Blog Archive - January 2008

What if you held a conference, and no (real) scientists came?

Over the past days, many of us have received invitations to a conference called "The 2008 International Conference on Climate Change" in New York. At first sight this may look like a scientific conference - especially to those who are not familiar with the activities of the Heartland Institute, a front group for the fossil fuel industry that is sponsoring the conference. You may remember them. They were the promoters of the Avery and Singer "Unstoppable" tour and purveyors of disinformation about numerous topics such as the demise of Kilimanjaro's ice cap.

A number of things reveal that this is no ordinary scientific meeting:

• Normal scientific conferences have the goal of discussing ideas and data in order to advance scientific understanding. Not this one. The organisers are suprisingly open about this in their invitation letter to prospective speakers, which states:
  

  "The purpose of the conference is to generate international media attention to the fact that many scientists believe forecasts of rapid warming and catastrophic events are not supported by sound science, and that expensive campaigns to reduce greenhouse gas emissions are not necessary or cost-effective."

  So this conference is not aimed at understanding, it is a PR event aimed at generating media reports. (The "official" conference goals presented to the general public on their website sound rather different, though - evidently these are already part of the PR campaign.)

• At the regular scientific conferences we attend in our field, like the AGU conferences or many smaller ones, we do not get any honorarium for speaking - if we are lucky, we get some travel expenses paid or the conference fee waived, but often not even this. We attend such conferences not for personal financial gains but because we like to discuss science with other scientists. The Heartland Institute must have realized that this is not what drives the kind of people they are trying to attract as speakers: they are offering $1,000 to those willing to give a talk. This reminds us of the American Enterprise Institute last year offering a honorarium of $10,000 for articles by scientists disputing anthropogenic climate change. So this appear to be the current market prices for calling global warming into question: $1000 for a lecture and $10,000 for a written paper.

• At regular scientific conferences, an independent scientific committee selects the talks. Here, the financial sponsors get to select their favorite speakers. The Heartland website is seeking sponsors and in return for the cash promises "input into the program regarding speakers and panel topics". Easier than predicting future climate is therefore to predict who some of those speakers will be. We will be surprised if they do not include the many of the usual suspects e.g. Fred Singer, Pat Michaels, Richard Lindzen, Roy Spencer, and other such luminaries. (For those interested in scientists' links to industry sponsors, use the search function on sites like sourcewatch.org or exxonsecrets.org.)

• Heartland promises a free weekend at the Marriott Marquis in Manhattan, including travel costs, to all elected officials wanting to attend.

This is very nice hotel indeed. Our recommendation to those elected officials tempted by the offer: enjoy a great weekend in Manhattan at Heartland's expense and don't waste your time on tobacco-science lectures - you are highly unlikely to hear any real science there.

The debate is just beginning — on the Cretaceous!

Most of us who are involved in research related to climate change have been asked at one time or another to participate in public debates against skeptics of one sort or another. Some of us have even been cajoled into accepting. In the pre-YouTube days, I did one against the then-head of the American Petroleum institute at the U. of Chicago law school. Gavin did an infamous one against Crichton and company.

People are always demanding that Al Gore debate somebody or other. Both Dave Archer and I have been asked to debate Dennis Avery (of "Unstoppable Global Warming" fame) on TV or radio more than once — and declined. It's a no win situation. If you accept you give the appearance that these skeptics have something to say that's actually worth debating about — and give their bogus ideas more publicity. If you decline there are all sorts of squawks that "X won't debate!" or implications that scientists have declared "the debate" (whatever that is supposed to mean) prematurely closed when in fact it is "just beginning."

Scientists tend to react badly to demands like this in part because the word "debate" is a rather poor description of the way disagreements get hashed out in science. John Ziman has a good discussion of the extent to which scientific questions are 'debatable' here. In a lawyerly debate, it is fair game for each side to pick and choose whatever argument has the most persuasive force with the audience, jury or judge, without any obligation to consider the force of counter-arguments except insofar as they affect one's defense against the opponent. Science, in contrast, is a deliberative, cooperative, yet still competitive enterprise, where each side is duty bound to fairly consider all arguments and data that bear on the matter at hand. This is not to say that scientific disputes are necessarily dispassionate or orderly. Indeed, I've seen near-fistfights break out over things like the Snowball Earth and the interpretation of Neoproterozoic carbon isotope excursions.

