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about Gavin Schmidt

Gavin Schmidt is a climate modeler, working for NASA and with Columbia University.

Who ya gonna call?

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Gavin Schmidt and Michael Mann

Scientific theories gain credence from successful predictions. Similarly, scientific commentators should gain credibility from whether their comments on new studies hold up over time. Back in 2005 we commented on the Bryden et al study on a possible ongoing slowdown in the North Atlantic overturning circulation. In our standard, scientifically cautious, way we said:

… it might be premature to assert that the circulation definitely has changed.

Our conclusion that the Bryden et al result ‘might be premature’ was based on a balance of evidence argument (or, since we discussed this a few days ago, our Bayesian priors) for what the consequences of such a slowdown would be (a (unobserved) cooling in the North Atlantic). We also reported last year on some data that would likely help assess the uncertainty.

Well, now that data has been properly published (reported here) and it confirms what we thought all along. The sampling variability in the kind of snapshot surveys that Bryden et al had used was too large for the apparent trends that they saw to be significant (which the authors had correctly anticipated in the original paper though).

Score one for Bayesian priors.

Musings about models

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With the blogosphere all a-flutter with discussions of hundredths of degrees adjustments to the surface temperature record, you probably missed a couple of actually interesting stories last week.

Tipping points

Oft-discussed and frequently abused, tipping points are very rarely actually defined. Tim Lenton does a good job in this recent article. A tipping ‘element’ for climate purposes is defined as

The parameters controlling the system can be transparently combined into a single control, and there exists a critical value of this control from which a small perturbation leads to a qualitative change in a crucial feature of the system, after some observation time.

and the examples that he thinks have the potential to be large scale tipping elements are: Arctic sea-ice, a reorganisation of the Atlantic thermohaline circulation, melt of the Greenland or West Antarctic Ice Sheets, dieback of the Amazon rainforest, a greening of the Sahara, Indian summer monsoon collapse, boreal forest dieback and ocean methane hydrates.

To that list, we’d probably add any number of ecosystems where small changes can have cascading effects – such as fisheries. It’s interesting to note that most of these elements include physics that modellers are least confident about – hydrology, ice sheets and vegetation dynamics.

Prediction vs. Projections

As we discussed recently in connection with climate ‘forecasting‘, the kinds of simulations used in AR4 are all ‘projections’ i.e. runs that attempt to estimate the forced response of the climate to emission changes, but that don’t attempt to estimate the trajectory of the unforced ‘weather’. As we mentioned briefly, that leads to a ‘sweet spot’ for forecasting of a couple of decades into the future where the initial condition uncertainty dies away, but the uncertainty in the emission scenario is not yet so large as to be dominating. Last week there was a paper by Smith and colleagues in Science that tried to fill in those early years, using a model that initialises the heat content from the upper ocean – with the idea that the structure of those anomalies control the ‘weather’ progression over the next few years.

They find that their initialisation makes a difference for a about a decade, but that at longer timescales the results look like the standard projections (i.e. 0.2 to 0.3ºC per decade warming). One big caveat is that they aren’t able to predict El Niño events, and since they account for a great deal of the interannual global temperature anomaly, that is a limitation. Nonetheless, this is a good step forward and people should be looking out for whether their predictions – for a plateau until 2009 and then a big ramp up – materialise over the next few years.

Model ensembles as probabilities

A rather esoteric point of discussion concerning ‘Bayesian priors’ got a mainstream outing this week in the Economist. The very narrow point in question is to what extent model ensembles are probability distributions. i.e. if only 10% of models show a particular behaviour, does this mean that the likelihood of this happening is 10%?

The answer is no. The other 90% could all be missing some key piece of physics.

However, there has been a bit of confusion generated though through the work of climateprediction.net – the multi-thousand member perturbed parameter ensembles that, notoriously, suggested that climate sensitivity could be as high as 11 ºC in a paper a couple of years back. The very specific issue is whether the histograms generated through that process could be considered a probability distribution function or not. (‘Not’ is the correct answer).

The point in the Economist article is that one can demonstrate that very clearly by changing the variables you are perturbing (in the example they use an inverse). If you evenly sample X, or evenly sample 1/X (or any other function of X) you will get a different distribution of results. Then instead of (in one case) getting 10% of models runs to show behaviour X, now maybe 30% of models will. And all this is completely independent of any change to the physics.

My only complaint about the Economist piece is the conclusion that, because of this inherent ambiguity, dealing with it becomes a ‘logistical nightmare’ – that’s is incorrect. What should happen is that people should stop trying to think that counting finite samples of model ensembles can give a probability. Nothing else changes.

1934 and all that

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Another week, another ado over nothing.

Last Saturday, Steve McIntyre wrote an email to NASA GISS pointing out that for some North American stations in the GISTEMP analysis, there was an odd jump in going from 1999 to 2000. On Monday, the people who work on the temperature analysis (not me), looked into it and found that this coincided with the switch between two sources of US temperature data. There had been a faulty assumption that these two sources matched, but that turned out not to be the case. There were in fact a number of small offsets (of both sign) between the same stations in the two different data sets. The obvious fix was to make an adjustment based on a period of overlap so that these offsets disappear.

This was duly done by Tuesday, an email thanking McIntyre was sent and the data analysis (which had been due in any case for the processing of the July numbers) was updated accordingly along with an acknowledgment to McIntyre and update of the methodology.

