Mg/Ca proxies measure the temperature of calcification of G. ruber, which is not necessarily the same as surface temperature. Dahl et al state
G. ruber is present year–round at Site 723B, but experiences blooms during both monsoon seasons and calcifies above 80 m water depth (14–16)…. In accordance with the seasonality of G. ruber in the western Arabian Sea, Mg/Ca–derived SST from modern RC2730 sediments is 25 deg C, approximately 1 deg C cooler than the annual average.
Compare this to Hansen’s splice which purported to equate instrumental SST with calcification temperature over the mixed layer, stating:
Accepting paleo and modern temperatures at face value implies a WEP 1870 SST in the middle of its Holocene range. Shifting the scale to align the 1870 SST with the lowest Holocene value raises the paleo curve by ~0.5°C
If one applied a 1 deg C adjustment to allow for the difference between temperature of calcification and surface SST as indicated by Dahl et al, then Hansen’s Figure 4 has no force whatever. Modern warming would then, at most, be reaching Holocene Optimum levels (and there’s other hair on the calculation). So what’s the justification for Hansen’s splice? What due diligence did Cicerone perform on Hansen’s splice as part of his review?
Update: As pointed out in a comment below, this particular comparison between the calcification temperature of G. ruber and SST was made in the Arabian Sea and may not apply to the Western Equatorial Pacific. I’m obviously not an authority on G. ruber calcification temperatures relative to SST, but I’ve made an attempt to see what information exists on the topic, as it seems pretty germane to the splicing and the Arabian Sea comment is the only information that I’ve located to date. I would presume that G ruber calcifies throughout the mixed layer in the WEP as well and that the average temperature of the WEP mixed layer (or G ruber calcification) is lower than the surface temperature as estimated by GISS SST. In any event, this is the crux of the issue and it’s ridiculous that the matter is not addressed in a PNAS article.
Reference: Kristina A. Dahl, Athanasios Koutavas, and Delia W. Oppo, Coherent ENSO and Indian monsoon behavior since thelast glacial period http://005c496.netsolhost.com/as_eep_paper_v2.pdf
Climate Audit 
62 Comments
These guys sure love their spliced records. We had Svalbard a few months ago, the CET, and now this.
I particularly enjoyed the part about aligning them “at face value” … meaning in plain language, we’ll assume that
a) the proxy measurement is exactly accurate, and
b) there was no temperature change in the intervening 6,000 years.
Then we have his idea of calculating the uncertainty, “Shifting the scale to align the 1870 SST with the lowest Holocene value raises the paleo curve by ~0.5°C”. This, in plain language, means:
c) 1870 was no colder than a single proxy temperature from 10,300 years ago.
Heck of a data choice on which to base an uncertainty estimate …
I’d pay big bucks to see Hansen defend those theses in front of a doctoral committee.
w.
Steve M., if you check I think you’ll find that in the WEP G. ruber lacks the seasonal behavior it has in the Arabian Sea, and so no seasonal adjustment is needed. Note that the WEP site is essentially at the equator, while the Arabian Sea ones are around 18N, so this difference is unsurprising. If for some reason an adjustment had been necessary, do you think David Lea of all people wouldn’t have known to make it?
Steve B., I think you are right about the reason for the temperature adjustment for G ruber in the Arabian Sea, and that this adjustment is not necessary in the ODP 806 core area in question.
But I think you are placing excess faith in the ability of scientists not to make mistakes, or to underestimate the confidence intervals on their own work. David Lea might have known not to make the adjustment … but he might not. I mean, take the paper we’re discussing, for example, and the necessity of an adjustment to avoid the absurd claim that we’re currently warmer than the Holocene optimum. If for some reason an adjustment had been necessary, do you think Jim Hansen of all people wouldn’t have known to make it?
For example, in the abstract of David Lea’s paper (Climate Impact of Late Quaternary Equatorial PaciàƒÅ¾c Sea Surface Temperature Variations, David W. Lea et al.), the paper from which Jim Hansen took the graph in his paper, Lea says:
This, of course, is a 95% confidence interval (CI) of 1.4°C.