The repeated challenges to debate are probably meant to imply that scientists — and their supporters, including Al Gore — are fixed in their ideas, unreceptive to the new and challenging, and unwilling to defend their ideas in public. This picture is hard to square with how scientists actually behave among themselves. It is not that scientists don't debate, dispute, disagree about matters related to climate. All those things happen, but not on the subjects that skeptics like Inhofe or Fred Singer or Dennis Avery would like to debate (like whether global warming is mainly caused by CO2 or solar variability, or whether the IPCC warming forecasts represent a credible threat.). Those sorts of things are indeed considered settled science by serious climate scientists. Then, too, scientists are justifiably wary of being drawn into staged debates on such diffuse, ill-defined and largely meaningless topics as whether global warming counts as a "crisis." In the war of the sound bites, the people who feel free to lie and distort can always win. David Mamet made this point eloquently in Bambi vs. Godzilla. A debate like that is not any kind of debate in the sense understood by scientists.

In fact scientists are probing theories and conceptions all the time, trying to break them. The best way to become famous is to overturn established wisdom, so scientists look hard all the time for opportunities to do this. The problem of Hothouse climate states like the Cretaceous and Eocene is a case in point.

The Cretaceous is the time period from 145 million years ago up to the demise of the dinosaurs about 65 million years ago. The Eocene is a more recent period, from 56 million years ago to 34 million years ago. In between is the Paleocene, which is generally somewhat cooler than the late Cretaceous or mid Eocene. It has long been known that the polar climate — particularly the Arctic climate — was very different from today's. Many lines of evidence indicate temperatures well above freezing, with little or no permanent land ice and infrequent or absent sea ice. Lemurs could live in Spitzbergen, and crocodiles on Hudson Bay, to name a few examples. Most evidence also points to an absence of ice in Antarctica as well. These Hothouse (or Super Greenhouse) climates have much warmer polar regions than is the case for today's climate, and winters were evidently very mild. These hothouse climates are idealized as having been almost completely free of significant ice sheets on land and sea ice cover in the ocean. Hothouse climates pose a challenge to our understanding of climate in general, but more particularly they serve as a critical clue as to what surprises a high-CO2 world might have in store for us.

This is so because, at present, the only viable theory for Hothouse climates is that they come about as a result of elevated CO2 concentrations, which in turn are due to long term changes in the Earth's carbon cycle. The CO2 theory has many problems, some of which I'll discuss below, but no theory without elevated CO2 has been able to even come close to accounting for the Hothouse states.

These climates would be just dandy as a natural test of the Earth's sensitivity to long lived greenhouse gas concentrations were it not for one nasty fact: it is very, very difficult to get an accurate idea of how high the CO2 concentrations were so far back in time (see Crowley and Berner or Broadly Misleading on RC). For example, estimates for the Eocene range from values similar to modern CO2

concentrations all the way up to 15 times pre-industrial CO2. This unpleasantly large range represents uncertainties in the proxies used to estimate CO2 in the distant past. Various general circulation models can achieve largely ice-free polar conditions with CO2 between 4 and 8 times present concentrations, though even at those levels there are difficulties in accounting for the mildness of the winters. And up until recently it was thought that the tropical temperatures in such simulations were far warmer than reality — but more about that anon.

In the past few years there has been a real shake-up in the conception of what hothouse climates are like. First, it was found that the Tropical regions in hothouse climates are not tightly thermostatted as had been previously thought. Prior indications of a cool tropics turned out to be an artifact of alteration of the chemistry of marine sediments after they were deposited — a nightmare known as diagenesis to paleoceanographers. The tropics are actually quite a bit warmer than today's tropics. For example, the Eocene tropical ocean may have been as warm as 35C, as compared to about 29C today. The upward revision of tropical temperatures is quite a good thing for the CO2 theory, since it removes a good part of the "low gradient" problem, wherein models were thought to systematically exaggerate the pole to equator temperature gradient.

So far, so good. But then, just last year through heroic efforts involving a nuclear icebreaker, a conventional icebreaker and an icebreaking drill-ship. a deep-time sediment core was recovered from the Arctic ocean. The results, which came out in a series of papers in Nature (here,here and here) were startling. At times the Arctic was practically a freshwater lake, indicating some quite dramatic changes in the hydrological cycle. And more germane to the matter at hand, in the early Eocene, the Arctic was much warmer than previously thought.

