New paper: The Atlantic Multidecadal Oscillation as a dominant factor of oceanic influence on climate

Just two explanatory variables (GHG and AMO) still account for 93% of the temperature variance.

Dr. Leif Svalgaard sends word of this article in Geophysical Research letters by Petr Chylek, James D. Klett, Glen Lesins, Manvendra K. Dubey and Nicolas Hengartner Article first published online: 5 MAR 2014 DOI: 10.1002/2014GL059274

Abstract

A multiple linear regression analysis of global annual mean near-surface air temperature (1900–2012) using the known radiative forcing and the El Niño–Southern Oscillation index as explanatory variables account for 89% of the observed temperature variance. When the Atlantic Multidecadal Oscillation (AMO) index is added to the set of explanatory variables, the fraction of accounted for temperature variance increases to 94%. The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO. In comparison, the Coupled Models Intercomparison Project Phase 5 (CMIP5) ensemble mean accounts for 87% of the observed global mean temperature variance. Some of the CMIP5 models mimic the AMO-like oscillation by a strong aerosol effect. These models simulate the twentieth century AMO-like cycle with correct timing in each individual simulation. An inverse structural analysis suggests that these models generally overestimate the greenhouse gases-induced warming, which is then compensated by an overestimate of anthropogenic aerosol cooling.

1 Introduction

During the past century the Earth has experienced considerable warming due to anthropogenic as well as natural causes. Although a substantial body of research suggests that most of the warming has been due to an increasing atmospheric concentration of CO2 and other greenhouse gases, an exact partitioning of the magnitude of the global warming due to the natural and anthropogenic causes remains uncertain. Most climate research has centered on the use of coupled AOGCMs (atmosphere-ocean general circulation models) to elucidate the climate system from near first principles representing physical, chemical, and biological processes.

Empirical statistical models have been used recently [Lean and Rind, 2008; Foster and Rahmstorf, 2011; Mascioli et al., 2012; Zhou and Tung, 2013; Canty et al., 2013; Chylek et al., 2013] to complement physics-based models and are contributing to our understanding of anthropogenic and natural components of climate variability. The method assumes a linear relation between the observed temperature and a set of selected physically plausible explanatory variables (predictors). A typical set of explanatory variables includes the known radiative forcing and an additional factor characterizing the oceanic influence on climate [Compo and Sardeshmukh, 2009; Zhou and Tung, 2013].

Major radiative forcing includes solar variability (SOL), volcanic eruptions (VOLC) [Douglass and Clader, 2002; Haigh, 2003; Scafetta and West, 2006; Camp and Tung, 2007; Lean and Rind, 2008], anthropogenic greenhouse gases (GHG), and anthropogenic aerosols (AER). The oceanic influence is usually characterized by the El Niño–Southern Oscillation (ENSO) index [Lean and Rind, 2008; Foster and Rahmstorf, 2011]. However, the AMO (Atlantic Multidecadal Oscillation) [Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Gray et al., 2004] also exerts a considerable influence on the global and regional climate [Polyakov and Johnson, 2000; Chylek et al., 2006, 2009; Chylek et al., 2010; Chylek et al., 2013; Zhang et al., 2007; Mahajan et al., 2011; Frankcombe and Djikstra, 2011; Zhou and Tung, 2013; Canty et al., 2013; Muller et al., 2013; Kavvada et al., 2013].

In this note we show that the observed annual mean global temperature variability is captured more fully by a regression model when the AMO is added to the set of explanatory variables. Considering a compromise between accuracy and complexity, the minimal regression model that accounts for 93% of the observed annual mean global temperature variance contains only two explanatory variables: anthropogenic greenhouse gases (GHG) and the AMO. Adding all other predictors increases the fraction of accounted for global temperature variance to 94%.

Figure 1.

