Guest commentary from Tim Osborn, Tom Melvin and Keith Briffa, Climatic Research Unit, UEA
Records of tree-ring characteristics such as their width (TRW) and density (usually the maximum density of the wood formed towards the end of the growing season – the “maximum latewood density” – MXD) are widely used to infer past variations in climate over recent centuries and even millennia. Chronologies developed from sites near to the elevational or latitudinal tree lines often show sensitivity to summer temperature and, because of their annual resolution, absolute dating and relatively widespread nature, they have contributed to many local, continental and hemispheric temperature reconstructions. However, tree growth is a complex biological process that is subject to a range of changing environmental influences, not just summer temperature, and so replication, coherence and consistency across records and other proxies are an important check on the results.
Tree-ring records have greater replication (both within a site and between nearby sites) than other types of climate proxy. Good replication helps to minimise the influence of random localised factors when extracting the common signal, and it also allows the comparison of information obtained from different independent sets or sub-sets of data. If independent sets of data – perhaps trees with different mean growth rates or from different sites – show similar variations, then we can have greater confidence that those variations are linked to real variations in climate.
In a new QSR paper (Briffa et al., 2013), (BEA13) we have used these approaches to re-assess the combined tree-ring evidence from the Yamal and Polar Urals region (Yamalia) of northern Siberia, considering the common signal in tree-growth changes at different sites and in subsets of data defined in other ways. Together with our Russian colleagues and co-authors, we have incorporated many new tree-ring data, to increase the replication and to update the chronology to 2005 and have reassessed the inferences about summer temperature change that can be drawn from these data. The paper is published as an open-access paper (no paywall) and supplementary information including the raw tree-ring and instrumental temperature data are available from our website.
Figure 1 illustrates our inferences about past summer temperature variations. Low tree-growth periods for which the inferred summer temperatures are approximately 2.5°C below the 1961-90 reference are apparent in the 15-year smoothed reconstructions (Figure 1d), centred around 1005, 1300 (Figure 1b), 1455 (Figure 1c), 1530, particularly the 1810s where the inferred cooling reaches -4 or even -6°C for individual years (Figure 1a), and the 1880s. These temperature estimates will be interesting for the current debate about the representation of volcanically-induced cooling in temperature reconstructions, and for testing of climate model simulations.
There are numerous periods (Figure 1d) of one or two decades with relatively high growth (and inferred summer temperatures close to the 1961-90 level) but at longer timescales (Figures 1e and 1f) only the 40-year period centred at 250 CE appears comparable with 20th century warmth. This early warm period was both preceded and followed by periods of low ring width and so the central estimates of the temperature reconstruction averaged over the warmest 100-year period near the 3rd century CE (205-304 CE) are 0.4°C cooler than the 1906-2005 mean. Allowing for chronology and reconstruction uncertainty, we find that the mean of the last 100 years of the reconstruction is likely warmer than any century in the last 2000 years in this region.
Figure 1 (from Fig. 13 of BEA13). Summer temperature reconstructions based on either the Yamal ring-width chronology (red line, orange confidence intervals) or by combining information from the Yamal and Polar Urals ring-width chronologies and the Polar Urals density chronology (blue line, blue confidence intervals). The latter is shorter because the Polar Urals data are shorter and also has two versions that differ in how they are calibrated and in the summer temperature that they represent (in panels (a)-(e) it represents mean June–August temperature shown by the black dotted lines, while in panel (f) it represents mean June–July temperature shown by black continuous lines). Each panel shows a different time period and degree of smoothing; the values near to the end of the smoothed series are more uncertain than shown here due to the presence of end effects on the spline filters. The low-frequency agreement between the series is expected because the Yamal ring-width data are common to both reconstructions.
A response to the critics
The publication of our paper provides a timely opportunity to revisit and respond to a series of unfounded criticisms that have been levelled at our work in recent years, mostly originating from Steve McIntyre at the ClimateAudit blog, though they have been widely repeated and embellished by other commentators.
It is of course usual for results to be improved and superseded as science progresses. Our new Yamalia ring-width chronology differs from the Yamal chronology published by Briffa (2000) – see Figure 2a for a comparison. The very recent values are now lower (and extend by a decade more), but so are the estimates around 1000 CE. The consequent differential between medieval and modern growth is hardly changed. The period of high growth centred near to 250 CE (noted above) is also relatively unchanged, and is now the most prominent pre-20th century period of anomalous growth in the last 2000 years. These changes are because of genuine scientific progress, not because – as our critics have claimed – we had previously presented a deceptive chronology. They arise from extra data collection and, particularly, developments in tree-ring standardization methods (see the paper for details).
Figure 2. (a) Comparison of the Briffa (2000) Yamal ring-width chronology (red) and the new Yamalia ring-width chronology (black). (b) Comparison of the new Yamalia ring-width chronology (black) and two chronologies that have been promoted by critics of our work, but which turn out to be biased: the Polar Urals “update” chronology (purple; from Esper et al., 2002) and the Yamal chronology with modern data coming only from the Khadyta River site (blue). All series were scaled to have unit variance before being smoothed with a 10-year filter.
Figure 2b compares the new Yamalia chronology with two alternative chronologies heavily promoted by McIntyre and others – the so-called Polar Urals “update” chronology and a Yamal chronology using modern samples from the Khadyta River site. Both chronologies present a different picture of the difference between peak medieval and peak modern growth rates, with elevated growth around 1000 CE and suppressed growth in the 20th century. Our paper demonstrates that these two alternative chronologies are flawed.
