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There has been a lot of discussion about decadal climate predictions in recent months. It came up as part of the ‘climate services’ discussion and was alluded to in the rather confused New Scientist piece a couple of weeks ago. This is a relatively “hot” topic to be working on, exemplified by two initial high profile papers (Smith et al, 2007 and Keenlyside et al, 2008). Indeed, the specifications for the new simulations being set up for next IPCC report include a whole section for decadal simulations that many of the modelling groups will be responding to.

This figure from a recent BAMS article (Hawkins and Sutton, 2009) shows an estimate of the current sources of prediction error at the global and local scale. For short periods of time (especially at local scales), the dominant source of forecast uncertainty is the ‘internal variability’ (i.e. the exact course of the specific trajectory the weather is on). As time goes by, the different weather paths get averaged out and so this source of uncertainty diminishes. However, uncertainty associated with uncertain or inaccurate models grows with time, as does the uncertainty associated with the scenario you are using – ie. how fast CO2 or other forcings are going to change. Predictions of CO2 next year for instance, are much easier than predictions in 50 years time because of the potential for economic, technological and sociological changes. The combination of sources of uncertainty map out how much better we can expect predictions to get: can we reduce error associated with internal variability by initializing models with current observations? how much does uncertainty go down as models improve? etc.

From the graph it is easy to see that over the short term (up to a decade or so), reducing initialization errors might be useful (the dotted lines). The basic idea is that a portion of the climate variability on interannual to decadal time scales can be associated with relatively slow ocean changes – for instance in the North Atlantic. If these ocean circulations can be predicted based on the state of the ocean now, that may therefore allow for skillful predictions of temperature or rainfall that are correlated to those ocean changes. But while this sounds plausible, almost every step in this chain is a challenge.

We know that this works on short (seasonal) time scales in (at least some parts of the world) because of the somewhat skillful prediction of El Niño/La Niña events and relative stability of teleconnections to these large perturbations (the fact that rainfall in California is usually high in El Niño years for instance). But our ability to predict El Niño loses skill very rapidly past six months or so and so we can’t rely on that for longer term predictions. However, there is also some skill in seasonal predictions in parts of the world where El Niño is not that important – for instance in Europe – that is likely based on the persistence of North Atlantic ocean temperature anomalies. One curious consequence is that the places that have skillful and useful seasonal-to-interannual predictions based on ENSO forecasts are likely to be the places where skillful decadal forecasts do worst (because those are precisely the areas where the unpredictable ENSO variability will be the dominant signal).

It’s worth pointing out that ’skill’ is defined relative to climatology (i.e. do you do a better job at estimating temperature or rainfall anomalies than if you’d just assumed that the season would be just like the average of the last ten years for instance). Some skill doesn’t necessarily mean that the predictions are great – it simply means that they are slightly better than you could do before. We should also distinguish between skillful (in a statistical sense) and useful in a practical sense. An increase of a few percent in variance explained would show up as improved skill, but that is unlikely to be of good enough practical value to shift any policy decisions.

So given that we know roughly what we are looking for, what is needed for this to work?

First of all, we need to know whether we have enough data to get a reasonable picture of the ocean state right now. This is actually quite hard since you’d like to have subsurface temperature and salinity data from a large part of the oceans. That gives you the large scale density field which is the dominant control on the ocean dynamics. Right now this is just about possible with the new Argo float array, but before about 2003, subsurface data in particular was much sparser outside a few well travelled corridors. Note that temperature data are not sufficient on their own for calculating changes in the ocean dynamics since they are often inversely correlated with salinity variations (when it is hot, it is often salty for instance) which reduces the impact on the density. Conceivably if any skill in the prediction is simply related to surface temperature anomalies being advected around by the mean circulation, it could be possible be useful to do temperature only initializations, but one would have to be very wary of dynamical changes and that would limit the usefulness of the approach to a couple of years perhaps.

