Here’s another excerpt from the draft manuscript of my forthcoming book, Computing the Climate.

The idea that the temperature of the planet could be analyzed as a mathematical problem was first suggested by the French mathematician, Joseph Fourier, in the 1820s. Fourier had studied the up-and-down cycles of temperature between day and night, and between summer and winter, and had measured how deep into the ground these heating and cooling cycles reach. It turns out they don’t go very deep. At about 30 meters below the surface, temperatures remain constant all year round, showing no sign of daily or annual change. Today, Fourier is perhaps best remembered for his work on the mathematics of such cycles, and the Fourier transform, a technique for discovering cyclic waveforms in complex data series, was named in his honour.

The temperature of any object is due to the balance of heat entering and leaving it. If more heat is entering, the object warms up, and if more heat is leaving, it cools down. For the planet as a whole, Fourier pointed out there are only three possible sources of heat: the sun, the earth’s core, and background heat from space. His measurements showed that the heat at the earth’s core no longer warms the surface, because the diffusion of heat through layers of rock is too slow to make a noticeable difference. He thought that the temperature of space itself was probably about the same as the coldest temperatures on earth, as that would explain the temperature reached at the poles in the long polar winters. On this point, he was wrong—we now know space is close to absolute zero, a couple of hundred degrees colder than anywhere on earth. But he was correct about the sun being the main source of heat at the earth’s surface.

Fourier also realized there must be more to the story than that, otherwise the heat from the sun would escape to space just as fast as it arrived, causing night-time temperatures to drop back down to the temperature of space—and yet they don’t. We now know this is what happens on the moon, where temperatures drop by hundreds of degrees after the lunar sunset. So why doesn’t this happen on Earth?

The solution lay in the behaviour of ‘dark heat’, an idea that was new and mysterious to the scientists of the early nineteenth century. Today we call it infra-red radiation. Fourier referred to it as ‘radiant heat’ or ‘dark rays’ to distinguish it from ‘light heat’, or visible light. But really, they’re just different parts of the electromagnetic spectrum. Any object that’s warmer than its surroundings continually radiates some of its heat to those surroundings. If the object is hot enough, say a stove, you can feel this ‘dark heat’ if you put your hand near it, although it has to get pretty hot before we can feel the infra-red it gives off. As you heat up an object, the heat it radiates spreads up the spectrum from infra-red to visible light—it starts to glow red, and then, eventually white hot.

Fourier’s theory was elegantly simple. Because the sun is so hot, much of its energy arrives in the form of visible light, which passes through the atmosphere relatively easily, and warms the earth’s surface. As the earth’s surface is warm, it also radiates energy. The earth is cooler than the sun, so the energy the earth radiates is in the form of dark heat. Dark heat doesn’t pass though the atmosphere anywhere near as easily as light heat, so this slows the loss of energy back to space.

The surface temperature of the earth is determined by the balance between the incoming heat from the sun (shortwave rays, mainly in the visible light and ultra-violet) and the outgoing infra-red, radiated in all directions from the earth. The incoming short-wave rays passes through the atmosphere much more easily than the long-wave outgoing infra-red.

To explain the idea, Fourier used an analogy with the hotbox, a kind of solar oven, invented by the explorer Horace Bénédicte de Saussure. The hotbox was a very well-insulated wooden box, painted black inside, with three layers of glass in the lid. De Saussure had demonstrated that the sun would heat the inside of the box to over 100°C, and that this temperature remains remarkably consistent, even at the top of Mont Blanc, where the outside air is much colder. The glass lets the sun’s rays through, but slows the rate at which the heat can escape. Fourier argued that layers of air in the atmosphere play a similar role to the panes of glass in the hotbox, by trapping the outgoing heat; like the air in the hotbox, the planet would stay warmer than its surroundings. A century later, Fourier’s theory came to be called the ‘greenhouse effect’, perhaps because a greenhouse is more familiar to most people than a hotbox.

While Fourier had observed that air does indeed trap some of the dark heat from the ground, it wasn’t clear why, until the English scientist John Tyndall conducted a series of experiments in the 1850s to measure how well this ‘dark heat’ passes through different gases. Tyndall’s experiments used a four foot brass tube, sealed at both ends with transparent disks of salt crystal—glass was no good as it also blocks the dark heat. The tube could be filled with different kinds of gas. A tub of boiling water at one end provided a source of heat, and a galvanometer at the other compared the heat received through the tube with the heat from a second tub of boiling water.

Tyndall’s experimental equipment for testing the absorption properties of different gases. The brass tube was first evacuated, and the equipment calibrated by moving the screens until the temperature readings from the two heat sources were equal. Then the gas to be tested was pumped into the brass tube, and change in deflection of the galvanometer noted. (Figure adapted from Tyndall, 1861)

When Tyndall filled the tube with dry air, or oxygen, or nitrogen, there was very little change. But when he filled it with the hydrocarbon gas ethene, the temperature at the end of the tube dropped dramatically. This was so surprising that he first suspected something had gone wrong with the equipment—perhaps the gas had reacted with the salt, making the ends opaque? After re-testing every aspect of the equipment, he finally concluded that it was the ethene gas itself that was blocking the heat. He went on to test dozens of other gases and vapours, and found that more complex chemicals such as vapours of alcohols and oils were the strongest heat absorbers, while pure elements such as oxygen and nitrogen had the least effect.

Why do some gases allow visible light through, but block infra-red? It turns out that the molecules of each gas react to different wavelengths of light, depending on the molecule’s shape, similar to the way sound waves of just the right wavelength can cause a wine glass to resonate. Each type of molecule will vibrate when certain wavelengths of light hit it, making it stretch, contract, or rotate. So the molecule gains a little energy, and the light rays lose some. Scientists use this to determine which gases are in distant stars, because each gas makes a distinct pattern of dark lines across the spectrum from white light that has passed though it.

