Changes occurring in marine terminating outlet glaciers of the Greenland Ice Sheet and ice shelves fringing the Antarctic Peninsula have altered our sense of the possible rate of response of large ice sheet-ice shelf systems. There is a shared mechanism at work that has emerged from the detailed observations of a number of researchers, that is the key to the onset and progression of the ice retreat. This mechanism is shared despite the vastly different nature of the environments of Jakobshavns Isbrae, Wilkins Ice Shelf and the Petermann Glacier.

We reviewed in a previous post the first mechanism for explaining the change in velocity of Greenland’s large outlet glacier - the Zwally effect - and why it is not the key. This mechanism relies on meltwater reaching the glacier base via moulins and reducing the friction at the base of the glacier. This idea was observed to be the cause of a brief seasonal acceleration of 10- 20 % on the Jakobshavns Glacier in 1998 and 1999 at Swiss Camp 35 km inland from the calving front (Zwally et al., 2002). Examination of recent rapid supraglacial (i.e. on the surface) lake drainage documented short term velocity changes due to such events around 10%, but little significance to the annual flow of the large glaciers outlet glaciers (Das et.al, 2008).

The second mechanism is a dynamic thinning of the terminus zone of the marine terminating outlet glacier reducing the effective bed pressure, allowing acceleration - the Jakobshavn effect. The reduced resistive force at the calving front due to the thinner ice, now experiencing greater flotation, is then propagated "up glacier" (Hughes, 1986; Thomas, 2003 and 2004). If the Jakobshavn effect is the key the velocity increase will propagate up-glacier, there will be no seasonal cycle, and thinning and acceleration would be greatest near the terminus.

That the thinning and acceleration is greatest for marine terminating outlet glaciers has indeed been demonstrated by Sole et. al. (2008). That acceleration began at the calving front and spread upglacier 20 km in 1997 and up to 55 km inland by 2003 (Joughin et al., 2004). On Helheim the thinning and velocity propagated up-glacier from the calving front. Each of the glaciers fronts did respond to tidal variations indicating they had started floating, detached from their bed (Hamilton et al, 2006). This summer, Jason Box and others at Ohio State University observed that Jakobhavns Isbrae retreated again, losing 15 km², and maintaining an accelerated pace from the northern branch of the ice stream as opposed to the greater retreat and acceleration of the southern branch 2001-2005 (Box, 2008). This was accompanied by the second consecutive year of substantial retreat of the glacier just north of Jakobshavn, Sermeq Avannarleq which had been quite stable for much of the last century (Box , 2008b). Sole et. al. (2008) also noted that the recent thinning and acceleration was not limited to just the now more famous Helheim, Jakobshavn and Kangderlugssuaq Glaciers, but included Rinks Isbrae, Equaluit, Cristian IV and all others they observed. Note the greater flow of the southern ice stream in 2000, compare to the northern ice stream in this image from Ian Joughin:

Petermann Glacier is a much different glacier than the others mentioned above. Its velocity of 2-3 m/day (Higgins, 1990) is much lower than 10-30 m/day observed on the other marine terminating outlet glaciers. It is located on the northwest corner of Greenland and certainly experiences less melting and less snowfall. The lower 80 km (in length) and 1300 km² (in area) of the glacier is afloat. This makes it (by area) the largest floating glacier in the Northern Hemisphere. The ice front is not impressive,unlike the faster outlet glaciers. The calving front protrudes a mere 5-10 m above sea level, reflecting the fact that the ice at the front is only 60-70 m thick. Further up-glacier, the ice at the grounding line is 600-700 m thick. The combination of velocity and thickness yield the volume of material calved each year. Petermann Glacier calves 0.6 km³ (Higgins, 1990), whereas Jakobshavns yields close to 40 km³. The thinning between the grounding line and the calving front is mainly via melting as the snowline is at 900 m. The low slope leads to very low velocities, giving the low-lying floating section plenty of time to melt, and surface melt ponds are common.

