The problem of global warming is so big that solving it will require creative thinking from many disciplines. Economists have much to contribute to this effort, particularly with regard to the question of how various means of putting a price on carbon emissions may alter human behavior. Some of the lines of thinking in your first book, Freakonomics, could well have had a bearing on this issue, if brought to bear on the carbon emissions problem. I have very much enjoyed and benefited from the growing collaborations between Geosciences and the Economics department here at the University of Chicago, and had hoped someday to have the pleasure of making your acquaintance. It is more in disappointment than anger that I am writing to you now.

I am addressing this to you rather than your journalist-coauthor because one has become all too accustomed to tendentious screeds from media personalities (think Glenn Beck) with a reckless disregard for the truth. However, if it has come to pass that we can’t expect the William B. Ogden Distinguished Service Professor (and Clark Medalist to boot) at a top-rated department of a respected university to think clearly and honestly with numbers, we are indeed in a sad way.

By now there have been many detailed dissections of everything that is wrong with the treatment of climate in Superfreakonomics , but what has been lost amidst all that extensive discussion is how really simple it would have been to get this stuff right. The problem wasn’t necessarily that you talked to the wrong experts or talked to too few of them. The problem was that you failed to do the most elementary thinking needed to see if what they were saying (or what you thought they were saying) in fact made any sense. If you were stupid, it wouldn’t be so bad to have messed up such elementary reasoning, but I don’t by any means think you are stupid. That makes the failure to do the thinking all the more disappointing. I will take Nathan Myhrvold’s claim about solar cells, which you quoted prominently in your book, as an example.

As quoted by you, Mr. Myhrvold claimed, in effect, that it was pointless to try to solve global warming by building solar cells, because they are black and absorb all the solar energy that hits them, but convert only some 12% to electricity while radiating the rest as heat, warming the planet. Now, maybe you were dazzled by Mr Myhrvold’s brilliance, but don’t we try to teach our students to think for themselves? Let’s go through the arithmetic step by step and see how it comes out. It’s not hard.

Let’s do the thought experiment of building a solar array to generate the entire world’s present electricity consumption, and see what the extra absorption of sunlight by the array does to climate. First we need to find the electricity consumption. Just do a Google search on “World electricity consumption” and here you are:

Now, that’s the total electric energy consumed during the year, and you can turn that into the rate of energy consumption (measured in Watts, just like the world was one big light bulb) by dividing kilowatt hours by the number of hours in a year, and multiplying by 1000 to convert kilowatts into watts. The answer is two trillion Watts, in round numbers. How much area of solar cells do you need to generate this? On average, about 200 Watts falls on each square meter of Earth’s surface, but you might preferentially put your cells in sunnier, clearer places, so let’s call it 250 Watts per square meter. With a 15% efficiency, which is middling for present technology the area you need is

or 53,333 square kilometers. That’s a square 231 kilometers on a side, or about the size of a single cell of a typical general circulation model grid box. If we put it on the globe, it looks like this:

So already you should be beginning to suspect that this is a pretty trivial part of the Earth’s surface, and maybe unlikely to have much of an effect on the overall absorbed sunlight. In fact, it’s only 0.01% of the Earth’s surface. The numbers I used to do this calculation can all be found in Wikipedia, or even in a good paperbound World Almanac.

But we should go further, and look at the actual amount of extra solar energy absorbed. As many reviewers of Superfreakonomics have noted, solar cells aren’t actually black, but that’s not the main issue. For the sake of argument, let’s just assume they absorb all the sunlight that falls on them. In my business, we call that “zero albedo” (i.e. zero reflectivity). As many commentators also noted, the albedo of real solar cells is no lower than materials like roofs that they are often placed on, so that solar cells don’t necessarily increase absorbed solar energy at all. Let’s ignore that, though. After all, you might want to put your solar cells in the desert, and you might try to cool the planet by painting your roof white. The albedo of desert sand can also be found easily by doing a Google search on “Albedo Sahara Desert,” for example. Here’s what you get:

So, let’s say that sand has a 50% albedo. That means that each square meter of black solar cell absorbs an extra 125 Watts that otherwise would have been reflected by the sand (i.e. 50% of the 250 Watts per square meter of sunlight). Multiplying by the area of solar cell, we get 6.66 trillion Watts.

That 6.66 trillion Watts is the “waste heat” that is a byproduct of generating electricity by using solar cells. All means of generating electricity involve waste heat, and fossil fuels are not an exception. A typical coal-fired power plant only is around 33% efficient, so you would need to release 6 trillion Watts of heat to burn the coal to make our 2 trillion Watts of electricity. That makes the waste heat of solar cells vs. coal basically a wash, and we could stop right there, but let’s continue our exercise in thinking with numbers anyway.

Wherever it comes from, waste heat is not usually taken into account in global climate calculations for the simple reason that it is utterly trivial in comparison to the heat trapped by the carbon dioxide that is released when you burn fossil fuels to supply energy. For example, that 6 trillion Watts of waste heat from coal burning would amount to only 0.012 Watts per square meter of the Earth’s surface. Without even thinking very hard, you can realize that this is a tiny number compared to the heat-trapping effect of CO2. As a general point of reference, the extra heat trapped by CO2 at the point where you’ve burned enough coal to double the atmospheric CO2 concentration is about 4 Watts per square meter of the Earth’s surface — over 300 times the effect of the waste heat.

The “4 Watts per square meter” statistic gives us an easy point of reference because it is available from any number of easily accessible sources, such as the IPCC Technical Summary or David Archer’s basic textbook that came out of our “Global Warming for Poets” core course. Another simple way to grasp the insignificance of the waste heat effect is to turn it into a temperature change using the standard climate sensitivity of 1 degree C of warming for each 2 Watts per square meter of heat added to the energy budget of the planet (this sensitivity factor also being readily available from sources like the ones I just pointed out). That gives us a warming of 0.006 degrees C for the waste heat from coal burning, and much less for the incremental heat from switching to solar cells. It doesn’t take a lot of thinking to realize that this is a trivial number compared to the magnitude of warming expected from a doubling of CO2.

