7 THE BIG PICTURE
HOW WEATHER, HEALTH, AND WEALTH ARE RELATED

What a chimera, then, is man! What a novelty, what a monster, what a chaos, what a subject of contradiction, what a prodigy! A judge of all things, feeble worm of the earth, depositary of the truth, cloaca of uncertainty and error, the glory and the shame of the universe!

—Blaise Pascal, Pensées


Past performance is no guarantee of future results.

—Mutual fund prospectus

CASE HISTORIES

The previous three chapters looked at short-term predictability in atmospheric, biological, and economic systems. Long-range forecasts, the subject of this last part, differ in that the aim is not to predict exactly what will happen at some fixed date, but to estimate major future effects. This may seem a fundamentally different task, but really the only things to have changed are the scales in time and space. Instead of predicting the local weather, averaged over an afternoon, some days in advance, long-range forecasters want to estimate the regional climate, averaged over a number of years, some decades in advance. In medicine, a long-range forecast might address the likelihood of large-scale pandemics emerging in the global population, while in economics, it could be concerned with the scope for, and consequences of, continued growth.

These systems are, of course, not independent of each other, especially over longer time periods. Global warming, for example, is a function of carbon-dioxide emissions, which depend on economic activity. To make a prediction for our civilization and the planet, we need to consider physical, biological, and social effects as, to use Keynes’s expression, an organic unity. The outcome also depends on the choices we make. Let’s look, for example, at two cases where older civilizations have tangled with their environment, to mixed effect. The first is notorious, the second less so.

Case history A is the nicely named Easter Island. This small island in the Pacific is a little off the beaten track, 3,700 kilometres from the coast of South America, but it’s famous among tourists and archaeologists for the amazing stone figures, the moai, that line the shores. When the Polynesians colonized it around 400 A.D., the island was a subtropical paradise alive with forests, birds, and animals, its seas rich with fish and dolphins that the islanders caught from canoes. Over the course of hundreds of years, a kind of small civilization grew up. The population reached around 10,000 and divided into clans and classes. Despite what we see as their remoteness, the islanders, like the citizens of Delphi, believed themselves to be at the centre of the universe. (The name of one spot translates to “navel of the world.”) They honoured their ancestors by carving the giant moai out of volcanic rock. Transporting and erecting the incredibly heavy moai required a great deal of ingenuity, and large numbers of log rollers. Between this, the clearing of land for agriculture, the use of firewood for heating, and other effects, the island was by 1400 completely deforested. The birds in the forests went extinct; exposed soil blew away into the ocean; there was no wood to make canoes; streams and lakes dried up; crop yields collapsed; wars were waged over the island’s remaining resources; everything went Malthusian. By the time the island was encountered by Europeans in 1722, there were only a couple of thousand people left, and they had taken up cannibalism. In the end, the islanders even turned against their stone gods, toppling and destroying them until not one was standing. Perhaps their promises, or predictions, had not come true. In 1900, after most of the remaining population had been ravaged by smallpox introduced by the Europeans, only 111 people remained.1

Case history B is the still smaller island of Tikopia, located just east of the Solomon Islands. Only 4.6 square kilometres in size, Tikopia was settled earlier than Easter Island. It was heading the same way until about 100 A.D., when it seems the population was converted to the benefits of orchard gardening and sustainable lifestyles. (I imagine an early, hard-core version of the Green Party.) Taboos developed that regulated both procreation and the consumption of food. Zero population growth was policy. It was enforced by the usual methods of celibacy and birth control, but also by more extreme techniques, such as abortion and infanticide (usually suffocation). Young men were sent out to sea on highly risky fishing expeditions, with the knowledge that only a few would return.2

The initial conditions in both cases were similar, but over time scales of centuries, the outcomes were completely different. Easter Island is a hit with tourists and a fright show for environmentalists. Many of the moai statues have now been restored, and they stare out from the covers of books and magazines as a kind of warning. No one will ever, ever mindlessly pollute there again. Some trees are beginning to return.3 Tikopia still supports around a thousand souls; zero population growth is assured because the young people tend to leave.

Who would have seen it coming? The course of civilization does not run smooth; ingenuity may not translate into survival skills, and technological achievements outlast their creators. The people of Easter Island and Tikopia have achieved a kind of quasi-balance with their environment. So how will the rest of us fare as we push against the limits of our much larger but still finite island? What type of story will ours be—horror, light-hearted comedy, or difficult European art film that offers no easy answers?

It seems unlikely that a kind of global civilization model, similar to the psychohistory in Isaac Asimov’s fictional Foundation Trilogy (“Q: Can you prove that this mathematics is valid? A: Only to another mathematician”4), can tell us the answer, given that we can’t predict next week’s weather. As Karl Popper asserted in 1957, “There can be no prediction of the course of human history by scientific or any other rational methods.”5 However, in 1968, the Club of Rome’s thirty members, drawn from science, business, and government, had a go. In collaboration with some professors from MIT, they loaded a computer model called World3 onto a mainframe, fed in some data, and stood back to see what would happen. The results, published in The Limits to Growth, were Easter Island: The Sequel. “If the present growth trends in world population, food production, and resource depletion continue unchanged,” the forecasters wrote, “the limits to growth on this planet will be reached sometime within the next one hundred years. The most probable result will be a rather sudden and uncontrollable decline in both population and industrial capacity.”6 The results appeared to imply that world population would peak at around 10 billion and crash to around half that in the middle of the twenty-first century.

The problem with such predictions, as the authors pointed out, is that present growth trends will not be sustained. Nothing in this world is fixed, especially not trends. The results implied that oil supplies would run out in the 1990s, but that didn’t happen (in part because more oil was discovered).7 Similarly dire warnings in the 1960s that the human population would collapse for lack of food did not come true because trends changed (population growth rates fell and food production improved).8 So how far can we see into the future? And can scientific models help?

