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Greenhouse
physics
All matter with a
temperature above absolute zero emits radiation. The hotter the substance, the
more radiation it emits and the shorter the average wavelength of the radiation
emitted. The sun emits much of its radiation as visible light, with an average
wavelength of about half a micrometer. The earth’s atmosphere emits as though
its average temperature were around 0°F,
at an average wavelength of about 15 micrometers. When an object emits
radiation it loses energy, and this has the effect of cooling it; absorption,
on the other hand, heats an object. Most solids and liquids absorb much of the radiation they intercept, and
they also emit radiation rather easily. Air is another matter. It is composed
almost entirely of oxygen and nitrogen. Such molecules barely interact with
radiation: they allow free passage to both solar radiation moving downward to
the earth and infrared radiation moving upward from the earth’s surface. If
that is all there were to the atmosphere, it would be a simple matter to
calculate the average temperature of the earth’s surface: it would have to be
just warm enough to emit enough infrared radiation to balance the shortwave
radiation it absorbed from the sun. Accounting for the amount of sunlight
reflected back to space by the planet, this works out to be about 0°F, far cooler than the
observed mean surface temperature of about 60°F. Fortunately for us, our atmosphere contains trace amounts of other
substances that do interact strongly with radiation. Foremost among these is
water, H2O, consisting of two atoms of hydrogen bonded to a single atom of
oxygen. Because of its more complex geometry, it absorbs and emits radiation
far more efficiently than molecular nitrogen and oxygen. In the atmosphere,
water exists both in its gas phase (water vapor) and its condensed phase
(liquid water and ice) as clouds and precipitation. Water vapor and clouds
absorb sunlight and infrared radiation, and clouds also reflect sunlight back
to space. The amount of water vapor in a sample of air varies greatly from
place to place and time to time, but in no event exceeds about two percent of
the mass of the sample. Besides water, there are other gases that interact
strongly with radiation; these include CO2, or carbon dioxide (presently about
380 tons for each million tons of air), and CH4, or methane (around 1.7 tons
for each million tons of air). Collectively, the greenhouse gases are nearly transparent to sunlight,
allowing the short-wavelength radiation to pass virtually unimpeded to the
surface, where much of it is absorbed. On the other hand, these same gases
absorb much of the long-wavelength, infrared radiation that passes through
them. To compensate for the heating this absorption causes, the greenhouse
gases must also emit radiation, and each layer of the atmosphere thus emits
infrared radiation upward and downward. As a result, the surface of the earth receives radiation from the
atmosphere as well as the sun. It is a remarkable fact that averaged over the
planet, the surface receives more radiation from the atmosphere than directly
from the sun! To balance this extra input of radiation—the radiation emitted by
atmospheric greenhouse gases and clouds—the earth’s surface must warm up and
thereby emit more radiation itself. This is the essence of the greenhouse
effect.
If air were not in motion, the observed concentration of greenhouse
gases and clouds would succeed in raising the average temperature of the
earth’s surface to around 85°F,
much warmer than observed. In reality, hot air from near the surface rises
upward and is continually replaced by cold air moving down from aloft; these
convection currents lower the surface temperature to an average of 60°F while warming the upper
reaches of the atmosphere. So the emission of radiation by greenhouse gases
keeps the earth’s surface warmer than it would otherwise be; at the same time,
the movement of air dampens the warming effect and keeps the surface
temperature bearable. Determining humanity’s influenceAn important and difficult issue in detecting anthropogenic climate
change is telling the difference between natural climate variations—both free
and forced—and those that are forced by our own activities. One way to tell the difference is to make use of the fact that the
increase in greenhouse gases and sulfate aerosols dates back only to the
industrial revolution of the 19th century: before that, the human influence is
probably small. If we can estimate how climate changed before this time, we
will have some idea of how the system varies naturally. Unfortunately, detailed
measurements of climate did not really begin in earnest until the 19th century;
but there are “proxies” for quantities like temperature, recorded in, for
example, tree rings, ocean and lake plankton, pollen, and corals. Plotting the global mean temperature derived from actual measurements
and from proxies going back a thousand years or more reveals that the recent
upturn in global temperature is truly unprecedented: the graph of temperature
with time shows a characteristic hockey-stick shape, with the business end of
the stick representing the upswing of the last 50 years or so. But the proxies
are imperfect and associated with large margins of error, so any hockey-stick
trends of the past may be masked, though the recent upturn stands above even a
liberal estimate of such errors. Another way to tell the difference is to simulate the climate of the last
100 years with climate models. Computer modeling of global climate is perhaps
the most complex endeavor ever undertaken by mankind. A typical climate model
