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%.
(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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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.
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?
Two thirds of the warming is caused by antroproghenic adjustment of the data?
THis paper has already been discussed/dissed at WUWT by separate blogposts by Willis E. and Bob Tisdale. http://wattsupwiththat.com/2014/03/16/chylek-imitates-ourboros/
and http://wattsupwiththat.com/2014/03/14/on-chylek-et-al-2014-the-atlantic-multidecadal-oscillation-as-a-dominant-factor-of-oceanic-influence-on-climate/
*sigh* Again? Regurgitation of garbage doesn’t change the smell much..
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.
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.
Oops, forgot to give a h/t to “Alec, aka daffy duck” for introducing me to Mann et al (2014).
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.
From 1945 to 1979 HADCRUT4 cooled at -0.015C per year.
According to the paper, 2/3rds of that cooling was caused by CO2.
http://www.woodfortrees.org/plot/hadcrut4gl/from:1945/to:1979/plot/hadcrut4gl/from:1945/to:1979/trend
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
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.
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.
This graph CET shows reality. The only surface temperature record I trust .. no urban island effect etc.and goes well back to 1660
http://notalotofpeopleknowthat.files.wordpress.com/2013/09/image16.png
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???
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.
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?
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.
Some works of Nobel Note may yet be accomplished.
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.
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
“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.
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”
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.
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.
the plot of linear versus dynamic did not appear , here it is .