How can you know that and still miss the obvious? You correctly note that a lot of former Baathists are working with ISIS. Presumably from your tone you also are not a fan of ISIS and believe that them expanding their control and influence is a bad thing.

Can I suggest taking the next step and asking we ponder what Iraq might be like if those guys controlled the entire country and whether or not you think that would be a positive change?

The reason I ask, is because that WAS the situation before bush and his warmonger buddies ousted them. No question Bush and co fouled things up from day one, but the actual decision to remove Saddam and the Baathists was hardly a bad thing. ISIS has yet to touch the atrocities that Saddam perpetrated while in power, and yes, keeping the remnants of his diseased regime out of power is certainly important and another reason to resist ISIS expansion. Just don't try and over simplify things to the point you start making the absolutely idiotic wish that Bush hadn't screwed up Iraq with his horrible war. Iraq was already an awful place long before, and the former Baathists that are working with ISIS are the biggest part of that.

Yes and no. The biggest distinction is between observation and modelling. We have a lot of direct observations of TOA energy that are more or less in agreement(more later), but in climate models the ability to simulate the observed TOA energy today and to maintain a neutral TOA energy when in equilibrium still requires tuning parameters like clouds by hand. Without that tuning, the TOA energy budget drifts off and all the model results drift out along with it. One of the reasons is that models leak or create energy at cell boundaries, which is to say that yes, conservation of energy is imperfect in the majority of current climate models. The other component of drift results from uncertainties in processes and parameters as the model runs.

Reality has dictated though that modellers jobs weren't hard enough with only the above to worry about. In reality, even the observational TOA energy imbalance is subject to a lot of instrumental uncertainty. The two main observational records used are from direct satellite measurements, but calibration issues for sensitive equipment on multiple satellites across the sky leave some uncertainty present. I don't have the exact numbers up in front of me, but the order of magnitude is approximately such that the total radiation coming in and going out at TOA is like 250W/m-2 going out, and 253W/M-2 going out. The error from calibration estimates vary, but something on the order of 1% or so is there, which sounds pretty tight. The trick for climate modellers is that the NET energy imbalance is 253-250, so 3W/M-2, where a 1% error adds up to +/-2.5W/M-2. Ocean heat content numbers are even tougher because you are trying to measure average temperature across the oceans and add up how much energy that is and watch for the delta from year to year, so an even tougher nut than the satellite data. The only good news for the poor guys trying to model this all is that ocean heat content estimates and satellite observation estimates line up within their margins of error. So we are pretty confident in an observed TOA energy imbalance of about 1W/M-2, but modelling that small an overall difference is very small eye of the needle to try and hit.

Why can't introduction to logic be mandatory education in our world already?

If we have the choice between preparing for catastrophe or not, and the possibility of catastrophe is unknown it is NOT proven that action must be taken.

For example, I can posit that there is an unknown possibility of a catastrophic earth impact from some massive rock out in space. That is an accurate enough statement, but it is not proof that we must immediately start a multi-billion dollar program to combat the threat of our extinction.

Climate change is in fact a bit different still, because contrary to what alarmists want to believe, the science is pretty clear that catastrophe is NOT coming down on our heads anywhere within the next 100 years. Best estimates from the IPCC's model projections of 2100(which yes have major uncertainty) is 1.5C higher than current(I'm going for memory so forgive if I miss a little on the scenario 4.5 projection), and a sea level rise of about 0.4m. That is over 100 years a temperature increase of 1.5C and sea level rise of 0.4m. Before crying wolf about how bad that really is, lets look backwards in time. Back in 1901 when we didn't have cars, or planes or pretty much the whole of modern technology, we can look at the climate change our great grand parents faced. In 100 years they would have to cope with a temperature rise of 1.1C and sea level rise of 0.19m. Far from being in a post-apocolyptic dystopia, the global standard of living has never been better. The biggest threat to mankind is still the damage and abuse we dish out upon one another. From the IPCC predictions for climate change, I'm gonna go ahead and predict that in 100 years the greatest threat to mankind is still the abuse we inflict on one another. You know, not much different than the last 2 millennia...

Apples meet oranges. With weather the prediction is based on well known conditions in the surrounding areas. We then are able to map out the likely changes for the next couple of days. The more days you go out, the less certain things become. The trick though is that when predicting tomorrow's weather, you are working with a very complete set of initial conditions.