consists of millions of lines of computer instructions designed to simulate an
enormous range of physical phenomena, including the flow of the atmosphere and
oceans, condensation and precipitation of water inside clouds, the transfer of
solar and terrestrial radiation through the atmosphere, including its partial
absorption and reflection by the surface, by clouds and by the atmosphere
itself, the convective transport of heat, water, and atmospheric constituents
by turbulent convection currents, and vast numbers of other processes. There
are by now a few dozen such models in the world, but they are not entirely
independent of one another, often sharing common pieces of computer code and
common ancestors. Although the equations representing the physical and chemical processes
in the climate system are well known, they cannot be solved exactly. It is computationally
impossible to keep track of every molecule of air and ocean, and to make the
task viable. The two fluids must be divided up into manageable chunks. The
smaller and more numerous these chunks, the more accurate the result, but with
today’s computers the smallest we can make these chunks in the atmosphere is
around 100 miles
in the horizontal and a few hundred yards in the vertical, and a bit smaller in
the ocean. The problem here is that many important processes are much smaller
than these scales. For example, cumulus clouds in the atmosphere are critical
for transferring heat and water upward and downward, but they are typically
only a few miles across and so cannot be simulated by the climate models.
Instead, their effects must be represented in terms of the quantities like wind
and temperature that pertain to the whole computational chunk in question. The representation of these important but unresolved processes is an art
form known by the term parameterization, and it involves numbers, or
parameters, that must be tuned to get the parameterizations to work in an
optimal way. Because of the need for such artifices, a typical climate model
has many tunable parameters, and this is one of many reasons that such models
are only approximations to reality. Changing the values of the parameters or
the way the various processes are parameterized can change not only the climate
simulated by the model, but the sensitivity of the model’s climate to, say,
greenhouse-gas increases. How, then, can we go about tuning the parameters of a climate model in
such a way as to make it a reasonable facsimile of reality? Here important
lessons can be learned from our experience with those close cousins of climate
models, weather-prediction models. These are almost as complicated and must
also parameterize key physical processes, but because the atmosphere is
measured in many places and quite frequently, we can test the model against
reality several times per day and keep adjusting its parameters (that is,
tuning it) until it performs as well as it can. But with climate, there are
precious few tests. One obvious hurdle the model must pass is to be able to
replicate the current climate, including key aspects of its variability, such
as weather systems and El Niño. It must also be able to simulate the seasons in
a reasonable way: the summers must not be too hot or the winters too cold, for
example. Beyond a few simple checks such as these, there are not too many ways to
test the model, and projections of future climates must necessarily involve a
degree of faith. The amount of uncertainty in such projections can be estimated
to some extent by comparing forecasts made by many different models, with their
different parameterizations (and, very likely, different sets of coding
errors). We operate under the faith that the real climate will fall among the
projections made with the various models; in other words, that the truth will
lie somewhere between the higher and lower estimates generated by the models.
The figure above shows the results of
two sets of computer simulations of the global average surface temperature of
the 20th century using a particular climate model. In the first set, denoted by
blue, only natural, time-varying forcings are applied; these consist of
variable solar output and “dimming” owing to aerosols produced by known
volcanic eruptions. The second set (in red) adds in the man-made influences on
sulfate aerosols and greenhouse gases. In each set, the model is run four times
beginning with slightly different initial states, and the range among the four
ensemble members is denoted by the shading in the figure, reflecting the free
random variability of the climate produced by this model, while the colored
curves show the average of the four ensemble members. The observed global
average surface temperature is depicted by the black curve. One observes that the two sets of simulations diverge during the 1970s
and have no overlap at all today, and that the observed global temperature also
starts to fall outside the envelope of the all-natural simulations in the
1970s. This exercise has been repeated using many different climate models,
with the same qualitative result: one cannot simulate the evolution of the
climate over last 30 years without including in the simulations mankind’s
influence on sulfate aerosols and greenhouse gases. This, in a nutshell, is why
almost all climate scientists today believe that man’s influence on climate has
emerged from the background noise of natural variability.
Editor's note: We are grateful to Professor Kerry Emanuel, and to
MIT Press for granting permission to post this excerpt from the book WHAT WE
KNOW ABOUT CLIMATE CHANGE
http://mitpress.mit.edu/9780262050890/
Further Reading:
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