(a) Radiative forcing due to greenhouse gases (red), solar variability (blue), and volcanic aerosol (black); (b) four considered models of anthropogenic aerosol radiative forcing; and (c) the observed mean global temperature (GLT) and the regression model temperatures without the AMO among the predictors (M1) and with the AMO (M1 + AMO). (d) The AMO index and residual of the regression model without AMO among the explanatory variables. (e) The division of the observed temperature variability between the statistically significant predictors. (f) The division of the observed temperature variability in two predictor models between the GHG and the AMO.

 

 

Summary and Discussion

A multiple linear regression model that uses the set of explanatory variables composed of radiative forcing due to anthropogenic greenhouse gases and aerosols, solar variability, volcanic eruptions, and ENSO accounts for 89 ± 1% of the global annual mean temperature variance. When AMO is added to the set of explanatory variables, the fraction of explained temperature variance increases to 94%. Just two explanatory variables (GHG and AMO) still account for 93% of the temperature variance. The improvement of the regression model by including the AMO is highly statistically significant (p < 0.01). For comparison, the CMIP5 ensemble mean of all simulations accounts for 87% of the observed temperature variance.

Our analysis suggests that about two thirds of the late twentieth century warming has been due to anthropogenic influences and about one third due to the AMO. This is a robust result independent of the parameterization of the anthropogenic aerosol radiative forcing used or of the considered regression model, as long as the AMO is among the explanatory variables.

An inverse structural analysis shows that all considered climate models (GFDL-CM3, HadGEM-ES, CCSM4, CanESM2, and GISS-E2) overestimate GHG warming that is then compensated by an overestimated aerosol cooling. The overestimates are especially large in models with an indirect aerosol effect. In these models a strong aerosol effect generates the AMO-like 20th century temperature variability. The apparent agreement with the observed temperature variability is achieved by two compensating errors: overestimation of GHG warming and aerosol cooling. This raises a question of reliability of these models’ projections of future global temperature. The inverse structural analysis underscores the significance of the AMO-like oscillation and therefore the need to establish its origin and to better simulate it in future climate models.

It is available as open access PDF here: http://onlinelibrary.wiley.com/doi/10.1002/2014GL059274/pdf

And as HTML here: http://onlinelibrary.wiley.com/enhanced/doi/10.1002/2014GL059274/

Also of interest:

CMIP5 multi-model hindcasts for the mid-1970s shift and early 2000s hiatus and predictions for 2016–2035

Gerald A. Meehl* and Haiyan Teng

Article first published online: 7 MAR 2014 DOI: 10.1002/2014GL059256

Abstract

Compared to uninitialized climate change projections, a multi-model ensemble from the CMIP5 10 year decadal prediction experiments produces more warming during the mid-1970s climate shift and less warming in the early 2000s hiatus in both the tropical Indo-Pacific region and globally averaged surface air temperature (TAS) in closer agreement with observations. Assuming bias in TAS has stabilized in the 10 year predictions, after bias adjustment, TAS anomalies for the 2016–2035 period in the 30 year predictions initialized in 2006 are about 16% less than the uninitialized projections. One contributing factor for the improved climate simulation is the bias adjustment, which corrects the models’ systematic errors and higher-than-observed decadal warming trend. Another important factor is the initialization with observations which constrains the ocean such that the starting points of the initialized simulations are close to the observed initial states.

http://onlinelibrary.wiley.com/doi/10.1002/2014GL059256/abstract

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54 thoughts on “New paper: The Atlantic Multidecadal Oscillation as a dominant factor of oceanic influence on climate

  1. Didn’t we just go through this nonsense? Hang on … yeah, we had their 2014 paper here, and then I discussed it in “Chylek Imitates Ourboros”.

    This is just Garbage V2.0. They are still using part of what they are trying to forecast as an explanatory variable.

    It was bad the first time … and no better this time.

    w.

  2. I don’t know if this is wishful reading on Anthony’s part or if I’m missing something, but to me this abstract looks like it’s staying with the party line that most of the warming since 1975 is our fault:

    “When the Atlantic Multidecadal Oscillation (AMO) index is added to the [explanatory variables composed of radiative forcing due to anthropogenic greenhouse gases and aerosols], the fraction of accounted for temperature variance increases to 94%.”

    and

    “The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO,”

    I don’t see anywhere that it backs off the human-caused warming line. It just adds in the AMO and PDO as well and claims a 94% accountability.