According to Sluijs et al ocean temperatures were as high as 23C — rather like Key West today. These temperatures come to you courtesy of a novel biochemical proxy known as Tex86, derived from certain lipids produced by tiny plankton called Crenarchaeota. Tex86 is the new wunderkind of paleoceanography.

Will wonders never cease? Evidently not. Just when the hothouse starts looking really, really hot, along comes a new Science article by Bornemann et al, dealing with climatic conditions in the Turonian (93.5 to 89.3 million years ago). The principal result of this paper is that there appears to have been a 200,000 year period right smack in the middle of one of the warmest periods of the past half billion years, when there were ice sheets (presumably in Antarctica) that were up to 60% the volume of today's Antarctic ice sheets. How in the world do you get such large ice sheets in a high CO2 climate warm enough for crocodiles to survive in the Arctic at the other side of the planet?

And this apparent glaciation is not the result of a global cold snap. As in the Eocene results quoted earlier, the tropical ocean surface temperatures are again on the order of 35C — courtesy once more of the wondrous Tex86 proxy.

How was the ice volume inferred? Primarily by an especially meticulous application of an old technique. When a glacier forms on land, the water it is made of is enriched in the lighter form of oxygen, 16O, which leaves the ocean enriched in the heavy form, 18O. Single-celled shelly creatures called foraminifera ("forams" for short) record this composition, but they are very subject to diagenesis. The key to the new estimate was to take samples from pristine glassy portions of exceptionally well-preserved foram shells. The sample was taken from a core in the Tropical Western Atlantic, so the investigators are able to determine tropical surface temperatures, making use of Tex86 proxies from organisms living near the surface. The ocean water isotopic composition is estimated using both surface-dwelling and deep-dwelling forams.

Since the oxygen isotope composition of forams depends on temperature as well as ocean water composition, the Tex86 proxy was used to correct for the temperature effect in forams living near the surface. There is no independent temperature proxy for the deep ocean, but the investigators assumed (a bit questionably) that deep ocean temperature did not change much over the time period. Be that as it may, the deep ocean oxygen isotope shift (uncorrected for any temperature effect) was similar in magnitude to the estimate from surface forams. Once you have the oxygen isotopic composition of sea water, you can translate that into ice volume by making an estimate of the isotopic composition of glacier ice. All this is easier said than done, but they did it. The glacial interval corresponds to the excursion of delta-18O toward positive values in the figure below, taken from the paper:

There is a useful commentary by Richard Kerr One must exercise the usual caution we urge in connection with radical results, and await confirmation before taking it to the bank. As Kerr points out, there is other data from this time period that doesn't show the isotope shift.

There are two additional things I myself noticed, which seem inconsistent. The first is that in order to get reasonable numbers for ice volume, the investigators needed to assume that Antarctic ice in the Cretaceous period had the same isotopic lightness as Antarctic ice today. Most theories of fractionation would have Antarctic ice being less fractionated in a warm climate, however. Perhaps the high Equator to Antarctic gradient helps keep the Antarctic ice light, but this is something that needs to be checked. What's even more troubling to me is that the bottom-dwelling forams (uncorrected for temperature) indicate the same ocean water isotopic shift as the temperature-corrected surface dwelling forams. However, if Antarctica glaciates, the deep ocean should be filled with cold Antarctic bottom water, which should produce an additional positive isotopic shift in the uncorrected bottom dwelling forams. That this shift isn't seen suggests that something is amiss to me.

Still, this paper already has a lot of modelers scratching their heads. To give an example of the magnitude of the problem, I reproduce below a figure from one of Rob DeConto's old simulations (Nature 421, (2003) ), showing the glacier distribution in Antarctica as a function of CO2, as CO2 is steadily decreased. These are done for orbital parameters favorable to Antarctic glaciation; the simulations don't use Late Cretaceous geography, but they do give a good idea of how hard it is to get a big glacier in Antarctica with anything much above twice the preindustrial CO2.

It is salutary to keep in mind that in many past cases where data conflicted with robust modeling results, it turned out to be the models that were right and the data that was wrong. This was the case for the early satellite reconstructions of twentieth century lower tropospheric temperature, which showed a spurious cooling. It was also the case for early reconstructions of tropical climate during the Last Glacial Maximum, which failed to show the cooling we now know to prevail in that region during glacial times.