Compare that to climate in 100 years or 300 years. You have the initial conditions still, but mapping out what planetary ice, plants, ocean currents and water vapor are gonna do to the TOA energy balance that drives climate decade after decade is hardly simple. Compare to a 5 day weather forecast, it is as a matter of fact much more challenging. Add onto that the fact that weather models can be tested against NEW data almost weekly, while climate models need to wait decades for actual true NEW data to compare projections against.

If you want to predict the average of weather, that's different than predicting changes to the climate. Average weather is as simple as observing something global average temperature next year will be much like this year +/- 0.5C. You can even confidently declare that global average temperature 25 years from today will again be the same as this year +/- 1.0 C. Climate scales stretch out to hundreds of years where the overall energy imbalance can swing things a couple of degrees. As I quoted directly above you, that projection of TOA energy is still an unsolved problem that requires corrections for modelled hindcasts to be reasonable.

Climate projections are every bit as challenging as weather projections, and when taken in context climate modelling faces many unique challenges that weather does not. Most importantly that weather models can be tested and refined on a much shorter time frame and against many, many more datasets.

More data is always good, but presenting any uncertainty and conditions on predictions is vital. Not only so we make properly informed decisions, but also so we don't tarnish trust by misrepresented predictions.

Climate models are really great science, but are also really ripe for this sort of problematic viewing from the public. Not just the laymen, but informed and educated public as well. To just quickly read and peruse climate model summaries you'd get the impression that confidence in models is really high. The reality is that confidence in PORTIONS of the models is really high. The whole however still has a long ways to go.

The IPCC fifth assessment report in chapter 9 notes the following:
Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).

That's taken context and backed up by over a dozen citations to relevant journal articles on model tuning. The short version is that tuning Top Of Atmosphere energy is still a required step to avoid climate models running out to unrealistic states. The journal articles all confirm this. With TOA energy being the ultimate overall driving force behind climate change, our predictions are still subject to the fact we aren't yet able to predict TOA energy. Without that we can make guesses what TOA energy might do, but the confidence in them is nothing like the confidence in other components of climate. Failing to qualify this though could leave us 20 years from now pointing at the AR5 projections and asking what went so terribly wrong with them, and the answer is that they had things largely right, save that TOA energy rose faster or slower than anticipated. That's in essence already the conversation over the IPCC First assessment projections from the 20+ years ago.

Mary Schweitzer's been working on this since 1993 when she found soft-tissue in a fossil and nobody would believe that's what she'd found. Her latest paper is getting fragments of the protein chains from a T-Rex. Back in 1993 she was told that any soft tissue at all was entirely and completely impossible. Don't know how realistic it is, but I really want to believe some manner of circumstances allow T-Rex clones in the future.

They want their headline back. Mary Schweitzer already made the same discovery in 1993, and she's been fighting for more than 2 decades to get her findings past the "consensus" that such long preservation was impossible. It seemed like she had gotten her findings verified again by 2000 but I guess it's still only now becoming generally accepted. Really unfortunate it can still take that long for a major discovery to become accepted.

I'm not sure of the 'this' that needs to be proven, but I'm going to interpret it as the GP statement scientists don't have the climate models rightas something that isn't nuts or crazy.

From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations:
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
And later the IPCC goes on to note:
Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation. Quantities closely related to those tuned will provide only weak tests of model performance.

So to recap, the singular driving force of all climate change, the energy imbalance at Top Of Atmosphere, is NOT an emergent property of the underlying climate models, but instead still requires hand tuning to avoid running out to an unrealistic state. More over, by the IPCC's own standards, quantities closely related to those tuned are only weak tests of model performance. How many components of the climate aren't at least weakly related to TOA energy?

It keeps going though, as if you hand adjust clouds or other parameters to balance energy, if your results were off you'd expect to get the macro of energy balance that was tuned correct on the mean, but because of compensating errors too high here and too low there. Read the entire IPCC chapter linked above and count the number of times compensating errors are observed in the unknown parameters like clouds.

If you further your reading beyond the IPCC chapter and read the linked journals you'll even find that climate models still regularly, as in more often than not, don't pass the conservation of energy test, it's even stated as part of the reason that tuning TOA energy is still necessary until bugs in code or algorithms can catch the leaking energy or additional energy that coming out of the ether.