Tyndall noticed that gases made of more than one element, such as water vapour (H2O) or carbon dioxide (CO2), tend to absorb more energy from the infra-red rays than gases made of a single type of element, such as hydrogen or oxygen. He argued this provides evidence of atomic bonding: it wouldn’t happen if water was just a mixture of oxygen and hydrogen atoms. On this, he was partially right. We now know that what matters isn’t just the existence of molecular bonds, but whether the molecules are asymmetric—after all, oxygen gas molecules (O2) are also pairs of atoms bonded together. The more complex the molecular structure, the more asymmetries it has, and the more modes of vibration and spin the bonds have, allowing them to absorb energy at more different wavelengths. Today, we call any gas that absorbs parts of the infra-red spectrum a greenhouse gas. Compounds such as methane (CH4) and ethene (C2H4) absorb energy at more wavelengths than carbon dioxide, making them stronger greenhouse gases.

Tyndall’s experiments showed that greenhouse gases absorb infra-red even when the gases are only present in very small amounts. Increasing the concentration of the gas increases the amount of energy absorbed, but only up to a point. Once the concentration is high enough, adding more gas molecules has no further effect—all of the rays in that gas’s absorption bands have been blocked, while rays of other wavelengths pass through unaffected. Today, we call this saturation.

Tyndall concluded that, because of its abundance in the atmosphere, water vapour is responsible for most of the heat trapping effect, with carbon dioxide second. Some of the other vapours he tested have a much stronger absorption effect, but are so rare in the atmosphere they contribute little to the overall effect. Tyndall clearly understood the implications of his experiments for the earth’s climate, arguing that it explains why, for example, temperatures in dry regions such as deserts drop overnight far more than in more humid regions. In the 1861 paper describing his experimental results, Tyndall argued that any change in the levels of water vapour and carbon dioxide, “must produce a change of climate”. He speculated that “Such changes in fact may have produced all the mutations of climate which the researches of geologists reveal”.



















One of the biggest challenges in understanding climate change is that the timescales involved are far longer than most people are used to thinking about. Garvey points out that this makes climate change different from any other ethical question, because both the causes and consequences are smeared out across time and space:

“There is a sense in which my actions and the actions of my present fellows join with the past actions of my parents, grandparents and great-grandparents, and the effects resulting from our actions will still be felt hundreds, even thousands of years in the future. It is also true that we are, in a way, stuck with the present we have because of our past. The little actions I undertake which keep me warm and dry and fed are what they are partly because of choices made by people long dead. Even if I didn’t want to burn fossil fuels, I’m embedded in a culture set up to do so.” (Garvey, 2008, p60)

Part of the problem is that the physical climate system is slow to respond to our additional greenhouse gas emissions, and similarly slow to respond to reductions in emissions. The first part of this is core to a basic understanding of climate change, as it’s built into the idea of equilibrium climate sensitivity (roughly speaking, the expected temperature rise for each doubling of CO2 concentrations in the atmosphere). The extra heat that’s trapped by the additional greenhouse gases builds up over time, and the planet warms slowly, but the oceans have such a large thermal mass, it takes decades for this warming process to complete.

Unfortunately, the second part, that the planet takes a long time to respond to reductions in emissions, is harder to explain, largely because of the common assumption that CO2 will behave like other pollutants, which wash out of the atmosphere fairly quickly once we stop emitting them. This assumption underlies much of the common wait-and-see response to climate change, as it gives rise to the myth that once we get serious about climate change (e.g. because we start to see major impacts), we can fix the problem fairly quickly. Unfortunately, this is not true at all, because CO2 is a long-lived greenhouse gas. About half of human CO2 emissions are absorbed by the oceans and soils, over a period of several decades. The remainder stays in the atmosphere  There are several natural processes that remove the remaining CO2 from the atmosphere, but they take thousands of years, which means that even with zero greenhouse gas emissions, we’re likely stuck with the consequences of life on a warmer planet for centuries.

So the physical climate system presents us with two forms of inertia, one that delays the warming due to greenhouse gas emissions, and, one that delays the reduction in that warming in response to reduced emissions:

  1. The thermal inertia of the planet’s surface (largely due to the oceans), by which the planet can keep absorbing extra heat for years before it makes a substantial difference to surface temperatures. (scale: decades)
  2. The carbon cycle inertia by which CO2 is only removed from the atmosphere very slowly, and has a continued warming effect for as long as it’s there. (scale: decades to millennia)

For more on how these forms of inertia affect future warming scenarios, see my post on committed warming.

But these are not the only forms of inertia that matter. There are also various kinds of inertia in the socio-economic system that slow down our response to climate change. For example, Davis et. al. attempt to quantify the emissions from all the existing energy infrastructure (power plants, factories, cars, buildings, etc that already exist and are in use), because even under the most optimistic scenario, it will take decades to replace all this infrastructure with clean energy alternatives. Here’s an example of their analysis, under the assumption that things we’ve already built will not be retired early. This assumption is reasonable because (1) its rare that we’re willing to bear the cost of premature retirement of infrastructure and (2) it’s going to be hard enough building enough new clean energy infrastructure fast enough to replace stuff that has worn out while meeting increasing demand.


Expected ongoing carbon dioxide emissions from existing infrastructure. Includes primary infrastructure only – i.e. infrastructure that directly releases CO2 (e.g. cars & trucks), but not infrastructure that encourages the continued production of devices that emit CO2. (e.g. the network of interstate highways in the US). From Davis et al, 2010.

So that gives us our third form of inertia:

  1. Infrastructural inertia from existing energy infrastructure, as emissions of greenhouse gases will continue from everything we’ve built in the past, until it can be replaced. (scale: decades)

We’ve known about the threat of climate change for decades, and various governments and international negotiations have attempted to deal with it, and yet have made very little progress. Which suggests there are more forms of inertia that we ought to be able to name and quantify. To do this, we need to look at the broader socio-economic system that ought to allow us as a society to respond to the threat of climate change. Here’s a schematic of that system, as a systems dynamic model:

The socio-geophysical system. Arrows labelled ‘+’ are positive influence links (“A rise in X tends to cause a rise in Y, and a fall in X tends to cause a fall in Y”). Arrows labelled ‘-‘ represent negative links, where a rise in X tends to cause a fall in Y, and vice versa. The arrow labelled with a tap (faucet) is an accumulation link: Y will continue to rise even while X is falling, until X reaches net zero.

