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Global Warming:
A True Story
for My Grandchildren
William Nordhaus
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Global Warming:
A True Story for My Grandchildren
William Nordhaus
Yale University
Draft for discussion purposes only
Copyright William Nordhaus
July 2010
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Table of Contents
Preface
Chapter 1. The Science of Global Warming
Chapter 2. Impacts of Climate Change on Human and Natural Systems
Chapter 3. Slowing Climate Change: Science, Economics, Politics
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Preface
If you read the newspaper or scan the daily blogs, you are virtually certain to
find a story about global warming. Here is a sample from diverse sources: [1]
“The last decade was the warmest on record.”
“Polar bears could disappear within a century.”
“Leaked emails are evidence of collusion among scientists.”
“Global warming claims a hoax.”
“The Greenland Ice Sheet has experienced record melting.”
“More Americans think global warming is exaggerated.”
What is the non-specialist to make of all these conflicting stories? How can
anyone sort through the contending voices without a lifetime of study? And how
should concerns about global warming fit into other societal concerns like persistent
unemployment, soaring public debt, low-intensity wars, religious conflicts, AIDS, and
nuclear proliferation?
The purpose of this small book is to put global warming in a perspective so that
the concerned citizens of the world can understand it. In these pages, I discuss the
problem from the beginning, where warming originates, to the end, where societies can
take steps to deal with the dangers of global warming. The book is written for nonspecialists. It is for people who follow daily events, perhaps have studied a bit of
science or economics, and are comfortable with numbers and logic. Most of all, it is for
people who are interested.
What is my perspective? My professional training is as an economist working in
a research and teaching university. I have taught and written in many areas of
economics, particularly environmental economics and macroeconomics. I am co-author
of a long-running textbook in introductory economics now in its 19th edition, and that
has given me a special appreciation for young people who are struggling with new
concepts.
I have also studied and written on global warming for more than three decades.
When I first started worrying about the subject, it was a zero in economics. Absolute
zero. Occasionally, along the way, it has been a joke among my colleagues, as if I was
analyzing the macroeconomics of alien societies. But today economists and other policy
analysts take global warming very seriously as a global threat.
Those who have read some of my earlier work sometimes ask me, “Didn’t you
think that global warming was a minor issue? Have you changed your mind?” Here is
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what I would say: When I first started working on global warming, it was not a minor
issue. It wasn’t an issue at all! I did not remotely expect that scientists and economists
would today be devoting so much research to the question or that it would dominate
scientific discussions.
But the evidence has accumulated, and the prospects of warming are grim. When
asked if I have changed my mind, I am reminded of the answer that the great economist
John Maynard Keynes gave to the same question in the middle of the Great Depression:
“When the facts change, I change my mind. Pray, sir, what do you do?” If we never
changed our minds, we would still be worshipping trolls and wandering around in
bearskins.
I write this book particularly for young people, and I dedicate it to my
grandchildren. They will inherit this world and are likely to live through the 21st
century. The globe they will inhabit at century’s end will differ greatly from today’s
world. The state of our planet will depend on the steps we take in the interim about
many things, but those to slow global warming are perhaps the most momentous for
the natural world.
The story of global warming is a fascinating one. It is as frightening as a Grimm’s
fairy tale. But it is a true story, and for that reason even scarier. And more interesting.
So to you who are just beginning the journey, I wish you, bon voyage.
New Haven
July 2010
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Chapter I
The Science of Global Warming
The world is so full of a number of things,
I’m sure we should all be as happy as kings.
Robert Louis Stevenson, A Child's Garden of Verses
First Words to Our Grandchildren: A Tale of Two Lakes
As parents and grandparents, we have an awesome responsibility to those who
will inherit this small, lovely, and often contentious planet. We feel this most tangibly
when we cradle a tiny and helpless baby in our arms. When we think about how long is
the road that baby will travel, how much the baby is dependent on parents and family
and friends, on the education, security, and public goods provided by schools, teachers,
and the institutions of our countries. The self-made man and the laisser-faire billionaire
would not survive a month without the intricate support systems of human societies.
Although the earth looks huge and impervious to human activities, in fact life on
earth is also a fragile and contingent system. We do not know whether the living
systems that have evolved on earth are unique in the universe, but it seems a highly
unlikely that the human and other life forms on Earth are found anywhere else. The
drama that is life on earth will play only once. [2]
I begin with the story of a small pond. In the summers, my family for four
generations has enjoyed a small salt lagoon in southern Rhode Island called
Quonochontaug Pond. [3] The picture on the cover of this book shows the night view of
Quonnie under a full moon in the middle of summer. Quonnie has lived an exciting life.
Fifteen thousand years ago, during the last glacial maximum, this area was buried
under a mountain of ice. The pond was one of the string of coastal estuaries left as the
glaciers retreated. It is home or touching point for piping plover, least terns, horseshoe
crabs, and multicolored jellyfish. On the ocean side of the pond is a long barrier beach
that has been free of houses since the 1938 hurricane tore through the area with winds
of 110 miles an hour and demolished all the human structures other than a few posts
and stone towers.
Quonnie is a vulnerable spot, subject to abuse from many quarters. Developers,
motor vehicles, hurricanes and nor’easters, septic systems, landscapers, pesticides, oil
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tankers, and motor boats all beat upon the fragile system. Conservationists, ecologists,
and coastal authorities beat back. In recent years, it has been a standoff between the
forces of preservation and the forces of degradation. Unchecked global warming will
change the ecosystems in unknown ways, and unchecked sea-level rise will eventually
inundate the entire area.
What will Quonnie look like in a century? Will it look like the inlets of more
southerly U.S. waters like those around Chesapeake Bay? Or will the combination of
warming, increasing carbonization and acidification, and other human insults turn it
into a dead salt marsh? I hope that we will take the steps to slow and reverse the
warming and carbonization and protect Quonnie and other spots that are beloved or
sacred to people who have visited them for generations.
If we look elsewhere, we can see how fragile lakes can be. One of the most
disastrous example is the Aral Sea, in Central Asia. This was once the fourth largest lake
in the world. But over the last four decades it has shrunk from 26,000 square miles to
about one-tenth that size. (See Figure I-1.) What were the reasons? Nothing dramatic
like global warming or war. The reason was primarily bad planning driven by bad
incentives. It occurred primarily because the centrally planned “socialist” Soviet Union
– not some runaway capitalist system -- decided to divert the rivers that feed the lake
for irrigation of marginal lands. [4] Like a child starved of nutrition, the lake is slowly
dying.
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Figure I-1. Satellite photos shows the shrinkage of the Aral Sea. [5]
____________________
This tale of two lakes tells the story of this book in the simplest way. We humans
control the future of our planet. The earth has many enemies – unbridled political
power, unchecked market forces, war, ignorance, wooden-headedness, and poverty.
But, through a combination of careful planning, good institutions, and appropriate
channeling of market forces, we can preserve the unique heritage around us.
This small book is about only one of the issues that we must address to preserve
our world – global warming. Humans have been contributing to a warmer globe for
centuries. But this 21st century is a critical period in which we must curb the unchecked
growth in greenhouse gases, particularly those that come from fossil fuels. If we have
not achieved a substantial reduction in these gases by 2100, then the environmental
future of the earth is grim.
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I write this book for my grandchildren because they will be living on this world in
the late 21st century. Statistically, unless humans do some dreadfully stupid things, at
least one of my grandchildren will be alive in 2100 to see what path human societies
have taken over the coming century. Sometime in the 2030s, perhaps they will read this
book and judge whether it served as an appropriate appraisal of the state of the art in
2010. Look online to see what remains of the Aral Sea. Judge whether your
grandparents and parents have continued to preserve beautiful places like
Quonochontaug Pond. And then, after you, my grandchildren, have learned the lessons
of both history and science, it will be your job to continue to protect this wonderful
planet.
A Summary of the Drama
Before beginning, I will summarize what follows. The story comes in three parts.
Figure 1 is a schematic that shows the circular flow of climate change and a visual
summary of the plot of this drama. Here, in the first part, we discuss the science of
climate change. We examine the economic roots of global warming – how economic
growth has led to rising carbon dioxide (CO2) emissions. This is the box at the upper left
of Figure 1. We will see in this chapter that human activities are definitely leading to
changes in atmospheric chemistry. This is the arrow from the upper left box to the
upper right box, which is the climate system. The present chapter discusses these two
boxes and the linkage between them.
The second act of our drama considers the impacts of climate change on human
and natural systems. This is actually the most difficult task of all because of the
evolving nature of human societies and technologies. So this second part will discuss
primarily the two boxes on the right side and the link between them.