Next, given any particular distribution of initialization data, how should this be assimilated into the forecasting model? This is a real theoretical problem given that models all have systematic deviations from the real world. If you simply force a model temperature and salinity to look exactly like the observations, then you risk having any forecast dominated by model drift when you remove the assimilation. Think of a elastic band being pulled to the side by the ‘observations’, but having it snap back to it’s default state when you stop pulling. (A likely example of this is the ‘coupling shock’ phenomena possibly seen in the Keenlyside et al simulations). A better way to do this is via anomaly forcing – that is you only impose the differences from the climatology on the model. That is guaranteed to have less model drift, but at the expense of having the forecast potentially affected by systematic errors in, say, the position of the Gulf Stream. In both methods of course, the better the model, the less bad the problems. There is a good discussion of the Hadley Centre methods in Haines et al (2008) (no sub reqd.).

Assuming that you can come up with a reasonable methodology for the initialization, the next step is to understand the actual predictability of the system. For instance, given the inevitable uncertainties due to sparse coverage or short term variability, how fast do slightly differently initialized simulations diverge? (Note that we aren’t talking about the exact path of the simulation which will diverge as fast as weather forecasts – a couple of weeks, but the larger scale statistics of ocean anomalies). This appears to be a few years to a decade in “perfect model” tests (where you try and predict how a particular model will behave using the same model but with an initialization that mimics what you’d have to do in the real world).

Finally, given that you can show that the model with its initialization scheme and available data has some predictability, you have to show that it gives a useful increase in the explained variance in any quantities that someone might care about. For instance, perfect predictability of the maximum overturning streamfunction might be scientifically interesting, but since it is not an observable quantity, it is mainly of academic interest. Much more useful is how any surface air temperature or rainfall predictions will be affected. This kind of analysis is only just starting to be done (since you needed all the other steps to work first).

From talking to a number of people working in this field, my sense is that this is pretty much where the state of the science is. There are theoretical reasons to expect this to be useful, but as yet no good sense for actually how practically useful it will be (though I’d welcome any other opinions on this in the comments).

One thing that is of concern are statements that appear to assume that this is already a done deal – that good quality decadal forecasts are somehow guaranteed (if only a new center can be built, or if a faster computer was used). For instance:

… to meet the expectations of society, it is both necessary and possible to revolutionize climate prediction. … It is possible firstly because of major advances in scientific understanding, secondly because of the development of seamless prediction systems which unify weather and climate prediction, thus bringing the insights and constraints of weather prediction into the climate change arena, and thirdly because of the ever-expanding power of computers.

However, just because something is necessary (according to the expectations of society) does not automatically mean that it is possible! Indeed, there is a real danger for society’s expectations to get completely out of line with what eventually will prove possible, and it’s important that policies don’t get put in place that are not robust to the real uncertainty in such predictions.

Does this mean that climate predictions can’t get better? Not at all. The component of the forecast uncertainty associated with the models themselves can certainly be reduced (the blue line above) – through more judicious weighting of the various models (perhaps using paleo-climate data from the LGM and mid-Holocene which will also be part of the new IPCC archive), improvements in parameterisations and greater realism in forcings and physical interactions (for instance between clouds and aerosols). In fact, one might hazard a guess that these efforts will prove more effective in reducing uncertainty in the coming round of model simulations than the still-experimental attempts in decadal forecasting.

One of the UK’s leading climate scientists will next week present new research findings on the increasing potential for a 4 degrees Celsius rise in global temperatures if the current high emissions of greenhouse gasses continue.

http://news.bbc.co.uk/1/hi/sci/tech/8279654.stm

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Climatologists are to meet in Oxford next week for a deeply depressing conference. Over three days they will discuss the world warming by a potentially calamitous 4°C as a result of human activities.

 

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It seems pretty clear that the long term agreement at Copenhagen will focus on some long term greenhouse gas emissions targets: a reduction of 80% by 2050 for the UK or 50% by 2050 for the world, for example. Such targets, if they are credible, set out long term direction, and have some value. However, there is also a need for immediate action. One such action that could make a big difference is a 'price on carbon', or, equivalently, higher energy taxes. In order to make such changes subject to an international agreement, we will need to measure these prices. if we can measure prices, we can write contracts (often known as 'derivatives') based on these prices. Measuring pre- and post-tax energy prices is a non-trivial task, but is being done by the International Energy Agency. Here I consider how such a task might be accomplished.