However, he then explains about how he adjusted the temperature for the depth of the drill core site … but he doesn’t mention the error in that process. Replicating his calculations from the graph, I find a ±0.6°C standard error, which is a 95% confidence interval (CI) of 1.2°C.
And in the tiny print in footnote 16, we find:
and
Now, for OPD806, the math works out that these are 95% confidence intervals of 0.44°C and 0.50°C respectively. Thus we have a claimed 95% CI in the abstract, for the difference between modern and LGM temperatures, of ±1.4°C. This is presumably comprised of all the errors listed in the paper. However, in the paper we find:
95% CI in the Mg/Ca to °C conversion formula (error listed on the first page of the paper), ±1.2°C.
95% CI in the depth adjustment calculation, ±1.2°C
95% CI in the long term reproducibility, ±0.4°C.
95% CI in the reproducibility of split samples, ±0.5°C.
Presumably all of these are orthogonal, so the total error is sqrt(1.2^2 + 1.2^2 + 0.4^2 + 0.5^2), which works out to a 95% CI of 1.8°C …
But it gets worse. Consider the problem of the 95%CI for the difference between two temperatures, such as the LGP and the present. While the depth adjustment is common to both the LGM and the present measurements, and thus cancels out, we are still left with the other terms, which do not cancel. The sum of these is sqrt(1.2^2 + 0.4^2 + 0.5^2), which is ±1.4°C, presumably the 95%CI claimed in the abstract.
But this 95%CI of ±1.4°C applies to both the LGM temperature and the modern (coretop) temperature, which means that the error in the temperature difference calculation is sqrt(2*1.4^2), or 2.0°C …
There’s a further difficult. In a footnote to Table 1, Lea talks about the error estimate presented in the abstract. He says:
Umm … no. Downcore preservation changes are likely to be age related. Thus, they will affect the slope of the calibration, and will introduce a new, age-linked error. Since the standard error of the calibration is not age-related, this error will be missed.
Finally, I disagree with the common practice of listing the standard error, rather than the 95%CI, as it tends to minimise the uncertainty.
In any case, I would say that, rather than his statement that the difference between the LGM and the present is 2.8°C ±0.7°C, the correct figure is that the temperature difference is 2.8°C with a 95%CI of ±2.0°C … which doesn’t tell us much.
w.
PS – for comparison, Dekens et al. (Core top calibration of Mg/Ca in tropical foraminifera:Refining paleotemperature estimation), who seem to be the authority in this question, say that the 95% CI for the Mg/Ca to temperature conversion (including the depth adjustment error I referred to above), is ±2.4°C. The number I have calculated for this study is ±1.8°C …
They also say that the depth adjustment 95%CI (which I calculated from the Lea graph as being 1.2°C) is ±0.6°C/kilometre of depth. As the depth of the OJP806 site is 2.5 km, this would make the depth adjustment 95%CI equal 1.5°C …
So I’d say my numbers are pretty close.
On this page:
http://npal.ucsd.edu/thermometry/index.htm
Don’t know if it applies well to the topic or not but the graph here seems to show a cooler Nothern Pacific basin using this technology.
I’ve added the following comment:
#2. David Lea did not make a comparison of this sort in any of his own papers, instead focussing only on comparisons between glacial and interglacial using the proxy. The PNAS article says:
It’s possible that the splice was made by Hansen and Lea did not turn his mind to it.
Tian et al 2005 say about G ruber in the South China Sea:
This might easily account for a difference of 1 deg C in that context as well.
I take your point that the mixed layer in the WEP is exceptionally deep but does that mean the G ruber lives at depths of between 30-60 m in the WEP or does it go lower with the deeper mixed layer following temperature rather than depth? Don’t you think that the splice should be discussed and justified?