So, what does all this mean for CO2 and anthropogenic global warming? Does it mean we don't know beans about climate, so let's have a party and why worry? No, actually. All this hothouse strangeness gives us a great deal more to worry about. The tropics is not strongly thermostatted, and there appear to be feedbacks in the system that can amplify polar warming more than previously thought possible.

Perhaps due to clouds? Matt Huber, one of the foremost Eocene modellers, stated in a recent seminar at the University of Chicago that he could get closest to reproducing the Eocene hothouse by assuming that the Earth's real climate sensitivity is at the high end of the IPCC range — around 4C per doubling of CO2. Or, perhaps there are mode switches in the climate system we know nothing about, which we are risking triggering by increasing CO2. Without understanding the Hothouse climates, it's impossible to say how close we are to the danger zone.

But what of this new riddle of Cretaceous ice? An optimist might say that the result shows that you can keep a lot of ice in Antarctica even in a very warm climate. On the other hand, the conditions allowing the ice to exist in a warm climate are evidently very fragile, since it was there (assuming the result holds up) for only 200,000 years — the wink of an eye, in geological terms. That could mean that the factors governing whether Antarctic ice stays or goes in a warm climate are more subtle than we thought, offering more possibilities for surprise transitions. Or it may turn out that Cretaceous CO2 is really only twice the pre-industrial level, but that there's some whopping positive feedback which bumps up tropical temperatures to 35C. In a scenario like that, the strange and unexplained resistance of Antarctica to warming might save some Antarctic ice, but that would be cold comfort, since the rest of the world would be toast.

Or, it may turn out that the processes determining the glaciation and deglaciation of a partly ice covered Antarctica have nothing to tell us about the present situation starting with a large Antarctic ice sheet. I'd be surprised if this turned out to be the case, but it could happen. One thing is for sure — if the result survives, it will provide an important and challenging test for the next generation of ice sheet models.

Could it be that the glaciation is telling us that we are completely barking up the wrong tree with the CO2 theory of hothouse climates?

Perhaps, but somebody will have to pony up a quantifiable alternative before that avenue can be pursued. And whatever the alternative is, the challenge of simultaneously explaining the coexistence of a super-hot tropics with Antarctic glaciation — and also explaining why this happened for only 200,000 years — is apt to be as big as any challenge posed to the CO2 theory. One could probably get a climate something like the suggested one by combining moderately elevated CO2 with making a lot of low clouds over Antarctica while making the rest of the world essentially cloud free (or somehow making the high cloud greenhouse effect dominant in the rest of the world), but that's quite a stretch. If somebody comes up with a way of doing that which can be expressed in a sound mathematical formulation, I'll be the first to want to have a look at it. Cosmic ray enthusiasts could have a field day with this, but I doubt they'd have much success.

However you slice it, it starts to look like the Eocene and Cretaceous are tugging at our sleeve, whispering to us "There are things going on with climate you don't begin to understand. Proceed with caution."

We already knew hothouse climates were interesting, but darned if they don't just keep getting more and more interesting. It puts me somewhat in mind of the old Yiddish curse– "May you live in interesting times." But, to paraphrase Maurice Sendak — Let the Wild Rumpus Continue!

Uncertainty, noise and the art of model-data comparison

John Tierney and Roger Pielke Jr. have recently discussed attempts to validate (or falsify) IPCC projections of global temperature change over the period 2000-2007. Others have attempted to show that last year's numbers imply that 'Global Warming has stopped' or that it is 'taking a break' (Uli Kulke, Die Welt)). However, as most of our readers will realise, these comparisons are flawed since they basically compare long term climate change to short term weather variability.

This becomes immediately clear when looking at the following graph:

The red line is the annual global-mean GISTEMP temperature record (though any other data set would do just as well), while the blue lines are 8-year trend lines - one for each 8-year period of data in the graph. What it shows is exactly what anyone should expect: the trends over such short periods are variable; sometimes small, sometimes large, sometimes negative - depending on which year you start with. The mean of all the 8 year trends is close to the long term trend (0.19ºC/decade), but the standard deviation is almost as large (0.17ºC/decade), implying that a trend would have to be either >0.5ºC/decade or much more negative (< -0.2ºC/decade) for it to obviously fall outside the distribution. Thus comparing short trends has very little power to distinguish between alternate expectations.