The final act considers policies and politics: How can we bend the trajectory of
global warming, and what are the most effective tools to do so? Even though this area is
the most controversial and divisive, from a scientific point of view it turns out to be a
straightforward issue. So this part discusses the lower left-hand box in Figure 1.
The dashed line indicates that climate-change policies close the loop. By taking
policies to limit emissions, we can slow climate change. But that last little arrow is
dashed to indicate that this is not an automatic link that will occur without countries
taking strong steps. If we continue along with current path of virtually no policies, then
the little dashed arrow will dissolve, and the globe will continue on the dangerous path
of unrestrained global warming.
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Chapter 1’s coverage
Economic activity and
fossil-fuel use leads to CO2
emissions (driving, air
conditioning,
construction,…)
Climate-change policies
adopted to reduce
emissions and adapt to
warmer world (carbon
taxes, cap-and-trade,
regulations,…)
Rising CO2 concentrations
along with other
greenhouse gases lead to
climate change
(temperature, precipitation,
sea-level rise, …)
Climate change imposes
ecological and economic
impacts (farming, coastal
structures, species loss,…)
Figure I-2. The circular flow of global warming
The discussion that follows can be seen as a circular flow. It starts at the upper
left with the economic determinants of the emissions of greenhouse gases like CO2.
These emissions lead to climate change, represented by the box at the upper right. As
climate changes, there are impacts on human and natural system, represented by the
box at the lower left. Human societies can take measures to reduce emissions as show in
the box at the lower left. These would then close the link as shown by the dashed
contingent arrow back to the starting box. The present chapter discusses the first two
boxes and the linkages between them.
__________________________________________
Introduction to the Science of Global Warming
We are barraged with terrifying stories about the future prospects if global
warming is unchecked. We hear about melting icecaps, extinct species, flooded subway
systems, hordes of environmental refugees, dying oceans, and threats to national
security. How secure are these projections? Are the projections based on the best
science? Or is this all a vast left-wing conspiracy to deindustrialize the world?
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A very short summary of climate science
Before we start on our journey, it will be useful to have a very short summary of
the basic science of climate change. We will expand and refine this summary in the
pages to come, but it will be important to look at the map before we start the journey.
The underlying premise of this book is that global warming is a serious, perhaps
even a grave societal issue. The underlying scientific basis of global warming is well
established. The core problem is that the burning of fossil (carbon) fuels such as coal,
oil, and natural gas leads to emissions of carbon dioxide (CO2).
Gases like CO2, which are called greenhouse gases (GHGs), accumulate in the
atmosphere and stay there for a long time. Higher concentrations of GHGs lead to
surface warming of the land and oceans. The mechanism by which GHGs lead to
warming can be understood as follows. The sun warms the earth with “hot” or shortwave radiation. Most of the radiation is sent back into space, but on its return voyage it
is “warm” or long-wave radiation.
We are fortunate that most atmospheric gases (especially water, but also CO2)
absorb more warm radiation than hot radiation. This process is like a blanket on a cold
winter’s night, which keeps us warm. As a result, the earth is about 33 °C warmer than
it would be without our normal blanket of greenhouse gases. However, we are adding
more blankets to the atmosphere in additional CO2. We are thereby increasing the
average temperature on the earth’s surface. Increasing the atmospheric composition of
CO2 by what seems a tiny fraction (from about 0.28 parts per thousand to 0.56 parts per
thousand) is projected to increase average temperature by around 3 °C. The reason for
this huge impact is the CO2 intercepts warm radiation in a very powerful fashion.
While the exact future pace and extent of warming is highly uncertain, particularly
beyond the next few decades, there can be little scientific doubt that the world has
embarked on a major series of geophysical changes that are unprecedented for the last
few thousand years. Scientists have detected the early symptoms of this syndrome
clearly in several areas: The emissions and atmospheric concentrations of greenhouse
gases are rising; there are signs of rapidly increasing average surface temperatures; and
scientists have detected diagnostic signals –such as greater high-latitude warming – that
are central predictions of this particular type of warming. Over the longer run, this
produces profound and potentially dangerous changes in many earth systems and
consequently to biological and human activities that are sensitive to the climate.
This in a few words is the syndrome that we will discuss in the pages that follow.
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Why are carbon dioxide emissions rising?
Our journey begins, and will also end, with the daily activities of humans around
the world. Because I am an American living in an urban environment, I will use that as
an example, but it could equally well involve an Iowa farmer, a German automotive
worker, a Chinese mechanic, an Indian farmer, or an Indonesian weaver.
The story starts with my decision to drive to a talk I am giving in upstate
Connecticut. I have no realistic alternative, so I take my car up and back. There is much
involved, but I will focus on the energy used. The trip is about 100 miles, and my car
gets about 20 miles per gallon, so I consume 5 gallons of gasoline. In an ideal situation,
with the gasoline burning cleanly, this will produce about 100 pounds of CO2, which
will come out the tail pipe and go into the atmosphere. I can’t see, hear, or smell it – and
indeed a few years ago I would not even have known about it. It seems unlikely to have
much effect on the world, so I probably will ignore it.
But there are 6 billion people around the world making similar decisions many
times, every day, every year, for decades and centuries. Suppose that everyone on earth
does the equivalent of my drive twice a week (through heating or lighting or cooking or
making steel or planting corn). Then this would add up to about 30 billion tons each
year, which is what total CO2 emissions were in 2009. In a sense, virtually everything
we do has some CO2 buried in the production process. You might think that riding your
bicycle is “carbon-free.” But there is a little carbon in the bicycle, and quite a bit
involved in making the road or sidewalk.
We can examine the trend in the carbon intensity of economic activity by looking
at the CO2-GDP ratio for the United States. We have reasonably good data going back
100 years, and the intensity is shown in Figure 2. [6] This is quite a fascinating picture. I
note one technical detail which will be used often in this book. The scale on the diagram
is a “ratio” or “logarithmic” scale. This is a diagram in which equal vertical distances
are equal proportions, so the vertical distance from 200 to 400 is the same as 400 to 800.
This is extremely convenient because it means that a straight line (or one with a
constant slope) has a constant rate of growth or decline. If you look at Figure 2, you see
that the carbon intensity of the U.S. economy increased until around 1910 (this was the
first age of coal). Since then, the CO2-GDP ratio has fallen at an average annual rate of
1.6 percent. There were wiggles along the way, but the long-term trend is clear.
The decline is sometimes called “decarbonization.” The reasons for the
decarbonization are many, but it involves two main factors. One is that we use less
energy per unit of output (say because our motors have become more efficient) and
secondly because the most rapidly growing sectors (such as electronics and health care)
use less energy per unit output than the average.
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CO2 emissions/GDP (ratio scale)
1,000
800
700
600
500
400
300
200
CO2 emissions/GDP
Trend (-1.6 percent per year)
100
00
10
20
30
40
50
60
70
80
90
00
10
Figure I-3. Carbon intensity of U.S. economy, 1900-2008
―――――――――――――――――――――――――――――――――――――――
While the carbon intensity of production is declining, it is not declining fast
enough to reduce total CO2 emissions, for either the world or for the U.S. If we take the
period since World War II, real output has grown at 3.0 percent per year and the carbon
intensity has declined at 1.6 percent per year, which means that CO2 emissions have
grown at 1.4 percent per year. We do not have such data of similar quality for the world
as a whole, but our estimates are that over the last half century, global output grew at
an average rate of 3.6 percent per year, the rate of decarbonization was 1.1 percent per
year, and CO2 emissions grew at 2.5 percent per year. The growth in CO2 emissions has
actually been slightly above trend in recent years, primarily because of the rapid growth
in developing countries like China. Figure 3 shows the long-term trend in total carbon
emissions. [7]
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Global CO2 emissions (billions of tons)
40
30
20
10
8
6
4
3
2
Global CO2 emissions
Trend growth at 2.7 percent per year
1
00
10
20
30
40
50
60
70
80
90
00
10
Figure I-4. Global CO2 emissions, 1900-2006
―――――――――――――――――――――――――――――――――――――――
So in a nutshell, here is the problem: Countries around the world are growing
rapidly (aside from some poor performers and putting aside recessions as painful but
temporary setbacks). They use carbon-based resources like coal and oil to fuel their
economies. The efficiency of energy use has improved over time, but the rate at which
the efficiency is improving has been insufficient to bend down the emissions curve.
Are humans changing the atmosphere in significant ways?
