by David Orrell
“It is easier to predict climate statistics than next week’s weather.” If the climate is completely stable, this is obviously true. But if the climate is changing, it should be no easier to predict than the short-term weather. This is reflected in the huge uncertainty in climate change estimates.
“The models don’t work now, but they will in ten to fifteen years.” That’s what was said ten to fifteen years ago, but uncertainty has only increased. We may have a better idea by then of what global warming entails, but it probably won’t be because of improved models.
“Different model versions cover everything from no warming to more than 11°C, so one must be right, at least for the global average.” True, but it doesn’t mean the models are right—just that they are very flexible.
“Criticisms are politically motivated.” Avoiding criticism by brushing it off as political is political.
Of course, the question of model validity is distinct from that of global warming. As in most areas of life, it is possible to believe there is a problem without claiming to be able to predict the future or have an accurate model.
THE BUTTERFLY EFFECT
This is not to say that it is impossible to make any kind of sense out of the global climate system, or to make any predictions. To take an example from a different context, suppose that a dietician monitors a child who has taken to eating a number of candy bars a day. It would be impossible to compute or predict the exact effect: in some children the candy will speed their metabolism so they burn off the energy (negative feedback), while in others it will slow them down and trigger much larger weight gains (positive feedback). However, the dietician can make an educated guess, and she would certainly expect the child to either gain weight or stay at the same weight, but not to lose weight.60
If measurements of the child’s weight over a few weeks showed that it was indeed increasing, while factors such as the amount of physical exercise remained more or less the same, then the dietician would have some confidence in saying that the candy was a likely cause. And if blood tests showed elevated levels of glucose, then immediate action would be called for. Glucose is present in our blood at a low concentration and is closely regulated by the body. If it doubles to twice its normal level, diabetes is suspected.
Similarly, scientists since Arrhenius have known that increased greenhouse gases will have a warming effect on the climate (which is why they are called greenhouse gases and not refrigerator gases). Observations of the ocean’s heat content, the worldwide retreat of glaciers, the melting of sea ice and permafrost in northern regions, and the rise in mean global temperatures all show that the planet is heating up in a manner consistent with an enhanced greenhouse effect.61 This in itself does not prove that increased carbon emissions are the cause, since the climate naturally fluctuates of its own accord, but it certainly makes it more plausible. Predicting the exact reaction, however, is not possible: the planet may limit the effect of greenhouse gases through negative feedback, or it may pass a threshold that leads to runaway warming or triggers a sudden shift to a different climate state. And tinkering with complex feedback loops—for example, by destroying tropical rainforests—may make the climate behave in a more erratic fashion, like a mutant strain of yeast without its control mechanisms.
One indicator of changes in the climate is the migration of species such as butterflies. The Edith’s Checkerspot butterfly, which lives along the Pacific coast and has been documented since 1860, has already begun moving north and to higher elevations. Its range now extends much deeper into British Columbia and Alberta. At the same time, colonies are disappearing from California and northern Mexico.62 If global warming continues, many species will be trying to do the same thing. Tropical diseases such as malaria may be carried right out of the tropics by their mosquito hosts. But some animals, like polar bears, will have no farther north to go. And of course, people will need to migrate if their situation becomes untenable.63
Such effects can in principle be assessed by ecosystem models (see Appendix III for a conceptual example). These attempt to capture the interactions between various species and their environment, and can be coupled with the output of GCMs. However, the uncertainty in the climate-change predictions—which are even worse at forecasting local effects than global trends64—is magnified by the great uncertainty in the ecosystem models. The results are therefore speculative, but they may help identify species and ecosystems at risk. For many species, the main problem is less global warming than the fact that we are eating large numbers of them (fish, for example).65
WEALTH: HOT ECONOMY
Global warming is directly linked to one species in particular—our own. The IPCC projections have all assumed a doubling of carbon-dioxide levels. When exactly this will happen depends on emission rates, which in turn depend on the course of the global economy. This is subject to even more vagaries than the global climate, and it’s even harder to predict.
To give one example: policy-makers would like to know how the stock market will grow over, say, the next seventy-five years so they can fund retirement programs. Economists can produce estimates based on a combination of numerical equations and historical analogies, but long-term predictions are no easier than short-term ones. (There is just a better chance that no one will remember your forecast.) Estimates of long-term U.S. stock-market returns typically vary from around 4.5 percent to 7 percent, which compounded over seventy-five years represents more than a factor of five difference.66
Perhaps for this reason, the IPCC decided, for its climate calculations, to study an array of different scenarios or storylines. These ranged from A1F1—whose “major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional difference in per capita income”—to B2, where “the emphasis is on local solutions to economic, social and environmental sustainability.”67 It is impossible to know which scenario is more probable, so all were assumed to be equally likely. The estimated uncertainty owing to different economic scenarios, for a fixed model, turns out to be about the same as the uncertainty owing to different models. This is despite the fact that the latter depends on things like the parameterization of clouds, while the former depends on the parameterization of Chinese consumers. When both sources of uncertainty are combined, the predicted range of climate warming for the year 2100 increases to 1.5°C to 5.8°C.68
The chosen economic scenarios add to the possible controversy. In 2005, one area of strong debate was whether the scenarios should rely on market-based exchange rates or purchasing-power parity.69 Some economists believe the latter results in unrealistic projections for economic growth, and therefore emissions growth. This is a fair point, and it highlights the need to treat predictions in a holistic manner. However, the fact that climate-change predictions are highly dependent on such effects is another sign of their sensitivity, and it means that results depend heavily on the biases of those doing the calculations.