So, it should be clear that short term comparisons are misguided, but the reasons why, and what should be done instead, are worth exploring.

The first point to make (and indeed the first point we always make) is that the climate system has enormous amounts of variability on day-to-day, month-to-month, year-to-year and decade-to-decade periods. Much of this variability (once you account for the diurnal cycle and the seasons) is apparently chaotic and unrelated to any external factor - it is the weather. Some aspects of weather are predictable - the location of mid-latitude storms a few days in advance, the progression of an El Niño event a few months in advance etc, but predictability quickly evaporates due to the extreme sensitivity of the weather to the unavoidable uncertainty in the initial conditions. So for most intents and purposes, the weather component can be thought of as random.

If you are interested in the forced component of the climate - and many people are - then you need to assess the size of an expected forced signal relative to the unforced weather 'noise'. Without this, the significance of any observed change is impossible to determine.

The signal to noise ratio is actually very sensitive to exactly what climate record (or 'metric') you are looking at, and so whether a signal can be clearly seen will vary enormously across different aspects of the climate.

An obvious example is looking at the temperature anomaly in a single temperature station. The standard deviation in New York City for a monthly mean anomaly is around 2.5ºC, for the annual mean it is around 0.6ºC, while for the global mean anomaly it is around 0.2ºC. So the longer the averaging time-period and the wider the spatial average, the smaller the weather noise and the greater chance to detect any particular signal.

In the real world, there are other sources of uncertainty which add to the 'noise' part of this discussion. First of all there is the uncertainty that any particular climate metric is actually representing what it claims to be. This can be due to sparse sampling or it can relate to the procedure by which the raw data is put together. It can either be random or systematic and there are a couple of good examples of this in the various surface or near-surface temperature records.

Sampling biases are easy to see in the difference between the GISTEMP surface temperature data product (which extrapolates over the Arctic region) and the HADCRUT3v product which assumes that Arctic temperature anomalies don't extend past the land. These are both defendable choices, but when calculating global mean anomalies in a situation where the Arctic is warming up rapidly, there is an obvious offset between the two records (and indeed GISTEMP has been trending higher). However, the long term trends are very similar.

A more systematic bias is seen in the differences between the RSS and UAH versions of the MSU-LT (lower troposphere) satellite temperature record. Both groups are nominally trying to estimate the same thing from the same data, but because of assumptions and methods used in tying together the different satellites involved, there can be large differences in trends. Given that we only have two examples of this metric, the true systematic uncertainty is clearly larger than the simply the difference between them.

What we are really after is how to evaluate our understanding of what's driving climate change as encapsulated in models of the climate system. Those models though can be as simple as an extrapolated trend, or as complex as a state-of-the-art GCM. Whatever the source of an estimate of what 'should' be happening, there are three issues that need to be addressed:

• Firstly, are the drivers changing as we expected? It's all very well to predict that a pedestrian will likely be knocked over if they step into the path of a truck, but the prediction can only be validated if they actually step off the curb! In the climate case, we need to know how well we estimated forcings (greenhouse gases, volcanic effects, aerosols, solar etc.) in the projections.

• Secondly, what is the uncertainty in that prediction given a particular forcing? For instance, how often is our poor pedestrian saved because the truck manages to swerve out of the way? For temperature changes this is equivalent to the uncertainty in the long-term projected trends. This uncertainty depends on climate sensitivity, the length of time and the size of the unforced variability.

• Thirdly, we need to compare like with like and be careful about what questions are really being asked. This has become easier with the archive of model simulations for the 20th Century (but more about this in a future post).

It's worthwhile expanding on the third point since it is often the one that trips people up. In model projections, it is now standard practice to do a number of different simulations that have different initial conditions in order to span the range of possible weather states. Any individual simulation will have the same forced climate change, but will have a different realisation of the unforced noise. By averaging over the runs, the noise (which is uncorrelated from one run to another) averages out, and what is left is an estimate of the forced signal and its uncertainty. This is somewhat analogous to the averaging of all the short trends in the figure above, and as there, you can often get a very good estimate of the forced change (or long term mean).

Problems can occur though if the estimate of the forced change is compared directly to the real trend in order to see if they are consistent. You need to remember that the real world consists of both a (potentially) forced trend but also a random weather component. This was an issue with the recent Douglass et al paper, where they claimed the observations were outside the mean model tropospheric trend and its uncertainty. They confused the uncertainty in how well we can estimate the forced signal (the mean of the all the models) with the distribution of trends+noise.

