique in the paper and its common use in other research fields.
Joe says:
January 4, 2013 at 6:25 am
richard telford says:
January 3, 2013 at 4:47 pm
[…} If Beenstock et al’s method cannot find the relationship between CO2 and temperature in the model, then it cannot be trusted if it cannot find the relationship in the real world.
Sorry, Richard, but that’s a complete logical falacy and displays a serious misunderstanding about the nature of scientific testing (whether statistical or physical). There are many valid tests which have assymetric reliability for positive and negative results.
—————
Since there is nothing wrong with what you wrote, and I don’t say anything about asymmetrical reliability, I can only assume that you misunderstood what I wrote.
Beenstock et al does not explored the Type II error rate of their method. Therefore when they find no relationship, how sure can we be that there is no relationship and not that the apparent absence of a relationship is because their method has little statistical power. I would not be in the least surprised if their method had little power. They would not be the first people to proclaim an important negative result while using a low-powered method.
I am simply proposing a means by which the Type II error rate of their method could be established. If they could demonstrate that their method had high power on artificial data, more credibility could be given to their analysis on real data.
richardscourtney,
E&E will publish pretty much anything. Saying your paper is peer reviewed does not put it in the same league as the papers in, for example, Wigley and Schimel. Gerlich & Tscheuchner’s falsification paper and Miskolczi’s papers were similarly peer reviewed. They’re still wrong. In the end, many peer reviewed papers in the mainstream journals will turn out to be wrong. Sturgeon’s Law (or Revelation) is that 90% of everything is crud. I haven’t read your paper, but I’m betting that you cite Beck and/or Jaworowski. If that is the case, then your paper is definitely in the 90% category.
The fact is that human emissions of CO2 are more than enough to explain the increase in atmospheric CO2. And the model using only human CO2 emission fits the observed levels very well. Any natural process would have to alter that relationship. The only alteration observed is the so-called missing sink. That caused a reduction in the rate of atmospheric CO2 concentration increase, not an increase in the rate.
DeWitt Payne says:
January 4, 2013 at 7:31 am
The fact is that human emissions of CO2 are more than enough to explain the increase in atmospheric CO2. And the model using only human CO2 emission fits the observed levels very well.
The model of the world being flat was more than enough to explain the observation that sailors never returned from over the horizon. Didn’t make it right though!
Joe,
I suggest you research the various unit root tests and cointegration theory. Beenstock, et.al. do not find that there is no relationship. They find that any relationship must be spurious because of the structure of the time series. But time series testing for unit roots has problems when there is a non-linear deterministic trend in the data. The tests will find unit roots when none are actually present. Worse, there is no consensus on whether or how to remove deterministic trends before testing.
In fact, there is good reason to believe that the unforced temperature series cannot have d > 0.5, the limit for long term persistence. Thus finding values of d ~ 1 should have been a red flag that the tests were being improperly applied and/or that the series was probably being forced. But Beenstock, et. al. are economists not physical scientists.
You really should read the article that was linked earlier. It’s a complete rejection of cointegration theory in no uncertain terms.
DeWitt Payne:
Your post at January 4, 2013 at 7:31 am consists solely of more unsubstantiated assertion from you.
Clearly, facts and evidence have no possibility of breaking through the armour you have put around your beliefs. You are entitled to believe whatever you want, but I prefer to consider the science and what it indicates.
You say
Yes, the “human emissions of CO2 are more than enough to explain the increase in atmospheric CO2”: I said that. And I also stated the fact that many natural effects also explain the increase in atmospheric CO2 much better, but you ignore that fact because it does not fit with what you want to believe.
And you don’t say which model you mean when you say “the model using only human CO2 emission fits the observed levels very well”. If you mean the Bern Model then it doesn’t fit the observed levels: it requires unjustifiable smoothing of the data to make it fit.
Our paper provides three models which each uses only human CO2 emission and each fits the observed levels perfectly within the measurement errors and with no smoothing. But so what? Our paper also provides three models which each has the change induced by a different natural cause and they each also fit the observed levels perfectly within the measurement errors and with no smoothing.
As I said,
I want to know the true cause(s).
Reality is what it is, and your beliefs cannot change reality whatever it is.
Richard
DeWitt Payne:
This is an addendum to my reply to your post at January 4, 2013 at 7:31 am as substantiation of my claim concerning your beliefs.
You say to me
Our paper mentions neither Beck and/or Jaworowski.
You would have been able to assess the paper if you had read it.
Your words I quote here are an example of you ‘making stuff up’ in fallacious attempt to justify your fallacious assertions.
If you had an argument worth making then you would make it instead of inventing things in your mind as self-serving justification of your assertions. Those assertions can only be beliefs because they are based solely on assertions justified by untrue assumptions.
Richard
Quoting from the paper:
“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.”
Informal inspection of the temperature data of panel c does show acceleration, matching that of the greenhouse gas forcing plots in a and b. The temperature rise appears less dramatic due to different scaling factors used in the 3 plots, but the acceleration of the temperature in the last 40 years compared to the previous 80 is clear to the naked eye. This is confirmed by a formal fit of temperature data to a nonlinear equation.
Apologies for not including the nonlinear plot in the previous post.
http://www.skepticalscience.com/pics/AMTI.png
A little late to the party here, but my friend in DC (must remain anonymous, but is an energy division lead economist for a prominent three-letter agency) says:
“This is interesting. I have no idea what climate change modelers have done but if their claims of causality in an empirical sense have not taken tests for stationarity of the underlying time series then the regression results would be possibly meaningless. A big if. This is not new statistics and I doubt folks have ignored it. But, like I said, I do not know what the empirical climate models have done.”
Any germane comments welcome.
DeWitt Payne says:
January 4, 2013 at 7:55 am
lots of irrelevent stuff
In case you hand’t noticed, my post was nothing to do with the validity or otherwise of the paper’s findings. I’ll leave that up to people far more qualified than me (or, likely, you) to determine.
But Richard Telford’s original post (which I ignored the fallacy in) and his follow-up contained a very basic logical fallacy that a test can’t be any good unless it provides reliable results in both directions – hence his concern about type 2 error levels in the original, when type 2 errors play no part in the vlaidity regarding type 1.
I considered building a nice analogy to demonstrate the flaw in his reasoning but decided it was easier, and more relevant, to explain it in terms of the paper under discussion. To explain the logical flaw in requiring tests to have equal (or even known) errors of both types didn’t require any discussion about whether or not the analysis in the paper is appropriate. Indeed, introducing such discussion would only obfuscate the point I was explaining to Mr Telford.
Perhaps you should try to fully understand what people are saying before you expect them to accept your own points. After all, given your apparent mis-comprehension of my post, one does wonder how much you might actually comprehend (as opposed to simply repeating from somewhere the far more technical matters that you’re using to criticise the paper?
richardscourtney,
“Those assertions can only be beliefs because they are based solely on assertions justified by untrue assumptions.”
They would still only be beliefs even if they were backed by true assumptions. It only matters that what backs the assertion is an assumption rather than evidence.
🙂
MattS:
re your post addressed to me at January 4, 2013 at 9:07 am.
Yes, of course you are right. I stand corrected. Thankyou.
Richard
“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.”
Gee a new branch of inquiry based on observations – this is the achilles heel that the hockey team will exploit in debunking this upstart idea.
JazzyT says:
January 4, 2013 at 12:12 am
“there will be a 180 degree phase shift due to the 24-month average”
The WoodForTrees site automatically shifts the moving average to have zero phase offset.
“But anthropogenic CO2 would show up mostly as a steady value (an offset) in the derivative of CO2, if it’s the relatively constant rise that people seem to think that it is, and that the Keeling curve indicates.”
Anthropogenic CO2 would show up as a trend in the CO2 derivative, because production has been steadily increasing. There is no room for such an additional term, because the slope is already accounted for by the temperature relationship.
“Measurements of carbon isotopes in atmospheric CO2 show a decreasing concentration of C-13, consistent with the notion that increased CO2 arises from these anthropogenic sources.”
“Consistent with” is not proof. The derivative relationship I have shown reveals that the consistency is spurious happenstance.
richardscourtney says:
January 4, 2013 at 6:21 am
“…but they don’t provide the complete ‘answer’ which Bart assumes.”
We’ve been over this many times and are not going to agree. But, for the record, I do not assume, I observe. The match is virtually perfect and seamless across the observable frequency spread. It is clear that temperature is in the driver’s seat.
DeWitt Payne says:
January 4, 2013 at 7:31 am
“The fact is that human emissions of CO2 are more than enough to explain the increase in atmospheric CO2.”
The fact is, this tells you nothing about whether it is responsible for it, only whether it could be.
“And the model using only human CO2 emission fits the observed levels very well.”
It fits very poorly in the fine detail. As I show, the model using temperature only fits the observed levels very well, too. But across all frequencies, not just in the quadratic term.
“Any natural process would have to alter that relationship.”
The relationship is spurious. It is happenstance. And, it is not at all an unlikely thing to have two increasing time series match a low order polynomial when you can add an arbitrary offset and scaling.
MattS says:
January 4, 2013 at 9:07 am
“richardscourtney,
“Those assertions can only be beliefs because they are based solely on assertions justified by untrue assumptions.”
And, so we reach a state in which an erroneous conclusion propagates from an initial erroneous conclusion which gets all but forgotten, and is always referred to, but never reexamined. A review of Feynmann’s recounting of the measurement of electron charge might be in order. Nobody wanted to go too far from Millikan’s value. Scientists are social animals, too, and they often seek safety in the herd.
Phillip
Given your claim that temperature change is accelerating over the last 40 years, the lack of acceleration over the last 15 years, nearly 40% of the period in question, contradicts your claim. Try fitting a linear trend, a concave trend and your convex trend to the data for the period 1970-2012 and report back with the R2. I have a hunch that your convex trend will produce the poorest fit.
Bart:
I am replying to a comment in your post at January 4, 2013 at 9:58 am for the information of others. You say
Yes, we have “been over this many times” and it is clear that we “are not going to agree”.
I have quoted your view here and my view is explained in my post at January 4, 2013 at 6:21 am which you cite.
There are those (e.g. DeWitt Payne) who state certainty that the recent rise in atmospheric CO2 concentration has an anthropogenic cause. And there are others (e.g. yourself) who state certainty that the recent rise in atmospheric CO2 concentration has a natural cause.
I remain ‘on the fence’ about the causality until I see data which convinces me to ‘get off the fence’ on one side. Your data convinces you but not me that I should ‘get off the fence’ on your side.
Richard
DeWitt Payne.
You are jumping the shark here. Cointegration is a longstanding and mainstream method; Granger and Engle won the Nobel prize for their work in pioneering the field. To rebut this, you repeatedly link to a single paper in a minor finance journal (as a financial professional, I have never even heard of the journal). According to Google Scholar, the paper has been cited a total of three times – all three by the author himself! Given the hundreds of papers published on cointegration in any year, this is a not very impressive achievement. So you disparage E&E, but somehow place significant reliance on this fringe paper in a fringe journal. Not very consistent.
Sorry if this is a repost but I think I messed up the first attempt.
Tom,
I was commenting on the authors statement about their figure presenting their data from 1880 to the present. That is their chosen data set.
As a scientist I am used to looking at such graphs but believe that even “informal” examination by the untrained eye can discern an accelerating trend in the data. In case some people were having trouble I simply suggested concentrating on the last 40 years of data compared to the previous 80 (actually 90) and “informally” making a linear fit with the minds eye. This impression can be confirmed by actual linear fits to temperature data for the period 1880 to the present, 1880 to 1969, and 1970 to the present.
http://www.woodfortrees.org/plot/gistemp-dts/from:1880/to:2013/plot/gistemp-dts/from:1970/to:2013/trend/plot/gistemp-dts/from:1880/to:1969/trend/plot/gistemp-dts/to:1880/to:2013/trend.
The fit for all the data is clearly inferior to the non linear fit. (Unfortunately the r square values for the linear fits are not given, nor is the function for the non linear plot but it appears to be second order polynomial or exponential.)
http://www.skepticalscience.com/pics/AMTI.png
With regard to a nonlinear fit for the past fifteen years, temperature data is much noisier than greenhouse gas concentration as the former is also dependent on factors such as solar output, volcanic eruptions, el nino and la nina events to name some of the most significant. Temperature trends must be analysed over multidecadal time periods. The noisy data means that the linear function from 1970 to the present is reasonable but is inferior to the nonlinear fit over he longer period.
Philip Shehan says:
“Informal inspection of the temperature data of panel c does show acceleration…”
Wrong.
But I knew this would happen. As Werner Brozek repeatedly shows in great detail and based on extensive data sets, there is no recent acceleration of global temperatures. Faced with that undeniable fact, the alarmist crowd has one of two choices:
1. Admit that despite the rise in CO2, there has been no acceleration of global temperatures, and reassess their failed conjecture, or…
2. Lie about it.
Global temperatures are not accelerating. In fact, as the WFT chart shows, global warming has stopped for the past decade and a half. Claiming that global temperatures are “accelerating” when the data shows otherwise is pure mendacity.
D.Boehm,
All I can do is redirect you to my 11.37 PM post.
Again I am specifically analysing the data and the claims made for it from 1880 by the papers authors. I have tried to be polite but since you are implying I am a liar, only wilful self delusion, ignorance or dishonesty can lead you ignore mathematical analysis of the entire data set and cherry pick a 5% segment of the total data carefully selected to begin with an extreme el nino southern summer of 1997-98, which in no way invalidates the 130 year trend.
Why didn’t you pick the 15 year period between 1940 and 1955 to prove that temperatures from 1880 to present have been dropping?
http://www.woodfortrees.org/plot/gistemp-dts/from:1940/to:1955/plot/gistemp-dts/from:1940/to:1955/trend
When the paper was first put up on Beenstock’s web page I bought a couple of books on the topic of non-linear co-integrated vector autoregressive (VAR) processes. Linear co-integrated VAR processes have been studied for decades. Except for the possibility of programming errors (and I hope that the authors follow a recently and widely but not universally promoted standard of putting all of their code, data, intermediate results, etc on line), I have two criticisms of the paper:
1. The standard: it is really hard to infer causation from vector time series without interventions (interventions can be conducted in chemical process control, where the VAR processes have been used with success.) All they have shown, with that caveat in mind, is that it is possible, contrary to a claim by IPCC AR(4), to create and estimate a reasonable model for 20th century temperature change that gives little or no weight to CO2 changes. In a sense, this is a complicated counterpoise to Vaughan Pratt’s modeling of a few weeks ago, in which he showed that: assuming a functional form for the CO2 effect he could estimate a filter to reveal that functional form. In each case, by enlarging the total field of functions under consideration, you can generally get a model to justify any a priori chosen conclusion.
2. I would like to have seen more graphs displaying the estimated non-linear relationships between the measured variables at each time point and the full set of variables at each lag: model and data. This is among the things that I hope they provide on line, but if they put up their data, model and results it will be possible for others (maybe I will) to produce those plots.
I think the paper is a solid contribution to the topic of modeling multivariate climate data. Now that their model has been published, its predictions can be updated as new data on CO2 concentrations and solar indices become available, and we can see how well it does on “1-year ahead”, “5-year ahead” and “10-year ahead” predictions without changing model parameters. As with all of the other models, if the mean square prediction error is small enough, we may begin to rely on its predictions.
Richard Telford: Beenstock et al does not explored the Type II error rate of their method. Therefore when they find no relationship, how sure can we be that there is no relationship and not that the apparent absence of a relationship is because their method has little statistical power. I would not be in the least surprised if their method had little power. They would not be the first people to proclaim an important negative result while using a low-powered method.
I agree with you.
I do not consider that in evaluating whether the paper is good, because it is already a long and technically dense paper, and I think that topic can be addressed later. However, I also think that it is possible to elaborate modeling sufficiently to achieve almost any desired result on extant data, so I believe that potential type 1 and type 2 error rates for particular hypotheses are 1. Not for a particular test, but for the procedure of multiple model fitting and multiple testing. Nobody is naive any more, the authors had already thought about potential models and what might produce null results for particular tests (I would bet) even before they started modeling. With so many people having already done so much modeling on so much data, the only hope for model comparisons and hypothesis tests must depend on future data.
richardscourtney,
I’m feeling the need for amusement, but there’s no way I’m going to spend 18£ to purchase a copy of your article. Put a pdf on line somewhere and post a link.
Pardon the error above. 15 out of 130 years is 11%
Philip Shehan says:
January 4, 2013 at 1:02 pm
Why didn’t you pick the 15 year period between 1940 and 1955 to prove that temperatures from 1880 to present have been dropping?
Because you see, we are trying to determine why the 1650 – 2000 year trend of slowly rising temperatures – all from natural causes unrelated to a CO2 increase between 1950 and 2013 – of 360 years length is being ignored by those whose funding and power and future promotions and employment are threatened by the evidence; while you are trying to force a rising-CO2 and rising-temperature relationship valid ONLY from 1973 to 1998 onto a 130 year period.
Further, why is a single 25 year period (1973 – 1998) “valid, critical, and worth destroying the world’s economy” about (while killing millions) important, while a 15 year period 1997 – 2012 ignored? the warming slope STOPPED in 1998. Why can you not recognize that fact? Whether it will begin again we do not know – but you cannot pretend that warming – and increase in temperatures – is continuing. The solar experts are predicting a 1.5 degree temperature drop over the next solar cycles … are you mentally ready for that?
Don’t blame aerosols either – the Mauna Loa atmospheric visibility index remain unchanged since before 1950, (other than two volcanic peaks).
Is your funding, your life, your health threatened by the 15 year period, but you are content urging the absolute, immediate and assured death of millions because of that one 25 year period out of 350 that you fear “might” cause minor problems 90 years from now? I could have saved millions from poverty, hunger, and disease through better water, better transportation, better heat, better food preparation and storage, and better shelter and clothing for a fraction of what YOU wasted on CAGW trips and politics alone. Today’s worldwide economic crisis BEGAN in the energy policies demanded by those in power in academia and the media and politics who used YOUR typical AGW propaganda to destroy energy production and movement.
In the meantime, while you fear a 1/10 0f 1% chance of a “might be a problem” for 20,000 you guarantee disaster now for billions more.
Here’s a Ph.D. dissertation on using VAR-ML to model temperature vs CO2 among other things. Conclusions include that there is two way Granger causality between temperature and CO2, particularly including the glacial epochs. Somebody, not me, might want to point this out to Willis E., who seems to think that Granger causality in this case is only one way.
DeWitt Payne:
Your post at January 4, 2013 at 1:23 pm says to me in total
You make two points and I address each of them.
If you want “amusement” then I suggest you read the posts you have made in this thread because you may give you the belly laugh which they give me.
If you don’t want to obtain a copy of our paper then watch the lecture by Murray Salby at
Salby’s lecture says the same as the analysis in our paper except that
(a) Salby does not conduct the attribution studies which we included to demonstrate the findings,
(b) we did not make Salby’s assessment of soil moisture effects, and
(c) Salby concludes that because natural changes can be the sole cause then they are the cause of the recent rise in atmospheric CO2, but we were not willing to accept that because – although unlikely – the anthropogenic emission could also be the cause.
Indeed, Salby uses some very similar words to paragraphs in our paper: n.b. this is NOT an accusation of plagiarism: clear statement of the same facts is likely to use the same or similar words.
A summary of Salby’s lecture with his main slides is at
http://hockeyschtick.blogspot.co.uk/2012/09/climate-scientist-dr-murry-salby.html
Watch the lecture, check its facts, and you may learn something despite your prejudice.
Richard
DeWitt,
I read your article (“Where Has All the Carbon Gone?”) at the Air Vent. It’s an informative article, with interesting results and interesting comments. I’m not sure it really provides much insight or evidence for Ferdinand’s Mass Balance argument, though. The Bern Carbon Cycle model you investigated in that article is for the most part an empirical model, is it not? I believe it is constructed under the assumption that humans are the cause of the recent increase in atmospheric CO2. I don’t think you can use an empirical model (which is tuned to provide a good fit to the data) as evidence to support one of its own input assumptions.
Regarding isotope ratio, the Earth has been sequestering carbon through the biosphere for billions of years. IMHO just about any reasonable terrestrial source for increased atmospheric CO2 will have been ultimately filtered by plants and would therefore likely have an isotopic ratio similar to that of burning fossil fuels.
The oxygen depletion evidence is also speculative and far from overwhelming. Correlation of oxygen depletion with fossil fuel burning is not that great and IMHO it is just as likely (and just as speculative to conclude) that the depletion is due to land use changes, or perhaps simply due to natural changes in the biosphere as temperature and CO2 rise.
RACookPE1978 says:
January 4, 2013 at 1:26 pm….
Once again.
I am commenting on the claims made in the paper for the temperature record from 1880. The authors’ claim that their is no accelerated warming over that period is not supported by examination of linear and non linear fits for that period.
My rhetorical question to D Boehm about why the 15 year period is any more indicative of a 130 year trend than the period 1945 -50 is intended to show that cherry picking data gives no indication whatsoever of the long term trend.
And I do not recognise the fact that warming has stopped since 1998 because it has not.