The real Yamal deception
Some background is perhaps needed regarding our preferred chronologies. Briffa et al. (1995) developed chronologies from Polar Urals ring width and density data. Subsequently, Briffa (2000) presented a 2000-year ring width chronology from nearby Yamal, which had much better replication (more trees) than the Polar Urals data and was therefore preferred. The Polar Urals data were later supplemented by additional samples which were used by Esper et al. (2002). Even including these additional samples the Yamal chronology remained better replicated: of the 1213 overlap years, the Briffa (2000) Yamal has 4 years with samples from less than 10 trees, while the “updated” Polar Urals chronology has 264 years with data from less than 10 trees, many of them in the medieval period (see here for more details). The additional sub-fossil data used in our new paper further increases the replication of the Yamal chronology compared with the Polar Urals chronology (Figure EC1 in the SI of the new paper). On the basis of replication and the strength of the common signal, the Yamal record was, and remains, superior to the Polar Urals chronology.
1: Why we didn’t use the Polar Urals “update”
We have been criticised for not archiving the Polar Urals “update” data. The “update” data were in fact archived at the ITRDB thirteen years ago. We have been criticised for not publishing an updated Polar Urals chronology using the updated data (and accused of worse here). The supposed reason for our decision not to do this was that the ‘update’ does not support our supposedly desired message of unprecedented modern warmth (because they appear to suggest that tree growth rate was greater during earlier times including the medieval period; Figure 2b, compare purple and black lines).
However, as reported in BEA13, it turns out that during the medieval period these Polar Urals “updates” are dominated by samples taken from the root collars of trees. Ring widths measured in such root-collar samples tend to be systematically larger than equivalent rings measured higher in the boles (stems) of the same trees. The reason for larger tree-ring widths during medieval times in the Polar Urals “updates” is now clear: it is because more samples were from the root collar with their inherently wider rings. Interpreting this as evidence for warmer temperatures is wrong.
Conclusion: the so-called “Polar Urals update” chronology is severely biased and should not be used as evidence of past changes in temperature; nor should our critics present it as evidence that we had committed scientific fraud by failing to publish a chronology using these data.
2: The Yamal record was not biased by omitting data
CRU has been accused of deception by presenting a Yamal tree-ring chronology biased by the omission of otherwise suitable data. A particular theme, originating again from ClimateAudit, is that tree-ring data from Khadyta River had not been used and would have dramatically altered the character of the chronology – and the NH temperature reconstructions that used the Yamal chronology – if these data had been used (Figure 2b, compare blue and black lines).
As reported in BEA13, through collaboration with our Russian colleagues who have extensive knowledge of tree-rings in this region, we have learnt that the Khadyta River site has problems related to the particular site conditions that differ from other sites in this region, and maybe influenced by changing permafrost. Certainly the trees have reduced growth and appear to be unhealthy, and some even dying. Thus the Khadyta River data that some claimed as being more representative than the data we used turn out to have a common signal that is inconsistent with the majority of site chronologies in this region. They could potentially bias the Yamal chronology had they been included and so for this reason we excluded these data from the main analysis in the new paper.
Conclusion: claims of a deceptive and biased Yamal chronology turn out to rely on outlier data that should be omitted; our new research, based on a greatly expanded dataset, supports the finding that tree-growth (and inferred summer temperature) in this region are likely greater in the last 100 years than for any previous century in the last 2000 years.
3: We did not withhold a combined Yamal and Polar Urals chronology
Separately, some of our incomplete and unpublished work on the Yamal and Polar Urals tree-ring data has been the subject of multiple requests under UK FOI/EIR legislation. (See this previous post for background). To be clear, this was not a request for the raw data that we were using in this area of northern Russia – the raw data were and are freely available. Instead, the request was for a tree-ring chronology that formed part of work that was, at the time, still ongoing.
The EIR has a (very sensible) exemption for material which is unfinished, incomplete or still in the course of completion. Our university (UEA) therefore refused the requests to release our incomplete research (see responses here and here). Steve McIntyre appealed and UEA reconsidered the issues but upheld the original decision. McIntyre then complained to the Information Commissioner’s Office (ICO). The ICO upheld UEA’s decision and rejected McIntyre’s complaint. McIntyre then appealed to the First-Tier Information Tribunal. Two weeks ago, after more than two years defending our right to publish our research at a time when we considered it to be complete rather than at a time dictated to us by Steve McIntyre, the Information Tribunal finally dismissed McIntyre’s appeal.
The research that was the subject of this information request has now been – as we said all along that it would be – completed and published, coincidentally, within days of the Information Tribunal’s decision. Our publication of this work contradicts McIntyre’s explicit accusations that we were hiding the requested chronology because it would have exposed long-standing scientific fraud on our part. These accusations were, and remain, baseless and mistaken.
Over the years, McIntyre has advanced a number of other criticisms of our tree-ring work in northwestern Eurasia. We note here that these too are also wrong.: 1) the original Polar Urals chronology was not wrongly cross-dated as claimed in a 2005 submission to Nature by McIntyre and McKitrick. When we demonstrated this in our response, Nature decided to publish neither their comment nor our response. It is worth noting that this rejection, nor any acknowledgement of his erroneous conclusions, were ever mentioned by McIntyre on his blog. (2) The Grudd (2008) Tornetrask chronology, promoted by some because of its elevated medieval growth (and implied much greater warmth) relative to the modern period, is biased by the issues noted in Melvin et al. (2013).
In conclusion, criticisms of our work have been based on misconceptions and misinformation. The so-called Polar Urals “update” chronology promoted by our critics turns out to be biased by inclusion of samples from tree root collars. The Khadyta River tree-ring data, whose exclusion from the Yamal chronology was portrayed as a severe example of cherry-picking to obtain a pre-conceived outcome, are from trees that appear to be dying and do not have a common signal with other regions. An updated Tornetrask chronology, with apparently elevated medieval warmth, turns out to be biased by combining incompatible groups of measurements.