With just a little more calculation, it’s possible to do a more precise and informative comparison. For coal-fired generation,each kilowatt-hour produced results in emissions of about a quarter kilogram of carbon into the atmosphere in the form of carbon dioxide. For our 16.83 trillion kilowatt-hours of electricity produced each year, we then would emit 4.2 trillion kilograms of carbon, i.e. 4.2 gigatonnes each year. Unlike energy, carbon dioxide accumulates in the atmosphere, and builds up year after year. It is only slowly removed by absorption into the ocean, over hundreds to thousands of years. After a hundred years, 420 gigatonnes will have been emitted, and if half that remains in the atmosphere (remember, rough estimates suffice to make the point here) the atmospheric stock of CO2 carbon will increase by 210 gigatonnes, or 30% of the pre-industrial atmospheric stock of about 700 gigatonnes of carbon. To get the heat trapped by CO2 from that amount of increase, we need to reach all the way back into middle-school math and use the awesome tool of logarithms; the number is

or 1.5 Watts per square meter. In other words, by the time a hundred years have passed, the heat trapped each year from the CO2 emitted by using coal instead of solar energy to produce electricity is 125 times the effect of the fossil fuel waste heat. And remember that the incremental waste heat from switching to solar cells is even smaller than the fossil fuel waste heat. What’s more, because each passing year sees more CO2 accumulate in the atmosphere, the heat trapping by CO2 continues to go up, while the effect of the waste heat from the fossil fuels or solar cells needed to produce a given amount of electricity stays fixed. Another way of putting it is that the climate effect from the waste heat produced by any kind of power plant is a one-off thing that you incur when you build the plant, whereas the warming effect of the CO2 produced by fossil fuel plants continues to accumulate year after year. The warming effect of the CO2 is a legacy that will continue for many centuries after the coal has run out and the ruins of the power plant are moldering away.

Note that you don’t actually have to wait a hundred years to see the benefit of switching to solar cells. The same arithmetic shows that even at the end of the very first year of operation, the CO2 emissions prevented by the solar array would have trapped 0.017 Watts per square meter if released into the atmosphere. So, at the end of the first year you already come out ahead even if you neglect the waste heat that would have been emitted by burning fossil fuels instead.

So, the bottom line here is that the heat-trapping effect of CO2 is the 800-pound gorilla in climate change. In comparison, waste heat is a trivial contribution to global warming whether the waste heat comes from solar cells or from fossil fuels. Moreover, the incremental waste heat from switching from coal to solar is an even more trivial number, even if you allow for some improvement in the efficiency of coal-fired power plants and ignore any possible improvements in the efficiency of solar cells. So: trivial,trivial trivial. Simple, isn’t it?

By the way, the issue of whether waste heat is an important factor in global warming is one of the questions most commonly asked by students who are first learning about energy budgets and climate change. So, there are no shortage of places where you can learn about this sort of thing. For example, a simple Google search on the words “Global Warming Waste Heat” turns up several pages of accurate references explaining the issue in elementary terms for beginners. Including this article from Wikipedia:

A more substantive (though in the end almost equally trivial) issue is the carbon emitted in the course of manufacturing solar cells, but that is not the matter at hand here. The point here is that really simple arithmetic, which you could not be bothered to do, would have been enough to tell you that the claim that the blackness of solar cells makes solar energy pointless is complete and utter nonsense. I don’t think you would have accepted such laziness and sloppiness in a term paper from one of your students, so why do you accept it from yourself? What does the failure to do such basic thinking with numbers say about the extent to which anything you write can be trusted? How do you think it reflects on the profession of economics when a member of that profession — somebody who that profession seems to esteem highly — publicly and noisily shows that he cannot be bothered to do simple arithmetic and elementary background reading? Not even for a subject of such paramount importance as global warming.

And it’s not as if the “black solar cell” gaffe was the only bit of academic malpractice in your book: among other things, the presentation of aerosol geoengineering as a harmless and cheap quick fix for global warming ignored a great deal of accessible and readily available material on the severe risks involved, as Gavin noted in his recent post. The fault here is not that you dared to advocate geoengineering as a solution. There is a broad spectrum of opinion among scientists about the amount of aerosol geoengineering research that is justified, but very few scientists think of it as anything but a desperate last-ditch attempt, or at best a strategy to be used in extreme moderation as part of a basket of strategies dominated by emissions reductions. You owed it to your readers to present a fair picture of the consequences of geoengineering, but chose not to do so.

May I suggest that if you should happen to need some friendly help next time you take on the topic of climate change, or would like to have a chat about why aerosol geoengineering might not be a cure-all, or just need a critical but informed opponent to bounce ideas off of, you don’t have to go very far. For example…

But given the way Superfreakonomics mangled Ken Caldeira’s rather nuanced views on geoengineering, let’s keep it off the record, eh?

Your colleague,

Raymond T. Pierrehumbert
Louis Block Professor in the Geophysical Sciences
The University of Chicago

Our study published in mid October in Geophysical Research Letters (Tedesco and Monaghan, 2009) documents record minimum snowmelt for Antarctica during austral summer 2008-2009 and lower-than-normal melt for several recent years, based on a 30-year satellite microwave record. Numerous blogs have cited the results as a challenge to previous studies reporting Antarctic warming, while also steadfastly ignoring other studies with similar results (e.g. Barrett et al., 2009). They have overlooked that these studies show that Antarctic warming has occurred mostly in winter and spring, whereas melting of course occurs in summer. And they oversimplify the causality and hence confuse our prediction for the future. We found that the same mechanism that has primarily caused low snowmelt in recent years will likely change in a manner that will enhance snowmelt in forthcoming decades. A brief summary follows.