Broadly speaking, decarbonization will require both changes in technology and changes in human behaviour. But before we can do that, we have to recognize and agree that there is a problem, develop an agreed set of coordinated actions to tackle it, and then implement the policy shifts and behaviour changes to get us there.

At first, this diagram looks promising: once we realise how serious climate change is, we’ll take the corresponding actions, and that will bring down emissions, solving the problem. In other words, the more carbon emissions go up, the more they should drive a societal response, which in turn (eventually) will reduce emissions again. But the diagram includes a subtle but important twist: the link from carbon emissions to atmospheric concentrations is an accumulation link. Even as emissions fall, the amount of greenhouse gases in the atmosphere continue to rise. The latter rise only stops when carbon emissions reach zero. Think of a tap on the bathtub – if you reduce the inflow of water, the level of water in the tub still rises, until you turn the tap off completely.

Worse still, there are plenty more forms of inertia hidden in the diagram, because each of the causal links takes time to operate. I’ve given these additional sources of inertia names:

Sources of inertia in the socio-geophysical climate system

Sources of inertia in the socio-geophysical climate system

For example, there are forms of inertia that delay the impacts of increased temperatures, both on ecosystems and on human society. Most of the systems that are impacted by climate change can absorb smaller changes in the climate without much noticeable difference, but then reach a threshold whereby they can no longer be sustained. I’ve characterized two forms of inertia here:

  1. Natural variability (or “signal to noise”) inertia, which arises because initially, temperature increases due to climate change are much smaller than internal variability with daily and seasonal weather patterns. Hence it takes a long time for the ‘signal’ of climate change to emerge from the noise of natural variability. (scale: decades)
  2. Ecosystem resilience. We tend to think of resilience as a good thing – defined informally as the ability of a system to ‘bounce back’ after a shock. But resilience can also mask underlying changes that push a system closer and closer to a threshold beyond which it cannot recover. So this form of inertia acts by masking the effect of that change, sometimes until it’s too late to act. (scale: years to decades)

Then, once we identify the impacts of climate change (whether in advance or after the fact), it takes time for these to feed into the kind of public concern needed to build agreement on the need for action:

  1. Societal resilience. Human society is very adaptable. When storms destroy our buildings, we just rebuild them a little stronger. When drought destroys our crops, we just invent new forms of irrigation. Just as with ecosystems, there is a limit to this kind of resilience, when subjected to a continual change. But our ability to shrug and get on with things causes a further delay in the development of public concern about climate change. (scale: decades?)
  2. Denial. Perhaps even stronger than human resilience is our ability to fool ourselves into thinking that something bad is not happening, and to look for other explanations than the ones that best fit the evidence. Denial is a pretty powerful form of inertia. Denial stops addicts from acknowledging they need to seek help to overcome addiction, and it stops all of us from acknowledging we have a fossil fuel addiction, and need help to deal with it. (scale: decades to generations?)

Even then, public concern doesn’t immediately translate into effective action because of:

  1. Individualism. A frequent response to discussions on climate change is to encourage people to make personal changes in their lives: change your lightbulbs, drive a little less, fly a little less. While these things are important in the process of personal discovery, by helping us understanding our individual impact on the world, they are a form of voluntary action only available to the privileged, and hence do not constitute a systemic solution to climate change. When the systems we live in drive us towards certain consumption patterns, it takes a lot of time and effort to choose a low-carbon lifestyle. So the only way this scales is through collective political action: getting governments to change the regulations and price structures that shape what gets built and what we consume, and making governments and corporations accountable for cutting their greenhouse gas contributions. (scale: decades?)

When we get serious about the need for coordinated action, there are further forms of inertia that come into play:

  1. Missing governance structures. We simply don’t have the kind of governance at either the national or international level that can put in place meaningful policy instruments to tackle climate change. The Kyoto process failed because the short term individual interests of the national governments who have the power to act always tend to outweigh the long term collective threat of climate change. The Paris agreement is woefully inadequate for the same reason. Similarly, national governments are hampered by the need to respond to special interest groups (especially large corporations), which means legislative change is a slow, painful process. (scale: decades!)
  2. Bureaucracy. Hampers implementation of new policy tools. It takes time to get legislation formulated and agreed, and it takes time to set up the necessary institutions to ensure they are implemented. (scale: years)
  3. Social Resistance. People don’t like change, and some groups fight hard to resist changes that conflict with their own immediate interests. Every change in social norms is accompanied by pushback. And even when we welcome change and believe in it, we often slip back into old habits. (scale: years? generations?)

Finally, development and deployment of clean energy solutions experience a large number of delays:

  1. R&D lag. It takes time to ramp up new research and development efforts, due to the lack of qualified personnel, the glacial speed that research institutions such as universities operate, and the tendency, especially in academia, for researchers to keep working on what they’ve always worked on in the past, rather than addressing societally important issues. Research on climate solutions is inherently trans-disciplinary, and existing research institutions tend to be very bad at supporting work that crosses traditional boundaries. (scale: decades?)
  2. Investment lag: A wholescale switch from fossil fuels to clean energy and energy efficiency will require huge upfront investment. Agencies that have funding to enable this switch (governments, investment portfolio managers, venture capitalists) tend to be very risk averse, and so prefer things that they know offer a return on investment – e.g. more oil wells and pipelines rather than new cleantech alternatives (scale: years to decades)
  3. Diffusion of innovation: new technologies tend to take a long time to reach large scale deployment, following the classic s-shaped curve, with a small number of early adopters, and, if things go well, a steadily rising adoption curve, followed by a tailing off as laggards resist new technologies. Think about electric cars: while the technology has been available for years, they still only constitute less than 1% of new car sales today. Here’s a study that predicts this will rise to 35% by 2040. Think about that for a moment – if we follow the expected diffusion of innovation pattern, two thirds of new cars in 2040 will still have internal combustion engines. (scale: decades)

All of these forms of inertia slow the process of dealing with climate change, allowing the warming to steadily increase while we figure out how to overcome them. So the key problem isn’t how to address climate change by switching from the current fossil fuel economy to a carbon-neutral one – we probably have all the technologies to do this today. The problem is how to do it fast enough. To stay below 2°C of warming, the world needs to cut greenhouse gas emissions by 50% by 2030, and achieve carbon neutrality in the second half of the century. We’ll have to find a way of overcoming many different types of inertia if we are to make it.