That the critics have promoted a series of results that have turned out to be flawed is unfortunate but not in itself reason to complain – as science progresses it is usual for results to be improved and superseded. What can be condemned, however, is the long campaign of allegations of dishonesty and scientific fraud made against us on the basis of these false claims. That is the most disquieting legacy of Steve McIntyre and ClimateAudit. The real Yamal deception is their attempt to damage public confidence in science by making speculative and scandalous claims about the actions and motivations of scientists while cloaking them in a pretense of advancing scientific knowledge.
Links to other relevant information
CRU response to Yamal criticisms in 2009
CRU comments on the Ross McKitrick 2009 article published in the Financial Post (17 June 2010).
References
- K.R. Briffa, T.M. Melvin, T.J. Osborn, R.M. Hantemirov, A.V. Kirdyanov, V.S. Mazepa, S.G. Shiyatov, and J. Esper, "Reassessing the evidence for tree-growth and inferred temperature change during the Common Era in Yamalia, northwest Siberia", Quaternary Science Reviews, vol. 72, pp. 83-107, 2013. http://dx.doi.org/10.1016/j.quascirev.2013.04.008
- K.R. Briffa, "Annual climate variability in the Holocene: interpreting the message of ancient trees", Quaternary Science Reviews, vol. 19, pp. 87-105, 2000. http://dx.doi.org/10.1016/S0277-3791(99)00056-6
- K.R. Briffa, P.D. Jones, F.H. Schweingruber, S.G. Shiyatov, and E.R. Cook, "Unusual twentieth-century summer warmth in a 1,000-year temperature record from Siberia", Nature, vol. 376, pp. 156-159, 1995. http://dx.doi.org/10.1038/376156a0
- J. Esper, E.R. Cook, and F.H. Schweingruber, "Low-Frequency Signals in Long Tree-Ring Chronologies for Reconstructing Past Temperature Variability", Science, vol. 295, pp. 2250-2253, 2002. http://dx.doi.org/10.1126/science.1066208
- H. Grudd, "Torneträsk tree-ring width and density ad 500–2004: a test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers", Climate Dynamics, vol. 31, pp. 843-857, 2008. http://dx.doi.org/10.1007/s00382-007-0358-2
- T.M. Melvin, H. Grudd, and K.R. Briffa, "Potential bias in ‘updating’ tree-ring chronologies using regional curve standardisation: Re-processing 1500 years of Torneträsk density and ring-width data", The Holocene, vol. 23, pp. 364-373, 2012. http://dx.doi.org/10.1177/0959683612460791
Hey Kevin, thanks for those review links. I’ve read both those books, but really enjoyed watching the Hoggan interview, and they did a great job summarising Mann’s book.
#41: in addition to the rebuttals above, a few questions:
Who in the Free Market is going to do the research from which the solutions will emerge? The biggest investment so far seems to be in denialism.
When solutions do emerge, who’s going to pay for them? Insurance companies? The Laissez Fairy?
And, as has been pointed out over and over, even if we survive for fifty years without terrible upheavals, how are we going to afford those global-scale multi-gigabuck solutions when more and more and more of our limited resources are going to disaster relief and remediation?
Yamal to the Rescue!
#50 Tim Osborn, Tom Melvin
Thank you for this additional information.
Is there any other tree you could remove from the chronology which would make more than a 1C difference to the result as the removal of YAD061 does according to your linked plot?
You state: “Of these 18 trees with the highest peak index values, 8 peak values occur in the 20th century and no more than 2 occur in any of the preceding 20 centuries.”
Please could you provide a table or plot showing the positions and durations of the samples from those 18 trees in the chronology.
Regarding the prevalence of peak values in the C20th, to what extent may they be due to increased CO2 fertilisation rather than increased temperature?
Thanks, Steve M! Glad to provide something worthwhile.
Sloop #49
> which I may be unfairly implying is a good example
I don’t think so. Denial of the first-order significance of water vapour feedback is a classical denialist talking point, at least among those denialists who have bothered to acquire a sciency-sounding vocabulary. And our friend CB clearly also belongs to the Church of the Free Market, where dogma has it that the Internet was a free-market product.
But, am I correct in seeing in your comment an illustration of the principle that we all have the greatest concerns about the fields of study we are most familiar with?
re: 50
Great description, thanks, I wondered what that was ever about, but the Lawson quote may be useful to Bob Ward, as per Lord Lawson’s climate-change think tank risks being dismantled after complaint it persistently misled public.
Back to science, or rather presentation thereof. Those images clearly convey the fact that there is little difference, but I found myself also wishing for the *addition* of a similar-scaled chart showing the actual differences, i.e., one line instead of two.
I generally find spaghetti charts challenging, especially in steep-sloped areas. I liked Nick Stokes’ display, where mouseover of a label highlighted its corresponding line , but that only works interactively, a bit like a “blink” At least with only 2 lines, one could show a single difference line.
Osborn&Melvin @50: Thank you! I’ll send this to my friend. Even if it doesn’t affect his opinions, it should prove useful in future.
I’ve wondered how are you taking into account the fertilizing effect of CO2?
In field trials in Finland, where trees were grown in increased CO2 atmospheres, scientists have noted that tree ring widths are really good indicator of CO2, but not so much temperature while tree growth is a good indicator of temperature, less of CO2.
