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.

Well, The Annals of Applied Statistics is the applied statistics journal of the Institute of Mathematical Studies which was founded in 1935 and currently has upwards of 4000 members. The Annals of Applied Statistics's is currently edited by Stephen Fienberg. If you'd care to be more specific with why you randomly believe the journal itself lacks all credibility aside from your personal disagreement with an article within it please do tell.

There are cases though where sample size is an inherent obstacle. Most popularly climate change is trying to study and predict impacts of processes that act over centuries and millenia, and our directly sampled data for temperature barely covers 1 century. Our direct data for CO2 concentrations covers less time than that. Our direct data for the global energy imbalance is even shorter than both. How to properly qualify and quantify the uncertainty with inherent limitations like this is a major challenge to studying serious matters. Plenty of medical research faces the same problem. How do you test a new Ebola vaccine before the next outbreak?

I did in fact, I even suffered through Mann's own angry blog post. Did you read just his blog post, or did you read the actual published discussion by the authors and publishers as well? If you did, it seems pretty clear the complaints against McShane and Wyner don't substantially refute any of their main criticisms. Might want to read the journals and discussion and not just Mann's own editorializing on his blog.

Funny, they don't look anything like Mann's original graph to me. If you view the full article the 30 yea weight means since 1900 are matched for a large portion of the time from 1AD through 1000AD.

He's the guy famous/infamous for the 'Hockey Stick' graph. He's also one of the main authors on the realclimate.org blog.

Obviously the whole thing isn't a crock, there is just a lot of noise in the field now largely owing to it being such a hot topic and gold mine for grants and publicity. The basics like the instrumental record warming for a century, CO2 measurements increasing for a century and the fact CO2 contributes to the greenhouse effect are all thoroughly solid. That doesn't mean a horde of soft science hasn't been piled on speculating about the social impacts of potential speculative future changes brought on by this all good or might mean getting published too, often with not much more underpinning scientific work than something by Asimov.

Even some of the harder science is problematic, and yes in particular that includes Mann's work. His whole hockey stick temperature reconstruction has been thoroughly rebuked by The Annals of Applied Statistics. The two most blatant criticisms being failing to provide any and later accurate error margins, and the blatant attachment of disparate datasets to create a desired impression by appending the instrumental record onto his reconstructed temperature. The lack of error bars and the fact the reconstruction was calibrated to that instrumental data the close fit the instrumental gives the false impression of much greater accuracy than is present.

Mann is thin skinned because his early work was thin, bordering on deliberate dishonesty, or more generously, ignorance of proper statistical practices and methods.

FTFY ;)

I agree there is lots of good science within the models. I still question the quality and confidence levels in them. The same IPCC link will also note that models in their set nearly universally are hand tuned to match the known historic record of TOA energy imbalance. That is the most important measure if climate change, energy change at top of atmo is corrected in the hind casts by hand to be correct. More over, the most common parameter used to tune that energy imbalance is cloud effects. If you look again at the discussion of how and why models missed the lowered trend after 98 one of the major suspects is inaccurate natural forcing a, like clouds, impacting the net forcings, AKA TOA imbalance. Basically the hand tuned parts work a lot better when we know what to tune them to than when moving into an uncertain future.

The models still cover the very challenging questions of how climate responds to increased incoming energy. For long term projections though the energy balance dominates and getting it right or wrong is greater than the difference between the many emission scenarios the IPCC uses. Exactly like it dominates the long term historical trends were researchers routinely tune the models until they match the known historic imbalances. Without a model that can predict energy imbalances I lack confidence in its long term reliability, much like the researchers themselves already know when making longer term hindcasts.

You want to link a journal over a blog please? There is no argument or discussion about the matter. The instrumental temperature record as recorded in the HADCRUT data set used in virtually every climate modelling experiment has a higher linear warming rate from 1950-2012 than from 1998-2012. There is no debate on the matter, that's simply a fact. Any source denying this is very simply being dishonest.

That said, as I pointed out before several times, the energy imbalance at TOA is where the actual greenhouse effect is going on. For pretty near the duration of satellite records there we have seen a consistent imbalance with more energy coming in than going out annually. That imbalance is also agreed to have had no annual trend since 2000 or longer. That means the planet's been gaining energy at the same rate before and after 98 and only temperature has been fluctuating rates. The question of importance is what is the real temp response to that increasing temperature? The linear rate from 1950-2012 or from 1950-1998? That's an active area of study and simply saying don't tLk about it, or it isn't important is just dishonest, as I've repeated a couple times now. The only guys I've seen really adamant about rejecting that are alarmists that want to claim greater than linear warming and catastrophe are near, since the data refutes them they reject or deny it.

I'd be interested which scientists you count on your side wishing the IPCC hadn't discussed it. I'm pretty sure guys like Cohen who's published work on the matter they reference among others consider it worthy of mention.

The reduced warming trend wasn't just compared to models, but compared to the linear trend over the last 50 years of the observed record. Given that CO2 has been steadily climbing the expectation, and the model predictions, were for accelerating warming rather than slowing. After the slowing persisted for more than a decade failing to talk about it is not honest.

When 111 out of 114 models are all running higher than the instrumental record that indicates something is systematically different in the real world than in the models. Whether error margins haven't been exceeded yet or not, if things were kosher in the models, the distribution above and below the observed should be more random than 111 out of 114 being on the high side, and badly so at that. It is dishonest to just say everything is ok and no need to even mention it.