    Did I miss the point of posting this?

  3. Couldn’t even agree with the first sentence of the Introduction “During the past century the Earth has experienced considerable warming…..” Considerable warming? How much warming was that again and compared to when? Maybe the beginning of the Dalton Minimum or the 1970’s or the last 17 years or maybe compared with the beginning of the last ice age.

  4. And hot off the press is Mann et al (2014), yup that Mann. It sounds as though they’re attempting to reverse the role of the AMO, saying that in recent decades the AMO suppressed the anthropogenic global warming signal. They also took a swipe at the Wyatt and Curry stadium wave paper. Link to Mann et al (2014):

    http://onlinelibrary.wiley.com/doi/10.1002/2014GL059233/abstract

    Link to press release:

    http://press-news.org/127589-slowdown-of-global-warming-fleeting.html

    Maybe Mann et al (2014) are discussing some planet other than Earth.

  5. Just one point of interest. I had a look at the paper and the references. Chylek cites himself 7 times. I certainly claim no high level expertise on his subject matter, I have a hard enough time trying to be an expert in my small area of atmospheric science, so I make no claims on the validity of his study. But I consider it a red flag when someone cites themself so many times. And others may not know, but you do get brownie points for how many times your papers have been cited.

  6. If the following quote is true: “using the known radiative forcing and the El Niño–Southern Oscillation index as explanatory variables account for 89% of the observed temperature variance. When the Atlantic Multidecadal Oscillation (AMO) index is added to the set of explanatory variables, the fraction of accounted for temperature variance increases to 94%. The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO.”

    How does AGW account for 2/3rds of something that is 94% covered by natural variance??
    Serious HS alert

    Kit

  7. Maybe throw out all the one-sided adjustments to the temperature records and you will have 99%. Willis points out that they are using large tracts of warming of the oceans as explanatory variables in determining Global warming. Possibly if you threw out the GHG altogether and included the rest of the ocean and the land you would have 100% correlation. Wait… that is the temperature record.

  8. If the AMO is causative (rather than just correlated) then there should be a host of non-temperature indicators for the state of the AMO…. I suspect these do not exist. Until/unless they do, Willis is right: the global temperatures correlate with the AMO index…. that does not prove causation. If there is a causal relation ship, then at a minimum, the AMO index should lead the rest of the global temperature by at least a year or so…. It doesn’t. http://woodfortrees.org/plot/hadcrut4gl/mean:49/plot/esrl-amo/mean:49 Chylek at al should find other metrics for the AMO which are NOT North Atlantic temperature surface temperature.

  9. This graph CET shows reality. The only surface temperature record I trust .. no urban island effect etc.and goes well back to 1660

    It shows absolutely nothing zilch nada therefore all the postings about ANY human influenced warming based on GISS, HADCRUT, NASA NOAA, BOM “adjusted data” etc are meaningless and food for warmist trolls. There is and has been NO significant global warming since 1660, just look at the CET graph! Do you see warming???

  10. Could some here please explain to me why the entire enormous southern half of this planet as no apparent effect or impact on the global climate judging by the total ignoring of the Southern Hemisphere climate by the northern hemisphere climate modellers.
    As far as climate science is concerned I think us Southern Hemispherians should just secede, take our half of the planet and go find somebody somewhere else who at least recognises that we have also have a real climate of our own down here in the Southern Hemisphere.
    A Southern Hemisphere climate that affects you lot in the Northern Hemisphere just as much and may be even more than your claiming that your “corrupted” [ according to your own climate modellers up there ] climate affects our mob down here.

  11. While I fully agree with Willis’ Ouroboros post and the inappropriateness of the AMO as a explanatory variable, it is only fair to note that a predictive model is not the goal of the paper. They are trying to make an explanatory model to see how relatively important different variables are to the temperature signal. I think (well, I hope) the authors would agree that it would be dreadful to try and use the model to forecast. So it is unfair to complain that they can’t use data they don’t have (next year’s AMO) to predict next year’s temp. That wasn’t their goal. It is fair to point out that this is still a dumb practice and that they could have easily substituted SSTs from other parts of the ocean or Siberia’s temp or any other things that couple with temp and got a similar response. What’s up with that?

