While we’re re-visiting bristlecones and foxtails, here are three interesting online articles, each of which discusses areas in the Sierra Nevada CA, which are now submerged, but where forests grew in the Medieval Warm Period. Many readers of this blog will have read articles about trees being disgorged from receding glaciers and it’s hard not to wonder about comparisons. Scott Stine, who has published in Nature about the phenomenon, has on line article here from 2001. Here is an interesting picture showing submerged medieval trees from Tenaya Lake, Yosemite National Park.
Picture from Stine article: Tenaya Lake, Yosemite National Park (elev. 8,150 ft). 1,000 years ago, trees grew in then-dry Tenaya Lake. Today only their tops show: still rooted in as much as 70 feet of water. Continue reading →
Constance Millar, url who wrote an excellent article on the medieval warm period in California, discussed here has written an interesting and timely article (presently in review) on thelate 20th century in the Sierra Nevadas, entitled: Response of high-elevation limber pine (Pinus flexilis) to multi-year droughts and 20th-century warming; Sierra Nevada, California. Continue reading →
Juckes stated:
MM2003 criticise MBH1998 on many counts, some related to deficiencies in the description of the data used and possible irregularities in the data itself. These issues have been largely resolved in Mann et al. (2004) [the Corrigendum].
Did Juckes carry out any due diligence in order to make the latter statement? Because I’m not sure what issues were “resolved” in the Corrigendum. Today I’ll mention one amusing issue that definitely wasn’t resolved in the Corrigendum.
In MM03, we reported that the instrumental precipitation record for the New England gridcell used in MBH98 did not match any historical data from the area or from the citation, but did match historical precipitation from Paris, France. (“The rain in Maine falls mainly in the Seine”). In experimenting subsequent to MM03, I determined that the MBH precipitation series assigned to the South Carolina gridcell matched a series from Toulouse, France. I was unable to match the MBH Bombay gridcell series to historical data from Bombay or anywhere else, but it was somewhat similar to the series from Philadelphia.
In our Materials Complaint to Nature, we requested details on the provenance of these series. The answer in the Corrigendum was “NOAA” – nothing more. However the Corrigendum SI stubbornly retained the fiction that MBH98 proxy data included precipitation from New England and South Carolina and Bombay. I asked Nature for further particulars on the actual provenance of this data, but they refused to provide it. Maybe Juckes can resolve this conundrum. How about it, Marty? Where the hell do the MBH98 precipitation series actually come from?
This is an interesting illustration of the teleconnection principle. In Mannian statistics, incorrect geographical locations “don’t matter” because of teleconnections. Rain in Maine or rain in Spain – doesn’t matter, put them in the teleconnection machine. Assume that they are temperature plus noise. See – that was easy.
In MM05 (EE), we reviewed literature on bristlecones because these trees were supposed to be unique radio receivers for world temperature. Obviously the specialist literature stood against this proposition. We cited a number of interesting articles by Mooney in American Midland Naturalist in the 1960s – none of which are considered by Juckes in his “evaluation” of millennial reconstructions, including the following:
Even in higher stands, their [bristlecone] principal botanical competition in many locations is with big sagebrush [Wright and Mooney, 1965; Mooney et al., 1964] with bristlecones outcompeting big sagebrush on moister dolomite substrate. This effect is vividly illustrated by Figure 2 of Wright and Mooney [1965], where the sharp geological contact between the dolomite and sandstone is clearly shown by the change from bristlecone pines to sagebrush at the same elevation The same effect is also perhaps shown in the charming 19th century painting (Figure 7), where a sharp change in vegetation at the same elevation is easily observed.
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Just a quick note that I have written an overview of how the blog is setup, with indications for what people should look for if they want a similar setup for their own blog.
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Wahl and Ammann 2006 reported that they could “get” something that was sort of HS-ish without principal component analysis. It wasn’t through a simple mean or CVM; it was through Mannian inverse regression. Juckes et al shows many reconstructions using “inverse regression”, mentioning in his conclusions that inverse regression caused over-concentration on a few proxies.
we have found that inverse regression tends to give large weighting to a small number of proxies and that the relatively simple approach of compositing all the series and using variance matching to calibrate the result gives more robust estimates.
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As you can see from the plot of the Juckes’ proxies, the Yang composite is a very important contributor to the 20th century blade. The Yang Composite is a mainstay of recent Hockey Team reconstructions – its use in Team reconstructions began in Mann and Jones 2003 and was then “randomly selected” into Osborn and Briffa 2006, Hegerl et al 2006 and now Juckes et al 2006.
The two series in the Yang composite that drive its 20th century blade are versions of two Thompson series from Dunde and Guliya. The Juckes “evaluation” did not evaluate the data versions. I’ve posted up on Dunde before, pointing out inconsistencies between the different versions (the Yang version has values only at 50 -year intervals!) Today I checked out the Guliya version. In addition to the Yang version (emailed to me by Dr Yang a couple of years ago), Thompson archived the version used in his Climatic Change 2004 article (only after my intervention) and has just archived a new version in connection with his PNAS 2006 article. The three versions are plotted below – the first 2 are at 10-year resolution; the last one at 5-year resolution:
Figure 1. Three versions of Thompson’s Guliya dO18 ice core series.
There is obviously a strong visual inconsistency between the different versions. This is reflected in the correlations. The correlation between the Yang version and the Climatic Change version is only 0.06. Remarkably, the correlation between the PNAS version (converted to 10-year intervals) and the Climatic Change version from only 2 years ago is -0.005. The data was collected nearly 15 years ago.
I’ve written to Science repeatedly asking them to require Thompson to archive and reconcile these conflicting results, but have been blown off completely.
Should Juckes have evaluated the conflicting versions of Thompson data used in the Yang composite? I think so.
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When one looks at the plots of the various Juckes proxies against gridcell temperature, the possibility of spurious regression must come to mind.
“Spurious regression” has been discussed on this blog from time to time and tries to provide a statistical framework for seemingly high correlations between unrelated series – things like Honduran births and Australian wine exports. The original article on the topic by Yule in 1928 observed a correlation of something like 0.97 between alcoholism and Church of England marriages. A prominent econometrician (Hendry) observed in the early 1980s that rainfall provided an excellent statistical explanation of inflation in an interesting article “Econometrics – Alchemy or Science?” (url).
The same question – Alchemy or Science? – is surely applicable to proxymetrics. In 1974, Granger and Newbold, the former a Nobel Prize winning economist, wrote an influential article on Spurious Regression, posted up here, which I discussed last year here.
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I’ve added a “page” – see left column – with links to various proxy data collations that I’ve archived here. I’ll try to archive some read scripts at some point.
Climate Audit 