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The Tyndall Centre brings together scientists, economists, engineers and social scientists, who together are working to develop sustainable responses to climate change through trans-disciplinary research and dialogue.

A carbon tax is a tax on fossil fuels according to the amount of CO2 they would produce on combustion (burning).  It would apply to all fossil fuels, and would be charged at extraction or importation of the fossil fuel. This tax would generally be passed on to consumers in an interim before alternatives are developed; but the revenue generated can also be refunded to taxpayers more generally so that average taxpayers are no worse off.

Michael Mann and Gavin Schmidt

The issues involved in science communication are complex and often seem intractable. We’ve seen many different approaches, but guessing which will work (An Inconvenient Truth, Field Notes from a Catastrophe) and which won’t (The Eleventh Hour) is a tricky call. Mostly this is because we aren’t the target audience and so tend to rate popularizations by different criteria than lay people. Often, we just don’t ‘get it’.

Into this void has stepped Randy Olsen with his new book “Don’t be such a scientist”. For those who don’t know Randy, he’s a rather extraordinary individual – one of the few individuals who has run the gamut from hard-core scientist to Hollywood film maker. He’s walked the walk, and can talk the talk–and when he does talk, we should be listening!

While there may be some similarities in theme with “Unscientific America” by Chris Mooney and Sheril Kirshenbaum that we reviewed previously, the two books cover very different ground. They share the recognition that there is currently a crisis in area of scientific communication. But what makes “Don’t be such a Scientist” so unique is that Olsen takes us along on his own personal journey, recounting his own experiences as he made the transition from marine biologist to movie-maker, and showing us (rather than simply telling us–you can be sure that Randy would want to draw that distinction!) what he learned along the way. The book could equally well have been titled “Confessions of a Recovering Scientist”.

More than anything else, the book attempts to show us what the community is doing wrong in our efforts to communicate our science to the public. Randy doesn’t mince words in the process. He’s fairly blunt about the fact that even when we think we’re doing a good job, we generally aren’t. We have a tendency to focus excessively on substance, when it is often as if not more important, when trying to reach the lay public, to focus on style. In other words, its not just what you say, but how you say it.

This is a recurring theme in Randy’s work. His 2006 film, Flock of Dodos, showed, through a combination of humor and insightful snippets of reality, why evolutionary biologists have typically failed in their efforts to directly engage and expose the “intelligent design” movement. In his 2008 film Sizzle, he attempted the same thing with the climate change debate–an example that hits closer to home for us–in this case using more of a “mockumentary”-style format (think “Best in Show” with climate scientists instead of dogs) but with rather more mixed results. Randy makes the point that the fact that Nature panned it, while Variety loved it, underlines the gulf that still exists between the worlds of science and entertainment.

However, the book is not simply a wholesale, defeatist condemnation of our efforts to communicate. What Randy has to say may be tough to hear, but its tough love. He provides some very important lessons on what works and what doesn’t, and they ring true to us in our own experience with public outreach. In short, says Randy: Tell a good story; Arouse expectations and then fulfill them; Don’t be so Cerebral; And, last but certainly not least: Don’t be so unlikeable (i.e. don’t play to the stereotype of the arrogant, dismissive academic or the nerdy absent-minded scientist). Needless to say, it’s easy for us to see our own past mistakes and flaws in Randy’s examples. And while we might quibble with Randy on some details (for example, An Inconvenient Truth didn’t get to be the success it was because of its minor inaccuracies), the basic points are well taken.

The book is not only extremely insightful and full of important lessons, it also happens to be funny and engaging, self-effacing and honest. We both agree that this book is a must read for anyone who cares about science, and the problems we have engaging the public.