  12. I must be interpreting the figures incorrectly. They have an increase in temperature from the AMO post 2000, as well as from CO2. Then they way that accounts for 97% of the variability?

    How do they reconcile that with flat temperatures?

    Folks, the earth is talking. Climate Scientists are not listening.

  13. Mann phaser blasted nearly every old school bit of climate history wisdom. Investors have moved on, as has the public, but what’s left is two generations of Gore’s indebted indoctrinates, who will turn on daddy, unless he keeps turning science into a more wound up cult, up, up and away, feeding their fantasy of being heroes. Still the hard sciences carry on, stoically.

  14. Eliza is right.
    High quality surface stations show zero warming for long periods of time.

    http://hidethedecline.eu/pages/ruti/europe/western-europe-rural-temperature-trend.php

    Dump the obviously corrupted urban data. Rural stations generally show zero warming over many decades. Also, Antarctic stations ( Amundsen-Scott, Vostok, Halley and Davis ) maintained with with scientific integrity also show zero warming since 1958.

    Satellite data show zero warming since 1980. Compare with BEST land surface data over the entire data set. This WFT plot shows the discrepancy since 1980

    http://www.woodfortrees.org/plot/best/scale:3/from:1880/plot/rss-land

  15. “The oceanic influence is usually characterized by the El Niño–Southern Oscillation (ENSO) index [Lean and Rind, 2008; Foster and Rahmstorf, 2011] …”

    and because these guys did not retract their papers, lets do the same error again.

  16. I was aware of the paper back in February but did not send it on because it is not very skeptical.

    “The anthropogenic effects account for about two thirds of the post-1975 global warming”

  17. I have been pointing out for years that the hypothesis of linearity in functional dependences is false:

    The method assumes a linear relation between the observed temperature and a set of selected physically plausible explanatory variables (predictors).

    Climate is one of the first examples of dynamical chaos. Dynamics means that the functional dependence to the assumed linearity of parameters can hold for only a limited interval on the variables, even for smooth dynamic variations as in the example.

    This is the basic reason why all these time consuming and expensive computer simulations of climate fail in predicting the future. Fitted by the linear method to the past, real time dynamics takes over and destroys their future predictions.

  18. Bob,

    “Maybe Mann et al (2014) are discussing some planet other than Earth.”

    I don’t think they are even discussing the same universe that we are.

  19. “An inverse structural analysis suggests that these models generally overestimate the greenhouse gases-induced warming, which is then compensated by an overestimate of anthropogenic aerosol cooling.”

    OK, they’re starting to move in the right direction.

    Now they need to account for fact that volcanic aerosols have already been severely ‘tuned’ down before their effects were “over-estimated” .

    The only way to explain that without frigging the input data is for there to be a strong negative feedback to changes in radiative forcing. Once they get that right the effect of CO2 is pretty small and the real problem stands out: there is a long term, centennial rise in temperature which is being spuriously correlated with CO2 because there is nothing else in their list of variables which can account for it.

    We know temperatures have risen since LIA and that started too early to be attributed to CO2. So there is a long term natural variability that is being correlated against a set of variables that include nothing that could account for it.

    By starting their regression in 1900 they mask this problem.

    The other thing they are trying to sweep under the carpet is the temperature drop in late 19th c. The temperature records go back further than 1900 , why do they cut off the earlier data? Doesn’t do the right thing to support the CO2 curve ??