To answer this question, we must consider long-term predictions of weather, health, and wealth. But we must first set the stage with a brief history of these three intertwined aspects of our lives— the story so far—and then go on to discuss future projections. Climate change is a highly contentious issue, so it gets the most space. Economic predictions that extend more than a few months ahead are more futurology than science, so economic growth is here discussed primarily in the context of how it will affect, and be affected by, climate change. Finally, we take a careful peak at global pandemics.

OUR HUMAN STOCK

The easiest place to begin a prediction is with the historical charts. The earth is billions of years old, and mankind has been around for hundreds of thousands of years—unless you believe in creationism, in which case you probably believe in Armageddon as well, which kind of takes the fun out of prediction.9 But for the rest of us, we join the story about 10,000 years ago, with the invention of agriculture in places such as the Fertile Crescent, in today’s Middle East. This technological and cultural leap was in part made possible by a relatively stable climate. For 3 million years, the climate had alternated between warm and cold periods, driven by subtle oscillations in the earth’s orbit.10 Ten thousand years ago, the last ice age, known as the Younger Dryas, had just thawed out, and average temperatures had increased from about 0°C to a relatively balmy 14°C. Sun hats were back in fashion.

Agriculture spread slowly, bringing improved nutrition and fuelling a rapid increase in population (from about 5 million to around 250 million at the time of Christ). Societies, which pre-agriculture had consisted mostly of roaming bands or tribes, grew into increasingly complex and stratified civilizations, with distinct classes of priests, soldiers, rulers, and labourers. Civilizations including the Greek, Roman, and Mesopotamian empires, developed money— usually coins of precious metals like gold, silver, and bronze, stamped with the images of gods and goddesses (like Apollo).

We tend to think of environmental problems as a modern phenomenon, but any civilization will grow until it encounters a limit of some type, and often it is a natural limit. Removal of forests and over-extensive agriculture led to droughts, floods, and topsoil erosion, sometimes causing local environmental collapse. Plato was aware of the dangers and famously described deforestation in Attica: “What now remains compared with what then existed is like the skeleton of a sick man, all the fat and soft earth having wasted away, and only the bare framework of the land being left.”11 Despite such warnings, forests continued to disappear across the Mediterranean region.

Along with agriculture, massive macro-engineering projects began to leave their mark. During the Qin and Han dynasties (221 B.C. to 220 A.D.), large parts of China’s forests were cut down to provide scaffolding, fuel, and housing for wooden cities and the Great Wall. The wood required during the building of the Great Pyramid of Khufu, in Egypt, came from cedar trees in Lebanon. Deforestation in many areas affected the local climate and led to permanently warmer and drier conditions—the Fertile Crescent is no longer so fertile.

The increasing size and density of cities led to rapid transmission of ideas and the development of sophisticated culture. Bigger cities also created the conditions to support and sustain epidemics. Rome in the third century A.D. had a population density similar to that of a tenement in today’s Mexico City, and there was no running water or sanitation. Deadly outbreaks were frequent occurrences. The Plague of Justinian (540–42), believed to have been bubonic plague, is estimated to have killed 25 to 40 percent of Europeans. A thousand years later, the disease returned as the Black Death, with a similar effect on population. The impact of these pandemics was so large that according to one theory, it can even be measured in carbon-dioxide emissions.12 Antarctic ice cores store air samples absorbed from the atmosphere over millennia, providing a record of atmospheric carbon dioxide; during plague years, farms were allowed to grow wild, so they absorbed carbon dioxide from the air, producing a dip in the records. Only smallpox would prove more deadly.

We didn’t get the upper hand over disease until the invention, by James Watt and others, of that great cure-all, the steam engine. This kick-started the Industrial Revolution in England by making it possible to transform coal into energy. Improved economic and material conditions, along with developments in sanitation and medical techniques such as vaccination, soon resulted in significantly lower death rates. The world population expanded, reaching the billion mark in the first half of the nineteenth century. By 1930, we were up to 2 billion souls, some with their own cars, now powered by that other carbon source. Birth rates slowed in industrialized countries, but elsewhere they remained high. Population grew in a roughly exponential fashion, and this growth was supported by the Green Revolution of the 1970s, which led to more productive agriculture and increased the planet’s capacity to feed the species. At the millennium, the world headcount had reached a frothy 6.1 billion. About 10 percent of the people who have ever lived are alive today. The earth has never before supported humanity on such a scale.

Economies also grew at a rapid clip. At the time of Christ, per capita income would equate to about a dollar per day (around what a person in an impoverished country can survive on today). Since then, it has increased, on average, to about fifteen dollars, with much of this increase occurring after 1700.13 Like successful suburbanites, we have grown both larger and richer. But how is the planet doing? What would Plato or his mentor, Socrates, say now?

THE WORLD: OVER 6 BILLION CUSTOMERS SERVED

As our population and economic processes have expanded, our impact on the environment has grown. Large areas of “new” countries like Brazil and Canada are fairly pristine, but in many areas of the planet, “only the bare framework of the land [is] left.” About a quarter of all ice-free land has been transformed into cropland or pasture, and much of the rest is exploited in some way for natural resources.14 Around half the world’s forests are now gone, and more have been significantly fragmented or otherwise degraded. Engineering works and other processes disrupt the earth’s surface on a scale comparable to that of erosion by wind or water. All life forms transform their local environments, but our impact is multiplied by technology. Other species don’t cover the land in concrete.