Map of Antarctica showing number of melting days in summer 2008-2009.

Our study demonstrates that low melt years during the 1979-2009 satellite record are related to the strength of the westerly winds that encircle Antarctica, known as the Southern Hemisphere Annular Mode (SAM). When the SAM is in a positive phase – meaning that the belt of winds is stronger than average – it has a cooling effect on Antarctic surface temperatures. The SAM was especially strong in austral spring and summer 2008-2009, and subsequently the 2008-2009 snowmelt was lower than normal. During the past 30-40 years, the SAM has gradually strengthened during austral summer (Marshall 2003), due mainly to human-caused stratospheric ozone depletion. In turn, the increasing SAM has weakened longer-term summer warming over Antarctica. The SAM index is not strongly positive every year of course, and particularly when combined with other atmospheric circulation changes (e.g. a strongly positive Southern Oscillation Index (SOI) – indicative of La Nina conditions) may contribute to anomalously high or low summer temperatures in any given year. The figures shown in our Supplementary Material section in our original paper illustrate this point nicely (below):

Monthly averaged December-January surface temperature anomalies (K) for 1998 (left, strong negative SAM and SOI) and 1999 (right, strong positive SAM and SOI).

The ozone hole is projected to recover significantly during the next 25 – 50 years due to the Montreal Protocol, which limits ozone-depleting substances used in industrial and household applications. As the ozone hole ‘heals’, the increasing summer SAM trends are projected to subside. As this happens, it is likely that summer temperature increases over Antarctica will become stronger and more widespread because the warming effect from greenhouse gas increases will no longer be kept in check by the dynamic
cooling impact of the SAM.

Therefore, the linkage between the SAM and snowmelt leads to our key conclusion: that enhanced snowmelt is likely in Antarctica as the SAM trends subside during the 21st century and summer temperatures become warmer. Our results agree with studies that have noted cooling and/or slower warming during the past three decades due to increasing SAM trends over the same period. Additionally, our conclusions do not contradict findings showing strong regional warming on the Antarctic Peninsula and in West Antarctica for the past 50 years, and warming over the entire continent for the past century. Our record is limited to the satellite era only, during which ozone depletion has dominated Antarctic summer temperature trends, and as already noted above, the observed warming in the last 50-100 years has occurred mostly in winter and spring. This context is important.

Gavin A. Schmidt, a climate scientist who works with Dr. Hansen and manages a popular blog on climate science, realclimate.org, said those promoting 350 or debating the number might be missing the point.
“The situation is analogous to people trying to embark on a cross-country road trip to California but they’ve started off heading to Maine instead,” Dr. Schmidt said. “But instead of working out ways to turn around, they have decided to argue about where they are going to park when they get to L.A.”
“If you ask a scientist how much more CO2 do you think we should add to the atmosphere, the answer is going to be none.”

I’ve been told that some readers may have misinterpreted the quote as a criticism of the 350.org campaign itself. This was not the intent and in fact my metaphor wouldn’t have made sense in that context at all. Instead, it was a criticism of people who are expending effort arguing about whether 350 is precisely the right number for a long term target, or whether it should be somewhat higher or lower. Since we aren’t currently headed anywhere near 350 ppmv (in fact we are at 388 ppmv CO2 and increasing by about 2 ppmv/yr), we need to urgently think of ways the situation can turn around. Tapping into the creativity and enthusiasm shown by the 350.org campaigners will certainly be part of that process.

We discussed some of the thinking behind this ‘Target CO₂‘ when Jim Hansen and colleagues’ paper first came out, where I think we made it clear that picking a specific CO₂ target to avoid ‘dangerous’ climate change is an inexact science at best. The comments by Robert Brulle and Ray Pierrehumbert at DotEarth and James Hrynyshyn also highlight some of that complexity. And I think the suggestions by ‘Paulina‘ for how a tweaked article might have been clearer are very apropos.

However, as the final line in my NYT quote should make clear, personally I think the scientific case not increasing CO₂ any further is very strong. Since the planet has not caught up with current levels of concentrations emissions (and thus will continue to change), picking an ultimate target that is less than today’s level is therefore wise. Of course, how we get there is much trickier than knowing where it is we should be going, but having a map of the destination is useful. As we discussed in the ‘trillionth ton‘ posting a couple of months back, how we get there also makes a difference.

In my original email to Andy Revkin, I had actually appended a line:

If you ask a scientist how much more CO2 do you think we should add to the atmosphere, the answer is going to be none.

All the rest is economics.

(and technology, and sociology, and psychology and politics etc.) but the point is that working out how we get there from here is the real challenge and the more people who are aware and involved in developing those solutions the better.

When it comes to the climate change disinformation campaign, we have chosen to focus on the intellectually bankrupt nature of the scientific arguments, rather than the political motivations and the sometimes intriguing money trail. We leave it to others, including organizations such as SourceWatch.org, the sleuths at DeSmogBlog, authors such as Ross Gelbspan (author of The Heat is On, and The Boiling Point), and edited works such as Rescuing Science from Politics to deal with such issues.

One problem with books on this topic is that they quickly grow out of date. Just over the past few years, there have been many significant events in the ‘climate wars’ as we have reported on this site. Fortunately, there is a book out now by our friends at DeSmogBlog (co-founder James Hoggan, and regular contributor Richard Littlemore) entitled Climate Cover Up: The Crusade to Deny Global Warming that discusses the details of the contrarian attacks on climate science up through the present, and in painstaking detail. They have done their research, and have fully documented their findings, summarized by the publisher thusly:

Talk of global warming is nearly inescapable these days — but there are some who believe the concept of climate change is an elaborate hoax. Despite the input of the world’s leading climate scientists, the urgings of politicians, and the outcry of many grassroots activists, many Americans continue to ignore the warning signs of severe climate shifts. How did this happen? Climate Cover-up seeks to answer this question, describing the pollsters and public faces who have crafted careful language to refute the findings of environmental scientists. Exploring the PR techniques, phony “think tanks,” and funding used to pervert scientific fact, this book serves as a wake-up call to those who still wish to deny the inconvenient truth.