  20. JamesS Apr 8 5:25pm asks “Did I miss the point of posting this? “. All sorts of things get posted here because they are interesting, not because they are “party line”. Even papers/articles touting AGW or aspects of it get posted, often without comment, because they are interesting.

    anna v Apr 8 9:05 pm says “Climate is one of the first examples of dynamical chaos. Dynamics means that the functional dependence to the assumed linearity of parameters can hold for only a limited interval on the variables, even for smooth dynamic variations as in the example.
    This is the basic reason why all these time consuming and expensive computer simulations of climate fail in predicting the future. Fitted by the linear method to the past, real time dynamics takes over and destroys their future predictions.
    “. Spot on. Correct me if I’m wrong, but AFAIK the “limited interval” is not decades or years or even months, but just a few days.

  21. It’s going to be a long haul. These guys will continue to produce this crap as long as the journals will print it. Comments at the journals may be an important leverage point.

  22. Bob Tisdale, in Mann et al. 2014 the abstract reveals his article is mostly in response to Curry and the “stadium wave” paper…
    “Abstract
    We estimate the low-frequency internal variability of Northern Hemisphere (NH) mean temperature using observed temperature variations, which include both forced and internal variability components, and several alternative model simulations of the (natural + anthropogenic) forced component alone. We then generate an ensemble of alternative historical temperature histories based on the statistics of the estimated internal variability. Using this ensemble, we show, firstly, that recent NH mean temperatures fall within the range of expected multidecadal variability. Using the synthetic temperature histories, we also show that certain procedures used in past studies to estimate internal variability, and in particular, an internal multidecadal oscillation termed the “Atlantic Multidecadal Oscillation” or “AMO”, fail to isolate the true internal variability when it is a priori known. Such procedures yield an AMO signal with an inflated amplitude and biased phase, attributing some of the recent NH mean temperature rise to the AMO. The true AMO signal, instead, appears likely to have been in a cooling phase in recent decades, offsetting some of the anthropogenic warming. Claims of multidecadal “stadium wave” patterns of variation across multiple climate indices are also shown to likely be an artifact of this flawed procedure for isolating putative climate oscillations.”

  23. Yet another model that presumes discarded variables and functions had no affect on the outcome, rendering its predictive value useless?

  24. Eliza

    “Do you see warming???”
    ————————————–

    yes

    if you decontextualise the last 30 years it looks like you could have an up trendline [except for the last bit]. Then if you project 100 yr prediction lines on that trendline with a few invented accelerator feedbacks then we all going to fry like its venus :)

  25. Hey, I added the color of my flashlight to the explanatory variables and see, there was a contribution of 1.43785×10-25 to the forcing. So that paper is wrong! Anthropogenic effects account for almost 100% of the warming.
    [/sarc]

  26. Santa Baby says: April 8, 2014 at 5:28 pm
    Two thirds of the warming is caused by antroproghenic adjustment of the data?”

    Bingo.
    I think it’s more like one third to one half, but they’ve adjusted the data just as you’ve adjusted the spelling.

  27. It appears they are finally getting around to admit they may have “missed” some factors in their previous models. But they are still hung up on their biases.

  28. I just have to laugh. All this AMO, PDO, Jetstream, solar cycle stuff does is illustrate the inaccuracy of our present method of estimating the current Global average temperature. Shifting energy within the system should have zero impact on the global average temperature. The error bars should be twice as large as the affects these cyclical changes have on the system.

    Of course that would mean the AGW’ers have no useful data to play with, but I can live with that.

  29. The question is, what is driving the 60 year oscillation in temperatures then?

    The Sun, GHGs, randomness, ocean circulation patterns like the AMO.

    One of these appears to be a better explanation.

  30. Are we saying that (insert something here) is such a large factor that it could halt warming for around 17 years?
    So, why wasn’t it built in to a single model, will all models now be updated to now include this feature and can all previous climate predictions be scrapped?

  31. Almost starting to look like there will be a strong summer El Nino.

    (The summer ones have a tendency to weaken rapidly/blow themselves apart at least in the last several decades, so I wonder if the same thing will happen).

    This is the most important thing happening in the climate right now.

  32. I still don’t understand why the “Climate Models” leave out water vapor as a major radiative forcing agent. I know, water vapor molecule is short lived before it is rained out but an other one takes its place, but the radiative forcing didn’t stop. I bet if water vapor was part of the “Climate Models” CO2 radiative forcing would be hidden in the error bars.