Our impact on the oceans is less immediately visible, since it is under water and out of sight. However, populations of large fishes such as tuna and cod have crashed by as much as 90 percent. Techniques like bottom trawling have laid waste to fragileocean-floor ecosystems and all but depleted fishing grounds like the Georges Bank off Nova Scotia.15 The water quality in some areas has recently improved—the Thames River, for example, is cleaner than it has been in decades—but those lakes, rivers, and streams that do not supply the wealthy with drinking water or recreation are often highly polluted. Changing rainfall patterns, combined with inadequate drainage, has led to increased flooding worldwide.16

In the air, fossil-fuel emissions have caused a rapid rise in atmospheric carbon dioxide. The Antarctic ice records show that the carbon dioxide level has stayed within a band of about 180 to 280 parts per million for the past few hundred thousand years—until recently. In 1958, it reached 315 ppm, and it is now about 380 ppm, and climbing.17 Much comes, almost invisibly, from the exhaust of private vehicles; every fifty litres of gasoline contributes about 115 kilograms of the gas. Local air pollution is of course not a new phenomenon—people complained about the soot in ancient Rome. However, pollution is now a global problem, and chemically synthesized molecules, with their complex and often unknown effects on atmospheric and biological chemistry, are ubiquitous.

The waste products of civilization also affect local and global climates. Carbon dioxide is a greenhouse gas and contributes to global warming. Over the course of the twentieth century, the planet’s average surface temperature rose by about 0.6°C. The 1990s were the warmest decade since record-keeping began, and the 2000s are so far following suit. Among other effects, this has led to increased forest fires across the world. Even the (normally damp) Amazon rainforest in Brazil and Venezuela is now vulnerable (as shown when a portion the size of Belgium was destroyed by fire in 1997–98).18

Human actions have therefore profoundly affected land, water, air, and fire. The only one of Plato’s elements not to have been touched is the ether—unless we count the electronic signals beamed around the world by satellite. And we have an even greater impact on life. While we as a species are doing well, our effect on creatures of the land, oceans, and skies has often been disastrous. We prosper as other species crash.19 Every extinction knocks another set of genes out of the world gene pool and affects the robustness of the global ecosystem.

Even this low-resolution history of the world is enough to show that we are living in unusual times: what we take as normal isn’t that normal. The climate is always prone to change, but over the past 10,000 years, humanity has enjoyed a warm and relatively stable period that has been suitable for the development of agriculture. We seized that window of opportunity, and now we have put almost all suitable land to the purpose of feeding ourselves. The length and quality of life in many countries has as a result increased enormously, but at the expense of degrading the existing air, water, and soil. And while our stone-age ancestors could react to land loss or shifting climates by moving, we don’t have that flexibility; there’s little slack in the system. As people in poor nations cluster in vulnerable areas, they are increasingly susceptible to natural disasters, such as the storm that hit Venezuela in 1999.

Many people in industrialized countries have grown up with a greatly reduced fear of infectious disease. Antibiotics and improved sanitation were a great success story of modern science, and they have vastly reduced death rates in many countries. The last really serious influenza pandemic to hit the rich world, in 1918, killed at least 20 million and made many more ill. Bubonic plague has been all but eradicated; smallpox exists only in the lab.20 Of course, the gains won were not permanent or global. The number of people infected with HIV/AIDS worldwide is about 40 million. New diseases such as SARS continue to emerge, and overuse of antibiotics has led to resistant strains of old diseases like tuberculosis.

Perhaps the most unusual thing about recent history, though, has been the extraordinary rate of economic growth. Industrialized countries like the United States commonly try to achieve annual growth rates in gross domestic product of around 3 or 4 percent. If the homme moyen had pulled in a dollar per day 2,000 years ago and a growth rate of only 1 percent over inflation was maintained since, he would today be enjoying a healthy pay packet of $439 million per day.21 Since not everyone plays NBA basketball, this is clearly impossible. Economic growth is the relatively recent by-product of the Industrial Revolution. In rich countries, we have until now had the best of all possible worlds: a good climate, excellent health, and an explosive economy. In bread-making terms, we are like a yeast colony in a dough: carefully nourished with all the requirements for life, covered with a towel, left undisturbed in a warm place, and growing exponentially. So how long can this go on?

LIVING IN A BUBBLE

According to chart-following optimists, the answer is forever. After all, many worriers have cried wolf in the past, and their dire predictions never came true. Malthus thought the world would disintegrate into famine, plagues, and wars, but while these have certainly happened, none has come close to stopping the growth of the total world population. We have the wind in our sails, momentum behind us, and nothing can hold us back. Global warming and overpopulation are just the fevered inventions of a neurotic society that is living in the safest period of human history and is egotistical enough to believe its challenges are unique.22

One such optimist is Michael Crichton, the author of thrillers such as Jurassic Park and the creator of the hit television hospital drama “E.R.” For his 2004 thriller, State of Fear, he spent three years researching climate change and the environment, and he came in on the side of the skeptics. In an appendix to that novel, which created a lot of controversy and was cited in the U.S. Senate as a useful contribution to the global-warming debate, he argued that climate predictions are hopelessly unreliable, and that improved technologies will mean that we never run short of resources. “For anyone to believe in impending resource scarcity, after 200 years of false alarms, is kind of weird,” he wrote. “I don’t know whether such a belief today is best ascribed to ignorance of history, sclerotic dogmatism, unhealthy love of Malthus, or simple pigheadedness.”23

Indeed, we have been told many times that we are on the verge of running out of oil or water or some other resource. The United States Bureau of Mines predicted in 1914 that American oil would run dry within ten years. Similarly alarmist predictions were made by the Department of the Interior in 1939 and 1951 (thirteen years of U.S. oil left), and by the Club of Rome in 1972 (not much of anything left). All, apparently, were proved wrong. As Adam Smith knew, scarcity means that the price goes up, so either new sources are found, which is what happened with oil (the Alberta tar sands are big), or an alternative is developed.24 In their book The Bottomless Well, Peter Huber and Mark Mills argue that energy use is positively virtuous, because “energy begets more energy. . . . The more energy we seize and use, the more adept we become at finding and seizing more.”25 According to this theory, when we approach a stop sign, we should step a little harder on the gas.