The bigger nitpick should obviously be that getting one thing wrong is in absolutely no way evidence for the GP's boldly false claim that nobody has gotten ANYTHING right.

A more important point is that temperature is just a proxy measure of the actual greenhouse effect of increasing energy within the climate. Temperature, as the 'hiatus' demonstrates, is also very dependent on the oceans and how much energy they are absorbing or releasing in a particular time frame. The IPCC notes in this section that:
Satellite records of top of the atmosphere radiation fluxes have been substantially extended since AR4, and it is unlikely that significant trends exist in global and tropical radiation budgets since 2000.

Meaning that we've been taking in more energy than we are dumping out at an unchanged rate since 2000, so the overall greenhouse effect never slowed even though temperatures did. You've already mentioned some of the speculated reasons for this, but the general simplistic consensus is that if the energy wasn't heating the air the oceans obviously stored the energy somehow. Ocean heat measurements have generally confirmed this, with some investigation still on margins of error.

That's a pretty long winded and well documented way of stating I agree with your points. I still hold to my context being just fine though in observing that climate models systematically overestimated temperatures since 1998. I still stand that it very much IS a relevant and important observation and criticism. Here's why with some more references to the IPCC's assessment of models linked earlier. As mentioned before, the heart of the warming problem is the global energy balance, and how much extra energy CO2 and other processes are helping to add to our planet each year. It's generally referenced as the Top Of Atmosphere energy balance(TOA), and in the long term, it's virtually the only variable that really truly matters to what is happening to our planet. Ultimately more energy in in the long run will raise temperatures, similarly more energy out will lower them. How that energy gets distributed between air and oceans and globally is a secondary consideration. I state all this because when the climate models are being prepared, one of the last steps is 'tuning' parameters in them so that they are accurate. Here's a quote from the IPCC on that:
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).

So, this leads me to one accusation, that hindcasting skill within the models is biased on the most important element(TOA Energy balance), by design. That's not declaring the models bad science, instead it is just observing that the climate models are merely hindcasting with the benefit of having the energy balance corrected by hand. Modelling the distribution of that energy between geography and air/ocean boundaries is still all good science and being done. The thing is, energy balance going forward matters a lot as we should expect to make predictions of increasing CO2 driving the energy balance up and predicting that is much more important while regrettably our hindcasting fails to test this.

The IPCC also further notes:
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.

Notably, clouds being a near universally tuned parameter in order to maintain reasonable TOA balance sets us up with models with built in limitations. Cloud behaviour can't really be tested because it is used to tune the energy balance. Energy balance can't really be tested because we hand tune it to would we 'know' it should be. The observed record tells us that energy imbalance since 2000 has no trend, it's been a steady increase neither gaining or losing rate(per IPCC). The observed record tells us that CO2 since 1900 has been rising very noticeably. With rising CO2 we should expect the energy imbalance to grow as well, but it isn't. That's about the time where I want to see climate models that can test if clouds are part of the equation mitigating the energy imbalance the CO2 should be causing. Regrettably as noted, clouds and TOA energy imbalance are two things that the climate models are tuned for or with...

If you don't get the drift, I lack confidence in the computer models overall veracity given the important role of clouds and the TOA energy balance. Something like the models systematically missing a trend like that since 1998 kind of confirms that. I'm a comp sci guy by trade, so computer models are something I get and understand. Modelling complex systems requires compromises and estimates, but I really have serious troubles swallowing the value in tuning for TOA when that is driving force for the entire system. Getting the macro long term temperature trends in the right ballpark over a few centuries is kind of a gimme if you don't have to predict the TOA changes accurately and can just tune it till your right.

Well, the IPCC disagrees with you in their fifth assessment report:

The observed global mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Section 2.4.3, Figure 2.20, Table 2.7; Figure 9.8; Box 9.2 Figure 1a, c). Depending on the observational data set, the GMST trend over 1998–2012 is estimated to be around one-third to one-half of the trend over 1951–2012 (Section 2.4.3, Table 2.7; Box 9.2 Figure 1a, c). For example, in HadCRUT4 the trend is 0.04C per decade over 1998–2012, compared to 0.11C per decade over 1951–2012. The reduction in observed GMST trend is most marked in Northern Hemisphere winter (Section 2.4.3; Cohen et al., 2012). Even with this “hiatus” in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST (Section 2.4.3, Figure 2.19). Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related questions of what has caused it and whether climate models are able to reproduce it.

Your rigorous statistical analysis doesn't look at the trend before 98 and after 98 and compare them, while the IPCC does. A linear trend at the rate from 1950-1998 would have warmed things faster than they have since 1998. That's what the IPCC says above. In your link you can clearly see the pattern. It's also true that things have continued to warm since 1998, it's just the linear average of warming from 1950-2012 is a slower warming than the linear average from 1950-1998 was. We are talking about climate and affects that span not just decades and centuries, but even millenia though so the divergence shouldn't be all that surprising.

What is more relevant and useful from it, as the IPCC goes on to note, is whether any of the existing climate models predicted the slower linear trend and if not why. Turns out 111 out of 114 overestimated the warming since 1998 and one of the biggest reasons is believed to be the already know poor understanding of clouds and water vapor.

ugh, should read Antarctic ice is about 26 million cubic kilometers. :(