Part 2

Climate Change

Let’s find out what the models say. If we end up on the highest emissions pathway (under scenario SSP5-8.5), how much warmer could our planet be in about 300 years?

Between 12ºF and warmer.

+0ºF +0.0ºC

+10ºF +5.5ºC

+20ºF +11.1ºC

+30ºF +16.6ºC

It’s at least this much 12ºF

Chapter 04

The climate is always changing

Figure 8

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Earth’s orbital eccentricity, axial tilt, and axial precession change in cycles. These cycles work together to trigger ice ages.

Earth’s orientation in space isn’t constant: our planet’s tilt, wobble, and the shape of its orbit around the sun cycles over the course of tens of thousands of years. These cycles are what cause the natural fluctuations between global ice ages and warmer periods. Our planet’s climate is also affected by volcanic activity and changes in how much energy from the sun hits the earth. One way that we know that the current warming is caused by human activity is because we are currently in a cycle that should be cooling the planet.

Voice of Laur Hesse Fisher MIT Climate

Video 2 The History of Climate Cycles (and the Woolly Rhino) Explained PBS Eons

Ice ages are controlled by how much sunlight is received by the Arctic region during summer.

In the early twentieth century, mathematician Milutin Milanković calculated (by hand) how the distribution of sunlight varies and speculated that variations in arctic sunlight caused the ice ages.

Video 3 A History of Earth’s Climate SciShow

The cause of these cyclic swings in temperature and the associated growth and retreat of great continental ice sheets was proposed by several scientists, notably by the Serbian mathematician Milutin Milanković in 1912. He recognized that the shape of Earth’s orbit around the sun varies cyclically over time—back and forth from more circular to more oval—with a period of about 100,000 years. Milanković also knew that Earth’s tilt with respect to the plane in which it orbits the sun wobbles over a cycle of 41,000 years, and that Earth’s rotation axis precesses like a top with periods of 19,000 and 23,000 years. These three factors—our planet’s tilt, wobble, and orbit—affect the way sunlight is distributed around the world, even though they hardly affect the total amount of sunlight received by the planet as a whole.

He speculated—correctly, it turns out—that ice ages are controlled by how much sunlight is received by the Arctic region during summer, and set about calculating this value from the basic laws of physics that control the earth’s orbit and rotation. After years of hand calculation, Milanković produced a curve showing how ice ages should behave. At that time, data such as those used to produce Figure 7 did not exist, and so there was only rough agreement with what little information there was. But today we know that the great ice ages were caused by the cycles computed by Milanković, though there are gaps in our understanding of the details of how Earth’s climate responded to these.

Figure 9

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Until about 1900, the summer temperature in the Arctic was going down.

Until the industrial revolution, the Arctic had been cooling for many thousands of years.

Unimpeded, this mechanism would lead the earth toward another ice age.

This cooling is illustrated in Figure 9, which zooms in on the last 2,000 years of temperatures in the Arctic. The slow, steady cooling trend from the beginning of the record to around 1700–1800 CE probably reflects the slow decline in sunlight reaching the Arctic due to the Milanković orbital cycles. Unimpeded, this mechanism would lead the earth toward another ice age, with continental ice sheets beginning to grow thousands of years from now. But note the strong uptick in temperature toward the end of the record, particularly after about 1900. This is quite unusual by the standards of the last few thousand years and reflects the increase in carbon dioxide and other greenhouse gases brought about by humanity’s rapid consumption of fossil fuels. We are certain that this increase in CO₂ concentration was caused by human activities because the isotopes of carbon in ice show that it comes from fossil fuel burning and the clearing of forests. Over the course of a few hundred years, humans rapidly burned fossil fuels that nature created over tens of millions of years.

Chapter 05

How much of the CO₂ increase is natural?

Figure 10

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Current CO₂ levels are well beyond anything our planet has seen for the past 800,000 years.

So, the evidence suggests that the warming of the last one hundred years is unusual compared to the last few thousand years and is almost certainly caused by higher concentrations of CO₂. But could the increased CO₂ concentrations themselves be natural?

Voice of Laur Hesse Fisher MIT Climate

Almost certainly not. Figure 10 shows the history of atmospheric CO₂ and Antarctic temperature going back 800,000 years, thus covering many Milanković cycles. Clearly, the atmospheric concentration of CO₂ does vary naturally, in tandem with temperature, ranging from about 180 to about 280 parts per million by volume (ppmv). But the Milanković cycles cannot account for the enormous spike at the end of the record, a spike to over 400 ppmv that humans put there. There is no evidence that it has been that large for many millions of years. If high economic growth continues with a strong reliance on fossil fuels, we may reach well over 1000 ppmv by the end of this century.