The Petermann Glacier lost a substantial area, 29 km² due to calving this summer (Box 2008c), and a crack well back of the calving front indicates another 150 km² is in danger. The volume of the ice lost is much less than that from the loss of a comparable area by Jakobshavn because the ice is an order of magnitude thinner. Once again the key to this glacier’s second major ice loss this decade after limited retreat in the last century, is thinning of the floating tongue, which weakens the glacier. The loss of this ice should then lead to acceleration, developing more crevassing and glacier retreat. The crack seen in the image of Petermann Glacier (ASTER image provided by Ian Howat of Ohio State) is more of a rift, like those on Larsen Ice Shelf, than a crevasse. This transverse rift is further connected to longitudinal-marginal rifts. Illustrating the poor connection of the Petermann Glacier to its margin and lack of a stabilizing force this margin has, even 15 km behind the calving front. This is not the only rift of its kind on the glacier. Also note that like on Larsen Ice Shelf the rift crosscuts surface streams.

A series of Landsat images from 2002, 2006 and 2007 illustrate the shift in the terminus and in the position of key rifts A, B and C. The distance back from the terminus has diminished for A and B from 2002 to 2007. In 2006 to 2007 the shift in the position of C is also evident.

As in the case on Jakobshavns, Helheim and others the key is the pre-conditioning phase of thinning, that leads to more calving, that leads to more acceleration, and that generates retreat. In a recent paper in press in the Journal of Glaciology Ian Howat and others examined changes in terminus position, surface elevation and flow on 32 glaciers along the southeast coast of Greenland from 200-2006. Their key conclusion was that the

… ratio of retreat to the along-flow stress-coupling length is proportional to the relative increase in speed, consistent with typical ice flow and sliding laws. This affirms that speedup results from loss of resistive stress at the front during retreat, which leads to along-flow stress transfer. Many retreats began with an increase in thinning rates near the front in the summer of 2003, a year of record high coastal-air and sea-surface temperatures.

This indicates again the importance of pre-conditioned thinning via melting.

Wilkins Ice Shelf (WIS) refused to hibernate this winter. A previous post noted that the recent collapse of Wordie Ice Shelf, Mueller Ice Shelf, Jones Ice Shelf, Larsen-A and Larsen-B Ice Shelf on the Antarctic Peninsula has made us aware of how dynamic ice shelf systems are.

The reasons for Ice Shelf collapse continue to be identified, but one key thread emerges. The decade prior to collapse the Larsen-B Ice Shelf had thinned primarily by melting of the ice shelf bottom (by the ocean) by 18 m (Shepard and others, 2003). Thinning preconditions the ice shelf for failure by weakening its connection to pinning points at the grounding line as the shelf becomes more buoyant. Glasser and Scambos (2008) observed that prior to collapse that rifts and crevasses parallel to the ice front crosscut the meltwater channels and ponds, hence, post dated them. The number and length of the rifts increased markedly in the year before collapse. There was no evidence of relict rifts, illustrating that these rifts are a feature of the last 20 years. After ice shelf collapse the ice front receded to the pre-existing rifts, and the pre-existing rifts defined the area of collapse. In this case the thinning and resultant structural weaknesses preconditioned the ice to rapid breakup, which proceeded to lose only the preconditioned portion of the ice shelf.

The WIS is buttressed by Alexander, Latady, Charcot and Rothschild islands and by numerous small ice rises, indicating that they are touching the ocean floor. WIS was examined by Braun, Humbert and Moll (2008). They found that drainage of melt ponds into crevasses were of no relevance for the break-up at WIS. On WIS the evolution of failure zones is associated with ice rises. In 1993/94, rift formation started to expand at the northern ice front. Today, the central part of WIS is intersected by long rifts formed in and around ice rises. The rifts up to tens of kilometers long evolve and coalesce prior to break-up events. The conclusion for WIS is that preconditioning of the ice shelf by connection of the rifts in the failure zones near ice rises trigger break-up events. The thinning and rifting lead to a cascade of failure.