Ref: Pinxian Wang, Ronghua Chen, and Xinrong Cheng 2005 Quaternary upper ocean thermal gradient variations in the South China Sea: Implications for east Asian monsoon climate PALEOCEANOGRAPHY, VOL. 20, PA4007, doi:10.1029/2004PA001115, 2005
Click to access 2004PA001115.pdf
Here’s another comment giving more details on G ruber (again in an Arabian Sea context):
Frank J.C. Peeters, Geert-Jan A. Brummer, Gerald Ganssen 2002, The effect of upwelling on the distribution and stable isotope composition of Globigerina bulloides and Globigerinoides ruber (planktic foraminifera) in modern surface waters of the NW Arabian Sea, Global and Planetary Change 34 (2002) 269–291 state:
I seem to recall that plankton undergo considerable daily variation in depth (up to 100m) in response to light. Whether G. ruber does this I have no idea.
Here’s another issue frpmm Lea et al 2000 ( noted up in Benway et al 2003). Lea, D. W., D. K. Pak, and H. J. Spero, Climate impact of late Quaternary equatorial Pacific sea surface temperature variations, Science, 289, 1719–1724,2000 http://www.geol.ucsb.edu/faculty/lea/pdfs/1719.pdf say:
ODP Site 806B is at 2520m. For comparing paleo proxies in the same core, the dissolution effect would be mitigated – but for comparing to SST, it would be substantial. I’ll try to see whether LEa et al did a dissolution adjustment.
Mg/Ca ratios of two species of Globigerinoides ruber (white) morphotypes: Implications for reconstructing past tropical/subtropical surface water conditions.
I was surprised to learn that while plankton drift horizontally they are able to maintain a fixed depth.
Israeli, U.S., German researchers unveil remarkable behavior of ocean plankton
I think that there is a very general kind of bias in all proxy reconstructions of anything in the past. If they find a proxy and it will indicate that things were changing intensely in the past, they abandon it as an unreliable proxy that is too random. In other words, they always selectively choose proxies that paint the past as more or less constant. Some of these proxies paint such a past because the ratio between the real fluctuations and the fluctuations of the proxies is underestimated; other proxies paint a constant past because of pure chance.
The result is that virtually all papers based on a hypothetical proxy will underestimate the fluctuations of everything in the past.
One would assume, also, that pH is a major factor determining the point of calcification. I truly believe that Hansen has gone stark, raving mad. Either that or he really is as crass as one must be to commit the sorts of acts he’s committed of late.
#12. pH looks like a very important issue in calcification. pCO2 also appears to be relevant. #10. Nice reference.
Re: #11 Well said, Lubos
This is exactly what has been admitted openly with the tree rings. They want to “select” the ones that “give a good signal.” Translation: if some tree ring records are uncorrelated with temp, they will not be used.
I could sure get better results from economic models if I could use this “method.” As in, oh, gosh, here’s a sample of gasoline consumption and prices that doesn’t show much variation. Guess it’s just a bad “signal.” I’ll just use these other samples where the variation of quantity is greater when price changes. Now I can produce a great “forecast” of gasoline consumption in the future. Good significance levels, too!!
Re #14
To be fair, that isn’t ALL they do with tree-rings. It isn’t all data-snooping. There is some substantial mechanistic reasoning behind the temperature response. So SOME of the cherry-picking (e.g. focus on tree-line sites) is justified. Some (chosing strip-bark bcps over whole-bark bcps) is not.
Still, Lubos’s point is a good one – and the less well understood the basic biology of the proxy, the worse the bias. (In the case of tree rings at least, the idea of a temperature response came long BEFORE the idea of AGW and the global search for a ‘fingerint’.)
#15. I’m not sure that there was a lot of looking at tree rings for temperature response prior to AGW. The earlier work seems to have focussed mostly on dating and on droughts. Jacoby seems to have been one of the first guys to try to connect tree rings to temperature; Hughes and Briffa took it up. This work was immediately inhaled by Bradley and Jones.
Earlier work on trees was much more interested in tree line changes (Lamb, Bryson) – a theme which was disparaged by the Team.
Re #16 Agreed, but my assertion is that the mechanistic basis for thinking of treeline environments as being temperature-limiting to annual tree growth is far older than even Douglas. (Maybe even thousands of years old?) Whereas the scientific basis for temperature responses in these other proxies is a much more recent development.