There are interesting new details about the Revelle/Singer/Lancaster affair and other tidbits that were new to me, and will likely to be new to others who been following the history of climate change contrarianism. Ross Gelbspan who has set the standard for investigative reporting
when it comes to the climate change denial campaign, had this to say about the book:

absolutely superb-one of the best dissections of the climate information war I
have ever seen. This is one terrific piece of work!

There is an important story behind the climate change denial effort that goes well beyond the scientific issues at hand. Its not our mission at RealClimate to tell that story, but there are others who are doing it, and doing it well. Hoggan and Littlemore are clearly among them. Read this book, and equally important, make sure that others who need to do as well.

Paul Krugman probably has the main issue right:

…it looks like is that Levitt and Dubner have fallen into the trap of counterintuitiveness. For a long time, there’s been an accepted way for commentators on politics and to some extent economics to distinguish themselves: by shocking the bourgeoisie, in ways that of course aren’t really dangerous.

and

Clever snark like this can get you a long way in career terms — but the trick is knowing when to stop. It’s one thing to do this on relatively inconsequential media or cultural issues. But if you’re going to get into issues that are both important and the subject of serious study, like the fate of the planet, you’d better be very careful not to stray over the line between being counter-intuitive and being just plain, unforgivably wrong.

Levitt was on NPR at the weekend discussing this chapter (though not defending himself against any of the criticisms leveled above). He made the following two points which I think go to the heart of his thinking on this issue: “Why would anyone be against a cheap fix?” and “No problem has ever been solved by changing human behaviour” (possibly not exact quotes, but close enough). He also alluded to the switch over from horse-driven transport to internal combustion engines a hundred years ago as an example of a ‘cheap technological fix’ to the horse manure problem. I deal with each of these points in turn.

Is geo-engineering cheap?

The geo-engineering option that is being talked about here is the addition of SO2 to the stratosphere where it oxidises to SO4 (sulphate) aerosols which, since they are reflective, reduce the amount of sunlight reaching the ground. The zeroth order demonstration of this possibility is shown by the response of the climate to the eruption of Mt. Pinatubo in 1991 which caused a maximum 0.5ºC cooling a year or so later. Under business-as-usual scenarios, the radiative forcing we can expect from increasing CO2 by the end of the century are on the order of 4 to 8 W/m2 – requiring the equivalent to one to two Pinatubo’s every year if this kind of geo-engineering was the only response. And of course, you couldn’t stop until CO2 levels came back down (hundreds, if not thousands of years later) without hugely disruptive and rapid temperature rises. As Deltoid neatly puts it: “What could possibly go wrong?”.

The answer is plenty. Alan Robock discussed some of the issues here the last time this came up (umm… weeks ago). The basic issues over and above the costs of delivering the SO2 to the stratosphere are that a) once started you can’t stop without much more serious consequences so you are setting up a multi-centennial commitment to continually increasing spending (of course, if you want to stop because of huge disruption that geo-engineering might be causing, then you are pretty much toast), b) there would be a huge need for increased monitoring from the ground and space, c) who would be responsible for any unanticipated or anticipated side effects and how much would that cost?, and d) who decides when, where and how much this is used. For point ‘d’, consider how difficult it is now to come up with an international agreement on reducing emissions and then ponder the additional issues involved if India or China are concerned that geo-engineering will cause a persistent failure of the monsoon? None of these issues are trivial or cheap to deal with, and yet few are being accounted for in most popular discussions of the issue (including the chapter we are discussing here).

Is geo-engineering a fix?

In a word, no. To be fair, if the planet was a single column with completely homogeneous properties from the surface to the top of the atmosphere and the only free variable was the surface temperature, it would be fine. Unfortunately, the real world (still) has an ozone layer, winds that depend on temperature gradients that cause European winters to warm after volcanic eruptions, rainfall that depends on the solar heating at the surface of the ocean and decreases dramatically after eruptions, clouds that depend on the presence of condensation nuclei, plants that have specific preferences for direct or diffuse light, and marine life that relies on the fact that the ocean doesn’t dissolve calcium carbonate near the surface.

The point is that a planet with increased CO2 and ever-increasing levels of sulphates in the stratosphere is not going to be the same as one without either. The problem is that we don’t know more than roughly what such a planet would be like. The issues I listed above are the ‘known unknowns’ – things we know that we don’t know (to quote a recent US defense secretary). These are issues that have been raised in existing (very preliminary) simulations. There would almost certainly be ‘unknown unknowns’ – things we don’t yet know that we don’t know. A great example of that was the creation of the Antarctic polar ozone hole as a function of the increased amount of CFCs which was not predicted by any model beforehand because the chemistry involved (heterogeneous reactions on the surface of polar stratospheric cloud particles) hadn’t been thought about. There will very likely be ‘unknown unknowns’ to come under a standard business as usual scenario as well – another reason to avoid that too.

There is one further contradiction in the idea that geo-engineering is a fix. In order to proceed with such an intervention one would clearly need to rely absolutely on climate model simulations and have enormous confidence that they were correct (otherwise the danger of over-compensation is very real even if you decided to start off small). As with early attempts to steer hurricanes, the moment the planet did something unexpected, it is very likely the whole thing would be called off. It is precisely because climate modellers understand that climate models do not provide precise predictions that they have argued for a reduction in the forces driving climate change. The existence of a near-perfect climate model is therefore a sine qua non for responsible geo-engineering, but should such a model exist, it would likely alleviate the need for geo-engineering in the first place since we would know exactly what to prepare for and how to prevent it.