As both temperature and CO2 have increased you can’t differentiate the effect of CO2 and temperature from each other from pure tree ring data. So how much of the tree ring width increase comes from CO2 and how much from temperature, in your study?
If the coefficient is too low, or not taken into account any pre industrial temperature swings are underestimated as the CO2 fertilizing effect increases modern temperature proxies and by calibrating to increased proxies, past temperature swings are dampened when read from past tree ring data.
Curious (59):
This was wrongly placed in the bore hole and I’ve retrieved it; it’s an entirely legitimate question.
The CO2 fertilization effect remains a very serious difficulty in tree ring analysis generally. There needs to be some serious thought given as to how to deal with it, and a general analytical solution proposed, to the extent possible. Currently none exists, but it would have to take advantage somehow of the differences in the spatio-temporal pattern of climate (highly heterogeneous), versus CO2 (highly homogeneous).
Jim
> 28, 50
> Response to Philip Cohen @27… (sorry for the long wait)
> YAD061 is not the “most influential tree in the world”.
Thanks much for that clear response.
I hope it starts showing up in ‘oogle searches — right now searching that quoted phrase returns nothing but the deniers’ assertions. Time will tell.
M Vermeer @ 55:
How we function cognitively and socially apparently results inevitably in functional, operative distinctions between the sciences of ecology and climatology. It is how, given the evolved nature of our brains, we must go about describing and understanding different facets of the same massive phenomenon. Of course though one of the great insights of the earth sciences is how the planet’s chemical, physical, and biological spheres alter and in turn are altered by each other.
Governance seeks to maintain pace with real and imminent planetary changes that affect the well-being of citizens. Humanity’s collective well-being stems from the tightly inter-related dynamics of biology and economy. To move people to action and sacrifice, Government tries continually to reveal and manage connections between human self interest and utility-maximization and healthy, high-quality, and resilient natural environments at multiple geographies. We have to begin where people perceive most tangibly the risks to their well-being and their children.
Simple principle. Devilishly difficult to apply well. Take lobster: commercial lobstering may disappear from southern New England waters in a matter of decades because of warming coastal ocean temperatures (and yes other factors including the historical harvests and multiple ecological impacts that ensue from warming water and altered current regimes). Convincing folks of the truth of this risk is difficult enough because livelihoods are threatened. It’s all the more difficult because the US is cutting funds for lobster monitoring and devoting paltry sums to the study of how lobsters respond as organisms and as populations to changing oceanographic conditions. Even long-term databases on coastal ocean temperature trends are surprisingly rare.
But commercial lobstermen don’t need multi-decadal temperature records and more science on lobster shell disease to conclude strange things are happening in the ocean waters they spend their lives on. They see the changes intimately and they want help to survive economically. They want the rest of us to both compensate them for losses they didn’t cause and to address proactively the causes of looming lobster recruitment failure. It’s a matter of understanding and connecting with them and building political support from there.
An incomplete, lunch hour response. Sure I have a predilection for science I’m more familiar with; but given the realities of policy and economy, which science to be particularly concerned with seems reasonably self evident.
First I would like to thank Tim Osborne (#32) for pointing me to the spline smooting algorithm used in the paper. I implemented it and compared the results to a 30 year FFT filter see http://clivebest.com/blog/?p=5040 . The results are surprisingly similar except for end effects.
To respond to some of the other remarks if I may:
One of the problems of climate science is that it has all become rather too devisive. You are either with us or you are against us. This then leaves little room for any middle ground, which is where I feel I stand. There is AGW but there is also still uncertainty about feedbacks. The models do not make firm predictions on future warming, and recent temperatures are significantly less than those predicted in AR4. It is indeed prudent to reduce CO2 emissions, but drastic action is both dangerous to society and even illogical (IMHO).
One should never propose a policy without proposing a realistic solution. To cut carbon emissions by say 80% in 30 years is fundamentally impossible through renewable energy. If you take for example the UK, power consumption averages around 40GW rising to a peak demand of ~70GW. This means that nationally we must have an installed capacity of 70GW, – perhaps rising to 100GW if electric transport takes off. However we will always need a 100% reliable base-load of 30GW. Currently there are 3700 wind turbines installed in UK receiving an annual subsidy of ~ 1.2billion. As I write – the sum total power being generated by ALL these turbines combined is 0.3GW (<1% of demand) – see http://www.gridwatch.templar.co.uk/. The maximum power generated on the windiest day this year was 5GW – but only for 2 days. The sums just don't add up. Suppose we quadruple the number of turbines – ignoring the environmental impact on our small island. This massive effort would be to no avail because instead of 0.3GW we would instead be generating 1.2GW right now – which is still insignificant. Renewables really do cost the earth!
Current energy policy is miss-conceived and physicists should stand up and say so. Myles Allen recently actually did stand up and say so! The only proven non-carbon energy source remains nuclear power. Nuclear Fusion could also work given the political will.
> CO2 fertilization effect remains a very serious difficulty
For what geological eras or time spans, Jim?
Where we have instrumental records for CO2 and temperature, is it understood?
(pointer welcome to discussion elsewhere, don’t want to derail this if it’s a long conversation)
[Response: It’s definitely a long conversation, getting deep into plant physiology quickly, but I meant “serious” in the sense of “difficult to separate two potentially (but strongly) confounded effects (CO2 fert. and climate), given the observational data available”. The data on CO2 effects, vs those used in dendroclimatology studies, are largely disparate and therefore difficult to connect. People are working on it, but it’s a big bridge to cross effectively.–Jim]
[Response: Here is an example of the type of study I referred to in my previous comment. I like it for its approach: the authors tried, given the limitations of the data, to separate out climatic from non-climatic effects in the ring response. They concluded that CO2 fert. (+/- a possible N effect) was likely a major contributor to increased ring width responses observed in
twoone species in the Alps (Pinus cembra), but that conclusion is less important, to me, than is their approach to the problem.–Jim]wait: over yonder at his blog, Clive Best says:
June 12, 2013 at 6:17 pm “… The new results already show about half the 20th century warming compared to their original 2000 paper – so the “consensus” is shifting.