Some scientists have always believed that science itself will learn to control the progress of the human race and steer us to safety. In A Masculine Birth of Time, Sir Francis Bacon wrote of science bringing about “a blessed race of Heroes and Supermen.” As the psychologist B. F. Skinner wrote in 1973, “What we need is a technology of behaviour. We could solve our problems quickly enough if we could adjust the growth of the world’s population as precisely as we adjust the course of a spaceship.”26 And if that doesn’t work, and we run out of room, we could always try a real spaceship and find another planet to colonize.

Optimism in the power of science and progress is heartwarming, but those pigheaded people who believe in fundamental analysis tend to take a more jaundiced, seasoned view. They will point out that recent successes took place in a very favourable environment, which may not continue; that these successes have been mostly limited to rich countries and have done little for a substantial proportion of the population; that excessive expansion often results in overshoot of fundamental limits (i.e., a bubble); and that when bubbles burst, they do so with a bang. In other words, all the boosterism for human ingenuity and technology is about as meaningful as that heard for the latest tech firm at the turn of the millennium.

These fundamental analysts will cast a questioning eye over S-shaped curves such as that shown in figure 7.1 (see page 282). Most current prognosticators have the world population shrinking slightly in rich countries and growing progressively more slowly in developing countries, until it gently coasts to an equilibrium around 2150. This is based on the observation that as developing countries get richer, the death rate decreases and population goes up. After a while, the birth rate also decreases (rich people have fewer children, for some reason), so growth slows or levels out.

Because the time scale for human reproduction is slow, population projections can be relatively accurate thirty or so years in advance—but only if nothing unusual happens (such as the baby boom, which forecasters failed to predict).27 Like economic models, demographic models tend to extrapolate the past and do not capture major turning points, like the one that is supposedly coming up. A fundamental analyst will therefore ask if this estimate of future growth, based on current trends, can be reconciled with the actual number of people the earth can support over the long term (its carrying capacity). If not, then we are living in a bubble. Rather than taper off at a sustainable limit, the population will overshoot and then crash—as has happened time and again on a more local scale with previous civilizations.28

i_Image2

FIGURE 7.1. Total estimated world population.29

The carrying capacity depends on, for example, whether everyone will be living at current First World standards. Its calculation implicitly assumes all kinds of things about economic and technological growth and the resilience of nature. (Even the definition of “sustainable” is nebulous. I will take it to refer, like homeostasis, to maintaining a kind of dynamic metabolic balance with the environment. Sometimes it is easier to define in the negative—Easter Island, not sustainable.) For everyone to enjoy a Western lifestyle, one estimate pegs the carrying capacity at around the 1930 population of 2 billion.30 This might seem low, but it accounts for the fact that rich people have far more impact on the planet than poor people. The environmentalist World Wildlife Fund estimates that by 2050, we will need almost three earths to support ourselves in the style to which we have become accustomed.31 Others believe that new technologies will allow us to support a much greater density, even higher than today’s. What is evident is that we are currently degrading the biological and physical systems that support life. In the words of the United Nations’ 2005 Millennium Ecosystem Assessment, we are “living beyond our means.”32

There is a chance that our technology will improve and growth rates will slow and reverse, steering the population back to a sustainable level; but there is also the possibility of a dramatic collapse. Maybe the Club of Rome was right. If so, then environmental skeptics are not skeptics at all—they are boosters who make naïve extrapolations based on recent performance. The true skeptics are people like Sir Martin Rees, Astronomer Royal, who pegs our civilization’s chances of survival to the end of the century at only 50 percent.33 (His predecessor in that position, John Dee, was also interested in predictions of the apocalypse, but his were based on the whispering of angels.34)

One study on opinions about climate change revealed a striking cultural divide between natural and social scientists.35 The latter, which includes conventional economists, tend to believe that the impact of even severe global warming will be low, and that we can invent replacements for “climatic services.” The former, which includes climate scientists, are far more pessimistic, and estimate the economic damage of global warming to be twenty to thirty times greater, with much of it assigned to non-market categories like nature or quality of life. Perhaps economists have more at stake in the existing economy.

So if we can’t even agree on the scale of the problem, what can computer models tell us about the future? Is it possible to make accurate predictions for the planet, or will the question be bounced back and forth between optimists and pessimists until time tells us the answer? And what can we say about the implications for our weather, health, and wealth?

WEATHER: GETTING WARM

While our impact on the planet is combinatorial, one issue that has received much recent attention and controversy is climate change caused by the accumulation of greenhouse gases in the atmosphere. We still fear the weather gods, and the more extreme possibilities— all-destroying hurricanes, killer droughts, apocalyptic floods—evoke images of almost biblical wrath. Sudden changes in climate have always been a risk—a thought here for the pre-Inca Moche of the Andes, or the Maya of Central America, whose water supplies were cut off by shifts in weather patterns—but we may become the first civilization to be affected by our own actions on a global scale. The scientist and environmentalist David Suzuki said, “Climate change is one of the greatest challenges humanity will face this century.”36 Meteorologists, meanwhile, see global warming as a tractable and interesting theoretical problem that can be addressed by the use of large mathematical models. But is it possible to predict the climate if we can’t predict next week’s weather?