Does reducing global warming imply changing human behaviour and is that possible?

This is a more subtle question and it is sensible to break it down into questions of human nature and human actions. Human nature – the desire to strive for a better life, our inability to think rationally when trying to impress the objects of our desire, our natural selfishness and occasionally altruism, etc – is very unlikely to change anytime soon. But none of those attributes require the emission of fossil fuel-derived CO2 into the atmosphere, just as they don’t require us to pollute waterways, have lead in gasoline, use ozone-depleting chemicals in spray cans and fridges or let dogs foul the sidewalk. Nonetheless, societies in the developed world (with the possible exception of Paris) have succeeded in greatly reducing those unfortunate actions and it’s instructive to see how that happened.

The first thing to note is that these issues have not been dealt with by forcing people to think about the consequences every time they make a decision. Lead in fuel was reduced because of taxation measures that aligned peoples preferences for cheaper fuel with the societal interest in reducing lead pollution. While some early adopters of unleaded-fuel cars might have done it for environmental reasons, the vast majority of people did it first because it was cheaper, and second, because after a while there was no longer an option. The human action of releasing lead into the atmosphere while driving was very clearly changed.

In the 1980s, there were campaigns to raise awareness of the ozone-depletion problem that encouraged people to switch from CFC-propelled spray cans to cans with other propellants or roll-ons etc. While this may have made some difference to CFC levels, production levels were cut to zero by government mandates embedded in the Montreal Protocols and subsequent amendments. No-one needs to think about their spray can destroying the ozone layer any more.

I could go on, but the fundamental issue is that people’s actions can and do change all the time as a function of multiple pressures. Some of these are economic, some are ethical, some are societal (think about our changing attitudes towards smoking, domestic violence and drunk driving). Blanket declarations that human behaviour can’t possibly change to fix a problem are therefore just nonsense.

To be a little more charitable, it is possible that what was meant was that you can’t expect humans to consciously modify their behaviour all the time based on a desire to limit carbon emissions. This is very likely to be true. However, I am unaware of anyone who has proposed such a plan. Instead, almost all existing mitigation ideas rely on aligning individual self-interest with societal goals to reduce emissions – usually by installing some kind of carbon price or through mandates (such as the CAFE standards).

To give a clear example of the difference, let’s tackle the problem of leaving lights on in rooms where there is no-one around. This is a clear waste of energy and would be economically beneficial to reduce regardless of the implications for carbon emissions. We can take a direct moralistic approach – strong exhortations to people to always turn the lights off when they leave a room – but this is annoying, possibly only temporary and has only marginal success (in my experience). Alternatively, we can install motion-detectors that turn the lights out if there is no-one around. The cost of these detectors is much lower than cost of the electricity saved and no-one has to consciously worry about the issue any more. No-brainer, right? (As as aside, working out why this isn’t done more would be a much better use of Levitt and Dubner’s talents). The point is changing outcomes doesn’t necessarily mean forcing people to think about the right thing all the time, and that cheap fixes for some problems do indeed exist.

To recap, there is no direct link between what humans actually want to do and the emissions of CO2 or any other pollutant. If given appropriate incentives, people will make decisions that are collectively ‘the right thing’, while they themselves are often unconscious of that fact. The role of the economist should be to find ways to make that alignment of individual and collective interest easier, not to erroneously declare it can’t possibly be done.

What is the real lesson from the horse-to-automobile transition?

Around 1900, horse-drawn transport was the dominant mode of public and private, personal and commercial traffic in most cities. As economic activity was growing, the side-effects of horses’ dominance became ever more pressing. People often mention the issue of horse manure – picking it up and disposing of it, it’s role in spreading disease, the “intolerable stench” – but as McShane and Tarr explain that the noise and the impact of dead horses in the street were just as troublesome. Add to that the need for so many stables downtown taking up valuable city space, the provisioning of hay etc. it was clear that the benefits of the horse’s strength for moving things around came at a great cost.

But in the space of about 20 years all this vanished, to be replaced with electrified trolleys and subways, and internal combustion engine-driven buses and trucks, and cars such as the Model-T Ford. Almost overnight (in societal terms), something that had been at the heart of economic activity had been been relegated to a minority leisure pursuit.

This demonstrates very clearly that assumptions that society must always function the same economic way are false, and that in fact we can change the way we do business and live pretty quickly. This is good news. Of course, this transition was brought about by technological innovations and the switch was decided based on very clear cost-benefit calculations – while cars were initially more expensive than horses, their maintenance costs were less and the side effects (as they were understood at the time) were much less burdensome. Since the city had to tax the productive citizens in order to clear up the consequences of their own economic activity, the costs were being paid by the same people who benefited.

Levitt took this example to imply that technological fixes are therefore the solution to global warming (and the fix he apparently favours is geo-engineering mentioned above), but this is a misreading of the lesson here in at least two ways. Firstly, the switch to cars was not based on a covering up of the manure problem – a fix like that might have involved raised sidewalks, across city perfuming and fly-spraying – but from finding equivalent ways to get the same desired outcome (transport of goods and people) while avoiding undesired side-effects. That is much more analogous to switching to renewable energy sources than implementing geo-engineering.

His second error is in not appreciating the nature of the cost-benefit calculations. Imagine for instance that all of the horse manure and dead carcasses could have been easily swept into the rivers and were only a problem for people significantly downstream who lived in a different state or country. Much of the costs, public health issues, etc. would now be borne by the citizens of the downstream area who would not be benefiting from the economic prosperity of the city. Would the switch to automobiles have been as fast? Of course not. The higher initial cost of cars would only have made sense if the same people who were shelling out for the car would be able to cash in on the benefits of the reduced side effects. This is of course the basic issue we have with CO2. The people benefiting from fossil fuel based energy are not those likely to suffer from the consequences of CO2 emissions.

