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Instrumental Record

2008 temperature summaries and spin

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Translations: (Italian)

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.

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Mountains and molehills

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Translations: (Español)

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).

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Tropical tropospheric trends again (again)

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Translations: (Italian)

Many readers will remember our critique of a paper by Douglass et al on tropical tropospheric temperature trends late last year, and the discussion of the ongoing revisions to the observational datasets. Some will recall that the Douglass et al paper was trumpeted around the blogosphere as the definitive proof that models had it all wrong.

At the time, our criticism was itself criticised because our counterpoints had not been submitted to a peer-reviewed journal. However, this was a little unfair (and possibly a little disingenuous) because a group of us had in fact submitted a much better argued paper making the same principal points. Of course, the peer-review process takes much longer than writing a blog post and so it has taken until today to appear on the journal website.

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Hypothesis testing and long range memory

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What is the actual hypothesis you are testing when you compare a model to an observation? It is not a simple as ‘is the model any good’ – though many casual readers might assume so. Instead, it is a test of a whole set of assumptions that went into building the model, the forces driving it, and the assumptions that went in to what is presented as the observations. A mismatch between them can arise from a mis-specification of any of these components and climate science is full of examples where reported mismatches ended up being due to problems in the observations or forcing functions rather than the models (ice age tropical ocean temperatures, the MSU records etc.). Conversely of course, there are clear cases where the models are wrong (the double ITCZ problem) and where the search for which assumptions in the model are responsible is ongoing.

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Global trends and ENSO

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Translations: (Español)

It’s long been known that El Niño variability affects the global mean temperature anomalies. 1998 was so warm in part because of the big El Niño event over the winter of 1997-1998 which directly warmed a large part of the Pacific, and indirectly warmed (via the large increase in water vapour) an even larger region. The opposite effect was seen with the La Niña event this last winter. Since the variability associated with these events is large compared to expected global warming trends over a short number of years, the underlying trends might be more clearly seen if the El Niño events (more generally, the El Niño – Southern Oscillation (ENSO)) were taken out of the way. There is no perfect way to do this – but there are a couple of reasonable approaches.

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North Pole notes

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I always find it interesting as to why some stories get traction in the mainstream media and why some don’t. In online science discussions, the fate of this years summer sea ice has been the focus of a significant betting pool, a test of expert prediction skills, and a week-by-week (almost) running commentary. However, none of these efforts made it on to the Today program. Instead, a rather casual article in the Independent showed the latest thickness data and that quoted Mark Serreze as saying that the area around the North Pole had 50/50 odds of being completely ice free this summer, has taken off across the media.

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Of buckets and blogs

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This last week has been an interesting one for observers of how climate change is covered in the media and online. On Wednesday an interesting paper (Thompson et al) was published in Nature, pointing to a clear artifact in the sea surface temperatures in 1945 and associating it with the changing mix of fleets and measurement techniques at the end of World War II. The mainstream media by and large got the story right – puzzling anomaly tracked down, corrections in progress after a little scientific detective work, consequences minor – even though a few headline writers got a little carried away in equating a specific dip in 1945 ocean temperatures with the more gentle 1940s-1970s cooling that is seen in the land measurements. However, some blog commentaries have gone completely overboard on the implications of this study in ways that are very revealing of their underlying biases.

The best commentary came from John Nielsen-Gammon’s new blog where he described very clearly how the uncertainties in data – both the known unknowns and unknown unknowns – get handled in practice (read that and then come back). Stoat, quite sensibly, suggested that it’s a bit early to be expressing an opinion on what it all means. But patience is not one of the blogosphere’s virtues and so there was no shortage of people extrapolating wildly to support their pet hobbyhorses. This in itself is not so unusual; despite much advice to the contrary, people (the media and bloggers) tend to weight new individual papers that make the news far more highly than the balance of evidence that really underlies assessments like the IPCC. But in this case, the addition of a little knowledge made the usual extravagances a little more scientific-looking and has given it some extra steam.
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Tropical tropospheric trends again

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Back in December 2007, we quite heavily criticised the paper of Douglass et al (in press at IJoC) which purported to show that models and data were inconsistent when it came to the trends in the tropical troposphere. There were two strands to our critique: i) that the statistical test they used was not appropriate and ii) that they did not acknowledge the true structural uncertainty in the observations. Most subsequent discussion has been related to the statistical issue, but the second point is perhaps more important.

Even when Douglass et al was written, those authors were aware that there were serious biases in the radiosonde data (they had been reported in Sherwood et al, 2005 and elsewhere), and that there were multiple attempts to objectively address the problems and to come up with more homogeneous analyses. We mentioned the RAOBCORE project at the time and noted the big difference using their version 1.4 vs 1.2 made to the comparison (a difference nowhere mentioned in Douglass et al’s original accepted paper which only reported on v1.2 despite them being aware of the issue). However, there are at least three new papers in press that independently tackle the issue, and their results go a long towards addressing the problems.
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Uncertainty, noise and the art of model-data comparison

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Translations: (Español) (Chinese (simplified))

Gavin Schmidt and Stefan Rahmstorf

John Tierney and Roger Pielke Jr. have recently discussed attempts to validate (or falsify) IPCC projections of global temperature change over the period 2000-2007. Others have attempted to show that last year’s numbers imply that ‘Global Warming has stopped’ or that it is ‘taking a break’ (Uli Kulke, Die Welt)). However, as most of our readers will realise, these comparisons are flawed since they basically compare long term climate change to short term weather variability.

This becomes immediately clear when looking at the following graph:

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