You are not just cherry picking a 15 year period to arrive at that conclusion, you are cherry picking the extreme southern el nino summer of 1997/1998. One summer does not a trend make.
Since 1996 there has been a warming trend of 0.1 C per decade. Since 1999 there has been warming trend of 0.1 degree per decade. And this even with the cololing la nina weather pattern for the last two years.
http://www.woodfortrees.org/plot/wti/from:1995/to:2013/plot/wti/from:1996/to:2013/trend/plot/wti/from:1998/to:2013/trend/plot/wti/from:1999/to:2013/trend
So explain to me how there can have been no warming since 1998, but warming since 1996 and warming since 1999.
By the way I am writing from Australia where the Bureau of meterology is debating whether the end of the el nino period means we are reverting to normal warmer and drier conditions or whether we are in for above average heat.
As far as tipping future temperature trends goes, it is unscientific to put too much reliance on individual weather events. That said, the entire continent is in the grip of a heat wave. It was 106 F here in Melbourne yesterday (way down south, the cool part) while bushfires are burning in Tasmania (the island state further south). Last night I have been unable to sleep with the heat, so typing out these pearls of wisdom in the night. So unscientific or not, my money is on a hot 2013.
From the Discussion section of ‘Polynomial cointegration tests of anthropogenic impact on global warming’ by M. Beenstock, Y. Reingewertz, and N. Paldor , published in the journal Earth System Dynamics
{all emphasis by me, John Whitman}
– – – – – – – – – –
Their conclusion’s significance lies in demonstrating that observations (time series data) cannot with significant confidence be interpreted as supporting a strong case for anthropogenic causes of recent global warming. There should be significant caution about any interpretations that support significant AGW causations.
And, I find they are bending over pretty far backwards to show how they might be wrong. Good for them. As part of that they directly allow for physical interpretation as well as data interpretation. I think the paper is a positive step in right path toward more analysis based on both its achievements and its shortcomings. Look forward to papers relating to it whether they support it or not.
Some might criticize them about the data sets they chose. But, to do exactly what they did with different data sets is very facilitated by their willingness to have the paper completely open and transparent in all respects.
It would interest me if the very same kind of study could be done over both the Holocene and also for the last 2000 yrs. Does anyone know if anyone has done such similar statistical studies or if someone is in the process of doing them?
John
Philip Shehan,
Your comment regarding ‘accelerating’ temperatures was pretty self-explanatory. However, temperatures are not accelerating. The only way to show they are is with a false artifact using a cherry-picked chart.
The longer the warming trend shown, the better. Here is a chart going back to 1850 and showing steadily rising global temperatures as the planet recovers from the LIA.
Note that there is no acceleration of global warming. None. The planet has been warming along the same trend line [the declining green line] for hundreds of years, and global warming has not accelerated. In fact, rather than accelerating, global warming has stopped for the past decade and a half.
There are any number of records that show that long term global warming has remained on the same trend line, and within well defined parameters. That trend has not changed despite the ≈40% rise in CO2.
Conclusion: the effect of CO2 is vastly overstated.
In fact, there is no evidence that the rise in CO2 does not have a cooling effect. The only empirical evidence we have shows that ∆CO2 follows. ∆T. There are no empirical measurements showing AGW. Thus, AGW is merely a conjecture, and until/unless it is reliably measured, no more public money should be wasted on such ‘climate studies’. Sorry if that gores your ox.
Philip Shehan says:
January 4, 2013 at 2:35 pm
“As far as tipping future temperature trends goes, it is unscientific to put too much reliance on individual weather events. ”
I disagree completely. What the CO2AGW scientists need to show is evidence for the positive water vapor feedback thermal runaway. For this, they need the following conditions:
No wind
High humidity
Elevated CO2
Insolation, no clouds
These conditions are the testbed. We should see a local thermal runaway within minutes, as radiative energy exchange is a fast process. This would be a local weather event not possible in the past, and proof for the thermal runaway conjecture. Specifically, we should see an absolute all time high temperature record for the continent on which it happens.
Good luck finding one.
From the Abstract section of ‘Polynomial cointegration tests of anthropogenic impact on global warming’ by M. Beenstock, Y. Reingewertz, and N. Paldor , published in the journal Earth System Dynamics
From the Discussion section of ‘Polynomial cointegration tests of anthropogenic impact on global warming’ by M. Beenstock, Y. Reingewertz, and N. Paldor , published in the journal Earth System Dynamics
– – – – – – – –
The possibility of a relatively ‘temporary’ anthropogenic effect on temperature rather than a relatively ‘permanent’ effect has suggestive implications:
a – Reconsideration needed about the adequacy of the carbon cycle being promoted via IPCC assessment reports. This paper is a challenge to that IPCC endorsed carbon cycle that carbon cycle that supports the case for anthropogenic cause of warming.
b – Wondering what it could mean if it is verified that a change in the rate of change of CO2 concentration may cause a ‘temporary’ change in temperature but a change in CO2 concentration may not cause any temperature change.
c – Is (b) possibly the CO2 lagged increase from natural caused heating/miniaturization of soil and heating of oceans?
John
[“Forcings” instead of “forgings” in their quoted text? Mod]
richardscourtney,
Salby’s slides were indeed as amusing as I expected. You do know that the seasonal and annual variability is imposed on a much larger trend, don’t you? Removing that trend is rather like ignoring an elephant in the room.
But if the world ocean is a source rather than a sink, how come the CO2 concentration at the South Pole station is lower than at Mauna Loa, which is lower than Barrow while the SH has a much lower percent land cover than the NH? The ocean as a sink is a much better explanation for the decrease in seasonal variability from Barrow to Mauna Loa to the South Pole as well as the concentration gradient. And you still haven’t explained where the 32 Gt of CO2 emitted in 2011, for example, goes. CO2 at Mauna Loa went up by 1.82 ppmv in 2011, that’s equal to about 15 Gt of CO2. I know some of you think mass balance is disreputable black magic, but it’s a standard tool in Chemistry and Chemical Engineering. Short of a nuclear reaction, matter must be conserved. The simplest explanation is that 32 Gt of CO2 went into the atmosphere while the biosphere and the oceans absorbed 17 Gt from the atmosphere. And the rate of removal is indeed proportional to the increase in atmospheric concentration, as one would expect from an equilibrium process. For example: CO2 emissions in 1965 was about 12 Gt while the increase in CO2 concentration was 1.34 ppmv and the concentration was 321 ppmv. That’s about 1 Gt absorbed and 11 Gt left in the atmosphere.
Also, since a lot of you believe that the global temperature hasn’t changed since 1998, how come the CO2 concentration is still going up? The lag time of the ocean isn’t a few years, btw, it’s closer to 2000 years and ocean temperature hasn’t gone up as fast as the land temperature. The dissertation I linked to above had the sensitivity of CO2 to temperature as 2.3 ppmv/degree. Of course CO2 is going up because global emission of CO2 has been increasing.
D Böehm says:
January 4, 2013 at 2:46 pm…
Oh please. Now I really don’t want to be rude but the presentation of your temperature data to include offsets for no other reason than to stretch the y axis to apparently flatten the real data is a cheap and ridiculously obvious attempt at deception that just gets my scientist dander up.
Once again the data presented realistically with linear and non;inear fits, both showing accelerataion of the warming trend for the period under discussion:
http://www.woodfortrees.org/plot/gistemp-dts/from:1880/to:2013/plot/gistemp-dts/from:1970/to:2013/trend/plot/gistemp-dts/from:1880/to:1969/trend/plot/gistemp-dts/to:1880/to:2013/trend
http://www.skepticalscience.com/pics/AMTI.png
I see the C isotope wars have broken out again.
http://chiefio.wordpress.com/2009/02/25/the-trouble-with-c12-c13-ratios/
We simply can’t know the source of CO2 from isotope ratios. Long list of problems, but one simple one is that we don’t know the isotope ratio in the fuels that were already burned ( it varies from fuel deposit to fuel deposit) and we have no clue what the ratio is from large quantities vented from geologic processes such as the mid-ocean vents, and even liquid BLOBS of CO2 seeping from the ocean at depths where it stays a liquid. And a whole lot more.
Oh, and CO2 just oozes from the ground all over the planet due to geological processes. In enough concentration to sporadically kill a lot of people and animals. Yellowstone, Mammoth, and many others have CO2 “issues” and have had to close off area or had animal kills. So unless you know the reason and amount of volcanic / geologic cyclicals and the variations in isotope ratios all over the world (including under the oceans) you can’t say squat. Similarly, do you know the degree to which plankton bloom live and die? That cycles a load of CO2 as well. How about “Fish gut rocks”? Nobody even knew they existed a few years ago. Turns out many ocean fish excrete carbonate deposits and poop them to the ocean floor. Now we’ve hauled vast quantities of fish out of the global oceans so there are a lot less rock-poopers “doing their thing”, which means less CO2 sequestration. Numbers? Hey, folks just figured out lately this was happening at all… but it’s big… and more…
Then there are the MASSIVE quantities of carbonate washed into the ocean every year from erosion of the rocks of the continents. Care to guess what they do? Yes, guess. Not going to have any actual way to say since it is highly variable and subject to a lot of estimation:
http://chiefio.wordpress.com/2011/12/12/ocean-carbonate-from-rocks/
Got any idea what the isotope ratios are for all the rock sources on the planet? Didn’t think so…
(It will vary, as some are ancient and some are freshly made, like those gut rocks or clam shell middens the Native Americans piled all over Florida.)
You can start to get an idea of the problem by looking at just some of the sources and sinks:
http://chiefio.wordpress.com/2010/10/17/where-co2-goes/
But since there are ‘lakes’ of liquid CO2 on the bottom of the ocean, I think that’s going to be hard to do:
http://chiefio.wordpress.com/2011/12/10/liquid-co2-on-the-ocean-bottom/
There’s also a bit of video with a shellfish (shrimp) playing with a bubble of liquid CO2, so “acidification” from CO2 concentration clearly isn’t an issue for him…
The simple fact is that asserting the CO2 rise is from fossil fuels is an assumption and a guess. It can’t be anything else as the needed data are missing. Yes, we release the CO2. What happens to it after that is anybodies guess and subject to gigantic natural processes that completely swamp it in scale.
Per the paper:
It would be nice to see the same treatment of “tide raising forces”. They have a cycle that matches the temperature history nicely. Tides are not just a monthly cycle. Since the moon has longer orbital changes, there are 60 year cycles (sound familiar?) and 1800 year cycles and several others.
Tides account for more than half of the vertical ocean mixing, so can easily account for moving cold water to the surface. That, then, can shift CO2 absorption and air temperatures and influence rainfall. As orbital resonance will keep lunar changes ‘in sync’ with planetary positions and solar motions (and potentially solar sunspot state if the match of sunspots to solar motion is valid) the lunar-tidal link can also explain some of the apparent solar correlation. They correlate, but via common orbital mechanics and tides.
http://www.appinsys.com/GLobalWarming/SixtyYearCycle.htm
cites: http://www.agu.org/pubs/crossref/2012/2012GL052885.shtml
with this nice graph:
http://www.appinsys.com/GLobalWarming/SixtyYearCycle_files/image002.jpg
http://www.pnas.org/content/97/8/3814.full
I would also suggest that the degree of ‘mixing’ will influence CO2 absorption rates.
More detail here:
http://chiefio.wordpress.com/2013/01/04/lunar-cycles-more-than-one/
The tidal cycles match temperature history on the 60 year, 1800 year and other periods as well. IMHO, it’s a strong contender and possible “smoking gun” for natural variability. At present we are at a dead bottom of mixing. Going forward, we ought to be getting much more. That ought to give cooling ocean surfaces, less CO2 out gassing and more absorbing, and a generally colder aspect to temperatures.
Graph of mixing power:
http://www.pnas.org/content/97/8/3814/F1.large.jpg
Peak in 1974, trough in 1990s, peak in 1787 (LIA), trough in 1920-30. etc etc.
So maybe CO2 and cold / hot cycle together because the come from the same ocean pot and are subjected to the same pot stirring…
richard telford says:
January 4, 2013 at 7:31 am
Since there is nothing wrong with what you wrote, and I don’t say anything about asymmetrical reliability, I can only assume that you misunderstood what I wrote.
Beenstock et al does not explored the Type II error rate of their method. Therefore when they find no relationship, how sure can we be that there is no relationship and not that the apparent absence of a relationship is because their method has little statistical power.
—————————————————————
That’s what i was trying to explain, Richard. There are two possible types of error when you’re testing for the presence of something.
Your test might tell you that “something exists when it doesn’t, or it might tell you it doesn’t exist when it does. Those are type 1 (a false positive result) and type 2 (a false negative result) respectively. The chance of each type of error will not usually be the same for any given test.
If you’re testing for “the absence of something” (as they are in this paper) then “NO CORRELATION” is the “something” that you’re looking for. So a type one error would mean finding “NO CORRELATION” when there is one (ie: falsely finding what you’re looking). A type 2 error in this case would be finding “CORRELATION” when one doesn’t actually exist.
So, in this case, finding “NO CORRELATION” is a POSITIVE result from the test whereas, had they found “CORRELATION” that would have been a NEGATIVE test result. It takes a little getting your head round that “there’s nothing there” can be the “positive” result but it’s only really a matter of perspective – if you’re looking for some solid ground to build on, finding a hole is a “negative” result, if you’re looking for holes to turn into swimming pools then finding solid ground is a negative..
Assuming for now that the method is appropriate to the data, that the data itself is reliable, and so on, the only error consideration then is “how likely was it to incorrectly give the POSITIVE result we obtained?” – or “what is the chance of a false positive?”.
The chance of a false positive is given entirely by the type 1 error rate, so the type 2 rate is irrelevant.
Philip Shehan,
Thank you for your chart, in which you have cherry-picked a short term trend artifact.
As I have repeatedly explained, the longer the time span of the chart, the more accurate the long term rising trend. Here is a chart showing what you are doing.
Rather than using a proper long term trend chart, you are cherry-picking a short time frame that supports your belief system. You may not even realize what you are doing, because your mind is already made up and closed air-tight. You believe that global temperatures are accelerating, so you cherry-pick a short time frame and say, “Aha!! Acceleration!”
But it is not so. The planet is recovering from the LIA along the same long term trend line, and it does not matter whether CO2 is low or high. In other words, CO2 does not matter. It is irrelevant. Sorry about your ox.
DeWitt Payne says:
January 4, 2013 at 3:17 pm
“Also, since a lot of you believe that the global temperature hasn’t changed since 1998, how come the CO2 concentration is still going up?”
I gave you the equation above, and explained how it can arise. It’s very simple, but apparently over your head.
DeWitt Payne:
I am acknowledging your post at January 4, 2013 at 3:17 pm so you know I have not ignored your twaddle.
I suggested that you watch Salby’s lecture and check his facts. But your post says you have not. Instead you continue to adhere to your irrational prejudice.
E.M.Smith gives detailed information at January 4, 2013 at 3:24 pm and he makes the only possibly valid conclusion when he says
And our paper assessed the entire carbon cycle not merely its parts which he mentions so I could add to his comments, but I see no reason to bother when you have repeatedly expressed your prejudice so clearly.
Be content with your prejudice if it makes you happy, but I will continue to adhere to the empirical evidence and it rejects your irrational beliefs.
Richard
DeWitt Payne:
My reply to your post at January 4, 2013 at 3:17 pm did not reply to a specific question you asked but I had repeatedly answered in posts whose contents you have ignored.
In retrospect, my failure to answer your question could imply that I have avoided it. The question was
My shortest answer to that was in my post at January 4, 2013 at 6:21 am addressed to Davidmhoffer.
Please note that you would not have asked your question if you were willing to learn from this thread instead of asserting your irrational beliefs.
Richard
Joe says:
January 4, 2013 at 3:25 pm
Your test might tell you that “something exists when it doesn’t, or it might tell you it doesn’t exist when it does. Those are type 1 (a false positive result) and type 2 (a false negative result) respectively. The chance of each type of error will not usually be the same for any given test.
If you’re testing for “the absence of something” (as they are in this paper) then “NO CORRELATION” is the “something” that you’re looking for. So a type one error would mean finding “NO CORRELATION” when there is one (ie: falsely finding what you’re looking). A type 2 error in this case would be finding “CORRELATION” when one doesn’t actually exist.
————————–
Please would you kindly give a reference in a statistical text book for this.
D Boehm,
Look, don’t try to blow smoke. You have been caught out manipulating the data sets to produce a chart which attempts to hide the trend.
Here is your chart going back to 1850:
http://www.woodfortrees.org/plot/hadcrut3vgl/compress:12/offset:14/plot/gistemp/compress:12/offset:13.885/detrend:-0.02/plot/hadcrut3vgl/trend/offset:-0.42/detrend:-0.23/offset:14/plot/hadcrut3vgl/trend/offset:14.1/detrend:-0.23/plot/hadcrut3vgl/scale:0.00001/offset:17/plot/hadcrut3vgl/from:1880/to:2010/trend/offset:14/plot/hadcrut3vgl/scale:0.00001/offset:9
and here is your chart with the irrelevant camoflage removed.
http://www.woodfortrees.org/plot/hadcrut3vgl/compress:12/offset:14/plot/gistemp/compress:12/offset:13.885/detrend:-0.02/plot/hadcrut3vgl/trend/offset:-0.42/detrend:-0.23/offset:14/plot/hadcrut3vgl/trend/offset:14.1/detrend:-0.23/plot/hadcrut3vgl/from:1880/to:2010/trend/offset:14
Your linear fit, stripped of the camoflage is inferior to the nonlinear fit:
http://www.skepticalscience.com/pics/AMTI.png
Philip Shehan,
As usual you are dissembling, by avoiding the clear points I made. The chart I posted is no different in principle from the one you posted under it. They both show that there has been no acceleration of global warming — the central issue. No acceleration. What is it about “no acceleration” that you can’t get your head around? Global warming has not accelerated, despite your desperate cherry-picked artifacts. In fact, global warming has stopped for the past decade and a half. You seem to be the only jamoke who can’t understand that plain fact.
And as I have pointed out before, your S.S. chart has no provenance; it is an invented fabrication with no connection to reality. No doubt a John Cook cartoon.
Once more for the thick-headed: there is no acceleration in the long term global warming recovery from the LIA.
Sorry about your ox. I never liked him anyway.
Philip Shehan says:
January 4, 2013 at 4:42 pm
That data set also shows ~5 ~32 year half-cycles. ~1848-1880 warmer, ~1880-1912 cooler, ~1912-1944 warmer, ~1944-1976 cooler, ~1976-2008 warmer, ~2008-?? cooler. sarc/
Anyone care to “fit” some other pattern….
JP Miller,
You’ve got to understand something about Shehan: he feeds at the public trough, so he feels he needs to spout his alarmist propaganda. But it’s only pseudo-science.
On a thread a few weeks ago I tricked Shehan into admitting that GCM’s were incapable of any sort of forecasting but could only project an indefinite warming trend. When he realized what he had done he tried to shift his line and wriggle out, but it was too late.
D Boehm,
You are still blowing smoke. Yes I cherry picked some short term data sets precisely to show that the preferred skeptic cherry pick starting with the extreme el nino southern summer of 1997-98 is contradicted by cherry picks starting at 1996 and 1999, thus demonstrtaing that by cherring picking you can argue any point you like. (Take note JP Miller) Therefore long term trends must be examined,
The data sets you provide beginning in 1850, which if properly examined after removal at your attempt to flatten the graph with bogus irrelevant lines, clearly show an accelerating trend. If you care to actually look at the data set in the figure from skeptical science, you will note that it is essentially the same temperature data set you present but is mathematically fitted by a non linear accelerating curve which is superior to your linear fit. It is thus no more a John Cook cartoon than your own presentation.
I do not feed at the public trough, unless you consider that being involved in cancer research at the University of Melbourne merits that label. It does demonstrate however that I know how to analyse scientific data, and can spot a shifty move and misrepresentation when I see it.
Philip Shehan:
Your post at January 5, 2013 at 1:43 am concludes saying
There is no need for you to be so modest.
Your posts in this thread clearly demonstrate your skills at misrepresenting data.
However, the data clearly and unambiguously show that global warming has stalled for the most recent ~16 years, and it is not possible for you to spin that stalling as being consistent with “acceleration” of global warming (unless you mean negative acceleration).
It is time for you to desist. If there are people reading this thread who are sufficiently gullible as to be gulled by your misrepresentation then you have already misled them. And you are demeaning yourself in the eyes of everyone who is not that gullible.
Richard
@Philip Shehan – the trouble I have is when people suggest that the trend in temperatures is accelerating. Look at the data:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1970/to:2013
Can anyone honestly look at that graph and say that the trend is accelerating? I think if you can, you are lying to yourself…and it’s the same with any of the datasets. Now the whole thing with the big El Niño year, 97-98, yes you can see the source of controversy, let’s also be honest. It does make a big difference to whether you can claim the trend stopped/slowed down 15/16 years ago or less than that. Whatever your opinion on that though, NO-ONE should be able to look at the data overall and say “clearly temperature increase is accelerating”…because onviously the exact opposite has happened, regardless of WHEN you say the change has occurred. It’s clear that over AT LEAST the last ten years, the rate has slowed, either to zero increase, or a very, very insignificant amount (possibly even decreasing, when you look over the more recent years). Much less than the value of the trend from the 1970s to late 90s. This is clear just from looking at the data, with no statistical analysis at all, just interpreting the graph. The trend CANNOT be said to be increasing whatever way you look at it, and it’s as simple as that. No need for “cherry-picking” to rebut the statement of increased warming trend, it’s just simple observable fact that it’s not the case.