There is some (moderate) AGW but in no way is it catastrophic !”
I saw the goalposts go by, they were going, uh, thataway ….
The catastrophe’s only begun to be detectable now against the background noise in the physics measurements, but it’s been clear in our more sensitive detectors — ecologies — for quite a while. Look up phenology.
The *actual* topic of this post was interesting, maybe it could be discussed, especially when some authors have taken the time to watch and answer questions.
Nuclear power and especially fusion power do not seem very relevant to this topic.
“The new results already show about half the 20th century warming compared to their original 2000 paper – so the “consensus” is shifting.”
Has Clive Best forgotten that real thermometers exist, and that the instrumental record, not regional proxy reconstructions, is what’s focused on when 20th century warming is discussed?
Isn’t it funny that everyone always thinks they are “the middle way”? Ain’t nothin’ in the middle of the road but yellow stripes and flat armadillos.
@(66) John Mashey.
I agree with you – my apologies for over-reacting !
“This then leaves little room for any middle ground, which is where I feel I stand.”
There is no scientific reason for choosing the middle ground, just because it is the middle. Clive Best should know this. But of course his “middle ground” is 1C warming per doubling of CO2, which is hardly a middle ground within science.
In geology, his “middle ground” would be to declare that the earth is 2 billion years old, splitting the difference between cosmologists and young-earth creationists, because, you know, the middle ground is most likely to be accurate …
Middle ground
Using tree rings to assert whether the trees had hot/dry/C02/infection/poisoning/..add more. Is a daft premise from the outset.
You cannot separate the variables.
Tree’s with high C02 grow faster. They can also withstand drought and freezing better due to the reduction of stoma that is required.
Linearity is not what tree’s do. Even the outer rings compress as new growth takes over.
[edit]
[Response: It’s definitely not simple or easy to separate out the various possible drivers of ring response in trees, but that doesn’t make it a “daft premise from the outset”. It does mean that some hard and creative thinking is required if the problem is to be solved correctly, and an ability and willingness to acknowledge when the limits of analysis have been reached. As is always true in all of science–Jim]
Andyj:
“Using tree rings to assert whether the trees had hot/dry/C02/infection/poisoning/..add more. Is a daft premise from the outset.”
And another internet genius shoots down an entire field of research with a SINGLE sentence!
Andyj #72,
don’t project your own ignorance on others. You (well, not you, but actual scientists) can separate out many of these factors. E.g., you use trees from locations where growth is temperature limited (i.e., at the tree line) to extract — you guessed it — temperatures. And so on.
[Response: That concept is certainly highly important and useful, but not necessarily fully effective either. For example, there is solid evidence that most–perhaps even all?–plants with the C3 photosynthetic pathway (which all trees used in dendroclimatology have) are carbon limited to a greater or lesser degree. Additionally, there is reason to believe that that limitation should be greater at higher elevations, for reasons that have to do with the reduced CO2 partial pressures there and tradeoffs between the biophysics of the resistance to CO2 diffusion/fixation, and the reduced carbon loss via reduced photorespiration as atmospheric [CO2] increases. It’s complex (and highly interesting to some of us), and a mistake to think that just because there is a known limitation in one environmental driver, that it necessarily predominates over others. It may–and it may also not.–Jim]
And do you think scientists are dumb? (No, don’t answer that.) Ever heard of multi-proxy reconstructions? It is routine to check that you get the same result (within uncertainties) taking tree-ring proxies along, and leaving them out. E.g., Mann et al. 2008 figures S5-S7.
And denialist’s should learn to spell :-)
A different question for the authors:
I’m always fascinated by thing paleoclimate researchers do to extract signal from noisy data for which one cannot simply replicate experiments in the lab.
So, for this region, what data do you wish you had, that could be plausibly gotten (i.e, not like 1000-year-old measured temperature records), that you don’t have? And why?
[Response: Great question John, and I’ll let them go first.–Jim]
Andyj’s post illustrates an important distinction. Laymen see complexity and despair. Scientists find a way to make sense of it. If it were easy, someone would have done it already.
Perhaps this should go in unforced variations, but as it’s plant related, will post here. Along with kudzu and poison ivy, it appears CO2 is good for brambles in Africa:
http://www.guardian.co.uk/environment/nature-up/2013/jun/21/blind-starving-cheetahs-climate-change
[Response: By coincidence, I just posted about that very topic on my blog late yesterday. As I mentioned to Martin, pretty much all C3 plant species (which are the vast majority of all species) are carbon limited, both plants we like and plants we don’t like. I like the science issues involved here, but I dislike the negatively-biased spin the author puts on it with several statements, especially regarding effects on cheetahs, which border on the ludicrous. Along those lines, it’s not “so-called ‘CO2 fertilisation'”– it’s real, actual CO2 fertilization, exactly in the sense of N or P fertilization, even if the biochemical mechanism is, of course, going to be different.–Jim]
Jim #74, now that I have your ear: is it true that RCS determination is still done by ‘stacking’ and averaging? Has anybody considered (this may teaching granny to suck eggs) using a simultaneous least-squares adjustment giving both the RCS and the adjusted chronology?