  33. “Our analysis suggests that about two thirds of the late twentieth century warming has been due to anthropogenic influences and about one third due to the AMO.”

    I see around a 0.3°C rise from 1965 to 1995, and from 1995 to 2005, again around a 0.3°C rise. So it may be that the AMO raised global mean surface temp by an extra 0.2°C from 1995, which is one third of the total 0.6°C: http://www.woodfortrees.org/plot/hadcrut4gl/from:1965

    That’s impressive for an increase in negative AO&NAO episodes, and definitely not what the models expected to happen to the AO&NAO from presumed increased forcing:

    http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch10s10-3-5-6.html

  34. anna v says:
    April 8, 2014 at 9:05 pm

    You point out another instance in which past is NOT prologue.

    In the post itself, I was most interested in the following statement made by the authors in their abstract:

    “In comparison, the Coupled Models Intercomparison Project Phase 5 (CMIP5) ensemble mean accounts for 87% of the observed global mean temperature variance. Some of the CMIP5 models mimic the AMO-like oscillation by a strong aerosol effect. These models simulate the twentieth century AMO-like cycle with correct timing in each individual simulation. An inverse structural analysis suggests that these models generally overestimate the greenhouse gases-induced warming, which is then compensated by an overestimate of anthropogenic aerosol cooling.”

    That would seem to contradict any conclusion that AMO accounts for anything; it also clearly (if only indirectly) hits on the central flaw of ALL of the climate models of the past 30 years. Which is the hazard of predicting both weather and climate. Making linear assumptions in a dynamic system.

  35. So….two more papers which suggest that the models have overestimated temperature increases, and thus climate sensitivity is lower.

    Can’t wait to see the complete lack of coverage in much to most of the MSM. MAYBE, just MAYBE, Andrew Revkin, who does have a bit of spine — enough of one to get the warmists angry with him, even though he (like me) does think CO2 causes some amount of warming — might cover these.

  36. When it comes to the models, it seems to me that they have tried to tune themselves to a certain amount of historical data that we as humans had collected. The unfortunate part of that, is that historical (or is that hysterical) data accounts for such an infinitesimal percentage of earth’s history, that it’s laughable that we think we can predict/project anything into the future. I kind of had this aha moment earlier. My analogy is this:

    Look at climate like an infinite road race. The climate has been running for Billions of years, sometimes fast (Hot) sometimes slow (Cold) and sometimes a nice decent pace with not much change. Just because we have 2,500, maybe 3,000 years of actual data, we think we can build a model, tune it to some of that recent past “running” of the climate and assume that in that small past, we can determine that the climate is going to continue to run at its current pace, whether that is Fast or Slow or on cruise control and we now know everything that constitutes climate.

    HA, that is the most egotistical thing I have ever heard. Just like driving down the highway going 60 MPH and a car passes you going 70 MPH. You assume in one hour he’s 10 miles ahead of you, and suddenly you pass him on the side of the road…… huh, must have been one of those “unknown” variables!

    That’s my layman’s two cents worth.

  37. Were the temperatures derived from thermometers or satellites? If thermometers then have we accounted for UHI, ‘necessary adjustments’ and the 70% of the Earth’s surface with no thermometers called oceans?

  38. A natural long-term trend is defined by a proxy factor times the time-integral of the difference between each annual average daily sunspot number and the average sunspot number for a long period (1610-1940). The net surface temperature change of all natural ocean cycles oscillates above and below this trend. The combination calculates average global temperature anomalies (AGT) since before 1900 with a correlation of 95% and credible AGT since the depths of the Little Ice Age. Search keywords AGW unveiled.

  39. Dan Pangburn says:
    April 9, 2014 at 10:15 am

    A natural long-term trend is defined by a proxy factor times the time-integral of the difference between each annual average daily sunspot number and the average sunspot number for a long period (1610-1940). The net surface temperature change of all natural ocean cycles oscillates above and below this trend. The combination calculates average global temperature anomalies (AGT) since before 1900 with a correlation of 95% and credible AGT since the depths of the Little Ice Age. Search keywords AGW unveiled.