The basic physics behind global warming was pointed out by the Swedish chemist Svante Arrhenius in 1896.37 The earth is warmed by energy from the sun, which arrives as a broad spectrum of electromagnetic radiation, including visible sunlight. Bodies at the earth’s temperature in turn emit long-wavelength, infrared radiation (which is how soldiers can detect people in the dark using night-vision goggles). Instead of radiating out into space, this energy is mostly absorbed by the atmosphere. The degree of absorption depends strongly on the exact concentrations of certain gases. These are known as greenhouse gases because they have the same effect as the glass in a greenhouse does: they let the light in, but don’t let the heat out. This is not a subtle effect. Without greenhouse gases in the atmosphere, the average temperature would be –18°C instead of +14°C. We have all been raised in a hothouse.

The greenhouse gases, which include water vapour, carbon dioxide (CO2), and methane, are therefore vital to our survival. However, you can have too much of a good thing. Increasing CO2 from its pre-industrial levels of about 280 ppm to 380 ppm is a substantial relative change. And even if we were to freeze CO2 emissions at current levels, its slow rate of decay means that the total amount will still continue to grow well into the future. Furthermore, because of the slow response of the ocean/atmosphere system, the effects of high CO2 levels will be with us for centuries, and may even be irreversible. A common property of non-linear systems is hysteresis: once a change has been made, it is difficult or impossible to undo.

In 1979, the meteorologist Jule Charney organized a meeting at Cape Cod to investigate what would happen if CO2 levels were double the pre-industrial level. (This may occur sometime during the present century, with climate effects tending to lag behind as the system adapts; though as discussed below, the exact rate of CO2 production depends on a wide range of social and economic factors.) At that time, there were only two American research groups actively involved in climate modelling: Syukuro Manabe’s at the Geophysical Fluid Dynamics Laboratory (GFDL) at Princeton, and James Hansen’s at NASA’s Goddard Institute. Manabe had been involved with climate models since 1963. (These are usually simplified versions of GCMs, with some of the details stripped out so they can be run over periods of hundreds or even thousands of years.) On the first day of the meeting, he told the assembled group that according to the model, a doubling in CO2 would lead to a rise of 2°C—not too bad. The next day, though, Hansen presented the results of his model: it indicated a much larger rise of 4°C, a factor of two difference. Since the aim was only to get a rough idea of the possible magnitude, Charney chose 0.5°C as the margin of error on both calculations, which left a range of 1.5°C to 4.5°C (Arrhenius had estimated 5°C back in 1896). The lower limit was not out of line with natural variations that have occurred in recent centuries, but the upper was a real icecap melter. In words, the estimate meant that either nothing much could happen or we could have a serious problem.38

Not everyone at the meeting was happy with the agreed range, and some described it as hand waving.39 However, it did seem to indicate that CO2 could have a considerable effect on the planet, and it helped mobilize scientific interest. The Intergovernmental Panel on Climate Change (IPCC) was established to report on the matter. At its second meeting in 1995, there were thirteen climate models to choose from; by the time of its 2001 meeting, climate modelling had grown into a major research area for groups all over the world, including the Max Planck Institute for Meteorology in Germany, the Hadley Centre in England, and the Lawrence Livermore National Laboratory in California. In a remarkable display of consistency, though, the estimate of potential warming, obtained by consensus among experts, remained little changed from what is sometimes called the “canonical” range of 1.5°C to 4.5°C, with an average of about 3°C.40 (The panel did not assign probabilities to the different outcomes, because there was no sensible way to determine them. One of the scientists observed, “The range is nothing to do with probability—it is not a normal distribution or a skewed distribution. Who knows what it is?”41)

Some more recent sensitivity studies indicate a rise beyond that canonical range. As the climatologist Stephen Schneider noted, “Despite the relative stability of the 1.5 to 4.5°C climate sensitivity estimate that has appeared in the IPCC’s climate assessments for two decades now, more research has actually increased uncertainties!” 42 This degree of uncertainty reflects the challenge of modelling the climate system. Just as a biological system incorporates complex regulatory loops that make it difficult to model, the climate system is made up of feedback loops that elude simple parameterizations.

GLOBAL COOLING


Can the future climate be predicted by looking at past climates? An alternative to using mathematical models is to adopt a data-driven approach, and study how greenhouse gas concentrations and climate have varied together with the ebb and flow of past ice ages—a field known as paleo-climatology. Sources of data include ice cores (which trap gases), petrified tree rings, and geological features such as sedimentary rocks.

There are two difficulties with this approach. First, the data contains a high degree of uncertainty. Scientists argue over how to interpret satellite pictures, let alone tree rings.Second, there is no exact analog in the past for the current situation—dinosaurs didn’t drive cars. Estimates of climate change based on paleoclimatology are therefore highly uncertain. As might be expected, they have a tendency to fall in the “canonical” range. Some scientists, though, point to extreme events in the past as proof that the climate system is more sensitive than models allow.

One such extreme event occurred during the Younger Dryas period. This was named after a small, cold-loving plant—Dryas octopetala—which pollen records show suddenly began to flourish across much of Europe about 12,700 years ago. The cause, it seems, was a sudden and precipitous drop in temperature, which plunged the warming earth back into ice-age conditions for over a thousand years. Some believe that it could have been triggered by an abrupt reversal of the ocean circulation pattern that drives the Gulf Stream, and that melting Arctic ice could initiate a similar event in the future. Global warming may mean that some areas, such as the U.K., become much cooler.

