This week has been dominated by questions of replication and of what standards are required to serve the interests of transparency and/or science (not necessarily the same thing). Possibly a recent example of replication would be helpful in showing up some of the real (as opposed to manufactured) issues that arise. The paper I’ll discuss is one of mine, but in keeping with our usual stricture against too much pro-domo writing, I won’t discuss the substance of the paper (though of course readers are welcome to read it themselves). Instead, I’ll focus on the two separate replication efforts I undertook in order to do the analysis. The paper in question is Schmidt (2009, IJoC), and it revisits two papers published in recent years purporting to show that economic activity is contaminating the surface temperature records – specifically de Laat and Maurellis (2006) and McKitrick and Michaels (2007).
Antarctic warming is robust
The difference between a single calculation and a solid paper in the technical literature is vast. A good paper examines a question from multiple angles and find ways to assess the robustness of its conclusions to all sorts of possible sources of error — in input data, in assumptions, and even occasionally in programming. If a conclusion is robust over as much of this as can be tested (and the good peer reviewers generally insist that this be shown), then the paper is likely to last the test of time. Although science proceeds by making use of the work that others have done before, it is not based on the assumption that everything that went before is correct. It is precisely because that there is always the possibility of errors that so much is based on ‘balance of evidence’ arguments’ that are mutually reinforcing.
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Warm reception to Antarctic warming story
What determines how much coverage a climate study gets?
It probably goes without saying that it isn’t strongly related to the quality of the actual science, nor to the clarity of the writing. Appearing in one of the top journals does help (Nature, Science, PNAS and occasionally GRL), though that in itself is no guarantee. Instead, it most often depends on the ‘news’ value of the bottom line. Journalists and editors like stories that surprise, that give something ‘new’ to the subject and are therefore likely to be interesting enough to readers to make them read past the headline. It particularly helps if a new study runs counter to some generally perceived notion (whether that is rooted in fact or not). In such cases, the ‘news peg’ is clear.
And so it was for the Steig et al “Antarctic warming” study that appeared last week. Mainstream media coverage was widespread and generally did a good job of covering the essentials. The most prevalent peg was the fact that the study appeared to reverse the “Antarctic cooling” meme that has been a staple of disinformation efforts for a while now.
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CNN is spun right round, baby, right round
With the axing of the CNN Science News team, most science stories at CNN are now being given to general assignment reporters who don’t necessarily have the background to know when they are being taken for a ride. On the Lou Dobbs show (an evening news program on cable for those of you not in the US), the last few weeks have brought a series of embarrassing non-stories on ‘global cooling’ based it seems on a few cold snaps this winter, the fact that we are at a solar minimum and a regurgitation of 1970s vintage interpretations of Milankovitch theory (via Pravda of all places!). Combine that with a few hysterical (in both senses) non-scientists as talking heads and you end up with a repeat of the nonsensical ‘Cooling world’ media stories that were misleading in the 1970s and are just as misleading now.
Exhibit A. Last night’s (13 Jan 2009) transcript (annotations in italics).
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FAQ on climate models: Part II
This is a continuation of a previous post including interesting questions from the comments.
More Questions
- What are parameterisations?
Some physics in the real world, that is necessary for a climate model to work, is only known empirically. Or perhaps the theory only really applies at scales much smaller than the model grid size. This physics needs to be ‘parameterised’ i.e. a formulation is used that captures the phenomenology of the process and its sensitivity to change but without going into all of the very small scale details. These parameterisations are approximations to the phenomena that we wish to model, but which work at the scales the models actually resolve. A simple example is the radiation code – instead of using a line-by-line code which would resolve the absorption at over 10,000 individual wavelengths, a GCM generally uses a broad-band approximation (with 30 to 50 bands) which gives very close to the same results as a full calculation. Another example is the formula for the evaporation from the ocean as a function of the large-scale humidity, temperature and wind-speed. This is really a highly turbulent phenomena, but there are good approximations that give the net evaporation as a function of the large scale (‘bulk’) conditions. In some parameterisations, the functional form is reasonably well known, but the values of specific coefficients might not be. In these cases, the parameterisations are ‘tuned’ to reproduce the observed processes as much as possible.
2008 temperature summaries and spin
The great thing about complex data is that one can basically come up with any number of headlines describing it – all of which can be literally true – but that give very different impressions. Thus we are sure that you will soon read that 2008 was warmer than any year in the 20th Century (with the exception of 1998), that is was the coolest year this century (starting from 2001), and that 7 or 8 of the 9 warmest years have occurred since 2000. There will undoubtedly also be a number of claims made that aren’t true; 2008 is not the coolest year this decade (that was 2000), global warming hasn’t ‘stopped’, CO2 continues to be a greenhouse gas, and such variability is indeed predicted by climate models. Today’s post is therefore dedicated to cutting through the hype and looking at the bigger picture.
Ozone holes and cosmic rays
Browsing through the blogosphere recently, I came across an interesting little story about the scientific method, scientific progress, and un-scientific spin (h/t Hank Roberts). The subject concerns the polar ozone hole in Antarctica and a possible role for cosmic rays in its variability on solar cycle timescales. The proponents of this link are a small research group at the University of Sherbrooke in Canada, who find themselves up against the mainstream stratospheric chemistry community and whose ideas are twisted out of all recognition by the more foolish of the usual suspects.
Contrarians and consensus: The case of the midwife toad
I recently came across an old copy of Arthur Koestler’s “The Case of the Midwife Toad”. Originally published in 1971, it’s an exploration of a rather tragic footnote in the history of evolutionary science. Back in the early years of the 20th Century (prior to the understanding of DNA, but after Mendelian genetics had become well known), there was still a remnant of the biological community who preferred the Lamarckian idea of the inheritance of acquired characteristics over the Darwinian idea of natural selection of random mutations. One of the vanguard for the Lamarckian idea was Paul Kammerer whose specialty was the breeding of amphibians that apparently few others could match. He claimed that he could get his toads and salamanders to acquire characteristics that were useful in the new environments in which he raised his specimens. This was touted loudly (in the New York Times for instance) as proof of Lamarckian inheritance and Kammerer was hailed as a ‘new Darwin’. It all ended very badly when one toad specimen was found to be faked (by who remains a mystery), and Kammerer killed himself shortly afterwards (though there may have been more involved than scientific disgrace).
The details of the experiments and controversy can be read online (with various slants) here and here, and a more modern non-replication of one of his experiments is described here. However, the reason I bring this up here is much more related to how the scientific community and Koestler dealt with this scientific maverick and the analogies that has for the climate science and its contrarians.
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Why don’t op-eds get fact checked?
Debra Saunders is a conservative columnist for the San Francisco Chronicle who has a history of writing misleading contrarian pieces on climate change. She contacted NASA Public Affairs recently for a comment on the initial glitch on the October GHCN numbers (see this earlier post for discussions of that). They forwarded the query to me and since her questions were straightforward, I answered them as best I could. Indeed in her subsequent column, she quotes me accurately and in context. However, the rest of her column shows none of the same appreciation for basic journalistic standards.
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Mountains and molehills
As many people will have read there was a glitch in the surface temperature record reporting for October. For many Russian stations (and some others), September temperatures were apparently copied over into October, giving an erroneous positive anomaly. The error appears to have been made somewhere between the reporting by the National Weather Services and NOAA’s collation of the GHCN database. GISS, which produces one of the more visible analyses of this raw data, processed the input data as normal and ended up with an October anomaly that was too high. That analysis has now been pulled (in under 24 hours) while they await a correction of input data from NOAA (Update: now (partially) completed).