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about Gavin Schmidt

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

A barrier to understanding?

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People don’t seem to embrace global measures of temperature rise (~0.2ºC/decade) or sea level rise (> 3mm/yr) very strongly. They much prefer more iconic signs – The National Park formerly-known-as-Glacier, No-snows of Kilimanjaro, Frost Fairs on the Thames etc. As has been discussed here on many occasions, any single example often has any number of complicating factors, but seen as part of a pattern (Kilimanjaro as an example of the other receding tropical glaciers), they can be useful for making a general point. However, the use of an icon as an example of change runs into difficulty if it is then interpreted to be proof of that change.

With respect to sea level, the Thames Barrier is a concrete example that has been frequently raised.
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Past reconstructions: problems, pitfalls and progress

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Many people hold the mistaken belief that reconstructions of past climate are the sole evidence for current and future climate change. They are not. However, they are very interesting and useful for all sorts of reasons: for modellers to test out theories of climate change, for geographers, archaeologists and historians to examine the impact of climate on past civilizations and ecosystems, and for everyone to get a sense of what climate is capable of doing, how fast it does it and why.

As a small part of that enterprise, the climate of the medieval period has received a very high (and sometimes disproportionate) profile in the public discourse – due in no small part to the mistaken notion that it is an important factor for the attribution of current climate change. Its existence as a period of generally warmer temperatures (at least in the Northern hemisphere) than the centuries that followed is generally accepted. But the timing, magnitude and spatial extent are much more uncertain. All previous multiproxy reconstructions indicate a Northern Hemisphere mean temperature less than current levels, though possibly on a par with the mid- 20th century. But there are only a few tenths of a degree in it, and so the description that it is likely to have been warmer now (rather than virtually certain) is used to express the level of uncertainty.

A confounding factor in discussions of this period is the unfortunate tendency of some authors to label any warm peak prior to the 15th Century as the ‘Medieval Warm Period’ in their record. This leads to vastly different periods being similarly labelled, often giving a misleading impression of coherence. For instance, in a recent paper it was defined as 1200-1425 CE well outside the ‘standard’ definition of 800-1200 CE espoused by Lamb.

Since a new ‘reconstruction’ of the last 2000 years from Craig Loehle is currently doing the rounds, we thought it might be timely to set out what the actual issues are in making such reconstructions (as opposed to the ones that are more often discussed), and how progress is being made despite the pitfalls.
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Global dimming and global warming

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Readers might remember a minor kerfuffle in EOS (the AGU house journal) in February this year in which Gerald Stanhill claimed to find a paradox in the contemporaneous effects of global warming and global dimming (a long term reduction of surface solar radiation, mainly due to aerosols and clouds). The article attracted attention mainly because the paradox was claimed to “pose [a challenge] to the consensus explanation of climate change”.

Rather than point out the subtle confusions (between surface and tropospheric forcing, and local and global signals) here, I and two co-authors wrote a comment to the journal. After a number of avoidable and unavoidable delays, this comment (along with another one and a reply) have all now appeared in EOS (Nov 6 edition). By now of course, the original piece has been long forgotten and so the point in having the correspondence printed is unclear, but still…

For those that care, I’ll link our comment once it’s been posted on the GISS website (now available here), but the bottom line is clearly seen in the following figure:

That is, if you take all of the IPCC AR4 models (now called the CMIP3 ensemble), then over the twentieth century all of them show varying degrees of global warming, while at the same time they show significant global dimming. An earlier paper of ours had pointed to the aerosols (unsurprisingly) being the dominant cause for long term changes in dimming, but that changes in clouds on a decadal basis were responsible for much of the shorter term variability. Thus there doesn’t appear to be much ‘paradox’ left to worry about – both dimming and warming are seen in models and in observations.

Apologies for appearing to push my own papers here (not something we like to do particularly), but the published comment would have been better done as a blog post in February. There may be a lesson there….