The Feb.-April break-up left a narrow 6 km wide fractured connection to Charcot Island. Existing rifts formed between already existing fractures, crossed almost the entire northern shelf. This fragile and vulnerable area was expected to collapse further the next austral summer. However, it instead has happened this austral winter with loss of an additional 160 km² of ice. It is the first winter ice loss of an ice shelf ever observed, and so was surprising. However, looking at the image below, from the European Space Agency showing the extent of the rifts as winter began, makes this less surprising. The question is more what can possibly hold this together? The area of interconnected rifts seen is 2000 km². If this is lost an additional 3000 km² of the 13 000km² of WIS, is at risk when this connection to Charcot Island is lost (Braun, Humbert and Moll, 2008).

It appears then that glacier or ice shelf thinning is the key preconditioning factor for collapse, retreat and acceleration, whether you are in Antarctica of Greenland. The mechanisms for ice shelf thinning include basal melting (from warming ocean waters), surface melting, reduction in glacier inflow and rift development. These are interrelated mechanisms that precondition the ice shelves to collapse. On marine terminating outlet glaciers the mechanisms to trigger thinning is surface ablation causing thinning, and potentially basal melting, though not yet observed (though see this recent paper by Holland et al, 2008). Once the process begins thinner less buttressed ice enables acceleration and more calving and more retreat. There is a potential difference between the two, in glacier such as most marine terminating outlet glaciers, where the glacier flow is rapid, acceleration results from retreat and thinning. In the case of ice shelves a glacier buttressed by them will accelerate after the loss, but the slow moving ice shelf may suffer from reduced inflow. Attention will continue to be focused on these rapid responders to climate change;marine terminating glaciers in Greenland and ice shelves in Antarctica. We can look forward to more details from the extensive 2008 summer field season in Greenland and the upcoming view of the Wilkins this fall.

Unlinked References:

Higgins, A. 1990. Northern Greenland glacier velocities and calf ice production. Polar Forschung, 60, 1-23.
Howat, I., I. Joughin, M. Fahnestock, B. Smith,T. Scambos 2008. Synchronous retreat and acceleration of southeast Greenland outlet glaciers 2000–06: ice dynamics and coupling to climate.Journal of Glaciology, 54(187).
Hughes, T. (1986), The Jakobshavn effect. Geophysical Research Letters, 13, 46-48.
Thomas, R. H. Abdalati W, Frederick E, Krabill WB, Manizade S, Steffen K, (2003) Investigation of surface melting and dynamic thinning on Jakobshavn Isbrae, Greenland. Journal of Glaciology 49, 231-239.
Thomas RH (2004), Force-perturbation analysis of recent thinning and acceleration of Jakobshavn Isbrae, Greenland, Journal of Glaciology 50 (168): 57-66.

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As people should know, designing truly objective surveys is very tricky. However, if you are after a specific response, it's easy to craft questions that will favour your initial bias. We discussed an egregious example of that from Steven Milloy a while ago. A bigger problem is not overt bias, but more subtle kinds - such as assuming that respondents have exactly the same background as the questioners and know exactly what you are talking about, or simply using questions that don't actually tell you want you really want to know. There are guides available to help in crafting such surveys which outline many of the inadvertent pitfalls.

Well, Bray and von Storch have sent out a new survey.

The questions can be seen here (pdf) (but no answers, so you can't cheat!), and according to Wikipedia, the survey respondents are controlled so that each anonymised invite can only generate one response. Hopefully therefore, the sampling will not be corrupted as in past years (response rates might still be a problem though). However, the reason why we are writing this post is to comment on the usefulness of the questions. Unfortunately, our opinion won't change anything (since the survey has already gone out), but maybe it will help improve the interpretations, and any subsequent survey.

There are too many questions in this survey to go over each one in detail, and so we'll just discuss a few specific examples (perhaps the comments can address some of the others). The series of questions Q15 through Q17, typify a key issue - precision. Q15 asks whether the "current state of scientific knowledge is developed well enough to allow for a reasonable assessment of the effects of turbulence, surface albedo, etc..". But the subtext "well enough for what?" is not specified. Global energy balance? regional weather forecasting? Climate sensitivity? Ocean circulation? Thus any respondent needs to form their own judgment about what the question is referring to. For instance, turbulence is clearly a huge scientific challenge, but how important is it in determining climate sensitivity? or radiative transfer? Not very. But for ocean heat transports, it might very well be key. By aggregating multiple questions in one and not providing enough other questions to determine what the respondent means exactly, the answers to these questions will be worth little.