#16
This change is reflected in the change of titles; in the early days it was refered to as dendrochronology. The term dendroclimatology came in later as you affirm. The study of droughts by people like Douglas are properly climate related and likely a more reasonable use of tree ring information as the extended debate about what the growth rings are reflecting on this blog attests. Certainly M. L. Parker’s studies showing the major predictor of growth was the amount of precipitation in the months of September and October of the preceding year. There were others working early in the field of temperature reconstruction from tree rings include Josza and Parker. Peter Scott at the University of Toronto attempted to deal with the problem of varying factors during the entire growth, possibly an early attempt to deal with the divergence problem, took cores at fixed intervals all the way up several trees.
Re #18
This only serves to emphasize the point that if you want to make cherry pie (temperature reconstruction) then you need to pick cherries (positive reponders). IOW the issue is not whether or not cherry-picking is justifiable; it’s necessary! The issue is whether the fruit that have been picked for the cherry pie are in fact ripe cherries.
Re 19; It’s a nice analogy, but the problem would appear to come in when you are picking something that appears to be cherries, but aren’t. No matter how well you pick and how carefully you make the pie, it will not be cherry pie. It seems that many if not most of the tree rings are not really a temperature proxy. Murray
Re #19 Exactly. [The analogy, not mine, has a delicious history.]
There is still a problem, Bender, concerning your statement in #19 that you “need to pick responders.” By choosing those that respond clearly to temp while leaving out those that have only a weak response, you are influencing the coefficient that you will get that relates ring size to temp.
Since you are testing the relationship on recent history where temps are available and then using the resulting coefficient to reconstruct earlier climate history, you will only get the “right” result if the “good-responders” in your sample respond ONLY to temp. Otherwise, you need to use all possible ring samples to “average out” the varying effects of other influences on growth. If you only choose the strong responders (i.e. those that had good temp responses in recent years) you will over-estimate temp response, because what you did was to choose those rings where other factors worked in the same direction as temp, while omitting those rings where other factors worked to offset the effect of temp on the rings.
Bender: Don’t most statistical operations still depend on random sampling of a population? How are you going to have a random sample, if you are picking cherries in the manner you describe. I suppose you might say, ‘the population consists of those trees that exhibit a response to temperature.’ But how would you ever know that the response was not to one of many other variables? I still fail to see how tree rings can possibly be used as temperature proxies (even without the non-linearity problems).
Re #22
No. Remember the question is not “how do tree rings in general respond to temperature?” but “what was past temperature?”. The bias you describe will affect the legitimacy of the first inference, not the second. As far as the second is concerned, linear regression will actually UNDERESTIMATE the true temperature response. (That is why it is reasoanble to posit that the MWP was swallowed up by reconstruction bias.)
Re: #24. Right, Bender, I had the effect backwards. As you say, picking only strong response trees today will underestimate temps in the past. So then I don’t understand your statement that you have to cherry pick. Why should you not include all trees in your estimate because of the variation of the other factors as I said above, except with your correction?
Re #23
The purpose of random sampling is to make an unbiased inference about a population. Why would you insist that that population include individuals that are incapable of bearing on the question of past temperature?
Get a grip, people. You are throwing the baby out with the bathwater, and there is no need to do this. The proxies are crappy enough on their own (huge uncertainty) that you don’t need to dismiss them – at least not using silly arguments like this that misrepresent the theory and practice of temperature reconstruction.
I am not going to reply to any more dendro questions if they’ve already been covered in past threads. I’m not the dendro whipping boy. I’ve read ~50% of the entire blog now – and so can you.
I’ve read it all.
Great. By “read” I mean “understand” and “retain”.
How much have you read, by the dictionary definition?
Re #25
If I wanted to know the temperature outside, would you insist I sample from all the thermocouples in my lab, including the broken ones that give variable readings that I keep in the junk drawer, or could I restrict myself to the subset that we keep in good working order that all give the same reading? Surely we can agree on the latter?
Think about it. Think about the relationship between “samples”, “populations”, and “scope of inference”. Last post.
Re #29 More to the point, how much have you read by my defintion?
(Careful, now, we don’t want another incident.)
I will answer if you answer my question first. I want to see if your percentage is the same by both definitions. Mine is certainly not.