A very close and careful analysis of the records of temperature and CO₂ in ice cores shows that during Milanković cycles, CO₂ mostly lags temperature, suggesting that the CO₂ variations were caused by the warming and cooling, not the other way around. In this case, the CO₂ was acting as a positive feedback, amplifying the Milanković oscillations. But in the last 100 years, the huge increase in CO₂ drove the temperature change.

The argument that one has to choose whether CO₂ is a forcing or a response is specious. The same agent can be a forcing in one circumstance and a response in another. Suppose you have a manual transmission car in first gear, pointed downhill, and you release the brake. The downhill motion of your car will spin up its engine. In fact, this is a good way to start your car if its battery is dead and you happen to be pointed downhill. But ordinarily, the engine powers the motion of the car.

Atmospheric Carbon Dioxide data from NOAA

Chapter 06

Video 4 Modeling the Future of the Greenland Ice Sheet NASA Climate Change

06a What are climate models?

The real issue, of course, is what will happen in the future. Although ultimately we want to know what the human and monetary risks are, we need to start with something simpler: how global temperature will evolve going forward. But the Earth’s climate system is immensely, almost overwhelmingly complex. To deal with this complexity, scientists have created computer models to map these interactions and simulate how the Earth might respond under different scenarios and assumptions—and we’re getting an increasingly clear picture of what kind of temperature changes we can expect in the coming decades.

Voice of Laur Hesse Fisher MIT Climate

To deal with the immense complexity of the climate system, scientists turn to comprehensive global climate models. The word “model” means many different things to different people and in different contexts. Models used for predicting weather, for example, are computational devices for solving large sets of equations. Using a computer to solve these equations is very similar to using a computer to, say, precisely land a spacecraft on Mars. This type of modeling is quite different from something like economic modeling. Economic models also solve equations, but unlike weather models, the equations are constructs based mostly on past economic data and records of human behavior.

But this comparison of climate models with the models used to land spacecraft is a little misleading. Although the equations governing climate are known rather precisely, there is no way they can be solved exactly using present-day computers. We cannot even begin to track each molecule of the climate system but must average over big blocks of space and time. For example, today’s climate models typically average over blocks of the atmosphere that are 100 kilometers square and perhaps 1 kilometer thick, and over time intervals of several tens of minutes. This averaging introduces errors and skips over important climate processes.

Cumulus convection—thunderstorms, for example—is the main way, other than radiation, that heat is transmitted vertically through the atmosphere. But cumulus clouds are only a few kilometers wide and so cannot possibly be explicitly simulated by models that average over 100 kilometer squares. Nevertheless, they must be accounted for, and so we turn to a technique awkwardly called “parameterization” to do so. Parameterizations represent processes that cannot be resolved by the model itself, and they attempt to be faithful to the equations underlying those processes. But many assumptions have to be introduced, and their efficacy is usually judged by how well they simulate past events. In many ways, parameterizations are closer in spirit to economic modeling than to programming spacecraft.

Thus climate and weather models are hybrids of strictly deterministic modeling (like programming spacecraft) and somewhat ad hoc parameterizations (closer to economic modeling).

Weather models can be tested over and over again, every day, and thereby progressively refined. Today’s weather models are far superior to those of a generation ago, partly because of improved computational technology, partly because of increased know-how, and partly because they can be repeatedly tested against observations and refined. But climate evolves slowly, and so there are not that many climate states against which to test models. So, in contrast with weather forecasting, in climate modeling we have neither the history of success nor the confidence that comes with it. But the fundamentally chaotic nature of weather imposes a predictability horizon on weather forecasting, whereas with climate we are trying to predict the slow response of the long term average statistics of the weather to changes in sunlight, CO₂, and other factors. For this kind of prediction, there may not be a fundamental predictability horizon. (We can say with confidence that summer will be warmer than winter for as many years in advance as we care to.) Instead, we have to deal with remaining uncertainties in the physics of climate.

Our climate system is incredibly complex. Features like clouds, ice, and vegetation must be handled approximately and that, among other things, introduces uncertainty in our models.

06b Uncertainty

Scientists face tremendous challenges when attempting to model the Earth’s climate system. While there is a solid understanding of how many parts of the system work, our incomplete understanding of several aspects of the highly complex climate system introduces uncertainty into our attempts to forecast how climate will change.