All that said, climate models truly are still good tools. The steps taken above are still good steps, and I honestly and truly mean that. They all still contribute to testing theories and ideas of how components of our climate function and are vital tool to furthering our understanding. At the exact same time though, to declare that lacking confidence in future prediction from them today is nuts or crazy is just wrong. There's very good reason to place very big caveats and conditions on the projections currently being generated. In particular hindcast skill should NOT be expected to be a very good indicator of predictive skill at all.

The Wind Shear's Full Scale Rolling Road was built in 2008, but I guess that also isn't currently under construction so you still might cling to that...

The underlying important point remains. When it comes to engineering planes and automobiles, computer modelling is heavily used, but the use of real world tests like wind tunnels are also still in use because the models are not yet perfect.

I think the more salient point is our call to action should take into account uncertainties within the models. If the actions we call people to are costly, people should reasonably expect that the evidence brought forward is certain enough to justify the cost...

Modelling climate is insanely challenging as the scope is our entire planet, and the components involved number in the hundreds or thousands, and the interactions between them are again almost universally dependant upon one another. Climate models are a great tool for us to investigate and test theories about those systems and their interactions. We are getting pretty good at sorting the important/dominant components from the less significant ones. That said, there IS still a long ways to go. I would strongly advocate for continued study and development of climate modelling. I would also strongly caution against placing high confidence in specific model projections out into the future. Evidence follows:

Climate models fail the conservation of energy test. That's pretty fundamental, and models very widely still 'leak' energy.
From Mauritsen et al.
Among the model simulations whose data were available at the time of this analysis, there is a tendency for drift in the CMIP5 models to be less pronounced than in some of the CMIP3 models, and there is a reduction in the number of warm and cold biased models in CMIP5. Only a few models are close to zero imbalance, or likely to relax to near-zero imbalance. If a model equilibrates at a positive radiation imbalance it indicates that it leaks energy, which appears to be the case in the majority of models, and if the equilibrium balance is negative it means that the model has artificial energy sources.

Climate model tuning today normally uses adjustments to cloud parameters to balance the Top of Atmosphere energy. The single central driving force behind climate change still gets tuned by hand and is not yet an emergent property of the underlying understanding or simulation of the system.
From Chapter 9 of the IPCC AR5, complete with more than a half dozen citations to articles on model tuning confirming exactly this.
For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to parameters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).

It's tempting to take the above and declare all climate models are bunk and toss them out, which would be very bad. Climate models are doing an important job of helping us get to a level of understanding where someday we hopefully can simulate TOA energy, we just aren't there yet. I would however for strongly advocate that until we are there, long term predictions, which very much depend on trend in TOA energy, are not at all sufficient for dictating policy changes.

The commentary you link is to a blog post by Mann himself in which, unsurprisingly, he declares himself the winner. I'd encourage you to read the full rebuttal despite it's length. Having read both it's full details and Mann's blog post after it seems rather clear Mann is glossing over some very clear and specific details that are devastating to his claims.

You shouldn't have stopped reading with Mann et al's reply, go ahead and read McShane and Wyner's rebuttal. They are rather more emphatic about Mann's failure to use statistics properly. Where Mann's reply consisted of larger hand waving and very general criticism like what data to include, McShane's rebuttal is heavy on detail and specifics. For example, the critique of McShane not filtering or excluding the data they used correctly is directly and exhaustive addressed:
SMR allege that we have applied the various methods in Sections 4 and 5 of our paper to an inappropriately large group of 95 proxies which date back to 1000 AD (93 when the Tiljander lightsum and thicknessmm series are removed due to high correlation as in our paper; see footnote 11). In contrast, the reconstruction of Mann et al. (2008) is applied to a smaller set of 59 proxies (57 if the two Tiljander series mentioned previously are removed; 55 if all four Tiljander series are excluded because they are ”potentially contaminated”). The process by which the complete set of 95/93 proxies is reduced to 59/57/55 is only suggestively described in an online supplement to Mann et al. (2008). As statisticians we can only be skeptical of such improvisation, especially since the instrumental calibration period contains very few independent degrees of freedom. Consequently, the application of ad hoc methods to screen and exclude data increases model uncertainty in ways that are unmeasurable and uncorrectable.

Moreover, our interpretation of SMR Figure 1 is quite different. ...
Smoothing exaggerates the difference and requires careful adjustment of fit statistics such as standard errors, adjustments which are lacking in SMR and which are in general known only under certain restrictive conditions. In contrast, consider the right panel of Figure 1 which is a reproduction of SMR Figure 1a without smoothing. The difference between a given model fit to the full dataset or the reduced data set is clearly dwarfed by the annual variation of the fit; ...
In short, while SMR allege that we use the ”wrong” data, the result remains the same (also see SI).

