![graphic_esd_cover_homepage[1]](http://wattsupwiththat.files.wordpress.com/2013/01/graphic_esd_cover_homepage1.jpg?w=114&resize=114%2C150)
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.
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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.
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: @ur momisugly 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: @ur momisugly 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