The notion of 'temperature observations' used in Q16 and Q17 is similarly undefined. Do they mean the global average temperature change over the 20th Century, or the climatology of temperature at a regional or local scale? Or it's variability? You might think the first is most relevant, but the question is also asked about 'precipitation observations' for which a century-scale global trend simply doesn't exist. Therefore it must be one of the other options. But which one? Asking about what the ability of models is for modelling the next 10 years is similarly undefined, and in fact unanswerable (since we don't know how well they will do). Implicit is an assumption that models are producing predictions (which they aren't - though at least that is vaguely addressed in questions 45 and 46). What 'extreme events' are being referred to in the last part? Tornadoes? (skill level zero), heat waves (higher), drought (lower), Atlantic hurricanes (uncertain). By being imprecise the likely conclusion that respondents feel that global climate models lack the ability to model extreme events is again meaningless.

Q52 is a classic example of a leading question. "Some scientists present extreme accounts of catastrophic impacts related to climate change in a popular format with the claim that it is their task to alert the public. How much do you agree with this practice?" There is obviously only one sensible answer (not at all). However, the question neither defines what the questioners mean by 'extreme' or 'catastrophic', or who those 'scientists' might be or where they have justified such practices. The conclusion will be that the survey shows that most scientists do not approve of presenting extreme accounts of catastrophic impacts in popular formats with the aim of alerting the public. Surprise! A much more nuanced question could have been asked if actual examples were used. That would have likely found that what is considered 'extreme' varies widely and that there is plenty of support for public discussions of potential catastrophes (rapid sea level rise for instance) and the associated uncertainties. The implication of this question will be that no popular summaries can do justice to the uncertainties inherent in the science of abrupt change. Yet this is not likely to have been the answer had that question been directly addressed. Instead, a much more nuanced (and interesting) picture would have emerged.

Two questions of some relevance to us are Q61 and Q62, which ask whether making discussions of climate science open to potentially everyone through the use of "blogs on the w.w.w." is a good or bad idea, and whether the level of discussion on these blogs is any good. These questions are unfortunately very poorly posed. Who thinks that anyone has any control over what gets discussed on blogs in general? The issue is not whether that discussion should take place (it surely will), it is whether scientists should participate or not. If all blogs are considered, then obviously the quality on average is abysmal (sorry blogosphere!). If the goal of the question was to be able to say that the level of discussion on specific blogs is good or not, then specific questions should have been asked (for instance a list of prominent blogs could have been rated). As it is, the conclusion will be that discussion of climate science on blogs on the w.w.w. is a good idea but the discussion is thought to be poor. But that is hardly news.

One set of questions (Q68+Q69) obviously come from a social rather than a climate scientist: Q68 asks whether science has as its main activity to falsify or verify existing hypothesis or something else; and Q69 whether the role of science tends towards the deligitimization or the legitimization of existing 'facts' or something else. What is one to make of them? There are shades of Karl Popper and social constructivism in there, but we'd be very surprised if any working scientist answered anything other than 'other'. Science and scientists generally want to find out things that people didn't know before - which mostly means choosing between hypotheses and both examining old 'facts' as well as creating new ones. Even the idea that one fact is more legitimate than another is odd. If a 'fact' isn't legitimate, then why is it a fact at all? Presumably this is all made clear in some science studies text book (though nothing comes up in google), but our guess is that most working scientists will have no idea what is really behind this. You would probably want to have a whole survey just devoted to how scientists think about what they do to get anything useful from this.

To summarise, we aren't in principle opposed to asking scientists what they think, but given the track history of problems with these kinds of surveys (and their remaining flaws), we do suggest that they be done better in future. In particular, we strongly recommend that in setting up future surveys, the questions should be openly and widely discussed - on a wiki or a blog - before the surveys are sent out. There are a huge number of sensible people out there whose expertise could help in crafting the questions to improve both their precision and usefulness.