We now move from economic activity to the geosciences. Is it true – indeed is it
possible – that human activities are significant enough to change the global climate?
After all, humans are but a tiny part of global activity. The answer here is unambiguous.
Thanks to the foresight of a few scientists, we began monitoring atmospheric carbon
dioxide in 1957 in Hawaii. Figure 4 shows a plot of monthly observations through the
end of 2009. Over the half-century, atmospheric concentrations have risen by more than
20 percent.8
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CO2 concentrations (parts per million)
390
380
370
360
350
340
330
320
310
55
60
65
70
75
80
85
90
95
00
05
10
Figure I-5. Atmospheric concentrations of carbon dioxide measured in Hawaii, 19572009
―――――――――――――――――――――――――――――――――――――――
How does this relate to human activity? We can compare the increase in
atmospheric CO2 with estimates of total emissions of CO2 from industrial activity,
shown in Figure 5. We see that somewhat more than half of emissions remain in the
atmosphere.
Where is the rest? To answer this question, we need to make our first use of
computerized models. (I defer the discussion of modeling until later and just use the
results here.) This question is subject to vigorous scientific debate, but most models find
that in the long run most of the non-atmospheric CO2 goes into the oceans, eventually
diffusing into the deepest parts, but that process takes place very slowly. [9] Models of
the carbon cycle developed by scientists estimate that around two-thirds of cumulative
CO2 emissions over the next century will be in the atmosphere at the end of that period.
[10]
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Fraction of emission remaining in the atmosphere
.60
.58
.56
.54
.52
.50
70
75
80
85
90
95
00
05
10
Figure I-6. The figure shows the fraction of each year’s CO2 emissions remaining
in the atmosphere. On average, about 55 percent of industrial emissions since 1970 are
still airborne
―――――――――――――――――――――――――――――――――――――――
The implication of the modeling results is that the residence time of CO2 in the
atmosphere is very long. For this we can look at computer models as well as to
statistical studies. A rough estimate is that if we emit 1 ton of CO2 today, about 40
percent of that will remain in the atmosphere after a century. [11] This has very
important implications for how we think about climate change. The long residence time
means that the effects of our activities today cast a very long shadow into the future.
They do not just wash away in a few days or months like many other kinds of pollution.
This long residence time will come back to haunt us when we consider the problem of
discounting in our chapter on economics.
The Use of Models to Project Future Climate Change
In order to understand the scientific and policy issues, we need to pause to
examine how scientists use their tools to understand future climate change. Up to now,
we have been looking at historical trends. But climate change is about the future, not the
past. We need to make projections – we hope accurate projections – about what climate
change will be over the coming decades, perhaps even for centuries.
A projection is a conditional or “If…, then…” statement. In other words, it states,
“If a given set of input events occur, and we use model X, then we calculate that the
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following output events will occur.” In the case at hand, the input events are things like
a path of CO2 emissions and the earth’s geography. The model might be one developed
by scientists at the Goddard Institute for Space Studies (GISS) in the U.S. And the
output events might be time paths for regional daily temperature and precipitation, sea
level rise, and sea ice.
We cannot do those calculations in our heads (at least, I cannot), so all these
calculations are done with computerized models – generally very large and complicated
computerized models. What is a model? You can think of it as akin to an architect’s
model of a building. Figure 6 shows a model of the iconic house designed by American
architect Frank Lloyd Wright along with a picture of the actual building. [12]
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Figure I-7. A model of “Falling Water” and a picture of the actual house designed
by Frank Lloyd Wright
A good model should capture the essence of the question at hand without
overwhelming the user with unnecessary clutter. In economics, we build models of
output and incomes, for example, to help the government forecast its revenues and
spending and provide an informed basis on what is happening to, say, the government
debt. In the area of climate change, we build models to estimate future emissions of
CO2, the impact on atmospheric CO2 as well as the pace and extent and even regional
dimensions of changes in climate. Other scientists estimate the impact on agricultural
output, on sea level, on the extent of malarial mosquitoes, and on snowpack for water
runoff.
In addition to very detailed models of specific areas (such as atmospheric
chemistry, ocean chemistry, agricultural response, economic growth, and the like), there
are also “integrated assessment models” or IAMs. IAMs link together the different
components of the global warming syndrome using several stripped-down modules. I
will often in this exposition rely on IAMs that we have developed at Yale known as the
DICE/RICE family of models. These models combine in one large computerized
package the end-to-end process from economic growth through emissions and climate
change to impacts on the economy and finally include certain policies to slow climate
change. The advantage of the IAM approach is that it can consider the entire process;
the disadvantage is that it has to simplify drastically some of the processes that are
captured in greater detail in the more complete models. Figure 7 uses the architectural
analogy of an IAM. When well done, they are as elegant as a building sketch by Frank
Gehry. [13]
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Figure I-8. Integrated Assessment Models are small-scale representations and are
similar to a Frank Gehry sketch, shown left, which was an early model for the Walt
Disney Concert Hall, right
_________________________
Two parts of a climate-change projection: emissions paths and climate modeling
With this background, we can now explain how scientists project future climate
change. It is necessarily a two-step procedure. The first step is to estimate the inputs
into the models, and the second step is to construct models and use them to project
future climate.
Emissions projections: techniques
The first step is to develop a set of projections for the inputs into the models.
These are primarily paths of emissions of CO2 and other greenhouse gases (GHGs).
While CO2 is the most important of the gases, others can make a significant
contribution. I will focus primarily on CO2 to keep the discussion manageable, but the
complete treatment will also include other gases. However, when looking at actual
projections, I use “CO2 equivalent,” or CO2-e, which adds together the contributions of
all the GHGs.
Most climate models have relied on a standardized set of projections known as
the SRES emissions scenarios, which were ones prepared in a Special Report on
Emissions Scenarios by the IPCC. These are essentially “stories” rather than projections
of future output and emissions paths. One has rapid population growth; another has
rapid technological change; one is eco-friendly. They are interesting scenarios rather
than attempting to be the most accurate projections.
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Why would scientists want to use “interesting” rather than accurate projections?
According to the SRES report, the reason is this: “However, many physical and social
systems are poorly understood, and information on the relevant variables is so
incomplete that they can be appreciated only through intuition and are best
communicated by images and stories.” [14] This is definitely not the way that
economists would recommend constructing long-term projections. We have no idea of
whether these are good guesses or wild fantasies. We have no way of judging whether
they span a reasonable range of possible future outcomes.
So, here is a first warning about projections about standard projections of future
climate change: They are based on emissions scenarios that are not based on standard
statistical techniques that are used in the social sciences. They do serve one very useful
function: They allow climate scientists to focus on a range of standardized emissions
scenarios to test and compare their climate and other natural-science models. But these
standardized test runs should not be confused with best-practice projections or
forecasts.
Statisticians and econometricians have studied techniques for making projections
for many years. The modern approach is to use a combination of demographic,
economic, and technological data; then estimate relationships using the historical data
and technological or scientific constraints. From these, we can obtain a statistically
based projection of future trends. This is the approach we use in constructing our
RICE/DICE models. It can be reproduced and easily updated, but the main advantage
is transparency. Unlike the SRES scenarios, we can understand the construction of
statistically based projections.
In the discussion of emissions, I will rely on economic models rather than the
SRES scenarios. Several modelers under the aegis of the Energy Modeling Forum (EMF)
have compared their results on the “baseline” or unconstrained emissions of CO2. This
result is extremely important because it gives us an idea of what the world will look like
if we take minimal action to control global warming. Basically, this projection is the
result of trends of decarbonization like those shown above along with economic
growth.
Using the statistical approach also allows us to judge the uncertainties associated
with the models. This is done using a technique called Monte Carlo simulation. This is
like spinning a roulette wheel 10,000 times to see how often different numbers or colors
come up. Figure 5 shows our estimates from the 2009 model along with four of the
standard SRES scenarios. It is important to note that these are uncontrolled emissions
runs. That is, they show what would occur if no policies are taken to slow global
warming.
Emissions projections: results
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We begin with the results of an important comparison study of different
Integrated Assessment Models undertaken in the EMF-22 project. This study included
modeling teams from around the world: 6 groups from Asia and Australia; 8 from
Western Europe; and 5 from North America. A subset of these provide results for a nopolicy run, for which Figure 8 shows projections of emissions for CO2 through 2100. [15]
The 11 lines without markers are from the EMF study, while the line with the circles is
the projection of the RICE-2010 model. [16] Two important points emerge from this
figure. First, all models project a continued growth in CO2 emissions. The range of
growth rates is between 0.5% and 1.7% per year over the 2000-2100 period. Even though
these seem to be small growth rates, they imply that emissions in 2100 will be 1.6 to 5.4
times higher than 2000. The problem is not going to disappear or be solved by standard
market forces.