When you’re asking yourself just how much mankind’s input to atmospheric temperatures, can possibly be, remember this: there is a movie by PBS called “400 Years of the Telescope” and it is very, very good; it takes us in a HIGHLY sped-up way from the very first monocular ship-to-ship magnifier scopes that gave a power of three magnification, to Galileo getting his hands on one; to Galileo building a bigger one and then on to people putting the first ones on mountains which were truly comparable to today. After this short intro into telescopic usage we are brought through the power of some GREAT editing into the modern electronically enhanced telescopes: every single type and kind is shown around the world and described as only a highly skilled video maker can do, and PBS gets it right as they spam you a history of the telescope.
Enter into the era when we began putting space-based telescopes into action.
Also the era when men began to make mirrors by filling the back with foam and pouring the glass mirror over a lightweight reinforcing lattice; and then, making many of these nearly perfect mirrors into tiles, which are used to construct truly gigantic mirrors themselves.
And enter into the era when expensive earth based telescopes have their mirrors bent by applying vacuum to the mirror, erasing the movement of atmospheric air due to heat wafting up from the earth’s surface.
These machines are COMPUTER CONTROLLED and can X out nearly ALL atmospheric gas movement over time, and yet, in all the past years since their invention, not one person who operates these telescopes has come out and told the world that not only do the computer driven mirror bending assemblies have to bend mirrors more and more, but that it’s directly seen that the hotter the atmosphere gets due to earth-shine frequency infrared light/heat wafting up.
No, to the contrary in the movie the people explaining the history of the scope while going into great detail about SO MUCH of the usage of these ground based scopes never mention ONE WORD about RISING HEAT in RECENT DECADES MAKING THE SCOPES’ MIRRORS NEED MORE FLEX.
Not one WORD????
Not
One
Not
Ever.
None.
Amazing isn’t it.
Think about this: in the past ten years ALONE, atmospheric CO2 has risen by A THIRD.
And not ONE WORD from a SINGLE SOUL about discovering MORE ATMOSPHERIC INFRARED?
From the SPACE BASED INFRARED ASTRONOMY field, not ONE word.
From the EARTH BASED INFRARED ASTRONOMY field, not ONE word.
From the EARTH BASED OPTICAL ASTRONOMY field, not ONE word.
In the past thirty years not ONE word from two VASTLY technologically adept fields whose ENTIRE EXISTENCE is DEPENDENT on knowing eXACTLY how much heat is in the atmosphere
so they can remove it’s effects from photographs of the sky
about how the Greenhouse Gas Effect is increasing the amount of infrared light in the atmosphere right in accordance with every other field of science we have, like the fields which sent those rovers to mars and landed them. They seemed to have a general idea of atmospheric density/energy conditions when they landed those,
but here on earth, the largest heater in the world, is – count my words, people: U.T.T.E.R.L.Y. UNDETECTABLE by ANYONE from ANY FIELD which measures light/heat in the atmosphere at ALL – the world’s LARGEST HEATER
is UTTERLY UNDETECTED?
Or there is no CO2 associated warming?
Which? And if there is ZERO associated warming while CO2 levels spiked upward by a third
then again just where is the science ‘proving’ there’s even any Greenhouse Gas effect, at ALL?
And where are the apologies to the DOZENS of scientists, the HUNDREDS of science field employees who told everyone who wouldn’t listen in MSM for the past decade, this is how it was always going to turn out?
Where is the admission by people worldwide who bought into this bunk and insulted those now proven to be their scientific betters, that they bought undrinkable Kool-Aid?
It’s disgraceful is what it is, and hundreds of thousands of texts have been written with this drivel as part of their ‘science’ sections.
A netflix link to the PBS movie; it’s well made and extremely important because it displays just how much lying and hubris go on regarding whether it’s ‘really possible to tell’ whether there’s a heating component associated with rising CO2.
https://movies.netflix.com/WiMovie/400_Years_of_the_Telescope/70116865?locale=en-US
Philip Shehan: The data sets you provide beginning in 1850, which if properly examined after removal at your attempt to flatten the graph with bogus irrelevant lines, clearly show an accelerating trend.
There is a difference between saying “The Earth has warmed since 1850” and saying “The Earth is warming.” Looking at the data of the last 16 years, it is hard to claim that the Earth “is warming”. Now your claim that the data, if “properly examined”, “clearly show” an accelerating trend is more dubious still. Perhaps you could elaborate on your concept of “properly examined” data, and show clearly where there is any recent acceleration in the data.
Philip Shehan: Your linear fit, stripped of the camoflage is inferior to the nonlinear fit:
That’s it? You fit one more model and stop because it displays what you want? You could get a much better fit with Bayesian Adaptive Regression Splines, or any of the many modern curve-fitting techniques: Fourier series, wavelets, polynomials of higher degree, piecewise exponentials. You might even try “basis pursuit”. Then you might evaluate whether, all things considered, some fits are better than others (model F, Akaike Information Criterion, Fisher Information of the parameters), and whether the best fits support or do not support your claim of acceleration.
But to select one more model and then claim it as the “proper examination” of the data is naive almost beyond words.
John Whitman: But, to do exactly what they did with different data sets is very facilitated by their willingness to have the paper completely open and transparent in all respects.
I am glad you wrote that. In particular, every data set cited in AR(5) can be analyzed using this method, in order to determine whether any temperature/CO2/Solar/other multivariate time series shows a strong CO2 effect. And for people who like Granger causality, this is the natural extension to nonlinear relations between antecedents and “consequences” (Granger causality basically says: If there is a causal relationship of antecedents to consequences, this is a way to measure its strength; true tests of causality require interventions.)
Allen Eltor,
Satellite measurements using instruments specifically designed to measure global temperatures clearly show that CO2 levels rise because global temperatures are rising, not vice-versa.
There are no comparable measurements showing that CO2 causes temperatures to rise. AGW may exist, but it is simply too small to measure, therefore it can be completely disregarded. AGW is nothing but a false alarm designed to extract money from a worried populace. In the real world, AGW is insignificant.
.Allen B. Eltor says: January 5, 2013 at 9:08 am
Think about this: in the past ten years ALONE, atmospheric CO2 has risen by A THIRD.
===============================
Yes, and I don’t need to think but a second to conclude that
1) You SHOUT to make a point
2) Your points are egregiously wrong as in above.
3) You are of the species of numb-brain SHOUTERS who come here from time to time, and the subject is way past you.
– – – – – – – –
Matthew R Marler,
Your use of the word ‘counterpoise’ in comparing this Beenstock et al paper with Vaughan Pratt’s modeling (at Curry’s place a few weeks ago) would to me indicate one approach somehow statistically uses a counterbalancing methodology that is fundamentally opposite/inverse of the approach of the other. Did I get that right?
Can you explain further? I would sincerely appreciate it. I am intrigued by the statistical significance of the differences in their approaches might imply. Although I am a retired engineer, statistics at the level you discuss makes me regret not having pursued more statistical knowledge.
John
D Böehm says:
January 5, 2013 at 11:01 am
Allen Eltor,
Satellite measurements using instruments specifically designed to measure global temperatures clearly show that CO2 levels rise because global temperatures are rising, not vice-versa.
No CO2 levels rise because of combustion emissions into the atmosphere with a small modulation due to temperature, your own graphs show this!
Phil says:
“CO2 levels rise because of combustion emissions into the atmosphere with a small modulation due to temperature, your own graphs show this!”
Wrong again, and you are alone here in making that claim. The Wood For Trees chart I posted shows very clearly that ∆CO2 follows ∆T. Here is another chart that shows the same thing: ∆T causes ∆CO2. There is no measurable scientific evidence that shows the reverse. There may be a slight warming effect from rising CO2, but the effect is inconsequential, and on balance it is beneficial.
The alarmist crowd got cause and effect mixed up right from the beginning, and now their consternation over the fact that the planet is not doing what they predicted is because of their original faulty premise.
Matthew Marler et al.
Once again I point out that my remarks began (Philip Shehan says:
January 4, 2013 at 8:43 am) as a comment on the temperature data the authors present from 1880 to the present (or more precisely 2007) where they claim that an “informal” examination of the data shows no acceleration in temperatures.
I contend that even to the untrained eye an “informal” (eyeballing) of the data shows that there has been an increase in the rate of warming over that period which is supported by “formal” curve fitting to linear and non linear functions.
Of course if you whish to do so you can get an even better fit with ever more complex mathematical functions. (I have experience in the application of such functions in the removal of baseline artifacts in Nuclear Magnetic Resonance Spectra.) With noisy data such as temperature over the period under discussion, you can always improve the fit with ever more complex fits but that tells you nothing about the underlying reality of the real time-temperature dependence without noise and accounting for all possible variables where temperature is clearly dependent on multiple real world forcing factors.
But again, debates with what happens if you examine an almost infinite supply of short term data subsets from the full data set (cherry picking) is, if you will pardon the expression, to lose sight of the wood for the trees.
Back to the point: An informal eyeballing, the application of a linear fit and a simple non linear fit (unfortunately whereas does give the r2 value it does not give the function but it appears to be a second order polynomial or exponential) for their temperature data from 1880 to 2007 in figure 3 does not support the no increase in rate assertion.
I only assert that the exponential fit is superior to the (debogussed) linear fit provided by D. Boehm for that period, which unfortunately does not have an r2 value but I suggest that if you cannot come to that conclusion by eyeballing the fits with the data, we must agree to differ.
Finally at the risk of producing more off-the-central-point discussion, with regard to your statement ‘There is a difference between saying “The Earth has warmed since 1850″ and saying “The Earth is warming.”’ Entirely agree. But again we are discussing a figure in the paper covering 1880 to 2007, or as a consequence of further discussion from 1850 to the present.
Mathematically, it is not possible to say what trend is happening at “the present”, at this instant in time. We can only discuss, as we have been doing, what trends are for a period of time in the past which may indeed conclude with the instant you are reading this.
I would also like to point out that nowhere in this analysis do I assert that there is a cause and effect relationship between time and temperature, still less a cause and effect relationship between greenhouse gas concentration and temperature.
Having a great deal of experience in analysing data in this way, I found the assertion by the authors in the caption to the figure untenable. I would have precisely the same opinion if the data were for stock prices or Norwegian lemming population. I suspect however that those here who cannot see an increase in the rate would see it differently in those cases.
Shehan says:
“I suspect however that those here who cannot see an increase in the rate” of global warming…
It appears that the only blinkered commenter here who can’t see reality is Shehan, who believes despite verifiable empirical evidence that global temperatures are currently accelerating upward. As the chart clearly shows, there has been no rising — much less, accelerating — temperature trend over the past decade. Global warming has halted, despite steadily rising CO2.
It gets tedious showing what is happening in the real world, but with such high site traffic it is best to make sure that new WUWT readers are made aware of the overwhelming scientific evidence that debunks the kind of climate alarmism and pseudo-scientific nonsense that Shehan has been peddling in this thread.
Noting mpainter seemingly, about to have a stroke, I re-read, what I wrote.
There are two typos: I said “Atmospheric CO2″ when I should have said
” MAN MADE atmospheric CO2″
risen about a third, since 1998 , which is fifteen years, not ten.
http://wattsupwiththat.com/2012/12/06/a-problem-nearly-one-third-of-co2-emissions-occured-since-1998-and-it-hasnt-warmed/
So the sentence should say ‘MAN MADE CO2’ and FIFTEEN years not ten.
Why in the world someone would be so offended over something obviously typographical in nature, is pleasantly, beyond me.
Once again, Boehm demonstrates that he is utterly incapable of sticking to the point of this thread: whether the data from 1880 to 2007 displayed in Fig 3 of the paper displays an accelerated warming trend.
He has now gone to reducing his prefered short term cherry pick of 1998 to the present (but not 1996 or 1997, because that does not give him his desired result) to the period from 2003 to the present. And yet again I must point out that none of this has any relevance to the argument put by the authors as to the trend for the period 1880 to 2007.
He has failed to answer my earlier query:
Why didn’t you pick the 15 year period between 1940 and 1955 to prove that temperatures from 1880 to present have been dropping?
http://www.woodfortrees.org/plot/gistemp-dts/from:1940/to:1955/plot/gistemp-dts/from:1940/to:1955/trend
And a late reply to Graham W says:
January 5, 2013 at 6:41 am…
I agree that the data from 1970 to the present is incapable of showing an accelerating trend for that period. I believe I made the point in one of my posts above (excuse me if I don’t go looking for it) that when data is sufficiently noisy or covers a short period , a straight line fit is the best you can hope for. Of course if I take your data set and make it even more restricted in time, the resulting graph shows no trend whatsoever. Its just too noisy:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:1995
If you can improve the signal to noise level within time period of your chosen data set (not possible with the temperature data here, but possible with Fourier transform NMR spectoscopy by accumulating more data – S/N improves with the square root of the number of scans ) you can get a cleaner line which is amenable to more complex fits.
What we are discussing here is a data set covering 130 years where the signal to noise ratio is sufficiently large to give a reasonably good linear fit, and even better nonlinear fit, in my opinion, based on about three decades experience looking at precisely these questions.
Shehan is still trying to peddle the anti-science nonsense that global warming is accelerating. That false narrative is so divorced from reality that only a cognitive dissonance-afflicted lunatic would try to peddle it on this “Best Science” site.
The longer the trend line the easier it is to see that the rise in [harmless, beneficial] CO2 has no measurable effect on natural global warming. There is no acceleration in the natural global warming trend since the LIA. Direct observation shows conclusively that CO2 simply does not have the claimed effect. AGW is a minor, 3rd order forcing that is swamped by many other negative feedbacks. AGW just doesn’t matter; it is too small to even measure.
This chart only goes up to year 2000, but it makes the point: the warming trend is the same whether CO2 is low or high. Everyone else can understand that logic, except Shehan, who has no understanding of the Null Hypothesis — which has never been falsified; current climate parameters have been routinely exceeded during the Holocene. Nothing unprecedented is happening now. Shehan actually believes that temperatures are rapidly accelerating, despite mountains of verifiable contrary scientific evidence. Global temperatures are not accelerating. Cognitive dissonance in spades: Shehan has it. Seriously.
There is always one hopeless nutcase who believes that War is Peace, Ignorance is Strength, and CO2 is a Problem. It is not; on balance, more CO2 is a good thing. At current and projected concentrations, CO2 is completely harmless, and beneficial to the biosphere. But when someone has bought into the insane demonization of “carbon”, they become impervious to facts and reason.
Reblogged this on The GOLDEN RULE and commented:
More and more evidence of the AGW scam!
Boehm, Still tap dancing around, blowing smoke, cherry picking partial data sets left, right and centre, and failing to deal with the only isue I am discussing here – Whether or not the data in figure 3 covering the period 1880 to 1997 is or is not showing an increase in the slope with time.
correction: the figure is panel c in figure 1, not figure 3, and the period 1880 to 2007
@Philip Shehan: I’m sorry but the period from 1970 – 2013 is not a short period of time. It is 43 years. It is more than enough time for any accelerating trend in temperatures to be visible in the graph, and it is not. In fact if you increase the time period beyond 43 years, the lack of an accelerating trend, or indeed any positive trend, in this millennium, is even clearer and more pronounced in contrast to the data that precedes it:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1940/to:2013
Again, no statistical analysis required. It’s simply obvious, using nothing but your eyes, that temperatures cannot be said to be “rising at an increasing rate”. They’re not.
Regarding Mr Shehan’s descent into ad hominem name-calling, he is trying to cover up the fact that there has been no acceleration of global warming — the crux of his alarmist beliefs, as demonstrated by his posting of John Cook’s dishonest cartoon of rapidly accelerating global temperatures — something that even the UN/IPCC admits is not happening.
I have posted numerous links above, showing conclusively that there is no acceleration in the natural global warming trend, and in fact, that global warming has stopped for the past decade. Shehan’s lame response is that I am “blowing smoke” and “cherry picking”. He needs to go tell the HadCRUT folks that their data is wrong. He needs to go tell U of Huntsville climate scientists that their satellite data is wrong. He needs to go tell Prof Richard Lindzen that Lindzen is wrong. And he needs to explain why the very long term trend charts I posted are “cherry-picking”. Simply asserting that nonsense shows Shehan’s desperation. In the end, Shehan’s argument rests on baseless assertions; opinions unsupported by testable, falsifiable scientific evidence.
But Shehan cannot go and argue with any of those sources, because he would be laughed at by folks who know more than he does — doubled and squared. Shehan hates the fact that I am simply showing what others have found: that there is no acceleration in the natural global warming trend, and that the trend is the same whether CO2 is low or high. Those experts debunk Shehan’s ‘acceleration’ nonsense. He doesn’t like it, but he is too chicken to go straight to those sources and argue with them. He would only be ridiculed by people much more knowledgeable than Mr Shehan.
Philip Shehan: I would also like to point out that nowhere in this analysis do I assert that there is a cause and effect relationship between time and temperature, still less a cause and effect relationship between greenhouse gas concentration and temperature.
I didn’t say you did. I said that you asserted there was an increase in the rate of change based on an inadequate analysis. In no way did you justify the claim that the exponential fit was the best.
Here is what you said: Informal inspection of the temperature data of panel c does show acceleration, matching that of the greenhouse gas forcing plots in a and b. The temperature rise appears less dramatic due to different scaling factors used in the 3 plots, but the acceleration of the temperature in the last 40 years compared to the previous 80 is clear to the naked eye. This is confirmed by a formal fit of temperature data to a nonlinear equation.
OK, you found a fit that confirmed your impression. Other people find different fits that confirm their impressions. In no way can you claim to have found a better fit, only that you found a fit that confirmed your impression. Besides, you cherry-picked your cut-off (80 vs 40 years); you can show by model fitting (others have done this plenty of times) that the late 20th century warming has almost the same rate of change as the early 20th century warming; in fact, piecewise linear fitting with knots chosen post-hoc [that is, chosen by the “naked eye”] provides the best fit.
Having a great deal of experience in analysing data in this way, I found the assertion by the authors in the caption to the figure untenable.
So far, so good. Then you made another untenable assertion that you claimed was superior. All you really did was show that a different conclusion follows from a different dubious model fit, based on a different cherry-picked segmentation of the time series. Anyone with experience in analyzing time series data can tell that there is no good evidence for an accelerated rate of warming.
D Böehm says:
January 6, 2013 at 5:57 am
Shehan’s comments are just stupid – fitting an arbitrary polynomial to data with inhomogeneous error distribution. There is no physical basis for it, and the error bars on the data increase the farther you go back in time to the point that they are useless for anything but spurious polemics, particularly in the era before 1900 or so. His purpose isn’t to enlighten, only to annoy. The only thing he is succeeding at is illustrating the weakness of the warmists’ feeble arguments. Don’t rise to the bait. Nobody is paying any attention anyway.
D. Boehm accuses me of descending into ad hominem attacks after calling me a liar in his
first comment to me (D Böehm says: January 4, 2013 at 12:17 pm). Others who have had a somewhat sneering tone in their remarks to me have received polite responses.
My first response to D Boehm’s accusation that I am a liar was to put this question to him and later repeated when in none of his subsequent responses has he been willing to provide an answer. So, for the third time:
If a 15 or 10 year period can be extracted from the data and held to be representative of the period 1880 to 2007 presented in figure 1 c, (which is what all his objections to my analysis boil down to) why does he not use the data for 1940 from 1955 to declare that temperatures between 1880 to the present have been falling?
http://www.woodfortrees.org/plot/gistemp-dts/from:1940/to:1955/plot/gistemp-dts/from:1940/to:1955/trend
So how about it Mr Boehm?
Matthew R Marler.
I did not claim that an exponential fit is best, I merely said that the non linear fit to the temperature data here
http://www.skepticalscience.com/pics/AMTI.png
was superior to a linear fit for that period thoughtfully provided by Mr Boehm.
http://tinyurl.com/af5xwmv
With regards to D.Boehms assertion that the non linear fit is a “dishonest cartoon” that is somehow incompatible with temperature data from the data from Hadcrut 4 and UAH, the temperature data in that fit is an averaging of 10 temperature data sets:
http://www.skepticalscience.com/pics/SummaryTable.png
which includes and is essentially the same as the two data sets Boehm uses in his plot. The nonlinear computer fit is as valid as the linear computer fit Boehm applies, making Boehms plot no more or no less a cartoon that the nonlinear graph.
Unfortunately no correlation coefficient is provided in the latter plot.
If it helps people to make an objective comparison of the two plots just forget that we are talking about the charged topic of global temperatures here, but imagine the data is for stock prices or the Norwegian lemming population.