The correct lesson is in fact the same as the one mentioned above: if costs and benefits can be properly aligned (the ‘internalising of the externalities’ in economist-speak), societies and individuals can and will make the ‘right’ decisions, and this can lead to radical changes in very short periods of time. Thus far from being an argument for geo-engineering, this example is an object lesson in how economics might shape future decisions and society.

Finally

To conclude, the reasons why Levitt and Dubner like geo-engineering so much are based on a misreading of the science, a misrepresentation of proposed solutions, and truly bizarre interpretations of how environmental problems have been dealt with in the past. These are, in the end, much worse errors than their careless misquotes and over-eagerness to shock highlighted by the other critiques. Geo-engineering is neither cheap, nor a fix, and the reasons why it is very likely to be a bad idea are ethical and legal, much more than its still-uncertain scientific merits.

My impression from the solicited talk, is that the confidence in the GCR hypothesis now rests on two points that were made explicit in the presentation, and that we have not adequately addressed here. So, here they are:

Point I: When I asked Svensmark why he presented a curve describing low cloud-cover from the ISCCP – used for correlation study with GCR (link) – that differed from the curves presented at the ISCCP web site (link), he informed me that he used a corrected version that has been published. Nevertheless, the ‘correction’ of the curve is controversial, and the ISCCP team is clearly not convinced, despite the likelihood of instrumental degradation.

Good practice would then be to present all the curves that cannot be ruled out because of errors. When asked why he didn’t present the other cures too, he said that he only wanted to show the one curve. Not a very convincing answer, and not very reassuring.

Point II involves a ‘remarkable’ correlation, meant to demonstrate a link between high GCR flux and cold conditions. This analysis is based on a comparison between band-pass filtered ice-rafted debris from iceberg drifts (Bond, 2001) and Carbon-14 (a cosmogenic isotope) over the last 12,000 years (e.g. after the most recent ice age).

The relationship between temperature and drifting icebergs, however, is complicated and not so straight forward. Icebergs are formed when chunks of ice break off glaciers and icesheets – a process known as ‘calving’.

On the one hand, icesheets and glaciers grow when the accumulation of precipitation at below freezing temperatures (snow) exceeds the summertime melting. Very low temperatures, tend to be associated with low precipitation, however. One the other hand, iceberg calving does not require very low temperatures (as long as the ice is present), but is favoured by reduced friction at the base of ice caps, resulting in a faster flow towards the sea. Melt water can lubricate the ice sheets and hence affect the ice flow.

Once the icesheets have calved and produced icebergs, they will drift according to the winds and ocean currents. The most influential ocean currents for iceberg drift in the North Atlantic include the East Greenland Current EGC), which follows the east coast of Greenland and flows from northeast to southwest, the West Greenland current (WGC) into the Labrador Sea, and the Labrador current (LC), a coastal current following along the perimeter of the Labrador sea basin in an anti-clockwise fashion.

Many of the cores used to study the ice-rafted debris were from locations away from these currents. It is not clear whether anomalous cold conditions produced more southerly winds and ocean currents. However, many of the core locations are associated with a surface flow from the south in the present climate, so it is possible that the icebergs transported by the EGC, WGC, and LC end up in the North Atlantic current. One explanation is that the icebergs got caught in the warm currents from the south, and melted on their way north, but that does not necessary imply cold conditions in that region, as these warm ocean currents provide a heat transport and the melting of icebergs suggest higher temperatures.

Cold conditions favour the formation of sea-ice, which have very different characteristics to icebergs. Sea-ice forms when the sea surface freezes, and can affect the ocean circulation through their effect on salinity. However, sea-ice does not create debris of rocks and minerals, as the icebergs do when the bottom of the sliding icesheets scrape the rocks.

It is plausible that very cold conditions can produce thick sea-ice that will lock icebergs in place near their sources in the Labrador sea and along the east coast of Greenland, but seasonal variations in the sea-ice may also imply open water in the summer. Nevertheless, very cold conditions may not necessarily favour the production of icebergs, as freezing temperatures will prevent the formation of melt water acting as lubrication and the accumulation of ice is expected to be less due to lower precipitation.

In summary, the ‘remarkable’ correlation does not seem to support the hypothesis that high flux of GCR produces a very cold climate. The question is rather whether the ocean and atmospheric circulation were influenced by the level of solar activity and associated changes in the total solar irradiation (TSI) – without involving GCR. After all, GCR is affected by the level of solar activity through its influence of the inter-planetary magnetic field, and anti-correlated with the sunspots.

When taken in the context of the global warming, there are other problematic issues such as the lack of trend in GCR (here and here), stronger warming during nighttime than daytime, large unknowns regarding the physical mechanisms involved in the growth of ultra-small molecule clusters to much larger cloud condensation nuclei (here and here), and questionable data handling and statistical analysis (here). In addition, it is difficult to statistically distinguish between the apparent response to solar forcing in the observations and GCM which do not take GCRs into account (link to a recent paper by Gavin and myself), implying that GCRs are not needed to explain past global temperature trends.

So what makes the GCR-hypothesis so convincing that warrants a solicited talk at the EMS annual meeting and an invited presentation at the NASL? Is the support based on the attention in media, or does it have a scientific basis?

I want a response from the community still supporting the GCR hypothesis, explaining why they find it convincing after all these misgivings. The spirit of science is about discussing different ideas and challenge unconvincing points of view. So far, I feel that many of these issues have gone unheeded outside the climate research community. Perhaps an improved dialogue between various research communities can help resolving these issues – the counter-arguments and GCR hypothesis represent a paradox that should be sorted out if the science is to progress. Either the supporters of the GCR hypothesis should convincingly explain why these misgivings are unfounded or irrelevant, or the GCR hypothesis should be buried. However, I feel that there is a lack of dialogue and willingness to listen, so I think that progress is not likely to happen regarding a commonly accepted solution on the GCR hypothesis.