This might seem confusing, but an dice-throwing analogy might be useful. If you have a bunch of normal dice ('models') then the mean point value is 3.5 with a standard deviation of ~1.7. Thus, the mean over 100 throws will have a distribution of 3.5 +/- 0.17 which means you'll get a pretty good estimate. To assess whether another dice is loaded it is not enough to just compare one throw of that dice. For instance, if you threw a 5, that is significantly outside the expected value derived from the 100 previous throws, but it is clearly within the expected distribution.

Bringing it back to climate models, there can be strong agreement that 0.2ºC/dec is the expected value for the current forced trend, but comparing the actual trend simply to that number plus or minus the uncertainty in its value is incorrect. This is what is implicitly being done in the figure on Tierney's post.

If that isn't the right way to do it, what is a better way? Well, if you start to take longer trends, then the uncertainty in the trend estimate approaches the uncertainty in the expected trend, at which point it becomes meaningful to compare them since the 'weather' component has been averaged out. In the global surface temperature record, that happens for trends longer than about 15 years, but for smaller areas with higher noise levels (like Antarctica), the time period can be many decades.

Are people going back to the earliest projections and assessing how good they are? Yes. We've done so here for Hansen's 1988 projections, Stefan and colleagues did it for CO2, temperature and sea level projections from IPCC TAR (Rahmstorf et al, 2007), and IPCC themselves did so in Fig 1.1 of AR4 Chapter 1. Each of these analyses show that the longer term temperature trends are indeed what is expected. Sea level rise, on the other hand, appears to be under-estimated by the models for reasons that are as yet unclear.

Finally, this subject appears to have been raised from the expectation that some short term weather event over the next few years will definitively prove that either anthropogenic global warming is a problem or it isn't. As the above discussion should have made clear this is not the right question to ask. Instead, the question should be, are there analyses that will be made over the next few years that will improve the evaluation of climate models? There the answer is likely to be yes. There will be better estimates of long term trends in precipitation, cloudiness, winds, storm intensity, ice thickness, glacial retreat, ocean warming etc. We have expectations of what those trends should be, but in many cases the 'noise' is still too large for those metrics to be a useful constraint. As time goes on, the noise in ever-longer trends diminishes, and what gets revealed then will determine how well we understand what's happening.

New rule for high profile papers

New rule: When declaring that climate models are misleading in a high profile paper, maybe looking at some model output first would be a good idea.

This is a reference to an otherwise interesting paper in Nature this week (Graversen et al) on the vertical structure of heating in the Arctic in recent decades. One of the key results is that during the summer, when temperatures near the surface are constrained to be close to zero by the presence of open water and sea ice, the troposphere heats up anyway. The mechanism for this heating is hypothesised to be related to changes in atmospheric heat transport. So far so good.

But towards the end, there is this curious line:

Our results do not imply that studies based on models forced by anticipated future CO2 levels are misleading when they point to the importance of the snow and ice feedbacks. …. Much of the present warming, however, appears to be linked to other processes, such as atmospheric energy transports.

The clear implication is that climate models don't suggest that atmospheric heat transports will change and that all polar amplification in those possibly misleading models is driven by snow and ice feedbacks. But is this correct? Well, it's hard to tell from this paper because they don't look at any model results!

This didn't stop the AP from declaring the heat transports to be part of some "natural and cyclical increase"! For National Geographic it was just 'mysteriously occurring'….

But in order to see what models have to say, all one has to do is look. With the easy availability of the CMIP3 archive, it's not too difficult to do the analysis for all the IPCC AR4 simulations for this exact period. As a short cut (and just because there is an easy interface) you can also go to the GISS archive and to pull down the figure for the summertime (Jun-Aug) temperature changes in the "all forcings" run for the same time period (1979-2001). If you do so, you'll see that in the Arctic, the models also suggest that summer time surface changes are small and that there is heating aloft - similar to the analysis in this paper. The match to the ERA-40 analysis isn't perfect by any means (but the match between different analyses products is not that great either). More analysis would need to be done to work out what was forced and how large the weather noise is etc, but the basic phenomena seems to be quite universal and not mysterious at all.

The point is that this isn't difficult stuff, and it should be standard practice to at least give a cursory look at what models actually show before accusing them of being misleading.