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.

Exactly, like in Michael Mann's "hockey stick" reconstructions and the data-gathering methodology changes at the year 1900 the sudden change in trend obviously is a result of the methodology change as well, and not something else....

For everyone worrying about our pending demise in 100 years based on climate models I would urge you to step back from the ledge.

Climate models are a great tool for expanding our understanding of climate processes and their interactions. They have been invaluable in gaining new knowledge and testing theories to better know how our climate behaves. At the same time, they also have a long ways to go.

The most basic primer on climate is that the greenhouse effect is basically the trapping of energy by gases in our atmosphere. The most basic and fundamental measure of this is the difference between energy coming in and energy leaving at the Top Of Atmosphere. This is more commonly referred to as TOA energy balance. This energy imbalance though is very small compared to the overall energy in and out, so measuring it is hard, let alone simulating it. Thus, parameters in climate models are hand tuned until TOA energy matches known and observed trends. This is a necessary step so that all the other modelled processes in the simulation operate under conditions that are reasonable and accurate and we can then compare their behaviour to the real world.

The alarmists, and maybe even some that don't count themselves such, will take huge issue with my next statement. With the climate models hand tuning TOA energy in order to avoid unrealistic conditions, and with TOA energy dominating long term climate trends, the climate models utility to long term predictions right now is poor at best and near nil at worst. You can't take a variable you've hand tuned and claim anything about it's predictive powers.

If you think I'm off my rocker, here's the IPCC saying the same thing:
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....
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.
I needn't point out that TOA energy is closely related to pretty near everything in our climate.

For climate models they are so tightly grouped partly because of model tuning. One of the steps in preparing climate models is tuning parameters to get the correct/observed Top Of Atmosphere(TOA) energy balance. Most commonly parameters for clouds are adjusted until the climate model's TOA energy results match the known values. Without doing that, the energy imbalance rapidly drives the climate into unrealistic states. That is straightforward as incoming/outgoing TOA energy is of course the singular long term driver of climate change. Of course, with it being almost universal practice to hand tune TOA energy to the same trends and values it shouldn't be too surprising that on the macro level, the climate models follow the same trends...

Now just watch some idiot alarmist come call me out for lying or something. The IPCC fifth assessment report states the following in Chapter 9:
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 the IPCC saying exactly what I just did, and a list of fully 8 different peer-reviewed journal articles backing their specific statement up. You want to call me out as wrong or cherry picking then provide something significantly more substantial than the above references if you want to have any leg to stand on.

Climate models have been a great means of testing and expanding our understanding of how our climate functions and interacts. That said, the limits on our climate models are GROSSLY underestimated by a great many people.

Why would I say that? Here's a quote from the IPCC's fifth assessment report:
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).

Before I'm get accused of cherry picking quotes, notice that the statement is backed by reference to 8 separate peer-reviewed journal articles on the subject of climate model tuning. You can use google scholar to look up the articles if you want to point out any misrepresentation by the IPCC, but to save time I've read them myself and the characterization is accurate.

That is to say that TOA energy is hand tuned as a general practice to prevent the models running into unrealistic states. The parameter most commonly used to tune it is the function of clouds. To be fair, the tuning is also restricted to values that are 'reasonable', meaning in keeping with existing observational limits if there are any for the parameters being tuned. The Golaz et al article though notes that even within those limits the choice of equally valid and realistic parameters can make a big difference in predictions, in their case a near complete failure to reproduce recent warming in simulation by changing cloud parameters between equally realistic values.

As I said, climate models are great for advancing and test our understanding of interactions of components of the climate. That comes with the huge caveat though that TOA Energy is the absolute driving factor of long term warming/cooling, and climate models absolutely do NOT predict it correctly as in my understanding from multiple journal articles on the subject, hand tuning is required to prevent unrealistic TOA values over even observed time frames.

Guess I needed a sarcasm sign with your response and a down vote too. What happened to everyone's sense of humor?

As important by what metric? Yes, we have a very good instrumental record of warming global temperature for nearly a century. Yes, we have a record about half as long showing CO2 concentrations rising. Yes, we have trivial physics to show that CO2 absolutely traps radiation and contributes to warming. Yes, we know that our actions have been contributing CO2 to the atmosphere over those time frames. Please point me to more sources, but this is about the extent of the very strongly agreed items on climate change. Further facts all start deriving from climate models, or statistically reconstructed models of proxy data. How do we quantitatively define the importance of reducing our CO2 emissions from this?

We need to know the cost we will bear from continuing temperature changes if we carry on our merry way. We need to know the reduction of those costs if we take a certain set of actions today or in the near future. We then can compare the costs of those actions today against the saved costs in the future and make an informed decision.

I hate to be that guy, but our climate models today are NOT sufficient for assessing this. Plenty of the guys working on the models will call me out, and maybe you should listen to them instead of me because they are after all the experts and know their work and field better than I. Scientifically speaking though, please also look at the facts I base my statement upon. The IPCC states the following information on climate models in general:
-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).
-Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evaluation.

Now, if you read those points something comes out and screams problem doesn't it? The heart and soul of all climate change is the increase or decrease of energy at the Top Of Atmosphere. The climate models nearly universally modify cloud effects to get their hindcasting of the energy at TOA correct. If they don't do this tuning, the models drift to an unrealistic state. The quantities that are tuned also should NOT be used in evaluation of the models. So TOA energy imbalance is one of the things that practically by DEFINITION the models are not meant to be able to be evaluated upon, let alone predictive for.

Let me humbly suggest that models that still aren't up to projecting TOA energy aren't exactly cut out for long term predictions of CO2 or any other impacts on TOA energy, which is THE centrally component of the greenhouse effect. The models are tackling problems like what happens to temperature and precipitation patterns under certain changes to TOA energy, but that's not the problem we are most interested in when assessing what level of energy changes CO2 is causing for us.

If I'm wrong or insane anywhere please, please correct me and point me in a good direction for some reasons I'm off base here. I'm a Comp Sci grad so I get computer modelling and the above concerns I've outlined seem terribly fundamental and after searching for a long time I can't find anywhere that the central concern I've outlined is meaningfully addressed.

But that is counter balanced by owing it to their editors and share holders to skip those few minutes and facts to publish an article that will catch more readers eyes.

I know nothing of particle physics, but I thought it was that the particles are very short lived?

Not exactly like the parent, but an example of the established knowledge refusing to acknowledge the data in front of it's face was experienced by Mary Schweitzer. In 1993 on a dig she was on a team that had to break a T-Rex bone open to transport it. Upon doing this she found some kind of reddish material and upon looking closer at it determined it was organic. The explanation that she had actually found some form of remaining soft tissue from a dinosaur was more or less dismissed out of hand because it's impossible for that to have been preserved that long. She was repeatedly rebuffed from calling it soft-tissue until the condition of proving HOW it was preserved could be demonstrated...

Eventually in 2000 another T-Rex bone was broken open and duplicated her findings. Since then proteins sequences have even been able to be pulled and line up similarly to birds.

The fact almost nobody has heard of her is a bit perplexing given the single most major obstacle to Jurassic Park in real life was turned on it's head by her discovery.

There is a much stronger reason to expect the RealClimate blog to take Mann's side, and that is because Mann founded RealClimate in the first place... Invoking the site as external support in Mann's defence is amusing.