Update: According to a recent (October 16) news relsease from the International Ice Charting Working Group (IICWG), over 1,200 icebergs drifted into the trans-Atlantic shipping lanes in 2009, making the iceberg season in the North Atlantic the eleventh most severe since the tragic loss of the RMS Titanic in 1912.

P.S. So far in 2009, three articles have been published in the arXhive on GCR and clouds (here, here, here). It is possible that such articles are more accessible to communities other than climate research, and hence enhances the awareness about the controversy surrounding the GCR-hypothesis.

(1) This discussion focuses on just a short time period – starting 1998 or later – covering at most 11 years. Even under conditions of anthropogenic global warming (which would contribute a temperature rise of about 0.2 ºC over this period) a flat period or even cooling trend over such a short time span is nothing special and has happened repeatedly before (see 1987-1996). That simply is due to the fact that short-term natural variability has a similar magnitude (i.e. ~0.2 ºC) and can thus compensate for the anthropogenic effects. Of course, the warming trend keeps going up whilst natural variability just oscillates irregularly up and down, so over longer periods the warming trend wins and natural variability cancels out.

(2) It is highly questionable whether this “pause” is even real. It does show up to some extent (no cooling, but reduced 10-year warming trend) in the Hadley Center data, but it does not show in the GISS data, see Figure 1. There, the past ten 10-year trends (i.e. 1990-1999, 1991-2000 and so on) have all been between 0.17 and 0.34 ºC per decade, close to or above the expected anthropogenic trend, with the most recent one (1999-2008) equal to 0.19 ºC per decade – just as predicted by IPCC as response to anthropogenic forcing.

Figure 1. Global temperature according to NASA GISS data since 1980. The red line shows annual data, the larger red square a preliminary value for 2009, based on January-August. The green line shows the 25-year linear trend (0.19 ºC per decade). The blue lines show the two most recent ten-year trends (0.18 ºC per decade for 1998-2007, 0.19 ºC per decade for 1999-2008) and illustrate that these recent decadal trends are entirely consistent with the long-term trend and IPCC predictions. Even the highly “cherry-picked” 11-year period starting with the warm 1998 and ending with the cold 2008 still shows a warming trend of 0.11 ºC per decade (which may surprise some lay people who tend to connect the end points, rather than include all ten data points into a proper trend calculation).

Why do these two surface temperature data sets differ over recent years? We analysed this a while ago here, and the reason is the “hole in the Arctic” in the Hadley data, just where recent warming has been greatest.

Figure 2. The animated graph shows the temperature difference between the two 5-year periods 1999-2003 and 2004-2008. The largest warming has occurred over the Arctic in the past decade and is missing in the Hadley data.

If we want to relate global temperature to global forcings like greenhouse gases, we’d better not have a “hole” in our data set. That’s because global temperature follows a simple planetary heat budget, determined by the balance of what comes in and what goes out. But if data coverage is not really global, the heat budget is not closed. One would have to account for the heat flow across the boundary of the “hole”, i.e. in and out of the Arctic, and the whole thing becomes ill-determined (because we don’t know how much that is). Hence the GISS data are clearly more useful in this respect, and the supposed pause in warming turns out to be just an artifact of the “Arctic hole” in the Hadley data – we don’t even need to refer to natural variability to explain it.

Imagine you want to check whether the balance in your accounts is consistent with your income and spendings – and you find your bank accounts contain less money than you expected, so there is a puzzling shortfall. But then you realise you forgot one of your bank accounts when doing the sums – and voila, that is where the missing money is, so there is no shortfall after all. That missing bank account in the Hadley data is the Arctic – and we’ve shown that this is where the “missing warming” actually is, which is why there is no shortfall in the GISS data, and it is pointless to look for explanations for a warming pause.

It is noteworthy in this context that despite the record low in the brightness of the sun over the past three years (it’s been at its faintest since beginning of satellite measurements in the 1970s), a number of warming records have been broken during this time. March 2008 saw the warmest global land temperature of any March ever recorded in the past 130 years. June and August 2009 saw the warmest land and ocean temperatures in the Southern Hemisphere ever recorded for those months. The global ocean surface temperatures in 2009 broke all previous records for three consecutive months: June, July and August. The years 2007, 2008 and 2009 had the lowest summer Arctic sea ice cover ever recorded, and in 2008 for the first time in living memory the Northwest Passage and the Northeast Passage were simultaneously ice-free. This feat was repeated in 2009. Every single year of this century (2001-2008) has been warmer than all years of the 20th Century except 1998 (which sticks out well above the trend line due to a strong El Niño event).

The bottom line is: the observed warming over the last decade is 100% consistent with the expected anthropogenic warming trend of 0.2 ºC per decade, superimposed with short-term natural variability. It is no different in this respect from the two decades before. And with an El Niño developing in the Pacific right now, we wouldn’t be surprised if more temperature records were to be broken over the coming year or so.

Update: We were told there is a new paper by Simmons et al. in press with JGR that supports our analysis about the Hadley vs GISS trends (sorry, access to subscribers only).

Update: AP has just published an interesting story titled Statisticians reject global cooling, for which they “gave temperature data to four independent statisticians and asked them to look for trends, without telling them what the numbers represented”.

Indeed, according to both the National Review and the Daily Telegraph (and who would not trust these sources?), even Al Gore’s use of the stair lift in An Inconvenient Truth was done to highlight cherry-picked tree rings, instead of what everyone thought was the rise in CO2 concentrations in the last 200 years.

Who should we believe? Al Gore with his “facts” and “peer reviewed science” or the practioners of “Blog Science“? Surely, the choice is clear….