Now look at the linear fit of Boehm’s data. If you can’t see the curve in your mind’s eye look at how the earlier and data is mostly above the blue line which is in line with the central section. Boehm has thoughtfully provided parallel lines above and below the fit which aid here by showing that the data curve below the purple line going through the early and late data, and curving away from the lower blue line at the earlier and later sections.
OK so I have over three decades experience in analyzing this kind of data, but I can’t believe that an objective untrained eye cannot tell that the non linear fir is superior.
Mathew Marler says to Shehan:
“Anyone with experience in analyzing time series data can tell that there is no good evidence for an accelerated rate of warming.”
Bart says about Shehan:
“Shehan’s comments are just stupid – fitting an arbitrary polynomial to data with inhomogeneous error distribution. There is no physical basis for it…”
What bothers me is this deceptive chart that Shehan repeatedly posts in an effort to show [non-existent] acceleration in global warming. That fabricated chart is a typical SkS invention. It does not reflect the real world. As regular WUWT readers know, people can lie with charts just like they can lie with statistics. Dishonest data manipulation is SkS’s stock in trade.
For a chart that shows what is really happening, this Phil Jones chart covers the same time frame. We can see that there is no acceleration of natural global warming, only that there are almost identical step rises in global temperature. Those rises are not geometric, and they show the same rate of warming whether CO2 was low or high. Thus, CO2 has had no measurable effect on natural global warming.
Given the choice of believing John Cook’s cartoon chart, or Phil Jones’ data-based chart, it is no contest. I have Climategate questions about Phil Jones, but comparing Jones’ data and methodology with Cook’s deception is night and day.
D Boehm once again refuses to deal with the 1880 to the 2007 data set as a whole, the analysis of which by the authors of the paper is what i have been discussing, preferring to present carefully selected stretches of data which he claims represent the whole, but yet again has refused to answer my question as to why the data set from 1940 to 1955 should not be used to conclude that there has been a linear drop in temperature since 1880.
His continuing silence on this point is deafening.
He has failed to explain in the light of earlier post he is still maintainng that the non linear fit using essentially the same data as his own plots is “deceptive” and continues in this vein:
“Given the choice of believing John Cook’s cartoon chart, or Phil Jones’ data-based chart, it is no contest.”
This is in spite of my posting of the temperature data sets on whch the “cartoon chart” is based and every bit as valid (if not moreso being an average of 10 data sets, including Jones’).
Then there is his complaint where he compares apples with oranges, complaining that an “arbitrary” baseline for temperature record calculated from the mean temperature which thus shows the temperature anomalies above and below the line and by definition is flat is “deceptive” while a line representing something else entirely, the slope of the data is not.
Now D. Boehm is a sensitive soul. In spite of starting out by calling me a liar and continuing in that vein including opining that my living off the public teat doing research into methods of the early detection and treatment of cancer somehow invalidates my professional expertise, he objects to ad hominem attacks (on himself). It therefore pains me to state that his above post, in line with his previous efforts, reveals him to a scientifically illiterate idiot.
I politely responded to Mr Marler’s critique above.
I let Bart’s slide but if could he explain to me why an arbitrary selection of a linear function as opposed to a non linear function ovecomes his objection about “inhomogeneous error distribution. There is no physical basis for it…”
This is the only substantive claim made in a post of nothing else but name calling. He is one of a number of posters here who have not engaged the debate but have warned others to ignore or not be seduced by my slick but fraudulent arguments, and who know by some powers of esp that no-one is reading them anyway.
Philip Shehan:
Your illogical assertions have been refuted – repeatedly and by several people – in this thread, but you continue with your blather.
Your most recent bloviation is at January 6, 2013 at 7:38 pm and begins saying
Nobody has been silent on that, but you persist in being deaf to the refutation of your nonsense.
If the trend is – as you claim – “accelerating” then successive sub-sets of the data set should show increasing trends. THEY DON’T. Indeed, the most recent ~16 years show no statistically significant (at 2-sigma) rise at all; none, zilch, nada.
You present the entire data series with an arbitrary curve that has no physical reality and say;
“See, the curve is increasing”.
Others reply to you saying, “So what? That curve has no physical reality”.
Your response has been to say, over and over again, “But the curve is increasing”.
The recent stasis is a physical reality.
It shows the trend has DECELERATED to zero over the most recent ~16years.
That is not consistent with your assertion of the trend accelerating.
The only deafness is yours, and it seems to be deliberate.
Richard
Philip Shehan is involved in cancer research at the University of Melbourne.
I am now resolved to eat more fruit and vegetables.
It is amusing watching Shehan impotently demanding that he should be allowed to frame this discussion. Me, I don’t care about his personal issues. What I care about is Shehan’s mendacious claim that global temperatures are accelerating upward. As I have shown in numerous charts, based on many different data bases, global temperatures are not only not accelerating, they have been flat to declining for quite a few years now despite the rise in harmless, beneficial CO2.
I suspected it would come to this eventually: faced with solid empirical evidence and verifiable observations showing conclusively that global temperatures have been flat to declining, a few of the less ethical alarmists would decide to simply lie about it, and claim that global temperatures are accelerating upward. Cook and Shehan continue to repeat that untruth. But as long as they do, I will be here to set the record straight.
As for the rest of Shehan’s nonsense, including his cherry-picked data set that ends in 2007… Pf-f-f-ft.
To the critics:
I am not framing the discussion. The authors of the paper are. I did not cherry pick the a data set that ends in 2007 (…Pft-f-f-ft). The authors of the paper did, and proceeded to make a statement about their “cherry picked” data set from 1880 to 2007 and its graphical presentation in Figure 1 C They then frame the interpretation of their “cherry picked” data set:
“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.”
They have thus framed the discussion in terms of a comparison of curve 1c with the accelerating curves in panels a and b.
The fitting of an accelerating curve to the data in 1 c is therefore in no way arbitrary. It is how the authors have framed the discussion.
My point is entirely a comment on the authors assertion – that the data from from 1880 to 2007 in panel 1 c is not well fit by an accelerating curve. It is, and none of the attempts by people like D. Boehm and others who wish to reframe the authors claims to their liking can alter this single fact:
http://www.skepticalscience.com/pics/AMTI.png
Philip Shehan:
Please accept some sincere advice.
You are wrong. Everybody can see you are wrong. If you cannot see you are wrong then you are deluding yourself so you need to step back and review the situation.
Your latest post January 7, 2013 at 12:35 pm is nothing short of silly. Please read it and see if you can recognise the blatant logical flaw which it contains. If you cannot see why it is flawed then ask somebody you trust to point it out to you.
Continuing as you are can only make you look even more foolish.
Richard
richardscourtney, Thank you for your sincere advice. I have an equally sincere request. I cannot see the blatant logical flaw in my my 12.35 post. Please point it out to me.
D Böehm says:
January 5, 2013 at 4:58 pm
Phil says:
“CO2 levels rise because of combustion emissions into the atmosphere with a small modulation due to temperature, your own graphs show this!”
Wrong again, and you are alone here in making that claim. The Wood For Trees chart I posted shows very clearly that ∆CO2 follows ∆T. Here is another chart that shows the same thing: ∆T causes ∆CO2.
Meaningless nonsense as usual, try being scientific and adding the actual scale for the CO2. As Ferdinand and I have pointed out multiple times the temperature change is insufficient to cause such a large change in CO2, only about 10% of it!
Richard Courtney,
I suspect that your good advice will fall on deaf ears. Once again, Shehan posts his thoroughly mendacious SkS chart, the cartoon chart fabricated by John Cook that dishonestly shows rapidly accelerating global temperatures. That is simply not happening. Compare that dishonest chart with what is actually occurring:
click1 [hadcrut3 and hadcrut4]
click2 [CET long term trend]
click3 [six separate data bases, from 2000]
click4 [hadcrut3, land temps]
click5 [ocean temps]
click6 [global satellite temps, various altitudes]
click7 [three U.S. data sets]
click8 [global mean T anomaly]
click9 [global surface vs models]
click10 [CO2: no T acceleration]
click11 [actual trend line vs IPCC’s predicted acceleration]
click12 [US temps, zero predicted acceleration]
click13 [global temps vs CO2]
click14 [CO2 vs global temps]
click15 [Temp vs CO2, past 17 years]
Shehan is peddling dishonest propaganda. There is no current acceleration of global warming, as numerous difference observations show. Also, note that John Cook’s SkS blog has it’s own special category: “Unreliable”.
That is because Cook alters the comments of skeptics to mean something entirely different than what they posted, and he does it without any comment. He just completely changes the meaning of the comments, and leaves the comment under the name of the person who made it. Dishonest, no? So why would anyone expect Cook to produce an honest chart?
Shehan is either dishonest or deluded. But by now I trust that other WUWT readers will see that there is no acceleration of the long term global warming trend, which has been rising at the same rate since the end of the LIA. And thus, CO2 has no measurable effect on global warming. It is merely a false alarm intended to generate grant money, which has been flowing into ‘climate studies’ at the rate of $7 – $* Billion annually. Big money is buying the global warming scare.
@Philip Shehan
Here is HadSST2 and a fitted 3rd order polynomial. Here are the (obviously non stationary) residuals. This model fails to explain anything about last 15 years (or anything prior).
Phil.,
Don’t be silly. This chart shows clearly that T controls CO2, and not just “10%” of it.
Philip Shehan:
I thought my advice would be an encouragement for you to obtain the help of your friends. But you say (in your post at January 7, 2013 at 1:56 pm) that you lack friends to help you.
Clearly, if I had known of your lack I would not have made such a cruel suggestion, and I hope you will accept my apology for having made such a hurtful mistake.
Perhaps if you were to be more open to accepting advice on WUWT then you may obtain some friends from it. Importantly, the practice at such openness on WUWT may gain you the ability to interact with those around you because it seems likely that your behaviour (as exhibited on this thread) is probably a major contributing factor in your ability to obtain and/or keep friends.
And in the hope of offering friendship, I respond to your request in your post at January 7, 2013 at 1:56 pm which says
I answer that in your post at January 7, 2013 at 12:35 pm you wrote
You then addressed the issue by providing a different and dubious data set without any explanation of how and why the “framing” of the authors was incorrect.
Simply you said the the authors of the paper framed the discussion by using specific data then you reframed the discussion by using different data.
Your reframing could hypothetically be justified by explanation of how and why their “framing” was incorrect and yours was correct. But you did not do that: you changed the subject. Your change of subject is a
.
I hope this answer – especially its first three paragraphs – helps.
Richard
Phil:
At January 7, 2013 at 2:25 pm you assert
Yes, and as I have repeatedly pointed out you cannot know that.
You are ‘blowing smoke’.
Anything which alters the equilibrium state of the carbon cycle will change the CO2 in the air, and it is not possible to know by how much or at what rate.
Richard
Layman Lurker,
Thank you from the bottom of my heart. I will no longer have to put up with the nonsense comments from D. Boehm like the one above:
“Once again, Shehan posts his thoroughly mendacious SkS chart, the cartoon chart fabricated by John Cook that dishonestly shows rapidly accelerating global temperatures.”
From here on in I will use your plot of HadSST2 fitted to a 3rd order polynomial, and let him blow raspberries at that one.
I thus represent it here
http://tinyurl.com/acr6opb
for direct comparison with the data set for 1850 to the present personally selected by D. Boehm in his post of 2:46 pm January4, with a linear data fit:
http://preview.tinyurl.com/bg6mgjp
Yours in eternal gratitude,
Phil Shehan
@D Böehm Stealey:
So “12 month change” means “year over year same month vs same month” delta?
That’s one very interesting chart…
rishardcourtney says:
“Philip Shehan:
Your illogical assertions have been refuted – repeatedly and by several people – in this thread…”
Shehan needs to understand what Layman Lurker is saying. Otherwise, Shehan will start posting LL’s 3rd order polynomial chart to make more of his specious claims… oh, wait. He’s already doing it!
Layman is correct when he says that we can’t really tell anything from the past 15 years from that model. As I have repeatedly pointed out, the only way to see if global temperatures are accelerating is by using a long term trend chart, based on verifiable data. When we view such a chart, it is clear that there is no acceleration of global warming. [The green line shows the long term global warming trend.]
Mike Smith,
I copied that chart from this site. Better toask them than to ask my interpretation. They are pretty responsive. [Note the first comment, which says: “…the large changes in both temperature and CO2 do not show any acceleration in warming as CO2 builds up and in fact the rate of warming declines as carbon dioxide increases…”].
Here is a similar one I made using the WFT data base.
@JP Miller:
Other patterns? Lunar tidal induced vertical ocean mixing.
http://www.pnas.org/content/97/8/3814/F1.large.jpg
https://chiefio.wordpress.com/2013/01/04/lunar-cycles-more-than-one/
@.Allen B. Eltor:
I see you found the caps lock key.
and
the
return
key.
But have you ever thought that more need for mirror bending might just indicate more convection and faster cooling of the planet?
https://chiefio.wordpress.com/2010/12/02/does-convection-dominate/
@richardscourtney:
You are a great optimist to think that sound advice for self inspection will be done by one so gifted in self delusion…
@Alarmed:
Taking added vitamins and avoiding bacon and BBQ as we speak! 😉
@Shehan:
You have an axe. You’ve ground it. We all have enjoyed watching you catch fire from the sparks. Maybe it’s time you quenched it and started with annealing again? (For those not a Smith, that’s the process of stress relieving an overworked article…)
richardscourtney says:
January 7, 2013 at 1:29 pm
Philip Shehan:
Please accept some sincere advice.
You are wrong. Everybody can see you are wrong. If you cannot see you are wrong then you are deluding yourself so you need to step back and review the situation….
>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Agreed He seems to think the people here are uneducated ignorami.
And if Shehan doesn’t like D Böehm Stealey’s graphs how about these.
length of Arctic Melt Season
Temperature Graph
Or Hansen’s Graphs
D Böehm Stealey says:
January 7, 2013 at 4:31 pm
Mike Smith,
I copied that chart from this site. Better to ask them…
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Actually it seems to have come from A.J. Strata (NASA engineer) the link is CO2 Does NOT Cause Climate Temperature Changes
He also did an article on the error in the temperature record
He does very nice analysis as you would expect from a top notch engineer.
E.M.Smith says: @ January 7, 2013 at 4:37 pm
@Shehan:
You have an axe. You’ve ground it. We all have enjoyed watching you catch fire from the sparks. Maybe it’s time you quenched it and started with annealing again? (For those not a Smith, that’s the process of stress relieving an overworked article…)
>>>>>>>>>>>>>>>>>>>>>>>>>
Darn it ChiefIO, now I have to clean my screen again. With visits from the farrier every 6 weeks I have a very strong visual of that process…..
D Böehm Stealey:
I am pleased that you take us back to discussion of the paper described in the above article in your post at January 7, 2013 at 4:31 pm.
Your post says
The carbon cycle is part of the climate system, and all parts of the climate cycle will adjust to a change of state. The dynamics of the carbon cycle system indicate that the system can easily sequester ALL the CO2 emission (both natural and anthropogenic) of each year. But the steady rise in atmospheric CO2 concentration (observed at Mauna Loa since 1958) shows that not all the emission is sequestered.
Your graphs (and those provided by Bart) show the rapid response of the climate system to temperature changes. This is important for two reasons.
Firstly, it is additional evidence that the carbon cycle is capable of sequestering all the total CO2 emission (both natural and anthropogenic) of each year although it does not. And this poses the question as to why the carbon cycle has changed state such that it is slowly seeking the new equilbrium indicated by the steady rise in atmospheric CO2 concentration.
This change of state may have been caused by the anthropogenic CO2 emission or any of several natural causes including the temperature rise which is recovery from the LIA. Some climate system processes have rate constants of years and decades so the steady rise would be an expected effect of such a change of state.
Secondly, and importantly, the above post reports the discussed paper as saying
On face value, that is a surprising conclusion. It implies that the climate system adjusts to negate a change to “greenhouse gas forcing”. (For clarity, I say that I think this is probably a result of the chaotic system seeking its strange attractor, but that is merely my opinion).
If the climate system in total naturally adjusts to changes in the carbon cycle then AGW cannot be a significant problem. Hence, the finding I quote from the paper warrants much work to confirm it or to refute it. Everything else is a secondary consideration.
Richard
richardscourtney ….
>>>>>>>>>>>>>>>>>>>>
I have a question.
1. According to the ‘consensus’ CO2 is Uniform in the atmosphere. The rest follows from that assumption.
2. According to all my reading plants are on the CO2 starved side and will suck CO2 down to ~ 200 ppm – 300 ppm within minutes of the sun hitting the leaves. For example
Given plants would much prefer ~1000 ppm CO2 and therefore are an unsatiated CO2 sink that gobbles down CO2 at measurable speed, I observed a 50 ppm drop within a tomato plant canopy just a few minutes after direct sunlight at dawn entered a green house (Harper et al 1979), – How the heck can the earth have a increasing CO2 level (blamed on mankind) unless it is because of the increase in SST?
The ‘Greening of earth’ has already been documented so the plant response is real.
I just can not see how mankind’s release of CO2, which is on land at ground level, would make it past the first set of trees before being happily yanked out of the air. Am I missing something?
Whatever.
No, the data is not “well fit by an accelerating curve” as the residuals of the fit to sst shows.
D Boehm Stealy
At last, we are in total agreement:
“Layman is correct when he says that we can’t really tell anything from the past 15 years from that model. As I have repeatedly pointed out, the only way to see if global temperatures are accelerating is by using a long term trend chart, based on verifiable data.”
Precisely what I have been arguing all through this discussion. And I agree with you that your chart the Hadcrut3 data from 1850 to the present and the linear fit are entirely appropriate for discussion of figure 1 c in the paper. So much so that I reproduced it in my 3:22 post. (For clarity I removed from your graph the other data set offsets and fits which obscure the Hadcrut3 data and linear fit. I know you did not intend to mislead people but the inclusion of these extraneous elements flattens the graph which makes “informal” examination of the trend more difficult)
http://preview.tinyurl.com/bg6mgjp
Now richardscourtney claims that you are indulging in a blatant logical flaw by using the Hadcrut3 data instead of the NASA GISS global temperature (meteorological stations) data the authors state they have used in Figure 1 c
http://data.giss.nasa.gov/gistemp/graphs_v3/
I do not agree that you are engaging in a blatant logical flaw here. It is an entirely permissable approximation given that given the noise levels of the various data sets are essentially the same for the period from 1880 to the present, and especially since we are discussing what the authors call an “informal” eyeballing of the data in 1c.
To keep richardscourtney happy I will use the WFT GIStemp data set which appears to be the NASA-GISS fit the authors are using, together with a linear fit:
http://tinyurl.com/bh5ua73
Unfortunatley, as the WFT does not have the capacity do apply nonlinear fits so I rely on the plot supplied by Layman Lurker of HadSST2 fitted to a 3rd order polynomial as an entirly adequate substitute for the purposes of the discussion
http://tinyurl.com/acr6opb
Alas, this is where our meeting of minds must end. You claim that from examination of the Hadcrut data and the linear fit “it is clear that there is no acceleration of global warming.” This is clearly not so, nor is it the case with the linear fit of the data used by the authors in figure 1c, nor the polynomail fit of HadSST2 data.
Shehan says:
“At last, we are in total agreement”
You have really lost it. We are not in any kind of agreement until you admit that global warming is not accelerating. That is the central issue in this debate. Trying to push me into a rhetorical corner like that won’t work. You’re not smart enough. And if you believe that Richard Courtney “claims that [I am] indulging in a blatant logical flaw”, please cut and paste his exact quote. Because I can’t find it. Make sure to show me the words “blatant logical flaw”. Thanx in advance.
Next, as I warned you following your fawning, sycophantic comment to Layman Lurker [“Thank you from the bottom of my heart”, etc.] when you thought he was agreeing with you, note that, as I told you, Layman was informing you that you can’t use that 3rd order polynomial model. But now you have posted it twice to support your belief in [non-existent] accelerating global warming. That model is nonsense. As Layman specifically informs you: “No, the data is not ‘well fit by an accelerating curve’…”. Yet you now use it as if it reflects reality. It doesn’t. The accelerating curve is bogus. It is an artifact.
Worse, it is deliberately deceptive, just like Cook’s invented SkS chart that you keep posting. I provided more than FIFTEEN legitimate charts made from various recognized data bases, showing conclusively that global warming is not accelerating. You ignored them all. Obviously, your mind is made up and closed air tight; you are unable to integrate reality into your world view.
You also ignored Gail Combs’ GISS chart showing that James Hansen has retroactively manipulated past temperatures in order to show nonexistent global warming. In fact, you ignore everything that does not support your belief system.
Mike Smith provides a good observation of your comments: “You have an axe. You’ve ground it. We all have enjoyed watching you catch fire from the sparks.” Mile thick glaciers could descend on Chicago, and you would still be trying to convince everyone that global warming is accelerating. You need help. Honestly. Get help.
Gail Combs says:
January 7, 2013 at 6:35 pm (Replying to )
richardscourtney ….
I do not remember the web site address, but the global CO2 level maps do show exactly that feature – that CO2 is NOT “uniform” across the planet at all latitudes and all seasons.