This would have the merit of producing uncertainty measures automatically for all the adjusted unknowns. Also the condition number of the normal matrix would tell you immediately how well you have separated growth curve and local climate (and CO2, if you try that). I suspect such an adjustment is formally equivalent to Briffa’s “signal-free” RCS determination, which avoids climatic trends leaking into the RCS curve when stacking. Least-squares would do this implicitly.
[Response: Martin, the RCS “stacking and averaging”, by ring number (= “cambial age”) is typically followed by fitting a smoothing spline to those averages (the underlying idea being that the age/size effect should be relatively smooth, which the un-smoothed averages alone will not always be), and in the literature both relatively stiff and flexible splines have been used to do so, which are mirrored in the two publically available softwares for performing RCS (ARSTAN and an R package called dplR). I’m not 100% sure what you mean by “simultaneous least-squares adjustment”, so hard to respond exactly. If indeed that method were +/- equivalent to the so-called “signal-free” standardization, then it would not prevent “climatic trends leaking into the RCS curve when stacking”, because that method does not guarantee any such result. The problem of an environmental trend being partially captured by the RCS curve is due to the inhomogeneity of the age/size structure of the sample combined with the existence of an environmental trend over time. This causes the entire RCS curve to be biased by a constantly increasing amount over time, not just at the series ends, and not with offsetting errors in the middle as claimed by Briffa and Melvin, but the entire curve, from one end to the other. This is the main point in my series on that whole topic at my blog, is easily demonstrable with a flexible growth model that can produce any type of age/size effect, and was the point of a PNAS paper I submitted last year (but which was rejected because the reviewers completely failed to understand this issue and the evidence I presented for it). So, even though I don’t know exactly what you have in mind, I highly doubt that it can solve this problem.–Jim]
Yep. I’ve been seeing poison oak show up at higher elevations in California the last few years, places it’s never appeared in the past. Likely birds have always pooped seeds, but the winters aren’t getting as cold.
Ever been fighting a fire downwind of burning poison oak? It’s nasty.
[Response: Gotta factor the fire regime changes into that calculus Hank. And never do the latter–highly dangerous if you breathe the active agent into your lungs. Firefighters have suffered serious injury that way.–Jim]
but,oops, offtopic. sorry
Jim #78, as standard least squares produces unbiased estimators for the RCS parameters (in fact for all unknowns — it’s a basic property), I don’t see how they could pick up biases from climatic trends.
[Response: Martin, we’re talking about two different types of bias here. You’re talking about the bias that could arise from curve fitting algorithms. I’m talking about the bias that arises when different ages/sizes of the sampled trees sample different portions of the environmental space. For RCS to work right, they all have to sample the full domain of that space, but they cannot if the age structure doesn’t allow it. Completely different things, and the latter is the much more potentially serious. The point is that no matter what curve fitting method you choose: least squares, robust regressions, whatever–they’re not going to be able to to remove the bias due to this sampling issue –Jim]
Jim, no, I’m not talking curve fitting. I must say that I sympathise with your PNAS reviewers… I don’t get what you mean either :-)
[Response:Readers will notice that someone here at RealClimate has seen fit to delete my extended comment to Martin (without justification and without any notice), so hold on while I re-compose it.–Jim]
[Response:Original comment: Then I’m not sure what application of least squares you are referring to. As for understanding the issue, the reviewers, who were (supposedly) tree ring experts, had available to them a huge amount of detailed information. I have discussed the issue in great detail beginning here and going to here. The problem is as follows. The RCS method is designed to estimate the age/size effect by taking an ~ mean ring response for each age/size in the data set. But this is only fully accurate if each sampled age/size fully samples the environmental space covered over the full time period. If instead you have, for example, a situation in which early rings preferentially sample one end of that continuum, while later rings tend to sample the opposite end, this will cause the entire RCS “regional curve” to be biased by a constantly increasing amount over time. Not just at the series ends, and not with offsetting errors in the middle as claimed by Briffa and Melvin, but the entire curve, from one end to the other. This is the main point in my series on that whole topic at my blog, is easily demonstrable with a flexible growth model that can produce any type of age/size effect, and was the point of a PNAS paper I submitted last year (but which was rejected because the reviewers completely failed to understand this issue and the evidence I presented for it). This problem is one reason–and only one–for why trying to estimate climate states over long periods, from tree ring widths, is completely unreliable. Completely.–Jim]
[Response: It’s probably worth adding that Jim’s last conclusion is not universally shared (e.g. fig 3 from Esper et al, 2012 shows a very good correspondence between TRW and MXD chronologies). That isn’t to say that there aren’t issues… ;-) – gavin]
Just now getting to respond to Gavin’s comment regarding the significance of Esper et al 2012 relative to RCS detrending concerns and the reliability of climate estimates from tree rings. Part (a) of Figure 3 is the relevant detail there, and the “very good correspondence” that it shows (r=0.58) between tree-ring widths (TRW, blue lines) and maximum latewood density (MXD, black) is driven primarily by the high-frequency variations, not by the long term trends, which the linear regressions to the two clearly show are different. This is one of the problems with using linear correlation to make assessments of similarity, and it is not at all uncommon to find very high correlations at short time scales, but much poorer ones at longer scales, including in sites showing the so-called “divergence problem”. Since the RCS method was used to detrend both series in their Fig 3a, there is no guarantee that the MXD long term trend is necessarily correct either. However, its estimated trend is supported much more strongly by the other types of truly independent long-term climate information supplied in their Figure S1, including glacial, treeline and data from two climate models (the gray/black lines in the figure are the tree-ring based estimates, green is treeline data, blue is glacial data and red and orange are climate model data; see the paper, linked to above, for the details on all figures). Therefore it is much more likely that the MXD is giving the better trend estimate. My guess for the likely reason for that is that the MXD data contain less of an age/size trend than the TRW data do, and there is therefore less confounding of size and climate, though I don’t know for sure. So, this lack of ability to capture long term trends, using RCS methods on ring widths, is exactly the concern that I have been trying to raise.