The second feature is that future emissions are highly uncertain. Because of inertia
of economic and technological systems, the near-term results are relatively certain. But
the range of estimates grows over time as the uncertainties about population,
technology, and particularly energy systems compound in the coming years.
We can also look at the uncertainty studies, or Monte Carlo estimates of the kind
discussed above. Using the RICE-2010 model, we estimate that there is a 10 percent
chance that the emissions will be greater than 116, and a 10 percent chance that they
will be less than 46 billion tons of CO2 per year. So the estimated 10-90 percentile range
from the RICE-2010 model is somewhat smaller than the range of model uncertainties
shown in Figure 8.
Although I don’t think the SRES scenarios are useful for scientific analysis, we can
compare the results just discussed with the standard scenarios. It turns out that two of
the scenarios (the A2 and the B1) are outside the 10-90 percentile range, and indeed are
outside the range of any of the models of the EMF study (A1B and B2) are closer to the
middle of the projections, although they appear to grow too rapidly in the early decades
and stagnate in the later decades.
I-21
CO2 Emissions from Fossil Fuels (billions of tons per year)
140
RICE-2010
120
100
80
60
40
20
0
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Figure I-9. Alternative projections for CO2 emissions
The lines show 11 models surveyed by the EMF-22 project. The RICE-2010 with the
circles is a slightly later projection from the Yale RICE-2010 model.
―――――――――――――――――――――――――――――――――――――――
Return a moment to the uncertainties about future CO2 emissions. Where does
this come from? A careful analysis finds that most of the uncertainty lies in our
uncertainty about future economic growth. Will the world continue to robust economic
growth of the period from 1950 to 2005? Or will it stagnate with slow technological
change, recurrent financial crises and perhaps depressions, spreading pandemics, and
occasional widespread wars? These are essentially unknowable. For this reason, the
uncertainty about future emissions paths shown in Figure 8 is unlikely to be
significantly narrowed in the next few years.
Climate models
The second part of the task of projecting future climate change is to take the
emissions paths shown above along with other important input data and put these into
climate models. Climate models, or what are technically known as atmospheric-oceanic
general circulation models (AOGCMs), are essentially translations of physics and
geography into computerized computational form.
I-22
So, to understand the AOGCMs, we need to understand the basic atmospheric
science underlying the equations. We sketched the basics of climate science above, and
will extend it slightly to describe climate models. Recall that hot or short-wave solar
radiation comes through the atmosphere and warms the surface. The surface returns an
equal amount of heat to the atmosphere. Part of the return is long-wave (warm)
radiation. The atmosphere is a natural “greenhouse” in which gases such as water and
CO2 absorb some of the outgoing radiation. If there were no greenhouse gases, the
earth’s surface temperature would be −19 °C (cold like the moon), whereas the actual
temperature is 14 °C. A useful but imperfect analog is that greenhouse gases are like a
blanket in winter, allowing someone under the blanket to retain the heat and stay
warm.
Scientists calculated, as we see, the natural greenhouse effect warms the earth by
about 33 °C relative to what would happen with no atmosphere. The enhanced
greenhouse effect is what happens when even more greenhouse gases, such as CO2 are
added. The current quantity of greenhouse gases absorb some but not all of the
outgoing long-wave radiation. As more and more gases are added, an increasing
fraction is absorbed, and the new equilibrium comes at a higher temperature. Some
saturation occurs, however, so adding doubling CO2 might raise the temperature of 3
°C, but adding the same quantity again would add only 1.8 °C. So there is a kind of
diminishing returns to the enhanced greenhouse effect.
Because of the complexities of these processes, the best current models have quite
different projections of climate change. Figure 9 shows the model recent model
comparison prepared for the IPCC. Each model ran an identical experiment: They
compared a run with no CO2 increase with a run that doubled atmospheric CO2 over 70
years and then held CO2 at that doubled level. The models calculated a “transient
response,” which is the temperature increase at the time of doubling; and an
“equilibrium response,” which is the long-run temperature increase when all
adjustments have taken place. [17] Our RICE model calculations suggest that CO2-e (the
CO2 equivalent of all greenhouse gases) will double around 2050. So, the blue lines in
Figure 6 show what the estimated temperature response would be around 2050
according to the best-guess emissions paths. The average of the models is 1.8 °C, which
compares with the actual increase of around 0.75 °C for the last 100 years.
I-23
Averages of models are at arrows:
6
5
Transient
Number of models
Equilibrium
4
3
2
1
0
0
1
2
3
4
5
6
Temperature increase for CO2 doubling (°C)
Figure I-10. Temperature response of 18 models in IPCC Fourth Assessment Report
The 18 models were run with an experiment in which CO2 concentrations doubled over
a 70 year period. The red bars show the transient response, which is the global mean
temperature increase at the end of the 70 years; the average of the models for this
experiment was 1.8 °C. The blue bars show the response in the long run (usually 200 to
300 years); the average temperature increase for the equilibrium was 3.2 °C. [18]
―――――――――――――――――――――――――――――――――――――――
Figure 9 also shows the equilibrium temperature-sensitivity coefficient. The
average long-run impact of the models is 3.2 °C, or almost twice as much as the
transient response. The transition to the equilibrium proceeds very slowly over 2 or 3
centuries. [19] This large difference reflects the great inertia in the climate system due to
the fact that the oceans warm very slowly. This slow response is another part of the
difficulty of dealing with climate change. Like smoking cigarettes, it may take a long
time to see the effects. If there is any happy note in this, it is that if the concentrations of
CO2 are reversed relatively quickly, then temperature will also come down because the
oceans have not yet warmed.
Many non-scientists look at the divergence among models and wonder why these
uncertainties cannot be resolved. One is reminded of the joke, “If you ask five
economists you will get six answers.” This is not even a joke with climate models
I-24
because the same research group gets different estimates in different models as the
models get refined.
The reason for the difference can be explained using an example from economics.
When the Obama administration proposed its stimulus package, it proposed about $250
billion of government purchases. Economists desired to estimate the impact of this on
the economy, and to do so they use a multiplier analysis. There are both amplifying and
damping second-round effects of government spending. The powerful amplifying
effects (or positive feedbacks) occur because each dollar of spending generates
additional income, and consumers tend to spend some fraction of the additional
income. However, there may be forces that dampen the response (negative feedbacks).
For example, if output rises, then interest rates may rise, reducing stock and bonds
prices and reducing investment. While there are uncertainties here, a standard estimate
is that each $1 of purchases generates $1½ of additional GDP, indicating that the
multiplier is around 1½.
A similar set of forces operate on the climate. Climatologists have estimated that if
there were no feedback effects, the global warming from doubling CO2 would be 1.2 °C.
But there are very strong multipliers at work in climate change. The estimated climate
multiplier ranges leads to an amplified total impact of 1.8 to 4.4 °C for the models
shown in Figure 9.
Why is the uncertainty be so large? There are some factors that amplify the
simplest effect and others that diminish it. For example, if a warmer earth melts snow
and ice, this means the earth reflects less sunlight and this amplifies the greenhouse
effect through what is called the “albedo effect.” The most important amplification
comes because higher temperatures lead to increased water vapor in the atmosphere,
and water is a powerful greenhouse gas. The biggest problem for modelers has been
clouds. Clouds pose difficulties because they both cool and warm – they cool when they
reflect sunlight and they warm because they trap warmth. It turns out that modeling
clouds formation is extremely difficult, and this produces a substantial amount of the
difference among models.
The climate models are extremely detailed and produce a fantastic array of
results, which can be assessed and used for studies of impacts. One of the most
important set of results is the impact on temperature by region. Figure 10 takes four
well-known models from the array and shown their estimated change in temperature
by latitude at the end of the 21st century. These projections use a standardized scenario
that is reasonably close to our economic projections (SRES scenario 1AB).
A few features stand out. First is the feature that the temperature increases are
much larger at the poles, particularly the Arctic region. This is due largely to the
melting of polar ice in summer. Second, the differences across models are particularly
striking in this comparison. The models have only modest disagreements in tropical
regions but it turns out that modeling high latitudes (particularly the behavior of ice
I-25
and snow) is extremely difficult. The differences in model projections are a longstanding feature of climate models and have not disappeared with improved models,
better resolution, and faster computers.