The same issue affects calculations of the effects of global warming on the economy. We often read in newspapers about how global warming could benefit certain regions. When Arrhenius first estimated the effects of global warming, he thought it would be a good thing because he lived in Sweden. Winters in my hometown of Edmonton might get shorter and milder, which is something we could probably adapt to. Before signing the Kyoto Protocol, Russia’s Vladimir Putin joked that a little warming might not be a bad thing for his part of the world. The U.S. government’s complacency about climate change, evident in the Bush administration’s unwillingness to sign the Kyoto treaty, may be based on a similar calculation made by the Pentagon. Their scientists predicted that extreme climate change would bring about a period of war, conflict, and instability, but they added that “with diverse growing climates, wealth, technology and abundant resources, the United States could likely survive shortened growing cycles and harsh weather conditions without catastrophic losses. . . . Even in this continuous st
ate of emergency the U.S. will be positioned well compared to others.”70 China and India would be more vulnerable to agricultural losses and population displacements, while Europe would have to cope with floods of refugees from North Africa and elsewhere.
From the Pentagon, global warming begins to sound like Von Neumann’s vision of the weather as a weapon of war. While it is obviously true that a change of any sort will affect some more than others, calculating the net effect on society is not easy. One study attempted to predict the total cost of global warming using the Regional Integrated Climate-Economy (RICE) model, an offshoot of the Dynamic Integrated Climate-Economy (DICE) model.71 Assuming a business-as-usual scenario, in which no action is taken to prevent global warming, the cost is $4,820 billion. But if humanity takes the optimal course of action, the total cost is found to be $4,575 billion, a net saving of only 5 percent. It has been claimed that this estimate is reliable because it agrees well with other similar models.72
The problem here is that if GCMs cannot predict the climate and economic models cannot predict the next recession, then the uncertainties only grow when the two are combined, and the results are certainly not reliable to within a few percent. When agreement does exist between different models, it says more about the self regulating group psychology of the modelling community than it does about global warming and the economy. It is an illustration of why ensemble forecasts can be highly misleading: an ensemble of wrong models does not make a right model, and the spread between the results is not an accurate measure of uncertainty.
When the economist Kenneth Arrow was working as an air force weather forecaster during the Second World War, he and his colleagues found that their long-range predictions were no better than random. They informed the boss but were told, “The commanding general is well aware that the forecasts are no good. However, he needs them for planning purposes.”73 We can’t exactly predict how the climate will change. In fact (here I agree with the random walk theory), to estimate the economic effects or precise causes you may as well toss the DICE. Projections may be useful for policy-makers, as a device to provoke ideas and aid thinking about the future, but they should not be taken literally. As Keynes once said of unforeseeable political events, “there is no scientific basis on which to form any calculable probability whatever. We simply do not know!”
Given the potential downside risk of global warming, perhaps the best approach is that of Warren Buffet, whose insurance companies, General Re and National Indemnity, are exposed to any increased risk of hurricane damage. As he told shareholders in 2005, it is unknown whether global warming will lead to more storms like Katrina, but “recent experience is worrisome. . . . Our ignorance means we must follow the course prescribed by Pascal in his famous wager about the existence of God.” Even if we’re not convinced about climate change, it would be prudent to pretend we are.
REASONS TO BE SKEPTICAL
Here are some arguments, heard from skeptics, for why predictions of climate change and environmental collapse are wrong—and some equally skeptical replies.
“Mathematical models of climate change are hopelessly unreliable.” Models of housing bubbles and disease epidemics are also unreliable, but these things still happen.
“It is ridiculous to believe that our puny species can affect the balance of an entire planet.” I wonder if the smallpox virus was plagued by similar doubts as it ran rampant through the New World. “Is it possible that I, a simple virus, can destroy a human being, let alone entire societies?”
“It is egotistical to think that we live in a special time, with unique challenges.” This argument might have been raised by Easter Islanders before they cut down the last tree. Easter Islander 1: “This is the last tree. If we cut it down, there will be no more.” Easter Islander 2: “Gee, what an ego.” (Fells last tree.)
“Human ingenuity will solve the problem.” Feel free to start any time.