Canada's Climate Targets



Today I’ve been tracking down the origin of the term “Greenhouse Effect”. The term itself is problematic, because it only works as a weak metaphor: both the atmosphere and a greenhouse let the sun’s rays through, and then trap some of the resulting heat. But the mechanisms are different. A greenhouse stays warm by preventing warm air from escaping. In other words, it blocks convection. The atmosphere keeps the planet warm by preventing (some wavelengths of) infra-red radiation from escaping. The “greenhouse effect” is really the result of many layers of air, each absorbing infra-red from the layer below, and then re-emitting it both up and down. The rate at which the planet then loses heat is determined by the average temperature of the topmost layer of air, where this infra-red finally escapes to space. So not really like a greenhouse at all.

So how did the effect acquire this name? The 19th century French mathematician Joseph Fourier is usually credited as the originator of the idea in the 1820’s. However, it turns out he never used the term, and as James Fleming (1999) points out, most authors writing about the history of the greenhouse effect cite only secondary sources on this, without actually reading any of Fourier’s work. Fourier does mention greenhouses in his 1822 classic “Analytical Theory of Heat”, but not in connection with planetary temperatures. The book was published in French, so he uses the french “les serres”, but it appears only once, in a passage on properties of heat in enclosed spaces. The relevant paragraph translates as:

In general the theorems concerning the heating of air in closed spaces extend to a great variety of problems. It would be useful to revert to them when we wish to foresee and regulate the temperature with precision, as in the case of green-houses, drying-houses, sheep-folds, work-shops, or in many civil establishments, such as hospitals, barracks, places of assembly” [Fourier, 1822; appears on p73 of the edition translated by Alexander Freeman, published 1878, Cambridge University Press]

In his other writings, Fourier did hypothesize that the atmosphere plays a role in slowing the rate of heat loss from the surface of the planet to space, hence keeping the ground warmer than it might otherwise be. However, he never identified a mechanism, as the properties of what we now call greenhouse gases weren’t established until John Tyndall‘s experiments in the 1850’s. In explaining his hypothesis, Fourier refers to a “hotbox”, a device invented by the explorer de Saussure, to measure the intensity of the sun’s rays. The hotbox had several layers of glass in the lid which allowed the sun’s rays to enter, but blocked the escape of the heated air via convection. But it was only a metaphor. Fourier understood that whatever the heat trapping mechanism in the atmosphere was, it didn’t actually block convection.

Svante Arrhenius was the first to attempt a detailed calculation of the effect of changing levels of carbon dioxide in the atmosphere, in 1896, in his quest to test a hypothesis that the ice ages were caused by a drop in CO2. Accordingly, he’s also sometime credited with inventing the term. However, he also didn’t use the term “greenhouse” in his papers, although he did invoke a metaphor similar to Fourier’s, using the Swedish word “drivbänk”, which translates as hotbed (Update: or possibly “hothouse” – see comments).

So the term “greenhouse effect” wasn’t coined until the 20th Century. Several of the papers I’ve come across suggest that the first use of the term “greenhouse” in this connection in print was in 1909, in a paper by Wood. This seems rather implausible though, because the paper in question is really only a brief commentary explaining that the idea of a “greenhouse effect” makes no sense, as a simple experiment shows that greenhouses don’t work by trapping outgoing infra-red radiation. The paper is clearly reacting to something previously published on the greenhouse effect, and which Wood appears to take way too literally.

A little digging produces a 1901 paper by Nils Ekholm, a Swedish meteorologist who was a close colleague of Arrhenius, which does indeed use the term ‘greenhouse’. At first sight, he seems to use the term more literally than is warranted, although in subsequent paragraphs, he explains the key mechanism fairly clearly:

The atmosphere plays a very important part of a double character as to the temperature at the earth’s surface, of which the one was first pointed out by Fourier, the other by Tyndall. Firstly, the atmosphere may act like the glass of a green-house, letting through the light rays of the sun relatively easily, and absorbing a great part of the dark rays emitted from the ground, and it thereby may raise the mean temperature of the earth’s surface. Secondly, the atmosphere acts as a heat store placed between the relatively warm ground and the cold space, and thereby lessens in a high degree the annual, diurnal, and local variations of the temperature.

There are two qualities of the atmosphere that produce these effects. The one is that the temperature of the atmosphere generally decreases with the height above the ground or the sea-level, owing partly to the dynamical heating of descending air currents and the dynamical cooling of ascending ones, as is explained in the mechanical theory of heat. The other is that the atmosphere, absorbing but little of the insolation and the most of the radiation from the ground, receives a considerable part of its heat store from the ground by means of radiation, contact, convection, and conduction, whereas the earth’s surface is heated principally by direct radiation from the sun through the transparent air.

It follows from this that the radiation from the earth into space does not go on directly from the ground, but on the average from a layer of the atmosphere having a considerable height above sea-level. The height of that layer depends on the thermal quality of the atmosphere, and will vary with that quality. The greater is the absorbing power of the air for heat rays emitted from the ground, the higher will that layer be, But the higher the layer, the lower is its temperature relatively to that of the ground ; and as the radiation from the layer into space is the less the lower its temperature is, it follows that the ground will be hotter the higher the radiating layer is.” [Ekholm, 1901, p19-20]

At this point, it’s still not called the “greenhouse effect”, but this metaphor does appear to have become a standard way of introducing the concept. But in 1909, the English scientist, John Henry Poynting confidently introduces the term “greenhouse effect”, in his criticism of Percival Lowell‘s analysis of the temperature of the planets. He uses it in scare quotes throughout the paper, which suggests the term is newly minted:

Prof. Lowell’s paper in the July number of the Philosophical Magazine marks an important advance in the evaluation of planetary temperatures, inasmuch as he takes into account the effect of planetary atmospheres in a much more detailed way than any previous wrlter. But he pays hardly any attention to the “blanketing effect,” or, as I prefer to call it, the “greenhouse effect” of the atmosphere.” [Poynting, 1907, p749]