IN THE LOOP

As mentioned in Chapter 4, one of the key processes involved in determining our weather is the formation and dissipation of clouds. They play an equally important role in the global climate. If the planet warms, more water evaporates into the atmosphere. Since water vapour is a greenhouse gas, this can trap heat and lead to further warming—positive feedback. If the water vapour condenses into clouds, though, these create shadow and lead to cooling— negative feedback. Except at night, when cloud cover prevents heat from escaping—positive feedback.

Water in the form of snow and ice also affects the planet’s albedo, which is its ability to reflect light. White surfaces have high albedo, and dark surfaces have low albedo (the word comes from the Latin for whiteness). Most of the earth is blue, green, or brown, colours that are somewhere in the middle; but snow, being white, reflects about 80 percent of the sun’s energy. It therefore acts to keep the planet cool. Antarctica is white all year-round, but in the Arctic, snow and ice cover is seasonal and sensitive to changes in climate. If temperatures increase, the cover reduces and the albedo goes down. The ocean, deprived of its ice layer, also releases more water vapour to the atmosphere, which accentuates the greenhouse effect. Together these cause further warming, in a positive feedback loop known as the Arctic amplification. Small differences in ice albedos have a large effect on model results.43

In fact, climate models must contend with feedback loops that are as complicated as those in any biological system—and usually have a biological component. Another example is the carbon cycle. Carbon is the most important building block for life. Its atomic structure gives it a unique ability to combine with other elements to form complex molecules such as DNA. Like the blood in our bodies or money in the economy, it is constantly being cycled around the biosphere. Plant life uses carbon dioxide for photosynthesis, combining it with water to create sugar and oxygen. The latter is breathed back out to the atmosphere.44 Algae on the ocean surface and other sea organisms similarly absorb huge amounts of carbon for photosynthesis. (Algae are also believed to be cloud-makers— they emit sulphur gases, which oxidize to form minute particles that encourage the formation of clouds.)

We too are now a major part of the carbon cycle: fossil-fuel emissions and cement manufacture release about 5.5 billion tons of carbon per year, while deforestation and other land use contribute another 1.5 billion tons.45 Furthermore, global warming can affect carbon levels in a positive feedback loop, by increasing the number of forest fires and reducing the amount of CO2 held in ocean water. Large amounts of carbon, as much as 450 billion metric tons, are also stored in the frozen tundra and boreal forests of the North.46 We have to hope that they stay frozen, since their release could lead to a runaway warming.

The climate system therefore consists of a nested series of nonlinear feedback loops that are in a kind of dynamic balance. Unlike short-term weather models, climate models do not suffer from sensitivity to initial condition, since the aim is to predict the average weather far in the future (which should be insensitive to the exact starting point). This is especially so if the model is run for a long period under fixed conditions, in which case it settles onto its own attractor. Error in climate models is therefore primarily a result of model error.47 If GCMs were perfect, so that “predictability limitations [were] not an artifact of the numerical model,”48 then climate prediction would be easy. But this is far from being the case. Weather and climate predictions are directly linked, and to believe that models can fail at the former but succeed at the latter is nothing but wishful thinking—especially when the most basic properties of the ocean/atmosphere system prove so difficult to model.49

WATER WORLD

One substance that consistently slips through the grasping fingers of weather and climate modellers like water is water. Because water is so ubiquitous, we don’t often reflect on its properties; but it is perhaps the most mysterious, upside-down, shape-shifting, and life-giving substance on earth. If carbon represents the earth’s yang, fixing and organizing other substances into its geometry, then water is its yin. The two are as different as water and oil (which is 85 percent carbon).

As Antoine Lavoisier demonstrated, a water molecule consists of a single molecule of oxygen (O) and two molecules of hydrogen (H). The frost on the ground, the mist in the air, the water in our bodies—all have this simple chemical structure. The molecule is highly polarized: one side has a positive charge, the other a negative. Since opposites attract, the positively charged side is drawn to the negatively charged side of nearby molecules, in a process known as hydrogen bonding. Collections of water molecules form a complex interacting group, each molecule aligning itself with the others in a vibrant dance, switching partners billions of times per second. This gives the substance a number of unique properties. For example, the solid form is less dense than the liquid form, so ice creates a protective layer that insulates the water below. From the level of the single cell to the entire globe, water is essential for life. About 70 percent of a typical cell is water, about 70 percent of our body weight is water, and about 70 percent of the earth is covered in ocean.

In the climate system, water appears in far more guises than is suggested by the simple categories of solid, liquid, and gas. In its solid form, it might be ice or snow or different types of crystal in the atmosphere. As a liquid, it can appear as rain, fresh water in rivers and streams, or brine in the sea. Water vapour can evaporate from the oceans or land or be absorbed by plants, and it can condense into every different type of cloud that you see in the sky as a mix of vapour, liquid, and crystals. And water constantly changes its form: a single molecule might start off in the ocean, evaporate into the atmosphere, join a passing cloud, fall to the ground as snow or rain, enter a river, and flow back into the ocean, all within days. As Leonardo da Vinci wrote, “The waters circulate with constant motion from the utmost depths of the sea to the highest summits of the mountains, not obeying the nature of heavy matter; and in this case it acts as does the blood of animals which is always moving from the sea of the heart and flows to the top of their heads. . . . These waters traverse the body of the earth with infinite ramifications.”50 Each ramification has a different effect on the climate. Water is therefore fluid, non-linear, constantly changing its appearance, and generally un-Pythago rean.

CLIMATE ATTRACTOR

Because of the importance of water in the global climate system, model predictions depend on exactly how its behaviour is represented. GCMs generally agree that as global warming continues, more moisture will evaporate and form clouds, therefore affecting the planet’s albedo; however, the exact change in albedo depends critically on the type of clouds.51 Climate modellers may in the future be able to hammer out a consensus on these and other difficult points, but there is no guarantee that the consensus will be right, because no model can capture the full scope of the climate system.