If the book has a flaw, it might be the seemingly implicit message that scientists all need to take acting or comedy lessons before starting to talk – though the broader point that many of us could use some pointers in effective communication is fair. More seriously, the premise of the book is rooted in perhaps somewhat of a caricature of what a scientist is (you know, cerebral, boring, arrogant and probably unkempt). This could be seen merely as a device, but the very fact that we are being told to not be such scientists, seems at times to reinforce the stereotype (though be fair, Randy’s explanation of the title phrase does show it to be a bit more nuanced that might initially meet the eye). Shouldn’t we instead be challenging the stereotype? And changing what it means to the public to be a scientist? Maybe this will happen if scientists spend more time not being so like stereotypical scientists – but frankly there are a lot of those atypical scientists already and the cliches still abound.

When it comes to making scientists better communicators, Greg Craven’s book “What’s the worst that can happen?” demonstrates how it can actually be done. Craven is a science teacher and is very upfront about his lack of climate science credentials but equally upfront about his role in helping normal people think about the issue in a rational way. Craven started off making YouTube videos explaining his points and this book is a further development of those including responses to many of the critiques he got originally.

Craven’s excellent use of video to discuss the implications of the science is neatly paired with the work that Peter Sinclair is doing with his “Climate Denial Crock of the Week” series. Both use arresting graphics and straightforward explanations to point out what the science really says, how the contrarians distort and misinform and take some pleasure in pointing out the frequent incoherence that passes for commentary at sites like WUWT.

Crucially, neither Craven nor Sinclair are scientists, but they are excellent communicators of science. Which brings up a point raised by both Mooney & Kirshenbaum and Olsen – what role should working scientists play in improving communications to the public? Video editing and scriptwriting (and even website design!) is probably best left to people who know how to do these things effectively, while content and context needs to be informed directly by the scientists themselves. To our mind this points to enhanced cooperation among communicators and scientists as the dominant model we should be following. We don’t all need to become film directors to make a difference!

The AGM was held on Tuesday 14th July 2009 at Wolfson college. This is the second of three general meetings to be held this year. You can view the AGM Minutes here (PDF).

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So you think you’re green, eh? But I bet you still fly. One return flight to Australia pretty much means that you shouldn’t breathe for the next 2 years, let alone burn any fuel!Ok, so you’re like me, you don’t fly, but you’d really like to. You don’t mind things taking a bit longer, which is why you will still get on the train to go to (fairly) interesting places and take a day rather than 2 hours (ok normally more like 6 hours once you take into account the trip to the airport, the checking in time, the flight, waiting for your luggage at the far end, and the trip from some airport in the middle of nowhere with sheep grazing on the runway). But! You don’t get to go to New York for a surprise trip for your wedding anniversary or a trip somewhere the other side of the world for some winter sun.

So,  you have either given flying up or you probably should. There is an element of sacrifice involved. And what hurts most, no one else seems to be giving it up. The UK seems determined to expand its air traffic business [1]. It’s not inconceivable that people could lose their jobs if they refused to fly when it was deemed necessary. Flying is now part of our way of life, and we’ll only stop flying with the collapse of society.

Let’s just accept this reality and consider how to make the best of an unfortunate situation.

Well, obviously, people could fly less. That is a nice easy glib thing to say. More realistically though, they could fly more efficiently [2]. There are many ways that this can be achieved. Better engines. Higher density of passengers on the planes. Slower. Not planes.

But I just said flying is here to stay!

Well, yes it is. And it is inevitable that it will be. You just need less infrastructure to set up a journey. However there are some wacky ideas that shouldn’t just be ignored. How about a fast train from London to New York

No, I mean faster than a plane. A LOT faster. Yes, even than Concorde (RIP). 4000 mph fast enough for you. Should only cost about $175 billion plus running costs [3].

However, that is still a bit of a red herring, I was talking about airships. Now bear with me a moment. It isn’t what you are thinking. Airships don’t blow up. They almost never did even when people were a bit careless in their construction. They are slow, but slow means up to 200mph. That’s less than a day from London to New York (as the crow flies, and I admit being somewhat naive about trade winds). I’d lose a day of holiday to fly across the Atlantic guilt-free!