Voice of Laur Hesse Fisher MIT Climate

To take one example, the water vapor content of the atmosphere varies, mostly in response to temperature itself. As the atmosphere warms, the concentration of water vapor increases. But water vapor is the most important greenhouse gas, and its increase leads to further warming. This is an example of a positive feedback in the system, and current understanding suggests that this one factor more or less doubles the warming that occurs in response to increasing CO₂ alone. But the true physics of climate is not that simple, and the distribution of water vapor is affected by many other variables besides temperature. Our incomplete understanding of water vapor is thus one source of uncertainty in modeling climate.

Much more problematic are clouds, which, regarding radiation, work both sides of the street. They account for most of the reflection of sunlight by our planet, thereby cooling it. But they also absorb and reradiate infrared radiation just like greenhouse gases, thereby exerting a warming effect. Which effect wins depends on the altitude and optical properties of the clouds. While major advances in the understanding of cloud processes have been made, clouds are still considered the main source of uncertainty in climate projections.

To this problem we can add many other issues that reflect the immense, almost overwhelming complexity of the climate system. As sea ice melts, a white surface is replaced by dark ocean waters, which absorb more sunlight (another positive feedback). In some places, jungles, which are relatively dark, may be replaced by deserts, which are highly reflective—a negative feedback. The rate at which the oceans absorb excess CO₂ may itself change in response to changes in ocean temperature and concentration of dissolved CO₂. Incomplete understanding of these processes also introduces uncertainty in climate projections.

Figure 11

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It takes time for the temperature of a system to increase after an initial burst of warmth.

Another source of uncertainty is the response of the deep ocean to climate change. The oceans act as a buffer to temperature change and delay the response of global temperature to increasing greenhouse gases. Here is a good way to think about the effect of the oceans. Suppose we have a sealed glass cylinder containing equal volumes of air and water. If it is just sitting at rest with no energy going in or out through the walls of the container, the air and water will settle down to the same temperature. Add enough black dye to the water to make it opaque and shine a powerful flashlight down through the glass top of the cylinder. The light passes through the air but is absorbed at the very top of the water, heating it. So the top of the water warms up, and since that is the part that is in contact with the air, the air warms up too. But the water below the surface is not heated by the light, which never makes it down below the surface, so it remains at the temperature it had before. But slowly—very slowly—the warmth of the surface water is diffused down into the deep water and this both warms the deep water and cools the surface water and with it, the air.

Thus after we turn on the flashlight there will be an initial fast warming of the air and surface water, followed by a very slow increase in the temperature of the whole system. Eventually, the water and air will reach a new, warmer temperature. How long it takes to do so will depend on how rapidly heat diffuses downward into the deep water.

Video 5 The Last Time the Globe Warmed PBS Eons

Our models could account for the lag between heat input and temperature change in the real world if we had a simple theory for how heat penetrates the ocean depths. We know that heat is mixed rapidly downward to a depth of between 20 and 150 meters (60 and 500 feet), depending on location and time of year. If heat did not penetrate deeper, then the 20–150 meter penetration would give a lag of around two years. But we know from measurements that heat manages to circulate much deeper in the ocean, taking quite a long time to do so, perhaps as much as 1,000 years. Just how this happens is complex, and is a source of uncertainty for longer range climate projections.

Finally, mathematical models of climate-like systems can exhibit sudden, unpredictable shifts. We don’t know for sure whether our climate is an example of such a system, but there is evidence encoded in ice cores from Greenland that ice age climates can jump rather quickly from one state to another. This evidence, together with our models, puts mathematical teeth on the idea of tipping points—sudden and largely unpredictable shifts in the climate state. This idea keeps many a climate scientist awake at night.

Figure 12

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The effort we make to curtail emissions today will impact greenhouse gas concentrations for centuries to come.

06c Dealing with uncertainty

As the Danish physicist Niels Bohr once remarked, “Prediction is very difficult, especially about the future.” Scientists have developed a number of different strategies to account for uncertainty, and have used these to estimate the range of possible temperatures that we will see in the coming century. There are roughly 40 climate models run by different organizations around the world, and they all give somewhat different predictions about the response of climate to increasing concentrations of greenhouse gases. In addition, we have to estimate just how the greenhouse gas content of the atmosphere will evolve over the coming centuries, which requires not just an understanding of the physics, chemistry, and biology controlling these gases but an assessment of human behavior—how much greenhouse gas will we end up emitting?