Rather, the CO2 DOES go down as the westerly winds cross the US (What??? You mean CO2 drops as it crosses the plains and trees and woodlands and forests of “industrial” north America??? Yeppers.) and heads across the Atlantic towards Europe. It also drops across Europe and northern Asia/Siberia. Deep drops in CO2 over the Amazon, southeast Asia jungles, parts of India. (Some of India is very dry = CO2 increase.)
China is a source of CO2.
Biggest CO2 “source” – or regions of highest concentration actually? Deserts. High mountain plateaus (Gobi, Mideast, Sahara, deep south Africa, US far west and Great Basin) where rocks and sand and no vegetation are the rule. Turkey is a “source” as is Iraq, Iran.
Makes you wonder doesn’t it – about where today’s CO2 is actually coming from.
RACookPE1978 says:
“…CO2 is NOT “uniform” across the planet at all latitudes and all seasons.”
Maybe this will help visualise it. [Might take some fiddling to get the slideshow to work.]
D Boem. I am devastated. The bromance is over, just when I thought we were getting on so well.
You wrote:
“we can’t really tell anything from the past 15 years from that model. As I have repeatedly pointed out, the only way to see if global temperatures are accelerating is by using a long term trend chart, based on verifiable data.”
Yet now you have reverted to berating me for not considering the last 15 years of the data or indeed any of your FIFTEEN short term data picks as representative of the 1880-2007 data set in fig 1 c.
The “central issue of the debate” is in fact this: the presenatation of data from 1880 to 2007 (not one of FIFTEEN, FIFTEEN HUNDRED or FIFTEEN THOUSAND subsets of that data) and their observation looking at that entire data set:
“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.”
As for the blatant logical flaw remark. richardcourtney told me that to use a data set other than the NASA GISS global temperature (meteorological stations) used by the authors in Figure 1 c is a “blatant logical flaw.” Now with his commitment to logic, I cannot believe that he would have a different opinion of your use of the Hadcrut 3 data instead of the NASA GISS data simply because he appears to agree with your standpoint and disagrees with mine. That would be illogical, and demonstrate an incapacity for objective argument. So I naturally assume that he would equally describe your use of another data set as a blatant logical flaw. ,(Don’t you Richard, and if not why not?). I was sticking up for you, bro.
(D Boehm, are you catching on that I had assumed you would recognise sarcasm in these posts with my having to flag them as sarc on/off?)
And yes I did ignore ” Gail Combs’ GISS chart showing that James Hansen has retroactively manipulated past temperatures in order to show nonexistent global warming.”
Because I cannot see how it had anything to do with (and I pause to take a deep breath while I prepare to repeat myself yet again for the slow learners)
The central issue of the debate is the presenatation of data from 1880 to 2007 in fig 1c of the paper and the authors observation looking at that entire data set:
“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.”
Philip Shehan says: January 7, 2013 at 10:42 pm
======================
Greetings fellow blogger, do you remember your comment that climate models cannot project anything but warming?
Layman,
I am not sure I understand your recent post. I am in agreement with your earlier post:
“Layman Lurker says:
January 7, 2013 at 2:54 pm
@Philip Shehan
Here is HadSST2 and a fitted 3rd order polynomial. Here are the (obviously non stationary) residuals. This model fails to explain anything about last 15 years (or anything prior).”
I am unsure as to what your point is about non stationary residuals. The discussion, as stated by the authors of the paper in reference to figure 1c, is about how the raw temperature data from 1880 to 2007 appears in that figure appears. As noted above I reject richardcourtney’s quibble that use of data other than the NASA GISS data is “a blatant logical flaw.” I am sure D Boehm will agree with me as he has also used other data sets as an entirely acceptable substitute.
I don’t see how the residuals, with which I have no argument, are relevant.
As to your statement “This model fails to explain anything about last 15 years (or anything prior).” I find this a little ambiguous. I am not sure “model” is the term I would use for a curve fit, linear or nonlinear, to data . I take model to mean a theory, giving a cause and effect explanation for data. The curves in themselves do not make any cause and effect statement, they simply indicate a correlation.
I agree that that the correlation or model if you prefer says explains nothing about the last 15 years or anything prior, in the sense D. Boehm enunciates:
“Layman is correct when he says that we can’t really tell anything from the past 15 years from that model. As I have repeatedly pointed out, the only way to see if global temperatures are accelerating is by using a long term trend chart, based on verifiable data.”
In that examination of the last fifteen years of the data set, or prior to 1880 cannot substitute for an examination of the entire data set from1880-2007.
The essential point is whether or not the curve fit whether linear or third order polynomial or something else provides a good fit (in terms of a high correlation coefficient r2)
Welcoming any furthercomment or expalnation on this, but for your plot showing good nonlinear curve fit to temperature data for the period presented and discussed in figure 1c, I remain sycophantically grateful
Postscript:
richardcourtney is making a category error in arguing that my statement contains a logical flaw.
Science uses inductive reasoning which draws conclusions from empirical evidence. Approximations routinely used as part of this process. This is what we are discussing here.
Logic is a branch of mathematics which employs deductive reasoning requiring no reference to real world empirical observation. All that is required is the argument be internally consistent with the conclusion
The following statements are logically consistent:
All cats have four legs
Tiddles is a cat
Therefore Tiddles has four legs.
All cats have five legs
Tiddles is a cat
Therefore Tiddles has five legs.
The following statements are logically inconsistent:
All cats have five legs
Tiddles is a cat
Therefore Tiddles has four legs.
The determination of how many legs a cats in the real world actually have must be determined by empirical scientific observation.
mpainter; I remember explaining to you repeatedly that I believe no such thing, never made any such statement, and that your interpretation of my actual comments on this was faulty.
Feel free to produce the actual comment here, and we can go through it yet again.
Gail Combs:
At January 7, 2013 at 6:35 pm you ask me
No, you are not “missing” anything: atmospheric CO2 measurements at localities show you are right. The sequestration is very fast and is mostly local to where the CO2 is emitted except for emission from the thermohaline circulation. Indeed, this is one of the reasons why atmospheric CO2 is seen to be “well-mixed” throughout the atmosphere.
However, that leads to the issue of why the atmospheric CO2 concentration (as measured at Mauna Loa since 1958) has been rising.
The carbon cycle has several components. Almost all CO2 is in the deep ocean, some is in the ocean surface layer, some is in the biosphere, a little is in the atmosphere, much is in limestone rocks, etc..
The equilibrium state of the carbon cycle determines the stable proportion of the CO2 in each of the cycle’s compartments. But the equilibrium state is not constant: it varies for many reasons. And any change to the equilibrium alters the amount of CO2 in the atmosphere. Indeed, this alteration is seen as the ‘seasonal variation’ in the Mauna Loa data.
Some processes of the carbon cycle have rate constants of years and decades. So, the carbon cycle takes decades to achieve true equilibrium (the Vostock ice core suggests achievement of equilibrium in response to temperature change takes ~600 years). But – as you observe – the rapid sequestration processes can sequester all immediate variations. This rapid sequestration is demonstrated by your observation, and by the temperature and CO2 variations compared in the plots of D Böehm, and etc..
This demands consideration of what has caused the change to equilibrium state of the carbon cycle which provides the atmospheric CO2 rise recorded at Mauna Loa. There are several possible causes and the anthropogenic emission is one (improbable) possible cause. Temperature rise which is recovery from the LIA seems to be the most likely cause, but there are several other natural possibilities, too.
Richard
Philip Shehan:
I take severe exception to your blatant misrepresentation of me especially when that misrepresentation follows my repeatedly taking the trouble of trying to help you.
At January 7, 2013 at 7:00 pm you say to D Böehm
NO! How dare you?!
It is YOU (i.e. Philip Shehan and NOT D Böehm) who has made the logical flaw.
You disagreed with the data used in the paper under discussion for no stated reason, and you replaced it with a graph of your own, then you discussed your graph and not the data which the paper analysed. Hence, you made the illogical claim that your discussion of something other than the paper refuted the paper.
D Böehm (and Layman) used other data to demonstrate why your graph is not valid for the application which you have applied. That is not a logical flaw.
You compound that at January 8, 2013 at 2:02 am when you write
I made no “category error”. In fact I pointed out that you did.
Indeed, your entire ‘Postcript’ is so wrong that it is risible. For example, it claims “logic is a branch of mathematics” when logic exists independently of mathematics and is the foundation of mathematics.
I am starting to suspect that you are not the sad, little, stupid man you pretend to be, and that your behaviour on this thread is egregious.
Richard
Gail Combs says: @ January 7, 2013 at 6:35 pm (Replying to )
….I just can not see how mankind’s release of CO2, which is on land at ground level, would make it past the first set of trees before being happily yanked out of the air. Am I missing something?
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
RACookPE1978 says: @ January 7, 2013 at 8:51 pm
…I do not remember the web site address, but the global CO2 level maps do show exactly that feature – that CO2 is NOT “uniform” across the planet at all latitudes and all seasons……
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
I think Chiefio (E.M.Smith) did a screen capture and article about it before it was ‘disappeared’ from the internet.
Japanese Satellites say 3rd World Owes CO2 Reparations to The West
Is that a map you were talking about?
the discussion ChiefIO captured:
I forgot I had that link bookmarked. Interesting that in the West, that proof is ignored. It certainly explains why Russia thinks Global Warming is hog wash. PRAVDA: Global warming, the tool of the West and several countries are dropped out of Kyoto and killed it.
Here is the latest mapfrom JAXA, Japanese Aerospace Exploration Agency, Greenhouse Gas Observation Satellite ‘IBUKI’ (December)
Here is the August 2012 map
Could someone screen capture in a website?? CHiefIO?
Since the scale goes from 350 ppm to 400 ppm and the reading range from 370 to 400, DEPENDING ON LOCATION, there goes the “Well Mixed” assumption that Ferdinand Engelbeen so faithfully defends. It vindicates Beck and the historic CO2 measurements too.
richardscourtney.
I am shamefaced at my ingratitude for your attempts to be so helpful with comments like:
“Your posts in this thread clearly demonstrate your skills at misrepresenting data.”
“If there are people reading this thread who are sufficiently gullible as to be gulled by your misrepresentation then you have already misled them. And you are demeaning yourself in the eyes of everyone who is not that gullible.”
“Your illogical assertions have been refuted – repeatedly and by several people – in this thread, but you continue with your blather.”
“you persist in being deaf to the refutation of your nonsense.”
“The only deafness is yours, and it seems to be deliberate.”
“Everybody can see you are wrong. If you cannot see you are wrong then you are deluding yourself”
“Continuing as you are can only make you look even more foolish.”
“see if you can recognise the blatant logical flaw which it contains”
“But you say (in your post at January 7, 2013 at 1:56 pm) that you lack friends to help you.”
(Didn’t actually say any such thing. Just asked for you to explain your comment rather than pester other people who probably would have no more idea what you meant than I did, but touched at your concern for my welfare in the friends stakes. It is true that I have no friends whatsoever on facebook.)
“Clearly, if I had known of your lack I would not have made such a cruel suggestion, and I hope you will accept my apology for having made such a hurtful mistake.”
“may gain you the ability to interact with those around you because it seems likely that your behaviour (as exhibited on this thread) is probably a major contributing factor in your ability to obtain and/or keep friends.”
I am devastated that I have caused you to “take severe exception to your blatant misrepresentation of me.” My only defence is the explanation that I gave to D Boehm:
“richardcourtney told me that to use a data set other than the NASA GISS global temperature (meteorological stations) used by the authors in Figure 1 c is a “blatant logical flaw.” Now with his commitment to logic, I cannot believe that he would have a different opinion of your use of the Hadcrut 3 data instead of the NASA GISS data simply because he appears to agree with your standpoint and disagrees with mine. That would be illogical, and demonstrate an incapacity for objective argument. So I naturally assume that he would equally describe your use of another data set as a blatant logical flaw.”
I appear to be mistaken in what appeared to be an entirely reasonable assumption- that your criticism of me would apply equally to the same alleged transgression when practiced by D. Boehm. Naturally I am shocked that you don’t, so request an answer to my question:
”(Don’t you Richard, and if not why not?)”
Please note that your statement here is incorrect:
“You disagreed with the data used in the paper under discussion for no stated reason, and you replaced it with a graph of your own, then you discussed your graph and not the data which the paper analysed.”
I don’t disagree with their data at all. If possible I would prefer to use it in my analyses. Where that is not possible, if you reread my above post, D. Boehm and I may (or anyone else) may use data sets other that used by used by the authors covering that period as there is no substantial difference between those sets when it comes to analysing the long term data trend.
I was perfectly happy to use the Wood For Trees data set corresponding to that used by the authors for with a linear fit and presented that plot. The difficulty with using WFT data for a non linear fit is that WTF has no provision for such a fit, so I used the plot provided by Layman Lurker as a perfectly reasonable substitute.
Finally with regard to your claim that:
“Indeed, your entire ‘Postcript’ is so wrong that it is risible. For example, it claims “logic is a branch of mathematics” when logic exists independently of mathematics and is the foundation of mathematics.”
I did formal logic components as part of my undergraduate degree in mathematics (got A’s too, so I seemed to understand it). It was certainly a branch of mathematics there. I am confused by your apparent description of this statement of mine as “risible’ when you and you seem to agree it by stating “logic…is the foundation of mathematics.
I also studied the History and philosophy of science where the distinctions between mathematical deductive reasoning and scientific empirical reasoning were covered at length and in detail (A’s again so I seemed to understand it).
Philip Shehan:
Thankyou for your post at January 8, 2013 at 6:07 am. It confirms my suspicion which I stated in my post at January 8, 2013 at 3:22 am: viz.
Other than that, your post at January 8, 2013 at 6:07 am says nothing worth the bother of a response.
Richard
richardscourtney,
You are correct that I am not a sad stupid little man. I am 6 foot 2 for starters, and it is the opinion of many people who I respect that I am far from stupid. As for sad, well that is subjective self judgement but all I can say is I don’t feel that way.
I reject that I was pretending to be sad stupid or little. I entirely reject the notion that my behaviour on this thread is egregious, unless by that you mean the part of the definition in my Oxford dictionary that reads “standing out from the flock”.
Certainly my latter posts have been increasingly smart arsed and sarcastic, but if you read your own highly personal attacks on me which I have quoted above, and those of D Boehm, can you really blame me? If you go back to the beginning you will see that I responded to slightly snarky comments from some people with resonably polite responses as they had some substantive points to make and I much prefer and as a scientist am used to, polite respectful and robust disagreement. I recognise that this is (sadly) not the culture of the blogosphere, and have come to the conclusion that there comes a point with the repeated efforts of people like D Boehm and sadly yourself, when turning the other cheek is no longer worth the effort.
However, there is nothing frivolous in my continuing to present substantive arguments on this subject. I have more than the usual professional interest in the presentation and intepretation of scientific data. I am sufficiently intersted in the subject of the nature of knowledge, scientific and otherwise to have taken philosphy units in my undergraduate degree, and after I completed my PhD, undertook post graduate studies in the History and Philosophy of Science at my own expense in addition to working full time as a research scientist.
My post of 6:07, may be a bit heavy on the sarcasm, but it’s intent is to present substantive arguments and I believe it does so.
This is wrong. Fitting a curve to temp does not “show” acceleration in the temperature series. When you have such obviously non stationary regression residuals, such a curve does not “confirm” or “show” anything. It is nonsense.
Your suggestion that the “noise” of ENSO and volcanoes explains residual departures from the accelerating function would be an interesting test. Rather than arm wave, why don’t you do a regression of said and report the results back to us?
Shehan is still desperately trying to justify his use of the false chart showing [non-existent] acceleration in global warming. Shehan loves that bogus chart, just like he loves the bogus SkS chart he constantly posts. Layman Lurker has repeatedly told Shehan that it is wrong to use that incorrect chart, adding that “This is wrong. Fitting a curve to temp does not ‘show’ acceleration in the temperature series.”
I note that no one else is agreeing with Shehan — not even über-warmists like Roger Harrabin, who gave a recent interview about the non-existence of any acceleration in global warming. In fact, Harrabin and the Met Office admit that global warming is slowing down, and that there will have been been ‘little additional warming’ for two decades. When even your own side contradicts your belief system, then your insistence that everyone but you is wrong indicates incurable cognitive dissonance:
Interviewer: The Met Office has revised downwards its projection for climate change through to 2017. The new figures suggest that – although global temperatures will be forced above their long-term average because of greenhouse gases – the recent slowdown in warming will continue. More details from our environment analyist Roger Harrabin.
Roger Harrabin: The new experimental Met Office computer model looking a year further ahead projects that the Earth will continue to warm but the increase will be about 20% less than the previous calculation. If the new number proves accurate there will have been little additional warming for two decades. [my emphasis] [note that Harrabin is referring to computer models, even as verifiable empirical observations in numerous temperature data bases show that global warming has stalled for the past decade or more].
Note that “slowing down” of global warming is deceleration — the opposite of acceleration. If Shehan does not now admit that global warming is not accelerating, he is crazy. What he pretends is science is simply his religious True Belief.
Layman Lurker says:
January 8, 2013 at 8:17 am
Your suggestion that the “noise” of ENSO and volcanoes explains residual departures from the accelerating function would be an interesting test. Rather than arm wave, why don’t you do a regression of said and report the results back to us?
It’s been done and published here:
http://iopscience.iop.org/1748-9326/6/4/044022/pdf/1748-9326_6_4_044022.pdf
Philip Shehan:
I write to reject your false and untrue assertion in your post at January 8, 2013 at 8:14 am which says I made “personal attacks” on you. I do this because my ignoring your unfounded assertion when you first made it (at January 8, 2013 at 6:07 am) has induced you to repeat it.
I made no “personal attacks” on you.
I said you were wrong, I said why and how you were wrong, and I gave you advice on how to understand why you were wrong but you rejected that advice (as E.M.Smith predicted you would in his post at January 7, 2013 at 4:37 pm).
Subsequently, when it became clear that you were incapable of the necessary introspection, I advised you to seek an explanation from someone whose opinion you trust. That advice was intended as a kindness, and I was shocked when you replied saying you needed me to provide the explanation. I then apologised for having made the assumption that you had such a friend, I kindly advised on how you could learn how to obtain and keep friends, and I provided the explanation.
Later I suspected you were only pretending to be a sad, little, stupid man and stated my suspicion. You replied by confirming my suspicion was correct.
Now, you egregiously suggest I have made “personal attacks” on you. Sad, very sad.
Richard
Richard,
How anyone could write that it is a false and untrue assertion that you made personal attacks on me and that it is I who lacks introspection after reading the quotes I presented would be astonishing anywhere but on this blog. I shall reproduce them here:
“Your posts in this thread clearly demonstrate your skills at misrepresenting data.”
“If there are people reading this thread who are sufficiently gullible as to be gulled by your misrepresentation then you have already misled them. And you are demeaning yourself in the eyes of everyone who is not that gullible.”
“Your illogical assertions have been refuted – repeatedly and by several people – in this thread, but you continue with your blather.”
“you persist in being deaf to the refutation of your nonsense.”
“The only deafness is yours, and it seems to be deliberate.”
“Everybody can see you are wrong. If you cannot see you are wrong then you are deluding yourself”
“Continuing as you are can only make you look even more foolish.”
“see if you can recognise the blatant logical flaw which it contains”
“But you say (in your post at January 7, 2013 at 1:56 pm) that you lack friends to help you.”
“Clearly, if I had known of your lack I would not have made such a cruel suggestion, and I hope you will accept my apology for having made such a hurtful mistake.”
“may gain you the ability to interact with those around you because it seems likely that your behaviour (as exhibited on this thread) is probably a major contributing factor in your ability to obtain and/or keep friends.”
And with regard to your repeated false claim that I could not find any friends to help me with my homework I re-present my initial response:
Didn’t actually say any such thing. Just asked for you to explain your comment rather than pester other people who probably would have no more idea what you meant than I did,
Philip Shehan:
re your post at January 8, 2013 at 1:02 pm.
Sorry, but I cannot help because I am too far away. You need to get somebody else to put your rattle back in your pram.
Richard
Layman,
I did not introduce the term acceleration to the discussion. I think my use of the word “show” rather than the phrasing “visibly different” is an acceptable substitute in my response to the authors’ paragraph:
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.
My use of the word “noise” to describe departures from a smooth line caused by real but irregular and currently unpredictable forcings such as ENSO and volcanoes which contribute to the temperature data caused me momentary hesitation. I am used to using the word in its correct technical sense in discussions of spectra – truly random spikeness caused by the electronics of the instument and other extraneous background electromagnetic radiation. However, I decided it was not a problem at the level of discussion here.
It is not only irregular forcings that I have covered by the use of the term noise, but regular predictable forcings like solar cycles, but Ihope you will not take me too much to task for that.
Which brings me to your suggestion that i should present the results of a regression analysis of I would not know where to start but I don’t feel that it is necessary. The observed temperature record presented by the authors is the result of the combined effects of a number of forcings, predictable and unpredictable, natural, and dare I say it, man made.