I asked first.
Re #29
Hard to say. There’s so much. The dictionary definition of “read” is irrelevant when it comes to the blogosphere. I intermittently & selectively scan, scrutinize, skip, revisit. It’s not at all like “reading” a book. Those caveats aside, I’d say I’ve “read” something between two thirds and four fifths. Scrutinized half.
26:
I agree with this. But you go too far. You have to define the population, before you can sample it and make inferences about it. Let’s say the population is ring widths of all trees at or near treeline. Now, if you sample from these trees, don’t look at the cores, and then make inferences, fine. But if you sample from the population, inspect the cores and throw away any of those that show a negative response (that is, cherry-pick only those that exhibit your bias), bad boy.
I think it is easier to say how much one has read by the dictionary definition (like reading a book). By that definition, I’ve read it all. I am kind of a glutton though. I got really hooked on the thing and then went over the thing from back to front methodically.
Your definition (understood and retained) is a tougher standard of course, so the amount will be less. Obviously reading > understanding > retention. I think it’s impossible to understand and retain something, you have not read unless you were one of those guys that could study by sleeping with the book under the pillow and it actually worked for you! I think also it is very unlikely that you can retain what you don’t understand unless you have a Rainman-like mind for trivial detail. Even what you do understand, you may not retain, just because of running out of brain cells or Tanqueray or whatever.
As a student (or a teacher), I’ve always found that being engaged helps drive understanding and retention. Do rather then show, show rather then tell, etc. With that in mind, I have made comments on almost every thread. Many of the comments are either critical or naively questioning. For me, I find that, this helps focus my mind on understanding the basic material, because one needs to have some comprehension to ask furthering questions.
With that: I think that I understood none of the vector algebra equations (have admitted that before). On the other stuff, I usually have a feel for what is going on (maybe from all the exposure here, or from just having a generalist technical mind) so that I have some conceptual understanding that AR has some understandable analogies with reservoir filling or stock market price variation. I don’t have a solid theoretical stats background and don’t remember the formulas for even the basic stuff like standard deviations and such that everyone has had in school. But I think I usually understand enough to get the implications, nonetheless. (like that standard deviation gives a feel for how broad the distribution is). I generate a lot of anger because of lumbering around like an idiot asking relevant, even critical questions with wrong terminology, but what can I do. I’m not going to leave unless asked by Steve and am too lazy to self-study. Rather have you all spoon-feed me. I think it is interesting (and a bit humanly understandable) that when I push some relevant critical point, that someone may get mad at me and tell me to go study the basics. And that anger is not always just from the butchering of basics, but is also from the contrarian questions themselves. I’ve had experiences like at Chefen’s blog, where he was incredibly angry at me, for not knowing the basics, not “rating” the right to make comments or ask questions and then having him admit that I raised a relevant concern that fundamentally affected an analysis and inference. I’m sorry to be tedious (and can understand how that could annoy those that know things gnat’s ass), but as long as this site is open to public lay comentary/discussion, I plan to continue to participate by asking “dumb questions”.
On the more basic (non-vector algebra stuff), I find that my understanding has grown over time as there were some concepts I did not understand immmediately and as Steve has had some gaps in his explication. In some cases, my opinion has changed on something as a result of re-reading it after some time had passed. For instance, my view of Huybers critique was rather biased as a result of reading Steve’s 3 posts on the subject first and as a result of not knowing what correlation and covariance matrix meant. But when I went back a while later and reread Huyber’s paper, I saw several good things about it in terms of describing issues and got a different picture of Steve being overly defensive and not conceding points on issues where there was agreement, to the extent that he could/should as a Feynman-ideal scientist. But that sort of change in understanding can happen in any new field as you get smarter. When you go back and reread papers, you get more out of them, find that some aspects you thought were bogus are not and visa versa.
Sorry for the wandering answer, but I guess if you put a gun to my head and ask for a number and don’t indict me for being immodest or hold me to too tough of a standard, I would say understand 80%. Retain 50%. With the VS06 paper that I wrote a long review on, I thought I got about 80-85% of it (the main gaps being matrix algebra, where I can’t follow the equations and a few other gaps where I have a general idea of a term, but don’t understand it gnat’s ass. Like I know now that normal and guassian are sorta somehow similar terms, but I had a mistaken impression that they meant the same thing.