You could make getting places part of the experience too. There is talk of airships with tennis courts on board! Seriously, who wouldn’t want to fly in something called a Mega-Airship![4]

[1] http://news.bbc.co.uk/1/hi/sci/tech/8243922.stm

[2] http://www.ecogeek.org/content/view/828/

[3] http://www.impactlab.com/2008/06/27/trans-atlantic-supersonic-maglev-vacuum-tube-train/

[4] http://www.ecogeek.org/automobiles/2885–solar-powered-mega-airship

I recently attended the World Climate Conference-3 (WCC-3), hosted by the World Meteorological Organization (WMO) in Geneva. Most of the talk was of providing “climate services” (CS) and coordinating these globally. But what are climate services, and how much of what was envisaged is scientifically doable?

Climate services is a fairly new term that involves the provision of climate information relevant for adaptation to climate change and climatic swings, long-term planning, and facilitating early warning systems (EW).

CS includes both data describing past and future climate, and usually involves downscaling to provide information on regional and local scales. It can be summarised by the contents of http://www.climateservices.gov/ (also see this link to an article discussing the US National Climate services).

It was stressed during WCC-3 that CS must not only communicate relevant information, but this information must also be ‘translated’ to non-expert in a way that it can be acted upon.

One concern expressed during WCC-3 was that global climate models still do not give a sufficiently accurate description of the regional and local aspects of the climate. The models also have serious limitations when they are to be used for seasonal and decadal forecasting. Climate models were originally designed to provide the large picture of our climate system, and the fact that ENSO, cyclones, various wave phenomena (observed in the real world) appear in the model output – albeit with differences in details – give us increased confidence that they capture real physical processes. For climate prediction, these details, often caricatured by the models, must be more accurate.

Although the dynamical aspects and regional scales are important, one must keep in mind that the atmospheric radiative transfer atmospheric models represent the core of the theory behind AGW, and that AGW involves longer time scales. Few scientists seriously doubt these radiative transfer models, which are closely related to the algorithms used in remote sensing, e.g. by satellites, to calculate temperatures. If one interprets the the New Scientist report from the WCC-3 as that the situation is no longer as dire previously thought, then one is in for a big disappointment. The sentiment is rather that climate change is unavoidable, and that we need to establish tools in order to plan and deal with the problems.

There are some signs, however, that biases and systematic errors in the global climate models (GCMs) can be reduced by increasing the spatial (and temporal) resolution, or by including a realistic representation of the stratosphere. Problems associated with the description of local and regional climates cannot merely be corrected through downscaling.

One concern was that the bit of code called ‘parametrisation’ (employed in the models to describe the bulk effect of physical processes taking place over a spatial scale too small for the model grid) may not be sufficiently good for the job of simulating all local climatic aspects. For this reason, there was a call for a globally coordinated effort in providing computer resources and climate simulation.

Some speakers stressed the importance of a truly global set of climate observation. In this context, it’s also crucial to share data without restrictions, in addition to aiding poor countries to make high quality measurements.

Although the focus during the WCC-3 was on adaptation, it was also stressed that mitigation is still a must, if we are to avoid serious climate calamities. It was concluded that we must move from a ‘Catastrophe handling’ strategy to a ‘Risk management’ policy.

One sad example showing that we are not there yet, was the forecasted June-August 2008 floods over the western/central Africa. It was the first time in history when Red Cross/Crescent launched a pre-emptive appeal based on a forecast. Unfortunately, there was a lack of willingness to donate funds before a disaster had taken place, and sadly, the forecasts turned out to be fairly accurate. The question is whether we are doing the same mistake when it comes to climate change.

Webcasts from the conference have been posted on the WMO WCC-3 web site. In addition to the science, a number of speakers discussed politics. There is also a new book – Climate Senses – that has recently been published for the WCC-3, dealing with climate predictions and information for decision making

This year's annual public debate discusses 'Is avoiding dangerous climate change compatible with economic growth?' We have five distinguished speakers:

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