    Go search it yourself, Dan. I don’t go on blind searches for any man, been there, done that, it doesn’t work. The problem is that whatever I bring back from my search, about half the time they say something like “Oh, no, that’s not what I was talking about” …

    If you want to get any credibility here, don’t play the “go search” card. Instead, LINK TO EXACTLY WHAT YOU THINK IS IMPORTANT. I don’t go on wild goose chases for anyone. Search keywords “osculate my fundament”.

    w.

  40. Seems like another Terraflop job to me; with a net result of overestimating this, followed by an overestimating of that , which presumably results in an overestimation of nothing.

    Next time ANY WUWT reader gets wind of mother nature caught in the act of doing any linear regressing, would they please alert WUWT, so we can all watch that happen.

    In a classic letter, sometime in the 1960s- 1970s, someone derived the numerical value of the fine structure constant (actually 1/FSC); which is close to 137, by simply frakking around with numbers.

    The derived result was of the form:

    1/alpha = (pi^a.b^c.d^e.f^g.h^i)^0.25 where a,b,c,d,e,f,g,h all have small integer values (not necessarily all different).

    Alpha is known to parts in 10^8, and this paper computed it simply from whole cloth, to within 60% of the standard deviation of the then best experimental value.

    So obviously, the paper had to be correct, because you couldn’t get that close by simply frakking around with numbers.

    The fact that NO observables from the physical universe, appeared anywhere in the paper, did not inhibit normally sane people from believing.

    Within a month, a couple of computer nerds derived all computed values of the fourth root of the product of pi and four small integers, each to some small integer power, that came within one standard deviation of 1/alpha. The list contained about a dozen values; the best of which was within 30% of the standard deviation from experiment.

    Proving that you can in fact “prove” anything you want, by simply frakking around with numbers.

    Close agreement with experiment is no proof of causality.

    Global Temperatures and radiant emittances are not even linearly related (in theory), so why would anyone do linear regressions of anything climate wise ??

    Why does this editor not understand the word emittances ??

  41. The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO.

    There is something funny going on here.

    If you look at their Figure 1[c] they have a thing they call the “Mean Global Temperature (GLT)”. This is the global temperature signature they are fitting to. Supposedly real world data.

    It is very strange. There is no ‘dip’ from 1950-1970 at all, instead it trends flat during that period. And there is seemingly no ‘pause’ at all.

    They do not define where GLT comes from or what it is. Eyeballing it suggests it is the worst and most UHIE contaminated dataset I have ever seen.

    In that case the statement I quote from their abstract is quite mendacious since using any of the credible UHIE free datasets the contribution of anthropogenic warming since 1975 may be about 20% at most.

    I’d say a fix is in. Watch for the climateers to spin this paper as “two-thirds of warming since 1975 due to evil CO2!!!!!!”.

  42. Bruce of Newcastle says:
    April 9, 2014 at 6:07 pm
    The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO.

    There is something funny going on here.

    If you look at their Figure 1[c] they have a thing they call the “Mean Global Temperature (GLT)”. This is the global temperature signature they are fitting to. Supposedly real world data.

    It is very strange. There is no ‘dip’ from 1950-1970 at all, instead it trends flat during that period. And there is seemingly no ‘pause’ at all.

    They do not define where GLT comes from or what it is. Eyeballing it suggests it is the worst and most UHIE contaminated dataset I have ever seen.

    Looks very like the NCDC/NESDIS/NOAA graph to me.

  43. Since Fourier analysis shows more than six cyclic components this is garbage in attributing all the ignorance factors to man made warming.
    Until climate scientists can look at the end of each year and show 100% correlation of their predictions after correction for random events of that year the science is pure speculation and a flaunting of their ignorance and intolerable arrogance. If they had engineering style quality assurance instead of crony nit grooming, chimpanzee style, glorified by the name of peer review this would be a pre requisite of publication.

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