Voice of Laur Hesse Fisher MIT Climate

Estimating future emissions is a problem of economic and behavioral forecasting, including, very importantly, predicting population growth. Will developed nations learn how to better conserve energy? Will the economies of countries like India expand rapidly, as China’s did, leading to rapid growth in energy demand? How far will low-carbon energy technologies penetrate the energy sector? There are strong interdependencies among these issues. For example, recent experience shows that as gross national product per capita expands together with per capita energy consumption, population growth tends to level off, ameliorating the growth in energy demand. All these factors strongly affect greenhouse gas emissions.

To deal with all this, the fourth Intergovernmental Panel on Climate Change (IPCC4) came up with a set of just four “representative concentration pathways” (RCPs), expressing plausible evolutions of greenhouse gases and other human made influences on climate, such as aerosols. The sixth assessment report of the IPCC (AR6) built upon the RCPs by creating five “Shared Socio-economic Pathways” (SSPs) that show low and high warming scenarios depending on different levels of GHG emissions. The high and very high GHG emissions scenarios (SSP3-7.0, shown in orange in Figure 12, and SSP5-8.5, in red) have CO₂ emissions that roughly double current levels by 2100 and 2080, respectively. The intermediate GHG emissions scenario (SSP2-4.5, in green) has CO₂ emissions remaining around current levels until the middle of the century. The low and very low GHG emissions scenarios (SSP1-2.6, in purple, and SSP1-1.9, in blue) have CO₂ emissions declining to net zero around 2050 and 2070, respectively, followed by varying levels of net negative CO₂ emissions.

In addition to the uncertainty surrounding the emissions, there is inherent uncertainty in the models themselves. Many important processes such as turbulence, convection, and the interaction of radiation with clouds have to be represented indirectly in the models and this is one of many sources of model error. One strategy to account for this important source of uncertainty is to run many different models and to run each of them many times with different initial states to produce a large ensemble of projections. While imperfect, comparing the results of the many members of such an ensemble gives us some idea of the inherent uncertainty in model projections. This strategy is also used in running weather prediction models and has proved valuable in quantifying the uncertainty of weather forecasts.

Figure 13

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If high economic growth continues with a strong reliance on fossil fuels, global mean temperature rise may reach 14°C by 2300 – a value not reached since around 50 million years ago.

06d What the models say

Thanks to current efforts to develop and deploy clean technology, and to implement policies that reduce carbon pollution, these models show that the odds of surpassing a global temperature rise of 4ºC are much lower than they were in the last IPCC report. Even so, if countries fail to meet their climate commitments–or if natural carbon cycle feedbacks are more positive than predicted–by 2100, global mean temperature may rise between 3.3°C and 5.7°C (that’s 5.9°F to 10.2°F), and by 2300, by between 6.6°C and 14.1°C (that’s 11.9°F and 25.4°F).

Voice of Laur Hesse Fisher MIT Climate

The red curve in Figure 12, SSP5-8.5, implies a doubling of CO₂ emissions from current levels by 2080, representing the very high warming end of future emissions pathways. This is the predicted result if high economic growth continues with a strong reliance on fossil fuels. While the plausibility of the SSP5-8.5 scenario has been debated in light of recent decarbonization efforts, due to uncertainty in carbon cycle feedback loops, it is still considered necessary to understand the potential impact of this high emissions, high warming scenario.

The other four SSPs assume higher levels of mitigation of greenhouse gas emissions and are useful for estimating how various mitigation strategies might ameliorate climate change.

In the most dire scenarios, humanity’s carbon emissions could deliver us CO₂ concentrations not seen for 50 million years.

This was when alligators roamed Greenland and sea levels were 230 feet higher than today.

In its Sixth Assessment Report, the IPCC projects that in the high emissions scenario SSP5-8.5, if high economic growth continues with a strong reliance on fossil fuels – or if natural carbon cycle feedbacks are more positive than predicted – then by 2100, global mean temperatures may rise by between 3.3°C and 5.7°C (that’s 5.9°F to 10.2°F), and by 2300, by between 6.6°C and 14.1°C (that’s 11.9°F and 25.4°F).

As Figure 13 shows, paleoclimate proxies suggest that such a value has not been seen since at least the Eocene period, roughly 50 million years ago, when alligators roamed Greenland, and sea level was 70 meters (about 230 feet) higher than today’s.

But what are the consequences of these changes? How will they affect us in human and economic terms? We next consider the set of real risks that climate change poses and how, at least for some risks, we might go about attaching actual numbers.

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Risks

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Climate Change

The climate is always changing

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Question

How much of the CO₂ increase is natural?

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