And he goes on:

The ” greenhouse effect” of the atmosphere may perhaps be understood more easily if we first consider the case of a greenhouse with horizontal roof of extent so large compared with its height above the ground that the effect of the edges may be neglected. Let us suppose that it is exposed to a vertical sun, and that the ground under the glass is “black” or a full absorber. We shall neglect the conduction and convection by the air in the greenhouse. [Poynting, 1907, p750]

He then goes on to explore the mathematics of heat transfer in this idealized greenhouse. Unfortunately, he ignores Ekholm’s crucial observation that it is the rate of heat loss at the upper atmosphere that matters, so his calculations are mostly useless. But his description of the mechanism does appear to have taken hold as the dominant explanation. The following year, Frank Very published a response (in the same journal), using the term “Greenhouse Theory” in the title of the paper. He criticizes Poynting’s idealised greenhouse as way too simplistic, but suggests a slightly better metaphor is a set of greenhouses stacked one above another, each of which traps a little of the heat from the one below:

It is true that Professor Lowell does not consider the greenhouse effect analytically and obviously, but it is nevertheless implicitly contained in his deduction of the heat retained, obtained by the method of day and night averages. The method does not specify whether the heat is lost by radiation or by some more circuitous process; and thus it would not be precise to label the retaining power of the atmosphere a “greenhouse effect” without giving a somewhat wider interpretation to this name. If it be permitted to extend the meaning of the term to cover a variety of processes which lead to identical results, the deduction of the loss of surface heat by comparison of day and night temperatures is directly concerned with this wider “greenhouse effect.” [Very, 1908, p477]

Between them, Poynting and Very are attempting to pin down whether the “greenhouse effect” is a useful metaphor, and how the heat transfer mechanisms of planetary atmospheres actually work. But in so doing, they help establish the name. Wood’s 1909 comment is clearly a reaction to this discussion, but one that fails to understand what is being discussed. It’s eerily reminiscent of any modern discussion of the greenhouse effect: whenever any two scientists discuss the details of how the greenhouse effect works, you can be sure someone will come along sooner or later claiming to debunk the idea by completely misunderstanding it.

In summary, I think it’s fair to credit Poynting as the originator of the term “greenhouse effect”, but with a special mention to Ekholm for both his prior use of the word “greenhouse”, and his much better explanation of the effect. (Unless I missed some others?)


Arrhenius, S. (1896). On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground. Philosophical Magazine and Journal of Science, 41(251). doi:10.1080/14786449608620846

Ekholm, N. (1901). On The Variations Of The Climate Of The Geological And Historical Past And Their Causes. Quarterly Journal of the Royal Meteorological Society, 27(117), 1–62. doi:10.1002/qj.49702711702

Fleming, J. R. (1999). Joseph Fourier, the “greenhouse effect”, and the quest for a universal theory of terrestrial temperatures. Endeavour, 23(2), 72–75. doi:10.1016/S0160-9327(99)01210-7

Fourier, J. (1822). Théorie Analytique de la Chaleur (“Analytical Theory of Heat”). Paris: Chez Firmin Didot, Pere et Fils.

Fourier, J. (1827). On the Temperatures of the Terrestrial Sphere and Interplanetary Space. Mémoires de l’Académie Royale Des Sciences, 7, 569–604. (translation by Ray Pierrehumbert)

Poynting, J. H. (1907). On Prof. Lowell’s Method for Evaluating the Surface-temperatures of the Planets; with an Attempt to Represent the Effect of Day and Night on the Temperature of the Earth. Philosophical Magazine, 14(84), 749–760.

Very, F. W. (1908). The Greenhouse Theory and Planetary Temperatures. Philosophical Magazine, 16(93), 462–480.

Wood, R. W. (1909). Note on the Theory of the Greenhouse. Philosophical Magazine, 17, 319–320. Retrieved from





I’m heading off to Florence this week for the International Conference on Software Engineering (ICSE). The highlight of the week will be a panel session I’m chairing, on the Karlskrona Manifesto. The manifesto itself is something we’ve been working on since last summer – a group of us wrote the first draft at the Requirements Engineering conference in Karlskrona, Sweden, last summer (hence the name). This week we’re launching a website for the manifesto, and we’ve published a longer technical paper about it at ICSE.

The idea of the manifesto is to inspire deeper analysis of the roles and responsibilities of technology designers (and especially software designers), given that software systems now shape so much of modern life. We rarely stop to think about the unintended consequences of very large numbers of people using our technologies, nor do we ask whether, on balance, an idea that looks cool on paper will merely help push us even further into unsustainable behaviours. The position we take in the manifesto is that, as designers, our responsibility for the consequences of our designs are much broader than most of us acknowledge, and it’s time to do something about it.

For the manifesto, we ended up thinking about sustainability in terms of five dimensions:

  • Environmental sustainability: the long term viability of natural systems, including ecosystems, resource consumption, climate, pollution food, water, and waste.
  • Social sustainability: the quality of social relationships and the factors that tend to improve or erode trust in society, such as social equity, democracy, and justice.
  • Individual sustainability: the health and well-being of people as individuals, including mental and physical well-being, education, self-respect, skills, and mobility.
  • Economic sustainability: the long term viability of economic activities, such as businesses and nations, including issues such as investment, wealth creation and prosperity.
  • Technical sustainability: the ability to sustain technical systems and their infrastructures, including software maintenance, innovation, obsolescence, and data integrity.

There are of course, plenty of other ways of defining sustainability (which we discuss in the paper), and some hard constraints in some dimensions – e.g. we cannot live beyond the resource limits of the planet, no matter how much progress we make towards sustainability in other other dimensions. But a key insight is that all five dimensions matter, and none of them can be treated in isolation. For example, we might think we’re doing fine in one dimension – economic, say, as we launch a software company with a sound business plan that can make a steady profit – but often we do so only by incurring a debt in other dimensions, perhaps harming the environment by contributing to the mountains of e-waste, or harming social sustainability by replacing skilled jobs with subsistence labour.