So what numerical percent of the blog have you read in the sense of “reading a book” and what percent have you understood and what percent retained?
Disregard last para. We cross posted.
Re #35
The reality, jae, is that the hypothesis evolved as the samples trickled in over the years. The reality is that that’s the way most natural science works. Ecology is the poor man’s science. It costs alot to develop a hypothesis and test it in the way Popper implores us to: conjecture, then test. The conjecture and refutation co-evolve in baby steps as the conjecture is continually being tweaked.
I agree completely that there is a significant problem in defining exactly what the population of responders are. But it is not “going too far” to conjecture/suppose/assume that treeline trees are likely to have temperature-limited annual growth rates. It is not “going too far” to suppose that some species and forms are more responsive than others. What *is* going too far is saying that strip-bark bcps in CA are a good global temperature proxy.
You are the one going too far in suggesting that all trees should be sampled randomly to get a representative sample of the whole population of all species & forms. But if you’re back-pedaling now, well, ok – it matters not how we got here, only that we’re here.
This has all been discussed so many times before. Let’s not do it again.
TCO, there’s a limit to the time that people can spend on things with you and people here are pretty generous with their time. You’ve been getting overly portentous on topics where you’ve only grasped part of the issue, but think that you’ve grasped it all. Comments not just from myself, but from some of the top commenters here (bender, James Lane, Jean S) should make you stop and think about the possibility that you’ve only grasped part of the matter before making pronouncements. You should also allow for the fact that people here, including myself, have other obligations and are not always going to be at your beck and call to explain things more than they have already, especially when you make a lot of demands.
Re #25
Maybe I was a little hasty to dispense with your statement here, John Hekman. If by “all trees” you mean all individuals that belong to the population of putative responders – then you are 100% correct. You can’t argue that “treeline bcps” constitute the population of strong responders, and then go and chuck out even one of the samples you obtain. Or treeline cedars. Etc. That’s not “cherry picking” in the dendroclimatologists’ sense of the term, that’s data snooping – the most egregious kind of cherry-picking there is. It’s like those people at the strawberry patch who won’t clean out the row, but will only take the cleanest, biggest ones. That’s not defensible. You gotta take the dirty ones too.
This has been discussed before in depth at CA.
39. Steve, you’ve made that claim already and I’ve already conceded the tediousness and even the incomplete grasp of issues, but disagreed that this is the only limiter in the responses. Let’s agree to disagree. FYI: I’ve also been right on some things, too. Look at the BC06 rejection…
P.s. Martin is my pick for an expert. And Jean and Bender are not always right either. Just because they know the math and stats doesn’t mean they can’t have a flaw in an argument. Usually I’m not debating the formulas, but am asking higher level questions or making philosophical points.
Being right is good. Contributing is good. Not many do both simultaneously. There are reasons for that.
Agreed.
38: Bender, if you get time and are in the mood, I would really like to see just how you would pick chronologies to include in a temperature reconstruction study…
Re #44
I thought I just explained that.
Perhaps someone has already made this observation, but Steve’s original posting seems to have little point. As far as I can understand, Hansen et al. used Mg/Ca proxies to estimate the red curve in the figure. The important finding seems to be that the Holocene range is around 0.5 deg C, while the “instrumental period” range (1870-1900 to 2110-2005) is significantly larger.
Period.
If the Mg/Ca proxy estimate of temperature is offset from the annual average SST, this doesn’t matter — it is the RANGE which counts. Am I missing something?
Did you check the contents of the Sept 25 & 27 threads from which this one developed?
#46 Since the Mg/Ca proxies are effectively smoothed to 1000 years, the only comparable range for the instrumental period is zero.
Re #48: You’re missing the point, Chris. The scales don’t need to match to be able to compare temperatures. There is a possibility that the thousand year scale could miss some short term spikes, but this is unlikely since other data (Hansen reference 29) with a much tighter scale doesn’t show anything like that.