More seriously, many of you will have noticed yet more blogarrhea about tree rings this week. The target de jour is a particular compilation of trees (called a chronology in dendro-climatology) that was first put together by two Russians, Hantemirov and Shiyatov, in the late 1990s (and published in 2002). This multi-millennial chronology from Yamal (in northwestern Siberia) was painstakingly collected from hundreds of sub-fossil trees buried in sediment in the river deltas. They used a subset of the 224 trees they found to be long enough and sensitive enough (based on the interannual variability) supplemented by 17 living tree cores to create a “Yamal” climate record.

A preliminary set of this data had also been used by Keith Briffa in 2000 (pdf) (processed using a different algorithm than used by H&S for consistency with two other northern high latitude series), to create another “Yamal” record that was designed to improve the representation of long-term climate variability.

Since long climate records with annual resolution are few and far between, it is unsurprising that they get used in climate reconstructions. Different reconstructions have used different methods and have made different selections of source data depending on what was being attempted. The best studies tend to test the robustness of their conclusions by dropping various subsets of data or by excluding whole classes of data (such as tree-rings) in order to see what difference they make so you won’t generally find that too much rides on any one proxy record (despite what you might read elsewhere).

****

So along comes Steve McIntyre, self-styled slayer of hockey sticks, who declares without any evidence whatsoever that Briffa didn’t just reprocess the data from the Russians, but instead supposedly picked through it to give him the signal he wanted. These allegations have been made without any evidence whatsoever.

McIntyre has based his ‘critique’ on a test conducted by randomly adding in one set of data from another location in Yamal that he found on the internet. People have written theses about how to construct tree ring chronologies in order to avoid end-member effects and preserve as much of the climate signal as possible. Curiously no-one has ever suggested simply grabbing one set of data, deleting the trees you have a political objection to and replacing them with another set that you found lying around on the web.

The statement from Keith Briffa clearly describes the background to these studies and categorically refutes McIntyre’s accusations. Does that mean that the existing Yamal chronology is sacrosanct? Not at all – all of the these proxy records are subject to revision with the addition of new (relevant) data and whether the records change significantly as a function of that isn’t going to be clear until it’s done.

What is clear however, is that there is a very predictable pattern to the reaction to these blog posts that has been discussed many times. As we said last time there was such a kerfuffle:

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.

Plus ça change…

The timeline for these mini-blogstorms is always similar. An unverified accusation of malfeasance is made based on nothing, and it is instantly ‘telegraphed’ across the denial-o-sphere while being embellished along the way to apply to anything ‘hockey-stick’ shaped and any and all scientists, even those not even tangentially related. The usual suspects become hysterical with glee that finally the ‘hoax’ has been revealed and congratulations are handed out all round. After a while it is clear that no scientific edifice has collapsed and the search goes on for the ‘real’ problem which is no doubt just waiting to be found. Every so often the story pops up again because some columnist or blogger doesn’t want to, or care to, do their homework. Net effect on lay people? Confusion. Net effect on science? Zip.

Having said that, it does appear that McIntyre did not directly instigate any of the ludicrous extrapolations of his supposed findings highlighted above, though he clearly set the ball rolling. No doubt he has written to the National Review and the Telegraph and Anthony Watts to clarify their mistakes and we’re confident that the corrections will appear any day now…. Oh yes.

But can it be true that all Hockey Sticks are made in Siberia? A RealClimate exclusive investigation follows:

We start with the original MBH hockey stick as replicated by Wahl and Ammann:

Hmmm… neither of the Yamal chronologies anywhere in there. And what about the hockey stick that Oerlemans derived from glacier retreat since 1600?

Nope, no Yamal record in there either. How about Osborn and Briffa’s results which were robust even when you removed any three of the records?

Or there. The hockey stick from borehole temperature reconstructions perhaps?

No. How about the hockey stick of CO2 concentrations from ice cores and direct measurements?

Err… not even close. What about the the impact on the Kaufman et al 2009 Arctic reconstruction when you take out Yamal?

Oh. The hockey stick you get when you don’t use tree-rings at all (blue curve)?

No. Well what about the hockey stick blade from the instrumental record itself?

And again, no. But wait, maybe there is something (Update: Original idea by Lucia)….

Nah….

One would think that some things go without saying, but apparently people still get a key issue wrong so let us be extremely clear. Science is made up of people challenging assumptions and other peoples’ results with the overall desire of getting closer to the ‘truth’. There is nothing wrong with people putting together new chronologies of tree rings or testing the robustness of previous results to updated data or new methodologies. Or even thinking about what would happen if it was all wrong. What is objectionable is the conflation of technical criticism with unsupported, unjustified and unverified accusations of scientific misconduct. Steve McIntyre keeps insisting that he should be treated like a professional. But how professional is it to continue to slander scientists with vague insinuations and spin made-up tales of perfidy out of the whole cloth instead of submitting his work for peer-review? He continues to take absolutely no responsibility for the ridiculous fantasies and exaggerations that his supporters broadcast, apparently being happy to bask in their acclaim rather than correct any of the misrepresentations he has engendered. If he wants to make a change, he has a clear choice; to continue to play Don Quixote for the peanut gallery or to produce something constructive that is actually worthy of publication.

Peer-review is nothing sinister and not part of some global conspiracy, but instead it is the process by which people are forced to match their rhetoric to their actual results. You can’t generally get away with imprecise suggestions that something might matter for the bigger picture without actually showing that it does. It does matter whether something ‘matters’, otherwise you might as well be correcting spelling mistakes for all the impact it will have.

So go on Steve, surprise us.

Update: Briffa and colleagues have now responded with an extensive (and in our view, rather convincing) rebuttal.

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.

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, it’s 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 to be fair, Randy’s explanation of the title phrase does show it to be a bit more nuanced than 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!