BBC contrarian top 10

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There is an interesting, if predictable, piece up on the BBC website devoted to investigating whether there is any ‘consensus’ among the various contrarians on why climate change isn’t happening (or if it is, it isn’t caused by human activity or if it is why it won’t be important, or if it is important, why nothing can be done etc.). Bottom line? The only thing they appear to agree about is that nothing should be done, but they have a multitude of conflicting reasons why. Hmm…

The journalist, Richard Black, put together a top 10 list of sceptic arguments he gathered from emailing the 61 signers of a Canadian letter. While these aren’t any different in substance to the ones routinely debunked here (and here and here), this list comes with the imprimatur of Fred Singer – the godfather to the sceptic movement, and recent convert from the view that it’s been cooling since 1940 to the idea that global warming is now unstoppable. Thus these are the arguments (supposedly) that are the best that the contrarians have to put forward.

Alongside each of these talking points, is a counter-point from the mainstream (full disclosure, I helped Richard edit some of those). In truth though, I was a little disappointed at how lame their ‘top 10’ arguments were. In order, they are: false, a cherry pick, a red herring, false, false, false, a red herring, a red herring, false and a strawman. They even used the ‘grapes grew in medieval England’ meme that you’d think they’d have abandoned already given that more grapes are grown in England now than ever before (see here). Another commonplace untruth is the claim that water vapour is ‘98% of the greenhouse effect’ – it’s just not.

So why do the contrarians still use arguments that are blatantly false? I think the most obvious reason is that they are simply not interested (as a whole) in providing a coherent counter story. If science has one overriding principle, it is that you should adjust your thinking in the light of new information and discoveries – the contrarians continued use of old, tired and discredited arguments demonstrates their divorce from the scientific process more clearly than any densely argued rebuttal.

Younger Dry-as dust?

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The Younger Dryas is so called because it corresponds, in the pollen record from Europe, to the latest (i.e. youngest) appearance of the Dryas octopetala pollen, an alpine flower in regions that are now far from alpine. It marks a clear period towards the end of the last ice age when the warming trend of the deglaciation in Europe particularly was interrupted for a period of about 1300 years before it got going again. There were clear glacier advances during this time and the moraines can be seen very clearly all around Europe and Scandinavia.

The clues to what caused this remarkable, if temporary, turnaround have always lain in assessing its spatial extent, the exact timing and correspondence with other events. Two recent papers have shed some welcome and potentially controversial light on the subject.

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CO2 equivalents

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There was a minor kerfuffle in recent days over claims by Tim Flannery (author of “The Weather Makers”) that new information from the upcoming IPCC synthesis report will show that we have reached 455 ppmv CO2_equivalent 10 years ahead of schedule, with predictable implications. This is confused and incorrect, but the definitions of CO2_e, why one would use it and what the relevant level is, are all highly uncertain in many peoples’ minds. So here is a quick rundown.

Definition: The CO2_equivalent level is the amount of CO2 that would be required to give the same global mean radiative forcing as the sum of a basket of other forcings. This is a way to include the effects of CH4 and N2O etc. in a simple way, particularly for people doing future impacts or cost-benefit analysis. The equivalent amount is calculated using the IPCC formula for CO2 forcing:

Total Forcing = 5.35 log(CO2_e/CO2_orig)

where CO2_orig is the 1750 concentration (278 ppmv).

Usage: There are two main ways it is used. Firstly, it is often used to group together all the forcings from the Kyoto greenhouse gases (CO2, CH4, N2O and CFCs), and secondly to group together all forcings (including ozone, sulphate aerosols, black carbon etc.). The first is simply a convenience, but the second is what matters to the planet. Many stabilisation scenarios, such as are being discussed in UNFCCC negotiations are based on stabilising total CO2_e at 450, 550 or 750 ppmv.

Magnitude The values of CO2_e (Kyoto) and CO2_e (Total) can be calculated from Figure 2.21 and Table 2.12 in the IPCC WG1 Chapter 2. The forcing for CO2, CH4 (including indirect effects), N2O and CFCs is 1.66+0.48+0.07+0.16+0.34=2.71 W/m2 (with around 0.3 W/m2 uncertainty). Using the formula above, that gives CO2_e (Kyoto) = 460 ppmv. However, including all the forcings (some of which are negative), you get a net forcing of around 1.6 W/m2, and a CO2_e (Total) of 375 ppmv with quite a wide error bar. This is, coincidently, close to the actual CO2 level.