However, even with the disagreement among models, we should not lose sight of
the central feature, which is that all major modeling groups (in both the full group of
Figure 6 and the regional detail of Figure 7) show major climate change over the 21st
century. These are the cutting edge of modern climate science, and the basic message
cannot be obscured by the discrepancies.
.28
14
Share population
T: Hadley
T: GISS
T: NCAR
T: GFDL
.20
12
10
.16
8
.12
6
.08
4
.04
2
.00
-80
-40
0
40
80
Temperature change (oC)
Share global population
.24
0
Latitude
Figure I-11. Estimated temperature change averaged by latitude for four models, 20802099
―――――――――――――――――――――――――――――――――――――――
In addition, to put a human face on these changes, we show the distribution of
human populations by latitude as the solid blue line in Figure 10. It is not generally
appreciated that almost half of human populations live between latitude 25 N
(approximately Miami and Hong Kong) and latitude 42 N (Boston and Rome). As can
be seen in Figure 10, there is likely to be substantial climate change in this region,
particularly in the northern end of this range. There is much more that can be learned
I-26
from climate models, particularly regarding impacts, but that will wait until the next
chapter.
Temperature projections from integrated models for uncontrolled paths
Next, we can put the different components together to project climate change over
the coming decades. For these estimates, we calculate the path with no climate-change
policies. In other words, we assume that no policies are taken – such as limiting
emissions or taxing carbon fuels – to slow the growth in CO2 and other greenhouse gas
emissions. This is probably not a realistic assumption, and in my view definitely not a
undesirable one, but it gives us a kind of “worst case policy assumption,” that is, a case
where countries just sit on their hands and let the dice roll.
Recall that the standard projections from climate models come from arbitrary
emissions trajectories rather than from models based on mainstream economic,
demographic, and technological analysis. We cannot, therefore, look to standard
projections from the IPCC to get an accurate picture.
Instead, the approach here will be to look at the Integrated Assessment Models –
models that combine climate and economic models to construct what might be called a
“combined best estimate” of climate change over the coming years. Even this is a
difficult task because models use different assumptions. So I will present a slightly
simplified approach. I will take the CO2 concentrations from different models shown in
Figure 8. I combine these with estimates of non-CO2 greenhouse gases from the GISS
model. And I will run them through the Yale RICE-2010 climate model. These RICE
model has a climate module that assumes a standard temperature sensitivity coefficient
of 3.2 °C per CO2 doubling. The runs apply the RICE climate module to the CO2
concentrations of the different models.
Figure 11 shows the results of these estimates. [20] This picture provides a good
overview of different future climate changes as seen by multiple modeling groups
around the world. The average change in temperature in 2100 is projected to be 3.5 °C
above the 1900 average. The average of the models is very close to the projection of the
RICE-2010 model, shown as the heavier line with the circle.
The spread among the models is discouragingly large, however, ranging from 2.8
°C to 4.3 °C in 2100. We must emphasize that the spread among the models is generated
only by the differences in CO2 concentrations of the models. These in turn are driven by
uncertainties in global and regional output growth, decarbonization, and the carbon
cycle. Alas, the true uncertainty is even larger. To get the complete picture, we would
need to add differences in other greenhouse-gas emissions, alternative land-use
patterns, and alternative climate models.
I-27
5.0
4.5
RICE-2010 model
Global mean temperature increase
(from 1900, ◦C)
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
2005 2015 2025 2035 2045 2055 2065 2075 2085 2095 2105
Figure I-12. Projected global mean temperature increase from 10 integrated assessment
models
―――――――――――――――――――――――――――――――――――――――
I noted above that the standard projections of climate change from the IPCC and
many climate models are based on unrealistic and poorly constructed economic models.
How do these projections compare with the economically based models shown in
Figure 10? Most of the standard scenarios track the economic models until the middle of
the 21st century. They begin to turn down relative to the economic projections after
2050, and most seriously underestimate temperature trends for 2100 and beyond. For
example, the average of the models shown in Figure 11 projects temperature increase
relative to 1900 of 3.5 °C, where middle scenario B1 projects 1.8 °C. While projections
beyond that are subject to even higher uncertainties, the RICE model projection for 2200
is 4.1 °C, while the scenario B1 projection is 2.1 °C.
The main reason why the economic projections show much higher temperature
trajectories appears to be because they are much more “optimistic” about economic
growth. As we noted at the beginning of this chapter, if there are no climate-change
policies, climate change is a race between economic growth and technological
decarbonization. Most economic models project continuing growth in the decades, and
that leads to continued strong growth in CO2 emissions in the absence of policies to
curb emissions.
I-28
Are we likely to encounter an unusually rapid climate change?
You might wonder whether we are making a mountain out of a bump in the road.
Climate change is part of earth’s history, from the warm periods of the dinosaurs to the
cold periods when New England lay under a mile of ice. Is this time really different?
The answer is yes and no. It is true that climate changes of similar magnitudes
have occurred, and some of them appear to have occurred extremely rapidly. During a
period known as the Younger Dryas about 12,000 years ago, the earth seems to have
gone into one-third of an ice age in a few decades. Similar periods of abrupt climate
change appears to have occurred in earlier periods, although the reasons are not well
understood.
But there is one major concern about the pace of uncontrolled climate change that
seems likely over the next century and beyond. Climatologists have concluded that no
climate changes of the speed and scope we are witnessing have occurred through the
course of human civilizations (say, the last 5000 years). While we do not have reliable
records akin to the instrumental records compiled in Figure 13, there are proxy records
that can be gathered from sources such as ice cores, tree rings, pollen of plants, and bore
holes in the ground. The best guess is that the rate of global climate change we face over
the next century will be about ten times as rapid as any change experienced during the
last five millennia. So, while not unprecedented on the scale of geological time, it is
unprecedented during the era of human settlements.
Further Downstream Impacts
We have emphasized the effects of human activities on temperature. At first
blush, a change of 2 or 3 °C was not that alarming. After all, we experience that much
change from 9 a.m. to 10 a.m. on the average day. Moreover, the climate changes
envisioned are not large relative to the changes that individuals and groups have
undergone through human migrations. People have moved from Moscow to Texas,
from southern Italy to New England. Many people today move from snowbelt to
sunbelt to enjoy the warmer lifestyle with an average temperature (in Phoenix) that is
12 °C warmer than where they left (say, Boston).
However, this comparison ignores the real problems raised by climate change.
The problems are not a simple rise in mean temperature but the accompanying
physical, biological, and economic impacts – and particularly the question of thresholds
and non-linear responses. This is easily illustrated by the example of driving on wet
roads. Consider what happens when the road surface goes from 1 degree above
freezing to 1 degree below freezing. You go from slippery to deadly conditions.
I-29
In this final section, I will discuss the issue of critical thresholds in the earth
systems that may be imperiled by the various aspects of climate change. This territory is
much less well understood: We leave the world of relatively (and I emphasize that
word) well understood implications of our emissions of CO2 and other greenhouse
gases and turn to much more complex and imperfectly understood implications.
Tipping points and safe operating spaces
Scientists believe Planet Earth has experienced an unusually stable climate for
almost 10,000 years (see Figure 16). This is the period during which human settlements,
written languages, cities, and human civilizations as we know them emerged.
However, the combination of larger populations, economic expansion, and new
technologies are changing the earth’s climate, ecosystems, land use, and water flows in
ever-larger ways. Scientists have asked whether we are changing the earth system
outside the biophysical environment within which human civilizations have developed
and thrived. [21]
Figure I-13. Estimated temperature over last 150,000 years from ice core proxy.
Note the stability of the temperature during the last 10,000 years [22]
===============================================
One set of concerns is that further climate change will trigger “tipping points” in
the earth system. A tipping point comes when a system sharp discontinuity in behavior.
I-30
We are familiar with tipping points and sharp discontinuities from our daily lives.
Some familiar tipping points are seen when a tree is uprooted when the stress is too
large or when a levy is ruptured when water is too high, as occurred with Hurricane
Katrina. Financial systems display tipping points when people lose confidence in banks
and cause a “run on the bank,” which can lead to severe financial crises like those of
2007-2008. Sometimes, there is a “good equilibrium” (banks healthy) and a “bad
equilibrium” (banks failing).
Figure 17 illustrates a tipping point using with ball in a double-bottomed bowl.