The manifesto characterizes a set of problems in how technologists normally think about sustainability (if they do), and ends with a set of principles for sustainability design:

  • Sustainability is systemic. Sustainability is never an isolated property. Systems thinking has to be the starting point for the transdisciplinary common ground of sustainability.
  • Sustainability has multiple dimensions. We have to include those dimensions into our analysis if we are to understand the nature of sustainability in any given situation.
  • Sustainability transcends multiple disciplines. Working in sustainability means working with people from across many disciplines, addressing the challenges from multiple perspectives.
  • Sustainability is a concern independent of the purpose of the system. Sustainability has to be considered even if the primary focus of the system under design is not sustainability.
  • Sustainability applies to both a system and its wider contexts. There are at least two spheres to consider in system design: the sustainability of the system itself and how it affects sustainability of the wider system of which it will be part.
  • Sustainability requires action on multiple levels. Some interventions have more leverage on a system than others. Whenever we take action towards sustainability, we should consider opportunity costs: action at other levels may offer more effective forms of intervention.
  • System visibility is a necessary precondition and enabler for sustainability design. The status of the system and its context should be visible at different levels of abstraction and perspectives to enable participation and informed responsible choice.
  • Sustainability requires long-term thinking. We should assess benefits and impacts on multiple timescales, and include longer-term indicators in assessment and decisions.
  • It is possible to meet the needs of future generations without sacrificing the prosperity of the current generation. Innovation in sustainability can play out as decoupling present and future needs. By moving away from the language of conflict and the trade-off mindset, we can identify and enact choices that benefit both present and future.

You can read the full manifesto at, and watch for the twitter tags  and .  I’m looking forward to lots of constructive discussions this week.




Tactics for Detecting Crap on the Internet


  1. Authorship
    • What do others say about the author?
  2. Sources cited
  3. Ownership of the website
  4. Connectedness
  5. Design & Interactivity
  6. Triangulation
  7. Check your own biases

(Download the full worksheet)


Here’s my abstract for the conference:

Constructive and External Validity for Climate Modeling




For more background, see:




Steve Easterbrook, University of Toronto


At the beginning of March, I was invited to give a talk at TEDxUofT. Colleagues tell me the hardest part of giving these talks is deciding what to talk about. I decided to see if I could answer the question of whether we can trust climate models. It was a fascinating and nerve-wracking experience, quite unlike any talk I’ve given before. Of course, I’d love to do another one, as I now know more about what works and what doesn’t.

Here’s the video and a transcript of my talk. [The bits in square brackets in are things I intended to say but forgot!] 

Computing the Climate: How Can a Computer Model Forecast the Future? TEDxUofT, March 1, 2014.

Talking about the weather forecast is a great way to start a friendly conversation. The weather forecast matters to us. It tells us what to wear in the morning; it tells us what to pack for a trip. We also know that weather forecasts can sometimes be wrong, but we’d be foolish to ignore them when they tell us a major storm is heading our way.

[Unfortunately, talking about climate forecasts is often a great way to end a friendly conversation!] Climate models tell us that by the end of this century, if we carry on burning fossil fuels at the rate we have been doing, and we carry on cutting down forests at the rate we have been doing, the planet will warm by somewhere between 5 to 6 degrees centigrade. That might not seem much, but, to put it into context, in the entire history of human civilization, the average temperature of the planet has not varied by more than 1 degree. So that forecast tells us something major is coming, and we probably ought to pay attention to it.

But on the other hand, we know that weather forecasts don’t work so well the longer into the future we peer. Tomorrow’s forecast is usually pretty accurate. Three day and five day forecasts are reasonably good. But next week? They always change their minds before next week comes. So how can we peer 100 years into the future and look at what is coming with respect to the climate? Should we trust those forecasts? Should we trust the climate models that provide them to us?

Six years ago, I set out to find out. I’m a professor of computer science. I study how large teams of software developers can put together complex pieces of software. I’ve worked with NASA, studying how NASA builds the flight software for the Space Shuttle and the International Space Station. I’ve worked with large companies like Microsoft and IBM. My work focusses not so much on software errors, but on the reasons why people make those errors, and how programmers then figure out they’ve made an error, and how they know how to fix it.

To start my study, I visited four major climate modelling labs around the world: in the UK, in Paris, Hamburg, Germany and in Colorado. Each of these labs have typically somewhere between 50-100 scientists who are contributing code to their climate models. And although I only visited four of these labs, there are another twenty or so around the world, all doing similar things. They run these models on some of the fastest supercomputers in the world, and many of the models have been in construction, the same model, for more than 20 years.

When I started this study, I asked one of my students to attempt to measure how many bugs there are in a typical climate model. We know from our experience with software there are always bugs. Sooner or later the machine crashes. So how buggy are climate models? More specifically, what we set out to measure is what we call “defect density” – How many errors are there per thousand lines of code. By this measure, it turns out climate models are remarkably high quality. In fact, they’re better than almost any commercial software that’s ever been studied. They’re about the same level of quality as the Space Shuttle flight software. Here’s my results (For the actual results you’ll have to read the paper):


We know it’s very hard to build a large complex piece of software without making mistakes.  Even the space shuttle’s software had errors in it. So the question is not “is the software perfect for predicting the future?”. The question is “Is it good enough?” Is it fit for purpose?

To answer that question, we’d better understand what that purpose of a climate model is. First of all, I’d better be clear what a climate model is not. A climate model is not a projection of trends we’ve seen in the past extrapolated into the future. If you did that, you’d be wrong, because you haven’t accounted for what actually causes the climate to change, and so the trend might not continue. They are also not decision-support tools. A climate model cannot tell us what to do about climate change. It cannot tell us whether we should be building more solar panels, or wind farms. It can’t tell us whether we should have a carbon tax. It can’t tell us what we ought to put into an international treaty.

What it does do is tell us how the physics of planet earth work, and what the consequences are of changing things, within that physics. I could describe it as “computational fluid dynamics on a rotating sphere”. But computational fluid dynamics is complex.