A typical GCM used for climate studies might divide the atmosphere into a three-dimensional grid with cells about the size of a small country like Belgium. For a particular property, such as albedo or cloudiness, they assign a single number or simple distribution to everything within a cell. Like fractals, though, natural surfaces reveal greater amounts of detail the more you zoom in. There is never a point where more resolution doesn’t reveal a finer degree of structure.52 Since GCMs can offer only rough param-eterizations of the physics of clouds—or the growth of trees, or changes in grasslands, or the deformation of ice under pressure, or the exact way water evaporates from exposed ground, or a whole host of other things—they are not true physics-based models. They cannot be demonstrated from fundamental laws. Like models of the economy, they are really a collection of approximate equations that have been combined and balanced to give reasonable results.

Many climate models, if run forward for thousands of years, would not give a realistic-looking climate at all without the use of so-called flux adjustments. Left to their own devices, models will cheerfully boil away all the water in the oceans or cover the world in ice, even with pre-industrial levels of CO2. (The fact that the earth doesn’t do this says something interesting about its regulatory networks, as we’ll see in the next chapter.) The only way to bring them back to something reasonable is to somehow fudge the heat balance—altering, for example, the transfer of heat between ocean and atmosphere.53

The models are also strongly affected by changes in parameters. In one recent Oxford University–led experiment, the largest of its kind, several key parameters controlling the representation of clouds and precipitation in a GCM were set to alternative values “considered plausible by experts.” The effect was to explode the range of predictions, pushing it as high as 11.5°C (after omitting some simulations that became unstable, or even showed cooling).54 The climate system consists of an intricate balance of opposing feedback loops—what Heraclitus described as a “harmony of opposite tensions”—and small changes in their representation can have large effects. This sensitivity does not imply that the climate system itself is unstable. All that can be concluded is that the models are sensitive to parameterization.55 The average rise was close to 3.4°C, but this only reflects the fact that perturbations were made around a model that happened to show that amount of warming.

Even such experiments do not reveal the true uncertainty in the calculation, because the very structure of the GCM equations fails to capture the underlying dynamics. Parameters are properties or constructs of the model, not the system. As one paper put it, “As soon as we begin to consider structural uncertainty, or uncertainty in parameters for which no prior distribution is available . . . tidy formalism breaks down. Unfortunately, the most important sources of model error in weather and climate

forecasting are of precisely this pathological nature.”56 Since the errors are “pathological” (i.e., not expressible by equations), it is as difficult to estimate the correct parameter range, or forecast uncertainty, as it is to predict the climate itself, and for exactly the same reasons. We don’t have the equations. In an uncomputable system, they don’t exist.

As the pioneer climate scientist Manabe put it, “Uncertainty keeps increasing with the more research money they put in. . . . It hasn’t gotten any better than when I started forty years ago.”57 Perhaps this is why, despite huge advances in computer speed, earth-observation techniques, and climate research, the IPCC and most climate scientists still quote the canonical band for carbon doubling of 1.5°C to 4.5°C.58 This range gives an image of stability, and it helps, as the Oxford scientist Steve Rayner described it, to “domesticate climate change as a seemingly manageable problem for both science and policy.” The estimates for global warming seem to say as much about the dynamics of science as they do about the dynamics of climate.59

REASONS TO BE VALID


Below are some common arguments for the validity of climate models—and the reasons why they are not valid.

“The models are derived from basic laws of physics.” There are no laws for the formation and dissipation of clouds or many other processes.

“The models can reproduce the current climate.” Versions with different parameters can adequately reproduce the current or recent climate, while giving a very different response to doubled carbon dioxide. It is always possible to tune models to fit past data; it’s much harder to predict the future.

“The models can simulate past climates.” Same problem. Also, there is a great deal of uncertainty in estimating climates in the distant past.

“The butterfly effect does not apply to long-term climate forecasts.” This isn’t very relevant, since chaos is rarely the most important factor in the modelling of physical systems.

“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 state 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 antibiotic 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.

Despite the efficiency of the human immune system, it occasionally loses the battle for prediction. AIDS, caused by the HIV virus, has infected tens of millions worldwide, and over 25 million in Africa alone; tuberculosis (from a bacterium) and malaria (a microscopic parasite) kill millions each year; and new diseases are constantly emerging. Creutzfeldt-Jakob disease (CJD), the human analog to mad cow disease, is caused not by a microbe but by mis-folded proteins known as prions. In the 1990s, it was feared that CJD could kill millions of people, though more recent estimates are far lower.76 The 2003 SARS outbreak was caused by a novel virus that was much less transmissible than influenza, but it still managed to spread from rural China to five countries within a single day. It killed under a thousand, far fewer than a normal flu, but the resulting panic caused an economic crisis in much of Asia and in cities like Toronto, with a total price tag estimated at $30 billion. It showed that we are only a viral mutation or two away from a modern-day plague—but it also showed that the response can be more damaging than the problem itself.