The discussion is in the context of the authors claims as to what an “informal” analysis (eyeballing) of the data sets show, if you will pardon the term in determing whether the overall trends of the curves are “visibly different” ignoring the lumps and bumps which I have termed noise along the way. In support of my view that they were mistaken that the temperature data thus observed showed no “accelerating” trend I produced a graph showing an accelerating cuve superimposed on a temperature dat set covering the period under discussionas an aid to the eye.
In no way do I claim that the curve fit is meant to account for all the forcing factors contributing to the actual data or that it has any specific physical relevance. Again, it is just an aid to the discussion of authors remarks as to what “informal” casual eyeballing of the data reveals.
This is the only issue I am discussing here, D. Boehms insistance that I must account for an ever expanding list of microinterpretatations of selected data sections and data sets not even mentioned by the authors notwithstanding.
Hope this clarifies my position.
Philip Shehan:
Your post at January 8, 2013 at 1:45 pm says
Say what!? Even by your standards that is a ridiculous statement
Your first post to this thread was at January 4, 2013 at 8:43 am and says
Richard
Thank you Richard for reinforcing my demonstration with a long list of your remarks to me above about your habit of engaging in personal attacks which you claim to have found “false” “untrue” and “egregious”.
You are aso exhibiting your staggering lack of comprehension and reasoning with this gem, commenting on my response to Layman which deserves repeating:
‘Your post at January 8, 2013 at 1:45 pm says
“I did not introduce the term acceleration to the discussion.”
Say what!? Even by your standards that is a ridiculous statement
Your first post to this thread was at January 4, 2013 at 8:43 am and says
“Informal inspection of the temperature data of panel c does show acceleration,”‘
From the very beginning, at my first comment, without rearrangement of the text:
‘Philip Shehan says:
January 4, 2013 at 8:43 am
Quoting from the paper:
“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.”
Informal inspection of the temperature data of panel c does show acceleration, matching…’
So Richard, who introduced the term acceleration into the discussion, the authors of the paper or myself?
And its not as if I did not include the quote from the authors in immediate reference to my statement in the comment to Layman that you take issue with.
Are you completely blind or completely stupid?
In my earlier post I wrote:
“I much prefer and as a scientist am used to, polite respectful and robust disagreement. I recognise that this is (sadly) not the culture of the blogosphere, and have come to the conclusion that there comes a point with the repeated efforts of people like D Boehm and sadly yourself, when turning the other cheek is no longer worth the effort.
Note that Layman knows how to conduct robust discussion without abuse, and I respond to him in kind, but you are getting worse and worse. I no longer bother in your case but return your manner in kind. I will add that I had thought that D. Boehm was the truly obtuse incorrigible abusive idiot in this thread. I have changed my mind.
Philip Shehan:
Having pretended to be a sad, little, lonely and stupid man but being revealed as something else, at January 8, 2013 at 7:55 pm you demonstrate you are a mendacious troll while trying to cause a rift between me and Layman.
You DID introduce temperature “acceleration” into the discussion.
The paper’s authors stated that rise in atmospheric GHG concentrations had accelerated but – as everybody knows – rise in global temperature has not. You joined the discussion at January 4, 2013 at 8:43 am saying you did also observe temperature “acceleration”.
Later, at January 8, 2013 at 1:45 pm, you claimed,
“I did not introduce the term acceleration to the discussion.”
As I said, that claim was “ridiculous”. Please note that I am aware of the tricks of warmunists so I did not say your claim was a literal untruth. The facts are:
1.
The authors mentioned that – as everybody knows – temperature rise has not accelerated.
2.
You joined the thread late and asserted that global temperature rise has accelerated.
3.
You maintained that assertion despite all evidence to the contrary and advice that your stance was making you look foolish.
4.
The UK Met. Office issued a press release admitting that global temperature rise has not accelerated but has decelerated,
5.
Following that press release, you claimed you had not introduced temperature acceleration into the discussion.
6.
I said your claim is “ridiculous”: IT IS.
7.
You have tried to dispute that that your claim is ridiculous and say I am being abusive.
8.
You are a deluded fool whose further comments I shall ignore. And,no, that is also NOT abusive.
Richard
Actually it would be more along the lines of LR08. I don’t see any discussion of their residuals in that paper though.
D Böehm Stealey says:
January 7, 2013 at 3:05 pm
Phil.,
Don’t be silly. This chart shows clearly that T controls CO2, and not just “10%” of it.
[snip.]
Your graph shows nothing of the sort and your refusal to give the scale of the CO2 rate indicates that you know that. In your graph you have subtracted ~2ppm/yr from the rate leaving a fluctuation of ~±0.2 on your graph related to temperature. The 2ppm/yr is the growth due to fossil fuel combustion etc., the slight modulation of sink/source due to temperature is a minor effect.
Your attempt to attach significance to the lag is flawed by your comparison of a global statistic with a local one, why not use the South Pole CO2? Of course it would make more sense to compare two global statistics.
Phil,
Anyone who looks at this chart can clearly see that ∆T leads ∆CO2. You are simply turning yourself into a pretzel trying to argue otherwise. Who should we believe? You?? Or our lyin’ eyes?
And I see that Shehan is still clinging to his preposterous notion of rapidly accelerating global temperatures, even as the rest of his climate alarmist crowd finally admits that global warming has stalled. Incurable cognitive dissonance.
D. Boehm
For the 463rd time (OK slight exageration).
Nowhere, absolutely nowhere, have I argued a causal relationship between temperature and CO2 concentration. I am perfectly happy, for the sake of argument here, to accept that delta CO2 follows delta T. Even that delta T causes delta CO2. (And indeed it does in the case of CO2 produced currently by melting of arctic permafrost with increased temperature and historically following the ice age melts)
The only point I am discussing. The ONLY point I am discussing. Thre ONLY point I am discussing is the claim introduced, that’s introduced, INTRODUCED (for benefit of the other slow learner here) by the authors:
“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.”
To my sad, lonely, stupid but very experienced old eyes in looking at such data, an informal eyeballing af the temperature data shows an acceleration for the temperature data in figure 1, panel c.
And in support of my eyeballing of this 1880 to 2007 global temperature graph (your chart showing short term data trends in central England notwithstanding) I have referred to a non linear accelerating curve of global temperatures for the period under discussion, which shows that my aging experienced eyes did not decieve me. There is a very good match. Superior, but only in the opinion of these ageing experienced eyes in the absence of the availability of an R2 parameter, to a linear fit of the authors data which I have twice provided.
And for the 364th time (OK slight exageration again) the fitting of a line whether linear or non linear is here only a visual aid to an “informal” eyeballing of the data as discussed by the authors. It deos not involve any assumptions of cause and effect, or any other random or non random link to the data on the x and y axes whatsoever.
Hoping that this finally clears up the only point I am making and that you will stop producing irrelevant data and assuming causal or other arguments i have nowhere made. Also hoping for the sudden appearence of a porcine airfleet.
Is Shehan still shoveling his ‘global warming acclerating’ horse manure? Even the most hard core climate alarmists now openly admit that there is no acceleration in global warming, and that global warming has been stalled for many years.
But not Shehan. No, Shehan still believes that global warming is rapidly accelerating. For proof, see the deceptive charts that he is still posting.
Shehan is the last climate alarmist claiming that global temperatures are currently accelerating. That tells us all we need to know about his mental state; pure cognitive dissonance, per psychologist Leon Festinger:
The Seekers have superior knowledge. You must believe their prophets, Mrs. Keech and SkS. The flying saucer will be along very soon to save the Believers. Have faith, Philip. You are right. Everyone else is wrong.
Boehm Stealey @11:37 am, January 9th links to a chart which compares CO2 concentration and temperature after removing all trends, and re-centering at zero. He purports that by removing the trends and showing a correlation, he can show that temperature is responsible for the trend in CO2. That, by itself, is quite an intellectual feat. Most of us would think that if you wish to analyse the relationship between two trends, you do not start by removing both trends from the data.
That, however, is not my primary concern. Taking Boehm Stealey’s chart at face value, I notice that for each 1 degree C fluctuation in temperature, there is a 4 ppmv fluctuation in CO2 concentration (scale factor = 0.25). Ergo, taken at face value, Boehm Stealey shows only that the approximately 1 degree C temperature rise over the course of the last century caused a 4 ppmv rise in CO2 concentration, with the rest of the rise coming from anthropogenic factors. Consequently, he is unable to explain the strong relationship between log CO2 concentration and global temperature.
Tom Curtis,
I deliberately scaled @0.25 to make the T/CO2 relationship clear. It would have been clear enough with, say, a 0.15 scale, but less so. This way, the peaks are about the same amplitude.
The point, however, is that ∆T leads ∆CO2, not vice-versa. If you can produce a similar chart showing that ∆CO2 leads ∆T, I will concede that you are correct. But if you can’t, then I am correct: ∆T causes ∆T. Anyone looking at the chart can see that.
Tom Curtis
As my post replying directly to Boehm’s 11:37 post notes, the major problem with his chart is the use of the isolate function, (See the explanation of this function on the WTF site which means that is neither a plot of CO2 concentration nor temperature but THE NOISE AFTER SUBTRACTION OF THE DATA.
Astonishing but true.
The only ‘astonishing’ thing is that Shehan continues to believe that global warming is accelerating. Even the BBC has now been forced to climb down, and admit that global warming is static.
To understand why Shehan cannot admit that there is no “acceleration” in global warming, and that global warming has in fact stalled for the past 16 years, a famous author has the answer:
OK so my first post may have been a little fruity in expressing my outrage at D Boehm’s conduct and I will accept that as an expalnation if why it has not appeared, but is the failure of my post (to Tom giving further explanation af Boehms analysis and a correct representation of the data to appear with an awaiting cosideration tag) indicative of anything?
Tom, if you want to analyse the relationship between two variables, detrending is often an essential step. If a relationship does not hold after removing the trends, then any correlation prior to detrending is likely spurious. If the cause of the trend is stochastic, then differencing might be necessary. However, differencing may not be necessary if the stochastic series are cointegrated
Here is an example showing how adding a trend creates a spurious correlation between two otherwise uncorrelated series.
Perhaps I was trying to include too many links in my earlier posts. Not sure haow many I used but is there a limit? I shall try 3.
Tom Curtis:
Anyway, it took me a while to track down that the isolate function used in the graph reduces it to a plot of noise.
http://www.woodfortrees.org/plot/esrl-co2/isolate:60/mean:12/scale:0.25/plot/hadcrut3vgl/isolate:60/mean:12/from:1958
An explanation of the functions discussed here is found on the WFT help page:
http://www.woodfortrees.org/help
My suspicions were aroused by informal inspection of the graph (and presentation of another graph from the same source in which extraneous processing had been used which did nothing but iintroduce extraneous lines which flattened the temperature data obscuring the curve of the data which the graph was supposed to be showing did not exist) shows that the match of the data sets is riduculaously good.
Given that temperature is affected by solar cycles, aerosols, volcanic eruptions, el nino and la nina events etc etc, how could there be such a near perfect correlation between temperature and CO2 content alone? Well we now know there isn’t. the graph is fraudulent.
Here is the temperature and Mauna Loa data and Hadcrut3 temperature with the only functions applied being a 12 month smoothing function for the temperature data and the normalise function which applies a scaling and offset adjustment.
http://www.woodfortrees.org/plot/esrl-co2/from:1958/to:2010/normalise/plot/hadcrut3vgl/from:1958/to:2010/mean:12/normalise
Layman Lurker: “Here is an example showing how adding a trend creates a spurious correlation between two otherwise uncorrelated series.”
I’m a little confused by that. If you have two variables X and Y, and you add trends to each, creating new variables X’ and Y’, the correlation between X’ and Y’ can hardly be called spurious, as they are correlated by construction. X and Y could well be uncorrelated but those aren’t the variables being compared, X’ and Y’ are.
Conversely, if I put the kettle on the stove and turn it on, and then I record the temperature and the burner setting every second for a minute or so, and then I detrend the data, I’m going to conclude that the burner has no impact on the temperature of the water (especially since the detrended burner value is zero the entire time). Or, even worse, I’m going to notice that the detrended temperature data starts curving down after a little while — since the water’s rate of warming will slow as it gets closer to equilibrium — and then conclude that the burner actually cools water down.
for Jason
JasonB I presume your burner kettle example is intended to be analagous to CO2 forcing of oceans? It is a great example. If you change the burner setting at defined time steps which do not allow a full temp response then is dT/dt vs dCO2/dt going to be linear? There is a great article and discussion on this at Lucia’s.
Layman Lurker,
The burner kettle example is meant to be literal: If detrending the data would lead us to falsely conclude that the burner is not warming the water, then a negative result in other detrended data could just as easily lead to the same mistake. It’s a question of the validity of the test.
As for correlation vs causation, deciding that causation does not exist because two variables are correlated seems rather perverse, and arguing that the only reason for believing causation to exist is due to the correlation is ignoring an awful lot of theory (and observation) going back quite a long way. In both the kettle/burner example and global temperatures, we have reasons to believe a-priori in both cases that one should cause an effect in the other from basic physical principles, and the predicted warming effect of increased CO2 predates any actual observation of that warming effect by, what, 100 years or so? The way to check that prediction is by comparing the data, not the detrended data.
Thanks for the response JasonB. I will respond later today or this evening.
D Boehm Stealey says:
January 9, 2013 at 11:37 am
Phil,
Anyone who looks at this chart can clearly see that ∆T leads ∆CO2. You are simply turning yourself into a pretzel trying to argue otherwise. Who should we believe? You?? Or our lyin’ eyes?
I’m not turning myself into a pretzel, I’ve been asking you to portray the data honestly but you refuse to do so. Clearly as pointed out below, your graph shows that only a very small fraction of the rate in change in CO2 depends on ∆T. Your “lyin’ eyes” clearly mislead you since they manage not to see most of the change in CO2.
As I pointed out above your graph shows that “CO2 levels rise because of combustion emissions into the atmosphere with a small modulation due to temperature”, so yes you should believe me!
D Böehm Stealey says:
January 7, 2013 at 3:05 pm
Phil.,
Don’t be silly. This chart shows clearly that T controls CO2, and not just “10%” of it.
No it doesn’t because you’ve removed 90% of the change in CO2 (~2ppm/yr) from your plot!
Mr Pretzel,
The chart I posted shows that changes in CO2 follow changes in temperature. You keep tap dancing around that fact. My challenge to you: produce a similar chart that shows that changes in CO2 are caused by changes in temperature. I have repeatedly challenged others to produce such a chart. Their response used up lots of pixels, but… still no chart.
Layman Lurker, your counter example consists of the correlation between to sets of white noise plus trend. Clearly if you eliminate the trends, you will find that there is no correlation between the white noise, but that will not enable you to explain the trend. My point stands.
D Böehm Stealey:
1) Whatever the reasons for your scaling, the fact of similar amplitudes shows the predicted correlation from the relationship you show. That shows unequivocally that global warming is not sufficient to explain any but a tiny fraction of the increase in CO2 concentration over the twentieth century.
2) I grant (because it is true) that year to year fluctuations in global temperature are the primary driver of year to year fluctuations. As shown above (and elsewhere) , the rise in temperature is not the cause of the vast majority of the rise in CO2 concentration over the twentieth century. You, however, wish to infer that because year to year fluctuations in global temperature largely cause the year to year fluctuations in CO2 concentration, that the rise in CO2 concentration cannot cause the rise in global temperatures over the twentieth century. To make that inference, however, it must be the case for all X,Y, that if X causes Y, Y cannot cause X. That is, for example, if the sound from the speaker causes the current in the microphone, the current in the microphone cannot cause the sound in the speaker.
You may be delusional enough to think that there are no feedback loops, but I am not. The essential premise of your argument is simply false, and demonstrated to be false in a host of physical systems.
As it happens, year to year fluctuations in temperature cause changes in CO2 concentration which are large relative to the annual increase in CO2 concentration due to anthropegenic emissions, but small relative to the decadal increase. Hence the short term fluctuations in CO2 concentration are dominated by global temperature, while the long term trend in dominated by anthropogenic emissions. In contrast, the expected equilibrium response to a years increase in CO2 emissions (0.02 C) is very small relative to year to year fluctuations in global temperature, so in the very short term changes in CO2 concentration have no discernible impact on temperature. But the accumulated impact of the accumulated anthropogenic emissions is large, and dominates the decadal trend in global temperatures.
3) I see that you are repeating Delingpole’s latest misrepresentation. The facts (confirmed by digitizing the graph and analyzing the result) is that the new Met Office prediction predicts that 2 out of the next five years will break the current global temperature record; the trend from 1996 to the end of the predicted interval will be approximately double the current trend from 1996 to the end of observations; and the trend from 1996 to the end of the predicted interval is statistically distinguishable from zero. I am sure you will continue to repeat Delingpole’s cannard; but you won’t mention these facts (or any facts) as evidence because that would reveal the claim to be a sham.
tom curtis,
1. You are still stuck on scaling. As I pointed out, the cause-and-effect relationship is the central point. ∆CO2 follows ∆T, and scaling has nothing to do with that fact. I challenge you to produce a similar chart showing the reverse.
2. Thank you for your assertion. It does not, however, change cause-and-effect.
3. I did not read anything by Delingpole regarding this issue. I don’t know what he wrote. So you are using him as a strawman in our debate; you set that strawman up and knocked it right down, you brave strawman slayer. That allows you to avoid the central fact that changes in CO2 are caused by changes in temperature, not vice-versa.
Your citing that the “Met Office prediction predicts” is simply an appeal to an authority that is wrong in it’s predictions far more than it is right. Their credibility is shot. They have now been forced to admit that global warming has stopped. If CO2 has any warming effect, it is negligible, and should be disregarded as trivial.
Wake me when you find a similar graph that shows CO2 causing temperature changes. The global warming hysteria is based on nonsense. There is simply no scientific evidence supporting the CO2=CAGW conjecture.
D Böehm Stealey:
1) Much as you dislike it, the scale is significant and shows the response of CO2 concentration to temperature. I know you like to only look at that part of the data that makes your point, lest you see all of it and be forced to change your opinion. I don’t like such blinkered views, however, and will note what the correlation says about the effect of temperature on CO2 levels.
2) Again, you simply evade the point that proving that temperature has a causal effect on CO2 concentration does not prove that CO2 concentration has no causal effect on temperature. That CO2 does indeed have a causal effect on temperature has been demonstrated beyond reasonable doubt, and is acknowledged by all AGW skeptics with a skerrick of scientific credibility (and quite a few without).
3) First, I apologize. It was David Whitehouse, not Delingpole, who started the deceptive spin on this story. You, apparently got the story second hand, but that in no way improves the basis of the story, and does not justify your ignoring the evidence that shows Whitehouse’s spin to be deceptive. You have no basis for evading the question as to why you consider a prediction that two out of five years setting new records, an increased warming trend, and (probably) statistically significant warming from the current cherry picked benchmark constitutes a prediction of no more global warming. (Note that my original analysis used HadCRUT4 data along with a digitized version of the prediction. I have since discovered that the Met Office used the HadCRUT3 data set for the prediction. This will change the detailed results of my analysis, but is unlikely to overturn the main points.)
Lets separate out what we know from what are guesses.
1) the greenhouse effect is real and can be shown by looking at the temperature of the earth without greenhouse gases. The physics for this is well established.
2) we know that temperatures have gone up and down over thousands of years. There are what appear to be cyclic patterns to thse movements. We’ve also clearly demonstrated a shorter term cyclic movement related to ENSO/amo/pdo
3) we know other things can have powerful effects on temperature including small movements in the earths gravity and changing it from the sun.
4) there is a relationship between co2 and temperature and historically we have seen that co2 does indeed rise as temperature rises due to outgassing from the oceans. Similarly co2 has declined as temperatures go down.
5) we don’t know what percentage of the temperature increases or decreases in the past are actually due to whatever forcing caused the initial temperature change and how much is related to changing gas concentrations. Several lines of analysis show that the feedback could be one ( null) to 9 times the original forcing. This is based on our understanding of what the conditions that caused the forcing and how the forcing effected the environment. It is clear that our understanding of solar forcing at this time does not allow it to account for the entire temperature change we’ve seen in the past. Therefore paleo methods for determining how much co2 effects temp are not reliable due to lack of sufficient proxies for all parameters, error in the proxies we have and a basic lack of understanding how the environment reacts to changing co2 or solar or other forcings.
6) a good part of the energy balance for the system is tied up in the oceans which represent 1000 times the heat capacity of the atmosphere. Without knowing precisely the movement of energy in and out of the ocean it is difficult to understand where energy may be coming from or going to. Satellites can measure numerous things in the atmosphere but only Argo has been able to give us the first accurate information about the top 3000 meters of the ocean. Unfortunately the ocean is so large that even Argo doesn’t really tell us everything we need to know to be sure what’s happening to energy in the system
7) models have been created with assumptions about how the earth oceans and atmosphere react to each other and we don’t know if the assumptions in these models are correct. Wellnwenknow they are wrong because in fact the models have been seen to be poor predictors of any variable including temperature.