Re the update to the main post:
“I would presume that G ruber calcifies throughout the mixed layer in the WEP as well and that the average temperature of the WEP mixed layer (or G ruber calcification) is lower than the surface temperature as estimated by GISS SST. In any event, this is the crux of the issue and it’s ridiculous that the matter is not addressed in a PNAS article.”
I just now got a chance to go back and re-read this material. Dahl et al used core data from a second site in the eastern equatorial Pacific and made no such adjustment there (nor any reference to needing to consider one). As well, the Hansen WEP site is noted to have almost no short-term SST variation (seasonal or otherwise), which would make sense given that it’s on the equator and in the warm pool. So, unless you want to try to make a case that G. ruber Mg/Ca isn’t a reliable proxy for SST anywhere, it would appear that there’s no remaining issue.
Re 46, 48, 48, Jim B. posed an interesting question:
What you are missing is that the entire proxy data record for the full holocene consists of only
. The minimum interval between data points in the entire proxy record is 200 years, and the next smallest is 400 years, so we have to assume that the data is averaged over at least 200 years. This gives us three problems.
1) With only six data point for the entire Holocene, the odds of us happening to hit either the warmest or the coldest times are very small.
2) Even if we hit say the highest year, we are averaging it with another 199 years of cooler years, so the data would be smoothed downwards (or upwards for the coolest year). This reduces the range, even if we hit the right years.
3) You are comparing apples and oranges. If we apply the same 200 year averaging to the modern data, we get a single data point. How can we compare a single point to a range?
Finally, Hansen’s paper doesn’t say a word about comparing ranges, only about the comparison of modern temperatures to the high points of the last millyun years … which is why Steve’s point about the spice is crucial.
w.
#30
Sampling over a wider area may provide an average condition, but when you sample one tree you are determining the microclimate of that tree, which, in turn, the tree itself influences. I don’t know if it is still the case, but the World Meteorological Organization rules for studying forest climates used to require that you clear a 200 m area and place your instruments in the middle. I always thought thismeant you were measuring the climate of a forest clearing, not the forest.
I advised the forstry industry in Canada many years ago about how clear cutting changed the climate of a region, by a variety of means, not least by changing the albedo. Fly over a forest with clear cut and you can see the lighter clear cut areas. Of course, you also change the entire evapotranspiration regime. I also advised them about the problem of the increased exposure to raindrop splash impact, one of the major physical processes in soil erosion. I suggested they plant a low fast growing dark ground cover through which the new planted trees coud grow to ameliorate both problems.
There are other factors that influence growth and therefore ring patterns, but are not to my knowledge considered. These influences show up better under extreme conditons, but undoutedly subtly affect growth everywhere. Near the treeline growth pattern is dramatically affected by wind and snow cover. I have a photo I took of a tree at Churchill with no branches on the windward side as the tree is dessicated on that side by the cold very dry arctic winds. Similarly, the lower branches are bigger because they are covered and protected by snow in the winter. Right at the snowline there is a gap in the branches partly because of wind, but also because of snow abrasion and small animals browsing in the winter.
I would think these sorts of conditions were very important in high alpine sites.
Does anyone know if there is a large base of theoretical papers that back-up the use of specific proxies used as paleotemperature reconstruction? I know in my own field of research there are papers that explain and justify the methods that are used. Good such papers usually clearly define the limits of the method, what you can and cannot determine with it. Since I only ever see the papers that use these methods (because I haven’t done the searching required), my question is, does anyone know of any solid papers that perhaps define the boundaries of using proxies and back up the thinking.
The reason I ask is I typically see people here discussing the various points of the papers that use these data sets and debating their use, but I rarely see papers linked that specifically talk about the method(s) and their relative strengths and weaknesses. It seems to me that if there is something to “attack” you would direct it at that work and not the subsequent work.
#53. That’s a big problem with much of this field. Mg/Ca calibraiton of foraminifera seems a little more conscious of the need to establish the proxies and there is a little literature which I’m familiarizing myself with. There seems to be a lot of arm-waving through these issues in tree ring studies leading to the occasional peculiar situation of equal numbers of “positive” and “negative” responders at the same site.