The height of the bowl represents the health of the economy or ecosystem. We start in a
good equilibrium in (a). Then stresses push on the right side of the bowl. At first, the
ball moves only a little. Then, once the tipping point is reached, the ball races to the
bottom of the second curve in (c). Here, we have multiple locally stable equilibria. So
once the ball is in the second curve, in (d), even though the stresses are removed, the
ball is stuck in the bad equilibrium. [23]
Health of
economy or
ecosystem
Health of
economy or
ecosystem
Original
good
equilibrium
(a)
Stresses from climate
change, pollution,
and deforestation
pushes on system
(b)
T
T
(d)
(c)
Figure I-14. Illustration of how stresses can change system slowly until tipping point is
reached, after which there are rapid and potentially catastrophic changes.
____________________________________________
I-31
Many earth scientists have warned that we are approaching or have passed
important tipping points in the earth’s systems. One study, shown in Table 1, illustrates
some important potential tipping points, time scales, and impacts. [24] This shows 14
potential tipping points of global or continental scale that were identified by a working
group of scientists. I have sorted these be the time scale on which they operate. In
addition, I have added a column indicating what I believe to be the level of concern,
from least to most serious. (I risk being misunderstood here. Least concern is still great
concern, but we need to set some kind of priorities in focusing our policies.)
We can consider two major dimensions here. First, is how soon we will cross the
threshold, which is related to both the warming trigger and the degree of inertia in the
system. Second, is the level of concern. The areas of most urgency are the three-star
issues that we may cross in the next hundred years. There are two in this category: the
disappearance of a substantial part of the Amazon rain forest and the reversal of the
Atlantic thermohaline circulation. I believe that we should add ocean carbonization to
this list (a topic discussed in the next chapter), although that has just appeared on the
radar screen of scientists in the last decade.
One interesting feature of this table is that it appears that there are no two-star or
three-star tipping points with time horizon less than 300 years until climate change
reaches 3 °C. At 3 °C, we encounter the bottom end of the tipping range for several
systems: Sahara/Sahel and West Amazon monsoon, Amazon rain forest, Boreal forest,
Atlantic thermohaline circulation, El Nino–Southern Oscillation, West Antarctic ice
sheet, and we have passed the bottom end of the range for the Greenland Ice Sheet. We
have not yet passed the 3 °C threshold.
The research on tipping points and safe boundaries for operating human
societies is in its infancy. We have already found new potential tipping elements since
the paper discussed in Table 1 was published, while others have receded in the timing
or level of concern. The following chapters will discuss how to stay within these and
other suggested limits. But the work on thresholds is a sober reminder of the potential
discontinuities that can occur in complex systems.
I-32
Threshold
warming
value
Time scale
Level of
Concern (most
concern = ***)
?
< 1 yr
*
Increased UV at surface
+0.5–2°C
10 yr
*
Amplified warming, ecosystems
Sahara/Sahel and West
African monsoon
+3–5°C
+3–4°C
10 yr
50 yr
**
***
Wet period
Amazon rain forest
Boreal forest
+3–5°C
50 yr
**
Biome switch
?
< 100 yr
**
Release of GHGs
+ 3–5°C
100 yr
***
Regional cooling
El Nino–Southern Oscillation
+3–6°C
100 yr
**
Drought
Antarctic Bottom Water
Unclear
100 yr
?
Ocean circulation,carbon storage
?
100 yr
?
Amplified warming,biome switch
+1–2°C
> 300 yr
***
Sealevel +2–7m
+3–5°C
> 300 yr
***
Sealevel +5m
Marine methane hydrates
?
100 to 10,000 yr
?
Ocean anoxia
?
10,000 yrs
***
Tipping element
Arctic ozone
Arctic summer sea-ice
Permafrost
Atlantic thermohaline circulation
Tundra
Greenland ice sheet
West Antarctic ice sheet
Biodiversity loss
Amplified warming
Marine mass extinction
Table 1. Tipping Points in the Earth System
___________________________________________________
Should we take climate models and projections seriously?
Unless you are a specialist in the geosciences, you may well find the discussion
complicated and even impenetrable. So before moving to the final section, I step back
and consider the overall reliability of the climate models, data, and projections. Is this
just some conspiracy of propaganda? Or is it real?
How can we deal with the possibility of a hoax? In my younger days, I studied
the philosophy of Bishop Berkeley, who was a radical “idealist.” His idealism is not that
of the protesters battling the police at a meeting of the World Trade Organization.
Rather it was the idea that we can never really be sure that something is real because
there is no absolute reference point from which to judge reality.
Some people claim similarly that the entire pageant of climate change science is a
hoax, a grand conspiracy, a kind of scientific Protocol of the Elders of Science, designed to
extract money from a gullible and frightened public. At some deep level, perhaps we
are just an audience in a theatre where the gods play some elaborate movie. But that
would apply to everything around us, not just climate science. So we have to judge
climate science by the same standards that we use in judging the reality of drug safety,
measuring gross domestic product, breaking a leg, and getting hit by a truck.
I-33
So in this discussion, I address three aspects of global warming science that have
come under attack. I begin with a discussion of bloginess. I then examine the question
of the reliability of the temperature data that are used to measure long-term trends. I
conclude with a review of the question of whether historical trends provide empirical
support for the climate models.
A measure of bloginess
One informal way to judge the seriousness of an issue is to see where it gets aired.
Serious scientific questions are discussed in scholarly journals, while issues that arise
from political groups or talk radio drift into the web from blogs and similar media.
Discussions that arise as if from nowhere have a high degree of what I will call
“bloginess.”
Here is a simple test: When I searched “climate change hoax” on Google, I got
312,000 results. When I searched it on Google Scholar (which requires some element of
publication in a recognized source [25]), I landed only 4 results. Let’s call the ratio of
overall web citations to Scholar web citations a measure of bloginess. For climate
change hoax, the bloginess index is about 80,000. In fact, climate change hoax has a
bloginess index that is close to that of “Lady Gaga.” For a serious term, “double CO2,”
the ratio was 39, For a technical term, “radiative forcing,” the bloginess index was 8.
Figure 13 shows the bloginess of the different terms. So, looking at its level of bloginess
suggests that climate change hoax is mainly a creature of the media and blogs.
Bloginess index (ratio scale)
100,000
10,000
1,000
100
10
1
"Climate change hoax"
"Double CO2"
"Radiative forcing"
Figure I-15. Bloginess for different climate change terms
I-34
Bloginess indicates the relative importance of a term on the web relative to its citations
in the scholarly literature.
―――――――――――――――――――――――――――――――――――――――
The reliability of the global temperature measures
Most people have seen graphs that show the rising trend of global temperatures
over the last century, and perhaps also reconstructions to earlier centuries. These were
recently questioned in the “Climategate” affair. This occurred when the web
organization known as “Wikileaks” released thousands of emails of climate researchers.
Critics claimed that climate scientists were cooking the books and falsifying the
historical record. (The present author was not an author of any leaked materials.) What
should the outsider think of this debate?
The issues involved here are very familiar to economists. Most economic
magnitudes are “aggregates” of underlying source data. If you have ever read about the
unemployment rate, the gross domestic product, or the consumer price index – each of
these is aggregated from a variety of data from surveys or administrative data.
Estimating the global mean temperature raises many of the same issues as does
estimating these economic variables. How do we aggregate over the subunits? How
reliable are the data? What do we do about missing observations?
My experience across a broad variety of fields tells me that the major problems
with scientific data are the questions just raised and rather than fraudulent creation of
data on a grand scale. Whether it is Watergate or Madoffgate, or less well known
scientific frauds, there are sufficient checks and balances in an open society to root out
fraud. In economics, the major fraudulent data were (and perhaps are) produced by
authoritarian governments, for example the economic data from the former Soviet
Union. There are two symptoms of data fraud. The first is that researchers do not make
their underlying data and methods freely available. If I had to point to one major set of
economic data that is suspicious on this count, it is the Chinese national output data,
which are completely opaque to outsiders. The other symptom is unexplained
inconsistencies between different data sources.
In climate science, perhaps the best check against fraud and error is the presence
of multiple research teams competing to produce the best model or data. One of the
reasons that the climate modeling efforts have credibility is that there are so many
groups racing to produce the best model. Similarly, there are multiple research teams
producing global climate data. Figure 13 shows, for example, the results of three
estimates of global mean temperature over the last century-plus. It is visually obvious
that the three reconstructions move quite closely. [26]
I-35
Temperature difference from mean (oC)
.8
GISS
NCDC
Hadley
.6
.4
.2
.0
-.2
-.4
-.6
80
90
00
10
20
30
40
50
60
70
80
90
00
10
Figure I-16. Global temperature reconstructions from three centers
――――――――――――――――――――――――――――――
“Let me do it myself”
The distinguished physicist Richard Feynman once said “What I cannot create, I
do not understand.” Children understand this when they say to parents who want to
cut their food or steady their bicycle, “Let me do it myself.” Children and Feynman are
saying that the only way to fully master something is to do it or reproduce it yourself.