In 2005 and 2006, the world was working itself into a similar panic over a disease that is highly contagious, extremely lethal, and absolutely terrifying—at least for birds. Like the 1918 influenza, which took more lives than the First World War, the H5N1 strain of avian flu is made up mostly of genes from our feathered friends. But unlike the 1918 flu, it has yet to become easily transmissible between humans.77 Spread around the world by migratory birds such as ducks, which can host the virus without dying from it, avian flu has killed hundreds of millions of chickens and other birds. Of the humans who have contracted it, usually through direct contact with birds, it has killed about half. (This compares with a mortality rate of 2 to 3 percent for the 1918 pandemic.)78 If it mutates to a different, human-transmissible form, with even half the original virulence, then things would look extremely bad. But as Laurie Garrett, the author of The Coming Plague, pointed out in May 2005, “We have no idea what exact genetic changes this would require, how difficult it is for the virus to make those changes and whether or not the virus would significantly sacrifice its virulence level in the process.”79 There is no “normal” size for an outbreak. Avian flu may therefore turn into something far worse than the 1918 epidemic, or it may never happen.80

Mathematicians and epidemiologists can model the spread of disease with computer simulations, which in their cruder form divide a population into three classes: those who have the disease, those who are susceptible, and those who are immune (possibly because they have died). The models assume that people encounter each other randomly, like molecules in a gas, and pass on the disease at a rate that depends on its transmissibility.81 Epidemics, if they become established, are then seen to follow a typical S-shaped curve, rather like that in figure 7.1 (see page 282).

More sophisticated models simulate a detailed population that statistically matches the properties of a given city. Each “individual” in the model will interact with a certain number of people each day. The number of interactions varies just as it does in a real town, so that students attending class come into contact with many people, while those who work at home do not. These simulations have shown that the speed with which health officials act—by isolating patients and telling people to stay at home—is critical.82 Indeed, the impact of SARS was much lower in Vancouver than in Toronto because healthcare workers there immediately managed to contain the disease.

However, the most important factor is the nature of the disease itself, and we cannot predict what exactly will emerge from the global ecosystem.83 The process by which viruses incorporate new genes is inherently random. The evolution of a microbe as it adapts to a new environment is also highly variable and unpredictable. In one experiment, two samples of a virus that usually infects the bacterium E. coli were introduced to Salmonella instead. The two virus samples adapted in completely different ways, and after just ten days, they were genetically distinct.84

Nor can we predict the lethality or transmissibility of a disease by sequencing a microbe’s DNA, since symptoms (like traits) are an emergent feature of the complex interaction between the microbe and its host. This was scarily demonstrated in 2001 by Australian researchers who were trying to develop a mouse contraceptive as a population-control device. Their approach involved the insertion of the gene interleukin-4 into the genome of mousepox (a version of smallpox that afflicts mice but not humans). The gene was expected to boost antibody production, but instead it transformed the virus into a raging killer that took out all the laboratory mice.85 A major concern is that biotechnologists will accidentally create novel diseases that control the populations of both mice and men.

According to legend, Apollo’s arrow enabled Pythagoras to cure plagues, but there seems little that mathematical models can do to offer similar protection. Building a model of an epidemic in progress is rather like working out how to double the cube at the altar of Apollo during the Athenian plague. So how can we prepare ourselves for the emergence of new diseases?

The best option, it seems, is to take a cue from our own immune systems, both innate and acquired. The innate response is to couple systems such as the Global Public Health Intelligence Network,86 which automatically searches news reports and websites worldwide to pick up signs of an outbreak, with available technologies such as antiviral drugs87 and antibiotics. The acquired response is vaccination tailored to the particular disease. The World Health Organization is attempting to improve systems for the design, production, and distribution of vaccines.88 New biotechnologies can play an important role by speeding the process, and high-throughput techniques can monitor the development of dangerous pathogens. Equally important will be low-tech plans to maintain basic health care and food supplies during an outbreak.

Of course, as Timothy Geithner, president of the Federal Reserve Bank of New York, remarked on the slightly different subject of hedge funds, “It is hard to motivate people to buy more insurance against adverse outcomes when the risks seem remote and hard to measure and when present conditions seem favourable.” 89 The price tag of the next epidemic is uncertain, but a protective network can be accurately costed.90 It is natural for those footing the bill to demand proof that the investment is worthwhile. But how can we prove whether avian flu is the revenge of chickens on the human race? As Garrett says, “The bottom line for policymakers: Science does not know the answer.”91 It is hard to get the balance right, and even our own immune system overreacts sometimes. All we can do is watch for coming storms.

WE DON’T KNOW

It might seem in this chapter that we have fallen into the trap of assuming that the future will resemble the past: just because we cannot predict atmospheric, biological, or economic systems now does not mean that we will not be able to do so one day, once we have better computers, observation systems, and models. Statements about the limitations of human ingenuity have a way of being proven wrong. However, the constraints here are the result of the nature of the systems themselves. In cellular automata, computational irreducibility does not (by definition) go away with a better computer, and emergent properties cannot (by definition) be expressed in terms of simple physical laws. Similarly, the dual nature of complex real-world systems means that they can be based on simple, local rules—avian flu is just a few bird genes—but at the same time be uncomputable. Errors in model parameteriza-tions are magnified by the complex feedback loops that characterize such systems. In a hundred years, we won’t have an equation for the Game of Life, and (to venture a prediction) we won’t have an equation for life.

Even if models do not have predictive accuracy, they are still useful tools for understanding the present, envisaging future scenarios and educating policy makers and the public. Scientific research into global warming—along with pictures of calving icebergs—has highlighted the changes that are currently happening in the climate system, and provoked debate about the possible consequences. Similarly, models of the spread of disease have helped focus people’s minds on the possible consequences of a pandemic, and economic models have shown how dependent we are on our relationship with the rest of the biosphere. For, in the long run, what unites our future weather, health and wealth is that they all rely on the state of the planet.

To summarize:

Given that optimists and pessimists are unlikely to convince one another on purely theoretical grounds of our future impact on the planet, and its impact on us, it is hard to imagine how the debate will be resolved until events unfold. To better prepare for an uncertain future, perhaps we need to discontinue the search for more accurate numerical predictions and do the opposite instead. We take up this theme in the next chapter.