I went into my son’s fourth grade class recently, and I started to explain what a climate model is, and the first question they asked me was “is it like Minecraft?”. Well, that’s not a bad place to start. If you’re not familiar with Minecraft, it divides the world into blocks, and the blocks are made of stuff. They might be made of wood, or metal, or water, or whatever, and you can build things out of them. There’s no gravity in Minecraft, so you can build floating islands and it’s great fun.

Climate models are a bit like that. To build a climate model, you divide the world into a number of blocks. The difference is that in Minecraft, the blocks are made of stuff. In a climate model, the blocks are really blocks of space, through which stuff can flow. At each timestep, the program calculates how much water, or air, or ice is flowing into, or out of, each block, and if so, in which directions? It calculates changes in temperature, density, humidity, and so on. And whether stuff such as dust, salt, and pollutants are passing through or accumulating in each block. We have to account for the sunlight passing down through the block during the day. Some of what’s in each block might filter some of the the incoming sunlight, for example if there are clouds or dust, so some of the sunlight doesn’t get down to the blocks below. There’s also heat escaping upwards through the blocks, and again, some of what is in the block might trap some of that heat — for example clouds and greenhouse gases.

As you can see from this diagram, the blocks can be pretty large. The upper figure shows blocks of 87km on a side. If you want more detail in the model, you have to make the blocks smaller. Some of the fastest climate models today look more like the lower figure:


Ideally, you want to make the blocks as small as possible, but then you have many more blocks to keep track of, and you get to the point where the computer just can’t run fast enough. A typical run of a climate model, to simulate a century’s worth of climate, you might have to wait a couple of weeks on some of the fastest supercomputers for that run to complete. So the speed of the computer limits how small we can make the blocks.

Building models this way is remarkably successful. Here’s video of what a climate model can do today. This simulation shows a year’s worth of weather from a climate model. What you’re seeing is clouds and, in orange, that’s where it’s raining. Compare that to a year’s worth of satellite data for the year 2013. If you put them side by side, you can see many of the same patterns. You can see the westerlies, the winds at the top and bottom of the globe, heading from west to east, and nearer the equator, you can see the trade winds flowing in the opposite direction. If you look very closely, you might even see a pulse over South America, and a similar one over Africa in both the model and the satellite data. That’s the daily cycle as the land warms up in the morning and the moisture evaporates from soils and plants, and then later on in the afternoon as it cools, it turns into rain.

Note that the bottom is an actual year, 2013, while the top, the model simulation is not a real year at all – it’s a typical year. So the two don’t correspond exactly. You won’t get storms forming at the same time, because it’s not designed to be an exact simulation; the climate model is designed to get the patterns right. And by and large, it does. [These patterns aren’t coded into this model. They emerge as a consequences of getting the basic physics of the atmosphere right].

So how do you build a climate model like this? The answer is “very slowly”. It takes a lot of time, and a lot of failure. One of the things that surprised me when I visited these labs is that the scientists don’t build these models to try and predict the future. They build these models to try and understand the past. They know their models are only approximations, and they regularly quote the statistician, George Box, who said “All models are wrong, but some are useful”. What he meant is that any model of the world is only an approximation. You can’t get all the complexity of the real world into a model. But even so, even a simple model is a good way to test your theories about the world.

So the way that modellers work, is they spend their time focussing on places where the model does isn’t quite right. For example, maybe the model isn’t getting the Indian monsoon right. Perhaps it’s getting the amount of rain right, but it’s falling in the wrong place. They then form a hypothesis. They’ll say, I think I can improve the model, because I think this particular process is responsible, and if I improve that process in a particular way, then that should fix the simulation of the monsoon cycle. And then they run a whole series of experiments, comparing the old version of the model, which is getting it wrong, with the new version, to test whether the hypothesis is correct. And if after a series of experiments, they believe their hypothesis is correct, they have to convince the rest of the modelling team that this really is an improvement to the model.

In other words, to build the models, they are doing science. They are developing hypotheses, they are running experiments, and using peer review process to convince their colleagues that what they have done is correct:


Climate modellers also have a few other weapons up their sleeves. Imagine for a moment if Microsoft had 25 competitors around the world, all of whom were attempting to build their own versions of Microsoft Word. Imagine further that every few years, those 25 companies all agreed to run their software on a very complex battery of tests, designed to test all the different conditions under which you might expect a word processor to work. And not only that, but they agree to release all the results of those tests to the public, on the internet, so that anyone who wanted to use any of that software can pore over all the data and find out how well each version did, and decide which version they want to use for their own purposes. Well, that’s what climate modellers do. There is no other software in the world for which there are 25 teams around the world trying to build the same thing, and competing with each other.

Climate modellers also have some other advantages. In some sense, climate modelling is actually easier than weather forecasting. I can show you what I mean by that. Imagine I had a water balloon (actually, you don’t have to imagine – I have one here):


I’m going to throw it at the fifth row. Now, you might want to know who will get wet. You could measure everything about my throw: Will I throw underarm, or overarm? Which way am I facing when I let go of it? How much swing do I put in? If you could measure all of those aspects of my throw, and you understand the physics of how objects move, you could come up with a fairly accurate prediction of who is going to get wet.

That’s like weather forecasting. We have to measure the current conditions as accurately as possible, and then project forward to see what direction it’s moving in:


If I make any small mistakes in measuring my throw, those mistakes will multiply as the balloon travels further. The further I attempt to throw it, the more room there is for inaccuracy in my estimate. That’s like weather forecasting. Any errors in the initial conditions multiply up rapidly, and the current limit appears to be about a week or so. Beyond that, the errors get so big that we just cannot make accurate forecasts.

In contrast, climate models would be more like releasing a balloon into the wind, and predicting where it will go by knowing about the wind patterns. I’ll make some wind here using a fan:


Now that balloon is going to bob about in the wind from the fan. I could go away and come back tomorrow and it will still be doing about the same thing. If the power stays on, I could leave it for a hundred years, and it might still be doing the same thing. I won’t be able to predict exactly where that balloon is going to be at any moment, but I can predict, very reliably, the space in which it will move. I can predict the boundaries of its movement. And if the things that shape those boundaries change, for example by moving the fan, and I know what the factors are that shape those boundaries, I can tell you how the patterns of its movements are going to change – how the boundaries are going to change. So we call that a boundary problem:


The initial conditions are almost irrelevant. It doesn’t matter where the balloon started, what matters is what’s shaping its boundary.