How can we possibly reproduce any of the data or models that are produced by
armies of climate scientists? This is a particularly daunting task here because of the
complexity of the constructs and the sheet size of the data sets. Nevertheless, like the
child trying to learn to ride a bicycle, I decided to see if I could reproduce the
complicated indexes of global average land temperatures.
With the help of my colleague Xi Chen, I took annual data on average
temperature for 23,019 grid cells around the world from the U.S. National Climate Data
Center (NCDC). I used data only for the period 1980 – 2008 because of the sheer size of
the calculations. These data exclude most of the high latitude regions. A further
question is whether the data underlying the reconstruction can be verified. We
undertook a spot check of a few stations around the world (in the United States, Ghana,
Morocco) and found that the gridded historical data matched the stations we looked at.
Finally, we used our data on average land area by grid cell from the Yale G-Econ project
to calculate a series on global mean temperature.
I-36
Figure 14 shows our calculations and the published estimates from the NCDC
and the Hadley Center in Britain. These move very closely together, although the Yale
reconstruction has a slightly higher time trend than the other two series. [27]
The conclusion on the historical time series is that they pass the usual tests of
scientific scrutiny. As with all empirical estimates, they are subject to a variety of errors
in estimation and construction. But they can be used for further analysis subject to the
appropriate caveats about all similar aggregate indexes, whether from economics or
from other areas.
Temperature (oC, difference from mean)
.8
T: Hadley
T: Yale reconstruction
T: NCDC
.6
.4
.2
.0
-.2
-.4
-.6
-.8
80
82 84
86
88
90 92
94
96 98
00
02
04 06
08
Figure I-17. Land surface temperature from NCDC, Hadley, and Yale reconstruction
――――――――――――――――――――――――――――――
Are the climate models consistent with recent observational data?
Another related question concerns the relationship between the increase in global
temperature and human activities. This has been one of the most contentious issues
debated in the IPCC, and critics of global warming have consistently questioned
whether the warming is due to rising CO2 and other greenhouse gases or instead to
natural changes such as due to the sun, random variation, and the like. Look at Figure
13, which shows rising temperatures particularly since around 1980. Can we separate
human intervention from background noise?
I-37
Global mean temperature (oC, difference from 1900)
A useful way to examine this question is to ask what a temperature path would
look like if the climate models are correct and we have the observed trends in CO2 and
other greenhouse gases. For this question, we use estimates of both CO2 only and the
CO2 equivalent of all greenhouse gases and other influences such as volcanoes. We take
these estimates and put these into a simplified climate model and then compare the
results with the actual trend in global temperatures. We normalize all the series so that
they are equal to 0 in 1900. [28]
1.0
0.8
Climate model: CO2 only
Actual temperature
Climate model: All influences
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
1900
1925
1950
1975
2000
Figure I-18. Actual and model temperature projections
――――――――――――――――――――――――――――――
The results are shown in Figure 15. The wiggly line shows the actual temperature
trend. The smooth line at the top shows what the models would predict with the
influence of CO2 only, while the bottom almost-smooth line shows the estimates with
all estimated forcings. The results here are similar to the more complete experiments of
scientists reported in the scholarly literature. One striking feature is that the projection
with all gases is far below the projection with CO2 only. This fact comes primarily
because of the estimated cooling effects of aerosols, which come from a wide variety of
sources such as burning fossil fuels and biomass.
Three (?) features emerge from the graph. The first is that the temperature trend is
very noisy. This arises from the complex dynamics of the earth-atmosphere-ocean
system. As an economist, I recognize this feature of complex systems, for we see a
similar volatility in stock markets, foreign-exchange markets, and in output and
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incomes. Neither economists nor climatologists can fully explain these erratic shortterm fluctuations, but they do complicate interpretation of the long-term trends.
Second, it is clear that global temperatures are rising, and this upward trend is
highly significant. There are several ways to test this. One way is to perform a
regression analysis. This is a technique for estimating the best fit of a line, but it also
allows us to estimate the reliability of the estimate. If we estimate the time trend using
regression analysis, we find that the coefficient is positive and highly statistically
significant. [29] This point is intuitively obvious from Figure 9, but it will also survive
statistical scrutiny. There is simply no doubt that the indexes are rising.
Third, it is clear that global temperatures are correlated with the predictions from
the calibrated climate model using all estimated influences (the bottom smooth line in
Figure 15). The rise in mean temperature from 1950-59 to 2000-09 was 0.60 °C for the
actual series and 0.45 °C for the prediction from the climate model. We can also test the
association with a regression analysis. If we use the predicted temperature as an
independent variable and actual temperature as the dependent variable, we find a
highly significant coefficient. Indeed, the regression suggests that the model is slightly
underpredicting climate change. [30]
We can use a statistical analysis to determine the temperature change is just a
time trend plus noise. For this purpose, we do another regression analysis. This adds a
time trend to the basic equation from the last paragraph. The time trend The analysis
suggests that CO2 is statistically significant, and indeed the time trend disappears when
the greenhouse gases are included. Again, examination of Figure 2 shows this
intuitively. The temperature trend moves around erratically in the first part of the
period and then moves up steadily in the last half century. [31]
Can we therefore conclude that humans are causing global warming? Statisticians
know that association does not prove causation. Attributing causes is especially difficult
when we cannot do controlled experiments. But scientists who look at the weight of
evidence in many different areas along with graphs like that in Figure 15 conclude that
it is causal. The Fourth Assessment Report of the IPCC concluded, “Most of the
observed increase in global average temperatures since the mid-20th century is very
likely due to the observed increase in anthropogenic greenhouse gas concentrations.”
[32]
Critics continue to attack these and similar conclusions using reasons that are
sometimes serious and sometimes ridiculous. One argument is that scientists are not
really 100 percent sure that global warming will occur. That is true. But a good scientist
is not 100 percent sure of any empirical phenomenon. This was explained by Richard
Feynman in a way that is humorous but very deep:
Some years ago I had a conversation with a layman about flying saucers
— because I am scientific I know all about flying saucers! I said “I don't think
I-39
there are flying saucers.” So my antagonist said, “Is it impossible that there are
flying saucers? Can you prove that it's impossible?”
“No”, I said, “I can't prove it's impossible. It's just very unlikely”. At that
he said, “You are very unscientific. If you can't prove it impossible then how can
you say that it's unlikely?” But that is the way that is scientific. It is scientific only
to say what is more likely and what less likely, and not to be proving all the time
the possible and impossible. To define what I mean, I might have said to him,
“Listen, I mean that from my knowledge of the world that I see around me, I
think that it is much more likely that the reports of flying saucers are the results of
the known irrational characteristics of terrestrial intelligence than of the unknown
rational efforts of extra-terrestrial intelligence.” [33]
This lovely story is a reminder about how good science proceeds – both in the natural
sciences and in the social sciences like economics: A cool head at the service of a warm
heart.
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Endnotes: These notes are for the curious or specialists who would like to know where
the statements in the text are from or their statistical support.
These are from diverse sources such as a Gallup poll, a conservative think tank,
a report on scientific views, and a newspaper.
1
This point is emphasized by Stephen Jay Gould in Wonderful Life: The Burgess
Shale and the Nature of History.
2
I know of no thorough history of the area. A short sketch is provided at
http://www.saltpondscoalition.org/quonny%20pond.html.
3
A short essay by the distinguished environmental scientists Michael H. Glantz
on the Aral Sea and a parallel story on Lake Chad is available at
http://www.fragilecologies.com/sep09_04.html.
4
Image accessed from http://www.treehugger.com/files/2010/04/worlds-4thlargest-lake-90-percent-dried-up.php.
5
Data on CO2 emissions are from the Carbon Dioxide Information Analysis
Center (CDIAC) at http://cdiac.ornl.gov/. GDP is from the Bureau of Economic
Analysis back to 1929 and by private scholars as prepared by the author before that
period.
6
The CO2 data are from CDIAC (see footnote 3). The output data are from many
sources, particularly the World Bank and the International Monetary Fund. They use
the “purchasing power parity” concept for putting countries on a common currency.
7
The CO2 concentrations are from Mauna Loa in Hawaii. The data can be found
at http://www.esrl.noaa.gov/gmd/ccgg/trends/. Data on atmospheric CO2 are
collected from many sites today, and the numbers agree closely with the Mauna Loa
observations.