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The surprising answer (for those who don't work it out) is A. It's easy enough to see why this is the case. If the driving distance is 100 miles, then for case A the saving in fuel used (and hence emissions) is 100/12-100/18 = 2.8 gallons, while for B, you have 100/25-100/46 = 1.8 gallons. The confusion arises because people like to think linearly about numbers, not inversely, and so tend to assume that a similar change in mpg has a similar impact on fuel usage. This is not however the case - improvements in efficiency at the low end of the scale are much more useful at reducing emissions. This is actually a very general point - when trying to raise efficiency it is always sensible to start with the least efficient processes.

This confusion got some attention a couple of months ago after a piece that was published in Science by Larrick and Soll. They tested peoples instinctive reactions to changes in mpg numbers and found that people very often got it wrong, leading to less than optimal decisions. They also tested a different way of giving fuel usage information (the number of gallons used per mile), and since this is linear in emissions, people made the correct judgment much more often (it's worth noting that the standard in most of Europe is already litres per 100 km). Rewritten in those terms, the choices above become:

A. Someone swapping their old SUV (which takes 8.3 gallons to go 100 miles) for a hybrid version (5.6 gallons/100 miles) or
B. someone upgrading their 4 gallons/100 miles compact to a new 2.2 gallons/100 mile Prius?

Much easier, right? The authors of the Science piece are trying hard to get US manufacturers and the EPA to switch over from mpg to this new standard (though they prefer gallons/10,000 miles). It all seems eminently sensible to us.

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I often get emails from scientifically trained people who are looking for a straightforward calculation of the global warming that greenhouse gas emissions will bring. What are the physics equations and data on gases that predict just how far the temperature will rise? A natural question, when public expositions of the greenhouse effect usually present it as a matter of elementary physics. These people, typically senior engineers, get suspicious when experts seem to evade their question. Some try to work out the answer themselves (Lord Monckton for example) and complain that the experts dismiss their beautiful logic.

The engineers' demand that the case for dangerous global warming be proved with a page or so of equations does sound reasonable, and it has a long history. The history reveals how the nature of the climate system inevitably betrays a lover of simple answers.

The simplest approach to calculating the Earth's surface temperature would be to treat the atmosphere as a single uniform slab, like a pane of glass suspended above the surface (much as we see in elementary explanations of the "greenhouse" effect). But the equations do not yield a number for global warming that is even remotely plausible. You can't work with an average, squashing together the way heat radiation goes through the dense, warm, humid lower atmosphere with the way it goes through the thin, cold, dry upper atmosphere. Already in the 19th century, physicists moved on to a "one-dimensional" model. That is, they pretended that the atmosphere was the same everywhere around the planet, and studied how radiation was transmitted or absorbed as it went up or down through a column of air stretching from ground level to the top of the atmosphere. This is the study of "radiative transfer," an elegant and difficult branch of theory. You would figure how sunlight passed through each layer of the atmosphere to the surface, and how the heat energy that was radiated back up from the surface heated up each layer, and was shuttled back and forth among the layers, or escaped into space.

When students learn physics, they are taught about many simple systems that bow to the power of a few laws, yielding wonderfully precise answers: a page or so of equations and you're done. Teachers rarely point out that these systems are plucked from a far larger set of systems that are mostly nowhere near so tractable. The one-dimensional atmospheric model can't be solved with a page of mathematics. You have to divide the column of air into a set of levels, get out your pencil or computer, and calculate what happens at each level. Worse, carbon dioxide and water vapor (the two main greenhouse gases) absorb and scatter differently at different wavelengths. So you have to make the same long set of calculations repeatedly, once for each section of the radiation spectrum.

It was not until the 1950s that scientists had both good data on the absorption of infrared radiation, and digital computers that could speed through the multitudinous calculations. Gilbert N. Plass used the data and computers to demonstrate that adding carbon dioxide to a column of air would raise the surface temperature. But nobody believed the precise number he calculated (2.5ºC of warming if the level of CO₂ doubled). Critics pointed out that he had ignored a number of crucial effects. First of all, if global temperature started to rise, the atmosphere would contain more water vapor. Its own greenhouse effect would make for more warming. On the other hand, with more water vapor wouldn't there be more clouds? And wouldn't those shade the planet and make for less warming? Neither Plass nor anyone before him had tried to calculate changes in cloudiness. (For details and references see this history site.)