8) fitting the models to the proxies and data we have produces numerous fits that sometimes look good and sometimes look bad. We know that when predicting the next year or 10 years that none of the models (23 or so) is any better than the other (papers available). So a mean is calculared but a mean implies that none of the models is correct and also that there is a big cicular element to the models that the scientists building the models know the data and are constantly trying to make the models better but since they all use the same data and there is common knowledge of what the models consist of with similar assumptions in all the models and common ideas the models could all be easily wrong and even predicting higher temperatures in 2100 from the models is poppycock. Error analysis of the models demonstrates that there is no way the models could produce a fixed prediction with small error. The error bars are so enormous that in 2100 a temperature +10 or -10 is possible.
9) we know that the models have consistently predicted higher temps and more effects from co2 than we’ve seen.
10) we know that a good fraction of the co2 increase is due to man burning fossil fuels. However the rate of increase in co2 is below projections of the ipcc. Apparently the environment can absorb the co2 better than we think. Further we assume that the oceans and other means of absorbing the co2 naturally will not work as well. We don’t know if that is really true.
11) nobody has tested the assumptions in the models about how water vapor will change and how other things will react. These are assumptions that are untested and can make a huge difference on rhe result
12) the models to this point have assumed a hockey stick as a base assumption of the temperature record. They cannot explain variations in temperature because of cyclical phenomenon except if co2’changes. Yet we know now that’s there have been variations in the past and these cycles absolutely seem to exist
13) while co2 has been correlated with temperature change so has cosmic rays and sunspot number. We can’t explain these relationships with our understanding of climate.
14) the last ipcc thought that the models were so good that taking them and adding effects of Enso and volcanoes we had largely been able to show a tight fit to the data. We therefore thought we had a good handle on natural variability and its causes. We therefore issues a statement that with 95% certainty the temp change from 1979-1998 was caused mostly by co2. However since 1995 there is no statistically observable trend in temperature which means the natural variability that we assumed we had a tight leash on in ipcc ar4 has proven to be wrong. That is a central failure that is not talked about. We now cannot say that we understand the level of other factors affecting the environment and how long such effects would dominate any co2 effect. However the fact that the entire release of co2 since 1995 has been neutralized by a “natural variability” that we cannot explain means that our “certainty abut the cause of the heating between 1079-1998 is now up in the air completely. Given how much variability has been observed its now clear that a similar level of variability could in fact be the entire cause of the 1979-1998 warmup meaning that the presumed fit of high sesitivity to co2 in the models could be completely wrong. This is a central point that is not understood. The global warming hypothesis rested on the idea we could ascribe within controlled ranges the effects of other forcings. Now that its clear we don’t have a good handle on these other forcings or natural variability we are forced to go back and rejustify the oft assumed now position that we know that co2 is the cause. It’s clear in fact that other factors such as El Niño could have been a large part of the warming from 1979-1998 or other unknown phenomenon related to longer cycles in the system. Since we don’t fully understand the cause of the 18 year hiatus in temperature we also dont know where that effect is going go in the future. It could as some have speculated actually drive temperatures colder for any period of time or make them warmer.
15) there is a high variability in the estimates from the paleo record on the climate sensitivity. We lost the assurance we had that the 1979-1998 period was “clean” and we could understand what caused that heating. The models are way too inaccurate and circular to be able to add credence to the assumed 3.0 climate sensitivity in the most widely publicized predictions of the ipcc.
16) the historical record of the us temperature has been fiddled with based on “in homogenous inconsistencies”. This algorithm which is used to blindly adjust our historic temperatures to make them mores accurate seems to have a strong bias to adding z0.3-0.5c to the temperature rise in the last century. This widespread and massive change in the historical record without being thoroughly vetted is a sore point. Nonetheless nobody would argue temperatures haven’t gone up. We simply don’t know with certainty why they have or what the real magnitude of the increase is.
17). While glaciers are melting they have been doing so for hundreds and hundreds of years. Satellite measurements clearly are showing that we overestimated the melting. We calculated 3 feet gain with one foot coming from Antarctica one foot from inland glaciers and one foot from warming water. Newest data shows that Antarctica is probably neutral or gaining ice mass. Inland glaciers are losing 1/4 the ice we assumed and 1/12 what some predicted. Further there is very little warming of the oceans since we have an accurate thermometer in argo that has been observed in the ocean. The result of this is that we understand why sea level has risen very little in the last 10 years. For whatever reason this is way less of a problem than people anticipated and is probably going to produce the same amount of sea level rise we’ve seen for hundreds of years which is about 6″ of rise not 3′. This is a miss of factor of 6. Huge error.
18) numerous other things predicted have proven wrong. There is no deonstrable increase in storms as has been predicted. We don’t see higher variability in rainfall but decreasing variability. We don’t see higher humidity in the atmosphere a critical important prediction that has failed. This tells us that key assumptions may be wrong.
What’s my conclusion after studying all this?
A) over the next 10 years some of the parameters in alternate theories show a declining trend in temperature. For the first time sincemthensatellite era we will be able to observe if these alternate causes of climates change play a larger role than co2 or lesser. Co2 is unmistakably climbing. The sun is unmistakably less intense and has lower sunspots. The oceans are in a negative pdo /amo phase. All of these were during the 1979-1998 period in sync and rising. Therefore ascribing the temperature change reliably to one or the other was difficult. In the nextn10 years if temperatures resume a 1979-1998 type rise or higher as cagw theory requires then its will show conclusively that co2 rules. If temperatures stays flat or goes down it means the assumptions that co2 was the lord of temperature change will be disproven. If temperatures go down a lot then coz2 probably has almost no effect on the atmosphere and all the research and assumptions will look ridiculous in their certainty. If temperatures stay the same it similarly means that co2 isn’t lord. Other things can overwhelm co2. It’s also possible temperatures go up modestly and this would mean that the climate sensitivity is weak but there is some effect of co2. All things are possible. It’s occurred to me that maybe co2 is powerful but so is solar and oceans. That a complex adding and subtracting of large effects has resulted in a more muted temp change. We need to observe more to conclude what the real CS for all these things are but happily nature over the next 10 years is going to give us that experiment to observe.
tom curtis,
As I have stated repeatedly: “Anyone who looks at this chart can clearly see that ∆T leads ∆CO2.”
That is my central point, and it is the point that you keep avoiding.
1. The scale is not relevant to my argument: that temperature leads CO2, not vice-versa. Scale is irrelevant.
2. You claim that “proving that temperature has a causal effect on CO2 concentration does not prove that CO2 concentration has no causal effect on temperature.” But you keep avoiding my challenge to produce a chart showing that changes in temperature cause subsequent changes in CO2. That is because there are no such charts; any effect on T from rising CO2 is so minuscule that it is unmeasurable — unlike empirical measurements showing conclusively that ∆T causes ∆CO2.
3. I am evading nothing, because I have read nothing by David Whitehouse, either. I think for myself, and if a growing number of writers agree with me, then they are on the right track.
You say: “You have no basis for evading the question as to why you consider a prediction that two out of five years setting new records, an increased warming trend, and (probably) statistically significant warming from the current cherry picked benchmark constitutes a prediction of no more global warming.”
Of course I have a basis for that fact. And the Met Office has finally agreed with me: global warming has stalled for the past decade and a half. Your argument is based on a prediction, therefore it is nothing but a hopeful conjecture. Such predictions have invariably turned out to be wrong.
None of the alarmist crowd ever predicted sixteen years of no global warming [whether Hadcrut3 or Hadcrut4; they both show the same lack of warming]. That is causing much consternation among the true believers. Rather than going on believing in alarmist pseudo-science, you should really just listen to what the planet is saying. Because Planet Earth is the ultimate Authority. And Planet Earth is disagreeing with the climate alarmist crowd.
D Böehm Stealey says:
January 10, 2013 at 3:25 pm
tom curtis,
…That allows you to avoid the central fact that changes in CO2 are caused by changes in temperature, not vice-versa.”
You are always ready to instantly jump down my throat but there is complete silence when you cannot answer questions from me. (Still unanswered after over a dozen posts of yours to me and after being asked three times – Why can the decrease in temperature from 1940 to 1955 not be taken as representative of the period 1880 to 2007?)
For the sake of argument here I will accept that your graph actually does show what you claim the global temperature Muana Loa data, in spite of your use of the WFT Isolate function which “Does the same running mean as ‘mean’, but then subtracts this from the raw data to leave the ‘noise’”
Since you have specifically claimed that changes in CO2 are caused by changes in temperature I will amend my earlier statement to substitute that for correlation and ask:
Given the complexity of the climate system which involves solar cycles, volacanic eruptions, el nino and la nina events, aerosols and particulates, etc , how does such a near perfect cause and effect relationship between CO2 concentration and temperature operate?
Tom Curtis: thank you for your comments elsewhere. Could you respond to the following here?
Tom Curtis:
Thank you for your explanation. I am still a little confused. According to the Wood For Trees help section:
Mean (Months) Running mean over the given number of months. Keeps the number of samples the same, but smooths them by taking the average of that number of months around each sample.
Isolate (Months) Does the same running mean as ‘mean’, but then subtracts this from the raw data to leave the ‘noise’
I thought the functions you mention were performed by (quoting from WFT help again)
Scale (Scale factor) Multiplies each sample by the given scale factor
Offset (Offset amount) Adds the given offset to each sample (can be negative)
Normalise – Scales and offsets all samples so they fall into the range 0..1
Shehan,
First off, I am not appealing to the authority of the Met. They have none. I simply pointed out that they finally agree with me that global warming has stalled.
Next, you keep trying to corner me into responding to issues that do not address my central concern: there is no acceleration of global warming as you believe. You can stop trying to corner me; as I’ve said before, you just aren’t smart enough to pull it off.
Now that you have finally acknowledged that temperature leads CO2, and that you have no chart showing the opposite, that issue is resolved. Further nitpicking is pointless. I disposed of the scale issue. My only two concerns are the facts that temperature leads CO2, and that there is no acceleration in global warming. The rest is uninteresting to me. I can easily answer the questions, but they are a needless time sink. And you don’t determine which questions I answer; I do.
Next, you are simply complaining when I point out that others do not agree with you. Complaining on the internet won’t get you anywhere. But complain, if you have to be a complainer.
Finally, if/when you admit that there is no current acceleration of global warming, we are done. You will have acknowledged reality, and at that point you can stop posting your mendacious charts showing rapidly accelerating global warming. That just does not exist, and you are about the last person on earth who clings to that anti-science nonsense.
1) Yet again, you wish to draw attention to only that point in the data, and to ignore the implications of the rest of the data. Your bias is duly noted.
2) In fact, images in which rises in CO2 precede rises in temperature are very easy to come by. You just need to plot the rise of both over the twentieth century, as in this example. It is even possible to determine that the best fit between annual CO2 and temperature data (HadCRUT4) is found when the temperature data lags the CO2 data by 18 years.
3) You claim to have made the discovery that the Met Office predicts no ongoing global warming by yourself. Therefore you are guilty not of being hoodwinked by the unscrupulous, but of unscrupulously hoodwinking others by making those claims despite the fact as noted, that prediction on which you made those claims was that “two out of five years setting new records, an increased warming trend, and (probably) statistically significant warming from the current cherry picked benchmark constitutes a prediction of no more global warming”. Please note, it was you who linked to discussions of the prediction when you made your claim. You now try to evade the issue by noting that the evidence you yourself cited was a prediction. Well, yes. But it did not turn from observation to prediction between the time you cited it and the time I looked it up.
tom curtis,
It is hard to know who you are responding to. That said, there are major problems with your comment above.
The most serious is the fact that neither chart that you linked to shows which leads and which lags, CO2 or temperature.
I have shown conclusively with empirical evidence that T leads CO2, and even Philip Shehan has finally acknowledged that fact in his post above. No one has accepted my chalenge to produce a chart showing that CO2 leads T. The reason is clear: there are no such charts, because any warming from CO2 is far too small to measure. The only thing your charts show is correlation, and a fictitious, non-existent acceleration in global warming. There is no such acceleration of global temperatures. Only the deluded true beleivers that inhabit the science fiction blog SkS believe in accelerating global temperatures. In reality, global warming has stalled for the past 16 years.
Even the ultra alarmist Met Office now acknowledges that global warming has stalled. Their predictions mean nothing, since their past predictions were wrong. They certainly failed to predict the current halt in global warming for the past decade and a half.
Your #3 is even more confused than your other points. Do you ever proof read your comments? It would take a mind reader to see what you are trying to communicate.
Face the facts that 1. global warming is not accelerating, 2. that CO2 lags temperature, and 3. that the naturally rising global warming trend since the LIA is unchanged, despite a large increase in harmless, beneficial CO2 — thus deconstructing the failed conjecture that CO2 causes any measurable warming. It does not, no matter how much you wish it were so. Those are all scientific facts, and they destroy your CO2=CAGW belief system.
@ JasonB
JasonB, in your example the reason for the false conclusion is the assumption of linearity when the true relationship is non-linear. Assuming there is no prior knowledge of a relationship, one should only conclude that there is no evidence of a relationship as specified by the (linear) test.
I have not suggested this. I have suggested that similar trends can cause (spurious) correlation between unrelated variables.
I have not suggested this either. A detrended correlation should only be interpreted as a failure to reject a hypothesis of causal relationship.
The existence of warming does not rule out all other causes besides CO2. I would also argue that there is plenty of uncertainty surrounding things like ocean response time, feedbacks, and sensitivity to argue that the current rate of warming follows exactly as expected from physical principles.
I have demonstrated that two unrelated variables can be correlated due to existence of similar trends. If you do not detrend you have no way to rule out a spurious correlation – particularly if that relationship does not hold in the detrended case.
Philip Shehan,
As the documentation notes, “Isolate” does an awful lot more than subtract “the trend”.
To illustrate, here is HadCRUT3 with the linear trend for the entire record overlaid:
http://www.woodfortrees.org/plot/hadcrut3vgl/mean:12/plot/hadcrut3vgl/trend
Detrending the data to reveal the residuals from the linear trend shows the familiar “W” shape, and the fact that a linear trend is not a good model for the entire record:
http://www.woodfortrees.org/plot/hadcrut3vgl/mean:12/detrend:0.741532/plot/hadcrut3vgl/trend/detrend:0.741532
Note that a linear trend for the entire record only has two parameters.
Using “Isolate”, however, completely eliminates the “W”:
http://www.woodfortrees.org/plot/hadcrut3vgl/mean:12/detrend:0.741532/plot/hadcrut3vgl/trend/detrend:0.741532/plot/hadcrut3vgl/isolate:60/mean:12
This is because every single sample is being given it’s own five-year trend, which it is then compared to. That’s a whopping 3,766 parameters. Any gradual changes in temperature that are roughly linear on the scale of five years get completelly wiped out, which is why the Isolate plot is completely flat (no “W”) and consists entirely of short-term fluctuations.
Fascinating. A poster is referring to a comment of mine which at the time of reading and typing this response not appeared. I had read elsewhere that this person was in fact a moderator here who has posted under more than one name without disclosing their status. Quoting this source:
“Now, I will note that I feel anonymity on the Web is a good thing. Sock-puppeting, however, is another story entirely – if a moderator on a site misrepresents himself/herself as a rather virulent poster or two (who seem oddly immune to moderation), that is not honest. I don’t care what a posters real name is, or where they work, their posts should make sense on their own. But if they are mixing roles as moderator of a site and an unrestrained sock-puppet poster of distorted information and insults, that’s just downright deceptive. And calls into question the site itself – if there’s deception in an aspect as important as moderation, what else is going on?”
As no evidence was presented to support the assertion, I made no judgement. Now I have the evidence and I make the judgement..
I believe your original point was that in order to analyse the relationship between two trends, the series being compared must not be detrended. You agree that uncorrelated variables can show spurious correlation due to similar trends. How do you establish a causal relationship then when you can’t rule out the spurious case?
Layman Lurker:
Suppose I have two functions; f(x) is a simple linear function of x, while g(x) is actually a simple linear function of f(x).
Their correlation is perfect, and their detrended (i.e. both zero) correlation is perfect.
Now I add a some noise to both. Their correlation is still close to perfect, but their detrended correlation is terrible because I have effectively eliminated the effect of the causal relationship by detrending them. Detrended correlations are highly sensitive to noise and other factors.
In the case of CO2 and temperatures, we expect:
1. The temperature to have a logarithmic relationship with CO2 concentrations, at least in the range of temperatures and concentrations that are relevant to us.
2. The change in CO2 levels to have a lagged effect on temperature.
3. The effect of the change in CO2 levels to be lost in the noise in the short term due to their magnitude relative to other influences in the short term.
If I simply compare the logarithm of annual average CO2 concentrations with annual average temperatures over the period 1958-2012, I get a correlation of about 0.9. If I compare the linearly detrended figures, the correlation drops to about 0.5. What should I conclude?
Indeed not; in fact there are many factors at play, some quite large and highly uncertain (e.g. the direct and especially the indirect effects of aerosols), which makes it difficult to establish climate sensitivity with any real precision using only the instrumental record.
What I believe you showed with your example is that two variables X’ and Y’ related by construction — because you added a linear trend to X and Y in order to create X’ and Y’ — will show correlation, but that if you detrend them, you simply remove again the relationship that you just added. My f() and g() example is similar — g() most definitely depended on f(), but as soon as I added another term to each (some noise) the correlation vanished in the detrended data. If we regard the noise added to f() as X and the noise added to g() as Y, and f() as X’ and g() as Y’, then it’s clear that inferring anything about the relationship between f() and g() from the lack of correlation between X and Y is difficult.
That’s not to say that correlation equals causation, because of course spurious correlation can exist. But in this case the causation is not being inferred by the correlation, it’s inferred from the physics.
JasonB, Thank you for your comment.
I understand the detrend function which shows that a linear fit of the data is inadequate and I beleive simple eyeballing of the data and subtracting the line “in the minds eye” or “informally” so to speak reveals the same information.
If you can point me to where in the documentation there is more explanation of the Isolate function from the WFT help page I was relying on as given in my post (and indeed I find that too brief), It would be helpful in understanding your points fully.
Layman Lurker, correlation is never sufficient by itself to show causation; but as JasonB’s example so, lack of correlation between detrended data does not show correlation of the original data to be spurious. The effect of detrending is that the data no longer contains information about the trend. Ergo, it is impossible from the detrended data to deduce causal relations about the trend. In fact, as Philip Shehan has pointed out, the Isolate function does not remove the trend, but the running mean. That means all data about variations with a period greater than 60 months were removed by Böehm Stealey’s data manipulation, including, of course, any effect of forcing from CO2.
(reply -There are numerous moderators. Speculating about motives is not welcome. ~mod)
Hi Philip,
I wasn’t relying on additional documentation, rather simply the observation that taking the average of the monthly temperatures surrounding a point is the same as finding the midpoint on the linear trend fitted to those same monthly temperatures.
So, in the example with isolate=60, finding the five-year average temperature centred on a point and subtracting it is exactly the same as finding the 5-year linear trend centred on that point, then finding what that trend says the point’s value should be, then subtracting that from the point to leave the residual (i.e. the noise) at that point.
As you can see from the graph, all those parameters give it a lot of freedom to “ride out” longer-term fluctuations and completely remove them from the data, which isn’t terribly surprising.
You are the slippery type, Shehan. I will try once more to pin you down.
GCM’s are, in effect, tinker toy contraptions used to support AGW theory but in fact are no more than that same theory transposed into algorithms. This is all that the global warmers have to offer as support for the unsupportable AGW theory: the cat chasing it’s tail.
What do you say?
I am sure that you are aware that this question is a trap because it puts you in the position of ultimately refuting AGW theory, which projects warming as inevitable, if you deny the premise of the GCM’s, which project inevitable warming .
If you try to finesse the contradiction, you will get tangled up and embarrassed, I can assure you. The world is watching, Shehan. Will you try to wriggle away like last time?
Thank you JasonB.
mpainter:
You have tried this on before, asserting that climate models are designed to only project warming. Further you have claimed that I have agreed with that proposition. Both these claims are nonsense.
@JasonB
As they should be. Failing to reject a null due to a weak S/N ratio is part of science. What is worse though, is rejecting a null because of an artifact of the method.
I would not argue with you on this. I would only point out that appealing to correlation as supportive of causation when the trend is included must consider the null (unrelated variables correlated by chance).
last time?
Philip Shehan says:
January 11, 2013 at 6:19 am
mpainter:
You have tried this on before, asserting that climate models are designed to only project warming. Further you have claimed that I have agreed with that proposition. Both these claims are nonsense.
=====================
From this, I assume that your stand is that GCM’s project other than global warming. Now, suppose you show us one that projects something other than warming, with the important qualifier that it incorporates AGW theory. I have another thread with Joel Shore on this, and he is trying out the idea that the GCM’s do not incorporate AGW theory, so perhaps you two should get together and decide what’s what before I start to quote you two against each other.
@Tom Curtis
Wrong method = right answer is still a spurious result. The null is not that a correlation exists, but rather that a correlation is due to chance. If a trend association cannot distinguish between causative relationships and unrelated variables with common trends, then what is the point of even calculating the correlation in the first place?
tom curtis,
Trying to re-frame the arguments I made is a strawman fallacy. Let me reiterate the only two points I have consistently made here:
1. There has been no acceleration in the long term global warming trend since the LIA, and
2. Empirical evidence shows conclusively that temperature leads CO2, not vice-versa
Respond to those facts instead of going off onto other tangents.