Jeremy, the best reference I’ve found is Dekens, Core top calibration of Mg/Ca in tropical foraminifera:Refining paleotemperature estimation. Unfortunately, it will cost you … fortunately, it’s only $9.00.
Dekens is quite detailed about the problems and error estimates of using foraminifera. The one area that seems to be lacking is much data on “downcore dissolution”, that is, the Mg tends to dissolve out of the foraminifera after they are buried. It seems to depend, as one might expect, on the chemical composition of the core, and does not seem to be well studied.
w.
Willis, looking at the Dekens article, one notes that carbonate concentration appears to affect what they are measuring. There has also been recent concern expressed (for example at a http://www.oar.noaa.gov/spotlite/archive/spot_gcc.html regarding possible effects of increased atmospheric CO2 on carbonate concentrations in the ocean.
One also wonders whether any possible difference in growth rates of the different foraminifera also affects the Mg/Ca ratio they are measuring. In turn, since these are photosynthetic organisms, one wonders whether CO2 concentrations in the atmosphere (and dissolved CO2 in the ocean) affects the relative foraminifera growth rates.
Putting these concerns together, do we need to worry about direct effects of CO2 on the measured ratio apart from any indirect effect operating via SST? I could not see how the measurements in the Dekens paper would allow one to correct for such effects, especially since they are made at just one point in time and thus for one atmospheric concentration level of CO2.
Might it be possible that increased CO2 directly raises the modern Mg/Ca ratio regrdless of any effect on SST? If so, would that not bias downward the implied temperatures from previous eras?
Slight correction to #56
I notice on looking again that the measurements were taken over a 2-year period. Nevertheless, that would not be long enough to tease out any possible effects like I am concerned about.
I’ve been looking at the Dekens article as well and plan to post on it. The Mg/Ca dissolution at Ongong Java Plateau is very intense at depth. Dekens Lea et al propose an adjustment for this effect, presumably superceding the formula in Lea et al 2000 which made no adjustment, but then Medina-Elizalde and Lea 2005 (which seems to be used in Hansen et al 2006) reverts back to the 2000 formula. Guess which one runs paleo warmer?
The adjustment looks peculiar as it appears that Mg is completely dissolved from G. ruber at OJP below 3500 m, but this isn’t reflected in the formula. More on this tomorrow.
I sent the following email to David Lea yesterday:
Some months ago, after a AGW construction was demolished, Steve Bloom wrote words to the effect “not to worry, the foramins are coming”. Are these the “foramins” that he was referring to? If so, his presience demonstrates impeccable connections to the dark side.
I’m watching to see if characters named Steve, bender and Willis end up here in Act III or IV.
53:
I doubt there is much “basic science” out there. Just like the tree ring fiasco. That is the main trouble with “paleoreconstructions,” nice hypotheses and no real empirical backup. I think “arm waving” is the term that is used on this blog.
0.3°N, 159.4°E
2.7°N, 78.0°E
2°N, 91°W
0.5°N, 92°W
And of course icecore Vostok.
Is there any compelling reason to believe what these six researchers are telling us about the entire planet? No. cause many other single-point measurements suggest something very different.
Let’s take a look at e.g Petit & etc 1999 (“Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica).
“the Holocene, which has already lasted 11,000 years, is, by far, the longest stable warm period recorded in Antarctica during the past 420,000 years,” (2) “the climate record makes it unlikely that the West Antarctic ice sheet collapsed during the past 420,000 years,” (3) “during glacial inception … the CO2 decrease lags the temperature decrease by several thousand years,” and (4) “the same sequence of climate forcing operated during each termination: orbital forcing followed by two strong amplifiers, greenhouse gases acting first, then deglaciation and ice-albedo feedback.”
They also note that the interglacials preceding and following the one at 238,000 years ago were warmer still. In fact, from the graphs they present, it can be seen that all of the four interglacials that preceded the Holocene were warmer than the current one, and by an average temperature in excess of 2°C.
—————
Surely little bit different with Hansen’s data?