Fritz Möller followed up with a pioneering computation that took into account the increase of absolute humidity with temperature. Oops… his results showed a monstrous feedback. As the humidity rose, the water vapor would add its greenhouse effect, and the temperature might soar. The model could give an almost arbitrarily high temperature! This weird result stimulated Syukuro Manabe to develop a more realistic one-dimensional model. He included in his column of air the way convective updrafts carry heat up from the surface, a basic process that nearly every earlier calculation had failed to take into account. It was no wonder Möller's surface had heated up without limit: his model had not used the fact that hot air would rise. Manabe also worked up a rough calculation for the effects of clouds. By 1967, in collaboration with Richard Wetherald, he was ready to see what might result from raising the level of CO₂. Their model predicted that if the amount of CO₂ doubled, global temperature would rise roughly two degrees C. This was probably the first paper to convince many scientists that they needed to think seriously about greenhouse warming. The computation was, so to speak, a "proof of principle."

But it would do little good to present a copy of the Manabe-Wetherald paper to a senior engineer who demands a proof that global warming is a problem. The paper gives only a sketch of complex and lengthy computations that take place, so to speak, offstage. And nobody at the time or since would trust the paper's numbers as a precise prediction. There were still too many important factors that the model did not include. For example, it was only in the 1970s that scientists realized they had to take into account how smoke, dust and other aerosols from human activity interact with radiation, and how the aerosols affect cloudiness as well. And so on and so forth.

The greenhouse problem was not the first time climatologists hit this wall. Consider, for example, attempts to calculate the trade winds, a simple and important feature of the atmosphere. For generations, theorists wrote down the basic equations for fluid flow and heat transfer on the surface of a rotating sphere, aiming to produce a precise description of our planet's structure of convective cells and winds in a few lines of equations… or a few pages… or a few dozen pages. They always failed. It was only with the advent of powerful digital computers in the 1960s that people were able to solve the problem through millions of numerical computations. If someone asks for an "explanation" of the trade winds, we can wave our hands and talk about tropical heating, the rotation of the earth and baroclinic instability. But if we are pressed for details with actual numbers, we can do no more than dump a truckload of printouts showing all the arithmetic computations.

I'm not saying we don't understand the greenhouse effect. We understand the basic physics just fine, and can explain it in a minute to a curious non-scientist. (Like this: greenhouse gases let sunlight through to the Earth's surface, which gets warm; the surface sends infrared radiation back up, which is absorbed by the gases at various levels and warms up the air; the air radiates some of this energy back to the surface, keeping it warmer than it would be without the gases.) For a scientist, you can give a technical explanation in a few paragraphs. But if you want to get reliable numbers - if you want to know whether raising the level of greenhouse gases will bring a trivial warming or a catastrophe - you have to figure in humidity, convection, aerosol pollution, and a pile of other features of the climate system, all fitted together in lengthy computer runs.

Physics is rich in phenomena that are simple in appearance but cannot be calculated in simple terms. Global warming is like that. People may yearn for a short, clear way to predict how much warming we are likely to face. Alas, no such simple calculation exists. The actual temperature rise is an emergent property resulting from interactions among hundreds of factors. People who refuse to acknowledge that complexity should not be surprised when their demands for an easy calculation go unanswered.

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That's not the start of a joke, but it is a good jumping off point for a discussion of the latest publication on paleo-reconstructions of the last couple of millennia. As has been relatively widely reported, Mike Mann and colleagues (including Ray Bradley and Malcolm Hughes) have a new paper out in PNAS with an update of their previous work. And this is where the question posed above comes in: the difference is that with time scientists can actually make progress on problems, they don't just get stuck in an endless back and forth of the same talking points.

We discussed what would be required in an update of these millennial reconstructions a few months back and the main principles remain true now. You need proxies that are a) well-dated, b) have some fidelity to a climate variable of interest, c) have been calibrated to those variable(s), d) that are then composited together somehow, and e) that the composite has been validated against the instrumental record.