8
A complete calculation is a somewhat complicated. The calculation uses
estimates of emissions from CDIAC and atmospheric concentrations from Mauna Loa,
but does not project earlier concentrations. This produces unreliable estimates of the
fraction for the first few years, so we begin the calculations in 1970. Also, some of the
atmospheric CO2 may come from cutting forests and other non-energy sources, but we
have not included those numbers because they are so unreliable.
9
10
An example of this is from the IPCC Fourth Assessment Report, Science, p. 536.
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The residence time of CO2 is a contentious issue. Carbon cycle models give an
estimate of between 30 and 50 percent (from the IPCC, Third Assessment Report,
Science, p. 536). In the integrated economic-climate models we have constructed at Yale
University (the DICE-RICE 2010 models), we calculate that along the reference path, 41
percent of an any change in emissions will remain after 100 years. The models are
available at the DICE web site at
http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm.
11
12
The two images are drawn from http://img.ffffound.com/ and
http://walworthcountytoday.com/news/2009/jun/14/architectural-models-display-lake-geneva/.
13
The two images are from www.flickr.com/photos/patrickgage/2165601214 and
http://www.writedesignonline.com/assignments/Project333.html.
This report is available online at
http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/. The
quotation is from section 1.2.
14
The EMF results are published in [??], and the detailed results were made
available by Leon Clarke. Source: emissions comparisons v2.xlsx.
15
The RICE model results along with references are available at
http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm.
16
This figure and discussion is drawn from Chapter 8 of IPCC, Science, 2007. We
have omitted models that do not calculate both equilibrium and transient temperatures.
17
18
Source: tsc_meta_chapter9_v3.xlsx
This turns out to be difficult to extract from IPCC reports. However, an
examination of Supplementary Figure S10.3 at http://www.ipcc.ch/pdf/assessmentreport/ar4/wg1/ar4-wg1-chapter10-supp-material.pdf shows clearly how slow the
long-term response it.
19
A technical note for the specialist. Although some of the models provide
temperature trajectories, they exclude short-lived greenhouse gases and forcings and
therefore do not provide an accurate temperature projection. The runs shown in Figure
10 take the industrial CO2 concentrations from the models. Only 9 models have
provided estimates. We then combine these with estimates of land-use CO2 emissions
and the radiative forcings from other GHGs from the RICE-2010 mode. We then put all
20
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these into the climate module of the RICE-2010 model. The RICE-2010 version is
RICE_042510.xlsm, available on the RICE web page. The ten models were RICE-2010,
ETSAP-TIAM, FUND, GTEM, MERGE Optimistic, MERGE Pessimistic, MESSAGE,
MiniCAM – BASE, POLES, SGM, and WITCH. A full description of the models is
contained in the source at ???. Source: iam_model_temp_sim.xlsx, sheet
“Concentrations”.
This concept is drawn from Johan Rockstrom et al., “Planetary Boundaries:
Exploring the Safe Operating Space for Humanity,” Ecology and Society, vol. 14, no. 2,
2009, online at http://www.ecologyandsociety.org/vol14/iss2/art32/.
21
Image is from NOAA at
http://www.ncdc.noaa.gov/paleo/globalwarming/paleobefore.html, drawn from
research by Petit et al.
22
Thanks to my students at Yale for helping to develop this diagram. Source:
tipping slides.ppt
23
This is drawn from Timothy M. Lenton, Hermann Held, Elmar Kriegler, Jim
W. Hall, Wolfgang Lucht, Stefan Rahmstorf, and Hans Joachim Schellnhuber, “Tipping
elements in the Earth’s climate system,” Nature, February 12, 2008, vol. 105,no. 6, pp.
1786–1793. I have simplified the table and omitted some of the details. Source:
thresholds_table.xlsx.
24
As with most things from Google, it is not clear exactly how Google Scholar
includes publications or how they are ranked. Google lists the appropriate sources as
including “articles, theses, books, abstracts and court opinions, from academic
publishers, professional societies, online repositories, universities and other web sites.”
See http://scholar.google.com/intl/en/scholar/about.html.
25
Source: bloginess.xlsx
For the statistically inclined, the correlation coefficient among the three series
in the levels is between 0.94 and 0.99. The more demanding correlation coefficient for
the change in the three series is between 0.71 and 0.96.
26
The gridded data are taken from data prepared by Cort J. Willmott, Kenji
Matsuura and David R. Legates and distributed by the Center for Climatic Research,
Department of Geography, University of Delaware and available at
http://climate.geog.udel.edu/~climate/html_pages/download.html#lw_temp2. We
have taken the area-weighted averages of all terrestrial grid cells, using the area data
27
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from the Yale G-Econ data set available at gecon.yale.edu. For the statistically inclined,
the correlation coefficient between the Yale series and the other two series in the levels
is between 0.95 and 0.97. The more demanding correlation coefficient for the changes in
is 0.85 and 0.91.
We can also do a test for a time trend. The coefficient of a regression of the series
on time has a coefficient of 0.0303 (+0.0042) for the NCDC series; a coefficient of 0.0229
(+0.0032) for the Hadley series; and 0.0355 (+0.0053) for the Yale reconstruction. All
three are highly significant using the standard tests. The Hadley coefficient is
significantly lower than the other two estimates. We have not attempted to reconcile the
estimates because there are simply too many differences. For example, the Hadley
global takes the average of northern and southern hemisphere temperature anomalies
even though the area in the northern hemisphere is twice as large as that in the southern
hemisphere. Our estimates exclude Antarctica, while the Hadley numbers include at
least part of Antarctica.
Source: ghg_temp_history_121809.wf1
The procedure was the following. First we calibrated a simple climate model
to the average of the IPCC climate models. The simple model has the form of Tt = Tt-1 +
0.0289 x [T*t-1 - Tt-1], where T* is the equilibrium temperature change and T is the actual
temperature change. This equation is calibrated to the results of climate models in the
Fourth Assessment Report, Science, Table 8.2. We then took radiative forcings for CO2
(which are available from published sources) and non-CO2 forcings (from the GISS web
site at http://data.giss.nasa.gov/, updated by the author). The actual temperature
series is the average of the GISS and the Hadley calculations.
The calibration program for the climate model is calc_model_trans_eq.prg, and
the calculations are made in the file below, page “est_eq_trans.” The program to
estimate the projected temperatures are model_trans_eq.prg. The file with the data,
results, and figure is ghg_temp_history_121809.wf1, page “reg_hist_c1.”
28
For specialists, here is the procedure. We take the average of the three global
temperature series mentioned above (Hadley, NCDC, and GISS). We then do
regressions of global temperature on a time trend. To test for robustness, we use rolling
sample periods starting with a sample of 1880 to 2009 and ending with 1990 to 2009.
The coefficient has a t-statistic of 17 for the longest time period and 5 for the shortest
time period. Using standard significance tests, the probability of the smallest coefficient
arising by chance is 0.0001. There is simply no doubt that the indexes are rising. Source:
is ghg_temp_history_121809.wf1, page “history.”
29
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The following explains this in detail. We can estimate either radiative forcing
or the prediction of the climate model, and for this purpose we take the latter. The
explanatory variable is “T_predicted_all,” which is the prediction using all radiative
forcing. The “T_actual” is the average of the three actual series. We have estimates from
1880 through 2008. These estimates are made using EViews 6.0. The following shows
the regression output. The most important variable is the coefficient on predicted
temperature, which has a coefficient of slightly above 1, indicating that the model
underpredicts actual temperature.
30
Dependent Variable: T_actual
Method: Least Squares
Sample (adjusted): 1880 2008
Included observations: 129 after adjustments
Variable
C
T_predicted_all
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient
Std. Error
t-Statistic
Prob.
-0.080237
1.173187
0.009872
0.049388
-8.128058
23.75444
0.0000
0.0000
0.816281
0.814834
0.111501
1.578929
100.9546
564.2732
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.055648
0.259119
-1.534181
-1.489842
-1.516165
1.132687
The results show a coefficient on the predicted T of 1.09 (+ 0.107) and a time
trend of 0.00051 (+0.00057). The results also hold with a first-order correction for
autocorrelation. We add a technical warning to statisticians using these data that the
underlying temperature data have all the attributes of a non-stationary series, so tests
must take this into account.
31
This is one of the central conclusions of the IPCC Fourth Assessment Report,
p. 10. The IPCC has a very precise definition here. “Likelihood … refers to a
probabilistic assessment of some well defined outcome having occurred or occurring in
the future.” The term “very likely” means “> 90% probability.”
32
33
Richard Feynman, The Character of Physical Law, MIT Press, 1970.
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