Implications The important number is CO2_e (Total) which is around 375 ppmv. Stabilisation scenarios of 450 ppmv or 550 ppmv are therefore still within reach. Claims that we have passed the first target are simply incorrect, however, that is not to say they are easily achievable. It is even more of a stretch to state that we have all of a sudden gone past the ‘dangerous’ level. It is still not clear what that level is, but if you take a conventional 450 ppmv CO2_e value (which will lead to a net equilibrium warming of ~ 2 deg C above pre-industrial levels), we are still a number of years from that, and we have (probably) not yet committed ourselves to reaching it.

Finally, the IPCC synthesis report is simply a concise summary of the three separate reports that have already come out. It therefore can’t be significantly different from what is already available. But this is another example where people are quoting from draft reports that they have neither properly read nor understood and for which better informed opinion is not immediately available. I wish journalists and editors would resist the temptation to jump on leaks like this (though I know it’s hard). The situation is confusing enough without adding to it unintentionally.

Perspectives from China

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I spent the last three weeks in China partly for a conference, partly for a vacation, and partly for a rest. In catching up over the last couple of days, I notice that the break has given me a slightly different perspective on a couple of issues that are relevant here.

First off, the conference I attended was on paleoceanography and there were was a lot of great new science presented, particularly concerning the Paleocene-Eocene Thermal Maximum (around 55 million years ago), and on past changes to tropical rainfall patterns (see this week’s Nature) – two issues where there is a lot of relevance for climate change and its impacts today. I’ll discuss the new data in separate posts over the next few weeks, but for now I’ll just mention a topic that came up repeatedly in conversations over the week – that was how to improve the flow of information from the paleo community to the wider climate community, as represented by the IPCC for instance.

There was a palpable sense that insights from paleo-climate (in this case referring mainly to the ocean sediment record rather than ice cores or records from the last millennium) were not being given their due, and in fact were frequently being misused. In a panel discussion (hosted by Stefan), people lamented the lack of ‘synthesis’ that would be useful for the outside community, while others stressed (correctly) that synthesis is hard and frankly not well regarded within the community or their funders. I think this is a general problem; many of the incentives for success within an academic field – the push for novel techniques, the ownership of specific slices of data, the desire to emulate the paths to success of the previous generation – actually discourage work across the field that pulls together disparate sources of information.

In the paleo-oceanography case, this exhibits itself in the overwhelming focus on downcore records (the patterns of change at a single point through time) and the relative lack of integrated products that either show spatial patterns of change at a single time, or that try to extract common elements from multiple events in the past. There are of course numerous exceptions – the MARGO project that compiled records from the peak of the last ice age, or the work of PMIP for the mid-Holocene – but their visibility makes their uniqueness all the more obvious. There were no ideas presented that would fix this overnight, but the discussions showed that the community realises that there is a problem – even if the solutions are elusive.

My second thought on China came from travelling through some of the most polluted cites in the world. Aerosol haze that appeared continuous from Beijing to Hong Kong is such an obvious sign of human industrial activity that it simply takes your breath away (literally). In places, even on a clear day, you cannot see the sun – even if there is no cloud in the sky. Only in the mountains or in deeply rural parts of the country was blue sky in evidence. This is clearly an unsustainable situation (even if you are only thinking about the human health impacts) and it points the way, I think, to how China can be engaged on the climate change front. If reducing aerosol emissions can be done at the same time that greenhouse gases can be cut, the Chinese will likely jump at the chance. As an aside, I noticed that Compact Florescent Light bulbs were being used almost everywhere you looked, and that the majority of Shanghai’s motorbikes and scooters were electric rather than gasoline powered. These efforts clearly help, but they are just as clearly not sufficient on their own.

Finally, the limited access to the Internet that one gets in China (through a combination of having better things to do with one’s time and the sometimes capricious nature of what gets through the Great Firewall) allowed me to take a bit of break from the constant back and forth on the climate blogs. In getting back into it, one appreciates just how much time is wasted dealing with the most ridiculous of issues (Hansen’s imagined endorsement of a paper he didn’t write thirty six years ago, the debunking of papers that even E&E won’t publish, and the non-impact of the current fad for amateur photography) at the expense of anything substantive. In effect, if possibly not in intention, this wastes a huge amount of people’s time and diverts attention from more significant issues (at least in the various sections of the blogosphere). Serious climate bloggers might all benefit from not getting too caught up in it, and keeping an closer eye on the bigger picture. We will continue to try and do so here.

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.