re: 99.999% likelihood GW is man made.

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The results of our statistical analysis would suggest that it is highly likely (99.999 percent) that the 304 consecutive months of anomalously warm global temperatures to June 2010 is directly attributable to the accumulation of global greenhouse gases in the atmosphere.

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There still is a 0.001% chance this isn't man made.

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Not the logic of a "PhD"

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so now it's not global warming but sea warming? Do I have that right?

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pretty sure the sea is part of the globe.

Motherfrick these threads have people asking more questions than a helicopter parent at a Harvard tour.

You are correct. Global warming was created in the minds of environmental wackos, so it is man made.

Let me make sure I get this.... the 19 year 'pause' in temp increases, even though we are pumping more and more CO2 into the air, is too immaterial and ridiculous to consider in the grand scheme of things.. barely a single data point if anything.....

But this 304 month period - that is nothing short of sufficient to make a 99.999% conclusion? I love the consistency.

And what is the starting basis of the study?:

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"There is strong physical evidence that the critical factors which influence global temperatures in the time-scale of human decision-making are atmospheric GHGs, aerosol and particulate concentrations, the El Niño Southern Oscillation (ENSO) cycle, solar radiation, and volcanic activity(IPCC, 2007, IPCC, 2013, Meinshausen et al., 2011, Allan, 2000, Benestadt and Schmidt, 2009, Gohar and Shine, 2007 and Wang et al., 2005)'

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Ok so we are starting by eliminating all but a few potential causes from the equation and assigning no percentage to the rest.... then from our 4 or 5 we kept allocating the 100% basically to 1.

Where in this paper (I read it), does it conclude that 0.000% should be allocated to any other factor or more specifically why in the name of science we should completely ignore any other factor while trying to calculate a finite percent.

The scientific method is dead and gone.

Man made GW/Climate Change is a redistributive scam.
Anybody stupid enough to fall for it deserves to be fleeced.

30 years...out of BILLIONS of years of the Earth's paleoclimatic history.

Definitely a valid sample for statistical purposes.

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There still is a 0.001% chance this isn't man made. We shouldn't act until we are certain.

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SpidermanTUba

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.......don't give a rat dog dick if it's man made or naturally occurring......you and the federal government cannot have one more fricking cent of my money for wealth redistribution!! Get it through y'all's thick skulls .....not one more red cent!!!

The big-assed ball of flaming Hydrogen!

It's god's fault!

Although this is a very thoroughly analysis, my understanding, albeit limited, of ARIMA modeling (or just ARMA in this case) with covariates makes me believe that the procedures of model identification is flawed.

The authors appear to model the covariates then identify the ARIMA parameters. I was under the impression that you identify a stationary time-series process for both the the DV and IV variables then find the cross-dependence of the variables. In this case, the ARIMA parameters are modeled with the covariates.

I don't find this especially problematic until they remove the CO2 as a predictor; this assumes that these are the same ARIMA parameters as the previous mode, when that may not be the case. I think this is why fitting the time-series models first, then adding the predictors is a better procedure. Regardless, not attempting to verify, and possibly reidentify, the ARIMA process seems suspect to me. Therefore, the comparison with the model without CO2, and their subsequent interpretations, may be based on a misidentified model. And although I'm a big fan of bootstrapping, jackknifing, and whatever other resampling or simulations techniques one can use to improve their study, they still require careful modeling by the researchers. Using resampling based on a misidentified model will lead to a robust estimate of the parameters, but misidentified parameters.

Anyways, this is just my evaluation of their modeling techniques. They may be generally correct, but I think their methodology could be improved before feeling as confident as the authors are, not to mention I'm sure some will misinterpret what the authors meant by 99.999% (i.e., p-values and effect sizes may be related but are surely not synonymous).

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This is circular reasoning. They're using their model of past data to predict past data. Then they take out variables and the "predictions" of past data change.

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The shortcomings of the model are clear when you look at the abysmal record for predicting future temperatures

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Is this like when Captain Picard was traveling backward and forward time and accidently had three different enterprises fire off tachyon bursts that inadvertently created an "anti-time" anomaly that got bigger as time went backwards and threatened to wipe out all of humanity?