The net effect of the change was to reduce mean US anomalies by about 0.15 ºC for the years 2000-2006. There were some very minor knock on effects in earlier years due to the GISTEMP adjustments for rural vs. urban trends. In the global or hemispheric mean, the differences were imperceptible (since the US is only a small fraction of the global area).

There were however some very minor re-arrangements in the various rankings (see data [As it existed in Sep 2007]). Specifically, where 1998 (1.24 ºC anomaly compared to 1951-1980) had previously just beaten out 1934 (1.23 ºC) for the top US year, it now just misses: 1934 1.25ºC vs. 1998 1.23ºC. None of these differences are statistically significant. Indeed in the 2001 paper describing the GISTEMP methodology (which was prior to this particular error being introduced), it says:

The U.S. annual (January-December) mean temperature is slightly warmer in 1934 than in 1998 in the GISS analysis (Plate 6). This contrasts with the USHCN data, which has 1998 as the warmest year in the century. In both cases the difference between 1934 and 1998 mean temperatures is a few hundredths of a degree. The main reason that 1998 is relatively cooler in the GISS analysis is its larger adjustment for urban warming. In comparing temperatures of years separated by 60 or 70 years the uncertainties in various adjustments (urban warming, station history adjustments, etc.) lead to an uncertainty of at least 0.1°C. Thus it is not possible to declare a record U.S. temperature with confidence until a result is obtained that exceeds the temperature of 1934 by more than 0.1°C.

More importantly for climate purposes, the longer term US averages have not changed rank. 2002-2006 (at 0.66 ºC) is still warmer than 1930-1934 (0.63 ºC – the largest value in the early part of the century) (though both are below 1998-2002 at 0.79 ºC). (The previous version – up to 2005 – can be seen here).

In the global mean, 2005 remains the warmest (as in the NCDC analysis). CRU has 1998 as the warmest year but there are differences in methodology, particularly concerning the Arctic (extrapolated in GISTEMP, not included in CRU) which is a big part of recent global warmth. No recent IPCC statements or conclusions are affected in the slightest.

Sum total of this change? A couple of hundredths of degrees in the US rankings and no change in anything that could be considered climatically important (specifically long term trends).

However, there is clearly a latent and deeply felt wish in some sectors for the whole problem of global warming to be reduced to a statistical quirk or a mistake. This led to some truly death-defying leaping to conclusions when this issue hit the blogosphere. One of the worst examples (but there are others) was the ‘Opinionator’ at the New York Times (oh dear). He managed to confuse the global means with the continental US numbers, he made up a story about McIntyre having ‘always puzzled about some gaps’ (what?) , declared the the error had ‘played havoc’ with the numbers, and quoted another blogger saying that the ‘astounding’ numbers had been ‘silently released’. None of these statements are true. Among other incorrect stories going around are that the mistake was due to a Y2K bug or that this had something to do with photographing weather stations. Again, simply false.

But hey, maybe the Arctic will get the memo.

Ozone impacts on climate change

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In a nice example of how complicated climate feedbacks and interactions can be, Sitch and colleagues report in Nature advance publication on a newly modelled effect of ground level (or tropospheric) ozone on carbon uptake on land (BBC). The ozone they are talking about is the ‘bad’ ozone (compared to ‘good’ stratospheric ozone) and is both a public health hazard and a greenhouse gas. Tropospheric ozone isn’t directly emitted by human activity, but is formed in the atmosphere as a result of photolytic reactions related to CH4, CO, NOx and VOCs (Volatile Organic Compounds like isoprene, benzene etc.) – the so-called ozone precursors.

It’s well known that increased ozone levels – particularly downwind of cities – can be harmful to plants, and in this new study with a carbon-climate model, they quantify how by how much increasing ozone levels make it more difficult for carbon to be sequestered by the land biosphere. This leads to larger CO2 levels in the atmosphere than before. Hence the ozone has, as well as its direct effect as a greenhouse gas, an indirect effect on CO2, which in this model at least appears to be almost as large.

Actually it’s even more complicated. Methane emissions are one of the principal causes of the rise of ozone, and the greenhouse effect of ozone can be thought of as an indirect effect of CH4 (and CO and VOCs). But while NOx is an ozone precursor, it actually has an indirect effect that reduces CH4, so that the net impact of NOx has been thought to be negative (i.e. the reduction in CH4 outweighs the increase of ozone in radiative forcing – see this paper for more details). This new result might prompt a re-adjustment of that balance – i.e. if the ozone produced by NOx has a stronger effect than previously thought (through this new indirect mechanism), than it might outweigh the reduction in CH4, and lead to NOx emissions themselves being a (slightly) positive forcing.

In a bizarre way this is actually good news. There are plenty of reasons to reduce NOx emissions already because of it’s impact on air pollution and smog, but this new result might mean that reductions wouldn’t make climate change any worse. It also, once again, highlights the role of CH4 (the second biggest GHG forcing), and points out a further reason (if that was required) why further methane reductions could be particularly welcome in moderating future changes in climate and air quality.

Green and Armstrong’s scientific forecast

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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.
[Read more…] about Green and Armstrong’s scientific forecast

Making sense of Greenland’s ice

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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?
[Read more…] about Making sense of Greenland’s ice

No man is an (Urban Heat) Island

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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:
[Read more…] about No man is an (Urban Heat) Island

Cockburn’s form

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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.
[Read more…] about Cockburn’s form

Hansen’s 1988 projections

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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?
[Read more…] about Hansen’s 1988 projections