It is really worth reading the detailed reasons I glossed over as well, as it contributes to the veracity and accuracy of their critique of Mann's usage of statistics.
 

McIntyre & McKitrick aren't statisticians at all, so no argument there. Science shouldn't be about credentials, but if it is...

McShane and Wyner ARE statisticians and they published this paper in The Annals of Applied Statistics regarding Mann's statistic uasge for proxy reconstruction methods. The abstract follows, mostly because it pretty much speaks for itself:
Predicting historic temperatures based on tree rings, ice cores, and other natural proxies is a difficult endeavor. The relationship between proxies and temperature is weak and the number of proxies is far larger than the number of target data points. Furthermore, the data contain complex spatial and temporal dependence structures which are not easily captured with simple models.

In this paper, we assess the reliability of such reconstructions and their statistical significance against various null models. We find that the proxies do not predict temperature significantly better than random series generated independently of temperature. Furthermore, various model specifications that perform similarly at predicting temperature produce extremely different historical backcasts. Finally, the proxies seem unable to forecast the high levels of and sharp run-up in temperature in the 1990s either in-sample or from contiguous holdout blocks, thus casting doubt on their ability to predict such phenomena if in fact they occurred several hundred years ago.

We propose our own reconstruction of Northern Hemisphere average annual land temperature over the last millennium, assess its reliability, and compare it to those from the climate science literature. Our model provides a similar reconstruction but has much wider standard errors, reflecting the weak signal and large uncertainty encountered in this setting.

Mann et al. of course filed a rebuttal, which more or less amounts to declaring that statisticians know nothing about handling climate data, and furthermore that McShane and Wyner used completely inappropriate statistical methods.

I've read the rebuttals and McShane and crew seem to be the most on the ball in the exchange from my reading, with Mann et al's arguments seeming to be tangential to the central meat of the article and concerns identified, but go read it yourself.

Who ever said it wasn't getting warmer? Sure wasn't me. Instrumental records since 1900 have shown a global upward trend. Sea level has been steadily rising over that same time.

You've maybe mistaken me for someone that wants to reject the science on this or something?

What I pointed to in the scientific literature was that climate models still aren't predicting TOA energy accurately and so they are still in the general practice of hand tuning parameters like clouds to correct it. Climate models are still terrifically useful for learning more about our climate, they are in fact fundamental to that end. I'm merely also noting that since TOA energy dominates long term climate trends, until models can predict it with being tuned by hand we can't rely on climate models for long term trends, or at the very least not without a lot of caveats and conditions.

TOA energy IS climate change. Increased CO2(and any other greenhouse gases) act by trapping more radiation coming in at the top of atmosphere boundary. Current climate models, as per the IPCC and 8 cited articles, drift to unrealistic states UNLESS they are tuned by hand for more realistic TOA.

Yes, my concerns are backed up by the papers if you go read them. I can give you a quick link to the one by Golaz in which they test 3 different equally valid seeming cloud tunings to compare the results and they conclude:
CM3w predicts the most realistic 20th century warming. However, this is achieved with a small and less desirable threshold radius of 6.0 m for the onset of precipitation. Conversely, CM3c uses a more desirable value of 10.6 m but produces a very unrealistic 20th century temperature evolution. This might indicate the presence of compensating model errors.

Choosing the most realistic tuning setting for clouds led the model to fail to reproduce the the last century of warming accurately. Choosing a less realistic one gave a better result. For further reference and those that won't read the whole article, ALL 3 tuning settings chosen additionally were chosen on condition of balancing TOA energy correctly.

What is more, this assessment is assuming that all the other forcings not being tuned are correct, because in practice the tuning for TOA could be correcting for other erroneous values across the models regarding energy transfer patterns. If that were the case, simply balancing the overall global TOA would lead to accurate macros, but possibly because of compensating errors... Like the ones that Golaz noted. Also like the observation made throughout the IPCC report on cloud behaviour in models...

Why does it matter? Because it's the driving force of the climate. The climate models ARE still good at helping us understand the climatic responses to the driving. Regrettably though as long as we are hand-correcting the driving variable, we can't say too much from the models about future driving from TOA.

This hardly seems a controversial observation at this point.