“Environmental scare stories have consistently turned out to be wrong.” Some have—but how do we know this isn’t the moment in the horror film when it turns out the geek was right?
“Models have shown that the economic benefits of a warmer planet will balance the harm caused.” We’re skeptical about climate models, but not about economic models?
“When I put my head out the window, the air is fresh, the trees are green, there is no sign of imminent environmental collapse.” I’m guessing there may also not be much visible proof of famine and extreme poverty, but apparently they do exist.
“CO2 is present only in trace quantities in the atmosphere, and it’s a carbon source for plants. What can happen if it doubles?” The carbon source known as glucose is present only in trace quantities in our blood. Double it, and you have diabetes. Triple it, and you may lose consciousness. No one can manage the environment, but just as we do with our own bodies, we can monitor health, practice moderation, and limit exposure to toxins.
HEALTH: NEXT YEAR’S DISEASE
While climate change may turn out to be a major threat to our societies and economies, an equally serious concern is our much older enemy, disease. Figure 7.1 (see page 282) shows a smooth increase in population from 1850, but a look at the time preceding that would show more of a roller-coaster ride. The population in Britain in 1348 was about 3.7 million; it dropped to 2.1 million in 1430 as a result of the Black Death and didn’t recover to previous levels until 1603.74 It is only since the Industrial Revolution that the human stock has been on a steady upwards trend. In the rich world, we now live longer and healthier lives than at any time in history. Just as climate change affects everyone on the planet, though, no population will be immune to global pandemics. This is especially true in our highly connected modern societies and economies. Diseases can be transported around the world in mere days, before any health organization has had time to react. And hastily imposed quarantines and other measures could bring global trade to a halt and devastate the world economy.75 The next major storm might be biological, not atmospheric or financial. In this section, we turn our attention from the large scale to the very small.
A good way to learn about prediction is to study how our own bodies resist disease. Our immune system has developed over millennia to identify and counteract pathogens such as bacteria and viruses. The latter are not autonomous living beings but packaged strings of genetic information (DNA or RNA) that invade the cells of other organisms. Once inside, they hijack the cell’s machinery to reproduce themselves. This often kills the cell, at which point the virus is released to find new cells to attack.
Kepler believed, at one time, that the universe was structured after the Platonic solids. While he later changed his mind on that score, he would have been interested to find that a broad class of viruses, including those for polio and the common cold, contain their genetic information in an icosahedral container known as a capsid. At each vertex are cell-surface receptors, like microscopic spikes, which attach themselves to the cell to be invaded. The influenza virus, named after the Latin word for influence (because epidemics were thought to be influenced by the stars), needs a set of only eight genes to construct itself.
The immune system’s task is complicated by the fact that bacteria and viruses evolve at a much faster rate than humans. Microorganisms are on the fast track of evolution, while our immune systems lumber along, always one step behind. Bacteria cells can reproduce in about twenty minutes (as opposed to twenty years for humans), and they happily swap portions of DNA, including those that grant immunity to antibiotics. Viruses too are unstable, so this year’s flu bug may be quite different from the one that was causing problems last year. Like hit singles on the radio, they have a finite span before their novelty wears off.
The immune system must also be able to distinguish between micro-organisms normally resident in the body and foreign invaders. About 10 percent of our body’s dry weight consists of bacteria (in the gut, skin, and elsewhere), and on the whole, they provide very useful functions. Taking a broad-spectrum ant
ibiotic can affect digestion by depleting the bacteria that form an essential part of the digestive system. To recognize foreign bacteria that may be harmful— those terrorist cells—the immune system must predict what such an intruder would look like.
In humans, the first line of defence is the innate immune system. This includes the white blood cells, which are lined with receptors that recognize certain features of microbial invaders, such as components of the cell wall. When an unwelcome intruder is recognized, the white blood cells engulf and annihilate it. They also trigger the production of cytokines, which produce inflammatory responses such as fever. This system can occasionally cause problems of its own, especially when it overreacts to its own predictions; this is what happens with allergies, where the body seems to be launching some massive shock-and-awe attack on a relatively harmless substance like pollen.
While the innate immune system is always ready with a quick response, the acquired immune system takes the slow, thoughtful approach. It doesn’t just eliminate intruders—it tries to get to know them first. And it has a great memory. Its antibodies and T-cells, which target bacteria and viruses, are produced in the thymus and bone marrow by a process that resembles the ensemble forecasting approach: you don’t know what the intruder will look like, so you try everything. The genes for these proteins are mixed and matched randomly, to generate an enormous sample of different shapes. When one of them matches an intruder, a positive feedback switch is activated and more copies are made in the same shape, at a rate of millions per hour. The swelling in the lymph glands during an infection is caused by the rapid growth of colonies of immune cells. When the infection is cured, the immune cells degrade—except for a few so-called memory cells, which remain and speed the reaction to any subsequent infection. Vaccines work by stimulating the production of such memory cells.