There is a new critique of IPCC climate projections doing the rounds of the blogosphere from two ‘scientific forecasters’, Kesten Green and Scott Armstrong, who claim that since the IPCC projections are not ‘scientific forecasts’ they must perforce be wrong and that a naive model of no change in future is likely to be more accurate that any IPCC conclusion. This ignores the fact that IPCC projections have already proved themselves better than such a naive model, but their critique is novel enough to be worth a mention.
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Making sense of Greenland’s ice
A widely publicised paper in Science last week discussed the recovery ancient DNA from the base of the Dye-3 ice core (in southern Greenland). This was an impressive technical feat and the DNA recovered may well be the oldest pure DNA ever, dating back maybe half a million years. However much of the press coverage of this paper dwelt not on the positive aspects of the study but on its supposed implications for the stability of the Greenland ice sheet and future sea level rise, something that was not greatly discussed in the paper at all. So why was this?
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No man is an (Urban Heat) Island
Observant readers will have noticed a renewed assault upon the meteorological station data that underpin some conclusions about recent warming trends. Curiously enough, it comes just as the IPCC AR4 report declared that the recent warming trends are “unequivocal”, and when even Richard Lindzen has accepted that globe has in fact warmed over the last century.
The new focus of attention is the placement of the temperature sensors and other potential ‘micro-site’ effects that might influence the readings. There is a possibility that these effects may change over time, putting in artifacts or jumps in the record. This is slightly different from the more often discussed ‘Urban Heat Island’ effect which is a function of the wider area (and so could be present even in a perfectly set up urban station). UHI effects will generally lead to long term trends in an affected station (relative to a rural counterpart), whereas micro-site changes could lead to jumps in the record (of any sign) – some of which can be very difficult to detect in the data after the fact.
There is nothing wrong with increasing the meta-data for observing stations (unless it leads to harassment of volunteers). However, in the new found enthusiasm for digital photography, many of the participants in this effort seem to have leaped to some very dubious conclusions that appear to be rooted in fundamental misunderstandings of the state of the science. Let’s examine some of those apparent assumptions:
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Cockburn’s form
Alexander Cockburn (writing in the Nation) has become the latest contrarian-de-jour, sallying forth with some rather novel arithmetic to show that human-caused global warming is nothing to be concerned about. This would be unworthy of comment in most cases, but Cockburn stands out as one of only a few left-wing contrarians, as opposed to the more usual right-wing variety. Casual readers may have thought this is a relatively recent obsession of his (3 articles and responses over the last month), however, Cockburn has significant form* and has a fairly long history of ill-informed commentary on the subject of global warming.
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Hansen’s 1988 projections
At Jim Hansen’s now famous congressional testimony given in the hot summer of 1988, he showed GISS model projections of continued global warming assuming further increases in human produced greenhouse gases. This was one of the earliest transient climate model experiments and so rightly gets a fair bit of attention when the reliability of model projections are discussed. There have however been an awful lot of mis-statements over the years – some based on pure dishonesty, some based on simple confusion. Hansen himself (and, for full disclosure, my boss), revisited those simulations in a paper last year, where he showed a rather impressive match between the recently observed data and the model projections. But how impressive is this really? and what can be concluded from the subsequent years of observations?
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Fun with correlations!
We are forever being bombarded with apparently incredible correlations of various solar indices and climate. A number of them came up in the excoriable TGGWS mockumentary last month where they were mysteriously ‘improved’ in a number of underhand ways. But even without those improvements (which variously involved changing the axes, drawing in non-existent data, taking out data that would contradict the point etc.), the as-published correlations were superficially quite impressive. Why then are we not impressed?
To give you an idea, I’m going to go through the motions of constructing a new theory of political change using techniques that have been pioneered by a small subset of solar-climate researchers (references will of course be given). And to make it even more relevant, I’m going to take as my starting point research that Richard Lindzen has highlighted on his office door for many years:
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Ocean Cooling. Not.
A lot has been made of a paper (Lyman et al, 2006) that appeared last year that claimed that the oceans had, contrary to expectation, cooled over the period 2003-2005. At the time, we (correctly) pointed out that this result was going to be hard to reconcile with continued increases in sea level rise (driven in large part by thermal expansion effects), and that there may still be issues with way that the new ARGO floats were being incorporated into the ocean measurement network. Now it seems as if there is a problem in the data and in the latest analysis, the cooling has disappeared.
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Learning from a simple model
A lot of what gets discussed here in relation to the greenhouse effect is relatively simple, and yet can be confusing to the lay reader. A useful way of demonstrating that simplicity is to use a stripped down mathematical model that is complex enough to include some interesting physics, but simple enough so that you can just write down the answer. This is the staple of most textbooks on the subject, but there are questions that arise in discussions here that don’t ever get addressed in most textbooks. Yet simple models can be useful there too.
I’ll try and cover a few ‘greenhouse’ issues that come up in multiple contexts in the climate debate. Why does ‘radiative forcing’ work as method for comparing different physical impacts on the climate, and why you can’t calculate climate sensitivity just by looking at the surface energy budget. There will be mathematics, but hopefully it won’t be too painful.
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A Tale of Three Interviews
The release of the IPCC Working Group II summary report (on climate change impacts) lead to a large number of stories on climate change in the media and, inevitably, lots of requests for media appearances for climate scientists on the journalists’ Rolodex. On the same day, there was a short article in Science on the ‘framing’ of science communication.
The Science piece, by Scibloggers Chris Mooney and Matt Nisbet, make the point that the way science is expressed in public makes a difference to how it is received. So much, so uncontroversial. However, it generated some trenchant counter–arguments, (and counter–counter–arguments), possibly because they start off criticising a bit of a strawman ‘scientist’ who thinks that ‘if only laypeople better understood technical complexities… controversies would subside’. It’s certainly possible that such people exist, however, they are unlikely to be found among the scientists who are active in trying to communicate to the public. However, instead of arguing about this in a rather abstract way, I thought I’d illustrate the issue by discussing three interviews I did last Thursday and Friday in relation to the IPCC WG II release.