The number of well-dated proxies used in the latest paper is significantly greater than what was available a decade ago: 1209 back to 1800; 460 back to 1600; 59 back to 1000 AD; 36 back to 500 AD and 19 back to 1 BC (all data and code is available here). This is compared with 400 or so in MBH99, of which only 14 went back to 1000 AD. The increase in data availability is a pretty remarkable testament to the increased attention that the paleo-community has started to pay to the recent past - in part, no doubt, because of the higher profile this kind of reconstruction has achieved. The individual data-gatherers involved should be applauded by all.

The increase in proxy records allows a whole bunch of new things to be done. First off, the importance of tree rings can be tested more robustly. With the original MBH98 proxies, there was only enough other data to go back to 1760 if you left out the tree rings. The match was pretty good over multi-decadal periods, but the interannual variability was much larger without tree-rings. Now though, the Northern hemisphere land temperature reconstructions without tree rings can go back to 1500 AD or 1000 AD depending on which of two methodologies are used. For the NH land and ocean target, it's even possible to get a coherent non-tree ring reconstruction back to 700 AD! As before, there are some differences (notably in the 17th Century where the tree rings indicate colder temperatures), but the recent warming is anomalous regardless.

Secondly, you can screen records and pick targets more finely: do you want only records that match local temperatures? Done. You want to get a handle on global and southern hemisphere means as well as the northern hemisphere? Done. Other screens could easily be implemented.

The two methodologies used themselves span the range of different approaches that people have used. 'Composite and scale' (CPS) is perhaps the simplest method - it is basically an average of all the temperature proxies scaled to the target time series. The other method is denoted 'Error in variables' (EIV) in this paper, but is really a simplified application of the RegEM climate field reconstruction method used in a couple of more recent papers. It is essentially a fancy multiple regression to the target time series that can incorporate non-local proxies as well. The point of using two methods is to demonstrate what is, and what is not, robust, and to give an idea of what the structural uncertainty in these estimates is - something not easily calculated using standard statistics. That uncertainty is clearly larger as you go back in time, and larger still for the southern hemisphere.

Other improvements over previous work are that more proxy data sets go past 1980, and so calibration up to 1995 is possible. That allows more of the recent trends to feed into the calibration and highlights the so-called divergence problem in some (but not all) recent tree-ring records. That divergence is significantly lessened without tree-rings or using the EIV method.

Figure: Spaghetti plot of the new reconstructions over a) 1800 and b) 1000 years
along with selected older ones for comparison.

So what does it all mean? First off, this paper (like MBH98 before it) is not an attribution study. That means that the reasons for any of the ups-and-downs in the records are not demonstrated by these papers alone. Attribution of the recent trends (as discussed in IPCC AR4) to anthropogenic effects has mostly focussed on the last 150 years and did not use any paleo-data. Nonetheless, there have been a couple of key studies that have used this kind of data along with simple energy balance models (Crowley, 2000; Hegerl et al, 2006 for instance) and it will be interesting to see if this new reconstruction will make any difference to their conclusions.

Secondly, in comparison with previous reconstructions, the current analysis does not provide many surprises. Medieval times are warmer than the Little Ice Age as before, and a little warmer using the EIV method than was the case in MBH99. The differences in the 11th Century are on the order of a couple of tenths of a degree - well within the published error bars in IPCC TAR though. Interestingly, there are quite rapid and strong drops in temperature near 1100 AD and around 1350 AD which may make interesting case studies for attribution to solar or volcanic forcings in future. Overall, there are a few more wiggles than before, but basically nothing much has changed. (Though one should always be aware of the maxim that one person's noise is another person's signal).

Finally, while the headline numbers 'likely warmest since XXXX' are of some contextual value, they aren't the real point of this kind of study. Most of the interesting work - looking for patterns associated with solar forcing say - will start when the spatial patterns of temperature change start to be discerned - and that is still a work in progress.

So, onto the inevitable discussion! One test of whether that discussion is more political than scientific will be the extent to which people acknowledge the progress that has been made. Repetitions of tired and oft-debunked one-liners will be telling!

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