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The German defense minister, Karl-Theodor zu Guttenberg, has resigned following the exposure of plagiarism on a massive scale in his PhD dissertation.  The figure above shows the results of a page-by-page Wiki effort to "audit" his dissertation.  The black and red colors indicate text that was directly (black) or partially (red) copied from other sources.  The white parts were judged OK and the blue represents the front and back matter.

Guttenberg's defense of his actions, which were supported by Chancellor Angela Merkel, sought to focus attention on those critiquing him in an effort to downplay the significance of the academic misconduct

But in the end, it appears that the presures brought to bear from Germany's substantial academic community made continuation for Guttenberg impossible

we need to understand the magnitude and characteristics and causes of natural climate variability over the current interglacial, particularly the last 2000 years.  I’m more interested in the handle than the blade of the hockey stick.  I also view understanding regional climate variations as much more important than trying to use some statistical model to create global average anomalies (which I personally regard as pointless, given the sampling issue).

I am really hoping that the AR5 will do a better job of providing a useful analysis and assessment of the paleodata for the last millennium.  However I am not too optimistic. There was another Workshop in Lisbon this past year (Sept 2010), on the Medieval Warm Period.  The abstracts for the presentations are found here.  No surprises, many of the usual people doing the usual things.

This raises the issue as to whether there is any value at all in the tree ring analyses for this application, and whether these paleoreconstructions can tell us anything.  Apart from the issue of the proxies not matching the observations from the current period of warming (which is also the period of best historical data), there is the further issue as to whether these hemispheric or global temperature analyses make any sense at all because of the sampling issue.  I am personally having a difficult time in seeing how this stuff has any credibility at the level of “likely” confidence levels reported in the TAR and AR4.

What happens if you weight the land surface record to account for this bias? Their preliminary result (which they emphasize is preliminary) is that land surface trends would actually increase if properly weighted. If this is the case then it potentially presents a headache for the climate modeling community because it would exacerbate the divergence between land surface and tropospheric trends that we documented in Klotzbach et al. 2009 (see this, this, and this).

My favorite climate scientist and several of his colleagues have a new paper out on global land surface temperature trends (Montandon et al. 2011).  They perform an interesting analysis in asking the degree to which the spatial distribution of land surface stations is representative of land surface types found on Earth. They find that the major surface temperature records (i.e., NCDC, GISS, CRU, GHCN) are not spatially representative (see their Figure 2 above).

The authors of the new paper point out just how hard it is to study languages. We're aware of over 7,000 of them, and they vary significantly in complexity. There are a number of large language families that are likely derived from a single root, but a large number of languages don't slot easily into one of the major groups. Against that backdrop, even a set of simple structural decisions—does the noun or verb come first? where does the preposition go?—become dizzyingly complex, with different patterns apparent even within a single language tree.

Linguists, however, have been attempting to find order within the chaos. Noam Chomsky helped establish the Generative school of thought, which suggests that there must be some constraints to this madness, some rules that help make a language easier for children to pick up, and hence more likely to persist. Others have approached this issue via a statistical approach (the authors credit those inspired by Joseph Greenberg for this), looking for word-order rules that consistently correlate across language families. This approach has identified a handful of what may be language universals, but our uncertainty about language relationships can make it challenging to know when some of these are correlations are simply derived from a common inheritance.

For anyone with a biology background, having traits shared through common inheritance should ring a bell. Evolutionary biologists have long been able to build family trees of related species, called phylogenetic trees. By figuring out what species have the most traits in common and grouping them together, it's possible to identify when certain features have evolved in the past. In recent years, the increase in computing power and DNA sequences to align has led to some very sophisticated phylogenetic software, which can analyze every possible tree and perform a Bayesian statistical analysis to figure out which trees are most likely to represent reality. By treating language features like subject-verb order as a trait, the authors were able to perform this sort of analysis on four different language families: 79 Indo-European languages, 130 Austronesian languages, 66 Bantu languages, and 26 Uto-Aztecan languages. Although we don't have a complete roster of the languages in those families, they include over 2,400 languages that have been evolving for a minimum of 4,000 years.