So can these models predict the future? Are they good enough to predict the future? The answer is “yes and no”. We know the models are better at some things than others. They’re better at simulating changes in temperature than they are at simulating changes in rainfall. We also know that each model tends to be stronger in some areas and weaker in others. If you take the average of a whole set of models, you get a much better simulation of how the planet’s climate works than if you look at any individual model on its own. What happens is that the weaknesses in any one model are compensated for by other models that don’t have those weaknesses.

But the results of the models have to be interpreted very carefully, by someone who knows what the models are good at, and what they are not good at – you can’t just take the output of a model and say “that’s how it’s going to be”.

Also, you don’t actually need a computer model to predict climate change. The first predictions of what would happen if we keep on adding carbon dioxide to the atmosphere were produced over 120 years ago. That’s fifty years before the first digital computer was invented. And those predictions were pretty accurate – what has happened over the twentieth century has followed very closely what was predicted all those years ago. Scientists also predicted, for example, that the arctic would warm faster than the equatorial regions, and that’s what happened. They predicted night time temperatures would rise faster than day time temperatures, and that’s what happened.

So in many ways, the models only add detail to what we already know about the climate. They allow scientists to explore “what if” questions. For example, you could ask of a model, what would happen if we stop burning all fossil fuels tomorrow. And the answer from the models is that the temperature of the planet will stay at whatever temperature it was when you stopped. For example, if we wait twenty years, and then stopped, we’re stuck with whatever temperature we’re at for tens of thousands of years. You could ask a model what happens if we dig up all known reserves of fossil fuels, and burn them all at once, in one big party? Well, it gets very hot.

More interestingly, you could ask what if we tried blocking some of the incoming sunlight to cool the planet down, to compensate for some of the warming we’re getting from adding greenhouse gases to the atmosphere? There have been a number of very serious proposals to do that. There are some who say we should float giant space mirrors. That might be hard, but a simpler way of doing it is to put dust up in the stratosphere, and that blocks some of the incoming sunlight. It turns out that if you do that, you can very reliably bring the average temperature of the planet back down to whatever level you want, just by adjusting the amount of the dust. Unfortunately, some parts of the planet cool too much, and others not at all. The crops don’t grow so well, and everyone’s weather gets messed up. So it seems like that could be a solution, but when you study the model results in detail, there are too many problems.

Remember that we know fairly well what will happen to the climate if we keep adding CO2, even without using a computer model, and the computer models just add detail to what we already know. If the models are wrong, they could be wrong in either direction. They might under-estimate the warming just as much as they might over-estimate it. If you look at how well the models can simulate the past few decades, especially the last decade, you’ll see some of both. For example, the models have under-estimated how fast the arctic sea ice has melted. The models have underestimated how fast the sea levels have risen over the last decade. On the other hand, they over-estimated the rate of warming at the surface of the planet. But they underestimated the rate of warming in the deep oceans, so some of the warming ends up in a different place from where the models predicted. So they can under-estimate just as much as they can over-estimate. [The less certain we are about the results from the models, the bigger the risk that the warming might be much worse than we think.]

So when you see a graph like this, which comes from the latest IPCC report that just came out last month, it doesn’t tell us what to do about climate change, it just tells us the consequences of what we might choose to do. Remember, humans aren’t represented in the models at all, except in terms of us producing greenhouse gases and adding them to the atmosphere.


If we keep on increasing our use of fossil fuels — finding more oil, building more pipelines, digging up more coal, we’ll follow the top path. And that takes us to a planet that by the end of this century, is somewhere between 4 and 6 degrees warmer, and it keeps on getting warmer over the next few centuries. On the other hand, the bottom path, in dark blue, shows what would happen if, year after year from now onwards, we use less fossil fuels than we did the previous year, until about mid-century, when we get down to zero emissions, and we invent some way to start removing that carbon dioxide from the atmosphere before the end of the century, to stay below 2 degrees of warming.

The models don’t tell us which of these paths we should follow. They just tell us that if this is what we do, here’s what the climate will do in response. You could say that what the models do is take all the data and all the knowledge we have about the climate system and how it works, and put them into one neat package, and its our job to take that knowledge and turn it into wisdom. And to decide which future we would like.

My department is busy revising the set of milestones our PhD students need to meet in the course of their studies. The milestones are intended to ensure each student is making steady progress, and to identify (early!) any problems. At the moment they don’t really do this well, in part because the faculty all seem to have different ideas about what we should expect at each milestone. (This is probably a special case of the general rule that if you gather n professors together, they will express at least n+1 mutually incompatible opinions). As a result, the students don’t really know what’s expected of them, and hence spend far longer in the PhD program than they would need to if they received clear guidance.

Anyway, in order to be helpful, I wrote down what I think are the set of skills that a PhD student needs to demonstrate early in the program, as a prerequisite for becoming a successful researcher:

  1. The ability to select a small number of significant research contributions from a larger set of published papers, and justify that selection.
  2. The ability to articulate a rationale for selection of these papers, on the basis of significance of the results, novelty of the approach, etc.
  3. The ability to relate the papers to one another, and to other research in the literature.
  4. The ability to critique the research methods used in these papers, the strengths and weaknesses of these methods, and likely threats to validity, whether acknowledged in the papers or not.
  5. The ability to suggest alternative approaches to answering the research questions posed in these papers.
  6. The ability to identify limitations on the results reported in the papers, along with their implications.
  7. The ability to identify and prioritize lines of investigation for further research, based on limitations of the research described in the papers and/or important open problems that the papers fail to answer.

My suggestion is that at the end of the first year of the PhD program, each student should demonstrate development of these skills by writing a short report that selects and critiques a handful (4-6) of papers in a particular subfield. If a student can’t do this well, they’re probably not going to succeed in the PhD program.

My proposal has now gone to the relevant committee (“where good ideas go to die™”), so we’ll see what happens…






Weather as an initial value problem


Climate as a boundary value problem


Climate change as a change in boundary conditions