@Tom
Not true. As per Jason’s example it might not be possible to deduce causal relations if the data are too noisy, but then if what we are comparing consists mostly of unrelated noise, there should be no expectation of seeing a relationship. While it is true that trend association might indicate a causal relationship, it means nothing if you cannot reject the null. The basis for statistical inference is rejecting the H0.
What does this have to do with my discussion with you or JasonB? I have not been following the Shehan / Boehm discussion or considering it in any of my comments.
moderator: regarding your comment:
Philip Shehan says:
January 11, 2013 at 1:15 am
(reply -There are numerous moderators. Speculating about motives is not welcome. ~mod)
I am aware there are many moderators. I did not assume or suggest anything else. That said, I apoloogise for any offence caused to moderators who I am sure do their job honestly, diligently and impartially.
I was critical of the behaviour of only one person, who appears to be a moderator.
This person is commenting on the posts of others when those posts have not appeared and that is clearly unnacceptable.
The fact that the suppressed comment was addresed to this person,who may have been acting as moderator at the time, gives rise to the suspicion that the supposedly impartial umpire is in fact a player who is rigging the game. That possibility should be alarming to everyone.
I will represent the post that failed to appear the first time.
D Böehm Stealey says:
January 10, 2013 at 3:25 pm
tom curtis,
…That allows you to avoid the central fact that changes in CO2 are caused by changes in temperature, not vice-versa.”
You are always ready to instantly jump down my throat but there is complete silence when you cannot answer questions from me. (Still unanswered after over a dozen posts of yours to me and after being asked three times – Why can the decrease in temperature from 1940 to 1955 not be taken as representative of the period 1880 to 2007?)
For the sake of argument here I will accept that your graph actually does show what you claim the global temperature Muana Loa data, in spite of your use of the WFT Isolate function which “Does the same running mean as ‘mean’, but then subtracts this from the raw data to leave the ‘noise’”
Since you have specifically claimed that changes in CO2 are caused by changes in temperature I will amend my earlier statement to substitute that for correlation and ask:
Given the complexity of the climate system which involves solar cycles, volacanic eruptions, el nino and la nina events, aerosols and particulates, etc , how does such a near perfect cause and effect relationship between CO2 concentration and temperature operate?
PS
Have to laugh at your criticism of Tom Curtis for his “appeal” to authority (the predictions of the Met office.) You appealed to precisely that authority January 8, 2013 at 10:07 am
“I note that no one else is agreeing with Shehan…In fact, Harrabin and the Met Office admit that…”
(Note the appeal to meta authority – the collective opinion of the committed “skeptics on this blog.) At 3:55 pm January 6 , you individually name two of these authorities:
“Mathew Marler says to Shehan:
Bart says about Shehan:”
You lift your game a little when you later appeal to the authority of the BBC.
mpainter says:
January 11, 2013 at 9:18 am…
Have you stopped beating your wife?
I reject the premise on which your question is based.
Your suggestion that the results of the application of a model shows a particular result means that it is designed to to produce no other result is ludicrous.
Application of models used by the weather bureau here in Australia have been predicting (accurately) extreme high temperatures. That does not mean they are designed to or are only capable of only to producing only that result. They are clearly not. They also accurately predict (here in Melbourne) the arrival of sudden cool changes resulting from the arrival of cooling winds from the southern ocean. Yessterday the temperature dropped at least ten degrees from 37 C in half an hour. Not at all unusual in “four seasons in one day” Melbourne.
Try reviewing this assumption of yours with regard to these comments:
“From this, I assume that your stand is that GCM’s project other than global warming.”
My chuckle of the day in this thread was this from Shehan, when he mistakenly believed that L. Lurker was in agreement with him:
“Thank you from the bottom of my heart… Yours in eternal gratitude…”
LOL! Then LL corrected his mistake, concluding: “Whatever” at Shehan’s sycophantic groveling.
In his next post, Shehan was miffed. Reality wasn’t what he assumed it to be — a recurring fault of his.
It’s the same thing with Shehan’s mistaken belief in ‘accelerating’ global warming. Empirical evidence shows conclusively that there is no acceleration in global warming.
If Shehan would acknowledge that plain fact, we would be done here. But it would be wrong to allow his ‘accelerating’ pseudo-science to go unanswered. New readers might accept Shehan’s false anti-science. Can’t have that nonsense posted here on the internet’s “Best Science” site without being corrected.
D. Boehm is commenting on anything but the content of my 2.30 post, and it is easy to understand why. He is a consummate ducker and diver.
See my comment on the BBC forced to admit thread:
Philip Shehan says:
Your comment is awaiting moderation.
January 11, 2013 at 4:35 pm
That is assuming you are not the umpire pretending to be a player over there rigging the game and it does get posted.
Boehm has not denied that he is wearing two hats at the same time and as I note above his silence is uncharacteristic and in this case deafening. If he is engaging in that conduct it I submit it is incompatible with the “Best Science” status for this site which Boehm himself mentions.
Note my “sycophantic grovelling” to Layman (January 7 3:22 pm) was sarcasm aimed at Boehm, in that I could henceforth use the plot and fit supplied by Layman and not be berated incessantly for using what he describes as John Cook’s cartoon.
I noted with regard to his outraged spluttering in another comment that he is slow to recognise the use of sarcasm when it is used against rather than by him.
D. Boehm. I am devastated. The bromance is over, just when I thought we were getting on so well.”
I was not “miffed” in my response to Layman (January 8 2:02 pm) I am politely discussing the matters raised. Specifically I am pointing out that I am not attributing any physical significance to the curve fit, only that it provides an excellent aid to the eye in demonstrating the nonlinearity of the temperature data.
D Boehm even gets a compliment from me . I here supply sarc on /sarc off indicators (typos corrected):
“I am unsure as to what your point is about non stationary residuals. The discussion, as stated by the authors of the paper in reference to figure 1c, is about how the raw temperature data from 1880 to 2007 appears that figure. As noted above I reject richardscourtney’s quibble that use of data other than the NASA GISS data is “a blatant logical flaw.” [sarc on] I am sure D Boehm will agree with me [sarc off] as he has also used other data sets as an entirely acceptable substitute.
I don’t see how the residuals, with which I have no argument, are relevant.”
Actually the more I read my post to Layman the more I think it worth quoting in detail:
‘I agree that that the correlation, or model if you prefer explains nothing about the last 15 years or anything prior, in the sense D. Boehm enunciates:
“Layman is correct when he says that we can’t really tell anything from the past 15 years from that model. As I have repeatedly pointed out, the only way to see if global temperatures are accelerating is by using a long term trend chart, based on verifiable data.” Examination of the last fifteen years of the data set, or prior to 1880 cannot substitute for an examination of the entire data set from 1880-2007.“
[This is a ‘Gotcha’ as Boehm completely reversing himself having interminably berated me for making exactly this point. He insisted over and over again that the last 15 years could substitute for that entire set. Astonishing.]
Returning to my reply to Layman:
‘The essential point is whether or not the curve fit whether linear or third order polynomial or something else provides a good fit (in terms of a high correlation coefficient r2).
Welcoming any further comment or explanation on this, but for your plot showing good nonlinear curve fit to temperature data for the period presented and discussed in figure 1c, I remain sycophantically grateful.’
[The final words a shot at Boehm]
I see Shehan is still fixated on me. He can’t help himself. Good. I like the entertainment.
The title of this article is: A new paper shows statistical tests for global warming fails to find statistically significantly anthropogenic forcing
My central — and really, my only point — is that there has been no acceleration in global warming. But Shehan avoids that fact like Dracula avoids the dawn. Instead, he engages in his wild-eyed, arm waving rants that have nothing to do with my central point.
If Shehan would simply acknowledge the scientific truth, that there is no acceleration in the natural global warming trend, we would be done here. But Shehan cannot admit to that observed fact, even though his entire side now admits that global warming has stalled. He has too much misplaced pride, and it is based on willful ignorance.
Shehan keeps me amused, ranting about everything except the central point I repeatedly make: there is no acceleration of global warming. None. Any claim that there is is based entirely on pseudo-science.
Au contraire,
It is Stealy who refuses to stick to the point here. The authors assertion as to the appearence of the temperature curve in figure 1panel c.
Philip Shehan says:
January 4, 2013 at 8:43 am
Quoting from the paper:
“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.”
Informal inspection of the temperature data of panel c does show acceleration, matching that of the greenhouse gas forcing plots in a and b. The temperature rise appears less dramatic due to different scaling factors used in the 3 plots…
In support of my eyeballing of the accelerating nature of the data from 180 to 2007 (based on over 3 decades experience in examining such graphs):
http://www1.picturepush.com/photo/a/11901124/img/Anonymous/hadsst2-with-3rd-order-polynomial-fit.jpeg
Compare this to the data set Stealy presents and the linear fit:
http://www.woodfortrees.org/plot/hadcrut3vgl/compress:12/offset/plot/hadcrut3vgl/from:1850/to:2010/trend/offset
QED
Shehan,
As usual you are lying through your teeth by posting your mendacious 3rd order polynomial graph. Yes: lying. Layman has explained to you that such a graph is not valid, yet you dishonestly keep posting it.
Honesty is not in you. QED
To repeat Joe Bastardi’s comment on another thread this morning:
The effect of CO2 is negligible. It is so small that it cannot be measured. But some folks are so invested in their lies about “accelerating” global warming that they become a laughingstock by repeating their pseudo-scientific nonsense.
Off and on I have looked at Cosmic Ray data from the Moscow Neutron Monitor. Since it starts at 1958 I have looked at fits with SST with the same start date. Since Philip Shehan likes to plot my 3op fit to the 1850 start date (rather than the more telling residuals), just for the heck of it here is the 3op fit to the 1958 start date.
There you go. My informal inspection of the data since 1958 “shows” deceleration in temperature, counter to the greenhouse gas forcing plots in Fig 1 (a) and (b) since 1958. My informal insepection puts the point of inflection sometime around 1990 or even earlier.
Still appealing to “authority”. No, he didn’t
I guess a link would help.
Actyually Layman, I think this fit is even better, but Boehm goes absolutely feral whenever I present it. I am afraid he may have a stroke, but I am prepared to risk it.
http://www.skepticalscience.com/pics/AMTI.png
Shehan,
You keep posting that mendacious chart invented by the cartoonist John Cook as if it has anything to do with reality. It doesn’t.
Going by your own 1997 date, this shows conclusively that there is no acceleration in global warming.
Question: Why do you keep lying about it?
Rats. My post to layman immediately before $:38 failed to appear. Trying again:
Layman, I have no problem with the graph you have presented.
I took your initial response to me to mean that these plots are not indicative of any causal relationship. I agreed, noting that I was only discussing, in reference to the comments by the authors the temperature data set from 1880 to 2007 and whether a linear or non linear fit is a better visual match to the data
I contend that the non linear plot you supplied covering that period:
http://www1.picturepush.com/photo/a/11901124/img/Anonymous/hadsst2-with-3rd-order-polynomial-fit.jpeg
appears to match the data better than a linear plot:
http://www.woodfortrees.org/plot/hadcrut3vgl/compress:12/offset/plot/hadcrut3vgl/from:1850/to:2010/trend/offset
Again, as with the authors of the paper, I am talking about nothing more than appearance here. On that basis only, in your opinion, which curve looks a better fit to the data?
I will reproduce a comment here from the BBC thread that more fully explains my position:
Philip Shehan says:
January 12, 2013 at 7:04 am
clivebest:
I do not disagree with much of what you write. But as I pointed out in my post to Werner it is possible to overinterpret the data by going into too fine a detail
.
The central issue I am interested in (as discussed in the “AGW Bombshell?” thread is the APPEARANCE (Don’t mean to shout. How do you do italics or bold on this site anyway?) of the temperature record from 1880 to 2007. Specifically whether or not a linear or nonlinear curve best fits the graphical presentation of the entire temperature data set.
This does not require a detailed examination of all the factors including solar cycles, aerosols, particulates, greenhouse gas concentrations el nino and la nina events etc contributing to the appearance of the final temperature graph. Nor does it require a theoretical or cause and effect explanation for the curve function, linear or otherwise, chosen to fit the data. (There is no reason to assume a priori that a linear fit, any more than a nonlinear function describes the underlying physical reality of the temperature data. Linear fits are easy to do and given the noise levels of data sets of a century or a few decades or less they give an acceptable quick and dirty visual summary of the trend, so we all use them.)
In my post to Werner I argue and present links to graphs which indeed show that short term linear fits to 15 or 10 year sections of the long term temperature data can go every which way (like a yo -yo as he says in his reply) and are therefore not a good guide to the appearance of a fit covering the whole data set.
And Werner, in response to your question:
It sounds like the La Nina in 1999 balanced out the 1998 El Nino. Then what is wrong with starting a slope in 1997?
Absolutely nothing. Your explanation about the la nina 1999 event balancing out the el nino of 1998 is probably correct, and is precisely why you need to look at the long term where such events balance out as much as possible. Starting with 1997 would just be another short term section indicating nothing about the whole data set. I can add it to my earlier plot. As 1997 also includes the southern hemisphere el nino summer of 1997-98, the linear fits are unsurprisingly very similar:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1993/plot/hadcrut3gl/from:2000.3/trend/plot/hadcrut3gl/from:1993/trend/plot/hadcrut3gl/from:1998/trend/plot/hadcrut3gl/from:1996/trend/plot/hadcrut3gl/from:1999/trend/plot/hadcrut3gl/from:1997/trend
Rats again. My post to Layman immediately preceeding my 4:38 pm post failed to appear. as did (I think) my attempted repost. Perhaps it is too large.
I will try in two halves: (Apologies two the moderator if my earlier attempt actually got through)
Layman, I have no problem with the graph you have presented.
I took your initial response to me to mean that these plots are not indicative of any causal relationship. I agreed, noting that I was only discussing, in reference to the comments by the authors the temperature data set from 1880 to 2007 and whether a linear or non linear fit is a better visual match to the data
I contend that the non linear plot you supplied covering that period:
http://www1.picturepush.com/photo/a/11901124/img/Anonymous/hadsst2-with-3rd-order-polynomial-fit.jpeg
appears to match the data better than a linear plot:
http://www.woodfortrees.org/plot/hadcrut3vgl/compress:12/offset/plot/hadcrut3vgl/from:1850/to:2010/trend/offset
Again, as with the authors of the paper, I am talking about nothing more than appearance here. On that basis only, in your opinion, which curve looks a better fit to the data?
I will reproduce a comment here from the BBC thread that more fully explains my position:
Continuing:
Philip Shehan says:
January 12, 2013 at 7:04 am
clivebest:
I do not disagree with much of what you write. But as I pointed out in my post to Werner it is possible to overinterpret the data by going into too fine a detail
.
The central issue I am interested in (as discussed in the “AGW Bombshell?” thread is the APPEARANCE (Don’t mean to shout. How do you do italics or bold on this site anyway?) of the temperature record from 1880 to 2007. Specifically whether or not a linear or nonlinear curve best fits the graphical presentation of the entire temperature data set.
This does not require a detailed examination of all the factors including solar cycles, aerosols, particulates, greenhouse gas concentrations el nino and la nina events etc contributing to the appearance of the final temperature graph. Nor does it require a theoretical or cause and effect explanation for the curve function, linear or otherwise, chosen to fit the data. (There is no reason to assume a priori that a linear fit, any more than a nonlinear function describes the underlying physical reality of the temperature data. Linear fits are easy to do and given the noise levels of data sets of a century or a few decades or less they give an acceptable quick and dirty visual summary of the trend, so we all use them.)
In my post to Werner I argue and present links to graphs which indeed show that short term linear fits to 15 or 10 year sections of the long term temperature data can go every which way (like a yo -yo as he says in his reply) and are therefore not a good guide to the appearance of a fit covering the whole data set.
And Werner, in response to your question:
It sounds like the La Nina in 1999 balanced out the 1998 El Nino. Then what is wrong with starting a slope in 1997?
D Böehm Stealey says:
January 11, 2013 at 6:17 pm
But Shehan avoids that fact like Dracula avoids the dawn.
===========================
aptly put. My read of Shehan is that he lacks the intellectual courage to confront valid objections to his viewpoints. I have been trying to pin him down on the product of GCM’s for weeks, and still he dodges. He is the slippery type.
Okay Shehan, once again: I say that the GCM’s project indefinite warming because that is what they are designed to do. If you disagree, show me a GCM projection that shows other than warming.
@Philip Shehan
Both curve fits are equally good……not.
The important point here is that GHG’s have accelerated, but I agree with the authors – there is no evidence that temperatures have accelerated. Not informally or formally, or by my eyeball or your eyeball, or your SKS curve fit or my 1958 start date.
Furthermore, I would also concur with the authors that co2 is of the integration order I(2) and needs to be differentiated in order to explore the possible linear relationship to temperature (which is I(1)).In time series analysis this is axiomatic but I have kept my opinions pretty much to myself because of what I believe is implied – that co2 changes are caused by accumulated changes in sst. Further support is lended when seeing the lagged co2 relationships in such comparisons.
I did a comparison myself by plotting the normalized derivative of annual co2 along side normalized Reynolds OI sst (which GISS uses). Hopefully, this will be my last comment as I have no interest in getting drawn into prolonged arguments over this. FWIW I am still open minded on this but I haven’t seen any satisfactory demonstrations (and certainly no arm waving) which goes the full distance to explain the data. Troy.ca (whom I have immense respect for) has come the closest. IMO someone must reproduce the diff(co2) vs sst fit (not a simulation) with a compelling alternative model for this to be explained.
I would ask readers to review and consider the literature on non-stationary time series analysis (and cointegration analysis) before being to quick to dismiss.
Is there anyone else here who can explain to mpainter his faulty reasoning in this matter because I have tried and he still doesn’t get it.
Then what’s the point? If your choice of model comes purely from which one informally “looks” prettier, without any consideration of the underlying data generating process and the physics of the system whatsoever, then any inferences made about “acceleration” are wrong. Plain and simple.
I haven’t read every comment in this thread, so I’m sure someone else has made the same point, but I could fit a higher order polynomial which “fits” the data better and shows deceleration, but without a proper discussion of model diagnostics, such an “analysis” would also tell us nothing.
Layman Lurker says:
January 12, 2013 at 11:19 pm
Furthermore, I would also concur with the authors that co2 is of the integration order I(2) and needs to be differentiated in order to explore the possible linear relationship to temperature (which is I(1)).In time series analysis this is axiomatic but I have kept my opinions pretty much to myself because of what I believe is implied – that co2 changes are caused by accumulated changes in sst.
The problem with this analysis by economists is that they don’t address the science involved. It appears that they use the approach: “If you have a hammer everything looks like a nail”!
It is well established physics that at the concentration of CO2 in our atmosphere the absorption of IR by CO2 is approximately proportional to log([CO2]), so at very least the should apply their method to log([CO2]). If they were to do that, with the exponential growth of [CO2], they’d find that the CO2 forcing is I(1).
Perhaps you could demonstrate that for us Phil? I transformed annual co2 into forcing (1.73*co2^0.263) as described in an article here. Then I normalized both co2 and the transformed co2 (forcing) and plotted for comparison. A straight log transformation had basically the same effect. From this it would appear that co2 forcing would have the same order of integration. If I have overlooked something please feel free to point it out.
In any event, the transformation does not apply when the frame of reference is to explain atmospheric co2 as a function of sst.
Phil.
Previous papers on the subject (e.g. Garcia 2009 & Estrada 2010) have found that both rfCO2 and temperature can be better described as trend stationary processes (i.e. I(0)) with at least one structural break.
Layman Lurker says:
January 14, 2013 at 1:23 pm
Phil. says:
January 14, 2013 at 9:22 am
“It is well established physics that at the concentration of CO2 in our atmosphere the absorption of IR by CO2 is approximately proportional to log([CO2]), so at very least the should apply their method to log([CO2]). If they were to do that, with the exponential growth of [CO2], they’d find that the CO2 forcing is I(1).”
Perhaps you could demonstrate that for us Phil? I transformed annual co2 into forcing (1.73*co2^0.263) as described in an article here.
The paper that is the source of that curve says the following:
“For changes in CO2 alone, radiative forcing can be approximated by a natural logarithmic function with a climate sensitivity parameter determining the change in temperature (McGuffie and Henderson-Sellers, 1997).”
This is what one expects from the physics (see ‘Curve of growth’).
However, in the model Lenton used he fitted the expression that you used above, for the opacity of CO2, it’s just an arbitrary fit with no fundamental basis in physics.
In any event, the transformation does not apply when the frame of reference is to explain atmospheric co2 as a function of sst.
Where did this come from?
If that’s what you’re interested in you should be considering the temperature dependence of the Henry’s Law coefficient. This is related to exp(1/T), in equilibrium with seawater pCO2 doubles for a 16K increase in SST.
I would not argue this point. Derivations based on Henry’s law obviously must be considered (along with all other ocean related components of the carbon cycle).