In my comment at #21, I failed to say how much I am enjoying both the primary post and the discussion that follows. RC does it again, and congrats to Tim, Tom, and Keith for an awfully nice job. While primary, peer-reviewed papers cannot be done without, this kind of medium let’s us all “under the wrappings” to see some of the materials and concerns which are necessarily implicit in published works. It is very informative and a good deal of fun. Thank you!
Now professionally, I’ve always found looking over the shoulders of people grappling with experimental problems in geophysics to be enlightening, principally because the character and distributions of your data are so different than most people are taught. I learnt about directional data that way and went on to appreciate things like Best Linear Unbiased Estimation in the spatial realm, a.k.a. “kriging”. So keep on!
Jim, actually it was having read your blog posts on this that made me bring up the least-squares proposition as a way of addressing at least this bias problem. I’m not sure you appreciate what least squares with a properly formulated model can do — there is no shame in that, I know some clever folks who don’t either :-)
[Response: Of course I can’t appreciate it–because you haven’t explained what you have in mind. If you’d like to do that, go ahead. Note also that my original, more extensive response to you was again deleted by someone here–Jim]
Comment 3. fred smith: “Aren’t the conclusions of 1 and 2 post hoc explanations for exclusion of data?”
Comment 4. Tim Osborne: No. The fact that some Polar Urals cores were taken from root collars was noted at the time the samples were taken … For the second case, the potential problems with the Khadyta River site were also noted at the time the samples were taken …
When ClimateAudit publicized information about Yamal in 2009, CRU published two responses at their website discussing McIntyre’s recent posts on this subject. Neither CRU post mentions root collars or permafrost (the postulated problem at Khadyta River). Now in 2013 – more than a decade after the field work was done and published and years after your initial reply to McIntyre – information appears concerning root collars and permafrost. Why doesn’t this constitute a post hoc exclusion of data? If this doesn’t, what would?
Those of us who have knowledge of fields where confirmation bias is a widely recognized problem find such situations problematic. In the ideal case, you design your experiment, you collect as much of the data as you intended as possible, you analyze it by a pre-planned method, and publish the result. This process may lead to the hypothesis that a better answer lies in a subset of the data you initially collected or an alternative statistical treatment; a post hoc analysis that always risks confirmation bias. In critical fields such as human clinical trials, post hoc analyses aren’t acceptable, because everyone knows that one can usually find some sub-population that behaves as originally anticipated and devise a post hoc explanation. In those cases, the FDA demands that drug companies collect a new, independent data set with the patients (or trees) that are now hypothesized to provide the “right” answer. Of course, this isn’t always practical in dendrochronology, climate science or drug development. In that case, you are stuck with two possible answers to the question you were investigating: one answer arising from analysis of all the data you collected and analyzed, and a second answer arising from a subset or re-analysis of that data.
Are you reviewing all of your earlier work to see if root collars or permafrost might have biased those results?
[Response: Comment restored from trash folder.–Jim]
Martin Vermeer wrote:
“Jim, actually it was having read your blog posts on this that made me bring up the least-squares proposition…”
Jim’s posts on this topic form a very powerful argument in my opinion. I share his bewilderment on how a least squares approach can resolve the issues he very carefully lays out. Can you show how the least squares adjustment would improve the results from Jim’s flexible age model approach with synthetic data?
[Response: Comment restored from the trash folder.–Jim]
Jim, sure, we can try… drop me an email.
[Response: You’re welcome to present it here Martin if you like–would be informative for everyone.]]
A question for Tim Osborn or someone from CRU. Various papers by Shiyatov state that he crossdated 888 subfossil trees and more than 400 trees from two Polar Urals transects, with coordinates and altitudes carefully recorded.
Why didn’t CRU use this dataset in Briffa et al 2013 instead of the inadequately replicated dataset that it reported on? Did CRU attempt to obtain access to this data and receive a rejection? And why didn’t CRU report the existence of Shiyatov’s crossdated dataset in its review of previous work at Polar Urals?
[Response: We were aware that many additional wood samples were collected, measured and cross-dated in the Polar Urals by our co-authors, as part of their excellent ongoing ecological monitoring at the tree-line. In fact there are even more data, from more recently sampled material. As with the previous data, these are a complex mix of stem, root, prostrate forms, etc. and indeed many samples will not be suitable for dendroclimatological analysis using the basic RCS approach.
A preliminary analysis of some of the “stem” samples produces a similar picture of tree-growth change when using RCS processing as that shown by the Polar Urals chronology in our paper. We made this preliminary check to satisfy ourselves that we could proceed with our publication with the knowledge that we were not publishing a chronology that was likely to be contradicted when the more recent samples are analysed and published.
It was evident that much additional work will be necessary to examine and assess their suitability (and potential biases) when processing these data, and we had already demonstrated in our paper the difficulties in using the existing root-collar samples for example. I repeat that some, or many, of these will not be suitable for straightforward RCS processing because they are from prostrate or root-collar samples.
The dendrochronological data from this recent sampling have not yet been published and it is the prerogative of the Ekaterinburg laboratory to publish the first dendroclimatological analysis of the data that they have spent many years and extensive effort in collecting and processing. We hope to continue our long-standing collaboration with our Russian colleagues in this work.
Your question and other commentary at your blog may give readers the false impression that we have published using an inadequate dataset. This is ironic given your advocacy for publishing (a biased version of) this chronology when you believed that it would support an elevated Medieval Warm period, advocacy that extended to questioning our integrity for not doing so. It is unfortunate that you fail to acknowledge the careful analysis reported in Briffa et al. (2013) to demonstrate the biases that would arise from a naïve use of the Polar Urals update data. It is also unfortunate that you fail to acknowledge that Briffa et al. (2013) was based on a significantly increased set of tree-ring data for the adjacent Polar Urals and Yamal regions, with overall replication that is much improved over previous work. –Tim Osborn]
Thanks for the interest in our work and the many interesting follow-up comments, and sorry we’ve not had time to respond to the many comments that came in since last week. We’ll read them all and respond where we can…
Armando @26
Sorry, missed this one before: “Could you translate the summer temperatures to yearly temperatures?”
Possibly. During the instrumental period, summer and annual-mean temperatures tend to be correlated, and that relationship could be used to make an estimate of annual temperatures. Probably it would be more uncertain because the summer-annual relationship is not perfect. I would also be concerned that seasonal insolation changes due to orbital forcing would change this relationship on the longer (millennial) timescales.
Lynn Vincentnathan @38
re. max/min day/night temperatures and trees… differing sensitivity to day/night temperatures is possible. Rob Wilson, for example, has found some cases where tree-ring chronologies are more strongly correlated with day (maximum) temperatures. See:
Luckman and Wilson (2005) Climate Dynamics
Wilson and Luckman (2003) Holocene
John Mashey @57
“I found myself also wishing for the *addition* of a similar-scaled chart showing the actual differences”
Ok, I’ve added an extra panel to each of those to show the difference:
Effect of YAD061 on original Yamal chronology
Effect of YAD061 on new Yamal chronology
Hope that’s useful.
Roger Tattersall @54
(1) In the original chronology, removal of YAD061 makes a bigger difference than the removal of any other individual tree core (note these are chronology values, not calibrated reconstruction, so it isn’t 1degC). This is a combination of the high values in YAD061 occurring in the final few years when the sample size is small. In our new chronology, many cores make a bigger difference than YAD061 if removed.
(2) Plot of trees with peak index values that exceed the peak YAD061 value is now here. Black lines show all tree index series, red lines show those that peak higher than YAD061 peak, purple line is the YAD061 index series.
Hope you find that useful.
Regarding CO2 fertilisation… I see further discussion of this in comments below, so I’ll read them before responding in one go.
Tim Osborne @89
I am not sure but looking at thermometer data from the region , it looks like the annual temperature anomalies are relatively higher than the summer anomalies in the period from 1925 to 1955.
Here’s a webinar link to an interesting presentation by a UMinn forest ecologist Dr. Jane Foster on her recent work to “use tree-ring reconstructions of biomass growth to ask (a.) how tree, stand, and weather interact to predict forest biomass growth, and (b.) whether the variance in growth due to climatic variability is as important as other factors.”
http://necsc.umass.edu/webinars/characterizing-sensitivity-tree-species-and-forest-types-past-weather-variability-using-tre
This study’s objectives are: “establishing a [long-term] monitoring network in Superior National Forest, gauging vulnerability of forests to climate change, examining forest management and carbon storage, simulating future forest productivity.”
This presentation was organized by the US Department of Interior’s relatively new Northeast Climate Science Center, whose on-line resources and webinars are well-worth checking out.
[edit–just stick to the science]
[Response: Those are closed forest stands in Minnesota, the trees in which have a very low response to climate because they’re growing in a highly competitive environment. Not relevant.–Jim]
Tim Osborn @ 91
Thanks! that is indeed useful (although you must have meant YAD061, not YAD06). No surprise, but I think the visual appearances make the point strongly.
John Mashey @96:
Re. YAD06 vs. YAD061. Depends whether you are talking about the tree or the tree core. In this case the tree has only one core so it’s the same thing.
Armando @94:
Yes, that’s correct. Winter and autumn anomalies were positive in that period — graph here.
Tim Osborn @98
Thank you for your time and answer.
Seeing the significant difference between yearly and summer anomalies in the period from 1925 – 1955 what does that mean for the uncertainty in yearly anomalies derived from tree rings?
Are there any southern hemisphere tree ring datasets? Assuming uniform CO2 atmospheric mixing, it would be interesting to see trees that have a growing season inverse to the CO2 concentrations in those seasons (i.e. max annual CO2 at max solar exposure).
[Response: Yes there are some. Remember though, we’re only talking about a few ppm of CO2 intra-annual variation–not enough to make a difference.–Jim]
Interesting stuff. Thanks for posting.
One thing I don’t understand is the 100-year smoothing graphs. I’m not really a math guy, but I would have thought that smoothing for 100 years would mean averaging every point with all years 50 ahead and 50 behind. That would mean (I think!) that the graphs should stop 50 years before the end of your study (i.e. 1963 at the latest). But the graphs go on to today.
To a naive reader it seems like the only way to have the 100 year moving average for the year 2000 is to have the data for 2050 (which, obviously we don’t have). So I assume you do something different for all the years past 1963. In looking at the graphs, the uptick at the end doesn’t seem to fit very well with those that are fit at 15 years.
Just curious how that works. Is there a CSV file with the unsmoothed data I could play way? Mostly I’m just curious.
Thanks!
tim
[Response: The raw data are at the links given above, so smooth away as you like. The smooths above are described in the caption as being spline fits, and indeed, there are important ‘end-point’ effects that can arise (as noted). – gavin]